Performance Optimization for Micro-Frontend-Based Applications: A Predictive Analysis Using XG Boost Regression

Objectives:The primary objective was to evaluate the impact of ulnar collateral ligament reconstruction (UCLR) on MLB pitcher performance, with a specific focus on advanced metrics such as fastball velocity, spin rate, FIP (fielding independent pitching), SIERA (skill interactive earned run average), and WHIP (walks and hits per inning pitched) when compared to both pre-injury performance and a non-injured cohort of pitchers. The secondary objective was to assess the extent to which velocity and spin rate are predictive of pitcher performance. The hypotheses for the primary objective was that there would be no difference in pre-injury and post-injury performance or in post-injury performance and healthy controls. The hypothesis for the secondary objective was that both velocity and spin rate would be predictive of performance regardless of injury status.Methods:Pitchers with confirmed UCL injuries between the 2017 and 2021 MLB seasons were identified using the Pro Sports Transactions Archive and baseball-reference.com. Inclusion criteria required pitchers to have thrown at least 8.0 innings in two consecutive seasons both pre- and post-injury. A control group of healthy pitchers was age-matched at a 1:2 ratio to injured pitchers (Fig 1). Key performance metrics, including FIP, SIERA, and WHIP, were extracted from fangraphs.com, and spin rate and velocity data were collected from Baseball Savant.Principal component analysis (PCA) was used to compress several pitching performance metrics (FIP, SIERA, WHIP) into a single, comprehensive performance measure, referred to as the first principal component (PC1), where lower values indicate better overall performance. The changes in performance before and after surgery were normalized to account for age-related decline, which was controlled for by using an age-matched group of healthy control pitchers. Pearson’s correlation coefficients were calculated to assess the relationship between spin rate, velocity, and performance. Comparisons between pre- and post-surgery performance and between injured and control pitchers were conducted using independent t-tests. A power analysis was conducted to ensure a sufficient sample size to detect meaningful differences in performance outcomes, and all analyses were performed using Python 3.7 and RStudio 2023. Statistical significance was set at α = 0.05.Results:The study included 34 MLB pitchers who had undergone UCLR, with an average age at the time of injury of 27.03 ± 3.05 years. Age-matched controls (n=68) were identified for comparison, allowing for the analysis of both performance differences and the potential influence of aging on pitching metrics. Performance was first analyzed in terms of pitching volume (number of pitches and innings pitched). While the injured group showed a slight decline in innings pitched post-surgery, this difference was not statistically significant when compared to the control group (p = 0.301). Furthermore, no significant differences were found in strikeouts (p= 0.992) or hits allowed (p= 0.207) at two seasons before versus after injury. The PC1 analysis of FIP, SIERA, WHIP revealed no significant difference between injured pitchers post-surgery compared to their control counterparts (p = 0.287) (Fig 2).Spin rate and velocity were further analyzed to determine their relationship with post-surgical performance. Fastball velocity showed no significant change post-surgery (p = 0.687), and spin rate also did not significantly differ between injured and control pitchers (p = 0.876). However, both spin rate and velocity were identified as a key predictors of performance for both groups for WHIP (pspin= 0.02, pvelo= 0.04), FIP(pFIPspin= 0.003, pFIPvelo< 0.001), SIERA (pSIERAspin< 0.001, pSIERAvelo< 0.001), and PC1 (pPC1spin< 0.001, pPC1velo< 0.001) (Fig 3). There were no significant relationship between age at the time of injury and changes in performance, spin rate, or velocity, indicating that age did not influence post-operative outcomes (Pearson’s r = -0.072, p = 0.685).Conclusions:Following UCLR, MLB pitchers maintain their pre-injury level of performance, with no significant decline in fastball velocity, spin rate, or advanced statistics post-surgery. Additionally, both spin rate and velocity emerged as significant predictors of pitching performance across both injured and healthy pitchers, highlighting their potential utility as a key metric in assessing pitching effectiveness. These findings support the conclusion that UCLR is an effective intervention for MLB pitchers, allowing them to return to competitive play without significant deterioration in their key performance metrics.

In 2017, Uber announced it had a “broken relationship” with its drivers. Yet what broke the relationship and what effect might it have on Uber’s organizational performance? This paper is a mixed-methods study of how conflict impacts the relationship between platforms and drivers. First, by drawing on 55 original interviews with rideshare drivers, this paper identifies conflict triggers that damage the relationship between platforms and drivers. Second, this paper uses new time diary data from 490 Uber, Lyft, Juno, and other Transportation Network Company (TNC) drivers from across the United States to empirically test if these conflict triggers are associated with key TNC performance metrics. Specifically, this paper finds that a “broken relationship” is associated with drivers withholding their working time from Uber and allocating it toward their competitor, Lyft. Additionally, this paper finds that drivers who have a broken relationship with Uber are more likely to recruit (‘steer’) passengers toward Lyft. These behaviors provide a link between organizational conflict and key performance metrics in the ‘gig economy.’

