Performance Analysis of R-DCN Architecture for Next Generation Web Application Integration

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.

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.

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.

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.

Breast cancer remains a major healthcare challenge, highlighting the need for early and accurate diagnosis. Shear-wave elastography (SWE), an ultrasound-based imaging technique that quantifies tissue elasticity, has emerged as a promising tool. Recent studies suggest that SWE may provide additional diagnostic value when used alongside conventional imaging methods. This study aimed to assess the diagnostic performance of SWE when combined with conventional ultrasound and magnetic resonance imaging (MRI) in the evaluation of breast lesions. This retrospective study included patients with breast lesions who underwent SWE, conventional ultrasound and MRI. The diagnostic performance of each modality was evaluated individually and in combination. Histopathological results served as the gold standard for diagnosis. Key performance metrics - sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy - were calculated for each imaging approach. A total of 99 patients were included in the study, comprising 64 with benign lesions and 35 with malignant lesions. Malignant lesions were generally larger and exhibited distinct imaging characteristics across ultrasound, SWE and MRI. When assessed individually, SWE, ultrasound and MRI showed comparable diagnostic accuracy (64.6%, 62.6% and 62.6%, respectively). However, combining all 3 modalities significantly improved diagnostic performance, yielding sensitivity, specificity, PPV, NPV, and overall accuracy of 94.3%, 89.1%, 82.5%, 96.6%, and 90.9%, respectively (p < 0.001). The area under the curve (AUC) for the combined approach was significantly higher than for any single modality (0.917 vs 0.642, 0.627 and 0.633; p < 0.001). While SWE alone offers diagnostic performance comparable to that of ultrasound and MRI individually, its greatest value lies in combination with these imaging modalities. Integrating ultrasound, SWE and MRI significantly enhances diagnostic accuracy, sensitivity and specificity, offering a promising multimodal approach for more reliable differentiation between benign and malignant breast lesions.

Twisted string actuators (TSA) exhibit desirable traits for robotic applications like enabling high-force actuation. It is important to understand TSAs' key performance metrics to allow for ubiquitous usage. However, performance analysis of TSAs is challenging due to the strong coupling between the TSA model parameters. Comparing TSA's performance to motor-based actuators is essential in understanding the trade-offs of TSAs compared to motor-based tendon-driven actuators. This letter presented a model-based performance analysis of TSAs with a focus on four metrics: contraction range, linear velocity, effective torque input, and force output. Furthermore, the performance of TSAs was compared to another motor-based actuation technology namely spooled motor-tendon actuator (SMTA). A comparison framework was proposed by synthesizing models of TSAs and SMTAs. Simulation studies were conducted using the same type of motors to construct TSAs and SMTAs. The results showed that TSAs performed better in force outputs and effective torque inputs, whereas the SMTAs outperformed TSAs in terms of linear velocity and contraction range. In addition, a robotic assistive glove leveraging the advantageous performance of TSAs was realized, demonstrating the importance of the results. The findings suggest that TSAs are more suited for compact, high-force applications while SMTAs are more appropriate for high-speed robotic applications.

Two-Way Ranging (TWR) is a widely used method for distance estimation in asynchronous wireless networks, but the deviation between the clocks can lead to errors in distance estimation. The ultra-wideband (UWB) technology provides the best accuracy in distance estimation, but total emission time is drastically limited by harmonized standards. Localization accuracy, latency and network scalability are key performance metrics in wireless location characterization. This article advances a new sequencing of the localization process, called Double-Beacon Two-Way Ranging (DB-TWR) algorithm, and presents the related time-of-flight (ToF) estimation formula, which is able to compensate for clock error and to reduce the localization period. The Fine Time Measurement (FTM) protocol, specified by the Wireless LAN (WLAN) standard, uses the TWR method and as a result the clock deviation leads to systematic errors in ToF estimation. The paper proposes two additional timestamps captures and a new ToF computation formula which compensates for clock deviation. The article provides a theoretical analysis and extensive simulations of errors propagation in the ranging process, both as a mean and as an uncertainty, and proves that the designed algorithms cancel the clock offsets entirely.

