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\"This book describes research on training using cognitive psychology to build a complete empirical and theoretical picture of the training process. It includes a review of relevant cognitive psychological literature, a summary of recent laboratory experiments, a presentation of original theoretical ideas, and a discussion of possible applications to real-world training settings\"--Provided by publisher.

High volume cataract lists are cost-effective, reduce waiting times, and facilitate surgical teaching. We propose a stepwise training model that incorporates human factor principles and a reflective pedagogical approach, which has not been documented previously. Surgical training in ophthalmology is effective when a modular approach is utilised. High volume lists further enhance training by increasing exposure to a newer way of learning and working. We evaluated the efficiency and safety of trainee-assisted cataract surgery across a single NHS eye unit and an independent sector (IS) provider. We examined results from audits of surgical efficiency and safety in trainee-assisted high-volume lists, including a single-centre comparative evaluation of consultant-only and trainee lists. The quantitative and qualitative information gained from these projects helped us to implement a modular, structured training programme that utilises a reflective cycle of pedagogy, suitable for any grade of trainee. Our projects included an audit following cataract surgery performed by a surgical trainee over a 5-month period, which showed excellent post-op refractive results and no cases of intra-operative and post-operative complications. A single-centre observational study demonstrated comparable surgical throughput and safety results for trainee and solo consultant high volume lists. Systemic and ocular complication rates were reported to be similar for low and medium risk cataract surgery among trainee supervised IS and NHS lists. Cataract surgery outcomes and patient feedback support the effectiveness of the surgical training model. Combining Gibbs' reflective cycle of critical reflection with the International Council of Ophthalmology's principles helped us to develop the QM Model of modular teaching for cataract surgery, which we believe is suitable for utilisation in all surgical centres in the NHS and IS settings, for both low volume and high-volume surgical lists regardless of trainee experience.

The health of humankind is intrinsically associated with the health of the marine and ocean ecosystems. The pollution of the coastal region due to urbanization, for example, principally harms the growth of the ecosystem with poor-quality of water, which aggravates the survival of marine organisms and animals. The toxicity of the contaminated seafood would affect the human-ocean ecosystem thereby bringing down the economic rank of the region as well. Therefore, it is mandatory to assess the quality of the marine and ocean water to initiate any statutory measures to protect the regional marine water against pollution and dumping of toxic matter. This paper, therefore, presented an architecture of machine learning techniques to assist in classifying marine water quality. The proposed framework evaluated various classification models and selected the best fit out of the top-performing algorithms through training and optimizing. The finalized model was a stacked classifier, which was then deployed to predict the marine water quality index from the physicochemical and biological properties of the water.

Background It has been demonstrated that medical students are capable of learning microsurgical techniques. We hypothesize that microsurgical training might give insight into the importance of delicate tissue handling and correct knot tying that could have a positive influence on macrosurgical skills. The primary aim of this study was to evaluate the effect of microsurgical training on macrosurgical suturing skills in novice medical students. Subjects and methods In 2018, 46 novice medical students were enrolled and randomized into two groups. The intervention group received both macro- and microsurgical training and the control group received only microsurgical training. Both groups underwent an assessment test that consisted of macrosurgical tasks of three simple interrupted sutures with a square knot and continuous three-stitch long over-and-over sutures. These tests were individually filmed and assessed using the University of Bergen suturing skills assessment tool (UBAT) and the Objective Structured Assessment of Technical Skill global rating scale (OSATS). Questionnaires regarding future career ambitions and attitudes towards plastic surgery were also completed both prior to and following the tests. Results The intervention group needed a longer time to complete the tasks than the control group (12.2 min vs. 9.6 min, p  > 0.001), and scored lower on both the UBAT (5.6 vs. 9.0, p  > 0.001) and the OSATS (11.1 vs. 13.1, p  > 0.001) assessments. The microsurgery course tended to positively influence the students’ attitudes towards a career in plastic surgery ( p  = 0.002). This study demonstrates poorer macrosurgical skills in the medical students group exposed to microsurgical training. The true effect of microsurgical training warrants further investigation. Level of evidence: Level I, diagnostic study.

Aim: This study aims to explore how the European Union Agency for Law Enforcement Training (CEPOL) positions itself as a transnational provider of law enforcement training across Europe and beyond. It identifies key trends from the literature and examines CEPOL’s role as a trusted training provider, focusing on its training model. Methodology: The paper adopts a literature-based analytical approach, supported by a review of CEPOL’s strategic and digital training developments, highlighting how education expertise is integrated into training programmes. Findings: The study reveals CEPOL’s strategic use of technology through its digital strategy, its implementation of a blended training model, and its focus on equipping law enforcement officials with digital-age skills. It also identifies the CEPOL Exchange Programme and online platform as flagship initiatives. CEPOL's continuous data collection and assessment strategies support ongoing improvement and alignment with strategic training priorities. Value: The paper provides insights into CEPOL’s evolving training portfolio and its alignment with European security needs. It demonstrates the agency’s commitment to innovation, quality, and evidence-based training approaches, contributing to the development of a more effective and cohesive European law enforcement community.

