Training Variables Research Articles

An Advanced Cardiac Life Support Application Improves Performance during Simulated Cardiac Arrest.

Variability in cardiopulmonary arrest training and management leads to inconsistent outcomes during in-hospital cardiac arrest. Existing clinical decision aids, such as American Heart Association (AHA) advanced cardiovascular life support (ACLS) pocket cards and third-party mobile apps, often lack comprehensive management guidance. We developed a novel, guided ACLS mobile app and evaluated user performance during simulated cardiac arrest according to the 2020 AHA ACLS guidelines via randomized controlled trial. Forty-six resident physicians were randomized to lead a simulated code team using the AHA pockets cards (N = 22) or the guided app (N = 24). The primary outcome was successful return of spontaneous circulation (ROSC). Secondary outcomes included code leader stress and confidence, AHA ACLS guideline adherence, and errors. A focus group of 22 residents provided feedback. Statistical analysis included two-sided t-tests and Fisher's exact tests. App users showed significantly higher ROSC rate (50 vs. 18%; p = 0.024), correct thrombolytic administration (54 vs. 23%; p = 0.029), backboard use (96 vs. 27%; p < 0.001), end-tidal CO2 monitoring (58 vs. 27%; p = 0.033), and confidence compared with baseline (1.0 vs 0.3; p = 0.005) compared with controls. A focus group of 22 residents indicated unanimous willingness to use the app, with 82% preferring it over AHA pocket cards. Our guided ACLS app shows potential to improve user confidence and adherence to the AHA ACLS guidelines and may help to standardize in-hospital cardiac arrest management. Further validation studies are essential to confirm its efficacy in clinical practice.

Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems

This study explores the implementation of physics-informed neural networks (PINNs) to analyze turbulent flow in composite porous-fluid systems. These systems are composed of a fluid-saturated porous medium and an adjacent fluid, where the flow properties are exchanged across the porous-fluid interface. The segregated PINN model employs a novel approach combining supervised learning and enforces fidelity to flow physics through penalization by the Reynolds-averaged Navier-Stokes (RANS) equations. Two cases were simulated for this purpose: solid block, i.e. porous media with zero porosity, and porous block with a defined porosity. The effect of providing internal training data on the accuracy of the PINN predictions for prominent flow features, including flow leakage, channeling effect and wake recirculation was investigated. Additionally, L2 norm error, which evaluates the prediction accuracy for flow variables was studied. Furthermore, PINN training time in both cases with internal training data was considered in this study. Results showed that the PINN model predictions with second-order internal training data achieved high accuracy for the prominent flow features compared to the RANS data, within a 20% L2 norm error of second-order statistics in the solid block case. In addition, for the porous block case, providing training data at the porous-fluid interface showed errors of 18.04% and 19.94% for second-order statistics, representing an increase in prediction accuracy by 7% compared to without interface training data. The study elucidates the impact of the internal training data distribution on the PINN training in complex turbulent flow dynamics, underscoring the necessity of turbulent second-order statistics variables in PINN training and an additional velocity gradient treatment to enhance PINN prediction.

PENGARUH PELATIHAN DAN MOTIVASI KERJA TERHADAP KINERJA PEGAWAI PADA DINAS PERIKANAN KABUPATEN SELUMA

This research aims to determine "The influence of training and work motivation on employee performance at the Seluma Regency Fisheries Service", either partially or simultaneously. The type of research used is quantitative research. The population in this study were 50 employees at the Seluma City sub-district office in the Seluma Regency Fisheries Service, using the side total technique, which means the entire population was used as the research sample. The collection technique uses questionnaire data that has been tested for validity and reliability. The data analysis technique used to answer the hypothesis is Multiple Regression. The results of this research show that: (1) The research results show a positive influence of training variables on employee performance. Based on the results of the partial test ( T test ), the T_count was 3.321 and the regression coefficient was 0.189 with a significance of 0.002. (2) The research results show a positive influence of the Work Motivation variable on Employee Performance. Based on the results of the partial test ( T test ), the T_count is 2,773 and the regression coefficient is 0.144 with a significance of 0.000. (3) The results of simultaneous testing or jointly obtained that the Training and Work Motivation variables have a positive effect on employee performance. This is proven by the test results obtained by an F_count value of 5,567, with significance (0.007 &lt; 0.050). Keywords: Training, Work Motivation, Employee Performance

Awareness of school teachers about students common emergencies during school time in the eastern region, Saudi Arabia: A cross-sectional study.

