The paper will elaborate on how author’s company implements a comprehensive engineering assurance program for pipe spring supports at one of its petrochemical plants, to improve piping system performance and reliability. Spring supports are required at a piping system which is subjected to significant vertical movement during operation. The spring support prevents damage to the piping by transferring the load due to piping movement to supporting structure. Spring support failures were also among contributing factors to some major incidents, including fire, at piping systems in the Plant. The failures are attributed to ineffectiveness of overall spring support care. The Assurance Program was aimed to provide more systematic and structured approach to spring inspection and maintenance throughout spring life at the plant, from initial installation and throughout operation stage. The implementation of the Program is based on a comprehensive guideline produced, which details out inspection interval, acceptance criteria and recommended mitigations on for common types of defects. In summary, the Assurance program was able to detect early sign of spring failures on 83 spring supports at the Plant, some of which located at highly critical piping systems. This had resulted in timely intervention for rectification and prevented major incidents such as fire, loss of product containment, and asset damage.
Mr. A Hidayat is a Mechanical Piping specialist with 15 years of working experience in design and maintenance of piping system at petrochemical plants. Mr. Jalil Masing, and Mr. Shahzulreza are both registered professional engineer, each having experience of more than 18 years in mechanical engineering work. Mr. Muzzahhir is an established PETRONAS piping specialist with more than 20 years of experience in piping design, inspection and maintenance work.
Aluminium alloys have been used for aircraft structures since the 1950s. The selection of aluminium alloys has been based on their relatively high structural performance, capability to support the loads and stresses involved, excellent manufacturability, and moderate costs. The 7xxx series aluminium alloys are widely used in aerospace industry for applications like fuselage stringers and lower wing skins. The beneficial strength-to- weight ratio and advantageous life-cycle assessment studies of these alloys are attractive for improved lightweight design of structural aircraft parts. In this study, with the objective of providing a viable solution to minimize the weight of aircraft stringers, the feasibility of depositing stiffeners by wire-arc additive manufacturing onto Z-shaped sections made of thin sheets of Alclad 7075-O aluminium alloy was assessed. The results obtained so far show that stiffeners can be consistently deposited, although an adequate balance between wetting and penetration is quite challenging and calls for further optimization of experimental setup and deposition strategies.
The purpose of this study is to identify the potentials and opportunities of tourism opportunities that can be opened and developed in Samosir regency. To better understanding of the study aim, the descriptive qualitative approach was conducted by interviewing the head of tourism business of regency tourism and culture office. The results showed that a wide range of opportunities and potentials related to tourism entrepreneurship in this regency can be developed, such as food and beverage, accommodation, tourism attractions, transportation, entertainment and recreation business, MICE businesses, tour guides and travel, and SPA treatment business. The opportunities and potentials are more considerably higher as there are two districts in this regency selected as the key tourism areas for Lake Toba super priority tourism destination. The findings of the article contribute to the entrepreneurs and investors to consider developing and investing the tourism business in Samosir island. Also, the study recommendation can help the local authorities in designing policies in the development of tourism entrepreneurship and tourism industry. There are some limitations of the study which can be the impetus for the future research, namely the scope of the study is only in Samosir island, without covering other areas in Lake Toba and the authors identified tourism business opportunities from the perspective of local government, without covering the perspectives from the other related stakeholders.
tourism entrepreneurship, business potentials and opportunities, key tourism areas, entrepreneurs, investors
The effects of different environmental factors i.e., rainfall intensities, hydrological conditions and geological formation are inevitable while studying the stability of any soil slope. In the present study, a soil slope located in Meghalaya, which is one of the North Eastern states of India and also a part of the Himalayan ranges is considered for performing the numerical simulation of a soil slope under the influence of rainfall. The effects of different rainfall properties to the soil slope behaviour in the form of pore water pressure, changing ground water table is analysed. The contribution of these parameters to the stability of the soil slope is analysed using finite element-based software (i.e., MIDAS GTX NX 2023). Six different rainfall intensities and two ground water level (GWL) (one for wet period and one for dry period) were taken to systematically and thoroughly understand the effects of rainfall infiltration on the slope displacement. The variation of factor of safety with respect to time for different rainfall intensities and GWL are reported. Parametric studies based on different soil parameters are also being done. The results obtained were compared and pore water pressure variation throughout the slope, lateral deformation of the slope have also been reported to get an insight into the response of the given slope due to various rainfall intensities and site characteristics. It was observed that during the wet period the slope stability was affected by the changing matric suction, which is a result of the high monsoon experienced during this season. However, during dry period the rainfall intensity was less, which did not allow rain water to infiltrate the soil enough to change the matric suction values and hence, had very less effect on the soil stability.
J Sharailin Gidon 1 , Dr. Smrutirekha Sahoo 2 1. PhD scholar NIT Meghalaya, India , 2. Assistant Professor, NIT Meghalaya, India
The occurrence of gem minerals is a rare phenomenon and in the same way the geological information, geophysical and geochemical information are also very rare. Visakhapatnam District of Andhra Pradesh state in India, located in parts of Eastern Ghats comprising valuable mineral resources especially gem minerals. The precious and semi-precious stones of Alexandrite, Chrysoberyl, Chrysoberyl cat’s eye, Garnets, Tourmaline, Sillimanite and a variety of quartz group are known to occur in this region. The gemstones are very much engulfed within the pegmatite both in primary and secondary stages. The geological studies on exploration, mining and environmental planning have shown very promising results, where the gem mineral resources were estimated to a depth of 15 mts. from the surface. Geo electrical resistivity surveys have been utilized and the results are favorable and correlated with the surface geological features. The geo-chemical investigation carried out for the host rocks and the associated gem-variety stones and analyzed for their major and minor elemental concentrations. Geochemical analysis of the petrological members of gem bearing tracts of the region was carried out by ICP-MS and elemental concentrations were determined. A few of the samples were processed for XRD analysis. The following important points are noted in (i) Invaluable gem minerals are found in association with the Khondalite suite of rocks, as per their geochemical evaluation. (ii) The secondary pegmatitic body indicated all the mineralogical characteristics of the primary pegmaties in a deeply altered stage (iii) The colluvium forms as the target of explanation for the gem minerals in this region. (iv) The study shows that the element fluorine is endangerous to the local rural people, which may result flourosis in parts of the this region.
Protection of generators against Power Swings possesses ambiguity due to complexities in detecting power swings with available technology. The decision regarding islanding and detecting the faults during power swings has also posed many challenges. Artificial Neural Networks (ANN) and Machine learning (ML) algorithms are employed to classify the power swing into a stable and unstable class. The features are extracted by applying Wavelet Transform (WT) to a signal consisting of a variation in generators' load angles. Other features are obtained from the original load angle signal. All these features are used to train ANN and ML models. Once the Power Swing classification is done, a Fuzzy logic algorithm is used to make the Trip decision. The fuzzy logic algorithm is proposed to detect faults during stable power swing with an index proposed in the method and denoted as "Energy in Power Signal" (EPS). Results obtained clearly depict swing classification and relaying signal with 100% detection accuracy. The power swing classification is done even before the load angle slips the pole (crosses 180 electrical degrees). The proposed method is tested by simulating IEEE 9 bus interconnected power system. The method and hardware testing for the intelligent relay proposed are demonstrated by executing the algorithms with the raspberry pi controller.
ANFIS, EPS, Fault during power swing, In-Step- Block, In-Step-Trip, Intelligent relays, Out-of-Step trip, Generator protection
Science Fiction (SF) plays a pivotal role for shaping the attitude of the readers towards the future; but the main objective of a science fiction novel or film is not to predict the future or to assess any technological advancement. SF, mainly, teaches us what it really means to be a humane in a changing world of citizenship cum globalization. To convey this message, novelists and movie makers portray Artificial Intelligence (AI) as autonomous or human-like character to ponder upon the condition of human in flux and the social, economic, and political issues with reference to advancement in technology. Kazuo Ishiguro SF, Klara and the Sun (2021), abets one make sense of the world in COVID-19 pandemic, thereby, giving us the opportunity to learn about our own consciousness. It is through the perspective of Klara, the robot protagonist, that we explore artificial intelligence and consciousness, the posthuman situation of mankind, future of Utopia, humanity’s own changing ideologies, and human-machine relationship or human-nonhuman relationship. The way AI enacts human is debatable question as AI introspection not only reflects new capacities for human potential, but it mirrors the limits of humanity i.e., creature is defined and associated its creator. Ishiguro has presented AI as conscious machine, rather than a manufactured risk, to be seen as a depiction of societal issues and its interrelation to science and technology.
Science fiction, Artificial Intelligence, Posthumanism, Dystopia, Consciousness
Introduction: In health, learner-centered education has proven to be a successful teaching method combining practical skills with theoretical knowledge. However, it remains unclear whether learner-centred features of education increase family motivation and learning engagement.
Methods: In this descriptive qualitative study, 54 households from landslide-prone areas of East Java were recruited to participate using purposive sampling. Data was collected by means of semi- structured face-to-face focus group interviews. The interview transcript study was qualitative. This study demonstrates the value of adopting an educational method that emphasizes family autonomy.
Result: Facing Landslide Disasters can satisfy the hedonic and practical nature of learning in disaster- prone regions, hence improving family motivation and educational involvement. The results also indicate that learner-centered education can improve families' educational experiences and facilitate their transition from passive and receptive to proactive learners.
Conclusion: This study provides deeper and specific knowledge about the components of student-centered education that increase learning motivation and involvement in disaster-prone households.
Landslides, Motivation, Focus Groups, Indonesia, Curriculum
The use of digital assets in financial markets has increased significantly in recent years, leading to an increase in the use of digital assets centralized exchanges (CEX) for trading. CEX has faced various security challenges including fraud, hacking, and market integrity. In response, the use of smart contracts has been proposed as a means of improving security in CEX, this research aims to address the security issues associated with CEX by proposing a design and implementation of smart contract security in these exchanges. The study will evaluate the proposed design and implementation through a series of smart contract security frameworks of Formal Verification, Penetration Testing, and Security Auditing, to determine the number of vulnerabilities found of the proposed solution. The study will also analyze the results with security methodologies and best practices in the development and deployment of smart contracts using the Open Web Application Security Project (OWASP), with a focus on improving security in CEX. The proposed solution will provide a framework for improving the security of CEX and promote trust and stability in the digital assets market.
