Real-time Performance Monitoring Research Articles

Optimalisasi Pengawasan Kinerja Karyawan Business Consultant PT XYZ : Implementasi Sistem RACI Melalui Project Google Spreadsheet

Employee performance monitoring is one of the key factors in human resource management, especially in a dynamic business sector such as futures trading. PT XYZ, a company engaged in futures trading, faces challenges in managing the performance of Business Consultants (BC) which often arise due to lack of clarity in the division of tasks, lack of accountability, and ineffective coordination. This study aims to optimize employee performance supervision at XYZ through the implementation of the RACI (Responsible, Accountable, Consulted, Informed) system integrated with Google Sheets. The research method used is qualitative with a case study approach, where in-depth interviews, observation, and documentation are used to collect data from employees and project managers who are directly involved. The results showed that the RACI system through Google Spreadsheet can clarify employee roles and responsibilities, increase accountability, and facilitate real-time performance monitoring. The modification of the RACI matrix in this spreadsheet also makes it easier for management to improve team collaboration and productivity. Despite technical challenges in its implementation, the system proved effective in optimizing team performance and communication.

Performance Predictions of Solar-Assisted Heat Pumps: Methodological Approach and Comparison Between Various Artificial Intelligence Methods

The coefficient of performance (COP) is a crucial metric for evaluating the efficiency of heat pump systems. Real-time monitoring of heat pump system performance necessitates continuously collecting and processing data from various components utilizing multiple sensors and controllers. This process is inherently complex and presents significant challenges. In recent years, artificial intelligence (AI) models have increasingly been applied in refrigeration, heat pump, and air conditioning systems due to their capability to identify and analyze complex patterns and data relationships, demonstrating higher accuracy and reduced computation time. In this study, multilayer perceptron (MLP), support vector machines (SVM), and random forest (RF) are used to develop COP prediction models for solar-assisted heat pumps. By comparing the predictive accuracy and modeling time of the three models built, the results demonstrate that the random forest model achieves the best prediction performance, with a mean absolute error (MAE) of 2.42% and a root mean squared error (RMSE) of 4.01% on the train set. On the test set, the MAE was 2.35% and the RMSE was 3.84%. The modeling time for the RF model was 6.57 s.

Santa María Academy: Shaping the Heroes of Today with Tomorrow's Technology

Santa María Academy has successfully integrated advanced technology into its educational framework to enhance the learning experience of its students, preparing them for future careers in law enforcement and the military. The academy's innovative approach includes personalized coaching, interactive resources, and smart tools that cater to individual student needs. By leveraging artificial intelligence and real-time performance monitoring, Santa María Academy has significantly improved student outcomes, motivation, and engagement. This case study explores the key elements of their success, including the implementation of personalized learning plans, interactive podcasts, a smart syllabus, and a real-time performance dashboard. The academy's model serves as a benchmark for educational institutions aiming to merge traditional values with cutting-edge technology to produce well-rounded and highly skilled graduates.

Label-Free Electrochemical Sensor Sensitivity Enhancement Using Field Effect Transistors: Fluoride Ions for Fuel Cell Degradation Analysis

In the context of proton exchange membrane fuel cells, precise monitoring of fluoride ions in the effluent water is crucial. Associated with the fluoride ion accumulation, membrane degradation and reduction of fuel cell efficiency arise. This study focuses on developing extended gate field-effect transistors (EGFET) sensors for real-time monitoring of fluoride ion concentrations. High sensitivity and specificity in detecting the concentration of fluoride ions using both N-type (TN0702) and P-type (LP0701) metal-oxide-semiconductor field-effect transistors (MOSFET) were achieved. The transfer characteristics (Id vs Vgs) owere thoroughly examined across a range of fluoride concentrations from parts per million (ppm) to parts per billion (ppb). N-type MOSFET-based sensors showed a distinct decrease in drain current (Id) with increasing fluoride concentration. With fluoride concentrations changed, the P-type MOSFET exhibited a notable shift in the threshold voltage (Vgs). A robust linear relationship was observed on calibration curves with R2 values exceeding 0.90 between the natural logarithm of fluoride concentration (lnC) and gate-source voltage (Vgs). This work demonstrates that MOSFET can significantly enhance the sensitivity of electrochemical sensors without complex labeling processes. The improvement has a future in the miniaturization of cost-effective sensor systems for fuel cell applications, facilitating real-time performance and degradation monitoring.

