Training Stage Research Articles

Features of the construction of the training program dragon boat rowing at the initial stage of preparation

The article reveals ways to improve the physical training of rowing athletes at the stage of initial training in the preparatory period for rowing on Dragonboats. Training in rowing at the stage of initial training is aimed at the harmonious development of all the main motor qualities of the athlete. At the same time, the training of rowers at this stage should ensure the versatile development of physical qualities. It should be noted that the physical fitness of rowers is the main component of general fitness. It is also known that physical fitness is characterized by the level of functional capabilities of various body systems - cardiovascular, respiratory and muscular. Therefore, the fact that it is necessary to pay more attention to the development of physical qualities, which in turn will be aimed at improving the types of training necessary for an athlete, is so important. The purpose of the study: To analyze the activities of the State University of Secondary Education in rowing on the "Dragon" boats and the peculiarities of the construction of the training program on rowing on the Dragonboats at the initial stage of preparation. Methodology. An analysis of sports training was conducted on the basis of the Irpin State University of Dragonboat, in the period from May to July 2024, the work of coaches with groups of initial training, analysis of plans and documents was analyzed. In the experiment, 2 groups of primary training, approximately 20 children, were accepted to learn. The training plans, standards, and working programs of the coaches were analyzed. Conclusions: Methodically and correctly organized training sessions effectively improve the physical and functional readiness of young rowers, which not only contributes to the improvement of sports results, but also creates opportunities for a successful transition to training at the next stage of multi-year training. The use of training without taking into account the level of physical and functional fitness of athletes at the stage of preliminary basic training not only negatively affects the dynamics of sports results, but also the state of health of young men.

Physical fitness as a factor in the effectiveness of the competitive activity of 17-18-year-old football players during the transition to professional teams

The need to demonstrate high results, on which a coach's career depends, forces the latter to practically ignore the psychophysiological features of the body of athletes aged 17-21 during the educational and training process and measures to restore players after exertion. As a result, the demands of professional football for most of the talented youth become excessive, which leads to overfatigue and a high level of injuries. The most rational approach to solving the problem of training football players during the transition to professional teams should be based on a systematic training methodology that will ensure effective development of sportsmanship, systematic growth of functional capabilities and their proper implementation. It should be noted that the indicators of physical and technical fitness of football players after graduating from the children's and youth football school do not meet the requirements of professional football. The article is devoted to solving the problem of the effectiveness of the competitive activity of 17-18-year-old football players during the transition to professional teams. The work revealed that the age range from 17 to 21 years falls on the period of an athlete's life, when the formation of all functional systems, which ensure high performance and immunity of the body against adverse factors that appear during intense training, is basically completed. It was noted that the physical and technical fitness indicators of football players after graduating from the children's and youth football school do not meet the requirements of professional football. The peculiarities of physical fitness in relation to the effective competitive activity of 17-18-year-old football players at the stage of specialized basic training were analyzed. The data of the study served as indicators of the players' physical fitness. As a result of the conducted correlational analysis of the physical fitness of young football players aged 17-18 years, an average and low value of the relationship was determined, which implies a fairly close relationship between the level of physical fitness and almost all indicators of the sportsmen's motor qualities, except for the indicator that characterizes the starting speed. It has been confirmed that the preparedness of athletes in football depends on their functional indicators, which, in turn, determine the effectiveness of certain tactical and technical actions.

Machine learning based prediction for bulk porosity and static elastic modulus of Yungang Grottoes sandstone

In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model’s error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone.

