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Abstract Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation’s (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter’s BCI performance in the vibrotactile stimulation group increased significantly by 9.13% (p

Brain-computer interface (BCI) has helped people by allowing them to control a computer or machine through brain activity without actual body movement. Despite this advantage, BCI cannot be used widely because some people cannot achieve controllable performance. To solve this problem, researchers have proposed stimulation methods to modulate relevant brain activity to improve BCI performance. However, multiple studies have reported mixed results following stimulation, and the comparative study of different stimulation modalities has been overlooked. Accordingly, this study was designed to compare vibrotactile stimulation and transcranial direct current stimulation's (tDCS) effects on brain activity modulation and motor imagery BCI performance among inefficient BCI users. We recruited 44 subjects and divided them into sham, vibrotactile stimulation, and tDCS groups, and low performers were selected from each stimulation group. We found that the latter's BCI performance in the vibrotactile stimulation group increased significantly by 9.13% (p < 0.01), and while the tDCS group subjects' performance increased by 5.13%, it was not significant. In contrast, sham group subjects showed no increased performance. In addition to BCI performance, pre-stimulus alpha band power and the phase locking values (PLVs) averaged over sensory motor areas showed significant increases in low performers following stimulation in the vibrotactile stimulation and tDCS groups, while sham stimulation group subjects and high performers showed no significant stimulation effects across all groups. Our findings suggest that stimulation effects may differ depending upon BCI efficiency, and inefficient BCI users have greater plasticity than efficient BCI users. [ABSTRACT FROM AUTHOR]

This paper describes a series of endurance and material property tests conducted on a pneumatic, fabric-reinforced inflatable soft actuator made of Dragon Skin 30 silicone, which exhibited performance variations during operation. It is important to understand the level of variation over time and how it affects the motions of the soft actuators. The tests were designed to investigate the repeatability and durability of the actuator by measuring changes in its trajectories after long working periods, determining its failure pressure, and examining its elasticity through tensile tests. The experiments were performed on multiple soft actuators, and the results show pertinent information about the variation in their motion and how it relates to the material behavior of the silicone. This information enhances our understanding of the real-world behavior of silicone soft actuators and enables us to better control their performance in our applications.

Objective: Assess radiologists' contribution to variation in clinically significant prostate cancer (csPCa) detection in patients with elevated prostate-specific antigen (PSA) and multiparametric MRI (mpMRI).Methods: This institutional review board-approved, retrospective cohort study was performed at a tertiary, academic, National Cancer Institute-designated Comprehensive Cancer Center with a multidisciplinary prostate cancer program. Men undergoing mpMRI examinations from January 1, 2015, to December 31, 2019, with elevated PSA (≥4 ng/mL) and biopsy within 6 months pre- or post-MRI or prostatectomy within 6 months post-mpMRI were included. Univariate and multivariable hierarchical logistic regression assessed impact of patient, provider, mpMRI examination, mpMRI report, and pathology factors on the diagnosis of Grade Group ≥ 2 csPCa.Results: Study cohort included 960 MRIs in 928 men, mean age 64.0 years (SD ± 7.4), and 59.8% (555 of 928) had csPCa. Interpreting radiologist was not significant individually (P > .999) or combined with mpMRI ordering physician and physician performing biopsy or prostatectomy (P = .41). Prostate Imaging Reporting and Data System (PI-RADS) category 2 (odds ratio [OR] 0.18, P = .04), PI-RADS category 4 (OR 2.52, P < .001), and PI-RADS category 5 (OR 4.99, P < .001) assessment compared with no focal lesion; PSA density of 0.1 to 0.15 ng/mL/cc (OR 2.46, P < .001), 0.15 to 0.2 ng/mL/cc (OR 2.77, P < .001), or ≥0.2 ng/mL/cc (OR 4.52, P < .001); private insurance (reference = Medicare, OR 0.52, P = .001), and unambiguous extraprostatic extension on mpMRI (OR 2.94, P = .01) were independently associated with csPCa. PI-RADS 3 assessment (OR 1.18, P = .56), age (OR 0.99, P = .39), and African American race (OR 0.90, P = .75) were not.Discussion: Although there is known in-practice variation in radiologists' interpretation of mpMRI, in our multidisciplinary prostate cancer program we found no significant radiologist-attributable variation in csPCa detection. [ABSTRACT FROM AUTHOR]