Comprehensive framework for data fusion in distributed ledger technologies to enhance supply chain sustainability

This review proposes a comprehensive framework that integrates data fusion with Distributed Ledger Technologies (DLT) to enhance sustainability in supply chain management. In today’s global supply chains, ensuring transparency, efficiency, and environmental responsibility is critical, yet the lack of real-time visibility and data fragmentation presents significant challenges. The framework addresses these issues by merging data from multiple sources, including IoT devices, operational databases, and external environmental factors, using advanced data fusion algorithms. DLT, with its decentralized, immutable, and transparent nature, ensures the integrity and security of the data, allowing all stakeholders to access accurate and tamper-proof information. The fusion of data within a DLT infrastructure not only improves traceability and accountability but also enables the automation of sustainability checks via smart contracts. These contracts can trigger actions based on predefined sustainability metrics such as carbon emissions, energy consumption, and resource efficiency. Furthermore, predictive analytics and machine learning algorithms integrated into the system provide real-time monitoring and optimization of sustainability performance throughout the supply chain. The proposed review offers numerous benefits, including enhanced transparency, reduced operational costs, improved sustainability outcomes, and risk mitigation. It also addresses challenges such as scalability, data privacy, and regulatory compliance, offering solutions to overcome these hurdles. By exploring case studies of successful implementations, this review demonstrates the practical applications and future potential of combining DLT and data fusion for sustainable supply chain management, positioning it as a critical tool for organizations aiming to meet environmental and regulatory demands in an increasingly digital and eco-conscious world.

Transparency as a Determinant of Financial Performance in State-Owned Commercial Enterprises in Kenya

State-owned commercial enterprises (SOCEs) in Kenya register below-par output in fiscal standards. Many explanations have been proffered for this state of affairs, including competition and fluctuations in the local and global economies. Additionally, it is important to examine how factors internal to these enterprises shape their financial performance. As such, this study aimed to examine the role of transparency as a corporate governance strategy in the fiscal output of state-owned commercial enterprises as anchored by legitimacy theory. Further, the study adopted explanatory research design. It targeted 476 board members and 379 corporate executives from 46 SOCEs in Kenya. The sample size was 295, consisting of 142 corporate executives and 153 board members, who were selected via stratified random sampling. To collect primary data, a questionnaire and document analysis were deployed. Data was analysed using descriptive statistics. Test for relationship between variables was done using covariance-based structural equation modelling (CB-SEM) along with Analysis of Moment Structures (AMOS) software. The study results revealed that transparency (estimate = 1.946, critical ratio = 3.676, p < 0.001) positively and significantly influenced the financial performance of SOCEs. It was thus deduced that transparency was a determinant of SOCEs’ fiscal output in Kenya. Therefore, SOCEs should deploy centralized real-time monitoring of financial performance through dashboards, build data management capacities and adopt business intelligence and data analytics technologies that allow for data driven and intelligence-based decision-making by the SOCEs’ board and corporate executives.

Real-Time Monitoring of Human and Process Performance Parameters in Collaborative Assembly Systems using Multivariate Control Charts

With the rise in customized product demands, the production of small batches with a wide variety of products is becoming more common. A high degree of flexibility is required from operators to manage changes in volumes and products, which has led to the use of Human-Robot Collaboration (HRC) systems for custom manufacturing. However, this variety introduces complexity that affects production time, cost, and quality. To address this issue, multivariate control charts are used as diagnostic tools to evaluate the stability of several parameters related to both product/process and human well-being in HRC systems. These key parameters monitored include assembly time, quality control time, total defects, and operator stress, providing a more holistic view of system performance. Real-time monitoring of process performance along with human-related factors, which is rarely considered in statistical process control, provides comprehensive stability control over all customized product variants produced in the HRC system. The proposed approach includes defining the parameters to be monitored, constructing control charts, collecting data after product variant assembly, and verifying that the set of parameters is under control via control charts. This increases the system's responsiveness to both process inefficiencies and human well-being. The procedure can be automated by embedding control chart routines in the software of the HRC system or its digital twin, without adding additional tasks to the operator's workload. Its practicality and effectiveness are evidenced in custom electronic board assembly, highlighting its role in optimizing HRC system performance.

