Training Process Research Articles

Text Command Intelligent Understanding for Cybersecurity Testing

Research on named entity recognition (NER) and command-line generation for network security evaluation tools is relatively scarce, and no mature models for recognition or generation have been developed thus far. Therefore, in this study, the aim is to build a specialized corpus for network security evaluation tools by combining knowledge graphs and information entropy for automatic entity annotation. Additionally, a novel NER approach based on the KG-BERT-BiLSTM-CRF model is proposed. Compared to the traditional BERT-BiLSTM model, the KG-BERT-BiLSTM-CRF model demonstrates superior performance when applied to the specialized corpus of network security evaluation tools. The graph attention network (GAT) component effectively extracts relevant sequential content from datasets in the network security evaluation domain. The fusion layer then concatenates the feature sequences from the GAT and BiLSTM layers, enhancing the training process. Upon successful NER execution, in this study, the identified entities are mapped to pre-established command-line data for network security evaluation tools, achieving automatic conversion from textual content to evaluation commands. This process not only improves the efficiency and accuracy of command generation but also provides practical value for the development and optimization of network security evaluation tools. This approach enables the more precise automatic generation of evaluation commands tailored to specific security threats, thereby enhancing the timeliness and effectiveness of cybersecurity defenses.

Characterizing and Understanding HGNN Training on GPUs

Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis. Prior to their practical application, identifying the optimal HGNN model parameters tailored to specific tasks through extensive training is a time-consuming and costly process. To enhance the efficiency of HGNN training, it is essential to characterize and analyze the execution semantics and patterns within the training process to identify performance bottlenecks. In this study, we conduct a comprehensive quantification and in-depth analysis of two mainstream HGNN training scenarios, including single-GPU and multi-GPU distributed training. Based on the characterization results, we reveal the performance bottlenecks and their underlying causes in different HGNN training scenarios and propose optimization guidelines from both software and hardware perspectives.

Diagnosis of Custard Apple Disease Based on Adaptive Information Entropy Data Augmentation and Multiscale Region Aggregation Interactive Visual Transformers

Accurate diagnosis of plant diseases is crucial for crop health. This study introduces the EDA–ViT model, a Vision Transformer (ViT)-based approach that integrates adaptive entropy-based data augmentation for diagnosing custard apple (Annona squamosa) diseases. Traditional models like convolutional neural network and ViT face challenges with local feature extraction and large dataset requirements. EDA–ViT overcomes these by using a multi-scale weighted feature aggregation and a feature interaction module, enhancing both local and global feature extraction. The adaptive data augmentation method refines the training process, boosting accuracy and robustness. With a dataset of 8226 images, EDA–ViT achieved a classification accuracy of 96.58%, an F1 score of 96.10%, and a Matthews Correlation Coefficient (MCC) of 92.24%, outperforming other models. The inclusion of the Deformable Multi-head Self-Attention (DMSA) mechanism further enhanced feature capture. Ablation studies revealed that the adaptive augmentation contributed to a 0.56% accuracy improvement and a 0.34% increase in MCC. In summary, EDA–ViT presents an innovative solution for custard apple disease diagnosis, with potential applications in broader agricultural disease detection, ultimately aiding precision agriculture and crop health management.

Multi-source partial discharge pattern recognition in GIS based on Grabcut-MCNN

Partial discharge (PD) surveillance constitutes a pivotal methodology for diagnosing insulation failures in electrical equipment. Enhancing comprehensively the precision of identifying PD anomalies in Gas Insulated Switchgear (GIS) is of paramount significance for ensuring the steady functioning of power grids. This study introduces a novel framework that integrates Phase-Resolved PD Graph Segmentation (PRPD-Grabcut) with a tailored MobileNets-based Convolutional Neural Network (MCNN) to classify GIS-related PD issues. Leveraging image segmentation via PRPD-Grabcut, crucial features are extracted from PRPD diagrams, which then facilitate the construction of the MCNN model. This model employs depth-wise separable convolutions alongside inverted residual architectures to tackle the vanishing gradient dilemma inherent in Deep Convolutional Neural Networks (DCNNs) during GIS PD pattern discernment. Upon the model's subsequent training and validation, empirical evidence illustrates that the PRPD-Grabcut-MCNN hybrid significantly alleviates the computational load and storage requisites of the model, concurrently enhancing the recognition precision and expediting the training process of the neural network. Relative to diverse established lightweight neural network architectures, MCNN manifests superior performance in terms of recognition accuracy, reduced cross-entropy loss, and expedited training duration.