In this study, we conducted a comprehensive DC and AC analysis of Gate-All-Around (GAA) devices, focusing on the implications of increased gate capacitance both for wider and narrower device on key performance metrics. Our findings reveal that the elevated gate capacitance due to thin GaN cap layer significantly affects the transconductance (gm), Drain-Induced Barrier Lowering (DIBL), subthreshold swing (SS) and the on/off current ratio (Ion/Ioff). A new analytical linkage is formulated connecting gate capacitance ( C g ) with key DC performance metrics. The results indicated a significant enhancement in both ft and fmax, attributed to the high Ion/Ioff ratio of 1.72 × 10 13 , confirming the superior high-frequency performance of GAA structures. Our analysis underscores the critical role of gate capacitance in optimising the electrical characteristics of GAA devices, paving the way for their application in 5G wireless communication.

With the astronomical growth in online presence vis-a-vis the IT industry across the globe, there is an urgent need to evolve cloud based DataCenter architectures that can rapidly accommodate web application integrations which can serve the energy industries, educational sector, finance sector, manufacturing sector, etc. Previous works in DataCenter domain have not investigated on cloud based DataCenter Servercentric characteristics for evaluation studies. This paper then proposed a Reengineered DataCenter (R-DCN) architecture for efficient web application integration. In this regard, attempt was made to address network scalability, efficient and distributed routing, packets traffic control, and network security using analytical formulations and behavioural algorithms having considered some selected architectures. In this work a simulation experiment was carried out to study six key performance metrics for all the architectures. It was observed that the network throughput, fault-tolerance/network capacity, utilization, latency, service availability, scalability and clustering effects of R-DCN responses with respect to above Key Performance (KPIs) metrics were satisfactory when compared with BCube and DCell architectures. Future work will show a detailed validation using a cloud testbed and CloudSim Simulator.

Financial organizations can no longer ignore the problem of managing risk. As a component of the financial system, efficient Financial Risk Management (FRM) may significantly affect business results. These findings show that the triangular neutrosophic numbers simulate capital asset key metrics. Performance metrics for capital assets may be modeled using this kind of value by accounting for all conceivable outcomes for their attainment. The profit on capital instruments, the risk of financial investments, and the covariance of capital instruments are the Key Performance Metrics (KPIs) modeled using triangular neutrosophic values. Specifically, this research employs two techniques. Prioritizing parameters for financial risk management dimensions using the Analytic Hierarchy Process (AHP). Second, the performance evaluation of financial risk management was evaluated from the perspective of several groups of specialists by the COCOSO method to assess the ranking of possible replies further. By using the presented method, we may better identify where to put our efforts and how to allocate our resources for greater effectiveness in managing financial risks.

With growing energy supply concerns and rising costs, energy efficiency is a critical component of industrial energy resilience and competitiveness by directly reducing energy operating costs. Energy efficiency projects in manufacturing also yield valuable benefits to other key metrics, such as improved quality, reduced maintenance costs, improved safety, decreased pollution, and enhanced productivity. However, it is difficult to receive approval for energy efficiency projects, so implementation rates are low, even when meeting capital project payback period criteria. The inclusion and quantification of non-energy benefits (NEBs) in the decision-making process for energy efficiency projects can improve the overall financial payback period while demonstrating a positive impact on the firm's key performance metrics and business strategy. Despite their significant financial and strategic value, NEBs are rarely factored into decision-making due to lack of tools to effectively identify and quantify them. Therefore, a comprehensive and integrative approach is needed for the rapidly evolving energy landscape. To address these challenges, through funding from U.S. Department of Energy, our new assessment methodology integrates common continuous improvement six sigma concepts, such as the DMAIC process, and a protocol of guiding questions, into energy efficiency assessments to identify NEBs. We have also developed open-source software, JUSTIFI, to guide users through this process, data collection, and quantification. It is designed to be used concurrently with DOE energy system analysis software suite, MEASUR. Our methodology and tools inform energy assessors, firm engineering, decision makers, and workforce seeking to increase energy resilience and to maximize benefits aligned with performance metrics.