Most of existing heat pump controls have been treated as local control problems where their control actions are solely based on local information, e.g. indoor air temperature. This study presents a real-life online federated control that can account for the interconnected dynamic behaviors in outdoor environment, an indoor thermal zone, and HP systems assisted by PV systems. The in-house federated simulation model consists of three artificial neural networks (ANNs) that predict dynamic behavior of the thermal zone temperature, HP energy consumption, and electricity generated by the PV, respectively. In other words, the dynamic inter-influences of the three systems are taken into consideration in the federated model where the time-varying input-outputs are cross-related. As a result, the federated model predictive control (MPC) demonstrated net energy saving by 40.4% compared to the local control over a week-long real-time implementation (local: 7,138 Wh vs. federated: 4,254 Wh). This study showcases how to federate the aforementioned entangled dynamic interactions between different dynamic systems for better control strategies. In other words, energy saving potential of multi-dynamic systems, e.g. room-HP-PV, would not be properly recognized unless they are federated. In addition, this study also emphasizes variations in the effectiveness of the federated control. [ABSTRACT FROM AUTHOR]

Motor imagery-based brain–computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel–Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies. [ABSTRACT FROM AUTHOR]

In a variable nozzle turbine (VNT), the nozzle vane and rotor clearance can generate complicated tip leakage flow, which produces a large flow loss and results in a noticeable reduction in the VNT performance. Therefore, it is necessary to study the influence of the nozzle vane and rotor clearance on the VNT performance to reveal the underlying mechanisms. In this study, the variation of VNT performance resulting from the nozzle vane and rotor clearance was studied numerically using the commercial CFD code. The numerical results were in good agreement with the experimental data. VNT performance under three varied clearance conditions under different clearance sizes, rotating speeds, and expansion ratios was analyzed. Clearance conditions included the individual effect of nozzle vane clearance, the individual effect of rotor clearance, and the combined effect of nozzle vane and rotor clearance. The results showed that the rotor clearance (clearance size equal to 0.4 mm) resulted in a certain reduction in thermal efficiency (−3.98%) and torque (−4.48%). Meanwhile, the nozzle vane hub and shroud clearance also affected the thermal efficiency (−3.66% and −13.37%, respectively) and torque (12.54% and −0.05%, respectively). Compared with the rotor clearance, the nozzle vane clearance dominated the variation in the VNT performance, and the values of the mass flow ratio, thermal efficiency, and torque increased by 12.15%, 5.43%, and 8.01%, respectively (The clearance on both sides of the vane was equal to 0.2 mm.). Under the combined effect of the nozzle vane and rotor clearance, the deviation of the thermal efficiency and mass flow ratio was smaller than the sum of the values of their individual effects; when the clearance on both sides of the vane was equal to 0.2 mm and the rotor clearance was equal to 0.6 mm, the value was reduced by 2.77% and 1.71%, respectively. This study also explains the influence mechanisms of clearance at both ends of the nozzle vane and the vane/rotor clearance interaction on the VNT performance in detail. [ABSTRACT FROM AUTHOR]

Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation. [ABSTRACT FROM AUTHOR]

Significant variation in performance in motor imagery (MI) tasks impedes their wide adoption for brain-computer interface (BCI) applications. Previous researchers have found that resting-state alpha-band power is positively correlated with MI-BCI performance. In this study, we designed a neurofeedback training (NFT) protocol based on the up-regulation of the alpha band relative power (RP) to investigate its effect on MI-BCI performance. The principal finding of this study is that alpha NFT could successfully help subjects increase alpha-rhythm power and improve their MI-BCI performance. An individual difference was also found in this study in that subjects who increased alpha power more had a better performance improvement. Additionally, the functional connectivity (FC) of the frontal-parietal (FP) network was found to be enhanced after alpha NFT. However, the enhancement failed to reach a significant level after multiple comparisons correction. These findings contribute to a better understanding of the neurophysiological mechanism of cognitive control through alpha regulation.

