Entire Training Research Articles

Use of modern technologies in the performance analysis of an elite runner

Modern technologies have become an indispensable tool in sports science, enabling a detailed analysis of performance parameters for elite and recreational athletes during selected activities or throughout an entire training cycle. This study focuses on the analysis of data collected using the Garmin application for an elite runner, specifically examining two key indicators: heart rate (HR) and cadence/stride length during a half-marathon. This study aims to analyze these key indicators, compare them with additional metrics, and identify how they influence the performance of the elite runner. Furthermore, it explores how the measured values can be utilized in training and competitive preparation. Based on the analysis, the study proposes general recommendations for applying insights gained from performance monitoring devices and suggestions for implementing these technologies in the training process to optimize both training methods and race strategies. The results highlight the importance of maintaining a stable HR, which the analyzed runner sustained at an average of approximately 175 bpm, as well as ensuring appropriate cadence and effective stride length aligned with the runner's characteristics. These findings provide practical recommendations for runners and their coaches on how to use available technologies not only for the analysis of specific performances but also for the long-term monitoring of training processes. The ultimate goal is to maximize individual performance and minimize the risk of overtraining.

Evaluating deep learning models for classifying OCT images with limited data and noisy labels

The use of deep learning for OCT image classification could enhance the diagnosis and monitoring of retinal diseases. However, challenges like variability in retinal abnormalities, noise, and artifacts in OCT images limit its clinical use. Our study aimed to evaluate the performance of various deep learning (DL) architectures in classifying retinal pathologies versus healthy cases based on OCT images, under data scarcity and label noise. We examined five DL architectures: ResNet18, ResNet34, ResNet50, VGG16, and InceptionV3. Fine-tuning of the pre-trained models was conducted on 5526 OCT images and reduced subsets down to 21 images to evaluate performance under data scarcity. The performance of models fine-tuned on subsets with label noise levels of 10%, 15%, and 20% was evaluated. All DL architectures achieved high classification accuracy (> 90%) with training sets of 345 or more images. InceptionV3 achieved the highest classification accuracy (99%) when trained on the entire training set. However, classification accuracy decreased and variability increased as sample size decreased. Label noise significantly affected model accuracy. Compensating for labeling errors of 10%, 15%, and 20% requires approximately 4, 9, and 14 times more images in the training set to reach the performance of 345 correctly labeled images. The results showed that DL models fine-tuned on sets of 345 or more OCT images can accurately classify retinal pathologies versus healthy controls. Our findings highlight that while mislabeling errors significantly impact classification performance in OCT analysis, this can be effectively mitigated by increasing the training sample size. By addressing data scarcity and labeling errors, our research aims to improve the real-world application and accuracy of retinal disease management.

Jerzy Grotowski – via negativa

In this article, we aim to provide a brief analysis of the concept of via negativa through which the renowned Polish artist and director Jerzy Grotowski articulates both his artistic view and the philosophy underlying his entire training of his acting method. This negatively formulated notion also serves as a practical research direction conducted in his theatrical laboratory. Our analysis begins with the religious implications of the term, extracts some consequences of the Grotowskian artistic vocabulary, and ends with some general observations regarding the essential features of the Polish theatre school and tradition.

Atenção primária e a população transgênero: da prática clínica às ações de educação em saúde

Introduction: discrimination against the transgender population has taken. The disparities us care in attention to health, causing one impact in this segment from the population. Objective: to analyze the performance of primary care health professionals regarding health care practices health developed for the transgender population. Methods: this is in one search descriptive with approach qualitative, carried out in Family Health Units belonging to the Health Strategy of Family (ESF) at the County in Victory, Spirit Holy. Results: it was observed that participants had little or no knowledge about the applicability of public health policies for transgender people, highlighting the lack of preparation of health professionals, the pathologization of the transgender experience and, mainly, the lack of acceptance. Conclusion: there are numerous barriers in the production of health care for the LGBTQIA+ population that involve different dimensions, which permeate the entire training and management of care and health care in the context of Primary Care.