Development and implementation of a raspberry Pi-based IoT system for real-time performance monitoring of an instrumented tractor

The tractor serves as a crucial power source in agricultural operations. However, the tractor's power often remains underutilized due to a mismatch between the tractor and implement, considering specific field conditions. To enhance system output, it becomes vital to acquire data on performance-related parameters for the tractor-implement combination. In this study we develop a real-time Instrumented Tractor Performance Monitoring System (ITPMS) using the Internet-of-Things (IoT). This system consists of a Raspberry Pi, a GPS sensor, a proximity sensor, a rotary potentiometer, and a three-point hitch dynamometer. The rotary potentiometer measures tillage depth, while the three-point hitch dynamometer used to measure data on draft force. Proximity sensors are installed on a two-wheel drive (2WD) tractor to measure forward speed and drive-wheel slip. We establish a dedicated web server using a Google® Firebase® project to store data from all sensors through Raspberry Pi. Additionally, we design a web interface and a mobile application to provide real-time data generated from the sensors. Field experiments were done to evaluate and monitor the performance parameters of the tractor-implement combination utilising the developed ITPMS. The results demonstrate that the system effectively monitors the performance parameters necessary for tractor-implement combination. Furthermore, the system's capability to update data to the IoT server in real-time is validated. Overall, the development and implementation of this Raspberry Pi based IoT system, provides a reliable and efficient solution for real-time performance monitoring of instrumented tractors.

Effects of Progressive and Velocity-Based Autoregulatory Resistance Training on Lower-Limb Movement Ability in Taekwondo Athletes.

Both autoregulatory progressive resistance exercise (APRE) and velocity-based resistance training (VBRT) utilize real-time monitoring of athlete physical performance to adjust training loads to provide appropriate training stimuli. However, the monitoring and adjustment approaches differ between both methods. This study aimed to compare the effects of APRE and VBRT on the muscle strength, power, and agility of college taekwondo athletes. Eight weeks of APRE and VBRT will promote similar results to strength gains in regards maximal strength, but VBRT will be superior to APRE in explosive power and agility. Clinical trial. Level 3. Thirty taekwondo athletes were divided randomly into 2 groups (VBRT/APRE), and all participants completed an 8-week APRE/VBRT intervention. Maximum strength, explosive power, and agility performance were assessed during the squat 1-repetition maximum (1RM), countermovement jump (CMJ), drop jump (DJ), kicking strength test (KST), taekwondo-specific agility test (TSAT), and hexagon test (HT). Highly significant time effects (P < 0.01) were observed for squat 1RM, CMJ, and TSAT in both the APRE and VBRT groups. However, there were no significant group-by-time differences for any of the measured outcomes to intergroup (P > 0.05), but APRE had a small effect size (ES) over VBRT for CMJ (ES = 0.48, = 0.06), TSAT (ES = 0.26, = 0.02), and HT (ES = 0.42, = 0.05). An 8-week autoregulatory APRE and VBRT can both effectively improve both the maximal strength, explosive power, and agility performance of taekwondo athletes, with APRE exhibiting potential advantages in improving CMJ, TSAT, and HT. These results provide important insights into the selection of suitable resistance training programs by professional coaches, taking into account athlete needs, training efficiency, and safety considerations.

Design and Implementation of Real-Time Sensors for Three-Phase Induction Motor Performance Monitoring using Internet of Thing (IoT)

This article delves into the design and implementation of cost-effective sensors tailored for real-time monitoring of three-phase induction motor performance. The research primarily centers on the assessment of the operational effectiveness of a suite of sensors, including the PZEM-004t power sensor, MLX90614 temperature sensors, NJK-5002C rotation sensors, and ADXL345 vibration sensors, within the context of three-phase induction motor performance analysis. The monitoring system for three-phase induction motor performance is built around the ESP-32 platform, incorporating the ADXL345 vibration sensor. This article successfully presents the individual sensor performance, which was systematically evaluated through testing on a three-phase induction motor that had been meticulously designed and closely observed. The unit tests reveal that all sensors employed in the experiments exhibit an average error rate of less than 5%. When assessed as an integrated system, the three-phase induction motor performance monitoring system demonstrates robust functionality, effectively measuring critical variables such as voltage, current, power factor, temperature, vibration, and rotational speed.

