Key Performance Metrics Research Articles

A Comparative Study of Parallel Processing, Distributed Storage Techniques, and Technologies: A Survey on Big Data Analytics

The significance of developing Big Data applications has increased in recent years, with numerous organizations across various industries relying more on insights derived from vast amounts of data. However, conventional data techniques and platforms struggle to cope the Big Data, exhibiting sluggish responsiveness and deficiencies in scalability, performance, and accuracy. In response to the intricate challenges posed by Big Data, considerable efforts have been made, leading to the creation of a range of distributions and technologies. This article addresses the critical need for efficient processing and storage solutions in the context of the ever-growing field of big data. It offers a comparative analysis of various parallel processing techniques and distributed storage frameworks, emphasizing their importance in big data analytics. Our study begins with definitions of key concepts, clarifying the roles and interconnections of parallel processing and distributed storage. It further evaluates a range of architectures and technologies, such as MapReduce, CUDA, Storm, Flink, MooseFS, and BeeGFS and others technologies, discussing their advantages and limitations in managing large-scale datasets. Key performance metrics are also examined, providing a comprehensive understanding of their effectiveness in big data scenarios.

Analysis of a Markovian Queue of Single Server Performing in MultiPhase Subject to Disaster, Recovery and Repair

In this paper, we can study about Markovian single-server queue with servers in distinct phases of disaster and repair. The server can stay in full-active Phase or passive Phase randomly and alternately. The server is set to serve in two Phases, such as the full active Phase and passive Phase. When the server operates in full active Phase, the customers arrive and served in First Come: First Serve (FCFS) queue discipline. However, when the server switches from full active to passive Phase, it can only offer to provide service at a rate lower than that of the server in full active Phase. During passive Phase, customers are not allowed to join the system. The server moves to repair Phase immediately after the last customer is served, irrespective of the Phase. The customers will be restricted from joining the queue in repair Phase. After the repair Phase, the server will enter the full active Phase. The server remains in the system for a random period of time. In the event of a disaster, in full active and passive Phases, all customers in system aredropped out, and system moves to repair Phase. The expressions for the steady state probabilities and some key performance metrics are obtained. A numerical illustration is made to study the effects of server shifting from full active phase to passive phase that affects the customers in the system and the occurrence of disaster which has an adverse effect on the customers in that phase.

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

Diagnostic Accuracy of Calcified Aortic Knob Found in Chest Radiograph for Detection of Coronary Artery Calcification

Aims: To validate the diagnostic performance of a calcified aortic knob found in chest radiographs in relation to coronary calcification. Study Design: This study is an observational analytical cross-sectional study. Place and Duration of Study: This study examined participants who were referred for a CT coronary calcium scoring (CT-CAC). at the Central Chest Institute of Thailand, Department of Medical Services, Ministry of Public Health, during the period from November 1, 2019, to October 31, 2021. Methodology: This cross-sectional study aims to evaluate the association between calcified aortic knobs on chest radiographs and coronary artery calcification as determined by CT-CAC. The study included 664 patients who underwent CT-CAC between November 1, 2019 and October 31, 2021. We selected participants aged 40 to 75 years without known history of coronary artery disease (CAD) or diabetes (fasting blood sugar levels under 126 mg/dl within past 6 months). A total of 441 eligible patients were included in the final analysis. Standard chest radiographs within 6 months interval were evaluated and classified into 4 grades, Grade 0 (no visible calcification), Grade 1 (<50% calcification of aortic knob), Grade 2 (>50% calcification of the aortic knob), and Grade 3 (circular calcification of the aortic knob). These findings were then compared with CAC obtained via dual source CT scanner to evaluate the diagnostic accuracy of chest radiographs. Key performance metrics, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated to determine the effectiveness of aortic knob calcification as a predictor for coronary calcification. Results: Positive CAC (CAC >0) was strongly associated with a calcified aortic knob (grades 1-3), with a high positive predictive value of 88.07%. The diagnostic accuracy, sensitivity, and specificity were 66.9%, 61.5%, and 79.8%, respectively. The negative predictive value was low, at 41.8%. Conclusion: A calcified aortic knob (grades 1-3) correlates strongly with positive CAC (positive predictive value 88.07%). The study’s findings of low sensitivity (61.5%) and negative predictive value (46.18%) indicate limitations in using calcified aortic knob on chest radiograph as a standalone screening tool for coronary artery calcification.

