Training Issues Research Articles

The level of preparedness of military personnel for hand-to-hand combat

Abstract. In modern conditions, the military profession does not exclude activities related to the forcible detention of a criminal or aggressor. A serviceman who is proficient in hand-to-hand combat often achieves superiority over a physically stronger enemy who outnumbers him. In this case, the defenders use motor actions that, if properly directed and applied by internal and external forces, provide either a gain in strength or a gain in time. Naturally, in the process of special physical training (SPT) the issue of effective training of hand-to-hand combat techniques plays an important role in achieving a positive result in the performance of the task.
 The existing practice of special physical training in higher military educational institutions of the Ministry of Defense of Ukraine shows that today, in the process of cadets' SPT, the rich material accumulated by specialists in the theory and methods of physical education and sports training is not yet fully used.
 The introduction of the latest technologies based on the use of hand-to-hand combat requires specialists to search for and apply innovative teaching methods and technologies, identify the most effective areas of the educational process that contribute to the achievement of a high professional level of students.

Rethinking copyright exceptions in the era of generative AI: Balancing innovation and intellectual property protection

AbstractGenerative artificial intelligence (AI) systems, together with text and data mining (TDM), introduce complex challenges at the junction of data utilization and copyright laws. The inherent reliance of AI on large quantities of data, often encompassing copyrighted materials, results in multifaceted legal quandaries. Issues surface from the unfeasible task of securing permission from each copyright holder for AI training, further muddled by ambiguities in interpreting copyright laws and fair use provisions. Adding to the conundrum, the clandestine practices of data collection in proprietary AI systems obstruct copyright owners from detecting unauthorized use of their materials. The paper explores the exceptions to copyright laws for TDM in the European Union, the United Kingdom, and Japan, recognizing their crucial role in fostering AI development. The EU has a two‐pronged approach under the Directive on Copyright in the Digital Single Market, with one exception catering specifically to research organizations, and another, more generalized one, that can be restricted by rightsholders. The UK allows noncommercial TDM research without infringement but rejected a broader copyright exception due to concerns from the creative sector. Japan has the broadest TDM exception globally, permitting the nonenjoyment use of works without permission, though this can potentially overlook the rights of copyright owners. Notably, the applicability of TDM exceptions to AI‐produced copies remains unclear, creating potential legal challenges. Furthermore, an exploration of the fair use doctrine in the United States provides insight into its potential application in AI development. It focuses on the transformative aspect of usage and its impact on the original work's potential market. This exploration underscores the necessity for clear, practical guidelines. In response to these identified challenges, this paper proposes a hybrid model for TDM exceptions emerges, along with recommended specific mechanisms. The model divides exceptions into noncommercial and commercial uses, providing a nuanced solution to complex copyright issues in AI training. Recommendations incorporate mandatory exceptions for noncommercial uses, an opt‐out clause for commercial uses, enhanced transparency measures, and a searchable portal for copyright owners. In conclusion, striking a delicate equilibrium between technological progress and the incentive for creative expression is of paramount importance. These suggested solutions aim to establish a harmonious foundation that nurtures innovation and creativity while honoring creators' rights, facilitating AI development, promoting transparency, and ensuring fair compensation for creators.

A Method for Analyzing the Operating Data of Electric Energy Meters Based on Data Mining Analysis

Aiming at the problem of error estimation of smart meters in distribution network, a method of error estimation of smart meters based on particle swarm optimization convolutional neural network is proposed. This method establishes an intelligent energy meter error estimation model through data collection, data prediction, and preprocessing. To address the convergence issue in training, the interlayer distribution of weights is adjusted to improve training quality. This method fully utilizes template calibration information to transform indicator detection under complex conditions into simple and effective isometric segmentation, transforming label recognition from complex text detection and recognition tasks to simple and efficient binary detection tasks, with better robustness. The effectiveness and high robustness of the proposed method have been demonstrated through experimental verification.

