Training Methodology Research Articles

Enhancing patient safety and risk management through clinical pathways in oncology

The increasing emphasis on risk management and quality assessment in hospital management necessitates strict adherence to government and international standards. Those standards are particularly important, and certification requirements further underscore...

Certificate program "Readiness, response and management of emergency situations" as a component in the formation of competencies for public health specialists

In the field of public health, the acquiring of relevant knowledge, skills and competencies for managing emergencies as well as the problem of effective response to them, and how to mitigate their consequences within the healthcare system are the crucial issues. The research employs bibliosemantic, informational, analytical, medical, statistical, sociological methods, method of content analysis, as well as surveys of practitioners from the Center for Disease Control and Prevention and the Public Health Center of Ukraine. The minimum sample size comprises 326 individuals, which was calculated using the statistical package G*Power; a total of 369 respondents participated in the survey. The statistical processing of the materials was carried out using the licensed program Microsoft Excel 2016. In addition, the inclusion criteria for respondents concerned the individuals who participated in rapid response groups (both primary and reserve). The exclusion criteria encompassed specialists who were on leave for various reasons, such as maternity or health-related issues, in addition to individuals who opted not to engage in the survey. When developing the requisite competencies, it is imperative to allocate appropriate consideration to the practical aspect, as evidenced by the information gathered through the survey. Thus, 47% of respondents believe that this aspect needs improvement. Furthermore, within the array of challenges linked to the attainment of requisite competencies, specialists from rapid response groups have delineated the subsequent factors: psychological and emotional stress, a perceived threat to personal safety (19%), inadequate levels of professional training (18.4%), health-related concerns (11.7%), and deficiencies in material and technical support (26.8%). Consequently, upon analysis of the acquired data, the imperative to initiate the subsequent modifications to the accredited program "Readiness, Response and Emergency Management" has been ascertained, namely: to enhance practical training methodologies, to underscore holistic competencies, to implement psychological training protocols, and to instruct on the operation with modern technical equipment.

ADA-NAF: Semi-Supervised Anomaly Detection Based on the Neural Attention Forest

In this study, we present a novel model called ADA-NAF (Anomaly Detection Autoencoder with the Neural Attention Forest) for semi-supervised anomaly detection that uniquely integrates the Neural Attention Forest (NAF) architecture which has been developed to combine a random forest classifier with a neural network computing attention weights to aggregate decision tree predictions. The key idea behind ADA-NAF is the incorporation of NAF into an autoencoder structure, where it implements functions of a compressor as well as a reconstructor of input vectors. Our approach introduces several technical advances. First, a proposed end-to-end training methodology over normal data minimizes the reconstruction errors while learning and optimizing neural attention weights to focus on hidden features. Second, a novel encoding mechanism leverages NAF’s hierarchical structure to capture complex data patterns. Third, an adaptive anomaly scoring framework combines the reconstruction errors with the attention-based feature importance. Through extensive experimentation across diverse datasets, ADA-NAF demonstrates superior performance compared to state-of-the-art methods. The model shows particular strength in handling high-dimensional data and capturing subtle anomalies that traditional methods often do not detect. Our results validate the ADA-NAF’s effectiveness and versatility as a robust solution for real-world anomaly detection challenges with promising applications in cybersecurity, industrial monitoring, and healthcare diagnostics. This work advances the field by introducing a novel architecture that combines the interpretability of attention mechanisms with the powerful feature learning capabilities of autoencoders.

Adaptation of the competence approach to the methodology of training it specialists in the field of programming

Adaptation of the competence approach to the methodology of training it specialists in the field of programming

Defining Running Intensity Domains from Critical Speed Derived from a 3-Minute All-Out Running Test

