Training Performance Research Articles

Effects on Performance, Perception, and Awareness of Plyometric Training in Youth Volleyball: A Novel Methodological Approach to Training

Plyometric training is known to improve jump height in volleyball, but few studies address athletes’ perception and awareness of its benefits. This gap limits its full potential for enhancing performance. This study examines young non-elite volleyball athletes’ awareness of plyometric training effects. A sample of 24 athletes (mean age 18.3 ± 3.8 years) was divided into an experimental group (EXP) and a control group (CON), each with 12 participants. The EXP group underwent plyometric training, while the CON group performed basic technical exercises. Performance and perceptions were assessed using the Spike Jump Test and surveys at pre-, mid-, and post-training phases. The EXP group showed significant vertical jump improvement, from a pre-training mean of 30.14 cm to 32.22 cm post-training, confirmed by the Friedman test (p = 0.00). In contrast, the CON group showed no significant changes (p = 0.47). Perception scores in the EXP group improved significantly, from 3.33 to 4.16, indicating enhanced awareness of plyometric training benefits, whereas the CON group showed no significant changes (p = 0.35). These findings highlight the dual benefits of plyometric training in improving both jump performance and awareness of its effectiveness, emphasizing the value of integrating perception into training for volleyball athletes.

The Effect of Training and Compensation on Employee Performance Through Job Satisfaction at The Sikka Regency Youth and Sports Education Office (PKO)

This study aims to describe training, compensation, job satisfaction and employee performance, analyze the effect of training and compensation on job satisfaction, analyze the effect of training and compensation on employee performance, analyze the effect of job satisfaction on employee performance and analyze the effect of training and compensation on employee performance through job satisfaction at the Office of Youth Education and Sports of Sikka Regency. The population in this study is all employees totaling 52 employees. The sample in this study is all members of the population totaling 52 employees of the Sikka Regency Youth and Sports Education Office. The data analysis technique uses path analysis. The results of the analysis showed that training and compensation had an effect on job satisfaction, training and compensation had an effect on employee performance, job satisfaction had an effect on employee performance and training and compensation had an effect on employee performance through job satisfaction at the Sikka Regency Youth and Sports Education Office.

Penerapan Sistem Pengendalian Manajemen pada Tim Bola Basket ITHB Arrows

This study explores the implementation of Management Control Systems (MCS) in the ITHB Arrows basketball team to achieve the team’s vision and mission while enhancing athlete motivation and performance. Key elements identified include structured training programs, performance evaluations, and motivation's role. Despite resource limitations, the findings suggest significant contributions of effective MCS practices to performance optimization. Recommendations include improved facilities and mission internalization for future strategy. Moreover, this study provides a comprehensive analysis of behavioral and motivational factors influencing success. By benchmarking against competitive teams, best practices are identified for long-term sustainable performance.

Does gamified training improve training performance? A dual-pathway moderated mediation model

Although gamification has grown in popularity in organizational training, scientific evidence verifying the benefits of gamified training is mixed, and the underlying mechanisms of it on training outputs remain unclear. Drawing upon the technology-enhanced training effectiveness model and cognitive-affective personality system theory, this research constructs a dual-pathway moderated mediation model explaining how gamified training shapes training performance, including knowledge, skills, and attitudes (KSAs). Adopting a randomized field experiment, the results indicated that gamified training significantly promoted knowledge and attitudes via enhancing goal commitment and perceived enjoyment, but exerted no significant influence on skills. Moreover, need for achievement moderated the effect of gamified training on goal commitment and its indirect effect on KSAs via goal commitment. Furthermore, hedonic need moderated the effect of gamified training on perceived enjoyment and its indirect effect on knowledge and attitudes via perceived enjoyment. Theoretical and practical implications are discussed.