Schools have to ensure student safety and well-being. However, medical emergencies involving injuries are common, requiring teachers to provide prompt first aid before professional care arrives. This study assessed knowledge and awareness of first aid among school teachers to identify areas needing improvement. This cross-sectional study surveyed 371 teachers from the eastern region primary schools using a standardized questionnaire assessing first aid knowledge across areas like bleeding control, fractures, seizures, choking, and burns. Demographic factors were analyzed for correlations with knowledge levels. Pearson's Chi-square tests analyzed bivariate relationships comparing correct knowledge identification percentages across teacher sociodemographic. The level of statistical significance was at a P value <0.05 across tests. Only 157 teachers felt able to appropriately handle medical emergencies. Major knowledge gaps existed regarding airway obstruction and chemical injuries, with 168 unable to identify correct responses for choking first aid and 105 unsure of chemical burn management steps. Appropriate fracture management was lacking, with just 70 correctly endorsing nose pinch for nosebleeds. Cardiopulmonary resuscitation knowledge was inadequate, with 57 of teachers unaware of the basics. Teachers demonstrating higher knowledge levels overwhelmingly had prior first aid training (153, 66.5%) compared to untrained counterparts (89, 63.1%). The study revealed poor first aid knowledge among elementary school teachers for effectively managing child emergencies. Strategies enhancing delivery must address barriers around training access, knowledge attrition, and variability in baseline skills through sustainable system-wide policies applied nationally. Fulfilling schools' ethical obligations toward maintaining student safety requires urgent reforms to bolster emergency preparedness.

Maximizing the performance of badminton athletes through core strength training: Unlocking their full potential using machine learning (ML) modeling

Core strength training plays an essential role in maximizing performance for badminton athletes. The core muscles in the abdominal, back, and hip regions provide stability, enable efficient power transfer between the upper and lower body, and allow for rapid changes in direction - all crucial components for success in badminton. However, optimizing core training requires an understanding of its impact on sport-specific skills. A variety of exercises targeting the abdominal, back, and hip muscles are discussed. Incorporating core strength training into regular regimens can improve athletes' overall strength, endurance, balance, control, and prevent injuries. This study investigates the effects of various core exercises on stability, agility, and power in badminton players. A comprehensive literature review was conducted to explore the biomechanical demands of badminton and how core musculature contributes to movements like serving, smashing, and lunging. Studies assessing the effects of core training programs in related racquet sports were also examined. The results indicate that targeted core exercises significantly improve athletes' stability, agility, and power output. Exercises targeting the abdominal, back, and hip muscles enhance performance capabilities while reducing injury risk. Machine learning (ML) techniques are then applied to further analyze the relationship between core training and athletic performance. An Artificial Neural Network (ANN) is developed using a dataset of athletes' training histories, metrics, and injury profiles. The model predicts enhancements to stability, agility, and strength from optimized core strengthening routines. Validation confirms the network accurately captures the complex interactions between training variables and physical attributes. This integrated approach provides evidence-based guidelines for tailoring individualized training regimens to unleash players' full abilities. ANNs hold promise for analyzing large datasets on athletes' performance metrics, training variables, and injury histories to design personalized training programs. Linear regression analysis confirmed the ANN's accurate predictions. The findings emphasize integrating data-driven core strength training tailored for badminton into comprehensive programs can help optimize physical abilities and elevate performance levels.

Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.

Endurance training and physiological variables: effects on sub-elite volleyball players

Volleyball is perceived as a physically demanding sport that involves rapid changes in speed, deceleration, and abrupt alterations in movement direction. It has been emphasized that physiological attributes such as VO2max, PIF, PEF, and FVC play a crucial role in the athletic performance of individuals participating in dynamic sports, aiding them in effectively managing critical situations. Hence, the aim of this research was to evaluate the impact of endurance training on physiological parameters among volleyball athletes. A randomized controlled trial was carried out with 24 male players (mean age 21,58; SD±2,46 years). The assessment of physiological characteristics involved the utilization of a dry spirometer for VO2max measurement and the Multistage Fitness Test for assessing PIF, PEF, and FVC. A two-way Anova analysis was executed to ascertain any disparities in physiological parameters from pre-test to post-test. The results indicated notable variances in physiological traits (VO2max, PIF, PEF, and FVC) among volleyball participants following the implementation of the Tabata intervention regimen. These metrics are instrumental in enhancing physiological capabilities to attain superior athletic performances. When designing training regimens, it is imperative to consider physiological functional elements aligned with the specific demands of the sport. Key words. VO2max; physiology; sport; exercise; Tabata training.

