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Available Transfer Capability (ATC) enhancement & optimization of UPFC shunt converter location with GSF in deregulated power system
Available Transfer Capability (ATC) is a measure for transmission system security margin in open access electricity market. Determining the Available Transfer Capability (ATC) of the transmission networks, Repeated Power Flow (RPF) approach have been used since it can satisfy voltage, thermal and stability constraints among all other methods available. The main objectives include identification of best location for UPFC to get maximum ATC enhancement and to propose a novel method for optimizing the UPFC PV bus location using Generation Shift Factor (GSF) so that power system transmission network can deliver more number of power trades. 2016 IEEE. -
Ban or boon: Consumer attitude towards plastic bags ban
In Tamil Nadu, the state government has imposed a ban on plastic bags two years ago. This has created a major impact of the day to day life of common people. Though it has positive effect on the environment, the common public had different perception as a consumer. This paper aimed at studying the consumer attitude towards the ban on plastic bags. A descriptive research design adopted to address the various dimension of consumer perception towards the ban on plastic ban. A sample size of 400 respondents was selected on the basis of systematic random sampling technique to collect data through structured questionnaire. For conducting the survey, consumers of retail shops in urban and rural places were chosen as target respondents. The collected data were analyzed with the help of statistical tools such as ANOVA, t-Test, Correlation, Linear Regression and Structural equation modelling and the interpretation reported. The result revealed that only 34 percentage of respondent were aware the environmental impact of plastic bags. About 71 percentage of consumers reported that they have faced difficulties in their day to day life due to plastic ban. 2021 American Institute of Physics Inc.. All rights reserved. -
Bard-Taylor ferroconvection with time-dependent sinusoidal boundary temperatures
The combined effect of centrifugal acceleration and time-varying boundary temperatures on the onset of convective instability of a rotating magnetic fluid layer is investigated by means of the regular perturbation method. A perturbation expansion in terms of the amplitude of applied temperature field is implemented to effectively deal with the effects of temperature modulation. The criterion for the threshold is established based on the condition of stationary instability manifesting prior to oscillatory convection. The modulated critical Rayleigh number is computed in terms of Prandtl number, magnetic parameters, Taylor number and the frequency of thermal modulation. It is shown that subcritical motion exists only for symmetric excitation and the destabilizing effect of magnetic mechanism is perceived only for asymmetric and bottom wall excitations. It is also delineated that, for bottom wall modulation, rotation tends to stabilize the system at low frequencies and the opposite is true for moderate and large frequencies. Furthermore, it is established that, notwithstanding the type of thermal excitation, the modulation mechanism attenuates the influences of both magnetic stresses and rotation for moderate and large frequencies. Published under licence by IOP Publishing Ltd. -
Barriers to Smart Home Technologies in India
Smart home technologies (SHT) are critical for effectively managing homes in a digital society. However, SHTs face challenges related to their limited use in developing country contexts. This study investigates the factors that act as barriers to SHT adoption among individuals in Bengaluru, India. The roles of perceived risk, performance and after-sale service, and demographics in using smart home technologies (SHT). This study used the data from the primary survey of 133 respondents. The collected data were analyzed using regression analysis. The results supported five of the proposed hypotheses, namely, perceived performance risk, perceived financial risk, perceived psychological risk, and technological uncertainty, which influence the Behavioral intention to adopt SHT. However, service intangibility is influenced by performance risk. Income and age influence the psychological risk and adoption of SHT. The study identifies the barriers to SHT adoption. The supportive environment for SHT needs to be strengthened to reduce the associated risks. IFIP International Federation for Information Processing 2024. -
Behavioral Time Management Analysis: Clustering Productivity Patterns using K-Means