Opportunities of digital health technologies for rheumatology: from clinics to quality and research

Recent developments in digital health technologies are overwhelming, and their use in routine work is still difficult to anticipate. This narrative review summarizes the concept of consecutive cohorts in the literature, together with local research experiences in consecutive rheumatic outpatients. Digital health techniques have to reflect the clinicians’ needs, support real-life care of patients, and allow for the specific assessment of quality parameters fulfilling the Donabedian aspect of qualified health care, using quality indicators to improve health care and research. Rapidly growing observational cohorts will perform best to provide follow-up data as the basis for further development of healthcare approaches for rheumatic patients. The challenges of a selection bias, patients with limited disease expression, and chances of early detection of patients with rare diseases are addressed. For research purposes, sequential analyses with growing cohort size, comparative cross-sectional studies with sequential hypothesis testing and other prognostic, diagnostic, and therapeutic aspects of patient management can be performed. With the support of new technologies, young clinicians can easily approach such clinical topics, and learn about clinical data analyses. The use of quality standards as proposed in international recommendations for diagnostic issues and classification criteria, management recommendations, monitoring, and training issues can be supported by digital technologies. In conclusion, collaborative projects allow detailed clinical analyses of large cohorts, but local initiatives can prepare these co-operations, provide first local logistics and research experiences, and teach clinicians how to perform clinical research. Digital health technologies will strongly support these local initiatives.

Possibilities of using virtual reality technology in skills development

With changing threats and rapid technological progress, society is facing significant changes and challenges. Ensuring that people have the skills and capabilities to meet these challenges is key. One ‘tool’ for this could be the use of virtual reality (VR). The VR research community is becoming increasingly active in the search for solutions. In this article, we have tried to summarize the concepts related to VR education and training, followed by the challenges of using VR technology and the solutions already implemented through examples from the US, France and China. We then concluded the article by outlining possible future concepts that will need to be developed to implement VR training, with a particular focus on the issues of military training

Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer

Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS’s ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.

Breastfeeding in primary health care: weaknesses and potentialities of nursing care

Objective: To identify the weaknesses and potentialities of nursing care for breastfeeding in Primary Health Care (PHC). Methods: A qualitative study developed through workshops held in September 2021 with 43 nursing professionals. Results: Two categories emerged: “Weaknesses of breastfeeding nursing care in PHC” and “Potentialities of breastfeeding nursing care in PHC”. Weaknesses permeate the discourse, which is often not in line with the reality of the postpartum period, lack of theoretical and practical knowledge, cultural influences, short maternity and paternity leave, shortage of professionals, absence of meetings, inappropriate physical space, limited time for care, failure to address the issue and lack of training. Presented as potentialities are the availability of professionals for guidance in different environments, the work of community health agents, the existence of the Women’s, Children’s and Adolescents’ Health Center, the Municipal Breastfeeding Support Network, the use of technology and encouraging the participation of partners from prenatal to postpartum. Conclusion: It is necessary to consider the weaknesses identified in order to develop improvement actions focused on increasing breastfeeding indices. As for the strengths, they reflect successful practices that can support and guide other nursing services.

I-FENN with Temporal Convolutional Networks: Expediting the load-history analysis of non-local gradient damage propagation

In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors [1,2], can efficiently simulate the entire loading history of non-local gradient damage propagation. To achieve this goal, we first adopt a Temporal Convolutional Network (TCN) as the neural network of choice to capture the history-dependent evolution of the non-local strain in a coarsely meshed domain. The quality of the network predictions governs the computational performance of I-FENN, and therefore we perform an extended investigation aimed at enhancing them. We explore a data-driven vs. physics-informed TCN setup to arrive at an optimum network training, evaluating the network based on a coherent set of relevant performance metrics. We address the crucial issue of training a physics-informed network with input data that span vastly different length scales by proposing a systematic way of input normalization and output un-normalization. We then integrate the trained TCN within the nonlinear iterative FEM solver and apply I-FENN to simulate the damage propagation analysis. I-FENN is always applied in mesh idealizations different from the one used for the TCN training, showcasing the framework’s ability to be used at progressively refined mesh resolutions. We illustrate several cases that I-FENN completes the simulation using either a modified or a full Newton–Raphson scheme, and we showcase its computational savings compared to both the classical monolithic and staggered FEM solvers. We underline that we satisfy very strict convergence criteria for every increment across the entire simulation, providing clear evidence of the robustness and accuracy of I-FENN. All the code and data used in this work will be made publicly available upon publication of the article.