Adaptive fractional physics-informed neural networks for solving forward and inverse problems of anomalous heat conduction in functionally graded materials

In this paper, adaptive fractional physics-informed neural networks (PINNs) are employed to solve the forward and inverse problems of anomalous heat conduction in three-dimensional functionally graded materials. In adaptive fractional PINNs, the finite difference L1 scheme is employed to discretize the time fractional derivatives in anomalous heat conduction problems. By combining the finite difference L1 scheme with automatic differentiation technique, adaptive fractional PINNs minimize a loss function constructed based on the governing equation, initial and boundary conditions to obtain solutions to heat conduction problems. To avoid competition among various loss terms during the training process, an adaptive loss balancing algorithm is adopted to balance the interactions among different terms of the loss functions. In addition, polynomial basis functions are used to expand unknown functions characterizing material parameters or heat sources, aiming to enhance the performance of adaptive fractional PINNs in resolving inverse problems. Five numerical examples involving forward, inverse, and nonlinear problems related to anomalous heat conduction are carried out to demonstrate the feasibility and effectiveness of adaptive fractional PINNs.

Implementation of innovative technologies in the training process of martial artists

Purpose: to establish leading directions for the implementation of innovative technologies in the practical activities of coaches and athletes in martial arts. Material and methods. A systematic review of scientific publications for the period from 2020 to 2024 related to the application of innovations in the sports field was carried out. The search was conducted in Google Scholar, PubMed, Scopus and Web of Science databases using the following keywords: «innovative technologies», «digital technologies», «sports», «training», «martial arts». The search was conducted by article titles. Full-text articles of a practical or review nature were analyzed. Results: it was established that the use of innovative technologies in the training of martial artists is an important component of the modernization of the training process. It contributes to improving the organization and control of training, increasing their effectiveness, forming training programs using remote technologies. This helps to create flexible training programs and predict their effectiveness. As a result of the bibliographic analysis of relevant studies, it was established that the key directions of the introduction of innovative technologies in the process of training martial artists are: digitization of individual components of training activities of athletes; introduction of technologies based on artificial intelligence; use of virtual and augmented reality technologies; systematic use of mobile applications and personal trackers in the training of athletes; the use of distance learning technologies in the organization of training activities. Along with the established advantages of using innovations in martial arts training, the problems of their implementation in the process of training athletes were identified, which consists in: limited availability of modern technologies to athletes and martial arts coaches; the need to develop programs for the use of technologies by participants in the training process. Conclusions. The use of innovative technologies is an important factor in the modernization of the training process of martial arts athletes. They are aimed at optimizing the training of martial arts athletes, reducing the risk of injuries and increasing the level of individualization of training. Despite the identified potential, the number of studies devoted to the practical implementation of innovations in sports training of martial artists is insufficient. This is due to the complexity of the wide application of technologies and the rapid pace of their modernization, which makes it difficult to assess their long-term effectiveness in various types of martial arts. In general, the findings of this study confirm that innovative technologies are a key driver of qualitative changes in the process of training martial arts athletes. Further research will focus on the development of comprehensive programs using digital technologies at the initial stage of multi-year martial arts training.