CURRENT TRENDS IN THE REHABILITATION OF PATIENTS WITH LOWER LIMB AMPUTATION

Amputation of a lower limb is a serious intervention that has a profound impact on a person's life and ability to work. The loss of a limb often causes significant physical and psychological changes that require comprehensive rehabilitation. The patient faces new physical limitations, such as loss of mobility, changes in balance and the need to adapt to prostheses. According to a report by the Wall Street Journal, since the beginning of the Russian-Ukrainian war, the number of people in need of amputation in Ukraine has risen to levels not seen since the First World War. The publication estimates that between 20,000 and 50,000 Ukrainians have lost one or two limbs. Therefore, the study of current trends in the rehabilitation of patients with lower limb amputation in the context of war in Ukraine is extremely relevant. Purpose. To analyse and systematise current trends in the rehabilitation of patients with lower limb amputation. Materials and methods. To achieve the objectives of the study, the researchers analysed scientific literature and systematised information using electronic databases such as PubMed, Google Scholar and others. Research results. Today, physical therapy for patients with lower limb amputation is characterised by a combination of traditional rehabilitation methods and innovative approaches. Kireev and others developed an algorithm that includes rehabilitation activities at 4 main stages. Preoperative rehabilitation, stage I, is aimed at improving postoperative outcomes. It has been suggested that the preoperative programme used in the treatment of young patients with lower limb amputation should consist of chest physiotherapy, muscle and joint mobility training, focusing on maintaining strength and function. During the second stage, before prosthetics, the rehabilitation process includes the aforementioned therapeutic gymnastics and physiotherapy, as well as massage and kinesiotaping. Important issues in preparing an amputated stump for prosthetics include preparing the postoperative wound and scar, controlling edema, preventing muscle atrophy, and preventing the formation of contractures in the joints of the amputated lower limb. In addition to the above-mentioned traditional methods, innovative approaches are actively used at the stage of training with a prosthesis: robotic prostheses, biofeedback technologies, and VR technologies. Giacomo Valle's research has shown that the use of a robotic prosthesis in combination with biofeedback technology not only increases the patient's movement speed, but also increases his or her confidence. Research by Rami L. Abbas demonstrated the feasibility of using VR technologies, which showed a significant improvement in the balance markers, TUG, DGI and BBS tests (P < 0.05). Lifelong rehabilitation, stage IV, involves monitoring the patient for psychosocial adaptation, stump skin pathologies, pain, musculoskeletal disorders, cardiovascular diseases, other chronic diseases, health, and ensuring the use of appropriate footwear for the contralateral limb. Conclusions. Thus, the current trends in combining traditional methods with innovative approaches in the rehabilitation of patients with lower limb amputation are appropriate and justified. It is through the use of therapeutic gymnastics, massage and kinesiotaping in combination with the use of biofeedback technologies, robotic systems and virtual reality that the best results are achieved.

Comparative Characteristics of Short-term and Long-term Adaptation by Blood Analysis of Foreign Female Students (India) to the Educational Process System

The article is devoted to the study of the comparative characteristics of the analysis of the leukocyte formula indicators of foreign female students from India during short-term and long-term sociocultural and other demographic factors of adaptation to educational process systems. We found little difference in the blood analysis of foreign Indian female students with short-term, long-term and social adaptation to the educational environment. Based on the evidence of the studied material, it is determined that the body reacts to each environmental requirement with a special effect. If, in one case, as at the initial stage of training, a student is faced with a situation of non-variability, then this requires the mobilization of many resources of the body, while in another case, at a longer and already established stage of training, the student’s body successfully copes with various influencing factors.