Mr. Sereyboth CHAMROEUN is pursuing a Master of Engineering in Computer Science at the Institute of Technology of Cambodia and earned double undergraduate degrees in Computer Science and Finance & Banking. He works at the Securities and Exchange Regulator of Cambodia, where he involves as a Project Manager for the development of a financial market surveillance system, evaluator of FinTech Regulatory Sandbox applicants, and member of the working group drafting Cambodia’s FinTech Development Policy. He also was Blockchain Engineer for Tokyo-based startup, focusing on Digital Identity on Blockchain.
Mobile-based augmented reality is a technology that uses a digital overlay on top of the real world to augment the perception of reality. Augmented reality enhances the learning experiences in a classroom. This technology can be used to create interactive and engaging educational content, allowing students to visualize complex concepts and theories in a more immersive way. Additionally, it can help to increase student engagement and motivation, leading to improved learning outcomes. The present research attempted to document the perspectives of learners on using mobile-based augmented reality applications to learn the mathematical concept of geometry. The researchers have also recorded the changes in mathematics classroom dynamics when technological tools were applied to learning. A total of 250 students from five different schools participated in the study. The instruments used were interest inventory for augmented reality-based mobile applications, focused group discussions, and observation schedule. The findings of the study suggest that the use of mobile-based augmented reality applications significantly enhanced students' understanding and retention of geometry concepts. Furthermore, the integration of technology in the classroom fostered a more collaborative and interactive classroom learning environment and promoted higher levels of student participation and engagement in classroom activities.
mobile-based augmented reality, educational technology, mathematics learning, classroom dynamics
Archana Yadav is a Ph.D. research scholar in the Department of Education, University of Delhi. She has completed her graduation, Post-Graduation, and M.Phil. (Education) from the University of Delhi. She completed her research work at M.Phil. under the supervision of Dr. Vinod Kumar Kanvaria. She has teaching and research experience with learners of all age groups in school. She is currently interested in writing and researching in her areas of interest namely educational technology, information and communication and technology, experimental research, and pedagogy of mathematics.
In a rapidly changing world, we must adapt to the new realities of the modern day. One such reality is the rapid adoption of Battery Powered Electric Vehicles (EV). Before we embark on a journey with our EV, we need to make sure our EV has sufficient charge to complete the journey. Sometimes we do not possess enough charge to make it to our destination in one charge. In these types of situations, having an optimal route with EV charging stations along with the way is a must. An optimal path to find the nearest EV charging station using Particle Swarm Optimization is proposed in this work. The built optimization model is tested by a scenario for finding out the electric vehicle ‘s charging station to verify the feasibility and effectiveness. The algorithm is implemented and the simulation is been carried out using the software NS2 (Network Simulator).
Electric vehicles, Charging Station, Particle Swarm Optimization, Optimal route, Optimal charging, Vehicle to Grid, NS2, AODV, DSR routing protocols, RERQ, RERP
Each country is unique in its competitiveness and this is illustrated by The Global Competitiveness Report (2019) into 12 pillars of competitiveness measurement. Information from these measurement pillars is then utilized by investor to understand, map, and decide where they should invest their capital which criteria are in accordance with their needs. The diversity of conditions that exist in a country is formed from the accumulation of the strategic competitiveness of each company (Antara et al., 2019) operating in the country itself. The achievement of successful business processes in each company can also be realized with the support of the government through policies that significantly affect the final conditions of the 12 pillars. It is known that the latest challenge in the industrial sector is the presence of Industry 4.0 which encourages the government to move faster and structured to create a strong and sustainable industrial ecosystem. The establishment of the Making Indonesia 4.0 road map places the government to create a strong industry through decent infrastructure facilities and has stable energy resources. In response to these conditions and situations, PLN's Board of Directors Decree No. 0778.K/DIR/2013 on Guidelines for Measuring the Maturity Level of Power Plant Governance within PT PLN (Persero) and its Subsidiaries in the Java Bali Sumatra System has issued by the state-owned enterprise who focused on the electricity sector. This decision was formed due to the variance in the results of asset management implementation shown by the three power plant management groups owned by PT PLN Group and eventually affecting the condition of electricitysupply stability in several areas in Indonesia. The three groups are PLN, PLN subsidiaries, and PLN affiliated company level 2. The purpose of the decree is to encourage all power plant units and subsidiaries to achieve optimal, effective, efficient, and sustainable company performance. Organization Logo Photograph Regarding to the previous explanation, the research aims to analyse the existence of the performance result gap in optimizing the use of management asset especially after the decree were issued. In the data collection process, it is known that PT PLN Group has adjusted the asset management framework according to the characteristics and needs of the company. There are five main components of asset management in PT PLN Group's power plant governance framework: (a) generation plan; (b) risk and control management; (c) life cycle delivery asset; (d) information management system; and (e) people and work culture which also includes 39 asset management subjects. The results of this research were obtained by comparing the company's performance data from the three power plant management groups and found that there was still a large gap in maturity level in the life cycle delivery component. This finding can help companies to focus on improving the subjects in the component to minimize or even eliminate the gap between the three of them.
Competitiveness, Power Plant Asset Management, Performance Result Gap, PT PLN Group, Life Cycle Delivery Aset
Iwan Agung Firstantara is currently a PhD student at the Industrial Engineering, Faculty of Engineering Gadjah Mada University. He is interested in the asset management area and trying to enhance his understanding by doing some developments and improvements for his workplace at PT PLN Group in Indonesia. Andi Rahadiyan Wijaya is working as a lecturer in Industrial Engineering, Faculty of Engineering Gadjah Mada University. His research intererst is on the operation and maintenance engineering and ergonomics. He has some research, publications, and service activities related with his research interest. Samsul Kamal is working as a lecturer at Industrial Engineering, Faculty of Engineering Gadjah Mada University. His research intererst is on the heat transfer and energy conversion system. He has some experiences on academic and non-academic, research, publications, and service activities related with his research interest. Budi Hartono is working as a lecturer at Industrial Engineering, Faculty of Engineering Gadjah Mada University. His research intererst is on the cognitive ergonomics, project management, risk and complexity analysis, and system approach. He has some research, honors and awards, publications, and service activities related with his research interest.
This study addresses the heterogeneity of concrete material and its impact on mechanical properties, which is often overlooked in concrete modeling. It focuses on the effects of variability and dimension, particularly in damage and concrete cracking modeling. Since cracks and damage in concrete depend on the tensile strength, variations in the value of tensile strength can significantly affect the outcomes of such studies. This research applies concrete variability in a finite element using Mazars Damage Model to govern the behavior law. To capture the variability of concrete damage, a random field generator called the Turning Band Method is used. A n Oil Palm Shell (OPS) concrete cube sample of 15x15x15 cm³ is modelled under tension test, varying the mesh size and length correlation to observe the damage response. The results show that a smaller mesh size of 1 cm leads to a more varied distribution of cracks compared to a mesh size of 2.5 cm. Moreover, a smaller correlation length causes the spread of microcracks on all sides of the concrete sample, whereas a larger correlation length localizes the cracks in certain areas. This study highlights the importance of considering concrete heterogeneity in numerical modeling for more accurate results in damage and cracking prediction. Index Terms— Heterogeneity, concrete, oil palm shell concrete, damage behavior, Mazars Damage Model, Turning Band Method.
Providing accurate soil humidity information with a capacitive sensor is challenging. The humidity of the soil is known to change slowly. However, due to the nature of the capacitive sensor, which is sensitive to environmental disturbance, the received humidity reading taken by the sensor could change drastically which is not reflecting the actual soil humidity conditions. In reducing the variations, data averaging could be incorporated. It is known that the longer the averaging points the lesser data fluctuation will be. However, in wireless sensor applications where energy usage should be as minimum as possible, the faster the sampling period gives more energy penalty. In this paper, an empirical experiment to determine how fast the data sampling and the number of averaging points result in the best soil humidity data taken by a capacitive sensor is presented. With the criteria of least data variance with the smallest sampling period result in 200 ms data sampling with a 1000-point average gives the best data quality.
soil humidity, wireless sensor, energy, data variance
In this research paper, we perform a comprehensive analysis of the current state of vulnerability detection tools for authentication. The increasing number of data breaches and cyber-attacks has made it essential for organizations to regularly assess the security of their authentication systems. The purpose of this research is to evaluate the effectiveness and efficiency of several commonly used vulnerability assessment tools for authentication/authorization and related areas. The study includes a comparison of the features, capabilities, andscope of the selected tools. The results of the analysis provide valuable insightsinto the strengths and limitations of the different tools andcan help bring light to some flaws. The paper concludes by providing recommendations for future research in this field.
Saru Chandrakar is a Senior Consultant at VISTA InfoSec Mumbai India. The writer is pursuing M.Tech in e-Security from Bhilai Institute of Technology Bhilai Chhattisgarh India. The writer has published various articles in National, Asian and International levels.
This study aims to demonstrate the role of Digital Marketing Co-Design in moderating research gaps, enhancing the transformation of the tourism industry and economic growth, and designing management design as a new approach for MSME entrepreneurs in creating added value services. The research design uses a sequential mixed method. The convenience sample is the participants in the Digital marketing workshop for MSMEs in Toba, Samosir, and Humbanghasundutan districts in October 2022 (N=325). Quantitative data were obtained through questionnaires and processed using the AMOS SEM tool, while qualitative instruments were obtained through unstructured discussion and question and answer. Digital Marketing Co- Design is proven not to increase Tourism Entrepreneurship Performance directly but needs to be done through Tourism Industry Transformation. Thus Digital Marketing Co-Design can answer the previous research gaps indirectly. The novelty presented by Digital Marketing Co-Design is increasingly sticking out when entrepreneurs can transform and adopt digital technology in their operational activities. Digital Marketing Co-Design is a transformative solution for improving the performance of MSMEs. Digital Marketing Co-Design is a solution for developing and enhancing Tourism Entrepreneurship Performance and strongly influences Accessible Tourism. As many as 38.15% of MSMEs are declared proficient and adaptive in using digital marketing; the rest still need further training. Digital Marketing Co-Design is a strategic tool and role model that provides consequences for stakeholder involvement in the digital era. Co-design is an innovative way and method to influence destination image differentiation, MSME profiles, and the variety of product and service offerings in tourist destinations. Training and workshops can bridge the MSME skills gap between districts. For this reason, employers, institutions, and local governments are expected to initiate the next training.