Dynamic wheel–rail adhesion characteristics on wet curved tracks considering surface roughness

Wheel–rail rolling contact is a complex tribological issue, especially the existence of a thin water film on the rail surface, which leads to low adhesion problems for the traction and braking performance of the railway trains. The objective of this paper is to propose a non-Hertzian dynamic wheel–rail adhesion model for trains passing through wet curved tracks considering wheel–rail surface roughness. A vehicle-track coupling dynamics model was first developed to output the wheel–rail dynamic contact parameters under wet conditions. These parameters were subsequently utilized as dynamic inputs for the present wheel–rail adhesion model to simulate dynamic curve passing. The present numerical model includes the normal contact model and tangential model. For the normal contact issue, an elastohydrodynamic lubrication model was used to accurately determine the normal contact stress within the contact patch along the contact traces. Based on the normal contact characteristics, the advanced extended creep force model was used to compute the tangential stress. Finally, the dynamic wheel–rail adhesion coefficient can be obtained. This model considers the effects of time variation and changes in contact geometry, resulting in sudden increases or decreases in the adhesion coefficient at certain locations. At a speed of 400 km/h, a higher spin creepage occurs, leading to an increased displacement of the third-body layer and causing the maximum tangential stress to shift from one side of the contact patch to the other. Under the combined influence of the creepages, the maximum tangential stress occurs at the trailing edge of the contact patch.

A deep learning based model for diabetic retinopathy grading

Diabetic retinopathy stands as a leading cause of blindness among people. Manual examination of DR images is labor-intensive and prone to error. Existing methods to detect this disease often rely on handcrafted features which limit the adaptability and classification accuracy. Thus, the aim of this research is to develop an automated and efficient system for early detection and accurate grading of diabetic retinopathy severity with less time consumption. In our research, we have developed a deep neural network named RSG-Net (Retinopathy Severity Grading) to classify DR into 4 stages (multi-class classification) and 2 stages (binary classification). The dataset utilized in this study is Messidor-1. In preprocessing, we have used Histogram Equalization to improve image contrast and denoising techniques to remove noise and artifacts which enhanced the clarity of the fundus images. We applied data augmentation techniques to our preprocessed images in order to tackle class imbalance issues. Augmentation techniques involve flipping, rotation, zooming and adjustment of color, contrast and brightness. The proposed RSG-Net model contains convolutional layers to perform automatic feature extraction from the input images and batch normalization layers to improve training speed and performance. The model also contains max pooling, drop out and fully connected layers. Our proposed RSG-Net model achieved a testing accuracy of 99.36%, specificity of 99.79% and a sensitivity of 99.41% in classifying diabetic retinopathy into 4 grades and it achieved 99.37% accuracy, 100% sensitivity and 98.62% specificity in classifying DR into 2 grades. The performance of RSG-Net is also compared with other state-of-the-art methodologies where it outperformed these methods.

Recalibrating the kidney failure risk equation for a Mediterranean European population: reducing age and sex inequality

IntroductionChronic kidney disease (CKD) patients may develop kidney failure (KF), receiving renal replacement therapy (RRT) in some cases. The Kidney Failure Risk Equation (KFRE-4), predicting RRT risk, is widely validated but not in a primary care Mediterranean European population. We aim to recalibrate KFRE-4 accordingly, considering death as a competing risk, to improve performance. Additionally, we recalibrate KFRE-4 for predicting KF, including all patients reaching CKD stage 5, not just those on RRT.MethodsRetrospective cohort study including individuals aged ≥50 years with confirmed glomerular filtration rate (eGFR) <60 mL/min/1.73m2 and measured albumin-to-creatinine ratio (ACR). Dataset was split into training and test sets. New KFRE-4 models were developed in the training set and performance was evaluated in the test set: Base hazard adapted-KFRE (Basic-RRT), Cox reestimation (Cox- RRT), Fine and Gray RRT reestimation (FG-RRT), and Fine and Gray KF reestimation (FG-KF).ResultsAmong 165,371 primary care patients (58.1% female; mean age 78.1 years; mean eGFR 47.3 mL/min/1.73m2, median ACR 10.1 mg/g), original KFRE-4 showed good discrimination but poor calibration, overestimating RRT risk. Basic-RRT showed poorer performance. Cox-RRT and FG-RRT, enhancing the influence of old age and female sex, diminished overprediction. FG-RRT, considering death as a competing risk, resulted the best RRT model. Age and sex had less impact on KF prediction.ConclusionA fully tailored recalibration model diminished RRT overprediction. Considering death as a competing event optimizes performance. Recalibrating for KF prediction offers a more inclusive approach in primary care, addressing the needs of women and elderly.