Background/Objectives: This study examines the utility of the 3-min all-out test (3MT) in determining exercise intensity domains through critical speed (CS), offering a practical alternative to traditional VO2max-based methods. Methods: Eighteen trained runners completed both the 3MT and a graded treadmill test. CS, VO2max, and HRmax were measured and compared to markers such as ventilation threshold (VT), gas exchange threshold (GET), lactate threshold (LT), onset of blood lactate accumulation (OBLA), CS, and VO2max, which are threshold markers for defining six exercise intensity domains. Results: Results demonstrate that CS reliably distinguishes among six exercise intensity domains with greater precision and less variability than heart rate (HR) or VO2-derived metrics. Notably, HR was unable to differentiate between high-intensity domains, highlighting its limitations for performance prescription. Compared to VO2max testing, the 3MT offers a simpler, non-invasive, and time-efficient approach to identifying training zones and monitoring performance. CS effectively delineated moderate-to-heavy, heavy-to-severe, and severe-to-extreme intensity domains, with strong correlations to performance thresholds. The findings emphasize the accessibility and reliability of the 3MT, particularly in overcoming the logistical challenges of traditional testing methods. Conclusions: This research underscores the potential of the running 3MT as a valuable tool for individualized training prescription and performance monitoring in both research and applied sports contexts, paving the way for a broader adoption in athletic training methodologies.

PREmbed: Balancing Conditional Generative Models with Embedding Pretraining and Regularization

Recent advancements in conditional generative models, e.g., Conditional Variational AutoEncoder (CVAE) and Conditional Denoising Diffusion Probabilistic Model (CDDPM), which utilize class-specific embeddings for generating images in specific classes, have demonstrated exceptional capabilities in producing high-quality images on balanced datasets. However, their performance decreases with imbalanced real-world data, affecting the fidelity and diversity of the generated images. This paper discusses the reasons and solutions for the imbalance issue in CVAE and CDDPM. By selectively reweighting the gradients of the embedding layer and main network, we identify the embedding layer’s bias as a critical bottleneck in these models. Motivated by this, we propose Embedding Pretraining and Regularization (PREmbed), a training methodology for addressing biased learning in the embedding layer of these models. PREmbed consists of two key components: (i) Masked Autoencoder pretraining on the original imbalanced dataset to obtain a robust initial embedding, and (ii) an embedding regularization loss to preserve class-level distances during the training phase to improve imbalanced embedding learning. Implementing PREmbed leads to the enhanced convergence of a robust embedding layer. Our experiments on imbalanced CIFAR-10 and CUB-200 datasets demonstrate that PREmbed enhances the performance of CVAE and CDDPM; it outperforms the baseline CVAE by 22.3% (FID 18.63) and the CDDPM by 30.3% (FID 12.70) in imbalanced CIFAR-10; 33.7% (FID 24.08) and 26.5% (FID 13.90) in CUB-200; and 16.4% (FID 49.41) and 15.2% (FID 33.05) in ImageNet-LT (which is where it achieves the state-of-the-art performance in imbalanced conditional image generation task).

Brain tumour histopathology through the lens of deep learning: A systematic review.

Machine learning (ML)/Deep learning (DL) techniques have been evolving to solve more complex diseases, but it has been used relatively little in Glioblastoma (GBM) histopathological studies, which could benefit greatly due to the disease's complex pathogenesis. Conduct a systematic review to investigate how ML/DL techniques have influenced the progression of brain tumour histopathological research, particularly in GBM. 54 eligible studies were collected from the PubMed and ScienceDirect databases, and their information about the types of brain tumour/s used, types of -omics data used with histopathological data, origins of the data, types of ML/DL and its training and evaluation methodologies, and the ML/DL task it was set to perform in the study were extracted to inform us of trends in GBM-related ML/DL-based research. Only 8 GBM-related studies in the eligible utilised ML/DL methodologies to gain deeper insights into GBM pathogenesis by contextualising histological data with -omics data. However, we report that these studies have been published more recently. The most popular ML/DL models used in GBM-related research are the SVM classifier and ResNet-based CNN architecture. Still, a considerable number of studies failed to state training and evaluative methodologies clearly. There is a growing trend towards using ML/DL approaches to uncover relationships between biological and histopathological data to bring new insights into GBM, thus pushing GBM research forward. Much work still needs to be done to properly report the ML/DL methodologies to showcase the models' robustness and generalizability and ensure the models are reproducible.

Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection

Ensuring airport security is of paramount importance to safeguard the lives of passengers and prevent acts of terrorism. In this context, developing advanced technology for early terrorist detection is crucial. This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. Our system utilizes state-of-the-art artificial intelligence techniques to analyze facial features. Our research uses VGG architecture and pre-trained with face data as a CNN model. This model is used to extract face embedding features from the dataset. These embedding features are then compressed with Principal Component Analysis (PCA) to obtain the meaningful feature as training data for the ANN algorithm. We trained our system using data from 500 identities data with 60 data for each identity. This training enables our system to recognize known terrorists and individuals on watchlists by comparing the facial features of individuals passing through security checkpoints with those in the database. The proposed CNN-ANN-based face recognition system not only enhances airport security but also significantly reduces the processing time for security checks. It can quickly identify potential threats, allowing security personnel to take appropriate actions in real time ensuring a rapid response to security concerns. We present the architecture, training methodology, and evaluation of the CNN-ANN model, achieving a high accuracy of 91.16% and precision of 91.36%. Through this research, we aim to increase airport security and strengthen efforts to combat terrorism, making air travel safer and more secure for all passengers.

Ph.D. Publication Requirement and Its Effects on Research Productivity Trends in Kazakhstan

ABSTRACTThe growing pressure to ‘publish or perish’, experienced by academia around the world, has pushed an increasing number of individual graduate programmes and universities, as well as entire higher education systems, to introduce a publication requirement as a prerequisite for the conferral of doctoral degrees. One example of the implementation of the requirement at the country level is Kazakhstan. This paper sheds light on the experience with publication requirement policies implemented at the country level by using bibliometric data from Scopus to statistically explore the effects of Ph.D. publication requirement policy on publication productivity trends in Kazakhstan. The findings reveal that the policy has increased the number of publications but has lowered the impact of research. The study suggests that measures focused on methodological training and improving the research capacity of future scholars may be more effective in strengthening the research competitiveness of countries than outcome‐focused policies.

X Ibero-American Meeting on Innovation, Research and Best Educational Practices (UNAM)

The X Encuentro Iberoamericano de Innovación, Investigación y Buenas Prácticas Educativas (EIBPE 2024), organized by UNAM, was held from November 4 to 6, 2024 in a virtual format. This event brought together teachers, researchers and professionals interested in exploring new emerging pedagogies, innovative activity design, emerging epistemologies and open educational resources. Key topics such as the use of digital technologies, educational inclusion and collaborative learning in post-pandemic contexts were addressed. Contributions on cognitive strategies, case studies for teacher training and creative teaching methodologies were highlighted. The meeting underscored the importance of universities as centers of innovation and action in the face of the educational challenges of the 21st century, strengthening academic networks and promoting the fulfillment of the Sustainable Development Goals. This event reaffirmed the commitment to educational excellence and social transformation.

Exploring the Rise of Smart Wearables: A Bibliometric Analysis on the Growing Use of Fitness Applications Among Athletes

This study explored the rise of smart wearables as an emerging area of research in the field of fitness applications by athletes and aims to contribute to the growing body of knowledge on the intersection of sports, technology, and health. Vos-viewer and R-studio were used to conduct the bibliometric analysis of the data extracted from the Scopus database in the usage of fitness applications by the participants in sports and other physical activities. To gain a holistic view of the current research field, clustering by keywords coupling was used to identify the significant research themes and provide suggestions for future research. The cluster labeled “Smart wearables to access the fitness” is ranked 1 based on highest cluster frequency, centrality, and impact. The findings of thematic analysis of keywords suggest some motor themes, basic themes, niche themes, and emerging themes, as the usage of smart wearables is found to be an emerging theme regarding the increasing utilization of fitness applications by athletes, which suggests the need to integrate fitness applications in physical education to promote fitness in children. The findings also provide valuable insights into trends and advancements in the field of fitness applications, highlighting the potential benefits for athletes, trainers, and researchers alike. As smart wearables continue to evolve, they are expected to play an increasingly integral role in optimizing sport performance and fostering a new era of data-driven training methodologies.

Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis

This article explores the practical implementation of autoencoders for anomaly detection, emphasizing their latent space manipulation and applicability across various domains. This study highlights the impact of optimizing parameter configurations, lightweight architectures, and training methodologies to enhance anomaly detection performance. A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. The results demonstrate that these models, when fine-tuned, achieve significant improvements in detection accuracy, specificity, and sensitivity while maintaining computational efficiency. The findings underscore the importance of lightweight, practical models and the integration of streamlined training processes in developing effective anomaly detection systems. This study provides valuable insights into advancing machine learning methods for real-world applications and sets the stage for further refinement of autoencoder-based approaches.

Innovative technologies for enhancing occupational safety in construction sites

The construction industry faces persistent challenges in ensuring occupational safety due to its high-risk environment and dynamic work conditions. Adopting innovative technologies has emerged as a transformative approach to addressing these challenges and improving worker safety. Wearable devices, drones, and smart sensors have been at the forefront of this shift, enabling real-time hazard detection, monitoring worker health, and enhancing safety protocols through advanced training methodologies. This article explores the role of these technologies in revolutionizing occupational safety in construction sites. Wearable devices, such as smart helmets and vests, monitor physiological parameters and environmental conditions, providing early warnings for potential hazards. Drones offer aerial surveillance capabilities, enabling site managers to identify unsafe conditions and ensure compliance with safety regulations. Smart sensors embedded in machinery and site infrastructure provide continuous data for monitoring equipment health and detecting environmental risks such as gas leaks or structural instabilities. The article also examines case studies of successful implementations, demonstrating how these technologies have reduced workplace incidents, enhanced productivity, and fostered a culture of safety. Challenges in adoption, including cost implications, integration with existing systems, and workforce training, are discussed, along with strategies to overcome them. By integrating innovative technologies, the construction industry can create safer and more efficient work environments, ultimately minimizing risks and enhancing occupational health. The study concludes by exploring emerging trends, such as AI-powered predictive analytics and robotic automation, which promise to further advance safety practices in construction.

POWERLIFTING TRAINING METHODS FOR WOMEN AGED 35–40 YEARS: ORGANIZATION AND STRUCTURE OF THE TRAINING PROCESS FOR ACHIEVING MAXIMUM SPORTS RESULTS

Introduction. Powerlifting by women aged 35–40 years in the challenges of today and the realities of Ukraine is relevant, as it contributes to physical and psychological and social communication. Aim. To conduct a comprehensive analysis of the factors impacting powerlifting on the health of women aged 35–40 years, to develop an author's method of training to achieve high sports results and prevent injuries. In this study, women aged 35–40 from the sports club "Olimp" and "Prometheus", Poltava, Ukraine, were involved. Materials and methods. A group of powerlifting women aged 35–40 years was selected based on age, training experience, physical parameters. The collected data was analysed to determine the relationship between indicators of physical and psychological health, and to identify injury risk factors. Results. Based on the results, an author's training method was developed, taking into account the peculiarities of the female body. Comparative analysis of the results was carried out with the control group. The training methodology developed on the basis of macrocycles and mesocycles of training, is an effective tool for preparing athletes for competitions with maximum sports results. The division of training into mesocycles with different durations and types of loads contributes to a gradual increase in results. The use of the technique allows to avoid overloads and injuries, provides control of cardiovascular activity and timely prevention of overtraining. Conclusions. The methodology is flexible to the needs and capabilities of female athletes. Development prospects and scientific significance of the research: The current research in powerlifting don’t focus sufficiently on optimal training approaches, taking into account physiological and hormonal characteristics, as well as their impact on training efficiency and achieving maximum results. This study makes a significant contribution to the field of powerlifting in women aged 35–40, a new training program contributes to a significant improvement in results on all indicators by 21%. This study confirms the effectiveness of the developed program and can become a valuable basis for developing personalized training approaches and improving athletic performance in women of this age range.