IT Use for Exploration, Digital Marketing Co-Design, Accessible Tourism, Tourism Industry Transformation, Tourism Entrepreneurship Performance
The entry level setup IDS is right now exceptionally intriguing as the main part of framework security. The IDS takes traffic data from line or framework then uses it to build more secure network. Attacks are really challenging and dangerous to eliminate street exercises. The entrance framework (IDS), which is a key component of framework security, is currently quite intriguing. The IDS collects traffic data from the network or framework and then uses it to improve security. Assaults are often extremely difficult and time-consuming to separate from street exercises. The examiner should look over all of the vast and varied material in order to screen the organisation association. The frequency of traffic is thus anticipated to be determined by an organisation search strategy. Another method for finding IDS identifiers was developed in this study by focusing on information mining techniques from a computation machine. Sorting the decision tree and the computation is the method used to establish the principles. A wide range of known (irresistible) assaults can be distinguished by evaluating the normal interruption pace of the framework for checking the means of misconception. In the case of something surprising occurs, the framework initially learns the ordinary profile and afterward records every one of the components of the framework that don't match the setup profile. The main benefit of discovery is its inability to discern between fresh or unexpected assaults at a high rate of occurrence. The upside of having the option to identify uncommon things is the capacity to recognize new (or startling) assaults that convey many advantages. Procedures dependent on innovation pipelines utilized in different ventures. We give general data to the investigation of traffic data and for the location of street mishaps utilizing the significant distance-course of-the-street .The proposed technique utilizes tests dependent on removing traffic statistics from internet media (such as Facebook and Twitter): This movement collects phrases related to any type of traffic workout, like traffic pauses or street terminations. The quantity of starting handling strategies is presently executed. breathing, signal presentation, POS signal, partition, and so forth to change the data acquired in the inherent structure. T information is then consequently shown as "traffic" or "traffic" utilizing the latent Dirichlet allocation (LDA) calculation. Vehicle enrollment data is isolated into three kinds; great, terrible and impartial. The response to this classification is the expression enraptured (positive, negative, or unbiased) as for street sentences, contingent upon whether or not it is traffic. To manage bi-directional LSTM organisations, each sentence is now converted to a single hot code using the bag-of-words (Bow) technique (Bi- LSTM). In the wake of preparing, a multi- stage muscle network utilizes soft max to arrange sentences as indicated by area, vehicle experience, and sort of polarization. The suggested approach compares the high-level preparing approaches with the preparation of various machines in terms of precision, F scores, and numerous standards. Building a Machine Learning-Based Network Intrusion Detection System for Software Defined Networks.
Diabetes, also known as chronic illness, is a group of metabolic diseases due to a high level of sugar in the blood over a long period. The risk factor and severity of diabetes can be reduced significantly if the precise early prediction is possible. The robust and accurate prediction of diabetes is highly challenging due to the limited number of labelled data and also the presence of outliers (or missing values) in the diabetes datasets. In the Project, proposing a robust framework for diabetes prediction where the outlier rejection, filling the missing values, K-fold cross- validation, and different Machine Learning (ML) classifiers (k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes and XG Boost) were employed. The weighted ensembling of different ML models is also proposed to improve the prediction of diabetes where the weights are estimated from the corresponding Area Under ROC Curve (AUC) of the ML model. AUC is chosen as the performance metric, which is then maximized during hyper parameter tuning using the grid search technique. All the experiments, in this literature, were conducted under the same experimental conditions using the Pima Indian Diabetes Dataset. From all the extensive experiments, our proposed ensembling classifier is the best performing classifier with the AUC as 0.97 respectively Our proposed framework for the diabetes prediction outperforms the other methods. The ML models were trained and tested on publicly available PIMA Indians Diabetes (PID) dataset of 768 female diabetic patients from the Pima Indian population near Phoenix, Arizona . This dataset consists of 268 diabetic patients (positive) and 500 non-diabetic patients Organization Logo [Type text] (negative) with eight different attributes. The descriptions of the attributes and brief statistical summary are shown in Table 1.
|SN||Attributes||Description||Mean ± Std|
|1||Pregnant (F1)||Number of times pregnant||3.85 ± 3.37|
|2||Glucose (F2)||Plasma Glucose Concentration||120.90 ± 31.97|
|3||Pressure (F3)||Diastolic Blood Pressure (mm Hg)||69.11 ± 19.36|
|4||Triceps (F4)||Triceps Skin Fold Thickness (mm)||20.54 ± 15.95|
|5||Triceps (F4)||Triceps Skin Fold Thickness (mm)||20.54 ± 15.95|
|6||BMI (F6)||Body Mass Index||32.00 ± 7.88|
|7||Pedigree (F7)||Diabetes Pedigree Function||0.47 ± 0.33|
|8||Age (F8)||Age in years||33.24 ± 11.76|
Srilatha was born in Ongole, Andhra Pradesh in 2002. She is pursuing Bachelor Of Technology in Information Technology at Prasad V Potluri Siddhartha Institute Of Technology (PVPSIT) 2019-2023 Vijayawada. Currently, she is working as an intern in the Virtusa as well as pursuing Final Year of B.Tech. in IT at PVPSIT. She has research interest in machine learning, deep neural networks, data mining, engineering and technology. She has publishedsome conference papers in these domains. Currently, she is working on some papers about these topics along with other students and professors.
The Theory of Constraints proposes that every manufacturing system has a limiting factor, commonly referred to as a bottleneck, which negatively affects overall throughput. Detecting and predicting bottlenecks is crucial for optimizing production systems since they provide the best starting point for any improvement measurements. However, dynamic bottlenecks that shift between processes can be challenging to detect and predict. Machine Learning, particularly deep learning, has the potential to improve bottleneck prediction by analyzing large amounts of data of past system behavior. This enables a model to identifying characteristic dependencies between stations and foresee future trends of a manufacturing system. Previous applications of Machine Learning in this context have shown the general potential that requires the deployment of appropriate algorithms. This paper presents a new approach for a multi-step prediction of dynamic bottlenecks using Long Short-Term Memory Networks. To validate the prediction performance of this approach, we evaluate several models in simulated manufacturing environment. The simulations differ in terms of the respective features of the systems’ bottleneck locations. We critically discourse the possible applications of the methodology against the background of the evaluation and make the entire program code of the simulations and the data analysis publicly available.
Nikolai West is a research associate at the Institute of Production Systems at the Technical University Dortmund and at the RIF Institute for Research and Transfer. He is pursuing his doctoral degree in the area of unsupervised and multivariate anomaly detection. The main goal is the adaptation of algorithms for a cross-functional evaluation of large data sets to predict defective parts. His further research focuses on the reusable application of industrial data analysis within dynamic value networks, on the development of intelligent methods for production planning and control, and on the prediction of dynamic bottlenecks using methods for time series forecasting.
Steels are the most coveted after material used in design of mechanical components. They are a perfect amalgamation of strength, ductility, and wear resistance. Common industrial components like bearings, couplings and gears are often manufactured using high strength steels. Stainless steels on the other hand are corrosion resistant in nature and hence used under applications needing corrosion resistance. The presence of high percentages of chromium results in resistance against corrosion in stainless steel. However, stainless steels often lack the required strength to work under high stress applications. In the food and beverage manufacturing industries components like bearings, gears, and couplings corrode in small time because of the use of chloride and sulfide cleaners in order to keep the surfaces clean and avoid contamination of edibles. High stresses will further increase the rate of corrosion. It is seen that often under such operating conditions, stainless steel too corrodes easily. Thus, the food and beverage industry need both high strength and high corrosion resistance from their steels. Since corrosion is a surface phenomenon, this can be achieved by through surface treatments imparting the required strength in stainless steel and increasing its corrosion resistance simultaneously. This paper studies the importance of composition and alloying elements that increase the corrosion resistance of steel and the surface treatments through which we can introduce them on the steel.
Stainless steel, corrosion, bearings, gears, washdown environment, food & beverage industry.
The development of the national shipping industry, especially shipyards, can increase the capacity of the national shipping industry in the transportation of goods and passengers between islands. Digitalization enables major changes in business models that allow the creation of new companies that grow very quickly. This study aims to analyze the process of shipyard availability in the Samarinda area using the fishbone diagram method. Data collection techniques in this study were carried out by literature study. The results of the research to be achieved are to create an application so that the administration process is neat, repair and shipbuilding are well programmed and scheduled, increasing the capacity of shipyards to build new building ships, also including repair / maintenance, with an application or web-based business can help improve business, because it will make it easier for ship owners to access where adequate shipyards are and scheduled and have a location and place that suits the condition of the ship without making many long voyages to get a docking place with schedule uncertainty.
shipyard, tranportation, digitization
The floor joists of cold-formed steel (CFS) structures may use large web openings to keep the floor height to a minimum. A cost-effective way to alleviate the detrimental effects of a large web opening is to affix appropriate reinforcements around the opening regions, to restore the original strength and stiffness of the member. A total of twenty-three laterally braced CFS joists were simply supported and subjected to uniformly distributed loads until failure for flexural tests, which considered solid sections, circular and square web openings (65% of web depth) and sections with reinforced web openings. The reduction in the flexural strength of a cold formed steel joist section due to a large web opening is less than 15%. Twenty-seven joist sections were subjected to short span, mid-span point load, shear tests to establish the shear resistances of sections having a large web opening and a reinforced web opening. The reduction in the shear strength of a CFS section with a web opening may be as high as 60%. Thus, the residual shear strength of a joist with a large opening may be as low as 40%. A Virendeel type reinforcement system can restore the original shear strength of a cold-formed steel joist section. Based on these studies it was concluded that; The mid 40% region of a joist (0.30L and 0.70L) can be defined as “Flexural Zone” and will need flexural reinforcements. The regions outside the mid 40% region of a joist can be defined as “Shear Zone” and will need the shear reinforcements.