Peningkatan Kemampuan Guru Dalam Menulis Karya Ilmiah Melalui Kegiatan Pengabdian Pada Masyarakat

One aspect of teacher professionalism involves scientific article writing skills. The purpose of this community service program is to improve teachers' scientific writing skills and competencies. This community service is planned for three months, with implementation methods including 1) discussion with the community, and 2) writing practice with the community. The evaluation of the activity showed good success and in accordance with expectations, reflected in the enthusiasm of teachers in participating in the entire training, both in providing material to the community and direct practice in writing scientific papers. Before the training, only 7 teachers had the potential to write scientific papers well, but after doing this service activity, there were about 20 teachers who could compile or make scientific papers, and there were about 5 teachers who were still constrained. And the quality of the article is quite good.

GB-RVFL: Fusion of randomized neural network and granular ball computing

The random vector functional link (RVFL) network is a prominent classification model with strong generalization ability. However, RVFL treats all samples uniformly, ignoring whether they are pure or noisy, and its scalability is limited due to the need for inverting the entire training matrix. To address these issues, we propose granular ball RVFL (GB-RVFL) model, which uses granular balls (GBs) as inputs instead of training samples. This approach enhances scalability by requiring only the inverse of the matrix of GBs’ centers and improves robustness against noise and outliers through the coarse granularity of GBs. Furthermore, RVFL overlooks the dataset’s geometric structure. To address this, we propose graph embedding GB-RVFL (GE-GB-RVFL) model, which fuses granular computing and graph embedding (GE) to preserve the topological structure of GBs and enhances the interpretability of the proposed model. The proposed GB-RVFL and GE-GB-RVFL models are evaluated on KEEL, UCI, NDC and biomedical datasets such as Alzheimer’s disease diagnosis and breast cancer prediction. The experimental evaluation demonstrates that the proposed models outperform baseline models in efficacy, robustness, scalability, and interpretability. The source codes of the proposed GB-RVFL and GE-GB-RVFL models are available at https://github.com/mtanveer1/GB-RVFL.

STUDY CONCERNING THE IMPROVEMENT OF SPEED THROUGH BASKETBALL SPECIFIC TRAINING

Background. The major importance of physical education lessons in higher education is achieved through their contribution to maintaining health, to compensating for the increased intellectual efforts generated by passing exams. Basketball training helps in the positive development of the general and specific motor capacity required by the subsequent professional activity, as well as to the achievement of the necessary framework for recreation and the practice of the favorite sport. Therefore, at this level, the achievement of the following objectives will be mainly pursued: – maintaining health and increasing the ability to adapt to changing conditions; – the development of the general motor capacity necessary to carry out the sports activity; – improving the somato-functional indices and optimizing the relationship between them. Physical training is a component of sports training that constitutes the pivot for all other components, constituting the starting point for the entire training process (Ardelean. T, 1996). General physical training has different weightages in training, depending on the experience of the students and for the training of beginner students it is very important that the time allocated is much greater than in the training of students from older years. Objectives. This study aims to find and offer, through specific basketball exercises, how to improve the quality of speed. Speed should be present in the equipment of the "motric luggage" of each student, considering the evolution of our modern society, to dynamic games. This speed quality motor offers several advantages to basketball players: – allows the execution of thechnical and tactic actions in a sustained rhythm. – gives the player a high degree of complexity and dynamic adaptation to playing basketball. – develops the player's thinking, attention, rationality and ability to concentrate. Methods. For this study we used the following research methods: documentation, observation method, statistical method, graphical representation method and experimental method. Results. For checking the motility of students enrolled in educational experiment we chose the following control samples: speed running 15m, speed running 20m and dribbling through cones. Conclusion. The results of this test depend both on the execution speed and the technique in performing the dribbling. We therefore recommend that these results can be improved both by increasing the speed of movement and the execution technique.