This paper focuses on investigating the efficiency profile through the three-time management behaviors using the K-Means clustering method. In the case of the study, the data gathered from digital time management tools for 100 participants for one month was preprocessed to distil features surrounding productivity, including daily working hours, focus time, break duration and frequency, and task completion ratios. The four groups that were agreed upon through K-Means clustering differed in terms of time management behaviours and productivity. Insert table 6 IT cluster 1 worked long hours with high productivity owing to the fact that they are IT professionals but had a tendency of multitasking. Employment Cluster 2 (marketing and sales professionals) achieved both personal and work-related self-care but identified the need for more concentrated time per task. As for the differences in the breaks, it can be noted that cluster 3 (management and administration personnel) had significantly higher task completion times and focus times, but their break intervals needed to be optimized. Hypothesis 2 stated that there will be many hours of leisure for Cluster 4 (students and interns) imply that their work hours should be adjusted to several small tasks a day, and their rates of task completion should be increased. From the study, it is possible to stress that time management should be considered as an individual activity that requires specific approaches to the given subject area and to the learner in particular. Specifically, demographic profiling identified the roles that age and occupational status may play in averting or exacerbating productivity deficiencies: insights that could be actionable in specific scenarios. The implications of this research offer practical insights into individual and organizational time management, as the usability aspects of machine learning techniques were considered and their applicability established, which further extends the scope of time management by revealing patterns and improving time management plans and practices. 2024 IEEE. -
Behavioural Intention towards adoption of Robotic Accounting for a profitable leading digital transformation
Leading digital transformation accelerates impactful changes in business environments and work places and helps them thrive in this age dominated by physical, emotional, and financial disruptions. This is very much evident during the pandemic-induced current economic climate; the Robotic Process Automation (RPA) industry has been found to grow at an exponentially increasing rate throughout 2020, and based on the response towards it, it can be logically predicted that this trend will continue to be in vogue for several years into the future. The use of RPA technology enables auditing firms to not only automate business processes but also significantly improve the way the company currently completes tasks. In view of the above, the present study focuses on the nature of digital automation of business processes in auditing firms using RPA and its impact on revenue management and client engagement. The study proposes to make use of qualitative research methods and also aims to theorize the role of various antecedents that develop a strong intention among the auditing firms to adopt RPA for the purposes of accounting and auditing. 2022 IEEE. -
Beyond Automation: Understanding the Transformational Capabilities of AI in Management
The investigation explores at the various ways that artificial intelligence (AI) is affecting management techniques. The study highlights the dichotomy between automation and augmentation, highlighting how artificial intelligence (AI) can replace human work through automation, but its ultimate use in augmenting human capabilities (augmentation) leads to better organisational performance. This analysis reveals how AI-driven tactics enhance operational efficiency, decision-making, and productivity by synthesising research findings from a variety of domains, including manufacturing, banking, municipal sectors, and remote work environments. It also looks at how AI may change management through big data and data analytics, recommending a shift to an integrated strategy that combines automation and human understanding to promote creativity and long-term growth. 2024 IEEE. -
Beyond the Stats: How Investment Decisions Are Influenced by Non-Accounting Data
Making investment decisions is a complex process that is influenced by data from non-accounting and accounting sources. In order to better understand the importance of financial reports in comparison to non-accounting data [1], this article examines this complexity. The study is guided by three main objectives: determining the relative importance of financial reports against non-accounting sources; determining the effect of non-accounting information on investment decisions [2]; and investigating the role of demographic factors on this effect. The study finds that, when making investment decisions, shareholders more frequently turn to non-accounting sources through thorough analysis and statistical testing. Notably, credit rating agencies, stock indices, and brokers all have a big say in how decisions are made, highlighting their significance. This work improves our knowledge of how accounting and non-accounting data interact to influence investment decision-making. It emphasizes how crucial it is to take into account a variety of information sources in order to make wise financial decisions [3]. When navigating the ever-changing market landscape of today, investors, financial analysts, and politicians can benefit greatly from these ideas. 2024 IEEE. -
Bibliometric Analysis: A Trends and Advancement in Clustering Techniques on VANET