Cold-formed Steel Joists, Large Web Openings, Openings Reinforcements, Experimental, Construction Guidelines.
The login behavior of users in HPC clusters will be examined in this research paper with the use of machine learning, and data analysis and probabilistic techniques to identify patterns that can be used to identify anomalous login behavior based on IP and timings. The customized model will monitor each user's login behavior and stop any unauthorized activity in HPC clusters.This paper also discusses the data preprocessing and feature engineering techniques to extract information from sshd logs. Multiple Machine learning models were experimented to recognize and store the pattern of users from the sshd logs, but most of the Machine Learning models failed. Machine Learning models are used to train on specific behavior or pattern from dataset, as the sshd logs doesn’t show any specific login behavior or pattern of users, hence most of the machine learning models fail to detect slight change in login behavior of users. Another reason for failure, the dataset only has true values and is highly imbalanced data which will make the job more tedious for any typical Machine Learning model to detect outliers or any pattern . As none of the users are following any pattern, it is required to consider one pattern and categorize the login behavior of all users in the same pattern.
Atharv Nagarikar received a B.E. degree in Electronics and Telecommunication from the Rashtrsant Tukdoji Maharaj Nagpur University, Nagpur, in 2019. Currently, he is working at the Centre for Development of Advanced Computing (C-DAC) Pune, India, as a Technical Associate in the HPCS department. As a Project Engineer, he is developing applications and conducting research in the technical areas of Deep Learning, Machine Learning, and Data Science. Abhishek Patel received a B.E. degree in Computer Science Engineering from Department of University Institute of Technology, Barkatullah Vishwavidyalaya (Formerly Bhopal University), Bhopal in 2021.Currently, he is working as a Technical Associate at the Centre for Development of Advanced Computing (C-DAC), Pune, India, as part of the High-Performance Computing and Storage (HPCS) department. In his current role, he is fully immersed in research and development related to the exciting fields of Deep Learning, Machine Learning, Data Science and HPC Environments. Krishna Gupta received B.E. degree in Computer Science & Engineering from RGPV University, Bhopal. Since Aug 2007, He has been associated with C-DAC, in various capacities. Currently as a member of HPC Technologies group, he is a member of a team of experts for PARAM Shavak DL GPU and PARAM Shavak VR product development and deployment. As part of National Supercomputing Mission, he is involved in the design, development, deployment and technical support of scientific applications with his expertise in HPC, Deep Learning, Machine Learning, Parallel, Distributed and Accelerator Computing. Shashank Sharma received an M.Tech degree in “Embedded Controls and Software” from the Indian Institute of Technology, Kharagpur, in 2014. Currently, he is working at C-DAC Pune as Senior Engineer, his current work area includes computer vision and deep learning application development. Mohammed Afzal is currently pursuing an M.Tech in Information Security from Savitribai Phule Pune University, and holds a B.E degree in Computer Science & Engineering from CSVTU, Bhilai. He is currently serving as a Module Leader at HPC-Technologies Group, C-DAC Pune, where his responsibilities include working on the security aspects as well as the solution design and deployment of HPC systems under the National Supercomputing Mission. As an active contributor to the next generation of supercomputers for India, he has been extensively involved in ensuring their security and Reliability. Samrit Kumar Maity received MCA from SRTMU, Nanded, and MS from BITS Pilani. Joined C-DAC in 2006. As a member of HPC Technology Group, he is involved in projects of national importance like NSM. High-Performance Computing (HPC), Deep Learning, Grid, and Accelerator computing are some of his research interests. Ashish Ranjan is currently working as an Associate Director in the HPC Technologies Department of C-DAC, Pune. He is leading the solution design and deployment of HPC systems under the National Supercomputing Mission. He is a System Architect for numerous HPC systems across various academic and R&D organizations in India and abroad. He has been actively working on the next generation of Supercomputers forIndia. Abhishek Das is Program Director for Exascale System Architecture and Design and HPC-AI Infrastructure Development at C-DAC working towards strategic planning and development of HPC and AI technologies. As a graduate of BTech (Computer Science & Engineering), he designed and implemented large-scale HPC and AI programs. Sanjay Wandhekar is a Senior Director at C-DAC, India, with over 25 years of management experience in HPC and IPTV/OTT technologies and systems. He holds a B.E. in Electronics & Telecommunication from College of Engineering, Pune and an M. Tech from IIT, Kanpur. He currently heads the National Supercomputing Mission (NSM) activities at C-DAC, which involve establishing Supercomputing facilities and ecosystem development in the country. His team has established over 20 Supercomputing facilities in the country and has received the Dr. A P J Abdul Kalam HPC award for their contribution to the Supercomputing ecosystem in India. Sanjay has a proven track record of building, mentoring and motivating high-performance teams and was the lead member in the development of India's first Supercomputer PARAM8000 and subsequent PARAM series of supercomputers.
A problem statement like "traffic congestion" has a wide variety of implications on society and the economy. Work is continuously being done in this area to make significant advancements. We have attempted to anticipate the network-wide traffic flow speed using time series analysis and cutting-edge deep learning techniques. For our work, we took into account the historical traffic data for Chicago, which includes the speed of the next time period's 1047 individual road segments. We converted the traffic data into a spatio- temporal matrix and added temporal data for each spatial road segment separately in each column because the traffic data contains time series for each spatial road segment. A RNN model with two layers of LSTM that allots one memory unit to each road segment was created. Using the spatio- temporal training matrix, we trained our model for 50 epochs and then received a vector containing the speeds of each road segment for the subsequent time step. For both the training set and the validation set, the model clearly displayed a learning tendency. Finally, for better visualization, we calculated the MSE and RMSE for the model on the spatio- temporal test matrix and also rendered the prediction as a spatio-temporal image. Keywords— RNN model, LSTM, Spatio-temporal analysis, Neural Networks, Classification, Optimization
In this paper, a multi-drone simulation is made using the fish school search algorithm. This simulation aims to create a multi drone simulator application where the simulator will be made based on one of the Swarm Intelligence algorithms. Fish School Search (FSS) is one of the swarm intelligence algorithms that imitates the behavior of a school of fish. In the multidrone simulation, the python programming language platform is used. The FSS algorithm is used to optimize the solution to a problem that is carried out by a multi drone. In the simulation, various variations of the number of drones are used so that the optimal number of drones is found each size of area and efficiency of the number of drones used to scan areas in each size of area. Based on the simulation that have been carried out, it is found that the greater the number of drones does not mean that the scan time decreases for each size of area.
Swarm Intelligence, FSS, Simulasi, platform python.
Job vacancies are jobs (positions) for employers who are looking for suitable employees (to fill vacancies). Job vacancies could be used as an indicator to see how good a country’s economy is. Until now there is still no research that produces job vacancy datasets in Indonesia. This job vacancy data can be used to assist other research that requires job vacancy data, such as creating a job recommendation system, identifying trends, and making predictions. Therefore, we would like to share our job vacancy dataset for researchers in this paper. The collected job vacancies were grabbed from several popular platforms in Indonesia. The collected job vacancies were grabbed from several popular platforms in Indonesia. The platforms we chose to create the datasets were glints.com, jobindo.com, jobstreet.co.id, and karir.com. We developed scrapers, one for each platform, and ran them to grab vacancies posted on the platforms. Afterward, we carefully analyzed the collected vacancy records and designed the pre-processing steps. In the end, we integrated all the records into one dataset. The final dataset contains 63,870 records and 20 attributes dated from 2015-2022. This paper also provides data visualization and data analytics to extract insights to help make better decisions.
My name is Triagus Abdi Simanjuntak. I am a final year student in Information Systems study program at Institut Teknologi Del. I am interested in projects related to data analytics. At the moment, me with the other authors are working on job vacancy aggregation projects to ease job hunters in looking for jobs.
Synthesis of AgNPs can be done by using bangun-bangun (Plectranthus amboinicus) leaf extracts because it contains carboxylic acid, phenol, ester, and amide that function as reducing agents. The objectives of this study are to determine the effect of bangun-bangun leaf extracts volume and the incubation temperature to AgNPs synthesis and their potential as antibacterial agent against Staphylococcus aureus and Escherichia coli, also to characterize the AgNPs synthesized. The research methods consist of preparation of culture and raw materials, isolation of bangun-bangun leaf extracts using squeezing method, AgNPs synthesis, antibacterial activity assay using disk diffusion method, and characterization of AgNPs using UV-Vis spectrophotometry, Scanning Electron Microscope-Energy Dispersive X-Ray (SEM-EDX), and Particle Size Analyzer (PSA). AgNPs synthesized at 90°C with ratio of leaf extract to silver nitrate solution is 0,09:24 has the greatest antibacterial activity for S. aureus and E. coli. AgNPs resulted an absorption peak at 400-475 nm, contained Ag elements of 82,22% w/w, sized at 63,9 nm, and formed aggregation. The potential of these AgNPs as antibacterial can solve the problem of bacterial resistance to ampicillin as commercial antibiotic.
Chintya Sinar Lumbantoruan is graduated with a Bachelor’s degree from Bioprocess Engineering, Institut Teknologi Del in 2019. Currently, she is the academic assistant in Bioprocess Engineering, Institut Teknologi Del.