Risk Factors for Injury in Border Collies Competing in Agility Competitions.

Border Collies are the most common breed in agility and their reported injury rate is much higher than that of other breeds. We aimed to identify demographic, training, and competition variables associated with the injury risk for this breed. We hypothesized that higher jump heights and competition at national/international levels would increase the injury risk. Data were collected from an internet-based survey. A logistic regression model was built using backward selection. There were 934 Border Collies in the sample, with 488 reporting an injury. The jump height relative to the shoulder height was associated with injury, with dogs jumping noticeably above or below shoulder height more likely to report a history of injury. Other identified risk factors included the number of weekends spent competing/year, the number of competitions at the national level, the age when starting elbow height jumps and backside jumps, the acquisition of the dog from a breeder, and the age of the handler. Factors associated with prolonged injury (>3-month duration) were the age when starting elbow height jumps and having a veterinary assistant as a handler. Border Collies jumping above shoulder height had an increased risk of injury. However, those jumping below shoulder height were also at a higher risk, which could have been due to reverse causality. Similarly, the observed associations regarding differences based on the number of trial weekends/year may have been impacted by reverse causality as well. The increased risk of injury with elbow height jump training at <10 months of age may indicate that the repetitive impact of jump training prior to skeletal maturity negatively influences musculoskeletal development. These data provide valuable information for further prospective studies.

ANALYSIS TO IMPROVE THE PERFORMANCE OF LEDOOKOMBO DISTRICT VILLAGE EQUIPMENT USING MOTIVATION AND JOB TRAINING DISCIPLINE VARIABLES

This research was conducted to determine the influence of work discipline, work motivation and work training on improving the performance of village officials in Ledokombo District. The research method used in this research is a quantitative method with a census sampling method of 117 respondents, using a questionnaire sheet distributed to village officials in Ledokombo District. This research uses multiple linear regression analysis methods, partial tests, and coefficient of determination (R2) tests. And the results of the analysis using SPSS 27 show that the three variables, namely motivational discipline and work training, have a significant influence on the performance of village officials in Ledokombo District. The results of the determination test (r2) were (41.8%) showing the large proportion or percentage contribution of the Discipline, Motivation and Job Training variables to performance. Meanwhile (58.2%) was contributed by other variables not included in this research, for example work burnout, compensation, work commitment and so on.

Trends in using intraoperative parathyroid hormone monitoring during parathyroidectomy: Protocol and rationale for a cross-sectional survey study of North American surgeons.

Hyperparathyroidism is a common endocrine disorder that occurs secondary to abnormal parathyroid gland functioning. Depending on the type of hyperparathyroidism, surgical extirpation of hyperfunctioning parathyroid glands can be considered for disease cure. Intraoperative parathyroid hormone (IOPTH) monitoring improves outcomes in patients undergoing surgery for primary hyperparathyroidism, but studies are needed to characterize its institutional adoption and its role in surgery for secondary and tertiary hyperparathyroidism, as these entities can be difficult to cure. Hence, we will perform a cross-sectional survey study of surgeon rationale, operational details, and barriers associated with IOPTH monitoring adoption across North America. We will utilize a convenience sampling technique to distribute an online survey to head and neck surgeons and endocrine surgeons across North America. This survey will be distributed via email to three North American professional societies (i.e., Canadian Society for Otolaryngologists-Head and Neck Surgeons, American Head and Neck Society, and American Association of Endocrine Surgeons). The survey will consist of 30 multiple choice questions that are divided into three concepts: (1) participant demographics and training details, (2) details of surgical adjuncts during parathyroidectomy, and (3) barriers to adoption of IOPTH. Descriptive analyses and multiple logistic regression will be used to evaluate the impact of demographic, institutional, and training variables on the use of IOPTH monitoring in surgery for all types of hyperparathyroidism and barriers to IOPTH monitoring adoption. Ethics approval was obtained by the Hamilton Integrated Research Ethics Board (2024-17173-GRA). These findings will characterize surgeon and institutional practices with regards to IOPTH monitoring during parathyroid surgery and will inform future trials aimed to optimize the use of IOPTH monitoring in secondary and tertiary hyperparathyroidism.

Interpreting Difficult Conversations-Evaluating How to Support Medical Interpreters in the Delivery of Serious News.

