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Multi-party conversations are a practical and challenging scenario with more than two sessions entangled with each other. Therefore, it is necessary to disentangle a whole conversation into several sessions to help listeners decide which session each utterance is part of to respond to it appropriately. This task is referred to as dialogue disentanglement. Most existing methods focus on message-pair modeling and clustering in two-step methods, which are sensitive to the noise classification pairs and result in poor clustering performance. To address this challenge, we propose a contrastive learning framework named IAM for end-to-end implicit addressee modeling. To be more specific, IAM makes utterances in different sessions mutually exclusive to identify the sessions of utterances better. Then a clustering method is adopted to generate predicted clustering labels. Moreover, to alleviate the lack of massive annotated data, we introduce a strategy to select pseudo samples for unsupervised training without manual annotations. Comprehensive experiments conducted on the Movie Dialogue and IRC datasets demonstrate that IAM achieves state-of-the-art in both supervised and unsupervised manners. [ABSTRACT FROM AUTHOR]

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability. [ABSTRACT FROM AUTHOR]

The increasing mental health challenges among elders demand specialized interventions, especially within Thai communities where resources are limited and stigma persists. While monks offer spiritual support, there is a gap in addressing complex mental health needs. This research aims to develop a monk-led elderly mental health counseling program in Thai Buddhist communities. From January 2023 to March 2024, this study underwent four phases. Initially, qualitative interviews with thirty-six monk and elder participants elucidated requirements. The program development integrated findings from the requirement study, the Solution-Focused Brief Therapy process, and Buddhist mindfulness principles to create a prototype. The quality assessment involved expert content validation, feasibility examination by stakeholders, and a small-scale pilot testing with five monks. Finally, the feasibility of the program was assessed with thirty-two monks. The study reveals three key components of the monk-led elderly counseling program focused on mental health: the counseling process known as MPS-MAV-PI (an Introduction to Mindfulness, Identifying Problems, Assessing the Severity, Mindfully Observing Thoughts and Emotions, Acceptance, Visualizing Success, Planning Strategies for Problem-solving, and Implementation and Subsequent Monitoring), the C-TIME strategy (Collaboration, Training Manual, Implementation, the Monitoring, and Evaluation), and the program manual. Moreover, feasibility assessments among monks show the high feasibility of the program for implementation. The monk-led counseling program holds promise in addressing these challenges, with high feasibility indicating potential effectiveness and scalability. Future research will prioritize evaluating its cost-effectiveness and overall effectiveness. [ABSTRACT FROM AUTHOR]

Along with seaports automation, there is growing interest in the automation of Grab-Type Ship Unloader (GTSU) that unloads coal and iron ore from bulk carriers. Autonomous unloading operations of GTSU offer the potential for significant productivity improvement and cost savings. In this paper, an AI model trained with manual operation data was designed for GTSU automation operation, and the AI model was verified through the equation-of-motion-based GTSU operation simulator. The operation data of hoist, grab, and trolley were predicted by training the designed AI model with the manual operation data of GTSU. Before applying the predicted data to the actual equipment, the predicted driving data was verified using the equation-of-motion-based GTSU operation simulator. The AI prediction model was designed using the Multi-Layer Perception network, a type of artificial neural network. The AI prediction model was evaluated with the Mean-Squared Error indicator, and the validation loss was found to be less than 0.02. In addition, verification of the prediction data was performed using the GTSU dynamics-based simulator. The Mean Relative Error was up to 0.50, and the R2 score value exceeded 0.92, indicating that the model is effective in predicting operation data. The proposed AI prediction model will be effectively utilized to implement a fully automated unloading system. [ABSTRACT FROM AUTHOR]

Nanomaterial properties and functionalities are influenced by their shape, size, and chemical composition. The importance of these parameters highlights the need for a statistically robust analysis of a large particle population, necessitating automation. This study introduces a neural network‐empowered smart scan technique that achieves a relative increase in speed compared to traditional energy‐dispersive X‐ray spectroscopy (EDX) mapping. The main advantage is that it reduces the required dose, decreasing potential damage to the sample by avoiding unnecessary exposure. It holds potential use in other multimodal scanning transmission electron microscopy or scanning‐based imaging approaches. In the first example, identifying particles in a matrix with a trained neural network reduces the acquisition time by two orders of magnitude. This acceleration enables a statistical compositional analysis of thousands of particles in less than 1 h. Similar improvements are observed for atomic resolution. The discrete positions of atoms identified by the trained network allow for selective EDX sampling at these centers, thereby identifying the atomic species of the column with much‐reduced sampling. Consequently, a lower sampling dose is required, enabling mapping of more delicate materials with high lateral resolution and at a high statistical confidence interval. Even though manual training is still required, this approach greatly benefits repetitive quality control tasks. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) quantification of intramuscular fat accumulation is a responsive biomarker in neuromuscular diseases. Despite emergence of automated methods, manual muscle segmentation remains an essential foundation. We aimed to develop a training programme for new observers to demonstrate competence in lower limb muscle segmentation and establish reliability benchmarks for future human observers and machine learning segmentation packages. Methods: The learning phase of the training programme comprised a training manual, direct instruction, and eight lower limb MRI scans with reference standard large and small regions of interest (ROIs). The assessment phase used test–retest scans from two patients and two healthy controls. Interscan and interobserver reliability metrics were calculated to identify underperforming outliers and to determine competency benchmarks. Results: Three experienced observers undertook the assessment phase, whilst eight new observers completed the full training programme. Two of the new observers were identified as underperforming outliers, relating to variation in size or consistency of segmentations; six had interscan and interobserver reliability equivalent to those of experienced observers. The calculated benchmark for the Sørensen-Dice similarity coefficient between observers was greater than 0.87 and 0.92 for individual thigh and calf muscles, respectively. Interscan and interobserver reliability were significantly higher for large than small ROIs (all p < 0.001). Conclusions: We developed, implemented, and analysed the first formal training programme for manual lower limb muscle segmentation. Large ROI showed superior reliability to small ROI for fat fraction assessment. Relevance statement: Observers competent in lower limb muscle segmentation are critical to application of quantitative muscle MRI biomarkers in neuromuscular diseases. This study has established competency benchmarks for future human observers or automated segmentation methods. Key points: • Observers competent in muscle segmentation are critical for quantitative muscle MRI biomarkers. • A training programme for muscle segmentation was undertaken by eight new observers. • We established competency benchmarks for future human observers or automated segmentation methods. [ABSTRACT FROM AUTHOR]

