Experiencing feelings of helplessness has repeatedly been reported to contribute to depressive symptoms and negative affect. In turn, depression and negative affective states are associated, among ...others, with impairments in performance monitoring. Thus, the question arises whether performance monitoring is also affected by feelings of helplessness.
To this end, after the induction of feelings of helplessness via an unsolvable reasoning task, 37 participants (20 females) performed a modified version of a Flanker task. Based on a previously validated questionnaire, 17 participants were classified as helpless and 20 as not-helpless. Behavioral measures revealed no differences between helpless and not-helpless individuals. However, we observed enhanced Error-Related Negativity (ERN) amplitude differences between erroneous and correct responses in the helpless compared to the not-helpless group. Furthermore, correlational analysis revealed that higher scores of helplessness were associated with increased ERN difference scores. No influence of feelings of helplessness on later stages of performance monitoring was observed as indicated by Error-Positivity (Pe) amplitude.
The present study is the first to demonstrate that feelings of helplessness modulate the neuronal correlates of performance monitoring. Thus, even a short-lasting subjective state manipulation can lead to ERN amplitude variation, probably via modulation of mesencephalic dopamine activity.
► Research on neural performance monitoring correlates after helplessness induction. ► Increased feelings of helpless induced ERN amplitude enhancement. ► Behavioral measures were not affected by feelings of helplessness. ► Subjective state manipulation manifests itself in increased ERN amplitudes.
Electronic performance monitoring (EPM) refers to the use of technological means to observe, record, and analyze information that directly or indirectly relates to job performance. The last ...comprehensive review of the EPM literature was published in 2000. Since 2000, dramatic advances in information technologies have created an environment in which organizations are able to monitor employees to a greater extent and with greater intensity than was previously possible. Moreover, since that time, considerable research has been devoted to understanding the effects of EPM on individual performance and attitudes. Contradictory findings in the EPM literature exist, suggesting that EPM is a multidimensional phenomenon and one for which contextual and psychological variables are pertinent. Thus, we propose a theory-based typology of EPM characteristics and use this typology as a framework to review the EPM literature and identify an agenda for future research and practice.
Operators are pressured to maximize the achieved capacity over deployed links. This can be obtained by operating in the weakly nonlinear regime, requiring a precise understanding of the transmission ...conditions. Ideally, optical transponders should be capable of estimating the regime of operation from the received signal and feeding that information to the upper management layers to optimize the transmission characteristics; however, this estimation is challenging. This paper addresses this problem by estimating the linear and nonlinear signal-to-noise ratio (SNR) from the received signal. This estimation is performed by obtaining features of two distinct effects: nonlinear phase noise and second-order statistical moments. A small neural network is trained to estimate the SNRs from the extracted features. Over extensive simulations covering 19,800 sets of realistic fiber transmissions, we verified the accuracy of the proposed techniques. Employing both approaches simultaneously gave measured performances of 0.04 and 0.20 dB of standard error for the linear and nonlinear SNRs, respectively.
Machine learning (ML) has disrupted a wide range of science and engineering disciplines in recent years. ML applications in optical communications and networking are also gaining more attention, ...particularly in the areas of nonlinear transmission systems, optical performance monitoring, and cross-layer network optimizations for software-defined networks. However, the extent to which ML techniques can benefit optical communications and networking is not clear and this is partly due to an insufficient understanding of the nature of ML concepts. This paper aims to describe the mathematical foundations of basic ML techniques from communication theory and signal processing perspectives, which in turn will shed light on the types of problems in optical communications and networking that naturally warrant ML use. This will be followed by an overview of ongoing ML research in optical communications and networking with a focus on physical layer issues.