Artificial intelligence for automatic echocardiography image view detection and left ventricle segmentation and ejection fraction prediction

Abstract Background Left-ventricular (LV) border segmentation is key to assess LV size and function. Current assessment has relatively large inter-observer variability, possibly impacting patients care. Artificial intelligence (AI) may permit faster and more reproducible assessment, possibly suitable to be used by non-experts. Purpose To train and test AI algorithms for (i) the identification of views, (ii) the segmentation of the LV in 2, 3 and 4-chamber views, and (iii) the computation of LV ejection fraction (EF). Methods Fifteen thousand echocardiography studies acquired in the Vall d’Hebron University Hospital (VH) for patients care were retrieved and anonymized. LV wall was identified via commercial clinical software in 619 videos with 2-, 3- and 4-chamber views (14082 frames) and 3 masks were created: LV cavity (within endocardial border), overall LV (within epicardial border) and LV wall. Annotated frames were split into independent training (465 videos) and testing (154) sets and performances were evaluated in terms of DICE score. End-diastolic and end-systolic LV volumes of 4-chamber views were identified and used to compute LV EF, which was compared with clinical reports in 488 patients. An external validation on CAMUS dataset was performed. Results A total of 6831 videos from 488 VH subjects were available for this analysis (Table 1). AI model identified image views with high accuracy (93%). AI performance on the segmentation of the LV cavity, overall LV and LV wall were satisfactory in 2- (0,86[0,79;0,90], 0,91[0,86;0,93], 0,79[0,74;0,83]), 3- (0,88[0,84;0,91], 0,91[0,90;0,93], 0,81[0,77;0,83]) and 4-chamber (0,90[0,86;0,93], 0,92[0,88;0,94], 0,82[0,79;0,85]) views. Segmentation performances were similar between sexes but were lower in case of atrial fibrillation and in images with low quality. In an external validation in 500 CAMUS patients performances were satisfactory, with DICE score of 0,91[0,87;0,94] and 0,80[0,73;0,84] for whole LV and LV cavity in 2 and 4-chamber views, respectively. In VH data EF predictions were in line with expected inter-observer reproducibility, with a good linear association (R=0.39, p<0.001) but remarkable underestimation (mean error = 12%). Models performed similarly in the external validation set, with a good linear association (R=0.31, p<0.001) and minimal underestimation (mean error = 2.2%). Conclusions AI models can identify echocardiography views, segment the LV in 2, 3 and 4-chamber views and use the segmentation to quantify EF. Care should be taken to avoid biases in AI performances, particularly in case of atrial fibrillation and limited image quality. Demographic and clinical information

Caffeinated chewing gum enhances maximal strength and muscular endurance during bench press and back squat exercises in resistance-trained men