The Sloyd Training School, an early twentieth-century private school for teachers in Boston, attempted to legitimize the Sloyd method of handiwork. Specifically, its Alumni Association's publication Sloyd Record brought together educators across the country to make a case for Sloyd's relevancy and impact on the academic and professional development of students, particularly students who were working poor or receiving educations in non-traditional settings. Its contributors painted Sloyd as a form of knowledge and a resource, as a literacy, and their rhetorical effectiveness was predicated upon Sloyd's ability to be painted as such in its far-reaching effects and comprehensiveness. [ABSTRACT FROM AUTHOR]

Background: Molar–incisor hypomineralisation (MIH) has been found associated with an increase in dental caries. Studies utilising the recent MIH diagnosis and calibration criteria recommended by the EAPD in 2021 are very scarce. Additionally, the available data in literature about the relationship between the hypomineralised teeth number (HTN) and caries come from only one study. Purpose: To assess the relationship between MIH and caries experience among children in the city of Fujairah, UAE, utilising the recent criteria recommended by the European Academy of Paediatric Dentistry (EAPD) in 2021, and to assess the relationship between the number of teeth affected with MIH, and dental caries. Methods: One hundred and sixty-two children were included in this cross-sectional study, aged 7–9 years old. Children were examined for MIH according to Ghanim et al. (Eur Arch Paediatr Dent 16:235–246, 2015. 10.1007/s40368-015-0178-8) criteria and Ghanim et al. (Eur Arch Paediatr Dent 18:225–242. 10.1007/s40368-017-0293-92017) training manual. Caries experience was assessed with decayed, missing, filled (dmft, DMFT) scoring system. Results: dmft mean was 6.56 (SD ± 3.78) and DMFT mean was 0.91 (SD ± 1.23). Children with MIH had significantly higher dmft (p = 0.003) scores. Children with higher HTN had significantly higher dmft (p = 0.008) scores. Conclusion: Children in Fujairah have extremely high caries scores. Children with MIH have more decayed, missing and filled teeth. Hypomineralised teeth number was positively associated with caries experience. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The classical P300 brain potential method was used to assess the cognitive capacity during training of manual docking in space. The aim of the study was to enhance the safety of this operation during a mission. METHODS: To examine this, N = 8 cosmonauts had to perform the manually controlled docking task simultaneously with an acoustic monitoring task. The P300 component was evoked by the acoustic stimuli of the secondary task. The docking task had to be executed at three difficulty levels: low (station not turning); medium (station turning around one axis); and difficult (station turning around three axes). In the secondary task, subjects had to discriminate between a low and a high tone, which occurred with a probability of 90% and 10%, respectively. Subjects had to count the high tones. After the 10th high tone, they had to inspect the power supply by giving an oral command. RESULTS: A methodology for event-related potentials was successfully demonstrated under space conditions. The P300 amplitude was largest and the latency shortest during the medium difficult task. DISCUSSION: The results suggest that P300 can be recorded during the complex manual docking task in space and could be used to assess individual available cognitive capacity of cosmonauts during a space mission. [ABSTRACT FROM AUTHOR]

The development of speech technology requires large amounts of data to estimate the underlying models. Even when relying on large multilingual pre-trained models, some amount of task-specific data on the target language is needed to fine-tune those models and obtain competitive performance. In this paper, we present a bilingual Basque–Spanish dataset extracted from parliamentary sessions. The dataset is designed to develop and evaluate automatic speech recognition (ASR) systems but can be easily repurposed for other speech-processing tasks (such as speaker or language recognition). The paper first compares the two target languages, emphasizing their similarities at the acoustic-phonetic level, which sets the basis for sharing data and compensating for the relatively small amount of spoken resources available for Basque. Then, Basque Parliament plenary sessions are characterized in terms of organization, topics, speaker turns and the use of the two languages. The paper continues with the description of the data collection procedure (involving both speech and text), the audio formats and conversions along with the creation and postprocessing of text transcriptions and session minutes. Then, it describes the semi-supervised iterative procedure used to cut, rank and select the training segments and the manual supervision process employed to produce the test set. Finally, ASR experiments are presented using state-of-the-art technology to validate the dataset and to set a reference for future works. The datasets, along with models and recipes to reproduce the experiments reported in the paper, are released through Hugging Face. [ABSTRACT FROM AUTHOR]

This article focuses on the performative textual strategies of Saʿd al-Dīn Ḥamūya (d. 1252), a Mongol-era Sufi whose arcane treatises both bewildered and inspired future generations of occultists, mystics, and messiahs. Framing Ḥamūya's work as avant-garde Sufism or Sufi free jazz, the article demonstrates how the shaykh self-consciously deconstructed medieval Sufi genre conventions to engage readers in an active process of meaning making. The training manuals penned by Ḥamūya's teachers and colleagues bound abstract metaphysical frameworks to the embodied and affective dimensions of Sufi praxis, teaching readers to negotiate multiple levels of reality through careful scrutiny of their own subjective experiences. With a deft use of structural allusion and ritual cues, Ḥamūya placed his own idiosyncratic formulations in conversation with these manuals while simultaneously deconstructing the very conventions through which they became meaningful. In so doing, the shaykh forced advanced Sufi readers to continually remap constellations of theory, practice, and experience without a stable hermeneutical center, thus leaving his work radically open to play and interpretation. While his colleagues claimed spiritual authority by subsuming a diversity of embodied practices and metaphysical frames within a single authorial vision, Ḥamūya parochialized their universalizing pretensions by recruiting readers to imagine and expand the endless possibilities of reality for him. [ABSTRACT FROM AUTHOR]