Design of a turbo-based deep semantic autoencoder for marine Internet of Things

With the rapid growth of the global marine economy and flourishing maritime activities, the marine Internet of Things (IoT) is gaining unprecedented momentum. However, current marine equipment is deficient in data transmission efficiency and semantic comprehension. To address these issues, this paper proposes a novel End-to-End (E2E) coding scheme, namely the Turbo-based Deep Semantic Autoencoder (Turbo-DSA). The Turbo-DSA achieves joint source-channel coding at the semantic level through the E2E design of transmitter and receiver, while learning to adapt to environment changes. The semantic encoder and decoder are composed of transformer technology, which efficiently converts messages into semantic vectors. These vectors are dynamically adjusted during neural network training according to channel characteristics and background knowledge base. The Turbo structure further enhances the semantic vectors. Specifically, the channel encoder utilizes Turbo structure to separate semantic vectors, ensuring precise transmission of meaning, while the channel decoder employs Turbo iterative decoding to optimize the representation of semantic vectors. This deep integration of the transformer and Turbo structure is ensured by the design of the objective function, semantic extraction, and the entire training process. Compared with traditional Turbo coding techniques, the Turbo-DSA shows a faster convergence speed, thanks to its efficient processing of semantic vectors. Simulation results demonstrate that the Turbo-DSA surpasses existing benchmarks in key performance indicators, such as bilingual evaluation understudy scores and sentence similarity. This is particularly evident under low signal-to-noise ratio conditions, where it shows superior text semantic transmission efficiency and adaptability to variable marine channel environments.

SAR-driven flood inventory and multi-factor ensemble susceptibility modelling using machine learning frameworks

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks.

THE POTENTIAL OF DIGITAL TECHNOLOGIES IN IMPROVING THE PROCESS OF TRAINING AND RETRAINING OF MILITARY DOCTORS

The issue of improving the process of training and retraining of military doctors is determined by changes in the security environment of the state today, and the need to develop the military medical sector in the conditions of long-term resistance to russian aggression. This article highlights the potential of using digital technologies to improve the process of training and retraining of military doctors. The results of a survey of trainees at the Ukrainian Military Medical Academy (a higher military medical educational institution that provides training, retraining and advanced training of specialists in almost all specialties for the Armed Forces of Ukraine and other military establishment of Ukraine) are presented. It is shown that for the implementation of changes in the system of training and retraining of military doctors the Academy provides for a number of measures: introducing innovative changes in the content and methodology of education; introduction of flexible technology for planning and organizing the educational process, focused on personalization of learning; development of international scientific and scientific-medical collaborations. Based on the conducted research, it was determined that in the conditions of active hostilities in Ukraine, it is digital technologies that make it possible to quickly introduce changes in the process of training and retraining of military doctors by improving the educational and informational environment. It was established that improving the readiness of military doctors to use digital technologies in their professional activities requires their end-to-end use in the entire training system. The article provides examples of the use of digital technologies in the process of training and retraining of military doctors.

Physics-guided neural network-based framework for 3D modeling of slope stability

Physics-informed neural networks (PINN) have gradually attracted attention in the field of geotechnical engineering. This paper proposes a novel PINN-based framework for the three-dimensional (3D) stability analysis of soil slopes. Based on the fundamental theorem of plasticity and limit analysis, the partial differential equations (PDE) with regard to slope collapse are derived and integrated into the physics-guided loss function. A kinematically admissible failure mechanism that rigorously satisfies the Mohr-Coulomb associated flow rule is obtained by minimizing the loss function, thereby circumventing complex mathematical calculations. The discrete points generated by PINN are selected and refined through a series of procedures to represent the failure block of slopes using a two-dimensional matrix. The entire training process of the PINN-based framework is conducted without the need for any labeled data. The resulting discretized failure mechanism is meshfree and capable of accommodating spatially discrete data. A validation exercise is performed to verify the proposed framework by comparing it with the classical 3D rotational failure mechanism. To further consider the impact of external excitation on slope stability, a hybrid PINN framework is developed to assess the stability of slopes subjected to complex external environments. In addition to the PINN to generate a failure mechanism, a parallel PINN is employed to acquire the corresponding spatially discrete data of specified external excitations. The hybrid PINN framework for seismic stability assessment of slopes is demonstrated by way of example, indicating favorable feasibility and applicability of the developed approach. The proposed PINN-based framework provides innovative and promising avenues for 3D slope stability analysis.