CLINICAL CONFERENCE ON PRACTICAL CLASSES IN DENTAL THERAPY

The modern education reform in Ukraine aims to provide high-quality subject training for specialists while reducing classroom load and increasing access to information. One of the ways to solve this problem is to introduce new, more effective methods and technologies of learning. An essential stage in training of doctors from the first years of study is the formation of clinical thinking, the ability to analyze and synthesize data, determine the criteria for making a diagnosis, and draw up a rational treatment plan. Developing this ability requires knowledge and practical experience, starting in educational institutions and continuing in clinical settings. Technologies and techniques of learning and teaching clinical disciplines are gaining particular relevance and determining importance. The training of dentists at the Department of Dental Therapy of I. Horbachevsky Ternopil National Medical University always meets the requirements of the times. Special attention is paid not only to the high-quality teaching of the discipline but also to the diversification of the organization of practical classes. The early years of medical specialists' training are crucial for developing clinical thinking. This process hones the ability to analyze and systematize data, define criteria for establishing a diagnosis, and compile a plan of effective treatment. At I. Horbachevsky Ternopil National Medical University, special attention is given to the qualitative teaching of the discipline and the diversification of the pedagogical process, with the clinical conference playing a pivotal role in this development. At the Dental Therapy Department, the study of the 'Dental Therapy' discipline concludes with a student clinical conference, a good tradition. This conference, held for the 5th-year students in the X semester, involves three groups of students who are currently completing their study of the subject. In each group, a student-curator who represents a specific clinical case is selected. Students present reports with a visual presentation of the patient's clinical condition, the results of the primary and additional methods of examination, differential diagnosis of the disease, the treatment scheme of the patient and its effectiveness. An important stage at the conference is the performance of prepared opponents, who are appointed from among the students of another group. On the eve of the meeting, the opponents directly familiarize themselves with the work of the student-curator, analyze the sequence of the patient's examinations, the correctness of the diagnosis, the preparation of medical documentation, the differential diagnosis of the disease, evaluate the treatment scheme, ask questions and express observations. The lecturers play a crucial role in this process, providing guidance and doing summaries of the clinical conference, ensuring that the learning objectives are met and the student's progress is acknowledged. Clinical conferences, are not just about deepening and systematizing theoretical knowledge or developing clinical thinking. They also provide a unique and invaluable opportunity for students to gain experience in public speaking. This experience, coupled with the development of personal qualities such as communication and cooperation, prepares students for the challenges they will face in their future careers. This holistic approach to medical education is a key part of the learning process, instilling confidence and readiness in the students.

Classification of Tile Productivity Data Based on Tile Type Using Random Forest Algorithm in Langkat Regency

This study aims to classify data on the productivity of census in Langkat Regency based on the type of census by using the Random Forest algorithm. Ubinan is a method used to measure the productivity of food crops, and in this study, the data was processed with various variables such as planting area, type of fertilizer, type of pesticide, and production volume. The Random Forest algorithm was used to build a classification model that could predict the productivity of the tares with very high accuracy, reaching 99.58% in the training stage. The model categorizes the productivity of the samples into several levels, namely Very Low, Low, Medium, High, and Very High. The implementation of this system is also equipped with a MATLAB GUI interface, which makes it easier for users to train and test data efficiently. With this system, users can see the prediction results through intuitive visualization. This research is expected to help farmers and policy makers in improving agricultural productivity through data-based analysis.

Enhancing local-scale groundwater quality predictions using advanced machine learning approaches

Assessing groundwater quality typically involves labor-intensive, time-consuming, and costly laboratory tests, making real-time monitoring impractical, especially at the local level. Groundwater quality projections at the local scale using broad spatial datasets have been inaccurate due to variations in hydrogeology, human activities, industrial operations, groundwater extraction, and waste disposal. This study aims to identify the most dependable and resilient machine learning algorithms for forecasting groundwater quality at nearby monitoring locations by utilizing simple water quality metrics that can be quickly assessed without extensive sampling and laboratory testing. The Entropy-weighted Water Quality Index (EWQI) was calculated using a large spatial and temporal dataset (2014–2021) of 977 wells with parameters including pH, total hardness (TH), calcium (Ca2⁺), magnesium (Mg2⁺), sodium (Na⁺), potassium (K⁺), sulfate (SO₄2⁻), chloride (Cl⁻), nitrate (NO₃⁻), total dissolved solids (TDS), and fluoride (F⁻). Further, similar parameters were also observed in 33 open wells at the three local monitoring sites from December 2022 to March 2023. The EWQI was predicted using a Random Forest (RF), eXtreme Gradient Boosting (XGB), and Deep Neural Network (DNN). The pH, TH, and TDS were used as input variables for EWQI predictions, as they can be easily measured using handheld probes or multi-parameters. The model performance was evaluated using R2, MAE, and RMSE. During the training stage, all three models predicted the EWQI with an R2 greater than 90%, with minimal errors when pH, TH, and TDS were input variables. To validate the models at a local scale, the EWQI was predicted at the village level (e.g., Antoli, Balapura, and Lapodiaya) using pH, TH, and TDS as input variables. The machine learning models were able to predict the EWQI very closely to the actual EWQI, with an R2 greater than 90%. It is also evident that the models could predict the EWQI using basic parameters that are easily measured, providing an overall idea of the water quality for a small area. Hence, these machine learning models could be useful for accurately representing groundwater quality, thereby avoiding the use of time-consuming and costly laboratory techniques.