Travel route recommendation with a trajectory learning model

This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.

The role of automated controls and streamlined compliance in managing risks in digital finance

This study investigates the role of automated controls and streamlined compliance in managing risk in digital finance institutions. Through a mixed-methods approach, combining quantitative analysis and qualitative insights from leading Indian digital finance companies (CRED, ZEST MONEY and PHONE PAY), the research examines the effectiveness of advanced technologies in enhancing risk management practices. The study found strong positive correlations between the adoption of automated controls and key performance metrics, including the fraud detection rate (0.68), regulatory reporting accuracy (0.72), and overall risk management effectiveness (0.75). Regression analysis revealed that automated control implementation was the strongest predictor of risk management effectiveness. A comparative analysis of different digital finance models highlights the varying challenges and benefits of implementing these technologies. This study proposes a holistic, future-proof framework that integrates AI, machine learning and blockchain for real-time risk monitoring, automated regulatory reporting and dynamic compliance management. This framework addresses the limitations of the current fragmented approaches and offers a scalable solution that is adaptable to evolving regulatory landscapes. The findings of this study have significant implications for practitioners and policymakers, emphasizing the need for investment in strong automated systems and adaptive regulatory frameworks. While acknowledging the limitations in sample size and longitudinal scope, this research provides a foundation for future studies and offers practical insights for enhancing risk management in the rapidly evolving digital finance sector. The study concludes that effective integration of automated controls and streamlined compliance is crucial for ensuring the resilience, integrity and sustainable growth of the digital finance ecosystem.

Leveraging Tropical Algebra to Assess Trustworthy AI

Given the complexity of the application domain, the qualitative and quantifiable nature of the concepts involved, the wide heterogeneity and granularity of trustworthy attributes, and in some cases the non-comparability of the latter, assessing the trustworthiness of AI-based systems is a challenging process. In order to overcome these challenges, the Confiance.ai program proposes an innovative solution based on a Multi-Criteria Decision Aiding (MCDA) methodology. This approach involves several stages: framing trustworthiness as a set of well-defined attributes, exploring attributes to determine related Key Performance Indicators (KPI) or metrics, selecting evaluation protocols, and defining a method to aggregate multiple criteria to estimate an overall assessment of trust. This approach is illustrated by applying the RUM methodology (Robustness, Uncertainty, Monitoring) to ML context, while the focus on aggregation methods are based on Tropical Algebra.

Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen

Optimizing CO2 injection for enhanced oil recovery (EOR) requires a precise estimation of the CO2-diffusivity coefficient in porous media. This study developed a predictive model for the molecular diffusivity coefficient of CO2 in bitumen and heavy crude oils using a Bayesian regularized artificial neural network. The unique contributions of the developed model compared to existing models include: First, a simple and accurate mathematical correlation between inputs and CO2-diffusivity has been presented, making it easy to use particularly for individuals with limited understanding of machine learning. Secondly, the time it takes the model to make a prediction (its inference latency) has been established and the model has been physically validated using trend analysis. Fourthly, independent datasets were used to test for the model generalizability.The model was evaluated using 260 data points from the literature, with 70 % for training and 30 % for testing. Key performance metrics were calculated, including root mean square error (RMSE = 0.03), and coefficient of determination (R2 = 0.996). Furthermore, an outlier detection analysis using the statistical leverage approach demonstrated that the data used was of high quality. A relevancy factor analysis revealed that pressure had the greatest impact on CO2 diffusivity, followed by temperature, while CO2 mass fraction had the least impact. The developed model operates within a temperature range of 295 K – 363 K and a pressure range of 1 MPa – 8MPa. Overall, the outcomes of this study contribute to the efficient prediction of CO2 diffusivity in heavy crude oil and bitumen.