The telecom industry’s revenue streams from core products are at stake due to digital disruption. Telcos have realised the need to keep up with fast pace of innovation for filling the gaps in top line, bottom line and the necessity to establish symbiotic relationships with start-ups that have the advantage of organisational agility and new business models. But so far, the successes of Telcos and collaborative undertakings through different “inside-out” and “outside-in” engagement models such as corporate venture capital (CVC), are inadequate and unsustainable. High focus on financial returns while losing sight of customer centric innovation for future / 5G and poor engagement are in conflict with the Telecom companies’ aspiration of focusing on next generation innovation. The objective of this research was to ascertain the strategies to overcome these issues, intra-firm specific success factors, and to explore the ways of achieving gainful engagements that lead to create shared value. Case Study combined with Grounded Theory (GT) was adopted as the research methodology. Data gathered through interviews, observations and documents are analysed by employing GT coding techniques to develop a conceptual framework for successful collaboration. This exercise is complemented and guided by an early-stage Literature Review which formulated the construct of the research. The Conceptual Framework developed through the Grounded Theory method of analysis, encompass four Core Categories such as Strategic Fit, Transformational Leadership, Collaborative Ecosystem, Responsive Co-creation and contextual enablers that primarily influence the Collaborative Ecosystem and Responsive Co-Creation. The resultant model that emerges from live data, comprised of four categories / themes that cover subject areas such as Strategy, Leadership, Ecosystem, Structure and Processes. The four categories correlate well with the three-dimensional Shared Value Creation model theorized by Porter and Cramer (2011). This comprehensive study provides the telecom industry and start-ups to understand a collaboration framework for engaging constructively. A noteworthy feature of this study is the inclusion of technological and organizational factors that make the startup and telecom successful in terms of “Creating Shared Value”. Future research scope can be widened by adding more engagement models and Telcos in different economies while focusing more on start-up intra-firm specific factors.
Strategic fit, Transformational leadership, Collaborative Ecosystem, Responsive Co-Creation, Creating Shared Value
A new kind of Distributed Denial-of-Service (DDoS) attacks called link flooding attacks (LFA) has been applied. LFA is easily implemented by launching large-scale legitimate low-speed flows and rolling target links to paralyze the provided services in a target network area. Many solutions were proposed to detect LFA, but only designed and optimized based on classical algorithms with handcrafted features, so they difficultly keep up with the rapid progress of self-organizing network structures and emerging network protocols. Thus, this study proposes a Convolutional-Neural-Network based LFA detection (CNN-LD) method, which collects the network status and determines whether LFA happens or not without any manual intervention. The experiment results demonstrate that CNN-LD can accurately detect LFA in a varying network structure with various flow patterns
Yuan-Cheng Lai received the Ph.D. degree in computer science from National Chiao Tung University, Hsinchu, Taiwan, in 1997. In August 2001, he joined the faculty of the Department of Information Management at National Taiwan University of Science and Technology, Taipei, Taiwan, where he has been a professor since February 2008. His research interests include wireless networks, network performance evaluation, network security, and content networking.
Load forecasting is one of the necessary tools for t h e modern energy management system. Forecasting power consumption and calculating load demand became a topic of great interest for th stakeholders in the electricity market. Decision- making, including purchasing and generating electric power, load switching, and demand side management are crucially dependent on load forecasting. This research work focuses on predicting power consumption in a Home Area Network (HAN) using time series forecasting methods like Long short-term memory neural Networks (LSTM). Our goal was to design a model that can precisely forecast the electrical load required in a Home Area Network. Utility companies and homeowners can utilize this model to better plan and control their power usage. It can assist in lowering the likelihood of power outages and enhancing the effectiveness of the electrical system. The Root Mean Square Error (RMSE) is used as the performance measure in our work. The dataset considered is from UCI repository with 350400 rows and 4 columns to achieve realistic data for training the model and used the encoder-decoder LSTM model for load prediction and achieved a root mean squared error value of 23 and a mean absolute error value of 18.6.
Time Series Forecasting, UCI repository, Normalization, Encoder-Decoder LSTM, RMSE (Root Mean Squared Error ), MAE (Mean Absolute Error)
E-commerce as a pathbreaking disruptive innovation of recent times, offers general businesses, a high level of customer satisfaction delivering noticeable cost benefits. Since the inception of E-commerce concept, we have been experiencing its exponential momentum in every corner of business. In fact, the significance of this concept is further recognized these days while experiencing the disruptions in global trade environment under the lock down scenarios in the ongoing Covid-19 pandemic. The Specialty Chemical Industry deals with chemicals that have an impact on some industrial chemical processes and final products, including those for our most fundamental requirements such as food and pharmaceuticals; it would be practically impossible to survive and thrive without this industry's products and services. It has been observed that, despite many growth stories, this industry is trailing to follow the latest technological suit versus peers. Challenges like fading innovation, evolving regulatory landscape, sustainability, rapid digitalization under Industry 4.0 and dynamic geo- political developments instigating trade barriers disrupting feedstock supplies are affecting this industry significantly. Such challenging situations do instigate a need for conducting business research around connecting topics, one of them being looking at a business model of E-commerce, to address and possibly mitigate some of such challenges. This exploratory study has been intended to run across South Asia and Southeast Asia cross-sectionally in the B2B Specialty Chemical Industry, and it is currently in the stage of quantitative assessment of the derived conceptual framework. This study was designed to run across South Asia and Southeast Asia. At this stage, I would like to share the background and significance of my research, as well as the literature review that was involved in arriving at my conceptual framework and leading to related research questions and hypotheses. In addition, I would like to provide some additional information regarding the research methodology that was adopted for the study. I will briefly describe the sampling strategy used for the pre-testing of the survey instrument and the pilot study, as well as the preliminary findings of the study. If available at the time of this conference, then I may be able to provide more specifics from my ongoing bulk study, which involves data collecting from key stakeholders and statistical analysis to verify my research hypotheses, draw findings, identify constraints, and suggest further research.
Vinod Agnihotri graduated in Chemistry from Bombay University, India in 1993. Subsequently, he earned another graduation in Fiber Technology and Textile Chemistry from the Institute of Chemical Technology, Bombay University in 1996. In 1998, while working, he also earned a Master Diploma in Business Administration from Symbiosis, Pune (distance learning). After gaining valuable professional experience in operations, sales, marketing, business development, product management, and international business while working for several local and global specialty chemical companies, he has been actively serving the industry for the past 27 years. Currently, he serves as the Managing Director and Vice President of LANXESS Pte Ltd., Singapore (German MNC).
In the world, multilingualism is very common in educational settings. Nonetheless, nothing is known about the connection between multilingualism and academic success. When research has been done on this subject, it has mainly concentrated on how having a home language influences academic success, lacking a more comprehensive understanding of multilingualism that goes beyond home languages and also takes into account the identity aspect of being multilingual. This essay investigates the connection between academic success, multilingual identity, and multilingualism. The article provides a succinct analytical assessment of multilingual practises, their effects, their educational advantages, and perspectives on the best ways to attain it. The term "multilingualism" refers to the ability to speak many languages well. There are generally authorised and illegal multilingual practises. The benefits of multilingualism practises in education include the enhancement of academic and educational value, the encouragement of creativity, the facilitation of adaption to social circumstances, and the respect for regional languages.
Education, Multilingual, Android, Native language.
Students with special needs have various strengths and weaknesses in themselves. Therefore, the teaching and learning of these students need to use a variety of appropriate techniques along with the use of resources taking into account the skills they have and need to achieve (Yasmin, 2000; Khatijah & Yasmin 2006). Students with special needs also have various problems such as behavior problems, observation problems, motor skill problems, socialization, and emotional problems. Therefore, teachers need to practice therapy activities during the teaching process inside or outside the classroom in an effort to help these students reach their maximum potential according to their abilities. In connection with that, this study was conducted to identify whether art therapy through coloring activities can increase concentration, and interest and reduce the stress of special education students. The special education students who were selected as study participants were two people, namely the hearing impaired, and the learning impaired, i.e. slow. These two students are usually slow to complete activities during teaching and learning either inside or outside the classroom. In addition, students who are slow have a weak mastery of learning. However, the findings of the study found that these students are very interested in the Color Reading Activity Book. They fully focused during the coloring activity conducted by the researcher so they successfully completed the coloring task in the activity book very well.
Special Needs; Therapy; Hearing problem; Learning problem; Color Reading Activity Book
Rural development is a priority for many developing countries in the world because it meets the world's food needs. Rural development requires the internet and digital technology for development sustainability. There is ample evidence that rural communities have lower internet and digital technology access than urban communities. This digital divide will impede development and promote village exclusivity. Therefore, efforts to bridge the digital divide are critical. Many studies have been conducted to investigate the digital divide between rural and urban communities, but studies on the impact of the digital divide and the contributing factors that cause it remain unexplored. This study aims to describe the impacts and factors that cause the digital divide in the context of rural development. This study is a narrative review of 16 articles selected from reliable sources such as scopus.com and sciencedirect.com. The results of the literature review are summarized to answer research questions. The impacts and drivers of the digital divide described in this study contribute to some of our efforts to bridge the digital divide while also providing some insight for future research. Photograph Organization Logo
Hanifah Ihsaniyati is a lecturer at Universitas Sebelas Maret. She was born in Temanggung on March 2, 1980. She is currently a doctoral student in agricultural and rural development communication at IPB University. She is involved in agricultural communication research groups and the center to study farmers' protection and empowerment (Pusdi Perlintan). Her published articles include development communication, agricultural extension, empowerment, management and strategy, social psychology, and information systems. Her Scopus ID is 57204942772, and her Orcid ID is 0000-0001-6416-1813. Her email address is firstname.lastname@example.org. Sarwititi Sarwoprasodjo is a lecturer at IPB University. Her research interests are social movement communication, development communication, and indigenous communication. She is also the editor of the Journal of Development Communication (Jurnal Komunikasi Pembangunan). Her research track record can be traced through SINTA ID: 6036868 and Scopus Author ID: 57188814546, and her ORCID ID is 0000-0003-3371-677X. Her email address is email@example.com, Pudji Muljono is a Professor of Community Development, his degree is in Educational Technology, State University of Jakarta, 2000. His research includes community development, empowerment, social mapping, communication, extension, education, librarianship, and educational technology. He has participated in many courses: scientific writing, research methodology, psychology for community development, participatory learning methods and media, learning and training methods, statistics for social analysis, and the basics of communication. From 2009-2017, he was entrusted with the mandate as Head of the Center for Human Resource Development, LPPM-IPB University, and since 2017 now has been assigned the task of Head of the Library Unit, IPB-University. Since 2010 until now, he has been the Chief Editor of the Extension Journal published by the Association of Indonesian Development Extension Experts and the Department of Communication Science and Community Development, IPB-University. His Scopus ID is 56544679600, and his ORCID ID is 0000-0002-3162-8816. His email address is firstname.lastname@example.org. Dyah Gandasari affiliate in Ministry of Agriculture Indonesia, Directorate General of Horticulture, Ministry of Agriculture Indonesia, Directorate General of Livestock Services, Ministry of Agriculture of Indonesia, Bogor Agricultural Development Polytechnic. She holds her master's degree in Agribusiness Management from Magister Management Agribusiness, IPB University. Her doctoral degree in communication was obtained Doctoral Program at IPB University. Currently, she is a faculty member at Agricultural Development Polytechnic (POLBANGTAN) Bogor, Indonesia. She lectures on communication and other social topics (agricultural extension, research methodology, statistics for social science, and rural sociology) POLBANGTAN Bogor. In the research field, she has published more than 20 publications in SCOPUS Listed Journal. Her main research of interest and areas of expertise are in Communication, Media Studies, Social Communication, and New Media. Her Scopus ID is 57195200058, and her ORCID ID is 0000-0002-1671-9760. Her email address is email@example.com firstname.lastname@example.org.