BackgroundWith advancements in deep learning-based dental imaging analysis, artificial intelligence (AI) models are increasingly being employed to assist in mandibular third molar surgery. However, a comprehensive overview of the clinical utility remains limited. This scoping review aimed to identify and compare deep learning models used in the radiographic evaluation of mandibular third molar surgery, with a focus on AI model types, key performance metrics, imaging modalities, and clinical applicability.Material and MethodsFollowing the PRISMA-ScR guidelines, a comprehensive search was conducted in the PubMed and Scopus databases for original research articles published between 2015 and 2024. Systematic reviews, editorial articles, and studies with insufficient datasets were excluded. Studies utilising panoramic radiographs and cone-beam computed tomography (CBCT) images for AI-based mandibular third molar analyses were included. The extracted data were charted according to the AI model types, performance metrics (accuracy, sensitivity, and specificity), dataset size and distribution, validation processes, and clinical applicability. Comparative performance tables and heat maps were utilised for visualisation.ResultsOf the initial 948 articles, 16 met the inclusion criteria. Various convolutional neural network (CNN)-based models have been developed, with U-Net demonstrating the highest accuracy and clinical utility. Most studies employed panoramic and CBCT images, with U-Net outperforming other models in predicting nerve injury and evaluating extraction difficulty. However, substantial variations in dataset size, validation procedures, and performance metrics were noted, highlighting inconsistencies in model generalisability.ConclusionsDeep learning shows promising potential in the radiographic evaluation of mandibular third molars. To date, most studies have relied on two-dimensional images and focused on detection and segmentation, while predictive modeling and three-dimensional CBCT-based analysis are relatively limited. To enhance clinical utility, larger standardized datasets, transparent multi-expert annotation, task-specific benchmarking, and robust external/multicenter validation are needed. These measures will enable reliable pre-extraction risk prediction and support clinical decision-making.

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.

Multivariate process control has recently gained significant attention because it takes into account the correlation between process variables and offers a more comprehensive understanding of the overall process behavior. In line with this phenomenon, this paper proposes the multivariate triple sampling Hotelling’s T 2 (MTS) control chart as an efficient process monitoring method in multivariate statistical process control. The mathematical properties of the MTS chart are also derived. The performance of the MTS chart is assessed using key performance metrics, such as average run length (ARL), average number of observations to signal (ANOS) and expected average number of observations to signal (EANOS). The performance of the MTS chart is compared with that of the existing multivariate double sampling Hotelling’s T 2 (MDS) chart. The findings indicate that the MTS chart consistently surpasses the MDS chart across all performance metrics, demonstrating its superior capability in detecting shifts for a multivariate process. Notably, the MTS chart exhibits lower ARL, ANOS and EANOS values, emphasizing its effectiveness in promptly identifying process shifts. In terms of practical application, an example is given to elucidate the execution of the MTS chart.

Spectrum sensing is an essential operation in cognitive radio networks. In this paper, we consider a dynamic spectrum sensing system consisting of both non-cooperative mobile cognitive radio(CR) secondary users and mobile non-cognitive radio primary users. Key performance metrics such as detection capability and sensing capacity are evaluated. Two mobility models are adopted for both secondary and primary users. The relative distance distribution associating the independent mobile users is analyzed and given in both 1- and 2-dimensions. Expressions for both performance metrics are derived, and simulations are performed to validate the theoretical analyses.

Design and development of routing protocols for Mobile Ad Hoc Networks (MANETs) is an active area of research. The standard practice among researchers working in this emerging domain is to evaluate the performance of their routing protocols in a network simulator. It is now a well known fact that the simulation studies are scenario specific and hence their results can not be generalized. In this paper, we present mathematical models of two key performance metrics, routing overhead and route optimality, of BeeAdHocMANET routing protocol. One of the key components of our BeeAdHocmodel is the collision model at Medium Access Control (MAC) layer. The mathematical expressions of the performance metrics provide valuable insight about the behavior of BeeAdHocin particular, and a typical ad hoc routing protocol in general, without resorting to scenario specific time consuming simulations.