АНАЛІЗ СТАНУ СВІТОВОГО РИНКУ ПРАЦІ В УМОВАХ ГЛОБАЛЬНОЇ ДІДЖИТАЛІЗАЦІЇ

In the context of the Fourth Industrial Revolution, there is a continuous transformation of processes in the labor market. Digitalization of society affects the structure of the employment sector, when some professions become obsolete due to automation of work processes. At the same time, there is a demand for new competencies aimed at working in an online environment. The purpose of this article is to analyze current global economic and technological trends affecting the transformation of the labor market and the formation of new socio-economic relations in the context of the formation of the digital economy. The author draws attention to changes in the nature of work and forms of work activity, requirements for professional skills and abilities. It shows how changes related to the digitalization of the economy directly affect the issues of training and retraining of the most popular specialists. The article systematizes the main global trends in the labor market caused by the digitalization of the economy and the widespread introduction of Information Computer Technologies. In the course of the research, the author revealed that digitalization of the economy has the opposite impact on labor markets, leading to multidirectional processes in the medium term, which is expressed in the reduction of low-skilled jobs, an increase in the level of robotization of production, differentiation of forms of Organization of production, revision of types of economic relations. As a result of digital transformation in industry, it became possible to produce products with higher quality, and the human factor became less significant in many production and technology sectors. The negative consequences of digitalization include the disappearance of a large number of professions, the release of workers, and an increase in unemployment. The author concludes that digital inequality in regional labor markets, with its further development, may negatively affect ensuring the country's economic sovereignty. At the same time, the results of the study suggest that no automation can completely replace a person, so forecasting total unemployment due to the robotization of production is temporary.

Digital Competence Among the Key Components of Future Masters of Primary Education Professional Training: Theoretical Interpretations

The article draws attention to the issue of professional training of future masters of primary education and the role of digital competence in it. It involves addressing various challenges and considerations to ensure that educators are well-equipped to meet the diverse needs of primary school students. The author outlines several aspects in this direction: innovative pedagogical knowledge and skills, active technology integration, proper classroom management, appropriate assessment and evaluation, and professional development. It is proved that integration of technology into the classroom results in digital competence improvement of future educators. Numerous interpretations of digital competence by Ukrainian and foreign researchers are presented. The international scientific findings proved, the concept of “information and communication competence” is not limited to technological or digital spheres only, on the contrary, it is transformed in a wider context and applied in almost all spheres of an individual’s life. In the context of digitalization of the education system, it is assured that the possession of digital competence aims at purposeful and independent use of ICT tools in educational activities, as well as in the process of methodical and research work; maximum use of the potential of digital technologies in the process of identifying and solving pedagogical tasks. The experience of formation of digital competence of future masters of primary education within their professional training at PNU is presented n the article.

Оптимизация процесса физической подготовки курсантов Министерства внутренних дел Российской Федерации