Objective: The present scoping review aims to transform the diverse field of research on the effects of mixed reality-based training on performance in manual assembly tasks into comprehensive statements about industrial needs for and effects of mixed reality-based training. Background: Technologies such as augmented and virtual reality, referred to as mixed reality, are seen as promising media for training manual assembly tasks. Nevertheless, current literature shows partly contradictory results, which is due to the diversity of the hardware used, manual assembly tasks as well as methodological approaches to investigate the effects of mixed reality-based training. Method: Following the methodological approach of a scoping review, we selected 24 articles according to predefined criteria and analyzed them concerning five key aspects: (1) the needs in the industry for mixed reality-based training, (2) the actual use and classification of mixed reality technologies, (3) defined measures for evaluating the outcomes of mixed reality-based training, (4) findings on objectively measured performance and subjective evaluations, as well as (5) identified research gaps. Results: Regarding the improvement of performance and effectiveness through mixed reality-based training, promising results were found particularly for augmented reality-based training, while virtual reality-based training is mostly—but not consistently—as good as traditional training. Application: Mixed reality-based training is still not consistently better, but mostly at least as good as traditional training. However, depending on the use case and technology used, the training outcomes in terms of assembly performance and subjective evaluations show promising results of mixed reality-based training. [ABSTRACT FROM AUTHOR]

Background: An ongoing barrier to sustainable obstetric fistula (OF) care is the lack of trained fistula surgeons. Despite a standardized training curriculum, data regarding OF repair training remain limited. Objectives: To assess the availability of literature on the case numbers or training duration required for OF repair competency and whether these data are stratified by trainee background or repair complexity. Search strategy: A systematic search of MEDLINE, Embase, and OVID Global Health electronic databases and gray literature. Selection criteria: All English sources from all years from low‐ and middle‐income and high‐income countries were eligible. Identified titles and abstracts were screened and full‐text articles were reviewed. Data collection and analysis: Data collection and analysis included a descriptive summary organized by training case numbers, training duration, trainee background, and repair complexity. Results: Of the 405 sources retrieved, 24 were included in the study. The only concrete recommendations were in the International Federation of Gynecology and Obstetrics 2022 Fistula Surgery Training Manual, which proposes 50 to 100 repairs (Level 1), 200 to 300 repairs (Level 2), and trainer discretion for Level 3 competency. Conclusions: More case‐ or time‐based data, particularly if stratified by trainee background and repair complexity, would be useful at the individual, institutional, and policy level for fistula care implementation or expansion. Synopsis: More research is required regarding the number of cases and time duration needed to achieve competency in obstetric fistula repair. [ABSTRACT FROM AUTHOR]

This manuscript offers a critical rhetorical analysis of a multi-site, longitudinal study’s procedures in collecting and recording biomarkers. This manuscript opens new areas of exploration for the field of the rhetoric of health and medicine as the biomarker sampling for measures of stress, and resilience tie to critical rhetorical theories surrounding power and the body. The training manuals and protocols disseminated to the multi-site research team serve as rhetorical arti facts to examine questions of how the choices of biomarkers and the procedures employed to collect the samples needed to measure them are in and of them selves a production of health knowledge of the bodies and identities of transgender and gender diverse people. This manuscript presents an investigation of the pro cesses of biomarker sample collection in conjunction with how the biomarkers are conceptualized as a means of deconstructing hegemonic assertions of gender and health normality. [ABSTRACT FROM AUTHOR]

The field of technology education in the United States has evolved from manual training, manual arts, industrial arts, technology education, to the current technology and engineering education. This historical review examines how the purposes of these approaches to K-12 STEM education in the United States have changed over time. In this paper, several key initiatives, including national learning standards for technology education, are identified, and the impacts of each milestone on current US technology and engineering education are investigated. Finally, the way in which the field of technology and engineering education has embraced and integrated other fields of study, as well as the current position of 'T' (Technology) and 'E' (Engineering) in STEM, is explored. This review will provide K-12 STEM educators with insights into the American historical development of problem- and project-based learning in technology and engineering education, helping them better understand how the contemporary integrated STEM education movement has evolved into its current state. [ABSTRACT FROM AUTHOR]

Copyright of Hipogrifo: revista de literatura y cultura del siglo de oro is the property of Hipogrifo: revista de literatura y cultura del siglo de oro and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50, MobileNetV2, EfficientNet-B0, and ConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably, ResNet50 and EfficientNet-B0 demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively. EfficientNet-B0 demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of this EfficientNet-B0 model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well. [ABSTRACT FROM AUTHOR]

In liberal political philosophy, from Michel de Montaigne to Judith Shklar, cruelty – the wilful inflicting of pain on another in order to cause anguish and fear – has been singled out as 'the most evil of all evils' and as unjustifiable: the ultimate vice. An unconditional rejection and negation of cruelty is taken to be programmatic within a liberal paradigm. In this contribution, two anthropologists triangulate cruelty as a concept with torture (Stade) and with love (Rapport). Treating the capability to practise cruelty and the liability to suffer from cruelty as universal aspects of a human condition, Stade and Rapport aim to instantiate the precise enactment of cruelty, firstly, and secondly, to propose a process of its social negation. CIA training manuals and quotidian practice within the British National Health Service are employed as illustrative materials. [ABSTRACT FROM AUTHOR]

Mining is a high-risk industry, so efficiency and safety are key priorities. Technological advancements, such as digitisation, digitalisation, and automation have made mines safer. These developments have also highlighted the need for operators with updated skills and improved education programs. This study analysed the training of semi-autonomous and manual Load Haul Dump (LHD) operators' at LKAB's Kiirunavaara mine, focusing on operators' training, perspective and integration of more recent tool such as simulator training. The survey questionnaire was sent to all 120 LHD operators. 86 answers were received, giving response rate of 70%. Results showed that operators generally were satisfied with how the training was structured, organised, and delivered. However, they wanted to add more topics, including practical loading, spending time with departments of other sub-processes, etc. In addition, 36% of the operators, including 20% of those operating semi-autonomous LHDs, and 80% of those operating manual LHDs, found simulator training difficult. [ABSTRACT FROM AUTHOR]