Training objectives and importance of multicultural assessment development for guidance and counseling teachers in Karanganyar

This training aimed to improve Karanganyar Middle School guidance and counseling (BK) teachers' ability to develop and implement culturally sensitive multicultural assessments. The program used a training methodology incorporating lectures, discussions, brainstorming, and experience sharing. Karanganyar Regency BK teachers already possessed some experience with multicultural assessments. Wilcoxon test results (p = 0.000< 0.05) showed a significant difference between pre- and post-test scores, with significantly higher post-test scores (average positive mean rank: 12.95; average negative mean rank: lower). Therefore, the training was effective in enhancing Karanganyar Middle School BK teachers' understanding, skills, and knowledge of multicultural assessments.

Barriers and Willingness to Undertake Cardiopulmonary Resuscitation Reported by Medical Students Dependent on Their Place of Residence-A Single-Center Study.

Bystander-administered cardiopulmonary resuscitation (CPR) is crucial for the survival of out-of-hospital cardiac arrests. However, only roughly 58% of bystanders would provide CPR, with wide variations across different regions. Identifying each factor affecting the barrier or readiness to perform resuscitation is a significant challenge for researchers. This study aimed to evaluate the obstacles preventing first-year medical students from initiating CPR, focusing on the size of domestic residential environments and the time and methodology of practical training. The original online questionnaire surveyed first-year medical students at the Medical University of Łódź from February 1 to March 2, 2024. The questionnaire development involved a literature review, expert evaluation, and pilot testing. Participation was voluntary and anonymous, with strict inclusion and exclusion criteria. The data were analyzed using PQStat software, employing descriptive statistics. The study revealed that 271 medical students reported a similar median of barriers regardless of the place of residence (median (Me) = 5, interquartile range (IQR) 2-6.25 vs. Me = 4, IQR 3-6 vs. Me = 4, IQR 3-6, p = 0.620). Out of 18 analyzed barriers, the only significant difference was found for crowded places. Medical students living in cities most frequently reported a willingness to perform CPR with rescue breaths for all victims. Those who grew up in towns <100,000 residents were less willing to start CPR if an unknown adult were the victim (rural area: 39.2% vs. town: 17.6% vs. city: 45.1%, p < 0.01). The number of reported barriers was similar regardless of training type and the time since training; however, the nature of these barriers varied after a year passed. Respondents across various locations reported similar number and types of barriers to performing CPR, including the most commonly declared fear of harm, uncertainty about recognizing cardiac arrest, and concerns about disease transmission. Although differences connected to the type of victims were observed, its low or moderate practical significance needs more comprehensive research on the impact of the size of the place of residence on the willingness to perform resuscitation and the related barriers.

Monitoring spatiotemporal changes in global change drivers and their effects on semiarid woodlands and forests - fieldwork protocol

Training schools play a vital role in COST actions, particularly for young researchers, as they provide opportunities to visit international laboratories and learn new methodologies. In May 2024, CLEANFOREST organized its first training school, Monitoring Spatiotemporal Changes in Global Change Drivers and Their Effects on Semiarid Woodlands and Forests, held at the Faculty of Science of the Universidade de Lisboa. The training school included a field trip designed to explore global change drivers and their impacts on semiarid woodlands and forests. Participants engaged in hands-on activities to understand how forest ecosystems interact with key global change factors such as air quality and climate change. They were introduced to various monitoring techniques and parameters for assessing forest health, including ecosystem fluxes, tree physiology, mortality, and regeneration. Additionally, participants examined plant biodiversity and functional ecology, focusing on lichens and their connection to air quality. Soil-related topics, such as the characterization of forest soils and their physico-chemical properties, were also addressed. Participants applied these methodologies in real-world scenarios, conducting measurements in different grazing management settings to assess their effects on tree growth, biodiversity, and soil properties. They then processed and analyzed the collected data, synthesizing their results to develop recommendations for sustainable forest management in the context of global change. This paper presents the field trip protocol used at Companhia das Lezírias, where simplified versions of established methodologies for sampling various ecosystem components were employed. The protocol provides a valuable reference for replicating similar studies, ensuring consistency in methodologies for future training and monitoring efforts.

