Training Model Research Articles

Attitudes and Values for Competent Psychology Practice (AV-CoPP): development and preliminary validation of the scale

ABSTRACT Objective Competency-based models of training have led to systemic and sweeping changes to psychologist training and supervision. However, this progress has mostly been in knowledge and skill competencies. Psychology currently lacks an instrument to track attitude-values changes across practitioner developmental stages. The current study describes the development and validation of the Attitude-Values for Competent Psychology Practice (AV-CoPP) scale. Method In Study 1, a set of 40 attitude-values across seven domains were selected. Content validity was examined by having each item rated for their importance by provisional psychologists (N = 61), psychologists (N = 94) and psychologists with endorsement (N = 113). Study 2 evaluated the convergent and divergent validity of the instrument by having psychology trainees (N = 92) rate the relative importance of these 40 items for psychologists, teachers, and plumbers. Results Results indicated excellent content validity and good internal reliability for the seven subscales. Further, the subscales adequately differentiated standards of professional practice expected of the three professional groups. Conclusion The research offers good preliminary psychometric support for the AV-CoPP. The AV-CoPP makes an important contribution to competency research by enabling the systematic measurement of attitude-value changes during training and supervision.

Implementation and delivery of electronic health records training programs for nurses working in inpatient settings: a scoping review.

Well-designed electronic health records (EHRs) training programs for clinical practice are known to be valuable. Training programs should be role-specific and there is a need to identify key implementation factors of EHR training programs for nurses. This scoping review (1) characterizes the EHR training programs used and (2) identifies their implementation facilitators and barriers. We searched MEDLINE, CINAHL, PsycINFO, and Web of Science on September 3, 2023, for peer-reviewed articles that described EHR training program implementation or delivery to nurses in inpatient settings without any date restrictions. We mapped implementation factors to the Consolidated Framework for Implementation Research. Additional themes were inductively identified by reviewing these findings. This review included 30 articles. Healthcare systems' approaches to implementing and delivering EHR training programs were highly varied. For implementation factors, we observed themes in innovation (eg, ability to practice EHR skills after training is over, personalizing training, training pace), inner setting (eg, availability of computers, clear documentation requirements and expectations), individual (eg, computer literacy, learning preferences), and implementation process (eg, trainers and support staff hold nursing backgrounds, establishing process for dissemination of EHR updates). No themes in the outer setting were observed. We found that multilevel factors can influence the implementation and delivery of EHR training programs for inpatient nurses. Several areas for future research were identified, such as evaluating nurse preceptorship models and developing training programs for ongoing EHR training (eg, in response to new EHR workflows or features). This scoping review highlighted numerous factors pertaining to training interventions, healthcare systems, and implementation approaches. Meanwhile, it is unclear how external factors outside of a healthcare system influence EHR training programs. Additional studies are needed that focus on EHR retraining programs, comparing outcomes of different training models, and how to effectively disseminate updates with the EHR to nurses.

CACER: Clinical concept Annotations for Cancer Events and Relations.

Clinical notes contain unstructured representations of patient histories, including the relationships between medical problems and prescription drugs. To investigate the relationship between cancer drugs and their associated symptom burden, we extract structured, semantic representations of medical problem and drug information from the clinical narratives of oncology notes. We present Clinical concept Annotations for Cancer Events and Relations (CACER), a novel corpus with fine-grained annotations for over 48000 medical problems and drug events and 10000 drug-problem and problem-problem relations. Leveraging CACER, we develop and evaluate transformer-based information extraction models such as Bidirectional Encoder Representations from Transformers (BERT), Fine-tuned Language Net Text-To-Text Transfer Transformer (Flan-T5), Large Language Model Meta AI (Llama3), and Generative Pre-trained Transformers-4 (GPT-4) using fine-tuning and in-context learning (ICL). In event extraction, the fine-tuned BERT and Llama3 models achieved the highest performance at 88.2-88.0 F1, which is comparable to the inter-annotator agreement (IAA) of 88.4 F1. In relation extraction, the fine-tuned BERT, Flan-T5, and Llama3 achieved the highest performance at 61.8-65.3 F1. GPT-4 with ICL achieved the worst performance across both tasks. The fine-tuned models significantly outperformed GPT-4 in ICL, highlighting the importance of annotated training data and model optimization. Furthermore, the BERT models performed similarly to Llama3. For our task, large language models offer no performance advantage over the smaller BERT models. We introduce CACER, a novel corpus with fine-grained annotations for medical problems, drugs, and their relationships in clinical narratives of oncology notes. State-of-the-art transformer models achieved performance comparable to IAA for several extraction tasks.