Objective: This research note introduces a set of tools to conduct analysis of household structure and composition with either limited or comprehensive longitudinal data. The data used here are from Health and Demographic Surveillance Systems in Africa, but the methods can be adapted and applied to other longitudinal micro-data such as register data. A training manual describing the procedures for creating time-varying household measures step-by-step is supplied as supplementary material to this note. Code is provided in STATA but can easily be translated for other statistical software, and the logic for each step remains the same. Results: The analysis of household structure demonstrates how with limited data (such as a household identifier), it is possible to construct time-varying measures of household membership, including household size or the number of members in specific age and sex groups. The analysis of household composition demonstrates how with expanded data (including links to parents in addition to residence status in the same household), it is possible to construct time-varying measures of household membership of specific kin, i.e. mother, sibling or grandparent. The results illustrated in this research note are a taste of what can be achieved by following the training manual in the supplementary material. [ABSTRACT FROM AUTHOR]

Background: The JOBS Program Germany is a labor market integrative and health promotion intervention for unemployed people. This study examines JOBS Program Germany trainers' view of (1) the theoretical concept of the JOBS Program, (2) its practical implementation in Germany on-site, (3) its acceptance by participants, and (4) the training effects. The study aimed at identifying potential for adaption allowing adjustments to improve the practical implementation and the training effectiveness. Methods: JOBS Program Germany trainers (two for each training) were interviewed via voluntary survey (computer-assisted web interviews) after each training. Results: Fourteen JOBS Program trainings have been conducted and all trainers responded resulting in 28 interviews. 78.5% and 85.7% of the respondents were rather or very satisfied with the theoretical contents and its practical implementation, respectively. Almost all trainers (96.4–100.0%) were satisfied with the on-site coordination, the cooperation with the organizer's employees, the room equipment, the training room size, and the environmental conditions in the training rooms. In 89.3% of all responses the trainers rated the last training a success. However, the trainers also provided valuable suggestions for further improvement in Germany. This concerns the revision of the training manual, the adjustment of the trainer training and the preparation of participants. Conclusion: Besides the trainers' positive view on the different dimensions of the training content and implementation, their suggestions can help ensure that many unemployed people in Germany could benefit from a continued regular implementation of the JOBS Program Germany in the future. Trial Registration: German Clinical Trials Register (DRKS), DRKS00022388. Registered on 20 July, 2020. [ABSTRACT FROM AUTHOR]

The dynamic field of food safety faces continuous challenges, prompting stakeholders to develop collaborative actions for improved food safety systems. As part of these actions, the EU‐FORA fellowship programme was dedicated to a multi‐actor collaboration addressing risks of the unregulated mycotoxins T‐2 and HT‐2 toxins in oats. Critical gaps in risk assessment procedures were identified, leading to a joint effort to develop a strategy for rapid data collection and risk assessment, including the development of a risk assessment toolkit comprising of a training manual and two intuitive Microsoft® Excel files. The toolkit enables efficient data collection and processing, facilitating risk assessment calculations and rapid risk detection. Applying the toolkit to assess T‐2 and HT‐2 toxin risks in Belgian oats revealed minimal concerns, except for children aged 3–9 years, likely due to an overestimation. The toolkit is available on the FoodSafety4EU Platform and will be refined based on user feedback, promoting better risk assessment practices. This approach empowers stakeholders, from professionals to policymakers, fostering collaboration and enhancing food safety practices. [ABSTRACT FROM AUTHOR]

Background: Psychological First Aid (PFA) is practiced worldwide. This practice in English is guided through a small collection of training manuals. Despite ubiquitous practice and formal training materials, little is known about what topics are covered and in what depth in these influential manuals. As such, we analyzed the topic structure of these training manuals. Objective: To model the PFA manuals' topics with the goal of identifying a set of topics with recurrent themes and evaluating the extent to which each manual demonstrated those themes. Method: This machine learning study employed an unsupervised topic modelling design using Latent Dirichlet Allocation. The variables are (1) the distribution of a word across documents and (2) the distribution of a word across topics. The level of measurement for all variables is continuous. The unit of analysis is words. Preprocessing and data analysis were carried out using the Orange Data Mining Toolbox (Demšar et al., 2013). This programme is a Python GUI. Results: Results indicated a ten-topic structure to the universe of the English PFA training manuals. These topics were: (1) Refugees, (2) Orientation Activities, (3) Community-Based Applications, (4) PTSD & Other Psychological Issues, (5) Training Materials, (6) Specific Helper Instructions, (7) PFA Scholarship, (8) MHPSS, (9) General Curriculum, and (10) Australian Specific Delivery. The depth of discourse on each topic varied widely between manuals. Conclusions: The Academics of the PFA topic shows a strong representation of the corpus and suggests current training manuals have stayed true to its evidence-supported practice. The topic of Community-Based Applications strongly represents the corpus and suggests that training models incorporate community-based applications. The scientific foundation and practical implementation of the training guides are essential elements. Limitations and implications were also discussed. Little is known about what topics are covered and in what depth in the influential PFA English manuals. We conducted a topic modelling study using Latent Dirichlet Allocation, aiming to discover a set of topics with recurring themes and analyze the degree to which each manual exhibited those topics. Results indicated a 10-topic structure to the universe of the English PFA training manuals. The training manuals' scientific basis and practical application are key components, while notable gaps presented. [ABSTRACT FROM AUTHOR]

In this paper, to better evaluate the flight performance of pilot cadets, a flight performance evaluation index system was constructed based on the task of the traffic pattern, the flight training manual, and interviews with instructors. The fuzzy comprehensive evaluation model established by the G1 method was used to evaluate the flight performance of pilot cadets. The flight data of 30 flight cadets were collected to verify the applicability of the fuzzy comprehensive evaluation model. The results showed that the index system established in this paper can meet the requirements of flight performance evaluation. In addition, the fuzzy comprehensive evaluation results were consistent with the evaluation results of experts. Therefore, the system is effective and feasible for the evaluation of pilot cadets' flight performance through the fuzzy comprehensive evaluation model established by the index system and G1 method. [ABSTRACT FROM AUTHOR]

