Procena operativnog uticaja SecuDroneComm-a - simulaciona analiza bezbedne komunikacije bespilotnih letelica u vojnim okruženjima

The objective of the present study is to investigate systematically the key metrics to evaluate the life extension performance of offshore wind farm operations. Finding the appropriate performance metric for an operation is essential for a durable, reliable, and profitable offshore wind farm operation. The analyzed key performance metrics are gross profit margin, return on asset, compounded annual rate of return of initial investment and levelized cost of energy. The mean value and standard deviation of each performance metric are calculated within a probabilistic techno-economic assessment framework for a single offshore wind asset, which is later extended to evaluate the whole offshore wind farm by a multi-asset portfolio optimization. The Markowitz modern portfolio theory is applied to estimate the maximum risk-adjusted ratio and Sharpe ratio, for the key performance metrics. Subsequently, the key performance metrics are compared to identify the most suitable metrics at different stages of the life extension. Moreover, the present study investigates the effect of different uncertainty levels associated with the stochastic variables in the techno-economic assessment. Finally, the suitability of performance metrics is analyzed and discussed for different offshore wind farm sizes and related recommendations are given.

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.

The performance of optoelectronic devices is affected by various noise sources. A notable factor is the 4.2% lattice mismatch at the Ge/Si interface, which significantly influences the efficiency of Ge-on-Si photodetectors. These noise sources can be analyzed by examining the impact of the Ge/Si interface and deep traps on dark and photocurrents. This study evaluates the impact of these charge traps on key photodetector performance metrics, including responsivity, photo-to-dark current ratio, noise equivalent power (NEP), and specific detectivity (D*). The trapping effects on charge transport under both forward and reverse bias conditions are monitored through hysteresis analysis. When illuminated with an unmodulated 1550 nm laser, all the key performance metrics exhibit maximum variations at a specific reverse bias. This critical bias marks the transition from saturated to exponential charge transport regimes, where intensified electric fields enhance trap-assisted recombination and thus maximize metric fluctuations.

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.’

IntroductionRecruitment and retention remain critical challenges in clinical trials, particularly in neurodegenerative diseases, which require large participant populations, rigorous screening, and prolonged follow-up periods. Care Access is a global research site management organization that operates clinical trial sites employing various operational models. This study evaluates the operational performance of Care Access site models—including traditional sites, hub-and-spoke, and decentralized community-integrated research (DCIR) sites—within a Phase 3 neurodegenerative disease trial, focusing on their relative efficiency in recruitment, randomization, and retention. The inclusion of multiple site models within the same trial presents a rare opportunity for direct comparison under uniform study conditions, providing unique insights into their respective advantages and challenges. By analyzing key site performance metrics and the role of innovative operational strategies, this study aims to identify effective approaches to enhancing trial efficiency and overcoming recruitment challenges to inform the design and conduct of future trials.MethodsThe trial involved 32 Care Access sites each employing one of these distinct operational models. Key performance metrics, such as participant screening rates, randomization rates, screen failure rates, and post-randomization discontinuation rates, were analyzed across (a) traditional, (b) hub-and-spoke, and (c) DCIR site models. We also compared the enrollment performance of Care Access to that of 196 non-Care Access sites using publicly available data.ResultsDCIR Sites demonstrated the highest recruitment efficiency, screening 20.61 participants per site per month and randomizing 0.79 participants per site per month, compared to 11.78 and 0.50 for traditional sites, and 12.20 and 0.45 for hub-and-spoke sites, respectively. Despite being newly established, and operating in a decentralized model, DCIR sites achieved post-randomization discontinuation rates (28.17%) comparable to those of traditional site models (26.28%), highlighting their effectiveness in maintaining participant engagement. All site models encountered high screen failure rates (~95%), consistent with Phase 3 trials for neurodegenerative diseases. Notably, a community-engaged, research-only facility achieved the lowest discontinuation rate (17.65%) among all sites, highlighting the potential of strong local engagement to significantly enhance retention and participation. Furthermore, when comparing Care Access sites with non-Care Access sites in this trial, Care Access sites achieved an average randomization rate of 15.6 participants per site, outperforming the 8.7 participants per site recorded by non-Care Access sites. Data quality, monitoring practices, and overall data integrity were consistent across all site models, supporting the reliability of findings across both decentralized and traditional approaches. This comparison highlights the effectiveness of the innovative operational framework and decentralized community engagement approach in overcoming traditional recruitment challenges and enhancing trial outcomes.DiscussionDCIR sites exhibited superior participant screening and randomization efficiency while maintaining discontinuation rates comparable to traditional site models. This success was driven by a combination of innovative operational strategies, including decentralized community-based outreach mechanisms that expanded population access to research by bringing trials directly to populations that previously lacked access to clinical research. At the same time, this approach helped reach underrepresented groups, thereby improving both geographic coverage and trial generalizability while enhancing overall trial performance. Additionally, other innovations like the deployment of centralized remote research coordinators also played a role by streamlining remotely-conducted tasks, allowing site staff, in all site models, to focus on participant care and engagement. These findings highlight the effectiveness of a flexible, multi-model site strategy in addressing recruitment and retention challenges in large-scale Phase 3 neurodegenerative disease trials and suggest that this approach may extend to other therapeutic areas facing similar challenges.