SIMULATION TRAINING: INTERNATIONAL EXPERIENCE AND DEVELOPMENT STRATEGY IN UKRAINE

The article presents information on the relevance of simulation training in modern medical education. Leading specialists emphasize the need to learn practical and communication skills, be able to work more intensively in a team; they also note that simulation training can significantly contribute to the achievement of these educational goals under present-day conditions. Simulation training, beginning at the undergraduate or graduate level, can help overcome a number of challenges, such as patient safety issues, ethical issues related to the acquisition and development of skills in interacting with real patients, the need to standardize learning experiences, competency assessments, and medical certification, the need for rapid staff adaptation (staff turnover), promotion of high standards of medical care, increasing the scope of the doctor's responsibility. In addition, simulation training makes it possible to practice more or less typical clinical situations with severe consequences, to promote the acquisition of relevant knowledge and skills, to improve technical skills, to practice the provision of medical care by an interdisciplinary team and individually. The experience of the simulation training development in some countries and a brief history of the foundation of the first simulation training centers in Ukraine are presented. The advantages of simulation training, which allow them to be widely used in educational activities are emphasized. The development of a training program is important in the work of the simulation center and is based on the capacity of simulation training and involves practicing technical and non-technical skills in managing certain situations in accordance with the guidelines. Debriefing (discussion) is necessary so that participants can evaluate their actions during training. Thus, simulation training is an effective method for practicing relevant skills and team interaction in doctor training. Team building experiences are offered using the simulation training, the individual characteristics of students that did not appear in traditional classes are revealed, the ability to conduct a discussion, dialogue and draw conclusions is acquired. A compulsory stage in stimulation training should be the assessment process, therefore it is necessary to notice the reaction of the participants, their satisfaction with the process, and the evaluation of the achievement of the tasks set. This will allow to improve the quality of the learning process and learning materials, to make changes in the training program. A questionnaire can be used for this purpose. The participants assess the relevance of the topic, individual levels of the simulation training and offer possible topics to advance the training program, express their wishes regarding the feasibility, frequency and points of further simulation training and the development of new frameworks. Communication skills are particularly important for doctors and can be actively developed in working conditions of our stimulation center. To conduct doctor training, it is optimal to establish simulation centers with experienced staff and a program developed to meet the needs of the rapidly changing medical landscape.

Improving the classification of rock samples with diffusion probabilistic machine learning

Machine learning is a powerful data-driven technology for regression and classification tasks in numerous branches of science and technology. With probabilistic machine learning, we can approximate the probability density function of a given digital data set, unlocking the sampling of new and realistic data. We implement probabilistic machine learning as a statistical-based data augmentation tool to effectively improve the performance of a classifier on scarce data sets. Limited data are the norm in most practical situations, as is the case with core samples retrieved from unconventional oil and gas reservoirs. Obtaining and properly labeling rock sample images is a manual and time-consuming task. This leads to scarce data sets that make the lithologic classification of rock samples challenging. We illustrate the technology on images taken from well cores from the Argentinean Neuquén and Austral basins, where by introducing generated samples in the training stage of a machine-learning classifier, we achieve a 5% higher accuracy with respect to the baseline obtained using the same number of samples from the original data set. We reach these results by proposing a lightweight workflow that can be run on very limited hardware. Our workflow is an alternative to classical data augmentation strategies for dealing with incomplete information, such as geometric transformations.