Objectives: The precise segmentation of atrophic structures remains challenging in neurodegenerative diseases. We determined the performance of a Deep Neural Patchwork (DNP) in comparison to established segmentation algorithms regarding the ability to delineate the putamen in multiple system atrophy (MSA), Parkinson's disease (PD), and healthy controls. Methods: We retrospectively included patients with MSA and PD as well as healthy controls. A DNP was trained on manual segmentations of the putamen as ground truth. For this, the cohort was randomly split into a training (N = 131) and test set (N = 120). The DNP's performance was compared with putaminal segmentations as derived by Automatic Anatomic Labelling, Freesurfer and Fastsurfer. For validation, we assessed the diagnostic accuracy of the resulting segmentations in the delineation of MSA vs. PD and healthy controls. Results: A total of 251 subjects (61 patients with MSA, 158 patients with PD, and 32 healthy controls; mean age of 61.5 ± 8.8 years) were included. Compared to the dice-coefficient of the DNP (0.96), we noted significantly weaker performance for AAL3 (0.72; p <.001), Freesurfer (0.82; p <.001), and Fastsurfer (0.84, p <.001). This was corroborated by the superior diagnostic performance of MSA vs. PD and HC of the DNP (AUC 0.93) versus the AUC of 0.88 for AAL3 (p = 0.02), 0.86 for Freesurfer (p = 0.048), and 0.85 for Fastsurfer (p = 0.04). Conclusion: By utilization of a DNP, accurate segmentations of the putamen can be obtained even if substantial atrophy is present. This allows for more precise extraction of imaging parameters or shape features from the putamen in relevant patient cohorts. Clinical relevance statement: Deep learning-based segmentation of the putamen was superior to currently available algorithms and is beneficial for the diagnosis of multiple system atrophy. Key Points: • A Deep Neural Patchwork precisely delineates the putamen and performs equal to human labeling in multiple system atrophy, even when pronounced putaminal volume loss is present. • The Deep Neural Patchwork–based segmentation was more capable to differentiate between multiple system atrophy and Parkinson's disease than the AAL3 atlas, Freesurfer, or Fastsurfer. [ABSTRACT FROM AUTHOR]

Deep learning networks might require re-training for different datasets, consuming significant manual labeling and training time. Transfer learning uses little new data and training time to enable pre-trained network segmentation in relevant scenarios (e.g., different vegetation images in rainy and dry seasons); however, existing transfer learning methods lack systematicity and controllability. So, an MTPI method (Maximum Transfer Potential Index method) was proposed to find the optimal conditions in data and feature quantity for transfer learning (MTPI conditions) in this study. The four pre-trained deep networks (Seg-Net (Semantic Segmentation Networks), FCN (Fully Convolutional Networks), Mobile net v2, and Res-Net 50 (Residual Network)) using the rainy season dataset showed that Res-Net 50 had the best accuracy with 93.58% and an WIoU (weight Intersection over Union) of 88.14%, most worthy to transfer training in vegetation segmentation. By obtaining each layer's TPI performance (Transfer Potential Index) of the pre-trained Res-Net 50, the MTPI method results show that the 1000-TDS and 37-TP were estimated as the best training speed with the smallest dataset and a small error risk. The MTPI transfer learning results show 91.56% accuracy and 84.86% WIoU with 90% new dataset reduction and 90% iteration reduction, which is informative for deep networks in segmentation tasks between complex vegetation scenes. [ABSTRACT FROM AUTHOR]

Copyright of Beiträge zur Lehrerinnen - und Lehrerbildung is the property of Schweizerische Gesellschaft fur Lehrerinnen- und Lehrerbildung (SGL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the advancements in technology, mixed reality (MR) has found numerous applications in industries and job training. However, most MR applications only provide visual feedback, lacking realistic haptic feedback. Nevertheless, certain jobs require haptic sensations in the hands. To address this issue, this study integrates realistic haptic feedback into an MR environment and develops a visuo-haptic MR milling simulator for conventional manual milling operation training using Microsoft HoloLens 2. This simulator comprises a virtual spindle, a virtual milling tool, a virtual workpiece, and a physical mockup of the milling table. It provides realistic cutting force feedback when the virtual milling tool collides with the virtual workpiece. Users can operate the milling simulator with their bare hands and feel cutting force as if they were operating a real milling machine. The developed training system allows novices to intuitively understand the relationship between milling parameters and milling conditions. The usability and immersiveness of the visuo-haptic MR milling simulator are studied, and the results show that realistic haptic feedback increases usability, realism, immersiveness, and presence. [ABSTRACT FROM AUTHOR]

The study focused on ergonomic principles for organizing garment-making work environment in South Eastern Nigeria. Specifically, it determined: important ergonomic principles for organizing garment-making work environment; ergonomic principles that are being practiced by garment workers, ergonomic principles that are needed by garment workers in their work environment, and work station design guidelines of the garment workers. Descriptive survey research design was used. The population was made up of 267 garment workers drawn from 19 registered garment companies in the area of the study. Questionnaire was used for data collection. Data were analyzed using mean, standard deviation, improvement need index and percentages. Findings include 19 important ergonomic principles for organizing garment work environment. These include; use of task lighting to distribute light evenly over the working surface to minimize shadow (X̅ = 3.61), maintaining adequate ventilation and good temperature in workplace to minimize fatigue (X̅ = 3.66), among others. Four (4) out of the 19 important ergonomic principles were being practiced include: alternating standing and sitting jobs to reduce fatigue (X̅ = 3.13), among others. Fifteen (15) ergonomic principles are needed by garment workers for organizing their work environment which include; use of task lighting on work surfaces to minimize shadow (X̅ = 3.06), maintaining adequate ventilation and good temperature to minimize fatigue (X̅ = 2.07), and others. Further findings are nine areas of work station design guidelines that are not practiced. These include: work surface height, designing of work to reduce static loading of muscles, among others. It was recommended among others that the ergonomic principles should be packaged in a training manual for training of garment workers by relevant bodies. [ABSTRACT FROM AUTHOR]