The objective of this study was to determine whether there is a decrease in the physical performance of the players in the most demanding passages (MDP) during periods of competition congestion. The study involved 15 under-19 players, belonging to a club in Spain’s first division (age: 18.1 ± 0.8 years, weight: 70.2 ± 4.9 kg and height: 1.78 ± 0.06 m), who were monitored during 23 national and international official matches in the 2018/19 season. The Youth League matches were played between two matches in the national championship in six different weeks. The league matches corresponded to the first 17 matches of the league championship, the period in which the 6 matches in the Youth League were played. The two physical variables analysed were total distance (TD) and distance at > 21 km·h-1 (TD21). Using the rolling average, four time windows were taken (1, 3, 5 and 10 min), and the values were relativized to the minute (m·min-1). The main results were that: there were more MDP in the first halves than in the second halves of all the time windows; 2) the central match in the week (Youth League) was the most demanding one; and 3) the players maintained the TD and TD21 in the MDP in the third match compared to the first. The results of this study could provide trainers with information on the need to design training tasks that simulate the demands of competition in relation to the TD and the TD21 according to different time windows.

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Motor imagery-based brain–computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel–Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user’s MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance.

One of the most significant challenges in the application of brain-computer interfaces (BCI) is the large performance variation, which often occurs over time or across users. Recent evidence suggests that the physiological states may explain this performance variation in BCI, however, the underlying neurophysiological mechanism is unclear. In this study, we conducted a seven-session motor-imagery (MI) experiment on 20 healthy subjects to investigate the neurophysiological mechanism on the performance variation. The classification accuracy was calculated offline by common spatial pattern (CSP) and support vector machine (SVM) algorithms to measure the MI performance of each subject and session. Relative Power (RP) values from different rhythms and task stages were used to reflect the physiological states and their correlation with the BCI performance was investigated. Results showed that the alpha band RP from the supplementary motor area (SMA) within a few seconds before MI was positively correlated with performance. Besides, the changes of RP between task and pre-task stage from theta, alpha, and gamma band were also found to be correlated with performance both across time and subjects. These findings reveal a neurophysiological manifestation of the performance variations, and would further provide a way to improve the BCI performance. [ABSTRACT FROM AUTHOR]

Electrical interconnects play important roles in both the performance and reliability of integrated circuits. As the operating frequency has increased and products have been exposed to harsh environments such as temperature extremes and thermal cycling, it has become essential to quantify the thermal effects on the reliability of such circuits. In particular, the effects on the signal transmission performance of the interconnects and the evolution of cracks caused by thermo-mechanical stresses need to be investigated. This study proposes that both research goals can be accomplished by S-parameter analysis alone. First, the performance variation was experimentally quantified by in situ measurements of S21 (an indicator of signal transmission performance) on bond-wire test vehicles at static temperatures between −60 °C and 120 °C. An impedance model of the interconnect was developed focusing on thermal expansion effects and it predicted the performance variation of the interconnect successfully at the given temperatures. Second, the results of ex situ measurements of S11 (an indicator of return signal) obtained using a test vehicle under thermal cycling were compared with scanning electron microscopy images of crack propagation in the interconnect. In the crack initiation phase, a resonance peak occurred on the S-parameter pattern and the peak moved toward lower frequencies as the crack propagated until failure. SPICE simulations using a suggested impedance model of the cracked interconnect confirmed the experimental results. In addition, this study demonstrates that monitoring the resonant frequency in the S-parameter pattern as a prognostic factor can predict the initiation and evolution of cracks earlier and is more sensitive than the conventional dc resistance measurement method. [ABSTRACT FROM AUTHOR]