Background: Despite their essential role in language concordant patient care, medical interpreters do not routinely receive training focused on difficult conversations and may not feel comfortable interpreting these encounters. Previous studies, while acknowledging the need for increased support, have provided limited strategies targeted at enhancing interpreter training and improving interpreter comfort levels in difficult conversations. Methods: Fifty-seven in-person medical interpreters providing services at our quaternary and community hospitals completed a 21-question mixed-methods survey regarding their comfort levels and experiences surrounding serious illness conversations. Results: Most medical interpreters reported being uncomfortable interpreting conversations surrounding difficult diagnosis, poor prognosis, and/or end-of-life. Nearly all respondents (98%) indicated that pre-meetings and/or debriefings with the medical team are helpful, yet only 25% reported frequent participation in these meetings. Conclusions: Our study highlighted the significant variability in medical interpreter training as well as ranging comfort levels in interpreting difficult conversations. Medical providers should not presume that interpreters are instantly prepared for these encounters. Current findings call for novel training opportunities specific to medical interpreters and difficult dialogues, as well as improved adherence of interprofessional pre-meeting/debriefings when serious news is discussed.

بررسی تأثیر آموزش ضمن خدمت بر توانمندسازی کارمندان

The purpose of this research is to investigate the impact of on job training on the employee’s empowerment of education department in Nimruz Province. This research is practical in terms of purpose and correlational in terms of method, the primary data was collected through questionnaire and was analyzed by SPSS program. Cronbach's alpha was found o.926 which it shows a strong reliability of the instrument. Also, the validity of the questionnaire has been confirmed by a number of experts in the field of economics. To conduct this research, among 130 employees of education department in Nimruz province, 97 people have been randomly selected. The results show that the variables of attitudinal training, knowledge training and skill training have a positive and significant effect on the level of employee empowerment. The research results show that skill training has the greatest effect of 0.409 and attitude training has the least effect of 0.282 on employee empowerment.

Analysis of Determinants Associated with the Activeness of Posyandu Cadres in the Working Area of the Tarus Health Center, Kupang Tengah Sub-District, Kupang District

Background: The activeness of cadres is very important in Posyandu activities because if cadres are not active then the provision of health services and Posyandu activity programs cannot run well, so cadre coaching is needed by health workers to increase the active role of cadres in the implementation of Posyandu activities. Purpose: This study aims to analyze the determinants associated with the activeness of Posyandu cadres in the working area of Puskesmas Tarus, Kupang Tengah District, Kupang Regency in 2024. Methods: This type of research is an analytic survey with a cross sectional research design. The sample in this study were 71 people. Sampling in this study using simple random sampling technique with a level of significance ? = 0.05. Results: The results showed that there was a relationship between the variables of knowledge (p=0.000), attitude (0.002) and training 0.002 with the activeness of Posyandu cadres in the working area of the Tarus Health Center, Kupang Tengah District, Kupang Regency. Conclusion: It is expected that health workers at the Tarus Health Center should always maintain the implementation of cadre training by providing counseling on the importance of activeness of Posyandu cadres so that Posyandu activities can run well, and it is also expected that Posyandu cadres can increase activeness as a social responsibility in the community.

Data-driven intelligent modeling of unconfined compressive strength of heavy metal-contaminated soil

This study focuses on empirical modeling of the strength characteristics of urban soils contaminated with heavy metals using machine learning tools and their subsequent stabilization with ordinary Portland cement (OPC). For dataset collection, an extensive experimental program was designed to estimate the unconfined compressive strength (Qu) of heavy metal-contaminated soils collected from a wide range of land use pattern, i.e. residential, industrial and roadside soils. Accordingly, a robust comparison of predictive performances of four data-driven models including extreme learning machines (ELMs), gene expression programming (GEP), random forests (RFs), and multiple linear regression (MLR) has been presented. For completeness, a comprehensive experimental database has been established and partitioned into 80% for training and 20% for testing the developed models. Inputs included varying levels of heavy metals like Cd, Cu, Cr, Pb and Zn, along with OPC. The results revealed that the GEP model outperformed its counterparts: explaining approximately 96% of the variability in both training (R2 = 0.964) and testing phases (R2 = 0.961), and thus achieving the lowest RMSE and MAE values. ELM performed commendably but was slightly less accurate than GEP whereas MLR had the lowest performance metrics. GEP also provided the benefit of traceable mathematical equation, enhancing its applicability not just as a predictive but also as an explanatory tool. Despite its insights, the study is limited by its focus on a specific set of heavy metals and urban soil samples of a particular region, which may affect the generalizability of the findings to different contamination profiles or environmental conditions. The study recommends GEP for predicting Qu in heavy metal-contaminated soils, and suggests further research to adapt these models to different environmental conditions.