Background: Muscle strength and dexterity impairments are common among patients with multiple sclerosis (MS) producing limitations in activities of daily living related to the upper limb (UL). This study aimed to evaluate the effectiveness of serious games specifically developed for the MYO Armband® capture sensor in improving forearm and wrist mobility, UL muscle strength, dexterity, fatigue, functionality, quality of life, satisfaction, adverse effects and compliance. Methods: A double-blinded (allocation concealment was performed by a blinded investigator and by blinding for assessors) randomised controlled trial was conducted. The sample was randomised into two groups: an experimental group that received treatment based on UL serious games designed by the research team and controlled by the MYO Armband® gesture capture sensor, along with conventional rehabilitation and a control group that received the same conventional rehabilitation for the UL. Both groups received two 60-min sessions per week over an eight-week period. Wrist range of motion (goniometry), grip muscle strength (Jamar® dynamometer), coordination and gross UL dexterity (Box and Block Test), fatigue (Fatigue Severity Scale), functionality (ABILHAND), quality of life (Multiple Sclerosis Impact Scale-29), adverse effects (Simulator Sickness Questionnaire, SSQ), perceived workload (NASA-Task load index), satisfaction (Client Satisfaction Questionnaire-8 (CSQ-8), Satisfaction with Technology Scale, System Usability Scale (SUS) and QUEST 2.0) and compliance (attendance) were assessed in both groups pre-treatment, post-treatment and during a follow-up period of 2 weeks without receiving any treatment. Results: Significant differences were observed in the experimental group compared to the control group in the assessment of forearm supination (p =.004) and grip strength (p =.004). Adverse effects were minimal (SSQ: 7/100 points) and perceived workload was low (NASA-Task Load Index: 25/100 points) in the experimental group. The MYO Armband® technology proved to be useful for the participants (SUS: 80.66/100) and the satisfaction scales received high scores (QUEST 2.0: 59.4/70 points; Satisfaction with Technology: 84.36/100 points). There were significant differences between the groups in terms of attendance percentage (p =.029). Conclusions: An experimental protocol using MYO Armband®-based serious games designed for UL rehabilitation showed improvements in active wrist range of motion and handgrip strength in patients with MS, with high satisfaction, minimal adverse effects and workload and excellent compliance. Trial registration number: This randomised controlled trial has been registered at ClinicalTrials.gov Identifier: NCT04171908. [ABSTRACT FROM AUTHOR]

This paper presents an iterative training framework with a binary classifier to improve the learning capability of a deep learning model for detecting abnormal behaviors in surveillance video. When a deep learning model trained on data from one surveillance video is deployed to monitor another video stream, its abnormal behavior detection performance often decreases significantly. To ensure the desired performance in new environments, the deep learning model needs to be retrained with additional training data from the new video stream. Iterative training requires manual annotation of the additional training data during the fine-tuning process, which is a tedious and error-prone task. To address this issue, this paper proposes a binary classifier to automatically label false positive data without human intervention. The binary classifier is trained on bounding boxes extracted from the detection model to identify which boxes are true positives or false positives. The proposed learning framework incrementally enhances the performance of the deep learning model for detecting abnormal behaviors in a surveillance video stream through repeated iterative learning cycles. Experimental results demonstrate that the accuracy of the detection model increases from 0.35 (mAP = 0.74) to 0.91 (mAP = 0.99) in just a few iterations. [ABSTRACT FROM AUTHOR]

Objective: To compare the efficacy of Dextrain Manipulandum™ training of dexterity components such as force control and independent finger movements, to dose-matched conventional therapy (CT) post-stroke. Methods: A prospective, single-blind, pilot randomized clinical trial was conducted. Chronic-phase post-stroke patients with mild-to-moderate dexterity impairment (Box and Block Test (BBT) > 1) received 12 sessions of Dextrain or CT. Blinded measures were obtained before and after training and at 3-months follow-up. Primary outcome was BBT-change (after–before training). Secondary outcomes included changes in motor impairments, activity limitations and dexterity components. Corticospinal excitability and short intracortical inhibition (SICI) were measured using transcranial magnetic stimulation. Results: BBT-change after training did not differ between the Dextrain (N = 21) vs CT group (N = 21) (median [IQR] = 5[2–7] vs 4[2–7], respectively; P = 0.36). Gains in BBT were maintained at the 3-month post-training follow-up, with a non-significant trend for enhanced BBT-change in the Dextrain group (median [IQR] = 3[− 1–7.0], P = 0.06). Several secondary outcomes showed significantly larger changes in the Dextrain group: finger tracking precision (mean ± SD = 0.3 ± 0.3N vs − 0.1 ± 0.33N; P < 0.0018), independent finger movements (34.7 ± 25.1 ms vs 7.7 ± 18.5 ms, P = 0.02) and maximal finger tapping speed (8.4 ± 7.1 vs 4.5 ± 4.9, P = 0.045). At follow-up, Dextrain group showed significantly greater improvement in Motor Activity Log (median/IQR = 0.7/0.2–0.8 vs 0.2/0.1–0.6, P = 0.05). Across both groups SICI increased in patients with greater BBT-change (Rho = 0.80, P = 0.006). Comparing Dextrain subgroups with maximal grip force higher/lower than median (61.2%), BBT-change was significantly larger in patients with low vs high grip force (7.5 ± 5.6 vs 2.9 ± 2.8; respectively, P = 0.015). Conclusions: Although immediate improvements in gross dexterity post-stroke did not significantly differ between Dextrain training and CT, our findings suggest that Dextrain enhances recovery of several dexterity components and reported hand-use, particularly when motor impairment is moderate (low initial grip force). Findings need to be confirmed in a larger trial. Trial registration ClinicalTrials.gov NCT03934073 (retrospectively registered) [ABSTRACT FROM AUTHOR]

Anterior clinoidectomy is an important procedure used in the treatment of a range of diseases of the frontotemporal region, both vascular and tumoral. Mastering this technique requires a high level of manual skills training. The objective of the study was to describe an easily accessible and economical alternative model of anterior clinoidectomy, with a principal focus on the significance of mastering technical skills and training tactile feedback. Five cadaveric sheep heads (10 sides) fixed in formalin and alcohol were injected with silicone and used to simulate extradural (5 sides) and intradural (5 sides) routes and 1 head was used to prepare an anatomic specimen for better demonstration of the anatomy of the paraclinoid region. A comparative anatomic analysis between the ovine and human anterior clinoid process was performed. Using cadaveric sheep models, all principal steps of the procedure for both the extradural and the intradural routes were imitated. A cadaveric sheep head model serves as a good model of anterior clinoidectomy regarding manual skills training and can serve as a good alternative to human cadaveric training. [ABSTRACT FROM AUTHOR]