Application Analysis of Artificial Intelligence Virtual Reality Technology in Fitness Training Teaching

The application of artificial intelligence virtual reality (VR) technology in fitness teaching and training is becoming increasingly widespread, and its significant advantages provide athletes with a more personalized, efficient and safe training experience. By simulating various fitness scenes, virtual reality technology enables athletes to train immersively, not only improving the fun of training, but also enhancing the training effect. At the same time, the introduction of artificial intelligence technology enables real-time monitoring and analysis of athlete performance during the training process, providing accurate data support for coaches and helping them develop more scientific training plans. This paper explores the application of virtual reality technology in physical education teaching and training, introduces the basic principles and characteristics of virtual reality and proposes several typical virtual reality application scenarios for the needs of physical education teaching and training. In football training, through virtual reality technology, athletes can conduct various technical and tactical exercises in simulated stadiums. At the same time, artificial intelligence systems can analyze the actions and decisions of athletes in real time, and provide feedback and suggestions. In track and field training, virtual reality technology can help athletes simulate competition scenes and improve their psychological adaptability during competitions. Through in-depth research, we have found that this technology provides athletes with a more personalized, efficient, and safe training experience. In football training, virtual reality technology can simulate real game scenes, allowing athletes to engage in various technical and tactical exercises in simulated sports stadiums. At the same time, artificial intelligence systems can analyze the actions and decisions of athletes in real time, provide accurate data support for coaches, and help them develop more targeted training plans.

Unlocking Interactive Learning: Building a Robust Quiz Portal with MERN Stack Technology"

Online learning has become increasingly popular in the digital era, necessitating innovative platforms to effectively engage students. This research outlines the development and implementation of an interactive quiz portal utilizing the MERN (MongoDB, Express.js, React.js, and Node.js) stack to enhance learning outcomes. The portal features a user-friendly interface for easy navigation and access to a diverse range of quizzes on various subjects. To ensure scalability, performance, and real-time interactions, Node.js handles backend operations, React.js manages dynamic interfaces, Express.js is used for server-side development, and MongoDB stores data. The portal includes instant feedback mechanisms, gamification elements, and personalized user profiles to enhance user motivation and involvement. Furthermore, adaptive algorithms are employed to adjust quiz content based on user proficiency levels. This paper presents the design, development, and implementation of an online test platform using the MERN (MongoDB, Express, React, and Node.js) stack. The portal functions as an all-inclusive platform for administering tests, assessments, and learning evaluations in an online setting. The portal provides smooth integration of frontend and backend activities, guaranteeing strong performance and user interaction, by utilizing the adaptability and scalability of the MERN stack. The portal enables quick development and deployment cycles with the help of MongoDB's document-based data storage, Express.js's effective middleware and routing, React.js's dynamic UI components, and Node.js's event-driven architecture. To improve user experience and learning results, features including interactive feedback mechanisms, real-time performance monitoring, quiz design and administration, and user authentication are easily integrated.

Real-time Performance Monitoring of HPC Clusters: Techniques and Challenges

In the era of exascale computing, High-Performance Computing (HPC) clusters have become essential for addressing complex computational challenges across various domains. The increasing scale and complexity of HPC systems pose significant challenges in ensuring optimal performance and resource utilization. Real-time performance monitoring has emerged as a critical requirement to address these challenges effectively. The paper offers a comprehensive analysis of real-time performance monitoring techniques and challenges within HPC clusters focusing on three key monitoring approaches: Prometheus for agent-based monitoring, the ELK (Elasticsearch, Logstash, Kibana) stack for log-based monitoring, and machine learning/artificial intelligence techniques for proactive analysis. The implementation of Prometheus and Node Exporter for real-time metrics collection, the utilization of the ELK stack for log aggregation and analysis, and the integration of ML/AI for anomaly detection and predictive analytics are explored. Challenges such as scalability, heterogeneity, and dynamic workloads are addressed in the context of each monitoring approach. The findings demonstrate that each technique offers unique advantages and trade-offs, providing valuable insights for improving HPC performance monitoring practices. Key Words: High-Performance Computing, real-time performance monitoring, Prometheus, ELK stack, Machine Learning, Artificial Intelligence, HPC clusters.