This study investigates the application of YOLOv11, a cutting-edge deep learning model, to enhance the detection of plant diseases. Leveraging a comprehensive dataset of 737 images depicting tomato leaves affected by various diseases, YOLOv11 was trained and evaluated on key performance metrics such as precision, recall, and mAP. Experimental results the model was trained and evaluated on key metrics including accuracy (75.6%), precision (0.80), recall (0.77), and mAP@0.5 (75.6%). Experimental through base architectural such as enhanced feature extraction with C2 modules, improved multi-scale detection using SPPF layers, and optimized non-maximum suppression techniques. These improvements enable the model to achieve stable precision and recall for each class, even in challenging scenarios with overlapping objects and diverse environmental conditions. By addressing practical usability challenges, this system offers a scalable, accessible, and impactful solution for precision agriculture, paving the way for sustainable with this pretrained model. This study underscores the potential of deep learning-based models, particularly YOLOv11, in transforming the way monitoring and disease management are approached, demonstrating its ability to stable accuracy and operational efficiency in real-world applications. Furthermore, the practical usability of the YOLOv11-based system addresses challenges in the domain of precision plant detection desease. By providing a scalable, accessible, and highly efficient solution, the model offering a significant advancement toward sustainable agricultural practices.

Call centre employees experience of mental game playing during work breaks were recorded in this study. The qualitative study involving two focus group discussions at different call centres, with 6 employees in one focus group and 9 in the second, stated employee's reasons for variations in the key performance metric by which they were being evaluated. In this case the key performance metric was the average speed of answer. In a previous study, where the mental games were introduced as an intervention during work breaks, the participants were asked to provide feedback on variations in productivity during a 4-week intervention study. Volunteer sampling was utilized, and the information was categorized into codes. Similar codes were grouped into themes. It was found that players are efficient in the middle of the week either because daily routine settles middle of the week or because employees try to average their metrics related to time. They also thought that productivity increased towards the end of the intervention because of games being accepted in the routine by that time, because the focus shifted from fun to work towards the end or because the employees knew they were being evaluated. According to participants, the non-players were more efficient than the players because games served as a bad distraction for the players and that players were considering games as something to do rather than an actual break. As a conclusion, it felt important to improve on the timings and frequency of the games played during work before it can be considered an effective intervention.

In this work, we develop novel disk characterization suite, DIG (disk geometry analyzer), which allows us to rapidly extract and to characterize the key performance metric of modern hard disk drive. Development of this tool is accompanied by thorough examination of four off-the-shelf hard disk drives. DIG consists of three key ingredients: O(1) track boundary detection algorithm,O (log n) zone boundary detection algorithm, and hybrid sampling based seek time profiling. We particularly focus on addressing the scalability aspect of disk characterization. With DIG, we are able to extract key metrics of hard disk drive within 3-20 min. DIG allows us to determine the sector layout mechanism of the underlying hard disk drive, e.g. hybrid serpentine, cylinder serpentine and surface serpentine, and to build complete sector map from LBN to three dimensional space of (cylinder, head, sector). Examining the disks with DIG, we found a number of important observations. Modern hard disk drive puts great emphasis on minimizing the head switch overhead. This is done via sector layout mechanism and and surface serpentine and hybrid serpentine is the typical way of avoiding it. Legacy disk seek time model leaves much to be desired to be used in modern hard disk drive especially in short seeks (less than 5000 tracks).

BackgroundDashboards are visual information systems frequently employed by healthcare organisations to track key quality improvement and patient safety performance metrics. The typical healthcare dashboard focuses on specific metrics, disease processes...

The explosion of data in the digital era has posed major challenges handling, computing and analyzing enormous and complex datasets. Cloud computing has arisen as a revolutionary solution providing scalable and elastic infrastructure necessary to deal with incoming big data workloads. This study employs an empirical approach to evaluate the performance, cost efficiency, and scalability of the three dominant cloud service models, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Function-as-a-Service (FaaS) on Amazon Web Services, Microsoft Azure and Google Cloud Platform. Standard big data analytics workloads running real-time stream processing and machine learning activities were then implemented in Apache Spark, Hadoop, and Kafka on harmonized cloud environments. Key performance metrics such as execution time, CPU utilization, memory, cost per task and throughput were taken, analyzed statistically using ANOVA and Tukey’s post hoc tests. Results show that FaaS configurations are always faster in execution speed, memory efficiency and cost compared to IaaS, while IaaS delivers better CPU usage for continual workloads. AWS and GCP platform performed relatively balanced when compared to Azure. It is concluded that serverless architecture is, in fact, optimal for modular and burst-oriented analytics, and hybrid models might be more appropriate for complex pipelines. These results can offer cloud architects practical directions towards scalable and cost-effective big data solutions.