Background: Traditional metrics may inadequately represent rates of attaining optimal oncologic care. We evaluated a composite "textbook oncologic outcome" (TOO) to assess the incidence of achieving an "optimal" clinical result after colon adenocarcinoma (CA) resection. Methods: The National Cancer Database (NCDB) was queried to identify patients undergoing colectomy for non-metastatic CA between 2010 and 2015. TOO was defined as a margin negative resection with an AJCC compliant lymph node evaluation, no prolonged length of stay (LOS) or 30-day readmission/mortality, as well as receipt of stage appropriate adjuvant chemotherapy. Results: Among 170,120 patients who underwent colectomy at 1315 hospitals, 93,204 (54.8%) achieved TOO with large variations observed among facilities. While certain factors were achieved nearly universally (R0 margin, 95.6%; no 30-day mortality, 97.2%), avoidance of prolonged LOS (77.3%) and appropriate adjuvant chemotherapy (83.0%) were achieved less consistently. On multivariable analysis, Black race/ethnicity (OR 0.82, 95% CI 0.80–0.85), Medicaid insurance (OR 0.64, 0.61–0.68), and low-volume facility (< 50/year) (OR 0.83, 0.77–0.89) were associated with decreased likelihood of TOO. Achievement of TOO was associated with improved long-term survival (HR 0.45; 95% CI 0.44–0.46). Conclusions: Roughly one-half of patients undergoing resection of CA achieved an optimal clinical outcome. TOO may be a more useful quality metric to assess patient-centric composite outcomes following surgical procedures. [ABSTRACT FROM AUTHOR]

This study investigates the impact of battery and fuel cell (FC) degradation on energy management of a FC hybrid electric vehicle. In this respect, an online energy management strategy (EMS) is proposed considering simultaneous online adaptation of battery and FC models. The EMS is based on quadratic programming which is integrated into an online battery and proton exchange membrane FC (PEMFC) parameters identification. Considering the battery and PEMFC states of health, three scenarios have been considered for the EMS purpose, and the performance of the proposed EMS has been examined under two driving cycles. Numerous test scenarios using standard driving cycles reveal that the ageing of battery and PEMFC has a considerable impact on the hydrogen consumption. Moreover, the proposed EMS can successfully tackle the model uncertainties owing to the performance drifts of the power sources at the mentioned scenarios. [ABSTRACT FROM AUTHOR]

Marginal differences in race results between top swimmers have evoked the interest in competition-based success factors of long-term athlete development. To identify novel factors for the multi-dimensional model of talent development, the aim of the study was to investigate annual variation in competition performance (ACV), number of races per year, and age. Therefore, 45,398 race results of all male participants (n = 353) competing in individual events, i.e., butterfly, backstroke, breaststroke, freestyle, and individual medley, at the 2018 European Long-Course Swimming Championships (2018EC) were analyzed retrospectively for all 10 years prior to the championships with Pearson's correlation coefficient and multiple linear regression analysis. Higher ranked swimmers at the 2018EC showed significant medium correlations with a greater number of races per year and small but significant correlations with higher ACV in 10 and nine consecutive years, respectively, prior to the championships. Additionally, better swimmers were older than their lower ranked peers (r = −0.21, p < 0.001). Regression model explained a significant proportion of 2018EC ranking for 50 m (47%), 100 m (45%), 200 m (31%), and 400 m races (29%) but not for 800 and 1,500 m races with number of races having the largest effect followed by age and ACV. In conclusion, higher performance variation with results off the personal best in some races did not impair success at the season's main event and young competitors at international championships may benefit from success chances that increase with age. The higher number of races swum per year throughout the career of higher ranked swimmers may have provided learning opportunities and specific adaptations. Future studies should quantify these success factors in a multi-dimensional talent development model.