The article describes the current developments in the field of nursing education in Austria. With the BGBl I 2023/62, the legal basis for the vocational training occupations of nursing assistant and nursing care assistant has been established. The training regulations have also been issued, and the pilot training facility is scheduled to start in the 2023/24 school year in four federal states. However, the training manual and the template for training documentation are still missing. Special provisions for nursing education are also listed, which deviate from the usual provisions of the Federal Health Act. It is emphasized that any work on patients should be avoided before the age of 17. The article deals with the normative foundations of the new vocational training occupations in nursing. It is emphasized that the legislator is striving to ensure the quality of education, the protection of young people, and the well-being of patients. However, it is also pointed out that it is questionable whether the extensive theoretical training can be conveyed in practice and whether the working conditions should be made more attractive. It is also noted that apprentices can be confronted with suffering and death in various situations, even if they do not perform nursing activities. [Extracted from the article]

Assessing the risk of bias (ROB) of studies is an important part of the conduct of systematic reviews and meta-analyses in clinical medicine. Among the many existing ROB tools, the Prediction Model Risk of Bias Assessment Tool (PROBAST) is a rather new instrument specifically designed to assess the ROB of prediction studies. In our study we analyzed the inter-rater reliability (IRR) of PROBAST and the effect of specialized training on the IRR. Six raters independently assessed the risk of bias (ROB) of all melanoma risk prediction studies published until 2021 (n = 42) using the PROBAST instrument. The raters evaluated the ROB of the first 20 studies without any guidance other than the published PROBAST literature. The remaining 22 studies were assessed after receiving customized training and guidance. Gwet's AC1 was used as the primary measure to quantify the pairwise and multi-rater IRR. Depending on the PROBAST domain, results before training showed a slight to moderate IRR (multi-rater AC1 ranging from 0.071 to 0.535). After training, the multi-rater AC1 ranged from 0.294 to 0.780 with a significant improvement for the overall ROB rating and two of the four domains. The largest net gain was achieved in the overall ROB rating (difference in multi-rater AC1: 0.405, 95%-CI 0.149–0.630). In conclusion, without targeted guidance, the IRR of PROBAST is low, questioning its use as an appropriate ROB instrument for prediction studies. Intensive training and guidance manuals with context-specific decision rules are needed to correctly apply and interpret the PROBAST instrument and to ensure consistency of ROB ratings. [ABSTRACT FROM AUTHOR]

Background and Aims: This systematic review examined healthcare students' attitudes, knowledge, and skill in Artificial Intelligence (AI). Methods: On August 3, 2022, studies were retrieved from the PubMed, Embase, Scopus, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta‐Analyses recommendations were followed. We included cross‐sectional studies that examined healthcare students' knowledge, attitudes, skills, and perceptions of AI in this review. Using the eligibility requirements as a guide, titles and abstracts were screened. Complete texts were then retrieved and independently reviewed per the eligibility requirements. To collect data, a standardized form was used. Results: Of the 38 included studies, 29 (76%) of healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in he future; however, in nine of the studies (24%), students considered AI a threat to healthcare fields and had a negative attitude towards it. Furthermore, 26 studies evaluated the knowledge of healthcare students about AI. Among these, 18 studies evaluated the level of student knowledge as low (50%). On the other hand, in six of the studies, students' high knowledge of AI was reported, and two of the studies reported average student general knowledge (almost 50%). Of the six studies, four (67%) of the students had very low skills, so they stated that they had never worked with AI. Conclusion: Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. Face‐to‐face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works and raising students' knowledge of the advantages of AI. [ABSTRACT FROM AUTHOR]

Introduction: Despite the public health system's critical role in non-communicable disease (NCD) surveillance in Vietnam, limited evidence exists on the implementation of NCD surveillance activities within these systems and the need for capacity building across different system levels to meet expected NCD surveillance goals. This study aimed to evaluate the status of and describe factors affecting the implementation of NCD surveillance activities and to identify the NCD surveillance capacity building needs of the public health system in Vietnam. Methods: We used a mixed-methods approach in four provinces, conducting self-completed surveys of staff from six Preventive Medicine Institutes (PHI), 53 Centres for Disease Control (CDC) and 148 commune health stations (CHS), as well as 14 in-depth interviews and 22 focus group discussions at four PHI, four CDC, and eight district health centres and CHS. Results: Study findings highlighted that although Vietnam has a well-functioning NCD surveillance system, a number of quality issues related to NCD surveillance data were salient. Multifactorial reasons were identified for incomplete, unconfirmed, and inaccurate mortality data and current disease surveillance data. Data on NCD management and treatment were reported to be of better quality than data for screening, targeted treatment, and counselling communication. Main factors affected the effective implementation of NCD surveillance, namely lack of complete and specific guidelines for NCD surveillance, limitations in human resource capacity within NCD departments, and shortage of funding for NCD surveillance activities. Conclusion: Study findings provide practical strategies for strengthening health system capacity for NCD surveillance through developing policies, guidelines, and standardised tools to guide NCD surveillance and a road map for integrated NCD surveillance, developing training packages and manuals for all levels of the health system, and conducting utilisation-focused surveillance training programs. [ABSTRACT FROM AUTHOR]

Fast assessment of the composition of amyloid fibril samples from cryo-EM data poses a serious challenge to existing image analysis tools. We develop a method for automated segmentation of single fibrils requiring only little user input during the training process. This is achieved by combining a binary segmentation based on a convolutional neural network with preprocessing steps to allow for easy manual generation of training data. Subsequent skeletonization turns the binary segmentation into a single-object segmentation. Then, we compute properties of shape and texture of each segmented fibril, including an estimation of the fibril width. We discuss the composition of the sample based on the distributions of these computed properties and outline how a classification of fibril morphologies might be performed using these properties. [ABSTRACT FROM AUTHOR]