Research into cutting-edge systems that can satisfy the rising needs for greater data rate and increased reliability is becoming more and more necessary as wireless communication technologies continue to evolve. To enhance the spectral and energy efficiencies, this study examine the performance of massive multiple input multiple output (mMIMO) communication systems using wavelet-order orthogonal frequency division multiplexing (W-OFDM). Also, a communication system was created based on W-OFDM Massive MIMO using MATLAB to model its operation. The simulated system's various stages of operation was examined through detailed analysis and its performance was compare with prior studies and practical networks on OFDM. Key performance metrics, specifically the data throughput, was evaluated to assess the effectiveness and viability of the system. The simulation results indicates that W-OFDM can achieve up to 35% reduction in peak-to-average-power-ratio (PAPR) compared to the traditional OFDM, with a 20% improvement in BER performance under Rayleigh fading conditions. Also, the spectral efficiency of W-OFDM mMIMO outperforms convectional OFDM by 15%, making it a viable technology for future communication networks requiring high data rates and reliability. This implies that, the analytical evaluation confirms that W-OFDM mMIMO provides substantial benefits for improving energy and spectral efficiencies in wireless communication systems, addressing the challenges of increased user demands for high spectrum. This study contributes to the understanding of MIMO systems' performance optimization, highlighting the importance of antenna configuration and cell distance. Comprehensive analysis of spectral efficiency and energy efficiency trade-offs in MIMO systems and identification of optimal antenna configuration for maximum energy efficiency, addressing some of the challenges of future wireless communication systems.

This study focused on investigating the relationship between emotional intelligence and key business performance metrics, such as profitability, employee retention, customer satisfaction, and leadership effectiveness. The researcher utilized the descriptive-correlational research design to determine the relationship between Emotional Intelligence and emotional intelligence and key business performance metrics, such as profitability, employee retention, customer satisfaction, and leadership effectiveness among the selected small business owners in San Pedro, Laguna. Based on the result, it was concluded that small business owners demonstrate a strong level of emotional intelligence, which encompasses skills such as recognizing and managing emotions, as well as understanding others. Emotional intelligence does not have a significant impact on profitability among the selected small business owners in San Pedro City, Laguna. Those who have higher levels of emotional intelligence are more likely to have better employee retention rates. Emotional intelligence does not have a direct impact on client satisfaction and lastly, emotional intelligence does not have a significant impact on the leadership effectiveness of small business owners in San Pedro Laguna. Thus, it is recommended that the small business owners in San Pedro Laguna focus on developing emotional intelligence skills, while also considering other factors such as market conditions, pricing strategies, and operational efficiency for profitability. Creating a positive work environment, enhancing client satisfaction through various factors, and taking a holistic approach to leadership development are also suggested.

Several active queue management (AQM) schemes have been proposed to address the bufferbloat issue, a long network delay and performance degradation issue caused by excess buffering of packets in network nodes. The solutions proposed up to now, however, satisfy only a subset of the key network performance metrics, i.e., end-to-end throughput, throughput fairness, and delay, in 802.11s-based multi-radio multi-channel wireless mesh networks (MRMC WMN). In this letter, we propose FD-AQM, a fairness-aware delay-controlled AQM to satisfy all three key network performance metrics simultaneously in the 802.11s-based MRMC WMN. The proposed FD-AQM achieves the goal by adopting a per-queue drop interval which is recalibrated according to the average round-trip time of flows, and a drop decision mechanism which considers average throughput as well as target delay. Through simulations, we show that FD-AQM is superior to other AQM schemes in terms of throughput and fairness.

Meeting future commercial sustainable aviation goals will require aircraft architectural configurations and propulsion systems that deviate from those currently employed today. Developing or selecting an optimal propulsion system solution for integration with novel aircraft configurations necessitates an understanding of how propulsion system performance and design requirements are impacted by such architectural changes at the aircraft level. With a focus on the structural characteristics of the propulsion system, this paper details how relevant architectural changes (mass, stiffness and location of powerplant and airframe integration) impact the key structural response of the propulsion system. A sensitivity study employing Taguchi Design of Experiments approach has been performed to identify the most dominant aircraft architectural parameters, and quantify how they impact the key structural performance metrics that inform propulsion system design. A whole aircraft coupled aerodynamic (DLM) – Flight Dynamics (FD) – Structural Dynamics (SD) airframe model has been generated, and integrated with a higher fidelity propulsion system idealization, for the purpose of acquiring key structural performance metrics under a range of static and dynamic load cases. The study concluded that, for a tube and wing aircraft configuration, the location of the powerplant under the wing had the most significant impact on airframe sizing loads, power plant sizing loads, and internal power plant deformations. Powerplant location combined with mass also had a significant impact on the dynamic response (natural frequencies) of the coupled propulsion system and airframe. Powerplant loading also exhibited a degree of sensitivity to pylon stiffness characteristics, particularly highlighting the interaction effects between pylon stiffness and powerplant mass parameters.