In recent years, Traffic management and road safety has become a major concern for all countries around the globe. Many techniques and applications based on Intelligent Transportation Systems came into existence for road safety, traffic management and infotainment. To support the Intelligent Transport System, VANET has been implemented. With the highly dynamic nature of VANET and frequently changing topology network with high mobility of vehicles or nodes, dissemination of messages becomes a challenge. Clustering Technique is one of the methods which enhances network performance by maintaining communication link stability, sharing network resources, timely dissemination of information and making the network more reliable by using network bandwidth efficiently. This study uses bibliometric analysis to understand the impact of Clustering techniques on VANET from 2017 to 2022. The objective of the study was to understand the trends & advancement in clustering in VANET through bibliometric analysis. The publications were extracted from the Dimension database and the VOS viewer was used to visualize the research patterns. The findings provided valuable information on the publication author, authors country, year, authors organization affiliation, publication journal, citation etc. Based on the findings of this analysis, the other researchers may be able to design their studies better and add more perception or understanding to their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Big Data Analytics Tools and Applications for Modern Business World
In the modern world, data is the unavoidable word. The digital environment in almost all our day to day life is linked with digital data. Effective data management is one of the important tasks. The gradual growth of technology in recent years, the generation of data has increased exponentially. Everything, ranging from sending a mail to simply browsing the internet generates data and this is collected and stored. This data has countless uses in various fields such as medicine, business, agriculture and marketing, but most of the time it goes unused. Business intelligence is a key factor in the current business world. Business growth is purely depending on technology. Technology is not only used in manufacturing it is applied to getting the customer. The data analytics is still in its earlier stages and has a long way to go before it yields favourable results. It is a good time as any to start working in this domain to utilize its prowess. This article has discussed the opportunities and growth of data analytics in the research domain. It can face soon when it reaches its advance stages. The big data is handling a larger amount of data in a conventional and non-conventional manner. Technology is playing a vital role to handle larger data from the database. This article is to discuss data analytics application in modern industry. In the technical perspective, big data Map-reduce is an advanced tool and for simulation part, R tool is used. 2020 IEEE. -
Bioinformatics Research Challenges and Opportunities in Machine Learning
This research work has studied about the utilization of machine learning algorithms in bioinformatics. The primary purpose of studying this is to understand bioinformatics and different machine algorithms which are used to analyze the biological data present with us. This research study discusses about different machine learning approaches like supervised, unsupervised, and reinforcement which play an essential role in understanding and analyzing biological data. Machine learning is helping us to solve a wide range of bioinformatics problems by describing a wide range of genomics sequences and analyzing vast amounts of genomic data. One of the biggest real-world problems is that machine learning is helping us to identify cancer with a given gene expression, which is done using a support vector machine. In addition, this study discusses about the classification of molecular data, which will help find out minor diseases. With the advancement of machine learning in healthcare and other related applications, data collection becomes a tedious process. This article also focuses on some of the research problems in machine learning domain. The uses of machine learning algorithms in bioinformatics have been extensively studied. These objectives will help to understand bioinformatics and different machine algorithms that are used to analyze the biological data. This research study presents different machine learning approaches like supervised, unsupervised, and reinforcement, which play an important role in understanding and analyzing biological data. Machine learning helps to solve a wide range of bioinformatics related challenges by describing a wide range of genomics sequences and analyzing huge amounts of genomic data. One of the biggest real-time challenges is that the machine learning is helping to identify cancer with a given gene expression, and this is done by using a support vector machine. Finally, this research study has discussed about the classification of molecular data, which will be helpful in finding out minor diseases. 2022 IEEE. -
Biomedical Mammography Image Classification Using Patches-Based Feature Engineering with Deep Learning and Ensemble Classifier