Development and validation of a novel liquid-liquid phase separation gene signature for bladder cancer

Bladder carcinoma (BLCA) represents a common urinary tract malignancy, characterized by aggressive behavior and high recurrence rates. The biological response regulation during tumor proliferation and metastasis is intimately associated with liquid-liquid phase separation (LLPS). For the purpose of enhancing early detection and treatment, this study employed transcriptomic data to examine the prognostic implications of LLPS-associated genes and formulate a predictive model. Clinical and transcriptomic data of bladder cancer patients were sourced from the GEO and TCGA databases. This study applied a clustering algorithm using non-negative matrix factorization (NMF) to classify samples, which were systematically compared based on their liquid-liquid phase separation characteristics. Prognostic models were developed using multivariate Cox regression and the Least Absolute Shrinkage Selection Operator (LASSO) algorithm to establish risk formulas for nine genes. The gene signature’s validity was tested across the entire TCGA cohort (406 cases), the TCGA testing cohort (120 cases), and the external validation dataset GSE13507. The predictive accuracy of the signature system was assessed using receiver operating characteristic (ROC) and Kaplan-Meier curves. Additionally, decision curve analysis incorporating clinicopathological parameters and the genetic signature was employed to predict individual survival. This study identified two distinct molecular subtypes, C1 and C2. Patients with the C1 subtype exhibited significantly better prognoses than those with the C2 subtype. Low-risk group patients consistently showed superior prognoses compared to high-risk groups across the entire TCGA, GEO, and TCGA training cohorts. Furthermore, the LLPS-related gene model demonstrated prognostic value independent of other clinical traits. This study identifies LLPS-associated gene clusters and establishes an independent, accurate prognostic model for BLCA. The model holds potential for clinical application in BLCA prognosis assessment.

Nekateri mehanizmi razvijanja bralne pismenosti na področju vzgoje in izobraževanja

This article highlights the issue of developing reading literacy in connection with revising the curriculum for kindergartens and curricula across the entire education and training vertical. It draws attention to the role of all professionals in kindergartens and schools in developing reading literacy. It also emphasizes that reading literacy is influenced by the way literacy is taught, quality teaching materials, the motivation of students for reading and appropriate strategies for working with students.

An integrated machine learning model enhances delayed graft function prediction in pediatric renal transplantation from deceased donors

BackgroundKidney transplantation is the optimal renal replacement therapy for children with end-stage renal disease; however, delayed graft function (DGF), a common post-operative complication, may negatively impact the long-term outcomes of both the graft and the pediatric recipient. However, there is limited research on DGF in pediatric kidney transplant recipients. This study aims to develop a predictive model for the risk of DGF occurrence after pediatric kidney transplantation by integrating donor and recipient characteristics and utilizing machine learning algorithms, ultimately providing guidance for clinical decision-making.MethodsThis single-center retrospective cohort study includes all recipients under 18 years of age who underwent single-donor kidney transplantation at our hospital between 2016 and 2023, along with their corresponding donors. Demographic, clinical, and laboratory examination data were collected from both donors and recipients. Univariate logistic regression models and differential analysis were employed to identify features associated with DGF. Subsequently, a risk score for predicting DGF occurrence (DGF-RS) was constructed based on machine learning combinations. Model performance was evaluated using the receiver operating characteristic curves, decision curve analysis (DCA), and other methods.ResultsThe study included a total of 140 pediatric kidney transplant recipients, among whom 37 (26.4%) developed DGF. Univariate analysis revealed that high-density lipoprotein cholesterol (HDLC), donor after circulatory death (DCD), warm ischemia time (WIT), cold ischemia time (CIT), gender match, and donor creatinine were significantly associated with DGF (P < 0.05). Based on these six features, the random forest model (mtry = 5, 75%p) exhibited the best predictive performance among 97 machine learning models, with the area under the curve values reaching 0.983, 1, and 0.905 for the entire cohort, training set, and validation set, respectively. This model significantly outperformed single indicators. The DCA curve confirmed the clinical utility of this model.ConclusionsIn this study, we developed a machine learning-based predictive model for DGF following pediatric kidney transplantation, termed DGF-RS, which integrates both donor and recipient characteristics. The model demonstrated excellent predictive accuracy and provides essential guidance for clinical decision-making. These findings contribute to our understanding of the pathogenesis of DGF.