Multiscale Spatio-Temporal Feature Fusion Based Non-Intrusive Appliance Load Monitoring for Multiple Industrial Industries

The appliance types and power consumption patterns vary greatly across different industries. This can lead to unstable identification results of traditional appliance load monitoring methods in different industries. A non-intrusive appliance load monitoring (NIALM) method for multiple industries based on multiscale spatio-temporal feature fusion has been proposed. Firstly, the ConvNeXt Block with efficient channel attention has strong feature extraction capability. Spatial features of appliance state changes and micro-variations generated during operation can be extracted from mixed industrial load information by it. Meanwhile, the bidirectional gated recurrent neural network is used to learn the bidirectional dependencies of the load data, obtaining temporal features. Then, the multi-scale feature extraction module is used to extract temporal and spatial features from different depths of the network layers. And the extracted multi-scale temporal and spatial features are fully integrated. Finally, the proposed model is optimized using the Stochastic Weight Averaging method. During the training process, a certain number of model weights are randomly averaged, which can improve the model's generalization ability and identification accuracy. The experiment was conducted on six different industries. The evaluation indexes such as accuracy, F1 score, and Wasserstein distance are also used to verify the effectiveness and superiority of the method.

A rolling bearing fault diagnosis method for imbalanced data based on multi-scale self-attention mechanism and novel loss function

Deep learning methods are widely used in the field of rolling bearing fault diagnosis and produce good results when faced with datasets with roughly equal numbers of normal and faulty samples. However, real-world data often has a serious imbalance, with the number of fault samples being significantly less than the number of normal samples. This dataset imbalance challenges the performance of traditional deep learning methods. To address this problem, this paper proposes an efficient imbalanced data rolling bearing fault diagnosis method. The method consists of two parts: a deep learning network based on a multi-scale self-attention mechanism and a novel loss function. In terms of the deep learning network, firstly, the one-dimensional vibration signal is converted into a two-dimensional image through the Gramian angular field. This conversion maximises the inherent feature extraction capability of the network. Subsequently, the multi-scale learning capability of the network is enhanced by implementing different expansion rates for the head of the multi-scale self-attention mechanism. This nuanced approach allows the network to capture the underlying information more efficiently. Finally, the inclusion of Ghost bottlenecks and feature pyramid networks (FPNs) helps to optimise network efficiency and improve generalisation performance. A novel loss function is also proposed to make the method more suitable for imbalanced data. During the training process, the classification of samples in each class is analysed using the recall metric of imbalanced classification and the real-time recall is used as a weight to weaken the dominance of the majority class. This weighting ensures the adaptability of the method to imbalanced datasets. The proposed method is evaluated using rolling bearing datasets from Case Western Reserve University, USA, and Southeast University, China. Comparison results with other state-of-the-art deep learning methods show that the proposed method has a robust performance when dealing with imbalanced data.

Comparative analysis of X-ray image classification of pneumonia based on deep learning algorithm

Abstract. In this paper, we compare and analyse the effectiveness of deep learning models such as AlexNet, Vgg, GoogleNet and MobileNet in the task of pneumonia image classification. During the training process, GoogleNet showed the fastest convergence speed, reaching convergence at the 3rd epoch; subsequently, the other three models gradually stabilised. In the end, the loss of AlexNet is 0.426, the loss of Vgg is 0.566, the loss of GoogleNet is 0.936, and the loss of MobileNet is 0.626. By selecting the weights of the round with the best training effect of each model, the results of classification accuracy are obtained: 88.9% for AlexNet, 92.6% for Vgg, and 92.6% for GoogleNet. 92.6%, GoogleNet is 85.2%, and MobileNet is 96.3%. MobileNet demonstrated the best prediction performance on the test set. These results provide a useful reference for the application of deep learning models in medical imaging..