Optimizing last-mile delivery of essential medicines in Nigeria: Insights from Niger State’s health-care supply chain

Last-mile delivery of health commodities is a critical component of efficient supply chains, and third-party logistics (3PL) providers are pivotal in ensuring cost-effective and reliable product distribution. This study explores the implementation of last-mile delivery outsourcing by government-operated warehouses and drug management agencies (DMAs). This research focuses on the relationship between 3PL utilization and key performance metrics, such as cost reduction, timely delivery, and end-user satisfaction. This study combines a literature review, surveys, and interviews conducted with employees of the DMAs in Minna, Niger State, to establish significant correlations. The findings show that using 3PLs significantly reduced operational costs, improved the frequency of timely deliveries, and enhanced end-user satisfaction. These findings underscore the need for continued engagement with 3PL providers to optimize supply chain performance and improve service delivery outcomes. Managers are recommended to maintain and expand such collaborations to sustain these benefits.

The Impact of Big Data and Artificial Intelligence on Influencer Marketing

Influencer marketing has rapidly emerged as a cornerstone of digital advertising strategies, enabling brands to connect authentically with target audiences through trusted voices on social media platforms. However, measuring the effectiveness of these campaigns has long been a challenge due to the complexity of digital ecosystems and the sheer volume of data generated by user interactions. Big Data and Artificial Intelligence (AI) are revolutionizing the way brands evaluate influencer marketing campaigns, offering unprecedented insights into key performance metrics such as engagement, audience sentiment, and conversion rates. This paper explores how data analytics and AI tools are transforming the identification of influencers, the optimization of campaign performance, and the prediction of future trends. Furthermore, we address the ethical considerations surrounding data privacy and the transparency of AI-driven decisions in influencer marketing. Ultimately, this study highlights how Big Data and AI are not only enhancing the efficiency of influencer marketing but also reshaping the future of digital advertising as a whole.

Perioperative risk scores: prediction, pitfalls, and progress.

Perioperative risk scores aim to risk-stratify patients to guide their evaluation and management. Several scores are established in clinical practice, but often do not generalize well to new data and require ongoing updates to improve their reliability. Recent advances in machine learning have the potential to handle multidimensional data and associated interactions, however their clinical utility has yet to be consistently demonstrated. In this review, we introduce key model performance metrics, highlight pitfalls in model development, and examine current perioperative risk scores, their limitations, and future directions in risk modelling. Newer perioperative risk scores developed in larger cohorts appear to outperform older tools. Recent updates have further improved their performance. Machine learning techniques show promise in leveraging multidimensional data, but integrating these complex tools into clinical practice requires further validation, and a focus on implementation principles to ensure these tools are trusted and usable. All perioperative risk scores have some limitations, highlighting the need for robust model development and validation. Advancements in machine learning present promising opportunities to enhance this field, particularly through the integration of diverse data sources that may improve predictive performance. Future work should focus on improving model interpretability and incorporating continuous learning mechanisms to increase their clinical utility.