Introduction. The specificity of tasks set for law enforcement agencies is characterised by excessive psychophysical tension. These strains increase fatigue, reduce mental and physical efficiency and, as a result, lead to decrease in the effectiveness of activity. Physical training is important for employees of all law enforcement agencies. Thus, physical training of employees of the Ministry of Internal Affairs of Russia in the process of professional activity contributes to the formation of special motor skills, improvement of the most important mental and physical qualities. One of the most significant components of professional training is to ensure the necessary level of psychophysical competency. The relevance of the current research is due to the shortcomings in the organisation of physical training for cadets of educational organisations of the Ministry of Internal Affairs of Russia. The experience of law enforcement activity shows that the lack of understanding of the importance of physical training of personnel often leads to injuries, wounds and deaths of police officers while performing their official tasks. It is important to identify the most significant problems and ways to solve them to optimise the physical training of future employees of the Ministry of Internal Affairs of Russia. Research methods. Analysis of normative legal acts regulating the issues of organisation of physical training for cadets of educational organisations of the Ministry of Internal Affairs of Russia, survey of specialists, research and synthesis of data of special scientific and methodological literature, monitoring of information resources of the Internet. Results. The research deals with the legislative regulation of the issues of physical training of cadets of educational organisations of the Ministry of Internal Affairs of Russia, reflects the views of scientists on the current problems of physical training of employees of the Ministry of Internal Affairs of Russia, analyses the indicators of their physical competence. On the basis of the research, specific measures to solve the problems of physical training of cadets of educational organisations of the Ministry of Internal Affairs of Russia are proposed.

Features of the development of speed skills in football players

This article examines the issues of speed training, training speed in football players aimed at increasing running speed and "transferring" this speed to running with the ball. This process of "transferring" speed abilities is more effective when an athlete in normal running emphasizes the frequency of movements, but not the length of running steps, that is, he overcomes training segments with slightly shortened steps, which in their rhythmic structure are similar to steps in running with m' cell. Speed training falls, in fact, on the pubertal phase of the body development of young athletes, when puberty is accompanied by a rapid increase in muscle mass, glycogen reserves, and an increase in the level of secretion of adrenaline and gonadal hormones. At this time, optimal biological prerequisites are created for the development of anaerobic capabilities, maximal strength, power endurance, and speed-power qualities. The volume of loads should increase at the fastest pace in the development of strength endurance, which reaches its maximum values by the end of the stage. Gradually, exercises with maximum efforts, performed in overcoming, yielding and isokinetic modes, are included in the training.
 Speed abilities are very difficult to develop. The possibility of speed increase in locomotor cyclic acts is very limited. In the process of sports training, an increase in the speed of movements is achieved both by influencing the actual speed abilities, and in another way - through the development of strength and speed-strength abilities, speed endurance, improvement of the technique of movements, etc., i.e. improvement of those factors that significantly depend on the manifestation of certain qualities of speed. Numerous studies have shown that the aforementioned types of speed abilities are specific. Direct positive transfer of speed takes place only in movements that have similar semantic and programming aspects, as well as motor composition. The specific features of speed abilities are noted, so they require the use of appropriate training tools and methods for each of their varieties.

A closer look at reinforcement learning-based automatic speech recognition

Reinforcement learning (RL) has demonstrated effectiveness in improving model performance and robustness for automatic speech recognition (ASR) tasks. Researchers have employed RL-based training strategies to enhance performance beyond conventional supervised or semi-supervised learning. However, existing approaches treat RL as a supplementary tool, leaving the untapped potential of RL training largely unexplored. In this paper, we formulate a novel pure RL setting where an ASR model is trained exclusively through RL via human feedback metrics, e.g., word error rate (WER) or binary reward. This approach promises to significantly simplify the annotation process if we could replace the conventional onerous annotation with a single numeric value in a human–computer interaction (HCI) way. Our experiments demonstrate the feasibility of this new setting and also identify two main inherent issues in conventional RL-based ASR training that may lead to performance degradation: (1) the mismatch issue between the action and reward has been commonly overlooked in Connectionist Temporal Classification (CTC) based models, which is attributed to the inherent CTC alignment mapping issue; (2) the classic exploration–exploitation trade-off still exists in the sampling stage of RL-based ASR, and finding the balance between them becomes a challenge. To address these issues, we first propose a new RL-based approach named CTC-aligned Policy Gradient (CTC-PG), which provides a unified formulation for different sampling strategies and alleviates the mismatch issue of action and reward in CTC-based models. Moreover, we propose Focal Sampling to balance the trade-off between exploration and exploitation with a flexible temperature parameter. Experiment results on LibriSpeech dataset showcase the effectiveness and robustness of our methods by harnessing the full potential of RL in training ASR models.

