VOLUME 8, ISSUE 12, DECEMBER 2020

Abstract: The present work proposes a prototype of the ARDUINO UNO application as an oximeter, with components that are obtained in the national market and with low power consumption, as well as being portable, economical and easy to use; and with this it can be built to make it affordable in homes to monitor the detection of oxygenation levels present in the blood in health emergencies.

Keywords: ARDUINO, oximeter, blood oxygen saturation, reductive hemoglobin, oxyhemoglobin, optical sensors.

Abstract: This paper proposes interval type 2 fuzzy proportional–integral–derivative controller plan technique and it is connected to the non-straight framework continuous stirred tank reactor. Interval type-2 fuzzy controller is composed such way that it is autonomous of process. To comprehend the impact of impression of uncertainty on the controller’s execution to two type of control bend to be specific forceful and smoother control bend are outlined. Popov-Lyapunov technique is utilized to discover stability of the framework.

Keywords: Interval type 2 fuzzy, continuous stirred tank reactor.

Abstract: The existing systems are either an electronic energy meter or an electro-mechanical meter which are currently in use is limited to record up to kWh units. The kWh units recorded by meter readers monthly, on foot which need to be processed by a meter reading company. A GSM based Energy meter with instant billing facility is introduced is efficient, but still the problem of missing SMS will degrade the accuracy and performance. A more reliable and user friendly system by creating web portal for multiple access using the advanced Visual studio .net frame work which will manage the data efficiently even if there is loss of SMS. It makes the design different from the previous proposals and also increases the throughput. The GSM/GPRS channel is a very useful means of communication as sending data as SMS turns out to be a very handy tool, due to its good area coverage capability and cost effectiveness. The front end web portal is User friendly and any employee with minimum knowledge of computers can work on this software. Employees can read the meter by sitting in their office. [1]

Keywords: Automatic Meter Reading System (AMRS), GSM, PIC, Short Messaging System (SMS), Visual Studio .NET

Abstract: The paper describes the modelling and simulation of a Distribution Static Synchronous Compensator (DSTATCOM) which injects the current at the point of common coupling in the network to eliminate or mitigate the voltage sag. It shows the capability and performance of DSTATCOM to mitigate and void out the voltage sag & a control technique based on Bang Bang controller and Hysteresis current controller is modelled to check the results. A VSC based D-STATCOM is modelled using MATLAB simulink tool. Thus the simulations are performed without & with DSTATCOM. Simulation results lead to the showings that the D-STATCOM mitigates voltage sag as well as improves power quality of system.

Keywords: DSTATCOM, Voltage Sag, Power Quality, VSI, Distribution System, Induction motor, Hysteresis Controller

Abstract: The purpose of the study was to effects of Plyometric exercise on Athletic Power in Women Volleyball players. The 25 volleyball players wer selected for sample size of the study and their age ranged between 20 -25 years. Only training was given to the experimental groups. Exclusion criteria were the presence of chronic medical conditions such as asthma, heart disease or any other condition that would put the subjects at risk when performing the experimental tests. The Plyometric exercise training program was planned as 12 weeks 4 day a week and 30 minutes in a day. The Plyometrics exercise includes Front Box Jump, Lateral Box Jump, Weighted Lateral Jumps, Broad Jumps,Skater Jumps, Scissor Jumps, Dot Drill,Lateral Box Shuffles, Athletic Power Athletic power measured by using the Standing Broad Jump etc . The result reveals that there was significant ieffect of Plyometric exercise on athletic power (p<0.05) in female volletball players. The athletic power significantly, improve due to Plyometrics exercise

Keywords: Plyometric exercise, Volleyball, Women, Athletic Power

Abstract: The purpose of this study was to assess the Self-efficacy between Indian and foreigner students. Total 250 Indian and 67 foreigner students from different university in India were selected for data analysis of the study. To measure academic self-efficacy, Yuen and his colleagues (2004B) Academic Self-efficacy scale extracted from the Life Skills Development Inventories were used. The findings of the study shows that only significant difference of time management was found between Indian and foreigner students. Foreigner students was found to have gor better time management skills as compare to their counterparts.

