A Multidimensional Comparison Assessing the Viability of Electric Vehicles in Jordan Across Key Performance Metrics

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

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 < 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 < 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.

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.

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.

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.’

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.

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

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 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.

Investigating greenhouse gas emissions and environmental impacts from the production of lithium-ion batteries in China

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.

Despite a rapid worldwide expansion of the biofuel industry, there is a lack of consensus within the scientific community about the potential of biofuels to reduce reliance on petroleum and decrease greenhouse gas (GHG) emissions. Although life cycle assessment provides a means to quantify these potential benefits and environmental impacts, existing methods limit direct comparison within and between different biofuel systems because of inconsistencies in performance metrics, system boundaries, and underlying parameter values. There is a critical need for standardized life-cycle methods, metrics, and tools to evaluate biofuel systems based on performance of feedstock production and biofuel conversion at regional or national scales, as well as for estimating the net GHG mitigation of an individual biofuel production system to accommodate impending GHG-intensity regulations and GHG emissions trading. Predicting the performance of emerging biofuel systems (e.g., switchgrass cellulosic ethanol) poses additional challenges for life cycle assessment due to lack of commercial-scale feedstock production and conversion systems. Continued political support for the biofuel industry will be influenced by public perceptions of the contributions of biofuel systems towards mitigation of GHG emissions and reducing dependence on petroleum for transportation fuels. Standardization of key performance metrics such as GHG emissions mitigation and net energy yield are essential to help inform both public perceptions and public policy.

Due to the emergence of sub-10 nm technologies, next generation CMP slurry formulations have continued to increase in additive (nanoparticle and chemistry) complexity to meet stringent device specifications. Therefore, it is essential to probe the molecular level interactions at the nanoparticle/slurry chemistry/substrate interface and in turn correlate them to key performance metrics such as removal rate, post CMP defects, planarization efficiency. More specifically, this work has developed a suite of techniques that explore the adsorption/complexation dynamics that evolve during the CMP process. Employing a suite of techniques such as modified atomic force microscopy (AFM), electrochemical quartz crystal nanobalance (EQCN), dynamic contact angle measurements, fluorescence microscopy, pre/post polish characterization of particle properties (size and zeta potential), and in-situ rotating disk electrode open circuit potential measurement the critical static and dynamic mechanisms can be exposed. Correlation of this information gives way to modulation (desired or undesired) of the CMP performance potentially providing tunabilty (process and formulation) at advanced technology nodes to meet stringent electrical, topography, and defect requirements. Furthermore, these methods have shown great promise as a vehicle to probe the molecular level interactions at the nanoparticle/chemistry/ substrate interface. Results have shown excellent correlation to key process performance metrics and have provided insight into relevant surface chemistry changes that impact critical interactions during the CMP process. This presentation will address key interactions through a series of case studies focusing on probing the impact of inhibitor structure on the substrate surface energy relevant to Cu CMP, the role of slurry chemistry on controlling defectivity during STI post-CMP cleaning, and effect of organic additives on the modulation of nanoparticle surface properties to enhance STI CMP performance.

The Rayleigh–Taylor instability (RTI) is an ubiquitous phenomenon that occurs in inertial-confinement-fusion (ICF) implosions and is recognized as an important limiting factor of ICF performance. To analytically understand the RTI dynamics and its impact on ICF capsule implosions, we develop a first-principle variational theory that describes an imploding spherical shell undergoing RTI. The model is based on a thin-shell approximation and includes the dynamical coupling between the imploding spherical shell and an adiabatically compressed fluid within its interior. Using a quasilinear analysis, we study the degradation trends of key ICF performance metrics (e.g., stagnation pressure, residual kinetic energy, and areal density) as functions of initial RTI parameters (e.g., the initial amplitude and Legendre mode), as well as the 1D implosion characteristics (e.g., the convergence ratio). We compare analytical results from the theory against nonlinear results obtained by numerically integrating the governing equations of this reduced model. Our findings emphasize the need to incorporate polar flows in the calculation of residual kinetic energy and demonstrate that higher convergence ratios in ICF implosions lead to significantly greater degradation of key performance metrics.

Theoretical models are essential for performance analysis and structure optimization design of large-scale piezoelectric micromachined ultrasonic transducers (PMUT) arrays. However, current models have rarely incorporated the inter-element crosstalk and oversimplified the electro-mechanical-acoustic coupling, leading serious discrepancies with experimental results and limiting the array optimization design and performance improvement. To address this, a novel electro-mechanical-acoustic coupling model and a spatial acoustic field modeling approach are proposed for PMUT arrays, incorporating distributed deformation functions of individual element and mutual acoustic impedance to analyse key performance metrics such as transmission power, frequency response, focal length, and beamwidth. Its accuracy is validated through finite element simulations, demonstrating small deviations of less than 3%. Parametric studies reveal that increasing the filling ratio from 20% to 60% improves transmission power and bandwidth but significantly increases crosstalk, reducing focusing efficiency. Enlarging the array size results in proportional increases in acoustic output power and focal pressure, while simultaneously reducing beamwidth, thereby improving directivity. As for array arrangements, circular array achieves higher focal pressure than square array, albeit with shorter focal lengths. Annular array, with its distinct mainlobe and ring-shaped sidelobes, demonstrates superior focal pressure at longer distances, ideal for extended-range applications. The theoretical models are further validated by experimental results from fabricated square, hexagonal, circular, and annular PMUT arrays. This study proposes an accurate theoretical model for PMUT arrays, enabling accurate and reliable prediction of key acoustic performance metrics in large-scale arrays, and facilitating the array structure optimization design and performance enhancement of PMUT.