Manual testing is a machine testing method in which testing cases are carried out manually rather than utilizing an automatic process. Both test cases were handled manually by the tester from the perspective of the end consumer. It guarantees that the program performs, regardless of whether or not it is specified in the prerequisite text. In order to complete approximately 100% of the application, test cases are planned and implemented. Manually produced test case reports are common. Since it can spot both visible and hidden program bugs, manual testing is one of the most effective testing techniques. A defect is described by the software as the difference between expected and actual performance. The bugs were corrected and turned over to the tester by the creator. Manual training is expected before automatic testing for any newly created software. This research requires a lot of time and resources, but it ensures the entire program is bug-free. Manual research entails knowledge of manual testing methods but does not require the use of an automated evaluation method. Manual checking is necessary since one of the fundamental software testing concepts is that "100% automation is not feasible." [ABSTRACT FROM AUTHOR]

Numerous training simulators have been developed using virtual reality (VR) owing to their various advantages. Systems for training machine operations with physical movements face differences in the operational feel between actual and virtual machines. Moreover, virtual training is problematic in safety education because trainees in safe virtual environments can exhibit unsafe behavior in reality. To solve these problems, a previous study developed a virtual reality (VR) system to train a lathe operation with mixed reality using a motion capture system. This study included a function to teach the procedure and safety precautions for straight turning operations using a lathe. To evaluate the training effectiveness of this system, an experiment was conducted to compare learning using a video. Testees were divided into a simulator group, who learned with the system, and a video group, who learned with the video material. Work on the actual lathe by each testee after learning, was evaluated. Consequently, the actual work by the testees who used this system had fewer errors and shorter standstill times in which they attempted to recollect the next phase task. Although the number of trainees was small, this relationship had a statistical advantage. In the actual work, all the testees in the video group entered the danger area; however, only half of the testees in the simulator group entered the danger area. Therefore, a trainee using a simulator can remember the work process more reliably and accurately and perform it safely. Moreover, trainees who have undergone training several times should be able to perform actual work without making operational errors or engaging in unsafe behaviors. [ABSTRACT FROM AUTHOR]

As funder, journal, and disciplinary norms and mandates have foregrounded obligations of data sharing and opportunities for data reuse, the need to plan for and curate data sets that can reach researchers and end-users with disabilities has become even more urgent. We begin by exploring the disability studies literature, describing the need for advocacy and representation of disabled scholars as data creators, subjects, and users. We then survey the landscape of data repositories, curation guidelines, and research-data-related standards, finding little consideration of accessibility for people with disabilities. We suggest three sets of minimal good practices for moving toward truly accessible research data: 1) ensuring Web accessibility for data repositories; 2) ensuring accessibility of common text formats, including those used in documentation; and 3) enhancement of visual and audiovisual materials. We point to some signs of progress in regard to truly accessible data by highlighting exemplary practices by repositories, standards, and data professionals. Accessibility needs to become a mainstream component of curation practice included in every training, manual, and primer. [ABSTRACT FROM AUTHOR]

To reduce the cost of manually annotating training data for supervised classifiers, we propose an automated approach to extract training data of urban objects in six classes: buildings, fences, man-made poles, vegetation, vehicles, and low objects. In this study, two segmentation algorithms are firstly implemented to generate meaningful objects from the non-ground point cloud. Then, we generated valid strict rules to label partial RANSAC (Random Sample Consensus) planes and meaningful objects as training data. The strict rules are built upon the semantic knowledge formed by the features of geometric, eigenvalue, RANSAC plane, multidimensional slice, and relative location. The accuracy of strict rule-based (SRB) training data is higher than 98.5 % for buildings, man-made poles, vegetation, and vehicles. The accuracy of low objects and fences reaches 97.10 % and 94.99 %, respectively. Finally, we compared the performance of the KPConv and PointNET++ networks trained by SRB and manually labeled training data to evaluate the effectiveness of our training data. The KPConv overall accuracy using manually labeled and (SRB) training data are 91.5 % and 86.8 % in the Paris dataset, 95.6 % and 92.0 % in the Freiburg dataset, respectively. The experiments demonstrate that automatically labeled training data can achieve similar accuracy compared to manual labels when coupled with two deep learning networks. Therefore, SRB training data extraction can effectively deal with the problem of training data scarcity and provide significant advancements in urban point cloud classification, where manual labeling of training data remains a crucial challenge. [ABSTRACT FROM AUTHOR]

Martial arts organizations can become Foucaultian institutions that discipline and punish practitioner bodies to enact ideologies of violence. In this article, I describe how these institutions function by examining the rhetorical history of one specific martial art, Taekwondo. My analysis extends Hawhee's examination of Ancient Greek athletics to include modern martial bodies and the associated non-Western rhetorical traditions underpinning these practices. Martial arts institutions operate in the following ways: (1) Invent traditions for rhetorical purposes, intended audiences, and desired effects; (2) produce discursive systems of control (like training manuals) to communicate institutional standards, expectations, and authorized methods of practice; (3) ascribe rhetorical/symbolic significance to body types and martial techniques; (4) and persuade global audiences through mass media and embodied performance. [ABSTRACT FROM AUTHOR]

This study aims to detail some aspects of the fieldwork of the security officer in the 1980s - especially that of the intelligence department - based on testimonies, which also involve interviews and archival documents. In addition, this information is linked to current studies on the training of security officers along with training manuals for officers, used in the form of courses. Some of the documents used are "Instructions for Surveillance and Investigations" from 1978 and "Current Issues and Changes in the Foreign Intelligence Service v. R.S.R." Both documents were published electronically by Remus Mircea Birtz on his personal blog; therefore, the task of researching and extracting these documents from the archives - often demanding and cumbersome - does not belong to us. It should be noted that the Security, as a body of utmost importance for the functioning of the R.S.R. In the foreseeable parameters for the regime of that time, it has known important fluctuations and transformations, even radical ones, on the current ones of what historiography is almost in the form of definition, "the history of communism in Romania". So, we can capture several stages in the evolution of the Security - almost a history segmented at intervals of ten years each. [ABSTRACT FROM AUTHOR]

Copyright of Historia y Memoria de la Educación is the property of Historia y Memoria de la Educacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)