Streamlining Smart Grids Reliability Assessment: An Innovative Mapping Approach

Assessing smart grid reliability, considering both cyber and physical components, typically involves a mapping step, escalating complexity and computational overhead. This paper presents a pioneering mapping approach that redefines the fundamental paradigm of smart grid reliability assessment. By leveraging a defined interconnection matrix, the method optimizes computational efficiency, curtailing the array of potential system states. Evaluation employing key performance metrics - Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS) - quantitatively demonstrates the superiority of our approach. Also, we explore the ramifications of integrating a dynamic thermal rating (DTR) in the process of reliability assessment, augmenting component safety through permissible enhancements in their ratings. Results underscore a notable reduction in total system states, from 221 to 214 for the bus topology and from 222 to 216 for the ring topology. Moreover, the analysis reveal substantial enhancements (Up to 43.58% ) in reliability indices upon consideration of the DTR system.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities.Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Bridging the gap: A comparative analysis of indoor and outdoor performance for photovoltaic-thermal-thermoelectric hybrid systems

This research evaluates the performance of a hybrid thermal-thermoelectric photovoltaic air collector system (PV/T-TE) through experimental investigations. By integrating photovoltaic (PV) panels with thermoelectric (TE) modules, the system aims to enhance efficiency and energy output by converting waste heat into additional electricity. The study examines the impact of radiation intensity, ranging from 455.65 to 795.18 W/m2, on the system's performance, utilizing output temperature (To) and plate temperature (Tp) as key metrics. Managing waste heat in PV technology remains a challenge, impacting efficiency. Traditional PV/T systems generate both electricity and thermal energy but suffer efficiency losses due to inadequate heat management. Integrating TE modules into PV/T systems offers a promising solution, but optimal configurations and performance impacts under varying conditions are underexplored. Limited research focuses on PV/T-TE hybrid systems, with most studies addressing PV-TE systems. The objectives of this research are to experimentally assess the impact of integrating TE modules on the thermal and electrical efficiency of PV/T systems under varying radiation intensities and air mass flow rates. The methodology involves setting up a PV/T-TE hybrid system, conducting experiments under controlled conditions, and analyzing key performance metrics. The study explores optimal TE module configurations and installation techniques to maximize heat transfer and electricity generation. Comparative analysis with conventional PV/T systems establishes the superiority of the hybrid system. Findings indicate significant benefits from incorporating TE modules, enhancing energy output and efficiency by maintaining optimal PV panel temperatures.

Low-Temperature Oxidation Pathways are Critical to Thermal Incineration of PFAS-Laden Materials

With growing desire to destroy per- and poly-fluoroalkyl substances (PFAS) now known to be detrimental to human health, a sound understanding of fluorocarbon combustion chemistry is important to efficient thermal destruction within incinerators. While most fluorocarbon combustion models and the sets of reactions contained within them were originally developed for the high temperatures encountered in flame suppression applications, they have often been used to assess PFAS destruction in incinerators, which emphasize a lower range of temperatures. We present results that demonstrate that low-temperature fluorocarbon oxidation pathways—not yet known to play a role in fluorocarbon combustion—impact key incinerator performance metrics, including: PFAS surrogate mole fractions, products of incomplete destruction, and waste destruction efficiencies. The results further point to the utility of NO as a potential additive. The present results show the influence of these pathways for CF3O2, for which some data are available, but analogous pathways would also occur for other fluoroalkylperoxy radicals, for which little is known. The results demonstrate the need for future work to identify and characterize low-temperature pathways more generally, consider such pathways in kinetic model development, and experimentally probe intermediate temperature conditions to better understand, design, and control thermal destruction technologies for improved PFAS management.

Test Methodology for Short-Circuit Assessment and Safe Operation Identification for Power SiC MOSFETs

The short-circuit (SC) immunity of power silicon carbide (SiC) MOSFETs is critical for high-reliability applications, where robust monitoring and protection strategies are essential to ensure system safety. Despite their superior voltage blocking capabilities and high energy efficiency, SiC MOSFETs exhibit greater sensitivity to SC-induced degradation compared to their silicon counterparts. This increased vulnerability necessitates the precise assessment of the key SC performance metrics, such as short-circuit withstand time (TSCWT), as well as a deeper understanding of the failure mechanisms. In this study, a comprehensive experimental methodology for evaluating the SC behavior of SiC MOSFETs is presented and validated using industrial references. The investigation further explores the concept of a Safe Operating Area (SOA) under SC conditions, highlighting the significant impact of quasi-simultaneous SC events on device lifetime. Additionally, an application case study demonstrates how these events can drastically reduce the device’s lifespan.