THE INFLUENCE OF TRAINING AND COMPETENCE ON THE PERFORMANCE OF HARPER HOTEL JAKARTA EMPLOYEES

The purpose of this study was to determine the effect of training and competence on employee performance at Harper Hotel Jakarta. The research method used in this research is descriptive with a quantitative approach. The population amounted to 58 people, so the sampling technique was used with the saturated sample method and the number of samples obtained in this study were 58 respondents. Data analysis techniques using multiple linear regression. The results showed that: (1) There is a positive and significant effect of training variables on employee performance at Harper Hotel Jakarta. This is evidenced by the value of tcount &gt; ttable, namely 4.352 &gt; 2.004 with a probability value (significance) = 0.000, which is below 0.005. (2) There is a positive and significant effect of competency variables on employee performance at Harper Hotel Jakarta. This is evidenced by the value of tcount &gt; ttable, namely 7.381&gt; 2.004 with a probability value (significance) = 0.000, which is below 0.005. (3) There is a positive and significant effect of training and competency variables on employee performance at Harper Hotel Jakarta. This is evidenced by the value of Fcount &gt; Ftable, namely 26.75 9&gt; 3.16 with a probability value (significance) = 0.000, which is below 0.005.

FORMATION OF EDUCATIONAL AUTONOMY OF STUDENTS OF ECONOMIC SPECIALITIES BY MEANS OF DIGITAL TECHNOLOGIES

The article is devoted to the problem of studying the peculiarities of the formation of educational autonomy of students of economic specialties by means of digital technologies. The purpose of the article is to identify the psychological and pedagogical features of the formation of educational autonomy of students of economic specialties by means of digital technologies. The tasks of the article are as follows: to characterize the theoretical foundations of the essence of educational autonomy as a pedagogical category; to determine the peculiarities of the formation of students’ learning autonomy skills; to prove the importance of the formation of educational autonomy among students of economic specialties; to determine the possibilities of digital technologies in the formation of educational autonomy among students of economic specialties. To achieve the goal, the following methods were used: literary analysis, generalization, comparison, systematization, etc. The article contains the following research results. A critical analysis of approaches to defining the essence of the concept of “learner autonomy” by various researchers was carried out. It was established that the concept of “pupil / student autonomy” was introduced in the early 1980s by Henri Holec, who defined this category as “the student’s ability to take responsibility for their own learning.” A theoretical study of the views of various researchers proved that the formation of students’ educational autonomy is mainly related to the process of mutual influence of motivation for educational activities and the success of completing educational tasks: motivation for learning is always characterized by the extent to which a student knows how to learn independently, demonstrating interest in learning, and, in turn, successful learning is impossible without motivation supported by the competence of self-regulated learning. In other words, learning autonomy is the student’s ability to be self-motivated and responsible for their own learning. It was found that one of the key aspects that should be taken into account when determining the independence of learning is whether we consider it as a means of achieving a goal (studying a certain educational module, subject, science) or as an end in itself (forming people as independent learners). These two options are not mutually exclusive, as they can both be part of our views on learning as a whole. Four main advantages of forming students’ learning autonomy have been identified: motivation (students strive to achieve their educational goals, but for this to happen, teachers must first help students set realistic learning goals); flexibility (an autonomous approach to learning is more flexible for both students and teachers, who can take on more of a coaching role, while students take the driver’s seat); lasting impact (learning to learn is an underrated skill, when students are in charge of their own learning, they learn what techniques and strategies work best for them); increasing involvement (using the autonomy of students, the teacher relies on their ability to solve problems, study the educational material and form their own opinion). The skills that students should develop in the process of autonomous learning are substantiated: setting goals; assessment of progress; time management; and self-motivation. Conclusions. The importance of the formation of academic autonomy of students of economics specialities has been proven, as independent thinking presents concepts, models and theories that students will use to solve real-world problems, honing their cognitive skills. The following advantages of using digital technologies in the direction of the formation of educational autonomy of students of economic specialties are outlined: making knowledge accessible; making knowledge understandable; providing flexibility and variability of training; and ensuring students’ interaction with the digital environment.