In order to reduce the expense of radiologists, deep learning algorithms have recently been used in the mammograms screening field. Deep learning-based methods, like a Convolutional Neural Network (CNN), are now being used to categorize breast lumps. When it involves classifying mammogram imagery, CNN-based systems clearly outperform machine learning-based systems, but they do have certain disadvantages as well. Additional challenges include a dearth of knowledge on feature engineering and the impossibility of feature analysis for the existing patches of pictures, which are challenging to distinguish in low-contrast mammograms. Inaccurate patch assessments, higher calculation costs, inaccurate patch examinations, and non-recovered patched intensity variation are all results of mammogram image patches. This led to evidence that a CNN-based technique for identifying breast masses had poor classification accuracy. Deep Learning-Based Featured Reconstruction is a novel breast mass classification technique that boosts precision on low-contrast pictures (DFN). This system uses random forest boosting techniques together with CNN architectures like VGG 16 and Resnet 50 to characterize breast masses. Using two publicly accessible datasets of mammographic images, the suggested DFN approach is also contrasted with modern classification methods. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Bioprospecting of Fungal Endophytes in Hulimavu Lake for Their Repertoire of Bioactive Compounds
Fungal endophytes hold a prominent position in the research world, in part due to the rich repertoire of bioactive compounds useful for industrial and environmental applications. The present study aims at bioprospecting few endophytic fungi isolated from Hulimavu lake flora (Bengaluru) for characterization of biological applications of their bioactive compounds. Among the lake plants screened, Alternanthera philoxeroides, Ricinus communis and Persicaria glabra were taken forward for isolation of fungal endophytes. Subsequent biochemical analyses were performed to quantify few fungal enzymes and bioactive compounds, followed by antimicrobial and cytotoxic assays. In conclusion, this pilot study aims to probe the plethora of bioactive compounds present in fungal endophytes that possess wide ranging biological properties. Due to the species richness and diversity of fungal endophytes across different host plants and habitats, bioprospecting fungal endophytes remains a very extensive yet promising topic for research, representing broad ranging environmental and industrial applications. The Electrochemical Society -
Bipolar Disease Data Prediction Using Adaptive Structure Convolutional Neuron Classifier Using Deep Learning
The symptoms of bipolar disorder include extreme mood swings. It is the most common mental health disorder and is often overlooked in all age groups. Bipolar disorder is often inherited, but not all siblings in a family will have bipolar disorder. In recent years, bipolar disorder has been characterised by unsatisfactory clinical diagnosis and treatment. Relapse rates and misdiagnosis are persistent problems with the disease. Bipolar disorder has yet to be precisely determined. To overcome this issue, the proposed work Adaptive Structure Convolutional Neuron Classifier (ASCNC) method to identify bipolar disorder. The Imbalanced Subclass Feature Filtering (ISF2) for visualising bipolar data was originally intended to extract and communicate meaningful information from complex bipolar datasets in order to predict and improve day-to-day analytics. Using the Scaled Features Chi-square Testing (SFCsT), extract the maximum dimensional features in the bipolar dataset and assign weights. In order to select features that have the largest Chi-square score, the Chi-square value for each feature should be calculated between it and the target. Before extracting features for the training and testing method, evaluate the Softmax neural activation function to compute the average weight of the features before the feature weights. Diagnostic criteria for bipolar disorder are discussed as an assessment strategy that helps diagnose the disorder. It then discusses appropriate treatments for children and their families. Finally, it presents some conclusions about managing people with bipolar disorder. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Blast resistance of steel plate shear walls designed for seismic loading
Since a blast loading or explosion can create nonlinear wave action and impact pressure on a structure, it necessary to construct a structure to resist blast loading as like other loads. In this study the nonlinear behaviour of a blast loading is simulated by calculating the pressure diagram with respect to time under the guidance of IS 4991-1968, code for "Criteria for Blast Resistant Design of Structures for Explosions above Ground". The study carried out for different charge weight (100kg TNT, 200kg TNT and 400kg TNT) and standoff distances of 20metre. Nonlinear behaviour of a Blast loading to steel structures with shear plates of thickness 6 mm, 8 mm and 10 mm are modelled in ETABS and the analysis is carried out to obtain base shear, story displacement, story deformation pattern, column forces, etc. Published under licence by IOP Publishing Ltd. -
Blockchain Computing: Unveiling the Benefits, Overcoming Difficulties, and Exploring Applications in Decentralized Ledger Infrastructure