The positioning accuracy of global and regional navigation satellite systems (GNSS/RNSS) depends on a variety of influence factors. For constellation-specific performance analyses it has become ...common practice to separate a geometry-related quality factor (the dilution of precision, DOP) from the measurement and modeling errors of the individual ranging measurements (known as user equivalent range error, UERE). The latter is further divided into user equipment errors and contributions related to the space and control segment. The present study reviews the fundamental concepts and underlying assumptions of signal-in-space range error (SISRE) analyses and presents a harmonized framework for multi-GNSS performance monitoring based on the comparison of broadcast and precise ephemerides. The implications of inconsistent geometric reference points, non-common time systems, and signal-specific range biases are analyzed, and strategies for coping with these issues in the definition and computation of SIS range errors are developed. The presented concepts are, furthermore, applied to current navigation satellite systems, and representative results are presented along with a discussion of constellation-specific problems in their determination. Based on data for the January to December 2017 time frame, representative global average root-mean-square (RMS) SISRE values of 0.2 m, 0.6 m, 1 m, and 2 m are obtained for Galileo, GPS, BeiDou-2, and GLONASS, respectively. Roughly two times larger values apply for the corresponding 95th-percentile values. Overall, the study contributes to a better understanding and harmonization of multi-GNSS SISRE analyses and their use as key performance indicators for the various constellations.
This paper reviews the recent advancement made in data-driven technologies based on SCADA data for improving wind turbines’ operation and maintenance activities (e.g. condition monitoring, decision ...support, critical components failure detections) and the challenges associated with them. Machine learning techniques applied to wind turbines’ operation and maintenance (O&M) are reviewed. The data sources, feature engineering and model selection (classification, regression) and validation are all used to categorise these data-driven models. Our findings suggest that (a) most models use 10-minute mean SCADA data, though the use of high-resolution data has shown greater advantages as compared to 10-minute mean value but comes with high computational challenges. (b) Most of SCADA data are confidential and not available in the public domain which slows down technological advancements. (c) These datasets are used for both, the classification and regression of wind turbines but are used in classification extensively. And, (d) most commonly used data-driven models are neural networks, support vector machines, probabilistic models and decision trees and each of these models has its own merits and demerits. We conclude the paper by discussing the potential areas where SCADA data-based data-driven methodologies could be used in future wind energy research.
Abstract
Bromley, T, Turner, A, Read, P, Lake, J, Maloney, S, Chavda, S, and Bishop, C. Effects of a competitive soccer match on jump performance and interlimb asymmetries in elite academy soccer ...players.
J Strength Cond Res
35(6): 1707–1714, 2021—The purpose of this study was to investigate the effects of a competitive soccer match on jump performance and interlimb asymmetries over incremental time points during a 72-hour period. Fourteen elite adolescent players from a professional English category 3 academy performed single-leg countermovement jumps pre, post, 24-, 48-, and 72-hour post-match on a single force platform. Eccentric impulse, concentric impulse, peak propulsive force, jump height, peak landing force, and landing impulse were monitored throughout. Interlimb asymmetries were also calculated for each metric as the percentage difference between limbs. Significant negative changes (
p
< 0.05) in jump performance were noted for all metrics at all time points, with the exception of jump height. Interlimb asymmetries were metric-dependent and showed very large increases, specifically post-match, with a trend to reduce back toward baseline values at the 48-hour time point for propulsive-based metrics. Asymmetries for landing metrics did not peak until the 24-hour time point and again reduced toward baseline at 48-hour time point. This study highlights the importance of monitoring distinct jump metrics, as jump height alone was not sensitive enough to show significant changes in jump performance. However, interlimb asymmetries were sensitive to fatigue with very large increases post-match. More frequent monitoring of asymmetries could enable practitioners to determine whether existing imbalances are also associated with reductions in physical performance or increased injury risk.