The effect of concurrent high-intensity interval training and resistance training on the lower body maximal strength and explosive power: A updated systematic review and meta-analysis

BACKGROUND: Endurance training can have a negative impact on strength training and may lead to reduced strength gains, known as the interference effect. However, high-intensity interval training (HIIT) as an endurance training mode may reduce this interference effect. OBJECTIVE: This systematic review and meta-analysis aim to investigate the effects of concurrent HIIT and resistance training (RT) on lower body explosive strength and maximum strength. METHODS: Five electronic databases were searched. Subgroup analyses were performed to assess the effects of HIIT modality, training status, and training duration on strength development following concurrent HIIT and RT. RESULTS: Meta-analysis showed that compared to RT alone, concurrent HIIT and RT will not affect the development of countermovement jump (CMJ) (WMD =-0.17, 95%CI =-1.45 to -1.11) and half squat (WMD =-0.05, 95%CI =-2.42 to 2.32). Further, subgroup analysis revealed that HIIT-running workout was conducive to the development of both CMJ and half squat. Longer training duration was found to be more effective in developing CMJ, while shorter training duration was more suitable for developing half squat. Additionally, athletes showed greater improvement than non-athletes. CONCLUSIONS: Combining HIIT and RT can enhance CMJ and half squat. The results of intervention are moderated by training variables and training status.

PENGARUH KEPEMIMPINAN, PELATIHAN DAN KOMPENSASI TERHADAP KINERJA KARYAWAN PADA PT TUNAS CAHAYA MANDIRI WIDYATAMA MEDAN

The purpose of this research is to determine and explain the influence of leadership, training and compensation on employee performance at PT Tunas Cahaya Mandiri Widyatama Medan. Data collection was carried out using a questionnaire. Before testing the hypothesis, the classical assumption test is first carried out. The analysis used multiple linear regression, hypothesis testing (F test and t test) and analysis of the coefficient of determination. From the results of data processing, a multiple linear regression equation is obtained where: Y= 1.106 + 0.319 X1 + 0.306X2 + 0.365X3 + ei. The results of this research show that the variables of leadership, training and compensation together (simultaneously) have a positive and significant effect on employee performance. Partially, it shows that leadership has a positive and significant effect on employee performance with a multiple regression coefficient value of 0.319 and a significant value of 0.036 &lt;0.05. Training has a positive and significant effect on employee performance with a multiple regression coefficient value of 0.306 and a significant value of 0.045 &lt;0.05. Compensation has a positive and significant effect on employee performance with a multiple regression coefficient value of 0.365 and a significant value of 0.040 &lt;0.05. The results of this research also conclude that to improve employee performance, companies should pay more attention to leadership, training and compensation.

Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling

Explainable artificial intelligence (XAI) models with Shapley additive explanation (SHAP) values allows multidimensional representation of movement performance interpreted on both global and local levels in terms understandable to human intuition. We aimed to evaluate the swimming performance (World Aquatics points) predictability of a combination of demographic, training, anthropometric, and biomechanical variables (inputs) through XAI. Forty-seven swimmers (16 males), after completing a training questionnaire (background and duration) and anthropometric assessment, performed, in a randomised order, a 25 m front crawl and three countermovement jumps, at maximal intensity. The predicted World Aquatics points (516 ± 159; mean ± SD) were highly correlated (r2 = 0.93) with the 529 ± 158 actual values. The duration of swimming training was the most important variable (95_SHAP), followed by the countermovement jump impulse (37_SHAP), both with a positive effect on performance. In contrast, a higher percentage of fat mass (21_SHAP) corresponded to lower World Aquatics points. Impulse, when interpreted together with dryland training duration and stroke rate, shows the positive effects of upper and lower limb power on swimming performance. Height should be interpreted together with arm span when exploring positive effects of anthropometric traits on swimming performance. The XAI modelling highlights the usefulness of specific training, technical and physical testing, and anthropometric factors for monitoring swimmers.

Standardizing clinician training and patient care in the neonatal neurocritical care: A step-by-step guide

Neonatal neurocritical care (NNCC) has emerged as an important specialty to address neurological conditions affecting newborns including a wide spectrum of brain injuries and developmental impairment. Despite the discipline's growth, variability in NNCC service delivery, patient care, and clinical training poses significant challenges and potentially adversely impacts patient outcomes. Variations in neuroprotective strategies, postnatal care, and training methodologies highlight the urgent need for a unified approach to optimize both short- and long-term neurodevelopmental outcomes for these vulnerable population. This paper presents strategic blueprints for establishing standardized NNCC clinical care and training programs focusing on collaborative effort across medical and allied health professions. By addressing these inconsistencies, the paper proposes that standardizing NNCC practices can significantly enhance the quality of care, streamline healthcare resource utilization, and improve neurodevelopmental outcome, thus paving the way for a new era of neonatal neurological care.