General Educational Institutions of Tyumen in 1945–1953

Introduction. The aim of the work is to determine the significance of the transformation of the educational process in general educational institutions of Tyumen in the post-war period. In the context of the study, the authors study the expansion of the network and the technical condition of general educational institutions, note changes in the staffing of schools, reveal various aspects of the methodological training of teachers, as well as the process of transforming the educational programs of educational institutions. Methods and materials. The basis of the research work was made up of previously unpublished archival records and statistical materials, regulations that were published as part of collections of documents during the period under study, as well as information from periodicals of the post-war years. In addition, over the past decade, a significant number of regional studies have appeared in historiography concerning the problem of school education in the period under study. Analysis. Soviet education in 1945–1953 was experiencing a serious crisis. School buildings in need of repair, lack of a sufficient number of classrooms and teaching staff, difficult financial and living conditions of general education institutions, difficulties in implementing the law on universal education, increasing requirements for the general education training of students and the teaching activities of teachers – these are not all the problems that Soviet education faced in the post-war period. Results. The researchers come to the conclusion that the post-war years became an important time for the transformation of the educational process in the schools of Tyumen, causing its transition to peacetime. During the study period, the network of educational institutions in the city was expanding, and the number of students was also growing. At the same time, a number of problems remained unresolved, which concerned the low technical condition of educational institutions and multi-shift classes. In the mid-1940s and early 1950s, in the schools of the city, there was an increase in the number, experience, and level of education of teaching staff. Author’s contribution. The search, systematization, and analysis of archival documents, regulations, periodicals, as well as research literature on the topic of research work, followed by composing the main part of the article, was carried out by Candidate of Sciences (History), Associate Professor of the Department of History of Tyumen State University, E.V. Borodulina. Posing general conclusions and checking references was carried out by A.V. Skochin. Funding. The study was carried out with the financial support of the Russian Foundation for Basic Research within the framework of the scientific project No. 20-49-720018.

Training in Marketing Skills Associated With The Fair Trade System For Sustainable Economic Development in Artisanal Fishermen in The Rural Area of Manta Canton, Ecuador

Objective: This article proposes a methodology for training in marketing skills, associated with the fair trade system for sustainable economic development in artisanal fishermen in the rural area of Manta canton, Ecuador. Theoretical Framework: The presented text offers a comprehensive vision of the training of marketing skills, with a particular focus on the context of artisanal fishermen and the application of fair trade. Method: The type of research assumed is quantitative, explanatory, experimental, and within it, the pre-experimental type, since it first explains the logic, the path, and the way to follow for the training of marketing skills in fishermen in the rural area of Manta canton, Ecuador. Likewise, the results that were achieved from a quantitative point of view with the implementation of the methodology are presented. Results and Discussion: This research represents a valuable contribution to the field of education and rural development, demonstrating the importance of comprehensive and contextualized training for the empowerment of producers and the construction of a more just and sustainable future. Training in marketing skills, based on fair trade, is revealed as a key tool to improve the living conditions of artisanal fishermen and promote more equitable and sustainable economic development. Implications of the Research: The results of this research have important implications for the formulation of public policies, education, and international cooperation, and can serve as a basis for future research that promotes sustainable development and equity in the fishing sector. Originality/Value: The research presented stands out for its originality in focusing on a little-studied sector such as artisanal fishermen and linking marketing training with fair trade. Its value lies in its ability to improve the living conditions of fishermen, strengthen local development, and promote fairer trade practices.

Analyzing Integration of Immersive Technologies in Aviation Education

As technology advances at a rapid pace, immersive tools such as Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) are increasingly being adopted in higher education, especially within universities. These technologies offer innovative approaches to enhance and digitize learning environments, opening up new avenues for enriched educational experiences and improved training methodologies. This paper investigates the integration of VR, AR, and MR in aviation education at the university level, evaluating their effectiveness, advantages, and limitations. It explores both the strengths and challenges associated with these technologies in relation to educational objectives, assessing their suitability for aviation training. Furthermore, the study examines emerging trends, particularly the synergy between artificial intelligence (AI) and immersive tools, underscoring the importance of continuous innovation and collaboration to maximize their potential. Through a comprehensive analysis of current practices and future directions, this paper sheds light on the transformative impact of VR, AR, and MR in aviation education. The conclusion reaffirms recent trends and positive outcomes of these technologies while also addressing cost implications for future developments.