Keywords: Study skills, Time management, Involvement in Learning, Critical and creative thinking

Abstract: Digital transactions are becoming faster and easier, providing numerous benefits to businesses and consumers alike. However, the increase in digital transactions is also fueling a rapid rise in digital payment fraud, an illicit phenomenon that attempts to exploit or bypass the control systems of online banking services. For banks and credit card companies, consumer protection against system failure is critical, and fast and real-time solutions are essential. Most traditional fraud detection methods rely on either approaches based on customer behavior, such as analysis of past transactions, or supervised learning based on domain-specific features, such as fraud rules and regulations. However, the rush to implement new services has left many banks and credit card companies ill-prepared to protect consumers from losses during online or mobile transactions. In this paper, we explore the use of novel machine learning techniques in the detection of online payment fraud. We explore the idea of deploying high-performing detections systems for real-time detection of payment fraud, while refining and learning local classifiers that can help even in situations that are very peculiar to a region. In addition, Big Data, if used in combination with proper validation processes, can facilitate rapid analysis of fraud schemes that require real-host interaction through the use of transaction fingerprinting techniques. This provides crucial information that could possibly lead to the unmasking of the true perpetrator. Furthermore, the use of actual user profiles can provide additional valuable data, but great care must be exercised to protect sensitive digital assets. Since it takes only seconds to commit digital payment fraud, business organizations must have not only the right technological framework, but also a detailed design and well-defined functional processes. By utilizing powerful detection systems, and also properly designing and implementing functional business processes, business organizations can implement a time-critical detection framework to efficiently handle the inherent unpredictability of this digital security threat.

Keywords: Real-time fraud detection, digital payments, machine learning, big data analytics, anomaly detection, transaction monitoring, behavioral analytics, supervised learning, unsupervised learning, feature engineering, predictive modeling, streaming data processing, fraud prevention, payment security, data pipelines, real-time scoring, model training, model deployment, decision trees, neural networks, support vector machines, clustering, risk scoring, false positive reduction, precision and recall, scalability, data lake, data ingestion, data enrichment, latency minimization, financial fraud, AI-driven security, fintech analytics.

Abstract: Besides traditional on-premise or virtual data centers, a growing number of companies process their data in cross-border cloud environments. The taxation of such companies is a highly regulated task. As a result, protecting sensitive data is of utmost importance, while also keeping records available on their whereabouts due to potential requests from financial authorities. The same challenges can be observed in other business areas, such as the health sector, with sensitive patient data. This paper presents early design considerations on how to orchestrate cloud services while being able to comply with existing laws and regulations. To this end, a coherent architecture is outlined and key building blocks are explained in detail. In order to validate compliance with the orchestration-specific designs, the architecture must be set up to create appropriate compliance enforcement strategies.
To comply with the laws and regulations explicitly, the authors introduce initial design considerations on the high-level architecture to orchestrate a cross-border data pipeline in a compliant manner. Further, the key components of the architecture are discussed. In general, compliance verification measures need to be reusable on different data flows without additional manual effort. Such measures can be captured in a compliance enforcement strategy, which must be preconfigured for every set of compliance requirements. Authors envision that in order for information that flows between blocks of a data flow to be compliant, it has to conform to associated strategies, just like how a business process is expected to follow a business process model. Cloud computing has raised new opportunities and challenges for information and knowledge management. Due to the availability of ever greater computational resources and data, the development of ever more complex algorithms and statistical data analysis methods, data-driven decision making is on the rise. Compliance with existing regulation frameworks is a key challenge when building and operating cross-border data pipelines.

Keywords: Data Security, Regulatory Compliance, Cross-Border Data Transfer, Secure Data Pipelines, Data Encryption, Tax Consulting, GDPR Compliance, Data Sovereignty, Audit Trails, Data Governance, PII Protection, Real-Time Monitoring, Compliance Automation, Cloud Security, International Tax Regulation.

Keywords: Green software, cloud-native, autoscaling, data tiering, server- less, energy efficiency, insurance workloads, Cloud-Native Architecture, Energy Efficiency Optimization, Green Computing, Scalable Microservices, Work- load Orchestration, Cost-to-Energy Ratio, High-Volume Transaction Processing, Sustainable IT Infrastructure, Dynamic Resource Allocation, Insurance Workflow Automation.

Abstract: Adolescent mental health has become a pressing public health concern, especially during the coronavirus disease 2019 pandemic, in light of prolonged lockdowns. A body of research has documented increased rates of depression and anxiety, and their evolution during subsequent lockdown periods. Notably, many studies indicate that few adolescents seek professional help during these distressing times. In line with health services utilization theory, these findings suggest a disconnect between expressed need and help-seeking behavior. Such research provides a wealth of information concerning the availability, accessibility, and effectiveness of mental health services during the pandemic. Perspectives on adolescents’ mental health service utilization—that is, whether and when to access services—have, however, remained marginal. By integrating information from multiple sources, these utilization patterns can be better understood. The present abstract aims to provide an overview of the determinants of adolescents’ mental health service seeking during prolonged periods of lockdown. The discussion focuses on issues within the adolescent’s life context and perceived needs, along with important influences originating from parents, the broader social environment, and the health system.