DiffGAN: An adversarial diffusion model with local transformer for MRI reconstruction

Magnetic resonance imaging (MRI) is non-invasive and crucial for clinical diagnosis, but it has long acquisition time and aliasing artifacts. Accelerated imaging techniques can effectively reduce the scanning time of MRI, thereby decreasing the anxiety and discomfort of patients. Vision Transformer (ViT) based methods have greatly improved MRI image reconstruction, but their computational complexity and memory requirements for the self-attention mechanism grow quadratically with image resolution, which limits their use for high resolution images. In addition, the current generative adversarial networks in MRI reconstruction are difficult to train stably. To address these problems, we propose a Local Vision Transformer (LVT) based adversarial Diffusion model (Diff-GAN) for accelerating MRI reconstruction. We employ a generative adversarial network (GAN) as the reverse diffusion model to enable large diffusion steps. In the forward diffusion module, we use a diffusion process to generate Gaussian mixture distribution noise, which mitigates the gradient vanishing issue in GAN training. This network leverages the LVT module with the local self-attention, which can capture high-quality local features and detailed information. We evaluate our method on four datasets: IXI, MICCAI 2013, MRNet and FastMRI, and demonstrate that Diff-GAN can outperform several state-of-the-art GAN-based methods for MRI reconstruction.

History, a compulsory component of basic education in South Africa from 2023 – Are South African archivists ready?

History helps society understand its past and its influence on the present and future. South Africa is a country with a unique history, comprising of varying accounts from the different people that make up its ‘rainbow’ nation. In 2018, South Africa’s Department of Basic Education; announced that history would be a compulsory subject for learners from Grades 10–12 starting from 2023. Changes would include a revised curriculum, teacher training and other issues. Expectedly archives would also be factored into this transformation process, as they are integral to history. Therefore, this study investigated the involvement and readiness of archivists in South African public archival repositories to support this development. Nineteen (19) archivists from the National Archives and Records Service of South Africa and the nine (9) Provincial Archives participated in an online survey. The findings indicate that there was minimal involvement of archivists. Nevertheless, the archivists were confident that they had the content and skills, to some extent, to support the revised curriculum. The study recommends that archivists be more proactive in raising awareness about the archives and linking various stakeholders, such as the Department of Basic Education, with these valuable records.

Models of practice and training in psychotherapy: cross-national perspectives from Italy and Canada.

Internationally, there is ongoing concern about accessibility to mental health care and training. The goal of this study was to explore commonalities and differences within models of clinical psychology and psychotherapy in Ontario, Canada, and Lombardia, Italy, respectively, to inform improvements to the accessibility of mental health care and training. Using key informant sampling, we recruited ten students and professionals in Italy and Canada who study or work in psychology for semi-structured interviews. We analyzed the interview content using an inductive approach for thematic analysis within countries and meta-theme analysis across countries. The findings indicated three cross-national meta-themes: the need to integrate evidence with practice, the limited accessibility of training for students and treatment for patients, and the importance of the quality of training programs. Despite some differences regarding the amount of scientific training, personal therapy for trainees, and the prominence of cultural diversity training, Canadian and Italian psychology professionals and students shared experiences of psychotherapy practice and clinical psychology training. The three cross-national meta-themes indicate which issues in training and practice may be relevant worldwide and where to focus resources. The findings can inform international collaborations regarding training model structures that may increase access to psychology training and may increase consensus on professional recognition standards to improve mobility for professionals. These changes could reduce barriers to mental healthcare services for patients.