An Unbiased Risk Estimator for Partial Label Learning with Augmented Classes

Partial Label Learning (PLL) is a typical weakly supervised learning task, which assumes each training instance is annotated with a set of candidate labels containing the ground-truth label. Recent PLL methods adopt identification-based disambiguation to alleviate the influence of false positive labels and achieve promising performance. However, they require all classes in the test set to have appeared in the training set, ignoring the fact that new classes will keep emerging in real applications. To address this issue, in this paper, we focus on the problem of Partial Label Learning with Augmented Class (PLLAC), where one or more augmented classes are not visible in the training stage but appear in the inference stage. Specifically, we propose an unbiased risk estimator with theoretical guarantees for PLLAC, which estimates the distribution of augmented classes by differentiating the distribution of known classes from unlabeled data and can be equipped with arbitrary PLL loss functions. Besides, we provide a theoretical analysis of the estimation error bound of the estimator, which guarantees the convergence of the empirical risk minimizer to the true risk minimizer as the number of training data tends to infinity. Furthermore, we add a risk-penalty regularization term in the optimization objective to alleviate the influence of the over-fitting issue caused by negative empirical risk. Extensive experiments on benchmark, UCI and real-world datasets demonstrate the effectiveness of the proposed approach.

Playing-related musculoskeletal disorders among Chinese conservatoire piano students: prevalence, risk factors and preventive interventions.

Both professional musicians and conservatoire students are at significant risk of developing playing-related musculoskeletal disorders (PRMDs) during their career life. With the growing number of students pursuing a conservatory degree and graduating from music conservatory in China, the aims of this study were: (1) to identify the nature of PRMD and explore the prevalence of PRMD in Chinese conservatoire students; (2) to determine the relevant risk factors with the presence of PRMD among Chinese conservatoire students; and (3) to suggest preventive interventions for young pianists at their early career stage. A self-reported online survey study was conducted among 363 Chinese conservatoire students who majored in piano performance. Of all respondents, 82.6% reported having had at least one PRMD. The wrist was proved to be the most affected body site, followed by the shoulder, finger and arm. Respondents who experienced PRMD reported "pain," "fatigue," and "stiffness" as the most frequent symptoms. The main risk factors associated with PRMD included gender, years of playing experiences, practice hours, warm-up habits before practice, and break-taking during practice. Female students, those with longer year of playing experience, those who practice longer hours, those who do not warm up before practice, and those who do not take breaks during practice were found to have more PRMD symptoms and higher level of severity. This study highlights the need to increase awareness of PRMD among conservatoire students and to understand the occurrence of PRMD; it is helpful for young pianists to prevent severe musculoskeletal disorders and implement preventive measures at early career stages. Further studies are suggested to follow up on music students who have had at least one PRMD at different stages of professional musical training.

Pre-stack seismic inversion based on one-dimensional GRU combined with two-dimensional improved ASPP

Abstract Pre-stack seismic inversion is essential to detailed stratigraphic interpretation of seismic data. Recently, various deep learning methods have been introduced into pre-stack inversion, effectively capturing the vertical correlations of seismic data. However, existing deep learning methods face challenges such as insufficient feature extraction, poor lateral continuity, and unclear inversion details. We introduce the atrous spatial pyramid pooling (ASPP) module into the pre-stack inversion process, modifying the connection order and mode of its three components. Additionally, we incorporate a triplet attention module to extract features at different scales and utilize a gate recurrent unit (GRU) module to extract global information. During the network training stage, we employ a multi-gather simultaneous inversion method, combining one-dimensional and two-dimensional inversions. The proposed method is named as IGIT (I for Improved ASPP, G for GRU, I for Initial model and T for Triplet attention). To verify the feasibility of this network model, we evaluate it using the Marmousi2 model, SEAM model, and field data, comparing the results with other deep learning methods. Experimental results demonstrate that the IGIT not only improves lateral continuity but also delivers accurate and clear inversion details. Notably, the inversion effect for density parameters shows significant enhancement.