In view of the deficiencies of peak factor,pulse factor and kurtosis factor in the monitoring of rolling bearing running condition,a new method to evaluate the variation of vibration performance of rolling bearing is proposed based on the fuzzy-set theory. Firstly,the original data sequence of the rolling bearing vibration is divided into the sample sequences,and then the intrinsic sequence is selected to calculate the standard deviation of each sample sequence and the absolute correlation degree,the gray confidence level and the closeness degree relative to the intrinsic sequence as new sample values. Then a fuzzy similarity matrix made up of these values processed in order of magnitude and by the linear mapping formula is further transformed into a fuzzy equivalence matrix via transitive closure method and the evaluation of the variation of vibration performance of rolling bearing is made by comparing equivalence coefficients in the fuzzy equivalence matrix with the set threshold（ λ = 0. 5）. Finally,the feasibility and correctness of the proposed method are verified by simulation data and experimental data.

Titanium hydride potassium perchlorate (THPP), which is one of the most commonly utilized pyrotechnic initiators, can fail or deviate from the desired performance when subjected to aging. The aging process is known to change its composition as well as its thermochemical kinetics; however, the variation in the performance of THPP in terms of its combustion behavior has not been addressed. This experimental paper reports new correlations between the ignition and combustion characteristics of THPP. A thermal analysis, along with morphological observations, conducted on aged samples revealed that aging caused progressive oxidation of the fuels and subsequent decomposition of the oxidants. The reaction kinetics extracted using the isoconversional method pointed to a decrease in the activation energy due to thermal aging while the opposite was noted in the case of hygrothermal aging. In particular, an increase in the activation energy limited the range of ignition temperature and decreased the reactivity, thereby increasing both the ignition delay time and burn time. Such altered characteristics due to aging could lead to an inconsistent performance of the pyrotechnic initiators. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine whether there is a decrease in the physical performance of the players in the most demanding passages (MDP) during periods of competition congestion. The study involved 15 under-19 players, belonging to a club in Spain's first division (age: 18.1 ± 0.8 years, weight: 70.2 ± 4.9 kg and height: 1.78 ± 0.06 m), who were monitored during 23 national and international official matches in the 2018/19 season. The Youth League matches were played between two matches in the national championship in six different weeks. The league matches corresponded to the first 17 matches of the league championship, the period in which the 6 matches in the Youth League were played. The two physical variables analysed were total distance (TD) and distance at > 21 km⋅h-1 (TD21). Using the rolling average, four time windows were taken (1, 3, 5 and 10 min), and the values were relativized to the minute (mmin-1). The main results were that: there were more MDP in the first halves than in the second halves of all the time windows; 2) the central match in the week (Youth League) was the most demanding one; and 3) the players maintained the TD and TD21 in the MDP in the third match compared to the first. The results of this study could provide trainers with information on the need to design training tasks that simulate the demands of competition in relation to the TD and the TD21 according to different time windows. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article addresses the values of a standard operating procedure based on review of articles. It describes the application and importance of a standard operating procedure from different perspectives while emphasizing its critical relevance to medical practice and pharmaceutical service. The review further discusses shortcomings related to nonexistence of standard operating procedure including inconsistent quality of service; performance variation; procedural mix-ups; and misinterpretation or miscommunication of information. In conclusion, standard operating procedure, if realized and materialized as a component of an effective management system, helps cultivate transparent functions; implement error prevention measures and facilitate corrective actions and transfer knowledge and skill. [ABSTRACT FROM AUTHOR]

Merging technique is widely adopted by I/O schedulers to maximize system I/O throughput. However, I/O merging could increase the latency of individual I/O, thus incurring prolonged I/O latencies and enlarged performance variations. Even with better system throughput, higher worst-case latency experienced by some requests could block the SSD storage system, which violates the QoS (Quality of Service) requirement. In order to improve QoS performance while providing higher I/O throughput, this paper proposes a reinforcement learning based I/O merging approach. Through learning the characteristic of various I/O patterns, the proposed approach makes merging decisions adaptively based on different I/O workloads. Evaluation results show that the proposed scheme is capable of reducing the standard deviation of I/O latency by 19.1 percent on average, worst-case latency by 7.3-60.9 percent at the 99.9th percentile compared with the latest I/O merging scheme, while maximizing system throughput. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller’s performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller’s performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300’s speller performance. We found that several of the RSVP’s event-related potential (ERP) and behavioral features were correlated with the P300 speller’s offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor (r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor (r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone.