In healthcare monitoring, the quality of medical services and patient outcomes are significantly influenced by the unnatural variations emphasizing the importance of effective control and monitoring ...strategies. In such scenarios the quality is compromised when a shift in the medical process is not detected timely. In this study, we commenced a comprehensive analysis of the variability present in the patient's hematocrit levels. We introduced a novel approach using fuzzy control charts for individual measurements to detect shift within hematocrit levels effectively. For the initial forecasting of hematocrit level variability, we employed the exponential smoothing method by using R software. Following this, we proposed a set of fuzzy control charts intended for individual measurements, including the fuzzy moving average control chart, fuzzy weighted moving control chart, and fuzzy moving range control chart. These control charts are defined with fuzzy control rules, allowing them to analyze and interpret the small shifts in the process precisely. Also, to measure the process's capability for providing a deeper understanding of the processes, fuzzy process capability indices are introduced. A Monte Carlo simulation approach is employed to obtain the performance metrics. Finally, a case study sourced from Kaggle was conducted to evaluate the performance of the proposed fuzzy control charts for assessing hematocrit levels.
•Innovative fuzzy control charts, including FMACC, FWMACC, and FMRCC, are proposed for individual measurements in health monitoring.•The proposed control charts use fuzzy control rules to detect small shifts in complex medical data.•Fuzzy Process Capability Indices are proposed for assessing process capability and improving the precision of healthcare decision-making.•The study aims to enhance patient care and treatment results by connecting healthcare practices with fuzzy control chart methods.•The research aims to improve patient care and treatment outcomes by enhancing data analysis and monitoring techniques.
Linear signal processing algorithms are effective in dealing with linear transmission channel and linear signal detection, whereas the nonlinear signal processing algorithms, from the machine ...learning community, are effective in dealing with nonlinear transmission channel and nonlinear signal detection. In this paper, a brief overview of the various machine learning methods and their application in optical communication is presented and discussed. Moreover, supervised machine learning methods, such as neural networks and support vector machine, are experimentally demonstrated for in-band optical signal to noise ratio estimation and modulation format classification, respectively. The proposed methods accurately evaluate optical signals employing up to 64 quadrature amplitude modulation, at 32 Gbd, using only directly detected data.
Performance engineering is a proactive and systematic approach aimed at designing, building, and enhancing software systems to ensure their efficient and reliable operation. It involves observing and ...measuring the operational behavior of a software system without interference, assessing performance metrics like response times, throughput, and resource utilization. This entails delving into kernel-level events related to performance monitoring, which play a significant role in understanding system behavior and diagnosing performance-related issues. Kernel-level events offer insights into how both the operating system and hardware resources are utilized. This information empowers system administrators, developers, and performance analysts to optimize and troubleshoot the system effectively.
A critical aspect of performance analysis is root cause analysis, which involves delving deep into kernel-level events connected to performance monitoring. These events provide valuable insights into the utilization of operating system and hardware resources, equipping system administrators, developers, and performance analysts with tools to effectively troubleshoot and optimize the system. Our study introduces an innovative artifact that captures kernel-level events using Elasticsearch and Kibana, facilitating comprehensive performance analysis under diverse scenarios. By defining both Light-load and Heavy-load scenarios and simulating CPU, I/O, Network, and Memory noise, we offer researchers a realistic environment to explore innovative approaches to system performance enhancement.
The artifact comprises both kernel events and system calls, resulting in a cumulative count of 24,263,691 events. The proposed artifact can serve three distinct applications. The first application emphasizes performance analysis by utilizing kernel events for monitoring. The second application targets noise detection and root cause analysis, again using kernel events. Finally, the third application investigates software phase detection through monitoring at the kernel level. These applications demonstrate that through our artifact, researchers can effectively analyze performance, detect and address performance noise, and identify software phases, contributing to the advancement of performance engineering methodologies.
All the system configurations, scripts, and traces can be found in the artifact GitHub repository.11URL: https://github.com/mnoferestibrocku/dataset-repo.
•The paper introduces a robust artifact with kernel-level events from ELK framework.•The artifact includes different workloads with diverse noise enhancing realism for evaluations.•Artifact application showcases capabilities, offering insights into performance research.
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