Tensor Based Semi-Blind Channel Estimation for Reconfigurable Intelligent Surface-Aided Multiple-Input Multiple-Output Communication Systems

Reconfigurable intelligent surfaces (RISs) are a promising technology for sixth-generation (6G) wireless networks. However, a fully passive RIS cannot independently process signals. Wireless systems equipped with it often encounter the challenge of large channel matrix dimensions when acquiring channel state information using pilot-assisted algorithms, resulting in high pilot overhead. To address this issue, this article proposes a semi-blind joint channel and symbol estimation receiver without a pilot training stage for RIS-aided multiple-input multiple-output (MIMO) (including massive MIMO) communication systems. In a semi-blind system, a transmission symbol matrix and two channel matrices are coupled within the received signals at the base station (BS). We decouple them by building two parallel factor (PARAFAC) tensor models. Leveraging PARAFAC tensor decomposition, we transform the joint channel and symbol estimation problem into least square (LS) problems, which can be solved by Alternating Least Squares (ALSs). Our proposed scheme allows duplex communication. Compared to recently proposed pilot-based methods and semi-blind receivers, our results demonstrate the superior performance of our proposed algorithm in estimation accuracy and speed.

Learning Styles of Medical Students, Surgical Residents, Medical Staff, and General Surgery Teachers When Learning Surgery: Protocol for a Scoping Review.

Learning styles are biological and developmental configurations of personal characteristics that make the same teaching method effective for some and ineffective for others. Studies support a relationship between learning style and career choices in medicine, resulting in learning style patterns being observed in different residency programs, including in general surgery, from medical school to the last stages of training. The methodologies, populations, and contexts of the few studies pertinent to the matter are very different from one another, and a scoping review on this theme will enhance and organize what is already known. The goal of this study is to identify and map out data from studies on the learning styles of medical students, surgical residents, medical staff, and surgical teachers. The review will consider studies on the learning styles of medical students in a clinical cycle or internship, surgical residents with no restriction on year of residency, medical staff in general surgery, or general surgery's medical faculty. Primary studies published in English, with no specific time frame, will be considered. The search will be carried out in four databases, and reference lists will be searched for additional studies. Duplicates will be removed, and two independent reviewers will screen the titles, abstracts, and full texts of the selected studies. Data collection will be performed using a tool developed by the researchers. A results summary will be presented with figures, narratives, and tables. A quantitative and qualitative analysis will be carried out and further results will be shared. The search was funded on September 25, 2023. Data collection was performed in the two following months. Of the 213 articles found, 135 were excluded due to duplication. The remaining 78 articles will have their titles and abstracts analyzed by three of the researchers independently to select those that meet the eligibility criteria. This data is expected to be published in the first semester of 2025. Conducting a scoping review is the best way to map what is known about a subject. Understanding how students, residents, staff, and even teachers prefer to learn surgery is key to staying up to date and knowing how to best educate those pursuing a surgical career. Open Science Framework 75ku4; https://osf.io/75ku4. DERR1-10.2196/57229.

Effects of Exercise Training on Mitochondrial and Capillary Growth in Human Skeletal Muscle: A Systematic Review and Meta-Regression.

