Prehospital transfusion training in Canada: a national survey of critical care transport organizations by Pierre-Marc Dion, Andy Pan, Andrew Beckett, Kanwal Singh, Adam Greene, Axel Benhamed, Melissa McGowan, Brodie Nolan

“…However, limited evidence exists on how providers are trained to deliver this intervention. While simulation-based education and instructional design features improve skill retention in other contexts, their application in prehospital transfusion training has not been systematically evaluated. …”
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The Сoncept of an Advanced Training Сourse in Information Culture for Students of Economic Spesialties by N. A. Panshin

“…Based on the analysis of the theory and practice of modern digital transformation, approaches and the concept of the content of an advanced training course in information culture (in modern conditions - digital culture) are offered for students of economic specialties. …”
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Challenges of Disaster Assessment of Readiness and Training Guideline (DART) in Iran’s Primary Health Care by Hamzeh Zarei, Mohammad H. Yarmohammadian, Nikoo Yamani, Golrokh Atighechian, Fatemeh Rezaei

“…Background: DART (Disaster Assessment of Readiness and Training program) is one of the five Disaster Risk Reduction Management Programs (DRRMP) in Primary Health Care (PHC) of Iran. …”
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Algorithm of Behaviour of Virtual Avatars During Public Speaking Training in a VR Environment by Karol Jędrasiak, Robert Robert Wolański, Damian Bereska

“…Results: The results of implementing this algorithm in VR-based training applications showed a significant improvement in the authenticity and effec - tiveness of public speaking training. …”
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PoseRL-Net: human pose analysis for motion training guided by robot vision by Bin Liu, Hui Wang

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Educational game for conflict mediation training in wartime conditions using large language models by Sophia V. Ilkova, Pavlo V. Merzlykin, Natalia V. Moiseienko

“…This research introduces an innovative educational game that teaches mediation skills through interactive dialogue with characters generated by large language models (LLMs). The game features dynamically generated conflicts and personalized responses based on player actions, allowing users to practice mediation strategies in a safe, repeatable environment. …”
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A large-scale dataset for training deep learning segmentation and tracking of extreme weather by Sol Kim, Andre Graubner, Lukas Kapp-Schwoerer, Karthik Kashinath, Konrad Schindler

“…Professional annotators were trained and guided to identify these features by domain-experts, and event-specific experts were consulted for each of the annotation guides. …”
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Physiology-informed regularisation enables training of universal differential equation systems for biological applications. by Max de Rooij, Balázs Erdős, Natal A W van Riel, Shauna D O'Donovan

“…However, on one hand the development and validation of knowledge-based mechanistic models is time-consuming and does not scale well with increasing features in medical data. …”
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Parameter-efficient adaptation with multi-channel adversarial training for far-field speech recognition by Tong Niu, Yaqi Chen, Dan Qu, Hengbo Hu, ChengRan Liu

“…To overcome these problems, we introduce a parameter-efficient speech prefix tuning (SPT) for FSR based on Whisper for the first time. This method uses fixed-length vector sequences appended to input features of each layer, enhancing the model’s capability for handling complex FSR tasks. …”
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Class Concept Representation From Contextual Texts for Training-Free Multi-Label Recognition by Dong Un Kang, Hyunwoo Lee, Se Young Chun

“…The power of large vision-language models (VLMs) has been demonstrated for downstream vision tasks, including multi-label recognition (MLR) with a training-free approach or prompt tuning by measuring the cosine similarity between the text features related to class names and the visual features of the images. …”
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Dual-Aspect Active Learning with Domain-Adversarial Training for Low-Resource Misinformation Detection by Luyao Hu, Guangpu Han, Shichang Liu, Yuqing Ren, Xu Wang, Zhengyi Yang, Feng Jiang

“…Although deep learning-based detection methods have achieved promising results, their effectiveness heavily relies on large amounts of labeled data, limiting their applicability in low-resource scenarios. …”
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Virtual Simulation Tools for Communication Skills Training in Health Care Professionals: Literature Review by Manuel Fernández-Alcántara, Silvia Escribano, Rocío Juliá-Sanchis, Ana Castillo-López, Antonio Pérez-Manzano, M Macur, Sedina Kalender-Smajlović, Sofía García-Sanjuán, María José Cabañero-Martínez