Pakcoy is a type of vegetable that is very difficult to distinguish to determine the maturity level of the greenish color intensity for determination of harvest readiness in the Pakcoy plant. There needs to be a system reliable and smart to help farmers in the harvest process. On research This camera is used to take images of the Pakcoy plant. This research aims to test the accuracy of the detection object with two categories namely Pakcoy plants "Ready to Harvest" and "Not Ready to Harvest" using the Tensorflow Lite framework with the EfficientDet Lite 2 architectural model, Pakcoy plant detection was tested via an Android device and website online real time to assess the performance of the detection model. The test results show the average detection accuracy of Pakcoy Ready to Harvest vegetables touched the number 98.02% and 99.00% for detection of Pakcoy vegetables not ready for harvest, this indicates that the detection model is working reasonably well with the device android as well as website.
Harvest Readiness Detector, Pakcoy, Tensorflow Lite, EfficientDet Lite 2, Deep Learning
A significant health problem with a consistentdevelopment rate is chronic renal disease, often known as chronic kidney disease (CKD). An irregular kidney function or a loss of renal function that worsens over months or years defines chronickidney disease. A global medical emergency is currently developing. One of the main reasons of this condition is unhealthyeating habits. Another major cause is not drinking enough water. It takes an average of 18 days for a person to survive without theirkidneys, needing dialysis and a kidney transplant. It may be difficult to identify chronic kidney disease (CKD) in its early stagesbecause there are no symptoms. The illness is often screened for in those who are known to be at risk for renal problems, such as those with high blood pressure, diabetes, or who have a blood relative who has the disease. As a result, treating the disease effectively requires early identification and treatment. Theprediction model used include ANN algorithm. For feature selection, Recursive feature elimination based on cross validation and analysis of variance have both been used.
Chronic Kidney Disease, Artificial Neural Network, Data Exploration, Model generation.
This research examines the history of encryption and the known types of ciphers. The study of various types of ciphers and their decryption showed that the main reason for disclosing their secrecy is the recognition of patterns in the form of a repeating sequence of characters. In this regard, the author aims to create a reliable program for encrypting texts in the Kazakh language, written using the Latin alphabet. For this, a type of multilayer encryption with the use of random number generation has been selected. Objective of the project: Creation of our own program for encrypting personal information submitted in the Kazakh language in Latin letters.
- Study of training material on cryptographic methods of information protection;
- Study of various types of ciphers;
- Creation of your own cipher;
- Creation of an encryption program;
- Creation of a decoder program.
Project hypothesis: The encryption program will cipher user's personal information in the Kazakh language securely and reliably. As a result of the project, a program was created that will encrypt any text in the Latin alphabet, including in the Kazakh language, using the principles of generating random numbers. This type of encryption can be used to create dynamic passwords for various electronic resources and to encrypt personal information.
Speech emotion recognition is gaining importance in various domains such as health care, product opinion mining, interactive voice-based assistant or caller agent conversation analysis. The existing system has some limitations in accuracy due to noise and variability in speech. In this work, we proposed CNN-LSTM-based model that overcome limitations by using a preprocessing technique called one hot encoder and produce more than the 80 percent of accuracy by the proposed model on dataset RAVDESS. The collected findings demonstrate that the suggested model performs comparably better than the existing models on the spectrogram and waveplots of the audio recordings on the same data.
The adoption of electric vehicle as the main transportation is increasing. The infrastructure is rapidly developing, especially to support the big city life. Medan City is one of the biggest city in Indonesia. This study aims to identify the factors affecting young generation to adopt electric vehicle as the main transportation mode. Using Technology Acceptance Model, some variables: knowledge, perceived ease of use, environmental concern, financial, usefulness, incentive policy, attitude and intention of use. The young generations in Medan City tend to adopt the electric vehicle as their transportation mode. The environmental concern dan knowledge of the electric vehicle relates each other that affecting the adoption the electric vehicle. There is no relationship between other variables
Niko Simamora is the lecturer in Engineering Management Study Program in Institut Teknologi Del. The research interest are mobility, transportation policy and management, tourism management, destination management.
Nature-inspired algorithms are a set of novel problem-solving methods and approaches that have attracted a lot of attention due to their good performance. Non-deterministic Polynomial (NP) challenging optimization problem that is inefficiently solvable using conventional methods. Thus, applying nature-inspired optimization approaches allows for a more precise resolution of the NP optimization of the clustering and they can solve complicated, multimodal issues, nature-inspired computing techniques are widely used in numerous challenging, practical optimization applications. Many different researchers have put out numerous nature-inspired algorithms over the past few decades. Comparing these algorithms to other traditional optimization techniques, several of them have been shown to be extremely effective. a young scientist attempting to use algorithms derived from nature to solve a problem. Not all problems can be solved by every algorithm. Some people outperform others. In order to make it easier for any newbie to comprehend the preceding path, we have attempted to describe a number of the top research proposals in this document. Here, we categorise the nature-inspired algorithms into three groups: natural-evolution based, swarm-intelligence based, biological science based and others. In this study, well-known algorithms influenced by nature include: GA,DE,ACO,PSO,BA,GWO,CSA have been studied. a thorough examination of several nature-inspired algorithms based on their inspiration, fundamental operators, control parameters, characteristics, variations, and the application domains in which they have been successfully used. It will also help in determining and narrowing down the approaches that are most appropriate for the problem.
nature-inspired algorithm; meta-heuristic algorithm; swarm intelligence algorithm; bio-inspired algorithm;
The most fascinating and revolutionary innovations of the last ten years has been cryptocurrency. Since the birth of Bitcoin in 2009, the cryptocurrency market has grown exponentially in value and popularity, with new coins and tokens being introduced every day. Predicting the price of cryptocurrencies has consequently gained popularity in the financial and technological sectors. We will look at the strategies and procedures we have used to forecast cryptocurrency prices in this research paper. Accurate prediction techniques should be developed in order to reduce risks and increase returns in the crypto market. The Long Short-Term Memory method will be utilized in this study along with the SVR and Ridge Regression algorithms to forecast the future price trend by learning past price trend information. In order to choose the best model, the data must be trained multiple times. Overall, the findings we get from this research paper will provide guidance for future research into bitcoin price forecasting using cutting-edge neural networks.
Price Prediction; LSTM; Cryptocurrency; SVR; Ridge Regression.
Mobile financial management is a tool that people usually used. This tool includes planning, budgeting money, and monitoring expenses. This study specifies the effectiveness of using this automated mobile financial manager called, Mr. FinMan: A Mobile Financial Manager that will suit everyone who is trying to plan and monitor their financial activities. In this, people will know how to have financial security and how to effectively set a journey or objective from the present condition to their desired period. The proposed system will provide a flexible and accurate result of data to the users. This automated mobile financial manager will be a huge help to individuals not only in guiding their financial activities but also in giving them advice on how to discipline their money wisely. This system will analyze the data that has been inputted data by the user and will give accurate result to them. It will lessen the hassle that user gets in budgeting their money, such as conserving their time and energy. This is also a friendly user system that will help the user to easily understand the usage or the utilization of the system. Biography: Experienced, passionate, and motivated computer science / information technology educator.
This study presents a small-scale water pumping system utilizing a fuzzy logic inference system attached to a renewable energy source. The result of the simulation was implemented in a microcontroller (Arduino Uno), together with two sensors (DHT22 for temperature and FC-28 for soil moisture) to gather data, two modules (real-time clock and secure digital card) to log the data, and photovoltaic cells. The study used a grand rapid variety of lettuce, organic substrates, and foliar for observation of the capability of the device to irrigate crops. The observation of the system took 22-31 days, which is one harvest period of the crop. Results showed a 22.55% increase in agricultural productivity compared to manual irrigation. Aside from reducing human effort, and time, the smart irrigation system could help lessen some of the shortcomings of manual irrigation. It could facilitate the economical utilization of water, reducing consumption by 25%. The use of renewable energy could also help farmers reduce the cost of production by,minimizing the use of diesel and gasoline. Also, the study includes a predicted model for the irrigation system of lettuce production, applying the decision tree algorithm. The system envisions to help farmers in setting up a controlled environment for the proper utilization of water and improving the mechanization of the irrigation system for the production of crops. There were 38266 observations taken from one harvesting period of the crop. The predictive capacity of the model was found to be almost perfect, considering the accuracy rate, precision, and recall of the model, which are 99.78%, 99.98%, and 99.82%, respectively.