Pruned tree-structured temporal convolutional networks for quality variable prediction of industrial process

In real industrial processes, the rapid and accurate acquisition of quality variables is essential. Therefore, this paper proposes a pruned tree-structured temporal convolutional network (PT-TCN) for efficient and accurate variables prediction. First, a novel tree network is developed, utilizing dilated causal convolution blocks as nodes to avoid the loss of local information. Each node extracts distinct local information, and by concatenating all tree nodes, the network can capture a comprehensive range of temporal scales. Then, to avoid the increased complexity caused by the tree structure, we design an online two-stage pruning strategy to compress the tree network without introducing additional computations. During the training process, blocks are initially pruned based on the correlation assessment between quality variables and tree nodes. Subsequently, weight normalization layers are employed to evaluate the importance of output channels in blocks, thereby enabling intra-block channel pruning. The effectiveness of PT-TCN is verified on Tennessee Eastman benchmark process. In addition, experiments on the real zinc flotation process demonstrate that the proposed PT-TCN improves in R2 and MAE by 1.32% and 1.26% respectively in predicting quality variables, and it can reduce 91.8% parameters of the initial tree-structured TCN without sacrificing accuracy.

Модель змагальної діяльності зв’язуючого гравця високої кваліфікації у волейболі

Increasing the competitive performance of Ukrainian volleyball players requires scientific support of the educational and training process and the use of modern methods of scientific research, one of which is modeling. Indicators of technical and tactical actions obtained by analyzing official competitions are the basis for creating models of competitive activity. The aim of the study is to develop a model of a high-skill link player on the basis of statistical data from the games of the Ukrainian Volleyball Championship among men's teams of the Super League. Material and Methods. The data of 18 qualified volleyball players who perform the function of a linking player in their teams were used for the study. We analyzed statistical data of link players of 8 teams participating in the Ukrainian Super League championship of the season 2023-24. To create a model of a linking player of Ukrainian Super League teams, 10 athletes were selected from the total sample, the main criterion was quantitative indicators of participation in the championship games (at least 80% of official matches). Research methods: analysis of literary sources and data in the Internet, pedagogical testing, analysis and generalization of statistical data of competitive activity, methods of mathematical statistics. Results. The analysis of game technical and tactical indicators of qualified volleyball players allows to understand more clearly the peculiarities of competitive activity of players of different roles. In accordance with the purpose of the study we analyzed the game activity indicators of linking players (number of matches and sets, total number of assists, effective assists, satisfactory assists, number of errors). It should be noted that all game indicators indicated the heterogeneity of the group of highly skilled binders, which is explained by different experience, players' participation in matches, and the variety of tactical actions of the teams. Conclusions. The developed model of competitive activity of highly skilled liaisons contains general data (height, age), indicators of physical abilities (height of block and attack), technical and tactical indicators (execution of passes, number of errors). In addition to the average data, the model presents a variation series from the lowest to the highest results.

Technical and vocational education training and industry collaboration: a bibliometric review

Technical and vocational education training (TVET) is an education and training process with a strong emphasis on industry practices that aims to generate competent workers in particular fields. Collaboration with industry in TVET has the potential to improve the quality and relevance of TVET programmes and equip students with the practical skills and knowledge demanded by employers. Thus, there is a need for a complete bibliometric study of research linked to collaboration between TVET and industry, despite the fact that the number of studies in this field continues to increase. The bibliometric analysis in this research which was extracted from Web of Science database is analyze using VOSviewer. The research conducted a descriptive analysis of the publication number trends, the top authors and leading journals in this field. Next, the researcher also analyzed the co authorship based on authors and countries, research trends, citation and keywords analysis as well as co citation analysis. The article found that most of the articles in this field are published by authors from developed countries where the majority is from the United States. Other than that, the recent research hotspot were also identified indicating the future direction of the research in this field.

POLICE RECRUITMENT AND TRAINING: ASSESSING THE IMPACT ON OFFICER BEHAVIOR AND CITIZEN COMPLAINTS IN NIGERIA

This study examines the impact of police recruitment and training practices on officer behavior and citizen complaints in Nigeria, identifying significant deficiencies that contribute to misconduct and public distrust. The research explores how inadequate recruitment standards, political interference, and insufficient training on ethics and community policing have undermined the professionalism of the Nigeria Police Force (NPF). Using an exploratory approach, the study analyzes literature reviews and secondary data from academic and governmental sources, comparing Nigeria’s practices with successful reforms in other African nations. Research indicates that the lack of a centralized background check system and inadequate training contribute to pervasive police misbehavior, as seen during the "End SARS" protests. Comparative analysis highlights best practices from countries like South Africa and Rwanda, where community-oriented policing and rigorous recruitment standards have improved public trust. The study concludes that reforming recruitment and training processes is crucial to promoting accountability and restoring public confidence in law enforcement. Key recommendations include establishing a centralized recruitment database, revamping training programs, and strengthening oversight mechanisms.