“…Health care professionals can improve their communication skills through simulation-based training, but our knowledge about virtual simulation and its effectiveness and use in training health professionals and students is still growing rapidly. …”
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PROFESSIONAL TRAINING OF FUTURE TEACHING STAFF: PHONATION-PHONETIC COMPLEX OF KNOWLEDGE, SKILLS AND ABILITIES by Nataliya V. Pavlova

“…Materials and methods. The article is based on the study of aspects of the content, essence, and features of the professional training of future teachers. …”
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Students’ Values of Various Training Profiles in the Digitalization of Society Context: Results of an Empirical Study by P. P. Deryugin, O. S. Bannova

“…All this is formed into a new group of student digital values, which are shaped differently among students of various professional training profiles. These new phenomena involve the search for new methodological and methodological approaches to conducting empirical sociological research, the search for relevant approaches to measuring the digital values of students of various training profiles, and identifying special characteristics and meaningful features of these values.Methodology and sources. …”
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The Impact of Virtual Reality on Technical Training: A Case Study in Live-Line Maintenance by Filip Gorski, Jakub Gapsa, Damian Grajewski, Przemyslaw Zawadzki, Mikolaj Maik, Pawel Sobocinski, Przemyslaw Starzynski, Rafal Wojciechowski, Krzysztof Walczak

“…A study was conducted involving 124 participants, divided into four groups, to compare the outcomes of VR-based training, traditional training, and a combination of both methods. …”
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Comparing UNet configurations for anthropogenic geomorphic feature extraction from land surface parameters. by Sarah Farhadpour, Aaron E Maxwell

“…Our results suggest that the incorporation of advanced modules can enhance segmentation performance, particularly in scenarios involving limited training data or complex geomorphic landscapes. However, differences were minimal when larger training set sizes were used (e.g., above 500 image chips) and the base UNet architecture was generally adequate. …”
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An efficient non-parametric feature calibration method for few-shot plant disease classification by Jiqing Li, Zhendong Yin, Dasen Li, Hongjun Zhang, Mingdong Xu

“…By leveraging the strict positive correlation between FAS scores and test accuracy, we can identify the Swin-Transformer V2-F6 network structure suitable for few-shot plant disease classification without training the network. Furthermore, based on this network structure, we designed the Plant Disease Feature Calibration (PDFC) algorithm, which uses extracted features from the PlantVillage dataset to calibrate features from other datasets. …”
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A systematic scoping review of mentor training in medical education between 2000 and 2024 by Jun Rey Leong, Adele Yi Dawn Lim, Nila Ravindran, Darius Wei Jun Wan, Varsha Rajalingam, Jun Kiat Lua, Hannah Yi Fang Kwok, Krish Sheri, Victoria Jia En Fam, Ranitha Govindasamy, Nur Amira Binte Abdul Hamid, Michael Dunn, Lalit Kumar Radha Krishna

“…Acknowledging mentor training as an essential aspect of mentoring programs, we propose a review to map current features and approaches to mentor training with the hopes of boosting the effective design of a proposed mentoring program. …”
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End-to-end feature fusion for jointly optimized speech enhancement and automatic speech recognition by Mohamed Medani, Nasir Saleem, Fethi Fkih, Manal Abdullah Alohali, Hela Elmannai, Sami Bourouis

“…The ASR results find that the joint training framework reduces the error rate from 32.99% (average noisy signals) to 13.52% (with the proposed SE and joint training for ASR).…”
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Enhancing Adversarial Transferability With Intermediate Layer Feature Attack on Synthetic Aperture Radar Images by Xuanshen Wan, Wei Liu, Chaoyang Niu, Wanjie Lu, Yuanli Li

“…To address this problem, this study proposes an intermediate layer feature attack algorithm that does not rely on training data for the adversary model. …”
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