Preparing Students for the 2024 Professionals Championship

Professional skill championships are one of the forms of assessment of professional competencies. The assessment of professional competencies is a matter of interest for employers. The requirements for a modern specialist are very high, these are: the ability to adapt to changing working conditions, have the necessary set of professional competencies, be able to navigate non-standard situations and make important decisions. To increase the effectiveness of training and improve results, it is necessary to systematize the entire training process.

Continuous fake media detection: Adapting deepfake detectors to new generative techniques

Generative techniques continue to evolve at an impressively high rate, driven by the hype about these technologies. This rapid advancement severely limits the application of deepfake detectors, which, despite numerous efforts by the scientific community, struggle to achieve sufficiently robust performance against the ever-changing content. To address these limitations, in this paper, we propose an analysis of two continuous learning techniques on a Short and a Long sequence of fake media. Both sequences include a complex and heterogeneous range of deepfakes (generated images and videos) from GANs, computer graphics techniques, and unknown sources. Our experiments show that continual learning could be important in mitigating the need for generalizability. In fact, we show that, although with some limitations, continual learning methods help to maintain good performance across the entire training sequence. For these techniques to work in a sufficiently robust way, however, it is necessary that the tasks in the sequence share similarities. In fact, according to our experiments, the order and similarity of the tasks can affect the performance of the models over time. To address this problem, we show that it is possible to group tasks based on their similarity. This small measure allows for a significant improvement even in longer sequences. This result suggests that continual techniques can be combined with the most promising detection methods, allowing them to catch up with the latest generative techniques. In addition to this, we propose an overview of how this learning approach can be integrated into a deepfake detection pipeline for continuous integration and continuous deployment (CI/CD). This allows you to keep track of different funds, such as social networks, new generative tools, or third-party datasets, and through the integration of continuous learning, allows constant maintenance of the detectors.

Domain knowledge-driven encoder–decoder for nasopharyngeal carcinoma segmentation

Accurate image segmentation is vital for the diagnosis and treatment of nasopharyngeal carcinoma (NPC). Although deep neural networks have shown promising performance in NPC segmentation, they depend on large-scale pixel-level annotation datasets for purely data-driven model training, which poses practical limitations. To address this challenge, this paper explores the domain knowledge, namely the expert knowledge from radiologists, and proposes a domain knowledge-driven encoder–decoder architecture. Specifically, the domain knowledge on image spatial information is formulated as Gaussian mixture distribution and then transformed into an optimal transport-based expert-prior regularization of the encoder, which enhances the model’s ability in capturing discriminative features. To project the encoded features onto pixel space and obtain the segmentation maps, a cross-scale feature refinement module is built in the decoder with theoretical justification, which integrates the domain knowledge that radiologists segment NPC in a gradual refinement process. Experimental results verify the effectiveness of the proposed method for NPC segmentation. Remarkably, despite using only 10% of pixel-level annotation data, the domain knowledge-driven model outperforms recent deep neural networks that use the entire training data.