Integration of IoT Technologies for Enhanced Monitoring and Control in Hybrid-Powered Desalination Systems: A Sustainable Approach to Freshwater Production

In the face of our rapidly expanding global population, the necessity of meeting the fundamental needs of every individual is more pressing than ever. Human survival depends upon access to water, making it a vital resource that demands novel solutions to ensure universal availability. Although our planet is abundant in water, 97.5% of it is saltwater, compelling nations to investigate ways to make it suitable for consumption. Seawater desalination is becoming increasingly vital for water sustainability. While seawater desalination offers a solution, existing methods often grapple with high energy consumption and maintaining consistent water quality. This paper proposes a novel hybrid water desalination system that addresses these limitations. Our system leverages solar energy, a readily available renewable resource, to power the desalination process, significantly improving its environmental footprint and operational efficiency. Additionally, we integrated a network of sensors and the Internet of Things (IoT) to enable the real-time monitoring of system performance and water quality. This allows for the immediate detection and improvement in any potential issues, ensuring the consistent production of clean drinking water. By combining solar energy with robust quality control via IoT, our hybrid desalination system offers a sustainable and reliable approach to meet the growing demand for freshwater.

Revolutionizing Sports Education: Harnessing Innovations and Technology for Enhanced Learning

This paper explores cutting-edge innovations in sports education, focusing on active learning strategies, the flipped classroom model, gamification, differentiated instruction, and the integration of technology. Active learning methodologies, such as problem-based learning and cooperative learning, engage students in critical thinking and collaborative problem-solving, while the flipped classroom model prioritizes experiential learning and learner autonomy. Gamification principles add elements of competition and rewards to enhance motivation and engagement, while differentiated instruction tailors learning experiences to individual student needs. Furthermore, technology integration, including virtual reality simulations, wearable technology, and online learning platforms, revolutionizes sports education by providing immersive experiences, real-time performance monitoring, and personalized learning pathways. Through a comprehensive review and analysis, this paper elucidates the transformative potential of these innovations in optimizing student learning outcomes and preparing athletes for success both on and off the field.

Integrating sports education with data analysis and computer technology: A new paradigm for enhanced athletic performance

This comprehensive study delves into the transformative integration of sports science, data analytics, and computer technology in athletic training and education. It highlights the multidisciplinary approach to enhancing athletic performance through evidence-based methodologies, including biomechanical analysis, physiological assessment, and psychological strategies. With a focus on data-driven training methodologies, real-time performance monitoring, educational implications, and the incorporation of ethical considerations, the article showcases how modern technology and data analysis can personalize training programs, optimize performance, and minimize injury risks. Through quantitative analysis and case studies, it demonstrates the significant impact of these integrations on athletic performance metrics, while also addressing the challenges and limitations faced in adopting these approaches. The findings suggest a promising future for sports education and training, advocating for interdisciplinary collaboration and the adoption of data analytics in sports.

Enhancing Reliability in Rural Networks Using a Software-Defined Wide Area Network

Due to limited infrastructure and remote locations, rural areas often need help providing reliable and high-quality network connectivity. We propose an innovative approach that leverages Software-Defined Wide Area Network (SD-WAN) architecture to enhance reliability in such challenging rural scenarios. Our study focuses on cases in which network resources are limited to network solutions such as Long-Term Evolution (LTE) and a Low-Earth-Orbit satellite connection. The SD-WAN implementation compares three tunnel selection algorithms that leverage real-time network performance monitoring: Deterministic, Random, and Deep Q-learning. The results offer valuable insights into the practical implementation of SD-WAN for rural connectivity scenarios, showing its potential to bridge the digital divide in underserved areas.