Skeletal muscle mitochondria and capillaries are crucial for aerobic fitness, and suppressed levels are associated with chronic and age-related diseases. Currently, evidence-based exercise training recommendations to enhance these characteristics are limited. It is essential to explore how factors, such as fitness level, age, sex, and disease affect mitochondrial and capillary adaptations to different exercise stimuli. The main aim of this study was to compare the effects of low- or moderate intensity continuous endurance training (ET), high-intensity interval or continuous training (HIT), and sprint interval training (SIT) on changes in skeletal muscle mitochondrial content and capillarization. Secondarily, the effects on maximal oxygen consumption (VO2max), muscle fiber cross-sectional area, and fiber type proportion were investigated. A systematic literature search was conducted in PubMed, Web of Science, and SPORTDiscus databases, with no data restrictions, up to 2 February 2022. Exercise training intervention studies of ET, HIT, and SIT were included if they had baseline and follow-up measures of at least one marker of mitochondrial content or capillarization. In total, data from 5973 participants in 353 and 131 research articles were included for the mitochondrial and capillary quantitative synthesis of this review, respectively. Additionally, measures of VO2max, muscle fiber cross-sectional area, and fiber type proportion were extracted from these studies. After adjusting for relevant covariates, such as training frequency, number of intervention weeks, and initial fitness level, percentage increases in mitochondrial content in response to exercise training increased to a similar extent with ET (23 ± 5%), HIT (27 ± 5%), and SIT (27 ± 7%) (P > 0.138), and were not influenced by age, sex, menopause, disease, or the amount of muscle mass engaged. Higher training frequencies (6 > 4 > 2 sessions/week) were associated with larger increases in mitochondrial content. Per total hour of exercise, SIT was ~ 2.3 times more efficient in increasing mitochondrial content than HIT and ~ 3.9 times more efficient than ET, while HIT was ~ 1.7 times more efficient than ET. Capillaries per fiber increased similarly with ET (15 ± 3%), HIT (13 ± 4%) and SIT (10 ± 11%) (P = 0.556) after adjustments for number of intervention weeks and initial fitness level. Capillaries per mm2 only increased after ET (13 ± 3%) and HIT (7 ± 4%), with increases being larger after ET compared with HIT and SIT (P < 0.05). This difference coincided with increases in fiber cross-sectional area after ET (6.5 ± 3.5%), HIT (8.9 ± 4.9%), and SIT (11.9 ± 15.1%). Gains in capillarization occurred primarily in the early stages of training (< 4weeks) and were only observed in untrained to moderately trained participants. The proportion of type I muscle fibers remained unaltered by exercise training (P > 0.116), but ET and SIT exhibited opposing effects (P = 0.041). VO2max increased similarly with ET, HIT, and SIT, although HIT showed a tendency for greater improvement compared with both ET and SIT (P = 0.082), while SIT displayed the largest increase per hour of exercise. Higher training frequencies (6 > 4 > 2 sessions/week) were associated with larger increases in VO2max. Women displayed greater percentage gains in VO2max compared with men (P = 0.008). Generally, lower initial fitness levels were associated with greater percentage improvements in mitochondrial content, capillarization, and VO2max. SIT was particularly effective in improving mitochondrial content and VO2max in the early stages of training, while ET and HIT showed slower but steady improvements over a greater number of training weeks. The magnitude of change in mitochondrial content, capillarization, and VO2max to exercise training is largely determined by the initial fitness level, with greater changes observed in individuals with lower initial fitness. The ability to adapt to exercise training is maintained throughout life, irrespective of sex and presence of disease. While training load (volume × intensity) is a suitable predictor of changes in mitochondrial content and VO2max, this relationship is less clear for capillary adaptations.

Optimizing modern surgical simulation through instructor feedback - insights from a retrospective observational study in a tertiary hospital.