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.

Background. Badminton, a racquet sport that has gained global popularity, demands technical precision, tactical awareness, and exceptional physical fitness. Skills such as smashing, footwork, and minimizing errors are critical to success. However, the specific influence of age and gender on these metrics, especially among younger players, remains underexplored. Objectives. This study aimed to examine the effect of age and gender on key badminton performance metrics, including smash ability, footwork, and unforced errors, in order to identify developmental and demographic factors influencing skill acquisition and execution. Materials and methods. A quantitative descriptive study involved 24 athletes (aged 9–14) from the Wincorp badminton organization in Surakarta, Indonesia. Participants were grouped by age (9–10, 11–12, 13–14 years) and gender, ensuring equal representation. Over two months, data on smashing, lobbing, driving, footwork, and error rates were collected. Descriptive statistics and MANOVA analyzed differences, with a significance level set at p &lt; 0.05. Results. MANOVA revealed significant age-related effects on smashing (p = 0.000), footwork (p = 0.000), and error points (p = 0.000), with beginners (13–14 years) excelling in most metrics. Gender differences were also found to be substantial for smashing (p = 0.000), footwork (p = 0.000), and error points (p = 0.003), with males outperforming females in most categories. Interaction effects between age and gender were significant for smashing and footwork (p &lt; 0.05). However, no considerable differences were observed for netting and serving strokes across age or gender. Conclusions. The study indicates that age and gender significantly influence badminton performance metrics. Beginner athletes (13–14 years) demonstrated superior skills compared to younger groups, while males generally outperformed females. These findings highlight the importance of tailoring training programs by age and gender to optimize skill development and reduce performance gaps. Further studies should be performed to investigate biomechanical and psychological factors to refine coaching strategies.

The optical system of the James Webb Space Telescope (JWS T) is split between two of th e Observatory’s element, the Optical Telescope Element (OTE) and the Integrated Science Instrument Module (ISIM). The OTE optical design consists of an 18-hexagonal segmented primary mirror (25m 2 clear aperture), a secondary mirror, a tertiary mirror, and a flat fine steering mirror used for fine guidance control. All optical components are made of beryllium. The primary and secondary mirror elements have hexapod actuation that provides six degrees of freedom rigid body adjustment. The optical components are mounted to a very stable truss struct ure made of composite materials. The OTE structure also supports the ISIM. The ISIM contains the Science Instruments (SIs) and Fine Guidance Sensor (FGS) needed for acquiring mission science data and for Observatory pointing and control and provides mechanical support for the SIs and FGS. The optical perform ance of the telescope is a key performance me tric for the success of JWST. To ensure proper performance, the JWST optical verification program is a comprehensive, incremental, end-to-end verification program which includes multiple, independent, cross checks of key optical performance metrics to reduce risk of an on-orbit telescope performance issues. This paper discusses the verification testing and analysis necessary to verify the Observatory’s image quality and sensitivity requirements. This verification starts with component level verification and ends with the Observatory level verification at Johnson Space Flight Center. The optical verification of JWST is a comprehensive, incremental, end-to-end optical veri fication program which includes both test and analysis. Keywords: JWST, Optical Verification, Cryogenic Te sting, Optical Analysis, Optical Testing

—The implementation of the Advanced Encryption Standard (AES) on RISC-V processors has gained attention for its potential in secure and efficient cryptographic operations. Researchers have explored hardware acceleration techniques, custom instruction set extensions, and vector-based optimizations to enhance performance. AES integration into RISC-V cores has demonstrated improvements in execution speed, energy efficiency, and memory footprint, making it suitable for IoT and embedded applications. Several studies propose hardware accelerators and co-processors that reduce encryption time while maintaining cryptographic security. Vector-based AES implementations fur- ther improve efficiency by leveraging parallel processing capa- bilities of modern RISC-V architectures. The introduction of custom AES instructions enables high-throughput encryption and decryption with minimal software overhead. FPGA-based AES accelerators have also been explored to enhance adaptability and flexibility in cryptographic applications. Experimental results in- dicate that RISC-V AES implementations outperform traditional software-based encryption in terms of speed and power con- sumption. The standardization of AES instruction set extensions in RISC-V continues to evolve, contributing to a more secure and efficient cryptographic ecosystem. This paper reviews recent advancements in AES integration with RISC-V, highlighting key performance metrics and optimization techniques. Index Terms—AES-128 Encryption, RISC-V Cryptographic Extensions, Hardware Acceleration, FPGA-based AES Co- processor.