Visual Search Interactive Model for Artificial Intelligence Robotics Model forthe Agricultural Field Analysis

The Visual Search Interactive Model for Artificial Intelligence (AI) is designed to enhance the efficiency and effectiveness of visual data analysis across various applications. By leveraging advanced computer vision techniques and machine learning algorithms, this model enables AI systems to interpret and analyze visual information in real-time, facilitating tasks such as object recognition, image classification, and scene understanding. The interactive nature of the model allows users to engage with the AI, refining searches and improving outcomes through iterative feedback. This paper introduces the Auxiliary Clustering k-means Machine Learning (AC k-means ML) model, designed to enhance agricultural efficiency through advanced data analysis and robotic integration. The study evaluates the performance of the AC k-means ML model using a dataset comprising 1,950 samples, achieving an overall accuracy of 91.5% and a precision of 89.2%. Key performance metrics such as F1 scores averaged 88.6%, with the highest individual cluster accuracy reaching 96% for Cluster 10. In addition to data classification, the model facilitated the completion of 250 tasks with a remarkable success rate of 92%, while maintaining an average task completion time of 15.4 minutes and an energy consumption of just 0.5 kWh per task. The implementation of the AC k-means ML model resulted in a 15% increase in crop yield and substantial cost savings estimated at $2,000. With a user satisfaction score averaging 8.7 and an adaptability score of 9.0, the findings indicate that the integration of machine learning and robotics significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.

Experimental Study of Bifacial Photovoltaic Module Performance on a Sunny Day with Varying Backgrounds Using Exergy and Energy Analysis

In this research, ethe performance of bifacial photovoltaic (PV) modules under varying background conditions is explored, specifically green grass, brown clay, and white gravel, on a sunny day. By leveraging both exergy and energy analysis, this research aims to provide a comprehensive evaluation of bifacial module efficiency compared to traditional monofacial modules. The experimental setup simulates diverse installation environments, including rooftops and ground-mounted systems, by varying background reflectance. Key performance metrics such as energy yield, exergy yield, and overall efficiency were measured. The findings reveal that bifacial modules installed over white gravel backgrounds achieve the highest exergy profile and efficiency during peak solar radiation periods, attributed to the enhanced reflectivity of white gravel. These insights can inform strategic decisions regarding the selection and placement of bifacial modules to optimize energy and exergy outputs in real-world scenarios. This study contributes valuable knowledge to the advancement of renewable energy technologies, offering guidance for researchers, developers, and policymakers focused on sustainable energy solutions.

Data-driven diabetes management: a statistical assessment information system leveraging big data

ABSTRACT Amidst the escalating global burden of diabetes and its multifaceted complications, there arises an urgent need for robust healthcare information systems tailored to address the complexities of diabetic patient management. This paper introduces a novel statistical assessment healthcare information system engineered to meticulously analyze diabetes-related data by harnessing the power of big data technologies. Diabetes, affecting millions worldwide, exerts substantial strain on healthcare infrastructures, necessitating innovative solutions to optimize patient outcomes and treatment efficacy. Leveraging advancements in data processing and analytics alongside vast medical datasets, this study pioneers a statistical assessment model explicitly crafted for diabetes analysis within a big data paradigm. The model’s efficacy is rigorously evaluated using key performance metrics such as accuracy and F-measure, within the Hadoop framework. Remarkably, the results demonstrate a significant enhancement in performance compared to conventional methodologies, affirming the model’s potential to revolutionize the accuracy and efficiency of diabetes analysis within a big data milieu. Moving forward, further research avenues include refining the model’s algorithms to accommodate diverse data sources and expanding its applicability to encompass a broader spectrum of diabetes-related factors.