Laparoscopic surgery is associated with a prolonged learning curve for emerging surgeons, and simulation-based training (SBT) has become increasingly prominent in this context due to stringent working time regulations and heightened concerns regarding patient safety. While SBT offers a safe and ethical learning environment, the accuracy of simulators in the context of evaluating surgical skills remains uncertain. This study aims to assess the precision of a laparoscopic simulator with regard to evaluating surgical performance and to identify the instructor's role in SBT. This retrospective study focused on surgical residents in their 1st through 5th years at the Department of Surgery of Linkou Chang Gung Memorial Hospital. The residents participated in a specially designed SBT program using the LapSim laparoscopic simulator. Following the training session, each resident was required to perform a laparoscopic procedure and received individualized feedback from an instructor. Both simulator and instructor evaluated trainees' performance on the LapSim, focusing on identifying correlations between the simulator's metrics and traditional assessments. Senior residents (n = 15), who employed more complex laparoscopic procedures, exhibited more significant improvements after receiving instructor feedback than did junior residents (n = 17). Notably, a stronger correlation between the simulator and instructor assessments was observed in the junior group (junior Global Operative Assessment of Laparoscopic Skills (GOALS) adjusted R2 = 0.285, p = 0.016), while no such correlations were observed among the senior group. A well-designed, step-by-step SBT can be a valuable tool in laparoscopic surgical training. LapSim simulator has demonstrated its potential in assessing surgical performances during the early stages of surgical training. However, instructors must provide intuitive feedback to ensure appropriate learning in later stages.

RELATO DE EXPERIÊNCIA: UM OLHAR HUMANIZADO COM PACIENTES ONCOLÓGICOS PEDIÁTRICOS NO SETOR DA ENFERMARIA

The objective of this work is to report the experience and describe the monitoring of pediatric oncology patients during their hospitalization carried out during the Mandatory Supervised Curricular Internship I. This is a descriptive, qualitative study of the experience report type, about the experience of the student covering the description of the discipline, internship field, activities carried out, challenges and personal and professional impact during the monitoring of pediatric oncology patients during their hospitalization, in the Mandatory Curricular Internship (ECS) l in the ninth period of the Nursing degree. The practice of supervised internship in health is a crucial stage in the academic training and professional development of students. Provides valuable opportunities to develop practical skills and apply theoretical knowledge in real situations. The work experience in the pediatrics sector also contributed to broadening their perspectives for a possible follow-up in the area and improving skills such as critical and reflective reasoning, decision-making and leadership, which are important for success in professional life.

A comprehensive investigation on the performance of reconstruction of noncircular fiber-representative volume elements in unidirectional composites using diffusion generative models

This study employs diffusion generative models to reconstruct random representative volume elements (RVEs) of unidirectional composites with noncircular fibers. Microscope images of these composites were prepared and trained with denoising diffusion probabilistic model (DDPM), denoising diffusion implicit model (DDIM), progressive distillation diffusion model (PDDM), and deep convolutional generative adversarial network (DCGAN). Hyperparameter tuning was performed for both DDPM and PDDM, and the generated RVE images were evaluated using the two-point correlation function (TPCF), Fréchet Inception Distance (FID), and computational cost. Furthermore, finite element (FE) models were generated using these images, and FE simulations were conducted considering interfacial debonding behavior. The resulting stress and strain curves from these simulations were compared. The results show that DDPM demonstrated the best performance in final image quality, while PDDM maintained stable performance from the early stages of training. Additionally, both models exhibited excellent agreement with the original images, indicating high quality, diversity, and resemblance.

SPECIFICITY OF WORK ON INTONATIONAL-SEMANTIC ORGANISATION OF PLAYING THE FLUTE WHEN MASTERING WORKS OF COMPOSERS OF THE SECOND HALF OF THE 20TH CENTURY AND EARLY 21ST CENTURY

The article is devoted to the formation of professional performers’ skills to interpret works for the flute created during the second half of the 20th century and early 21st century, that is, related to the so-called “modern repertoire”. The author concentrates on the intonation-semantic organisation of performance when mastering works of this category and identifies two key problems: the distance of the style and language of the works from the classical-romantic “basis” formed in performers at the initial and secondary stages of training, as well as a huge variety of musical languages, each of which a performer needs to master. In conclusion, the author of the article proposes a project of his own methodological model that involves mastering of the art of composition by students as a means to learn the principles of modern compositional techniques and, consequently, musical languages and styles as deeply as possible, and thereby to obtain the “keys” to the correct intonation-semantic organisation of performance.