A deep-learning-based approach for simulating pedestrian turning flow

The applications of artificial intelligence technology in pedestrian and evacuation dynamics research have achieved gratifying progress recent years. Benefiting from the non-linear fitting ability of deep learning algorithm, such learning-based method might have better performance in modeling the individual micro behaviors comparing to traditional pedestrian and evacuation dynamics models. Hence, this paper proposes a deep-learning-based pedestrian dynamics model which can simulate the pedestrian flow in right-angled corridors. The training process is conducted with a deep learning framework composed of two functional layers namely Scene Perception layer (SP layer) and Motion Dynamic layer (MD layer). The input features of the SP layer and MD layer are obtained from a defined ‘sense field’ which captures information about walking facility structures and neighbors. Dataset generated from twelve groups of pedestrian turning flow experiments is used for data training. The initial experiments are further adopted to evaluate the model at both qualitative and quantitative level from pedestrian motion data simulated by trained model. Qualitatively, the simulation results align with the corresponding experiments in terms of fundamental diagrams in different measurement areas and the headway distance-velocity relationship, demonstrating realistic motion characteristics, proper reactions to changeable walking facility structures and collision avoidance tendencies of agents driven by our model. For quantitative evaluation of the model precision, two indicators respectively calculate the duration and trajectory disparities are introduced and both of them yield relatively small values. Moreover, eight groups of external experiments completely independent from training data are introduced to validate the generalization ability of our model, the simulation results are found to match reality well without prior knowledge. The proposed framework presented is a success trial for simulating pedestrian turning flow and have the potential to be adapted to different scenarios. Outcomes presented will be of beneficial guidance for different engineering application such as performance-based fire design and crowd management.

Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques

Brain cancer poses a significant global health challenge, with mortality rates showing a concerning surge over recent decades. The incidence of brain cancer-related mortality has risen from 140,000 to 250,000, accompanied by a doubling in new diagnoses from 175,000 to 350,000. In response, magnetic resonance imaging (MRI) has emerged as a pivotal diagnostic tool, facilitating early detection and treatment planning. However, the translation of deep learning approaches to brain cancer diagnosis faces a critical obstacle: the scarcity of public clinical datasets reflecting real-world complexities. This study aims to bridge this gap through a comprehensive exploration and augmentation of training data. Initially, a battery of pre-trained deep models undergoes evaluation on a main brain cancer MRI “BT-MRI” dataset, yielding remarkable performance metrics, including 100% accuracy, precision, recall, and F1-Score, substantiated by the Score-CAM methodology. This initial success underscores the potential of deep learning in brain cancer diagnosis. Subsequently, the model’s efficacy undergoes further scrutiny using a supplementary brain cancer MRI “BCD-MRI” dataset, affirming its robustness and applicability across diverse datasets. However, the ultimate litmus test lies in confronting the model with synthetic testing datasets crafted to emulate real-world scenarios. The synthetic testing datasets, a BCD-MRI testing sub-dataset enriched with noise, blur, and simulated patient motion, reveal a sobering reality: the model’s performance plummets, exposing inherent limitations in generalization. To address this issue, a diverse set of optimization strategies and augmentation techniques, ranging from diverse optimizers to sophisticated data augmentation methods, are exhaustively explored. Despite these efforts, the problem of generalization persists. The breakthrough emerges with the integration of noise and blur as augmentation techniques during the training process. Leveraging Gaussian noise and Gaussian blur kernels, the model undergoes a transformative evolution, exhibiting newfound robustness and resilience. Retesting the refined model against the challenging synthetic datasets reveals a remarkable transformation, with performance metrics witnessing a notable ascent. This achievement underscores the important role of correct selection of data augmentation in fortifying the generalization of deep learning models for brain cancer diagnosis. This study not only advances the frontiers of diagnostic precision in brain cancer but also underscores the paramount importance of methodological rigor and innovation in confronting the complexities of real-world clinical scenarios.