Intelligent System, Data Analytics, Precision Agriculture, Digital Agriculture
Enhancing students' controllability awareness is critical for improving their mental health and reducing the risk of depression. Research conducted worldwide has revealed a disturbing prevalence of depression among students was 33.6%. Thus, wearable technology can be leveraged to address this issue to deliver persuasive strategies that encourage students to improve their controllability awareness. This technology can monitor physical activity, sleep patterns, vital signs, and movement characteristics to identify signs of depression or deterioration over time. In this study, a conceptual design model named integrated wearable persuasive-multimedia for controllability awareness (iWPM4CA) design model has been developed to investigate the potential of persuasion design and multimedia in wearable platforms to enhance one’s controllability awareness. This paper discusses the validation process of the iWPM4CA model through evolutionary prototyping, which was conducted to test the feasibility and desirability of the conceptual design model. Following a successful launch on the Google Play Store, this systematically designed wearable application, known as beHappy, has the potential to make a significant impact on enhancing controllability awareness among university students, thereby improving their mental health and well-being. The wearable application is a proof of concept that helps improve the iWPM4CA design model through the implementation of external feedback, identification of new requirements, and conformity of compatibility as new requirements are added.
Umi Hanim Binti Mazlan is a senior lecturer at the College of Computing, Informatics and Media. She obtained her Bachelor of Science (Computer Science) in 2008 and Master of Science (Computer Science) in 2009 from Universiti Teknologi Malaysia, UTM. In 2010, she joined Universiti Teknologi MARA Perlis Branch and Photograph Organization Logo taught there for about 13 years. She is currently pursuing her doctorate at Universiti Utara Malaysia.
Magnesium alloys grade AZ91 are known for their light-weight and good mechanical properties, making them attractive for various applications in the automotive, aerospace, and biomedical industries. Electrical Discharge Machining (EDM) is a non-conventional material removal process that can be used to machine these alloys. Copper electrodes are commonly used due to their high electrical and thermal conductivity. This study aimed to optimize the EDM parameters for machining AZ91 magnesium alloy using a copper electrode and the Taguchi method. The focus was on the evaluation of material removal rate (MRR), electrode wear ratio (EWR) and surface roughness as the response parameters. The study employed the Taguchi method to analyze the experimental results and determine the optimal combination of input parameters, such as pulse current, pulse duration, and duty factor, for the EDM of magnesium alloy grade AZ91 using a copper electrode. The findings can provide valuable insights for developing more efficient EDM processes, reducing machining time, and improving surface roughness in the machining of magnesium alloys.
Dr. Apiwat Muttamara is an expert in electrical discharge machining (EDM), with a Doctor of Engineering degree from Nagaoka University of Technology, Japan. He is currently a faculty member at Thammasat School of Engineering (TSE), Thammasat University, where he teaches and conducts research in the field of manufacturing engineering. He earned his Bachelor of Engineering degree in Industrial Engineering, and has since developed a strong expertise in EDM. With his extensive knowledge and experience, Dr. Muttamara is making significant contributions to the advancement of manufacturing processes and technologies.
Metamorphic malware poses a significant threat to conventional signature-based malware detection since its signature is mutable, and multiple copies can be created from metamorphic malware. As such, signature-based malware detection is impractical and ineffective. Thus, research in recent years has focused on applying machine learning- based approaches to malware detection. Profile Hidden Markov Model is a probabilistic model that uses multiple sequence alignments and a position-based scoring system. An enhanced Profile Hidden Markov Model (PHMM) was constructed with the following modifications: n-gram analysis to determine the best length of n-gram for the dataset, setting frequency threshold to determine which n-gram opcodes are going to be included in the malware detection, and adding consensus sequences to multiple sequence alignments of each malware family. 1000 malware executables files and 40 benign executable files were utilized in the study. Results show that n-gram analysis and adding consensus sequences help increase malware detection accuracy. And setting the frequency threshold that will involve more n-gram opcodes to take part in malware detection gives better accuracy in most of the malware families than just getting only the top 36 most occurring n-gram opcodes, which has been done in previous studies.
Ken Carlo D. Javier is a fourth-year student at the Pamantasan ng Lungsod ng Maynila (University of the City of Manila) where he is pursuing a bachelor’s degree in Computer Science. He was the Public Relations Officer of the PLM Computer Science Society. He’s the former Web Development Lead and Chief Technology Officer of Google Developer Student Clubs PLM. He worked as a software engineer intern at Dahslabs.ai. And he’s currently working as software engineer in GoMedia, a private- limited company under GoVA, a real-estate company based in Singapore.
currently the development of wireless communication is growing rapidly. One of them is the use of Long Range (LoRa) in various applications such as agricultural monitoring, health, animal husbandry and tracking systems. LoRa implementations are often used for long-distance transmission, so LoRa is becoming a promising technology as a future wireless communication standard for the Internet of Things (IoT). This is supported by its advantages such as long communication range, low cost, and low power Consumption. However, LoRa transmission is very sensitive to the surrounding environment such as buildings, trees, humans, hillsides and other frequency disturbances, making researchers conduct an analysis of its capabilities. In this study, we succeeded in analyzing moving objects (vehicles) as transmitter nodes. We analyze LoRa with distance and speed variables. Where it will be tested using vehicle speeds of 10 km/hour, 20 km/hour, and 30 km/hour to monitor movement tracking. This study also analyzes LoRa with the distance variable by measuring the RSSI, SNR, and packet loss values which aim to determine the appropriate performance in sending data in vehicle tracking in Line of Sight (LoS) and Non Line of Sight (N-LoS) areas. In the speed analysis it is concluded that the higher speed, the distance between points will be farther and the delay will be greater so that the displayed map will correspond to the actual path traversed by the vehicle.
LoRA, Line of Sight, Non-Line of Sight, Packet Loss, RSSI, SNR, Tracking.
This research is to find out the factors that affect employee performance through work discipline as a mediating variable. This study uses a quantitative approach and exploratory study through hypothesis testing. The study used a survey method with a total sampling of research subjects, namely employees of PT Bank Negara Indonesia (Persero) Tbk. UGM Yogyakarta Branch. The technique used is SEM modeling with the SmartPLS program. The results of data processing show that the relationship between variables is significant. Organizational climate has a direct effect on employee performance, training has a direct effect on employee performance, work discipline has a direct effect on employee performance, organizational climate has an indirect effect on employee performance through work discipline, training has an indirect effect on employee performance through work discipline. The results of data processing also show that the training independent variable is relatively more dominant than organizational climate in influencing employee performance. In addition, the results show that organizational climate, training, and work discipline variables jointly affect employee performance and are also influenced by other variables not examined in the model.
Tatar Bonar Silitonga, born in Deli Serdang, North Sumatra on July 25, 1967, is a TNI soldier from the Air Force branch. At present he has obtained a rank at the level of the first marshal of the Indonesian National Armed Forces. As a soldier, currently also serves as a lecturer at the defense management faculty of the Indonesian Defense University. As a lecturer, he is also active in researching and writing scientific articles and several books, both reference books and textbooks.
Metamorphic malware poses a significant threat to conventional signature-based malware detection since its signature is mutable. Multiple copies can be created from metamorphic malware. As such, signature-based malware detection is impractical and ineffective. Thus, research in recent years has focused on applying machine learning-based approaches to malware detection. Profile Hidden Markov Model is a probabilistic model that uses multiple sequence alignments and a position-based scoring system. An enhanced Profile Hidden Markov Model was constructed with the following modifications: n-gram analysis to determine the best length of n-gram for the dataset, setting frequency threshold to determine which n-gram opcodes will be included in the malware detection, and adding consensus sequences to multiple sequence alignments. 1000 malware executables files and 40 benign executable files were utilized in the study. Results show that n-gram analysis and adding consensus sequence help increase malware detection accuracy. Moreover, setting the frequency threshold based on the average TF-IDF of n-gram opcodes gives the best accuracy in most malware families than just by getting the top 36 most occurring n-grams, as done in previous studies.
consensus sequence, metamorphic malware, n-gram analysis, profile hidden Markov model, TF-IDF
This paper discusses computer vision-based human activity recognition. The major issue is being able to identify human behavior. The main issue for video categorization systems is common human actions in videos. For instance, a running motion will be included in a long jump or running sports film. Due to its multiple applications in areas like person monitoring, human-to- object interaction, and more, human action recognition is a crucial study subject in the science of computer vision. A pre-trained CNN model for feature extraction serves as the foundation for human action recognition. Deep learning methods include convolutional neural networks (CNN). The majority of convolutional neural networks (CNNs) used for recognition tasks are constructed using convolution and pooling layers, followed by a small number of fully connected layers, and identifying similar patterns in an interval to recognize the action with accuracy of 79–90% depending on the task. The computer vision community finds the video classification problem to be very difficult. The main reason that the video categorization problem is so challenging is the shared activities that are seen in the video. A high jump sport film, for instance, combines two distinct actions—running and high jumping—that are also shown in other videos, like running or hurdling sports videos. With just one frame that captures the specific action of the event, the human brain can quickly identify the correct occurrence in a film. By removing a few significant frames from the video and using those frames to conduct the classification procedure, the same premise may also be used to video classification systems.
Object Detection, Video Classification, open-CV, Human Activity Recognition, Human Behavior Analysis, CNN.
The public’s ability to get a great amount of valuable information from the online network has increased dramatically as a result of the popularity of the internet. Sentiment analysis, often known as opinion mining, uses machine learning and natural language processing (NLP) to automatically identify the emotional tone of online conversations. Sentimental analysis based on text information has developed into a useful tool in daily life that aids the public in enhancing the standard of living and the quality of goods. Word, phrase, and document levels are the three categories that make up sentimental analysis. In this main project, we carry out a number of duties involving reviews of drugs. We start by conducting a sentimental analysis. Sentiment analysis is a type of research that interprets and extracts opinions from reviews about a particular drug’s side effects, general contentment, and level of activity from users. Aspect-oriented sentiment analysis has been significantly impacted by the development of machine learning and deep learning
This research is to determine the influence of organizational climate and training variables on employee performance through work discipline as a mediating variable. This study uses a quantitative approach and exploratory study through hypothesis testing. The study used a survey method with a total sampling of research subjects, namely employees of PT Bank Negara Indonesia (Persero) Tbk.. The technique used is SEM modeling with the SmartPLS program. The results of data processing show that the relationship between variables is significant. Organizational climate has a direct effect on employee performance, training has a direct effect on employee performance, work discipline has a direct effect on employee performance, organizational climate has an indirect effect on employee performance through work discipline, training has an indirect effect on employee performance through work discipline. The results of data processing also show that the training independent variable is relatively more dominant than organizational climate in influencing employee performance. In addition, the results show that organizational climate, training, and work discipline variables together affect employee performance and are also influenced by other variables not examined in the model. Index Terms - Organizational Climate, Training, Work Discipline, and Employee Performance
Exposure to ionizing radiation results in the generation of reactive intermediate species such as singlet excited state, cation radicals, anion radicals, and free radicals that can kill cells or induce mutation of cells. Epigallocathecihn gallate (EGCG) and ascorbic acid (AA) are well known as good radical scavengers and antioxidants. Here, the radiation protection effects in the presence of EGCG and AA via scavenging process of free radicals (mainly hydroxyl radicals) were examined. Yeast cells were cultured in YDP liquid medium containing yeast extract, peptone, and dextrose/glucose in the presence and absence of EGCG and AA. In this study, cobalt-60 gamma rays and helium ion beams were used as the sources of ionizing radiation. The survival rates of yeast cells irradiated with various concentrations of EGCG and AA in the growth medium were analyzed and efficiencies of radiation protections were compared. Both of EGCG and AA play effectively important roles as a radiation-protective agent for yeast cells and the effectiveness in radiation protection of EGCG and AA were almost same between these two additives.
