JUSTIFI: Software for Improving Performance Objectives via Energy Efficiency

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.

The emergence of micro frontend architectures has revolutionized the way organizations approach frontend application development, enabling distributed teams to work independently while maintaining system consistency. However, performance optimization in these distributed systems presents unique challenges that differ significantly from traditional monolithic approaches. This study examines performance strategies for micro frontend-based applications through a comprehensive analysis of 30 applications across six key performance metrics. The research reveals significant performance variations across micro frontend implementations, with bundle sizes ranging from 345KB to 550KB and API response times ranging from 155ms to 300ms. Our analysis demonstrates strong correlations between optimization strategies and application performance, particularly highlighting the critical role of lazy loading implementations. Applications achieving lazy loading rates above 50% consistently outperformed those below 40%, with performance score improvements of up to 37 points. The study uses XGBoost regression models to predict key performance metrics, identifying challenges in CPU usage prediction due to overfitting concerns, while achieving exceptional accuracy for bundle size prediction (R² = 0.9647).The performance patterns indicate that successful micro frontend applications require integrated optimization across multiple dimensions, including composition strategies, dependency management, and inter-service communication protocols. The research identifies threshold values for optimal performance, including maintaining bundle sizes below 400KB and implementing aggressive lazy loading strategies. These findings provide actionable insights for development teams working with micro frontend architectures, providing data-driven guidance for architectural decisions and performance strategies in distributed frontend systems.Objective: This study examines performance optimization in micro-frontend-based applications. It analyzes 30 settings across six key metrics, focusing on bundle size, lazy loading, CPU utilization, and response times. The research highlights optimization constraints and a predictive model to guide architectural decisions for scalable, efficient distributed frontends using XGBoost regression.

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.

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.

Rapid energy estimation for energy efficient applications using field-programmable gate arrays (FPGAs) remains a challenging research topic. Energy dissipation and efficiency have prevented the widespread use of FPGA devices in embedded systems, where energy efficiency is a key performance metric. Helping overcome these challenges, Energy Efficient

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.

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.

Caching is a promising technique that can effectively reduce peak traffic by pushing popular content items proactively to the users during off- peak hours. Two key performance metrics for proactive caching are the energy efficiency and hit ratio. In this paper, we are interested in balancing the hit ratio and energy efficiency in social-aware proactive caching. More specifically, we present a social behavior driven pushing and caching policy that is capable of maximizing the hit ratio while satisfying the power constraint. For two notable schemes, namely uncoded caching and coded caching, we formulate two optimization problems that give the optimal tradeoff between the energy efficiency and caching hit ratio. Furthermore, the optimal solutions present the energy efficient joint pushing and buffer update polices. Our simulation results show that the social-aware pushing and update policies may bring a significant hit ratio gain when the buffer size is far less than the number of items.

A protocol for position-responsive routing (PRRP) was developed dynamically to minimize the maximum distance between nodes and gateways/cluster heads, for maximum energy utilization of a deployed wireless sensor node. A tree routing technique was employed to display notable gains in key performance metrics. Network Simulator (NS3) software was utilized to evaluate and compare the effectiveness of the suggested technique using two selected conventional techniques: LEACH (Low Energy Adaptive Clustering Hierarchy) and PEGASIS (Power Efficient Gathering in Sensor Information System). The node energy efficiency of the proposed PRRP was obtained as 0.3881J, LEACH as 0.2249J and PEGASIS as 0.2755J. The total energy remaining after 100 second iteration process was evaluated as 19.4060J, 11.2490J and 13.7745J for PPRP, LEACH and PEGASIS respectively. The result obtained showed that PRRP achieved a 63% energy efficiency improvement over LEACH and 41% over PEGASIS, demonstrating superior energy conservation capabilities.

This paper focuses on the challenges and opportunities of achieving sustainability in smart cities. It depends on maintaining the balance between energy efficiency and connectivity through energy-efficient protocols. The study surveys systems with energy-efficient protocols, emphasizing their strengths and limitations in maintaining sustainability in smart cities. The need for energy-efficient protocols in smart city settings is that these protocols dynamically adjust operations to optimize energy consumption within sensor nodes in communication networks while considering parameters: like heterogeneity, traffic patterns, and environmental factors. Evaluation against key performance metrics such as energy consumption, data delivery ratio, network lifetime, and throughput identify promising technologies to achieve an optimal balance between connectivity and energy efficiency in smart cities. It concludes by proposing future research directions and considerations to make the development of sustainable networking solutions rapidly tailored to the evolving landscape of smart cities. This paper aims to present a comprehensive overview of the pivotal role played by energy-efficient protocols in fostering sustainable networking architectures.

The composition of electronic government (E-government) cloud services is essential for optimizing resource allocation, load balancing, and energy efficiency, thereby ensuring seamless digital service delivery. optimizing cloud service composition remains a massive challenge because of dynamic workloads and varying aid needs. This research investigates the implementation of Particle Swarm Optimization (PSO) to improve the cloud service composition in egovernment platforms by effectively distributing workloads, minimizing execution time, enhancing Virtual Machine (VM) usage, and decreasing energy consumption. The performance of the proposed PSO-based technique was evaluated using CloudSim 5.0, and the results were compared with the Ant Colony Optimization (ACO) algorithm across key performance metrics, such as execution

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.

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...