State-of-Charge Evaluation for Real-Time Monitoring and Evaluation of Lithium-Ion Battery Performance

Nowadays, lithium-ion batteries have been garnered significant attention as the primary energy source for energy storage devices within the renewable energy sector. Key concerns surrounding the utilization of lithium-ion batteries include ensuring satisfactory design lifespan and safe operation. Consequently, there's been a practical need for battery management. Responding to this demand, various battery state indicators have seen widespread implementation. Among the battery state indicators, accurate state-of-charge (SOC) estimation is an essential requirement for many situations where Li-Ion batteries (LiBs) are used. The effectiveness of a Battery Management System (BMS) safeguards the battery against deep discharging and over-charging to maximize its lifespan. This paper conducts state of charge (SOC) evaluation of a Li-ion battery module (12V, 13 Ah lithium titanate oxide (LTO) battery) for battery management systems (BMS) in energy storage systems (ESSs).

A machine learning-driven approach to predicting thermo-elasto-hydrodynamic lubrication in journal bearings

Traditional methods of evaluating the performance of journal bearings, for example thermal-elastic-hydrodynamic- lubrication theory, are limited to simplified conditions that often fail to accurately model real-world components. Numerical models that include additional phenomena such as cavitation and fully coupled effects like deformation, temperature, pressure and viscosity can be more accurate but require a large amount of computational overhead, making analysis slower and more costly. To address this limitation, a novel machine learning-driven approach is developed to predict the 2D distribution of surface deformation, film thickness, temperature, and pressure across the bearing surface as a function of design variables such as load and speed. The training dataset, generated using a fully coupled Reynolds’ Equation solver implemented in OpenFOAM, contains a significantly extended range of conditions than in previous studies with approximately 39000000 points encompassing 4925 different test cases. Modelled bearing speeds range from 2000 to 10000rpm, while load values are varied between 1 and 30kN. Predicting surface deformation, film thickness, temperature and pressure across the bearing surface results in a mean absolute percentage error below 0.4% or better. The work also demonstrates that the trained models have a strong ability to generalise the prediction beyond the original training data range with only a 1% error at up to 200% of the highest trained speed. This work also demonstrates that machine learning-based processes are a practical alternative to physics-based numerical modelling, especially in cases where rapid performance evaluation is desired as real-time calculation is possible with significantly reduced computational cost. This has the potential to enable development of rapid design optimisation tools and real-time performance monitoring at high resolution and with low latency. Using consumer hardware, it is found that the neural network-based approach is faster than the existing numerical modelling technique by a factor of over 10000, enabling real-time predictions of lubrication systems.

Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks

Recently significant advances in the field of medical diagnostics can be seen. Nonetheless, diagnosing Parkinson's disease (PD) remains difficult, especially in terms of timeliness and precision. The inability of present technological methods to identify this complicated neurodegenerative disease emphasizes the urgent need for more research. PD is a complicated neurological condition that is becoming more commonplace worldwide. A timely and precise PD diagnosis is critical because it directly determines the quality of patient care and the success of treatment. However, our current diagnostic approaches, which primarily rely on clinical assessments, have substantial limits in terms of sensitivity and specificity. Thus, the need for a more reliable and objective diagnosing procedure is essential. Convolutional neural networks (CNNs) are employed in this study to investigate complex spiral and wave patterns and to overcome this significant challenge. Drawings from both PD patients and their healthy counterparts were included in the large dataset that was used to train the robust CNN model, which is a key component of the research technique. The training and intensive testing of the model on this dataset confirms its capacity to distinguish between PD instances and non-PD cases. The results of this investigation demonstrate how well the CNN model can predict Parkinson's disease (PD) based on spiral and wave patterns, with a 93% classification accuracy. The high sensitivity and specificity of the model are especially impressive because they significantly lower the chances of false positives and false negatives. The discussion that follows offers a considered evaluation of these findings, emphasising the model's potential for early diagnosis, its capacity to support professional judgments, and its crucial role in enhancing patient outcomes. This study highlights the great potential of deep learning methods for disease diagnosis, especially for PD. The effective integration of the CNN model into a React application for real-time prediction and continuous monitoring of patient performance has important implications for telemedicine and remote healthcare management. This breakthrough makes Parkinson's disease treatment more approachable and proactive.