Antioxidant, Radical scavenger, EGCG, AA, radiation.
For the engineering industry and other related fields, additive techniques represent a versatile category of production processes. Compared to chip machining, moulding, and casting, they offer new manufacturing options for complex shape components. Designing efficient machine parts necessitates balancing limits and variations. Hardness and dimensions are crucial quality considerations. These elements have an impact on how a plant component is formed and functions. They describe the use of the part and the cost-effectiveness of production to create the finished product with the least amount of postprocessing, together with structural material characteristics. The goal of this study was to identify the idealhardness and surface roughness related process parameters for DMLS components. The process elements of hatch type, laser spacing, sintering speed, and post contouring will all be investigated in this study. Taguchi's orthogonal array was used in this study's statistical trial design. Data from the experiments were examined using ANOVA. Laser scan spacing, one of the process variables, has been found to have a significant influence on the hardness of items made using this technique.
DMLS; Hardness; Surface Roughness; 3D Printing; ANOVA;
The project suggests a reliable method to safeguard women.The project's objective is to give women security so they never feel powerless.This paper's major goal is to develop and execute a highly dependable method for shielding women from harassment.The system is composed of a variety of components, including an Arduino microcontroller, GSM, GPS, memory cards, buzzer, and shock circuit.When a woman is engaged in this activity, she must firmly grasp the trigger of the gadget when she feels threatened. When the device is turned on, the GSM (Global System for Mobile Communications) sends an emergency message to the specified mobile number while the GPS (Global Positioning System) tracks the device's current location.When an attacker attacks a woman, a shock circuit is utilized in self-defense to harm the attacker. Key words: GPS Tracker and GSM, Microcontroller, Women Safety, ESP32 MCU
The system presented in this document aims to reduce the penalty for Industrial Units using automatic power factor correction units. The microcontroller used in this project is Arduino Uno. Two zero-cross detectors are used to detect voltage and current zero-crossings. The time difference between the zero voltage pulse and the zero current pulse is created by the appropriate operational amplifier circuit in comparison mode and fed to the two interrupt pins of the microcontroller. The program takes over to open the appropriate number of relays from its outputs and insert a shunt capacitor into the load circuit until the power factor reaches unity. The capacitor bank and relays are interfaced with the microcontroller using a relay driver. It shows the delay time between current and voltage on the LCD screen. In addition, this project can be developed by using thyristor control switches instead of relay control to prevent contact pitting, which is often encountered in switched capacitors due to high inrush current. Keywords— Arduino Uno, Zero-crossing detectors, operational amplifier, LCD, thyristor, relay
Face detection techniques have been expanded since the 1960s, and advances in machine learning and deep learning have revolutionized the field. Face detection algorithms are widely used in various applications, including security, surveillance and social media. TheViola–Jones object detection framework is the first to give a competitive object detection rate in real-time, introduced by authors Paul Viola and Michael Jones in 2001. Although it can be trained to detect different object classes, that was primarily inspired by the faces detection problem. The most common features used for facial recognition include shape, color, and texture. These systems are often combined with other technologies, such as facial recognition, to improve accuracy and performance further. Face detection systems are becoming increasingly popular because they help identify people quickly and accurately. This review paper explores and reviews the basis of mathematical equations and the evaluation of various techniques and models, such as CNN-based models, their architecture and their pros and cons. In addition, facial colour, gender bias, dataset bias, and facial features in low-resolution images are still a challenge that needs to be worked on.
Digital predistortion (DPD) plays a significant role in wireless communication for smart systems like Internet of Things (IoT) applications in smart cities. It is employed in transmitters to mitigate distortions caused by nonlinearities, including those associated with amplifier characteristics and local oscillator leakage. This paper presents the Verilog implementation of the modified differential evolution (MDE) based optimization algorithm for the DPD. Compared to the conventional exhaustive search method which is computationally intensive, our implemented algorithm enables the identification of a best-fit DPD model from a combinatorically large model space in a short time. The digital predistortion of radio frequency power amplifiers (RF PA) using the modified differential evolution (MDE) algorithm enables us to derive optimum DPD models. It was found that, MDE based optimization technique for determining the DPD models of the PA, saves lot of computer resources and computational time. Verilog facilitates the hardware implementation of memory polynomials, enabling parallel processing. This capability leads to accelerated and optimized computation of the memory polynomial's output, rendering it well-suited for real-time applications with strict latency constraints. By implementing the memory polynomial in Verilog, it becomes possible to deploy it on FPGA or ASIC devices, unlocking the potential for hardware acceleration. This capability can greatly enhance computational performance and efficiency, making the memory polynomial well-suited for applications demanding real-time processing or high throughput.
I am Akanksha U Hiremath, I have completed my bachelors in Electronics and Communications, I was very much interested in VLSI and was looking for opportunities to enter this field. Before joining for my masters I worked in Tata Consultancy Services Pvt Ltd to gain some industry experience, post two years of working in an industry I was determined to go ahead and pursue higher education in VLSI which would open doors for me to enter this industry. So now I am in my final year of postgraduation striving and learning to achieve-
In passenger Cars A-pillar has an important role in the safety of a vehicle, at the time of a vehicle accident (vehicle front crash impact, roll-over of vehicle, side impact) this side pillar takes a large load to avoid cabin deformation to maintain safety. In this study, the A-pillar of passenger car vehicles has been analyzed in order to get maximum strength and avoid maximum damage to the passengers during accidents. This A-pillar is reinforced with the composite material of carbon fiber with some orientation angle and then applied layer by layer to A-pillar to improve its strength and minimize layer-wise damage of every separate ply. It is difficult to investigate the layer-wise damage of every separate ply in research hence, ANSYS software was used for this. The ANSYS software's advanced composite pre-post [ACP] tool serves to analyze these layers. Then layer-by-layer damage on the model is tested and this testing is completed after this we get the analytical results value which is 16988 N and in experimental analysis, we get 16990 N which is very close to the analytical results value. Hence, we conclude that A-pillar has reduced the chances of injuries Which is caused by vehicle accidents has reduced.
Reinforcement, Passenger Car, Composite Material, Safety.
Sales prediction is a critical aspect ofCbusiness strategy that can help organizations make informed decisions about inventory management, pricing, and marketing. Machine learning techniques have been increasingly employed in recent years to predict sales accurately. One of the most popular datasets used for sales prediction is the BigMart Sales dataset, which contains sales data for multiple products across various outlets of a retail chain. The motivation behind the model is that it helps various businesses to predict their sales so that businesses can perform on their supply chain and other traits to gain maximum profit. Python and machine learning has been used to analyze the BigMart Sales dataset and develop a model that can predict future sales for the retail chain. Various Machine learning algorithm were compared like, Linear regression, random forest regression, KNN and XGBoost. The results of Linear regression as well as Random Forest regression are the best fit for the model.
Machine Learning Techniques, Prediction, Sales Forecasting, regression, KNN etc.
In this study, the effect of surface tension on droplet spreading after the droplet was impacted on a solid substrate was investigated numerically. It aims to analyze when the surface tension of the liquid was changed how spreading diameter of the droplet was affected . Ethyl Alcohol and water is selected as liquid droplet. The study focused on three different contact angle where 60 0 have high surface wettability and 90 0 and 120 0 has low surface wettability. We also observed some amount of air is entrapped during the droplet impact on the solid substrate .In this we also discussed how the air entrapment was depends on surface wettability and surface tension. We observed the air entrapment is more with low surface tension (Ethyl Alcohol) liquid and less in high surface tension liquid. We investigate the heat transfer characteristics when the temperature of the wall is 360 k and the droplet temperature 303 k. It shows the effect of air entrapment in heat transfer from wall to fluid in application of rapid spray cooling. The impact of a liquid droplet on a solid surface has recently attracted the attention of researchers due to the rise in technical applications such as inkjet printing, spray coating, rapid spray cooling of hot surfaces, manufacturing of big displays, glueing, etc.
Project planning and scheduling plays a vital role in estimating the time and cost angle of a project. Finalizing a project on time and within the budget is challenging. This study focused on finding the shortest possible time required to complete the phase 1B Building project of the University Health centre, FUTA and also to evaluate the probability of completing the project within the stipulated time. This project work has been able to give a concise view about network analysis in building construction, definitions of some terms commonly used in network analysis was stated also. Also an introduction to CPM and PERT was stated, assumptions of CPM, advantages and disadvantages of CPM were stated, assumptions, advantages and disadvantages of PERT were also stated. Data analysis was carried out and all the possible route of the activities involved in the building project was found, and location of the critical path of the building project was also found, the expected duration for every activity in the building project was determine, the probability of completing the project within the stipulated time was evaluated. The results showed that there is a 50% chance for the project to be completed within the stipulated time. With the information gather on the building project, the project was delayed due to some factors such as low circulation of money, inadequate equipment’s, loss of some weeks due to a change in plan, and inadequate manpower, thus; there was two month difference between the initial completion and the now actual completion time.
Project, cost angle, network, critical path, manpower