This article uses a factor analysis model to conduct an empirical analysis using data analysis. The data comes from a questionnaire survey. The subjects of the survey are college students in Guangxi, China. The research topic is the dual “3+1” training model for compound talents. Based on the SPSS analysis that KMO=0.812 and spherical Bartlett=0.000, the contribution rates of the extracted 8 common factor variances are: H =13.487%, H 2 =8.535%, H 3 =8.303%, H 4 =6.152%, H s =5.814%, H 6 =5.622%, H 7 =4.925%, H 8 =4.870%, the above data results indicate: government support and social support, school publicity, government and university implementation efforts, student satisfaction with the training model, students’ willingness for future development, etc. The Factor Analysis Model provides reference and enriches the theoretical basis.

Objective: This study aimed to explore the molecular response mechanisms of differential blood metabolites before and after 8 weeks of threshold and polarized training models using metabolomics technology combined with changes in athletic performance. Methods: Twenty-four male rowers aged 14–16 were randomly divided into a THR group and a POL group (12 participants each). The THR group followed a threshold training model (72%, 24%, and 4% of training time in low-, moderate-, and high-intensity zones, respectively), while the POL group followed a polarized training model (78%, 8%, and 14% training-intensity distribution). Both groups underwent an 8-week training program. Aerobic endurance changes were assessed using a 2 km maximal rowing performance test, and untargeted metabolome analysis was conducted to examine blood metabolomic changes before and after the different training interventions. Aerobic endurance changes were assessed through a 2 km maximal rowing test. Non-targeted metabolomics analysis was employed to evaluate changes in blood metabolome profiles before and after the different training interventions. Results: After 8 weeks of training, both the THR and POL groups exhibited significant improvements in 2 km maximal rowing performance (p < 0.05), with no significant differences between the groups. The THR and POL groups had 46 shared differential metabolites before and after the intervention, primarily enriched in sphingolipid metabolism, glutathione metabolism, and glycine, serine, and threonine metabolism pathways. Nine unique differential metabolites were identified in the THR group, mainly enriched in pyruvate metabolism, glycine, serine, and threonine metabolism, glutathione metabolism, and sphingolipid metabolism. A total of 14 unique differential metabolites were identified in the POL group, predominantly enriched in sphingolipid metabolism, glycine, serine, and threonine metabolism, aminoacyl-tRNA biosynthesis, and glutathione metabolism. Conclusions: The 8-week THR and POL training models demonstrated similar effects on enhancing aerobic performance in adolescent male rowers, indicating that both training modalities share similar blood metabolic mechanisms for improving aerobic endurance. Furthermore, both the THR group and the POL group exhibited numerous shared metabolites and some differential metabolites, suggesting that the two endurance training models share common pathways but also have distinct aspects in enhancing aerobic endurance.

The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements. We aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies. We conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items. In total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting. Our findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.

The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

Offline reinforcement learning leverages previously collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also desirable for multi-agent reinforcement learning (MARL) tasks, given the combinatorially increased interactions among agents and with the environment. However, in MARL, the paradigm of offline pre-training with online fine-tuning has not been studied, nor even datasets or benchmarks for offline MARL research are available. In this paper, we facilitate the research by providing large-scale datasets and using them to examine the usage of the decision transformer in the context of MARL. We investigate the generalization of MARL offline pre-training in the following three aspects: 1) between single agents and multiple agents, 2) from offline pretraining to online fine tuning, and 3) to that of multiple downstream tasks with few-shot and zero-shot capabilities. We start by introducing the first offline MARL dataset with diverse quality levels based on the StarCraftII environment, and then propose the novel architecture of multi-agent decision transformer (MADT) for effective offline learning. MADT leverages the transformer’s modelling ability for sequence modelling and integrates it seamlessly with both offline and online MARL tasks. A significant benefit of MADT is that it learns generalizable policies that can transfer between different types of agents under different task scenarios. On the StarCraft II offline dataset, MADT outperforms the state-of-the-art offline reinforcement learning (RL) baselines, including BCQ and CQL. When applied to online tasks, the pre-trained MADT significantly improves sample efficiency and enjoys strong performance in both few-short and zero-shot cases. To the best of our knowledge, this is the first work that studies and demonstrates the effectiveness of offline pre-trained models in terms of sample efficiency and generalizability enhancements for MARL.