Weighting non-IID batches for out-of-distribution detection

A standard network pretrained on in-distribution (ID) samples could make high-confidence predictions on out-of-distribution (OOD) samples, leaving the possibility of failing to distinguish ID and OOD samples in the test phase. To address this over-confidence issue, the existing methods improve the OOD sensitivity from modeling perspectives, i.e., retraining it by modifying training processes or objective functions. In contrast, this paper proposes a simple but effective method, namely Weighted Non-IID Batching (WNB), by adjusting batch weights. WNB builds on a key observation: increasing the batch size can improve the OOD detection performance. This is because a smaller batch size may make its batch samples more likely to be treated as non-IID from the assumed ID, i.e., associated with an OOD. This causes a network to provide high-confidence predictions for all samples from the OOD. Accordingly, WNB applies a weight function to weight each batch according to the discrepancy between batch samples and the entire training ID dataset. Specifically, the weight function is derived by minimizing the generalization error bound. It ensures that the weight function assigns larger weights to batches with smaller discrepancies and makes a trade-off between ID classification and OOD detection performance. Experimental results show that incorporating WNB into state-of-the-art OOD detection methods can further improve their performance.

Diagnostic Evaluation of the Contribution of Complementary Training Subjects in the Self-Perception of Competencies in Ethics, Social Responsibility, and Sustainability in Engineering Students

Higher education institutions, as organizations that transform society, have a responsibility to contribute to the construction of a sustainable and resilient world that is aware of the collateral effects of technological advances. This is the initial phase of a research that aims to determine whether subjects in the complementary training area have a significant effect on ethical, social responsibility, and sustainability (ERS) competencies in engineering students at a public higher education institution (HEI). To this end, a quantitative approach was used, adopting a descriptive comparative cross-sectional design and applying a self-perception instrument to a population of 418 students in the first and last semesters of engineering undergraduate programs. As a result, it was found that students that took subjects in the complementary area did not show significant development in social responsibility and sustainability competencies but did in ethical dimensions. Furthermore, in the triad of ERS competencies, there was a significant difference in students over 36 years of age, suggesting that time and academic experience contribute to a greater understanding and application of ethical and moral principles. These results support the idea that ethics and social responsibility are the pillars of sustainability. The findings highlight the importance of intervening in these subjects and complementing them with educational strategies that promote the continuous development of ERS competencies throughout the entire training itinerary, not only in complementary subjects, but as cross-cutting components of all specific engineering discipline subjects.

Leitura e escrita acadêmica na formação inicial de professores: Que letramentos?

From a historical perspective on the initial training of teachers in Brazil, a survey of research developed was carried out, locating in articles published in the last two decades the modes of functioning of the school institution, in the continuous movement of learning-teaching-learning reading and academic writing. Historically, Brazil has presented many difficulties in universalizing the learning of reading and writing in a socially significant way. In the direction of the formation of the critical citizen, we discuss the difficulties to make academic reading and writing effective as processes of forming and transforming the condition of citizenship of the Brazilian population. Proceeding with a qualitative approach, the articulation between the lines of investigation of the selected studies, which focused on teacher training, teaching work and the construction of a teacher's professional identity, were analyzed in the defense that being a teacher is to develop a work based on a solid specific training and that initial training courses should also be concerned with teacher professionalization (Nóvoa, 2017). In the case of initial training, it should allow students to go through teaching and learning experiences that are based on the use and reflection of the characteristics of academic writing, so that future teachers know how to deal with the prescriptions inherent to their work context and document their experiences, as the written record becomes part of a routine developed since initial training when the triad reading is adopted, analysis and production of academic texts as a formative and evaluative principle. In this sense, the analyses carried out reveal that academic literacy should permeate the entire initial training of teachers, with objective prescriptions, considering the autonomy of the trainer to plan and replan his classes coherently with his didactic-methodological choices (Mizukami, 2005-2006; 2000), in a political and pedagogical project committed to an education linked to more assertive teaching practices (Gatti, 2014; André et. al., 2010), with academic writing as one of the constitutive elements of a socially referenced quality education.