IntroductionCaffeinated chewing gum, known for its rapid absorption, has not been previously studied for its effects on maximal strength and muscular endurance in resistance exercise. The aim of this study was to determine the efficacy of caffeinated chewing gum on maximal strength and muscular endurance during bench press and back squat exercises.MethodsIn a randomized, double-blind design, 16 resistance-trained males (age: 21.6 ± 2.0 years, height: 176.8 ± 6.1 cm, mass: 79.6 ± 8.8 kg) chewed either caffeinated gum (3 mg/kg) or a placebo gum on two occasions, 1 week apart. After a standardized warm-up, participants chewed the gum for 5 min before performing maximal strength test (one-repetition maximum [1RM]) and muscular endurance test (60% 1RM repetitions to failure) for bench press and back squat exercises. 1RM, number of repetitions, ratings of perceived exertion and pain perception were assessed.ResultsCaffeinated chewing gum significantly improved 1RM in both bench press (105.3 ± 14.5 vs. 100.3 ± 13.4 kg, +5.0% [95% confidence interval (CI): 3.7–6.3%], p < 0.01) and back squat (172.3 ± 20.2 vs. 161.9 ± 22.3 kg, +6.8% [95%CI: 4.1–9.5%], p < 0.01) exercises with small effect size (Cohen’s d: 0.36 [95%CI: 0.09–0.63] and 0.49 [95%CI: 0.22–0.76], respectively), despite similar levels of pain perception and RPE (p > 0.05). It also increased the number of repetitions in both bench press (20 ± 5 vs. 17 ± 4, +18.8% [95%CI: 11.5–26.1%], p < 0.01) and back squat (37 ± 11 vs. 28 ± 8, +33.3% [95%CI: 23.1–43.4%], p < 0.01) exercises with moderate-to-large effect size (Cohen’s d: 0.76 [95%CI: 0.48–1.03] and 0.89 [95%CI: 0.60–1.16], respectively), despite similar levels of pain perception and RPE (p > 0.05).DiscussionCaffeinated chewing gum (3 mg/kg) improved both maximal strength and muscular endurance during bench press and back squat exercises in resistance-trained men. This approach offers a practical and time-efficient method to improve training performance while minimizing the risk of side effects.

Effect of coatings on the tribological performance of engine valve train: A review

Coatings play a crucial role in engine valve train components, reducing friction and wear to enhance engine efficiency and longevity. Common coatings in internal combustion engines include Diamond-Like Carbon (DLC) and Hard coatings, all of which improve tribological performance. These coatings are vital for valve trains, which face high stress and temperature variations. Deposition techniques like Physical Vapor Deposition (PVD), Chemical Vapor Deposition (CVD), and Plasma-Assisted Chemical Vapor Deposition (PACVD) influence coating performance. DLC coatings, often metalloid-doped, are noted for their low friction and high hardness. Hard coatings excel in wear resistance and friction reduction. This review brings together experimental findings on tribological coatings for engine valve train components, focusing on materials, deposition methods, testing conditions, and performance metrics. It highlights the best coatings for reducing wear and friction, ultimately enhancing the performance of internal combustion engines. Among the coatings evaluated, Chromium Nitride with Tungsten Carbide/Carbon (CrN + WC/C) stood out. This coating significantly reduced friction torque at low to moderate rotational speeds (with reductions of 20%–30% at 800 rpm and 1400 rpm) and maintained this reduction at high speeds (30% reduction at 2000 rpm). Additionally, under high load conditions, the CrN + WC/C coating achieved a remarkable 75% reduction in mean friction torque (reducing it from 4 to 1 Nm at both 800 and 2000 rpm). These tests were conducted using SAE 10W-30 oil at 80°C. The consistent performance of CrN + WC/C across varied conditions makes it the superior choice. CrN + WC/C coatings optimize the efficiency and longevity of valve train components by reducing friction, wear, and maintenance needs, significantly enhancing engine system reliability.

Using Asynchronous Training and Emailed Performance Feedback to Change a Teaching Behavior of Preservice Teachers

The current study used a multiple probe design to determine the effects of an intervention package (asynchronous training plus emailed specific performance feedback) on the rate of opportunities to respond (OTR) of three preservice elementary teachers providing small group mathematics instruction. The study utilized Swivl to record and upload lessons, which were viewed and coded to determine the rate per minute of OTR. Results show a functional relation between the intervention package and increased rates of OTR across all participants. Calculations using Nonoverlap of All Pairs (NAP) and Log Response Ratio (LRRi) suggest strong effects on the rate of OTR. Social validity was determined by semi-structured interviews, during which participants noted the intervention package to be effective at changing their behavior and simple to implement. Limitations and future research directions are addressed.