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.

The main objective of this study is to develop an optimal life extension management strategy for ageing offshore wind farms. Finding the appropriate performance metric for an operation is essential for a durable, reliable, and profitable offshore wind farm operation. To this end, the key metrics to evaluate the life extension performance of an offshore wind farm are investigated. The mean value and the standard deviation of each performance metric are calculated using a probabilistic techno-economic assessment framework for a single offshore wind asset, which is later extended to evaluate the whole offshore wind farm by the multi-asset portfolio optimization. In this regard, the Markowitz modern portfolio theory is applied to estimate a risk-adjusted return parameter, the Sharpe ratio of the overall portfolio of offshore wind assets. Later on, the key performance metrics are compared to identify the most suitable metrics at different stages of life extension, and a further discussion is given for different offshore wind farm sizes. Moreover, the optimal management strategy, which maximizes the Sharpe ratio of the overall offshore wind farm, is analyzed using one of the key performance metrics under optimistic, moderate, and pessimistic scenarios. Finally, the optimal allocation (portfolio) of offshore wind assets attained based on the mean-variance optimization is presented for the different stages of the life extension of the offshore wind farms accounting for the uncertainty propagation during the life extension.

Evaluating performance metrics in maritime supply chain in crucial for achieving operational efficiency, resilience, and service reliability in an increasingly complex global shipping environment. This Study systematically reviews 31 peer-reviewed articles published between 2010 and 2024 to identify, classify, and analyse key performance metrics used in the maritime logistics sector. The review was conducted using four major databases-ScienceDirect, Google Scholar, SpringerLink, and EBSCO Host- following the PRISMA framework. This study employs a systematic literature review (SLR), incorporating thematic analysis to identify and synthesize common patterns across the selected literature. The findings are categorized into three main dimensions: operational efficiency (e.g., berth productivity, vessel on-time performance, ship turnaround time), resilience (e.g., disruption recovery time, supply chain redundancy, routing flexibility), and servicer reliability (e.g., customer satisfaction, delivery accuracy, schedule adherence) The novelty of this paper lies in the development of a comprehensive and structured framework that integrates these key performance metrics, providing maritime stakeholders with actionable insight for performance evaluation and strategic alignment. This farmwork not only synthesizes current academic perspectives but also incorporates digitalization and technological readiness as enablers of enhanced Supply Chain performance. The outcome offers valuable guidance for decision-makers aiming to optimize resource allocation, mitigate risks, and improve overall competitiveness in maritime logistics.

Purpose The literature on warehouse performance assessments is mainly focussed on the efficiency and effectiveness of an action or activity due to customer demand and tailored fulfilment, with less attention being given to the performance measurement of each function of the warehouse and its overall productivity. Therefore, this study was aimed at revising the key warehouse performance metrics to a set of productivity measurement indicators that can be adopted internationally for benchmarking productivity performance. Design/methodology/approach A literature review and semi-structured survey questionnaire were used for this study. The importance of warehouse productivity performance was reviewed to revamp the measurement indicators. Through the use of a directed content analysis and descriptive analysis, an extensive study was carried out to analyze existing warehouse productivity indicators. Findings The findings of this study provide comprehensive references for practitioners and academicians for improving the classification of productivity measurements from existing key performance metrics for warehousing. Also, this paper highlights the warehouse resources related to the respective warehouse operation activities. Research limitations/implications The study was limited to productivity performance indicators adapted from Staudt et al. (2015). Furthermore, the samples for this study comprised Malaysian academicians and practitioners in the related field. The findings can be adapted on a global scale as this study implemented general warehouse operation processes. Originality/value Consequently, the contributions of this study are that it provides relevant benchmarks for key productivity performance indicators in the warehousing sector that has worldwide applicability and the developed model provides a conceptual platform from which further theoretical and empirical developments can be carried out.

Bioeconomic and greenhouse gas emissions modelling of the factors influencing technical efficiency of temperate grassland-based suckler calf-to-beef production systems

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.