Objective: To examine the readiness of general surgery residents in their final year of training to perform 5 common surgical procedures based on their documented performance during training., Design: Intraoperative performance ratings were analyzed using a Bayesian mixed effects approach, adjusting for rater, trainee, procedure, case complexity, and postgraduate year (PGY) as random effects as well as month in academic year and cumulative, procedure-specific performance per trainee as fixed effects. This model was then used to estimate each PGY 5 trainee's final probability of being able to independently perform each procedure. The actual, documented competency rates for individual trainees were then identified across each of the 5 most common general surgery procedures: appendectomy, cholecystectomy, ventral hernia repair, groin hernia repair, and partial colectomy., Setting: This study was conducted using data from members of the SIMPL collaborative., Participants: A total of 17,248 evaluations of 927 PGY5 general surgery residents were analyzed from 2015 to 2021., Results: The percentage of residents who requested a SIMPL rating during their PGY5 year and achieved a ≥90% probability of being rated as independent, or "Practice-Ready," was 97.4% for appendectomy, 82.4% for cholecystectomy, 43.5% for ventral hernia repair, 24% for groin hernia repair, and 5.3% for partial colectomy., Conclusions: There is substantial variation in the demonstrated competency of general surgery residents to perform several common surgical procedures at the end of their training. This variation in readiness calls for careful study of how surgical residents can become more adequately prepared to enter independent practice., (Copyright © 2023 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practical use, for example, selecting the best compromise between spelling accuracy and information transfer rate (ITR; speed) so that the P300 speller can maintain high accuracy while increasing spelling speed. Therefore, seeking correlates of, and predicting, the P300 speller's performance is necessary to understand and improve the technique. In this work, we investigated the correlations between rapid serial visual presentation (RSVP) task features and the P300 speller's performance. Fifty-five subjects participated in the RSVP and conventional matrix P300 speller tasks and RSVP behavioral and electroencephalography (EEG) features were compared in the P300's speller performance. We found that several of the RSVP's event-related potential (ERP) and behavioral features were correlated with the P300 speller's offline binary classification accuracy. Using these features, we propose a simple multi-feature performance predictor (r = 0.53, p = 0.0001) that outperforms any single feature performance predictor, including that of the conventional RSVP T1% predictor (r = 0.28, p = 0.06). This result demonstrates that selective multi-features can predict BCI performance better than a single feature alone. [ABSTRACT FROM AUTHOR]

As the size and complexity of high performance computing (HPC) systems grow in line with advancements in hardware and software technology, HPC systems increasingly suffer from performance variations due to shared resource contention as well as software- and hardware-related problems. Such performance variations can lead to failures and inefficiencies, which impact the cost and resilience of HPC systems. To minimize the impact of performance variations, one must quickly and accurately detect and diagnose the anomalies that cause the variations and take mitigating actions. However, it is difficult to identify anomalies based on the voluminous, high-dimensional, and noisy data collected by system monitoring infrastructures. This paper presents a novel machine learning based framework to automatically diagnose performance anomalies at runtime. Our framework leverages historical resource usage data to extract signatures of previously-observed anomalies. We first convert collected time series data into easy-to-compute statistical features. We then identify the features that are required to detect anomalies, and extract the signatures of these anomalies. At runtime, we use these signatures to diagnose anomalies with negligible overhead. We evaluate our framework using experiments on a real-world HPC supercomputer and demonstrate that our approach successfully identifies 98 percent of injected anomalies and consistently outperforms existing anomaly diagnosis techniques. [ABSTRACT FROM AUTHOR]

Performance variation is a critical issue in motor imagery brain–computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user’s sense of the motor imagery process and directly estimate MI-BCI performance through the user’s self-prediction are lacking. In this study, we first test each user’s self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject’s self-prediction of MI-BCI performance. The subjects’ performance predictions during motor imagery task showed a high positive correlation (r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self-experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI.