Influences of Feedback Gains on Vehicle–Bridge Coupled Vibration for a Mid-Mounted Maglev Train

Levitation control systems play a vital role in ensuring the operational performance of medium and low speed maglev trains. Improper gain values may induce excessive vibrations in vehicles and bridges, potentially leading to collisions. This research investigates the influence of feedback gains on the levitation subsystem and the vehicle-bridge coupled system, leveraging a newly designed maglev vehicle equipped with mid-mounted suspension and a state feedback control strategy. A dynamic model of the vehicle-bridge coupled system was developed, incorporating control strategies, track irregularities, and bridge flexibility, and was validated through experimental data. The dynamic responses of the bogie and bridge under varying gain values were analyzed in-depth. By examining pole locations and frequency responses, the mechanisms through which each gain impacts the coupled system were clarified. The study identifies that gains associated with acceleration, velocity, and displacement each have unique stability domains that interact with one another. To maintain system stability, the gains must adhere to the Routh-Hurwitz stability criterion while fulfilling specific bandwidth and damping requirements. Gains set too low or too high may result in levitation failures. Recommended ranges for these gains are as follows: acceleration (0.2–0.6), velocity (30–100), and displacement (5,000–10,000). These findings could provide valuable guidance for designing control systems that enhance the stability and safety of maglev train operations.

Estimating Winter Wheat Canopy Chlorophyll Content Through the Integration of Unmanned Aerial Vehicle Spectral and Textural Insights

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models.

Integrated analysis of bioinformatics, mendelian randomization, and experimental validation reveals novel diagnostic and therapeutic targets for osteoarthritis: progesterone as a potential therapeutic agent

BackgroundOsteoarthritis (OA), characterized by progressive degeneration of cartilage and reactive proliferation of subchondral bone, stands as a prevalent condition in orthopedic clinics. However, the precise mechanisms underlying OA pathogenesis remain inadequately explored.MethodsIn this study, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning techniques were employed to identify hub genes. Based on these hub genes, an Artificial Neural Network (ANN) diagnostic model was constructed. The Drug Signatures Database (DSigDB) was utilized to screen small-molecule drugs targeting these hub genes, and molecular docking analyses and molecular dynamics simulations were employed to explore and validate the binding interactions between proteins and small-molecule drugs. Expression changes of the hub genes under inflammatory conditions were validated through in vitro experiments, including RT-qPCR and Western blotting, and the therapeutic effects of the identified small-molecule drug on chondrocytes under inflammatory conditions were further verified in vitro. Lastly, Mendelian randomization analysis was conducted to examine the causal association between progesterone levels and various OA phenotypes.ResultsIn this study, we identified three hub genes: interleukin 1 receptor-associated kinase 3 (IRAK3), integrin subunit beta-like 1 (ITGBL1), and Ras homolog family member U (RHOU). An Artificial Neural Network (ANN) diagnostic model constructed based on these hub genes demonstrated excellent performance in both training and validation phases. Screening with the Drug Signatures Database (DSigDB) identified progesterone as a small-molecule drug targeting these key proteins. Molecular docking analysis using AutoDock Vina revealed that progesterone exhibited binding energies of ≤ -7 kcal/mol with each of the key proteins, indicating strong binding affinity. Furthermore, molecular dynamics simulations validated the stability and strength of these interactions. RT-qPCR and Western blotting confirmed the downregulation of the hub genes in IL-1β-treated chondrocytes. Western blotting also demonstrated the potential therapeutic effects of progesterone on IL-1β-treated chondrocytes. Finally, Mendelian randomization analysis established a significant association between progesterone levels and multiple OA phenotypes.ConclusionIn our study, IRAK3, ITGBL1, and RHOU were identified as potential novel diagnostic and therapeutic targets for OA. Progesterone was preliminarily validated as a small-molecule drug with potential effects on OA. Further research is crucial to elucidate the pathogenesis of OA and the specific therapeutic mechanisms involved.