The protocol known as blockchain, which is composed of blocks, utilizes a decentralized distributed system of nodes (miners). There are three parts to every block: information, which is represented by a hash, and the hash of a previous transaction. In order to regulate data after it has been stored, it is quite difficult to make changes. Mining is compensated for each encrypted function computation they carry out to verify the transaction. This research paper will provide a comprehensive understanding of blockchain-based technologies and how they are applied in a variety of industries, including those that deal with digital currencies, financial services, medical manufacturing, privacy, and a number of other fields. Digital money, notably the cryptocurrency Bitcoin, had previously been one of the most well-known network applications. As there have lately been several studies about the unique utilization of this sort of technology, we will discuss some of these academic works as well as the challenges encountered during the development of these kinds of applications. Blockchain technology is a quickly growing area of database technology that has recently found use in a wide range of industries, including the use of digital money, hospital administration, and other academic subjects. Because of how blockchain technology works and operates, these types of applications are now possible. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Blockchain Empowered IVF: Revolutionizing Efficiency and Trust Through Smart Contracts
Couples who are having trouble becoming pregnant now have hope thanks to in vitro fertilization (IVF), a revolutionary medical advancement. However, the IVF procedure calls for a large number of stakeholders, intricate paperwork, and highly confidential management of information that frequently results in inaccuracies, mistakes, and worries about data confidentiality and confidence. In this study, the revolutionary potential of the blockchain and smart contracts enabling the treatment of IVF is investigated. The IVF procedure may be accelerated by utilizing smart contracts, resulting in improved effectiveness, openness, and confidence among everybody involved. The paper explores the primary advantages of using smart agreements in IVF, including automation, implementing obligations under contracts, doing away with middlemen, assuring confidentiality and anonymity, and enabling safe and auditable operations. The implementation of electronic agreements and blockchain-based technologies in the discipline of IVF is also investigated, along with the problems it may face and possible alternatives. This study offers insightful information about the use of intelligent agreements and blockchain technology in the field of IVF, accompanied by conducting an in-depth evaluation of the literature on the topic, research papers, and interviews with professionals. The results demonstrate the possibility of lower prices, more accessibility, higher success rates, and better patient experiences in the IVF field. In general, this study intends to illuminate how blockchain and smart contracts have revolutionized IVF technological advances, opening the door for a more effective, transparent, and reliable IVF procedure. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Blockchain Enabled Model for Minimizing Post Harvest Losses
Post-harvest loss (PHL) leads to both decline in quantity and quality of food processing output from harvest to consumption. They can be caused by a wide range of circumstances, from growth conditions to retail handling. As storage loss is considered one among the detrimental factors, in this study, 25 data units were collected from a cold storage facility to analyze and focus specifically on post-harvest losses of vegetables. Various data analysis was carried out using SPSS tool. It was found that majority of losses were due to pest infection, weight losses due to climatic conditions, and transportation losses. On the other hand, block chain being a trend setter in the recent technology evolution which is providing fruitful outcomes in all the integrated fields, we have chosen the same for obtaining a better solution for the afore mentioned problem. Integrating blockchain technology into the structure can significantly reduce storage losses and support producer-consumer lines. The Electrochemical Society -
Blockchain Integrated Pharmaceutical Cold Chain: An Adoption Perspective
A complex and sensitive chain needs to be appropriately maintained to manage public health and people's lives. This is especially true of the cold pharmaceutical chain. The primary objective of this study is to explain how blockchain adaption might meet a pharmaceutical cold chain's requirements. A comprehensive technological adoption model, partial least square structural equation modeling, and a quantitative cross-sectional survey approach were utilized to identify stakeholder adoption intentions toward a blockchain-enabled cold supply chain. This study provides evidence that blockchain technology has the potential to support the objectives of the cold pharmaceutical chain. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Blockchain Integrated Retail Logistics Chain: An Adoption Perspective
The task of managing public health and safety is a multifaceted and delicate one that demands the careful upkeep of numerous processes and systems, with a particular emphasis on cold chain logistics. The primary objective of this research is to investigate how blockchain technology can meet the needs of a retail cold chain. To accomplish this goal, we employed a comprehensive technological adoption model, partial least squares structural equation modeling, and a quantitative cross-sectional survey approach to ascertain stakeholder adoption intentions toward a blockchain-enabled cold supply chain. Our findings suggest that blockchain technology has the capacity to effectively facilitate the goals of the retail cold chain. 2024 IEEE.