Performance variation is a critical issue in motor imagery brain--computer interface (MI- BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of poor performers, studies which focus on the user's sense of the motor imagery process and directly estimate MI-BCI performance through the user's self-prediction are lacking. In this study, we first test each user's self-prediction idea regarding motor imagery experimental datasets. Fifty-two subjects participated in a classical, two-class motor imagery experiment and were asked to evaluate their easiness with motor imagery and to predict their own MI-BCI performance. During the motor imagery experiment, an electroencephalogram (EEG) was recorded; however, no feedback on motor imagery was given to subjects. From EEG recordings, the offline classification accuracy was estimated and compared with several questionnaire scores of subjects, as well as with each subject's self-prediction of MI-BCI performance. The subjects' performance predictions during motor imagery task showed a high positive correlation (r = 0.64, p < 0.01). Interestingly, it was observed that the self-prediction became more accurate as the subjects conducted more motor imagery tasks in the Correlation coefficient (pre-task to 2nd run: r = 0.02 to r = 0.54, p < 0.01) and root mean square error (pre-task to 3rd run: 17.7% to 10%, p < 0.01). We demonstrated that subjects may accurately predict their MI-BCI performance even without feedback information. This implies that the human brain is an active learning system and, by self- experiencing the endogenous motor imagery process, it can sense and adopt the quality of the process. Thus, it is believed that users may be able to predict MI-BCI performance and results may contribute to a better understanding of low performance and advancing BCI. [ABSTRACT FROM AUTHOR]

Background: Most investigators of brain-computer interface (BCI) research believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI)-based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation. Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task-related states. Findings: We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we showed contralateral ERD and ipsilateral ERS in the somatosensory area, which are well-known patterns of MI. Finally, we showed that 73.08% of datasets (38 subjects) included reasonably discriminative information. Conclusions: Our EEG datasets included the information necessary to determine statistical significance; they consisted of well-discriminated datasets (38 subjects) and less-discriminative datasets. These may provide researchers with opportunities to investigate human factors related to MI BCI performance variation, and may also achieve subject-to-subject transfer by using metadata, including a questionnaire, EEG coordinates, and EEGs for non-task-related states. [ABSTRACT FROM AUTHOR]

In the current quality paradigm, the performance of a product is kept within specification by ensuring that its parts are within specification. Product performance is then validated after final assembly. However, this does not control how robust the product performance is, i.e. how much it will vary between the specification limits. In this paper, a model for predicting product performance is proposed, taking into account design, assembly and process parameters live from production. This empowers production to maintain final product performance, instead of part quality. The PRECI-IN case study is used to demonstrate how the monitoring system can be used to efficiently guide corrective action to improve product performance. It is claimed that the monitoring system can be used to dramatically cut the time taken to identify, plan and execute corrective action related to typical quality issues. To substantiate this claim, two further cases comparable to PRECI-IN, in terms of complexity, material and manufacturing process, were taken from different industries. The interviews with quality experts revealed that the typical time taken for corrective action for both cases was accounted to be seven days. Using the monitoring system for the PRECI-IN case, similar corrective action would have been achieved almost immediately. [ABSTRACT FROM AUTHOR]