Effect of Single-Leg Compound Training Program on Power of College Student High-Level Basketball Player

Background and Aims: There is widespread asymmetry in the body of athletes, which can affect and limit their performance in training and competition, leading to the potential and realistic risk of injury. This paper aims to improve the asymmetry of athletes and improve their performance and prevent injuries. The single-leg compound training method was used to explore the effect and degree of the improvement of the lower limb asymmetry in basketball players. Methodology: Experimental method, measurement method and questionnaire survey method are divided into pre-experiment and formal experiment. The measurement takes 38 male high-level basketball players to measure and evaluate the strength index. The formal experiment was designed with one-factor completely randomized experiment. High-level basketball players were randomly selected as the main subjects and randomly divided into experimental group and control group. The experimental group conducted single-leg compound training intervention for 8 weeks, and the control group conducted normal teaching training. The Pre-and Posttest tested the degree of lower limb asymmetry and physical fitness performance of the lower limb asymmetry and physical fitness performance indicators between the experimental group and the control group before and after the intervention. Result: The results found that: (1) Male college basketball second-level players have the asymmetry of the lower limb strength, and the asymmetry degree of the strength index on both sides of the lower limbs is more obvious. (2) 8-week single-leg compound training can effectively improve the asymmetry of lower limb strength in male college basketball players. (3) The decrease of lower limb asymmetry in male college basketball players can significantly improve the explosive force of lower limbs. This shows that the improvement of lower limb asymmetry can improve the physical performance of basketball players. Conclusion: The study shows that male college basketball players' explosive force and overall physical performance on the court can be greatly improved by targeting their lower limb strength asymmetry through targeted training. More balanced strength distribution can result from effective training interventions, which will ultimately enhance athletic performance.

A tale of two ages: fluid reasoning as a predictor of working memory training efficacy in middle-aged and older adults

ABSTRACT Research on working memory (WM) training reveals significant variability in training effects, indicating that pretraining cognitive abilities might account for these differences. However, consensus on whether higher (magnification account) or lower (compensation account) pretraining abilities predict greater training effects remains elusive. Our study aimed to clarify the role of fluid reasoning in predicting training performance (i.e. training scores at each session) and gains on near transfer WM tasks. We conducted two studies: Study 1 focused on middle-aged adults (47–65 years) and Study 2 on older adults (65–83 years). Participants in both studies were randomly assigned to either adaptive n-back training or an active control group and have all completed three WM tasks before and after 20 training sessions – the trained n-back task and two structurally different untrained tasks. Generally, greater average training scores were found in individuals with higher fluid reasoning for both age groups, although this trend did not reach statistical significance in older adults. Similarly, higher fluid reasoning predicted greater training gains only in the sample of middle-aged adults. Further analysis showed that both, middle-aged and older participants in the training groups exhibited higher gains on the trained n-back task but not on two other WM tasks. Additionally, fluid reasoning predicted n-back gains in both the training and control group. Consistent with a growing body of research, our results show limited generalization of training effects across untrained tasks. It seems that factors beyond pretraining ability should be considered when explaining between-participant differences in training performance.

Effect of internet shutdown on postgraduate trainees in Manipur: A qualitative study