Diagnóstico situacional del proceso enseñanza aprendizaje en posgrados de una unidad académica de Medicina. Una aproximación desde la percepción de docentes y profesionales estudiantes

IntroductionThe objective of the study was to verify the progress of implementation of the pedagogical model oriented to postgraduate competencies of a medical school, since it implemented a new curricular framework since 2016, achieving a first cohort of graduates, who will seek to consolidate their training in subspecialties offered by the academic unit. MethodsQualitative study with a hermeneutic, interpretive approach with teachers and final year residents of selected postgraduate courses. The data were obtained through semi-structured interviews and focus groups. The training programs were analyzed. The pedagogical model oriented to postgraduate competencies was the main category that guided the questions, the following dimensions being analyzed: academic management, teaching practice, professional student learning and curricular structure. Data collection concluded once theoretical saturation was obtained. ResultsAnalyzing 22 interviews with teachers and focus groups with 16 final-year postgraduate residents, it was identified that the pedagogical model currently implemented is mostly content-based, with insufficiently developed innovations. Teachers with heterogeneous training and involvement in higher education and implementation of the new model. Residents of specialties perceived as spectators, not involved in the training process that should have been integrated into the postgraduate degree to ensure the necessary competencies indicated. ConclusionIn the postgraduate field, the pedagogical model by content prevails, without the orientation by competencies having had continuity, verifying the need for interventions in teacher training, student involvement, improvement of infrastructure and assurance of educational quality, to saturate the objectives of the plan institutional strategic.

Проблема футбольного інтелекту у сучасних дослідженнях

Speed, dynamism, high physical and mental loads characterize modern soccer. In this regard, the player's intelligence plays an important role during the game, because it: affects the form of tactical behavior; allows to make effective decisions in stressful situations; increases the player's ability to understand and establish links between game episodes and solve problematic game episodes using available tactical information. Therefore, the study of theoretical, tactical and psychological aspects of the development of intellectual abilities of a player is important for obtaining high results in this sport. The aim of the study is to analyze theoretical research on the peculiarities and development of intellectual abilities of soccer players. Research methods: theoretical analysis and generalization of scientific and methodological literature. Research results. As a result of the conducted analysis of literary sources it was determined that soccer intelligence is a way of thinking that allows a player to solve problem situations using knowledge, experience, skills and natural data, which are best suited for a particular game situation. Player's intelligence is a complex multilevel structure that is the result of player's adaptation to the conditions of the game space, constitutes a special form of tactical behavior of the player, is the result of purposeful tactical training of the player as a set of elementary processes of information processing and a system of cognitive processes. Conclusions. In soccer, the intellectual component is an integral part of both the game itself and the training process. Coaches are interested in its improvement and effective use because soccer intelligence is a way of thinking that allows a player to successfully solve problem situations using knowledge, experience, skills, and natural data that are best suited for a particular game situation.

Wearing myopia glasses on GANs: Mitigating bias for pre-trained Generative Adversarial Networks via online prior perturbation