The Usefulness of the Telegram App in Learning English as a Foreign Language: Teachers and Students Voices

The study attempted to investigate the effectiveness of the Telegram application (app) in learning English as a foreign language for undergraduate EFL learners. The study looked at how Telegram app constraints EFL learners experience when utilizing Telegram in learning a foreign language. This research, using a descriptive-diagnostic research approach, used a questionnaire and semi-structured interviews to collect the data from 40 students and 20 teachers at Najran University which were chosen through a stratified sampling technique. The results revealed that both teachers and students think that Telegram can highly improve learning English as a foreign language. The results also showed the participant (student, teacher) played a role in varying the study sample's responses in favor of teachers. The findings of the interview show that many students struggle to learn the English language through Telegram due to constraints like device compatibility, health issues, lack of motivation, loneliness, distraction, and difficulty in uploading and downloading files, often unable to receive feedback or academic assistance due to technical staff and training issues. The results of this study have implications for teaching and learning English that can motivate both instructors and students to use social media-based tools.

Research and Practice on the Reform of Practical Training for Field Engineers in Mold Design and Manufacturing Majors in Higher Vocational Education

This article aims to study and explore the reform issues of practical training for on-site engineers majoring in mold design and manufacturing in vocational colleges. By analyzing the relevant background and needs, an innovative practical training plan is proposed, and the effectiveness of this plan in practical application is explored. This practical training program has positive significance in improving students practical operation ability, meeting enterprise needs, and promoting industry university cooperation. This study provides certain reference value for the practical training of on-site engineers in the field of mold design and manufacturing in vocational colleges.

A multi-residency elective to address well-being as a systems issue

What was the educational challenge? Burnout is a well-established issue in medical training, but optimal systems approaches for well-being and ways to engage residents in well-being work are unknown. What was the solution? The authors developed a multi-residency well-being elective with participants from internal medicine, anesthesiology, and urology residency programs. The elective included an asynchronous learning curriculum, a mentored independent project on system drivers of well-being, and participation in a cross-residency group that set elective priorities. How was the solution implemented? The authors worked with each residency’s leadership to protect time for participation. Concepts from Quality Improvement were used to structure the elective. Project work and resident participation were assessed continually to monitor engagement. What lessons were learned that are relevant to a wider global audience? Projects led to short- and long-term changes to support well-being in residency programs. Creating opportunities for residents from different specialties to discuss well-being work allowed ideas to spread across residencies. Protecting time to work on well-being issues may enhance a culture of well-being by demonstrating commitment to well-being as a priority equivalent to other educational endeavors. What are next steps? Further research is needed to assess the impact on resident participants and to understand how to optimally incorporate resident interventions into broader organizational strategies for well-being.

Attitudes of Greek Foreign Language Teachers Regarding the Role of Authentic Assessment in Foreign Language Teaching

The evaluation of student performance, which is closely tied to all school functions and is a fundamental responsibility of teachers in their role as educators, has been a challenging and contentious issue in educational policy and practice in recent years. It plays a crucial role in the educational process, as it is essential for fostering creativity and human development within the school environment. Between traditional assessment based mainly on written and oral measurable tests and authentic assessment that highlights the role of the pupil through the self-evaluation or other techniques such as the materials folder, teachers are called upon to undertake a difficult task. In this article the results of a qualitative research in relation to the role of authentic assessment in foreign language teaching that took place in the Attica Region in Greece from March 10 to April 10, 2022 were presented. A total of 20 foreign language teachers (English, French, German) participated in semi-structured interviews and expressed their views on the relevant questions. The research problem posed in this study was to investigate the views and attitudes of foreign language teachers (English, French, German) regarding the framework of authentic assessment in foreign language teaching. The survey asked teachers to share their opinions on the role of authentic assessment in foreign language teaching, their preferred assessment criteria, the use of alternative forms of assessment, and their satisfaction with their training in authentic assessment. In general, it emerged that permanent secondary school teachers with many years of service prefer traditional forms of assessment and choose techniques that result in measurable criteria. In contrast, younger contractual teachers are more receptive to alternative forms of evaluation. However, no significant difference was observed on the issue of training. This survey aimed to gather language teachers' perspectives on authentic assessment in order to enhance textbooks and training programs.