Background & objectives Internet shutdowns, although rare can be imposed by governments or affiliated entities and can intentionally disrupt online information and communication systems. While justified for maintaining public order, these shutdowns cause substantial economic losses, public suffering, and reputational damage. The internet shutdown in Manipur, initiated on May 3, 2023, due to civil unrest, has affected various sectors. The impact on doctors, amidst an era where internet access is considered as an integral part of practice, warrants deeper exploration of the challenges faced during such a prolonged shutdown period. Therefore, this study was conducted to explore the impact of internet shutdown among postgraduate trainees from two tertiary care hospitals in Manipur. Methods A study was conducted in two tertiary healthcare centers in Manipur from July to August 2023 among medical postgraduate trainees. Participants were selected using purposive sampling. Twenty-two in-depth interviews (IDIs) were conducted, recorded, and transcribed verbatim. Thematic analysis was done to identify codes, categories and themes. Results This qualitative study was undertaken among postgraduate trainees in two different academic institutes. A total of 21 IDIs were carried out. The major themes emerged from the study, namely, impact on professional life, impact on personal life, social consequences and coping. Internet shutdown affected patient care such as seeking timely opinions from consultants, delays in sharing patient reports and inconveniences in the hand-off process and the academic performance of postgraduate trainees like unavailability of study materials or keeping up with recent advancements. On a personal level, it negatively affected communication with family and friends, mental well-being and caused inconvenience in shopping and transactions. The study also found some positive impacts, such as less screen time leading to better sleep and increased physical activities. Interpretation & conclusions The findings of this study suggest that the internet shutdown significantly affected both the professional and personal aspects of postgraduate trainees’ lives in Manipur. The implementation of a targeted ban on social media platforms and the establishment of designated internet access points are proposed as potential alternatives.

Global sensitivity analysis of high-speed train dynamics system with high-dimensional inputs and multiple outputs

A high-speed train dynamics system involves numerous uncertainty parameters, and there are multiple evaluation indices for its dynamic performance. In this paper, we conduct high-dimensional and multi-output global sensitivity analysis for the dynamic performance of high-speed trains by comprehensively considering these dynamics parameters and indices. Firstly, the dynamics simulation model of the high-speed train is established and six dynamics indices for evaluating the safety and comfort of the train are obtained under operating conditions. Subsequently, considering the multi-output and high-dimensional characteristics of train sensitivity analysis, we propose a multi-output global sensitivity analysis method based on the correlation-analysis-guided Kriging model (CA-K). Finally, global sensitivity analysis of 96 uncertainty parameters on six dynamics indices is conducted under three scenarios utilising the proposed method. The results show that (1) CA-K achieves higher accuracy in approximating high-dimensional train dynamics indices than existing conventional surrogate models. (2) The multi-output global sensitivity analysis method can simultaneously identify all important dynamics parameters and exclude unimportant parameters.

Future Directions in Understanding Acute and Chronic Effects of Mental Fatigue in Sports: A Commentary on Bridging Laboratory Findings and Real-World Applications.

Mental fatigue (MF) is a psychobiological state that could negatively impact physical and cognitive performance, although the extent of this impact remains controversial. While laboratory studies have provided valuable insights into the acute effects of MF, their ecological validity in real-world sport settings remains limited. Recent research indicates that MF can naturally arise during sport activities, leading to chronic effects on endurance and perceptual-cognitive skills. These effects may persist beyond individual sessions, potentially increasing the risk of injury due to their cumulative nature. This commentary aims to guide future research by emphasizing the need to investigate the natural occurrence and chronic effects of MF in applied sport settings. Understanding how MF develops and manifests in real-world settings, particularly individual differences in perception and response, is crucial for creating more effective management strategies. Addressing these factors will enable better support for athletes who may experience prolonged periods of MF that could affect their training and competitive performance. Future research should prioritize studying MF in natural sport settings to enhance the ecological validity of findings. By focusing on the chronic and cumulative nature of MF, as well as individual variability, researchers can develop more tailored and effective strategies for managing MF. This research will not only bridge the gap between laboratory studies and real-world applications but also contribute to more precise monitoring and intervention techniques. Ultimately, these advancements will improve athletes' ability to maintain peak performance and reduce the risk of injury, thereby enhancing overall athletic well-being.

Pengaruh Lingkungan Kerja dan Pelatihan Terhadap Kinerja Karyawan Balai Pengelola Transportasi Darat Kelas Ii Banten

Every organization or company has various important parts to support the running of the organization's or company's activities in achieving organizational or company goals. This research aims to determine the influence of the work environment, training and employee performance. This research used a sample of 80 respondents. The research method used is causality research. To answer the hypothesis using Structured Equation Model (SEM) data analysis using PLS (Partial Least Square). Based on the testing, the results showed that the work environment and training had a positive and significant effect, both partially and simultaneously.