Pre-trained Generative Adversarial Networks (GANs) can provide rich information and make various downstream tasks beneficial. However, the training process of GANs potentially learns pre-existing biases in the historical data, which are not only inherited, but also amplified to some extent when using the generated data for prediction. Most available methods for bias mitigation utilize pre-processing or in-processing strategies to retrain GANs, which needs to access the raw data and also increases the time and computational cost required to train a fully expressive GAN. Therefore, we propose a novel post-processing mechanism to achieve fairness outputs of GANs through online prior perturbation. Unlike traditional output-based offline processing, our method mitigates biases from model inputs by designing a prior perturbation network, called “prior perturber”, to form a combined network with the pre-trained GAN. Specifically, we introduce a bias prediction network, called “bias predictor”, for online adversarial training with the prior perturber, which can decouple the generated representations from the bias features and improve the fairness of the downstream prediction results. In addition, the training process is independent of specific target labels, and the generated fair representations have good transferability. Experimental evaluations on five real datasets validate the effectiveness of the proposed method and the best utility is obtained on the same fairness level as compared to other output-based baseline methods. The result on fairness-utility trade-offs significantly improves the fairness of original samples by 56.40%, while the utility is reduced by only 2.60%.

The Effect of Multi-Ingredient Protein versus Collagen Supplementation on Satellite Cell Properties in Males and Females

ABSTRACT Introduction Skeletal muscle satellite cells (SC) contribute to the adaptive process of resistance exercise training (RET) and may be influenced by nutritional supplementation. However, little research exists on the impact of multi-ingredient supplementation on the SC response to RET. Purpose We tested the effect of a multi-ingredient supplement (MIS) including whey protein, creatine, leucine, calcium citrate, and vitamin D on SC content and activity as well as myonuclear accretion, SC and myonuclear domain compared with a collagen control (COL) throughout a 10-wk RET program. Methods Twenty-six participants underwent a 10-wk linear RET program while consuming either the MIS or COL supplement twice daily. Muscle biopsies were taken from the vastus lateralis at baseline and 48 h after a bout of damaging exercise, before and after RET. Muscle tissue was analyzed for SC and myonuclear content, domain, acute SC activation, and fiber cross-sectional area (fCSA). Results MIS resulted in a greater increase in type II fCSA following 10 wk of RET (effect size (ES) = 0.89) but not myonuclear accretion or SC content. Change in myonuclei per fiber was positively correlated with type I and II and total fiber hypertrophy in the COL group only, indicating a robust independent effect of MIS on fCSA. Myonuclear domain increased similarly in both groups, whereas SC domain remained unchanged following RET. SC activation was similar between groups for all fiber types in the untrained state but showed a trend toward greater increases with MIS after RET (ES = 0.70). Conclusions SC responses to acute damaging exercise and long-term RET are predominantly similar in MIS and COL groups. However, MIS can induce greater increases in type II fCSA with RET and potentially SC activation following damage in the trained state.

Implementation and Impact of a Lifting Cushion for Care Home Residents Who Have Fallen.

Falls are a global public health problem and the second leading cause of death from unintentional injury. Globally, approximately 30%-50% of people living in nursing or residential care homes fall each year. Falls have an impact on quality of life and morbidity. Prevention of falls is gold standard care. When falls do occur, implementation of safe strategies to help the person rise is required. Structured risk assessment and the use of a 'lifting' cushion are one such strategy. To evaluate the impact of the lifting cushion on management of falls and assess barriers and facilitators to staff use of the lifting cushion in 18 care homes. Two-phase study involving (i) capturing quantitative pre- and post-cushion implementation data along with comparison of means testing and (ii) theoretically underpinned qualitative semi-structured interviews to explore barriers and facilitators to cushion implementation with inductive and deductive data analysis. The cushion was used a total of 32 times out of 567 post-implementation recorded falls (6% of all falls). Barriers and facilitators to cushion use aligned to the Theoretical Domains Framework include knowledge, skills and confidence, emotion, beliefs about safety and decision processes, environmental context and resources and social influences. The lifting cushion was poorly adopted. Identified barriers to adoption would not be addressed using routine train and cascade processes. We identified facilitators that could be enhanced to promote uptake. Theoretically underpinned implementation strategies, tailored to assess determinants, are known to be more effective; however, this approach has rarely been used in care homes. We have demonstrated a structured approach to implementation of cushion use; this may be transferable to other care home practices. Cae home leaders should be aware that giving information alone will not change practice. Implementation or improvement strategies will be more effective.