Service utilization trends can shed light on whether adolescents sought care when required, and the reasons that might explain patterns. Evidence suggests an overall increase in the need for psychological care during lockdown periods, reflected in rising demand from both adolescents and their parents. However, adolescents appear to prefer communicating their emotional distress with family members or friends instead of professionals. Most adolescents experiencing risk factors for depression and anxiety do not seek help, even though those with higher levels of perceived need are likelier to do so. Help-seeking behavior is influenced by multiple factors, including age, gender, ethnicity, socio-economic status, stigma, and accessibility. Survey results indicate that stigma represents one of the main barriers to seeking help.

Keywords: Adolescent mental health, service utilization, lockdown effects, telehealth, accessibility, stigma, Adolescent mental health, Mental health service utilization, Prolonged lockdowns, COVID-19 pandemic impact, Tele-mental health services, Access to mental health care, psychological distress in adolescents, Health-seeking behavior, Barriers to mental health services, Youth coping and resilience.

Abstract: Research addresses Statistical Quality Control (SQC) techniques designed specifically for semiconductor manufacturing processes. Packaging, test, and assembly steps normally receive little SQC analysis although proper control is equally important. Steps assigned high defect rates require special attention to offset the effect on product yield. SQC tools directly influence semiconductor production yield, for both internal process yield and wafer-scale yield. The SQC solutions incorporate process capabilities, control charts, and sampling strategies. Recommended charts and control limits for all critical processing steps are provided by data from a major electronic product manufacturer. Special CUSUM and EWMA charts assess subtle process drifts. Process capability indices for non-normal data address the unique nature of defect counts. Bayesian techniques allow prior information in the analysis of any semiconductor step and posterior updating improves decisions on unknown parameters such as process mean and variance. A full integration with yield and defect analysis quantifies the contribution of control on yield improvement. Further development of statistical techniques is supported as overwhelming volumes of process data made real-time monitoring possible.

Keywords: Data; Tax compliance monitoring; Public revenue systems; Decision support; Policy evaluation; Policy design; Administration arrangements; Policy instruments; Tax gaps; Revenue risk.

Abstract: Optimizing the ingestion, transformation, and storage of real-time healthcare data pipelines can enhance information quality and enable critical alert systems and advanced analytics. Stream processing architectures, telemetric data models, and smart telemetric enrichment guarantee low-latency data delivery while upholding provider and regulatory data quality constraints. A rich set of storage technologies meets real-time and near-real-time data availability needs and enables high-performance analytics. Practical considerations such as security, compliance, and operability further strengthen streaming data flows, which support timely detection of critical patient events, accelerate population health insights, and shed light on the impact of data changes on clinical workflows.

Healthcare information systems face increasing pressure to deliver and utilize data in near-real time. Optimized end-to-end pipelines—tailored for ingestion, transformation and enrichment, and storage—boost data quality and internal responsiveness while unlocking more complex external uses, such as alerts for critical events. Timely detection of incidents ranging from falling out of bed to rapid deterioration can help mitigate patient harm and improve clinical outcomes. Broad support for population health and research is likewise crucial, particularly as data privacy legislation toward more open sharing of health data begins to emerge. Acknowledging operational domains and feasibility for wider adoption enables a more complete roadmap for practical implementations.

Keywords: Real-time pipelines, healthcare data, streaming analytics, data governance, privacy, interoperability, operational reliability.

Abstract: Simulating the market demand and supply interaction on individual banks and applying econometric techniques on the simulated bank data to statistically establish a significant operational relationship between the banking industry and the real sector of the economy. Stress-testing models have also been developed for some aggregate banking sector indicators for work. Cost and benefits of fraud detection solutions were evaluated to substantiate the business justification and return on investment for building, maintaining and supporting fraud detection solutions. The NIST Big Data Interoperability Framework and the DAMA-DMBOK framework were utilized to define the data ecosystem along six perspectives—data sources, data governance, data quality, data storage, data privacy and data provenance. The analysis included a taxonomy of financial fraud, and the definitions and characteristics of fraud detection, risk assessment and fraud monitoring. Some of the trends emerging in the digital banking ecosystem were also discussed. Banks need to achieve a balance in the prevention and detection of fraud, which is seen as never ending and deserves constant attention. The playbook defines how the operation teams respond to fraud. Such resources should ideally reside in the same location and have clear visibility for decision-making.

Given the extensive amount of available data, fraud detection solutions for banks require solid scientific foundations. Data-driven approaches that leverage machine learning and data-mining techniques are therefore being explored as tools for improving the prediction and detection of fraud events. Supervisory authorities require banks to incorporate fraud detection systems into their digital banking ecosystems. Such Data Driven Decision Systems must be capable of automatically detecting suspicious activity within acceptable limits of cost, accuracy, risk, coverage and performance level without too many false positives and as few wrong flags as possible.

Keywords: Banking; Big Data; Cloud Computing; Data Mining; Digital Forensics; Decision Theory; Risk Management; Real-time Analytics; Telecommunication; Fraud Detection.eva.