Although most current cloud providers, such as Amazon Web Services (AWS) and Microsoft Azure offer different types of computing instances with different capacities, cloud users tend to hire a cluster of instances of particular type to ensure performance predictability for their applications. Nowadays, many large-scale applications including big data analytics applications feature workload patterns that have heterogeneous resource demands, for which, accounting for heterogeneity of virtual cloud instances to allocate would be highly advantageous to the application performance. However, performance predictability has been always an issue in such clusters of heterogeneous resources. In particular, to precisely decide on what instances from which types to enclose in a cluster, such that the desired performance is attained, remains an open question. To this end, we devise a resource allocation mechanism by formulating it as a Mixed-Integer programming model representing an optimization problem. Our resource allocation mechanism incorporates predictable average performance as a unified performance metric, which concerns two key performance-related issues: (a) Performance variation within same-type instances, and (b) Correlations of performance variabilities across different types. Our experimental results demonstrate that target performance is predictable and attainable for clusters of heterogeneous resources. Our mechanism constructs clusters whose performance is within 95 percent of the performance of optimal ones, hence deadlines are always met. By reoptimisation, our mechanism can react to performance mispredictions and support autoscaling for varying workloads. We experimentally verify our findings using clusters on Amazon EC2 with MapReduce workloads, and on a private cloud as well. We conduct comparison experiments with an existing recent resource allocation approach in literature. [ABSTRACT FROM PUBLISHER]

Abstract: The failure of a rolling bearing in running usually has an early sign along with a time history, in which a time series of its performance performs abnormally. There should be a mutant gene driving the time series change according to genetics point of view, but so far it has not been found, having difficulty in mathematical revealing the inherent mechanism of variation of the rolling bearing performance. For this reason, fusing the fuzzy set theory and the chaos theory, a fuzzy chaos method is proposed to evaluate the nonlinearly evolutionary process of the rolling bearing performance as a time series. The fuzzy equivalence relation based on a phase space is employed to represent the mutant gene and the optimum measure for fuzzy equivalence relation is advanced to uncover the essence of variation of the rolling bearing performance. The experimental investigation on the vibration acceleration of the small size bearing and the friction torque of the large size bearing shows that the fuzzy chaos method is correct and effective. [Copyright &y& Elsevier]

Purpose – Considerable research efforts have been made to investigate the relative importance of firm-specific vs industry structure factors in relation to performance variation among firms in the past. However, the vast majority of the research comes from the USA and very little is known about results outside of this domain. The aim of this study was to investigate industry and firm factors producing performance differences among Turkish firms. In order to explore the contributions of firm-level factors and structural characteristics of industries, the study decomposes the relative impact of industry and firm effects on overall performance which includes the performance items such as sales turnover, market share and profitability. Design/methodology/approach – A quantitative, positivistic approach was adopted with respect to the methodological choice for this study. In order to measure the relative impact of industry and firm effects on performance, the questionnaire developed by Galbreath and Galvin was sent to the e-mail addresses of the general managers or the other executives at the top level as a web-link with a covering letter. Because unit of analysis is at the firm level, a single informant is used in the study and the questionnaire was mailed to only one executive from each firm. Having collected the data, the effects of firm-level factors (resources and capabilities) and industry structure on performance variation were analyzed by hierarchical regression method. Findings – A total of 259 firms from different industries were analyzed and the findings revealed that firm-level resources had a greater effect in explaining performance variation than industry structure in the Turkish business context. The results of this study confirm that in the resource-based view of the firm, the firms in Turkey "demonstrated a quite developed form of organizational learning" just like the other emerging economies (i.e. Taiwan, Brazil, Poland and South Korea). Within this framework, Turkish firms especially in automotive, textile, food, tourism and construction industries became important players in the global arena. Originality/value – This study contributes to the strategic management literature, particularly, in terms of providing comparable data from an emerging country, which is significant in verifying resource-based theory and generalizing results in a global context. The findings also suggest that the firms need to focus on their unique resources rather than try to control and manipulate structural forces in their industries since "the economies today might best be viewed as resource-based economies". It should be noted that, in this business era, the key challenge for the managers is the optimal deployment of existing strategic resources in order to make their organizations achieve sustainable competitive advantage and superior firm performance. [ABSTRACT FROM AUTHOR]