Positive Youth Development at Sea: A Case Study of the Shenandoah Model

Background: Tall ship sail training, a form of outdoor adventure education, has historically been used with youth to build competency in seamanship and social and emotional skills. However, there is a void in the literature documenting precise program models connected to specific goals. Purpose: This paper presents a case study of the Shenandoah Model of sail training. It details its Positive Youth Development (PYD) framework and mean changes in PYD assets reported by participants. Methodology/Approach: Participants were surveyed using a one-group retrospective pretest-posttest pre-experimental design to assess the impact of participation. Findings/Conclusions: The participants reported significant mean increases across all PYD assets (Caring, Connection, Contribution, Competence, Character, Confidence, and Happiness), including moderate effect sizes for all measures except Happiness. In addition, over 70% of the participants would recommend the program and/or do it again, suggesting program satisfaction. Implications: Connecting different PYD assets to various program activities allows future program designs to intentionally develop sail training voyages to build competencies. Future follow-up research is needed, including qualitative methods to capture the impact of these programs from the participants’ viewpoint.

Predicting peritumoral glioblastoma infiltration and subsequent recurrence using deep-learning-based analysis of multi-parametric magnetic resonance imaging.

Glioblastoma (GBM) is the most common and aggressive primary adult brain tumor. The standard treatment approach is surgical resection to target the enhancing tumor mass, followed by adjuvant chemoradiotherapy. However, malignant cells often extend beyond the enhancing tumor boundaries and infiltrate the peritumoral edema. Traditional supervised machine learning techniques hold potential in predicting tumor infiltration extent but are hindered by the extensive resources needed to generate expertly delineated regions of interest (ROIs) for training models on tissue most and least likely to be infiltrated. We developed a method combining expert knowledge and training-based data augmentation to automatically generate numerous training examples, enhancing the accuracy of our model for predicting tumor infiltration through predictive maps. Such maps can be used for targeted supra-total surgical resection and other therapies that might benefit from intensive yet well-targeted treatment of infiltrated tissue. We apply our method to preoperative multi-parametric magnetic resonance imaging (mpMRI) scans from a subset of 229 patients of a multi-institutional consortium (Radiomics Signatures for Precision Diagnostics) and test the model on subsequent scans with pathology-proven recurrence. Leave-one-site-out cross-validation was used to train and evaluate the tumor infiltration prediction model using initial pre-surgical scans, comparing the generated prediction maps with follow-up mpMRI scans confirming recurrence through post-resection tissue analysis. Performance was measured by voxel-wised odds ratios (ORs) across six institutions: University of Pennsylvania (OR: 9.97), Ohio State University (OR: 14.03), Case Western Reserve University (OR: 8.13), New York University (OR: 16.43), Thomas Jefferson University (OR: 8.22), and Rio Hortega (OR: 19.48). The proposed model demonstrates that mpMRI analysis using deep learning can predict infiltration in the peri-tumoral brain region for GBM patients without needing to train a model using expert ROI drawings. Results for each institution demonstrate the model's generalizability and reproducibility.

Quantitative strategies of different loss functions aggregation for knowledge distillation

<p>Deep learning models have been successfully applied to many visual tasks. However, they tend to be increasingly cumbersome due to their high computational complexity and large storage requirements. How to compress convolutional neural network (CNN) models while still maintain their efficiency has received increasing attention from the community, and knowledge distillation (KD) is efficient way to do this. Existing KD methods have focused on the selection of good teachers from multiple teachers, or KD layers, which is cumbersome, expensive computationally, and requires large neural networks for individual models. Most of teacher and student modules are CNN-based networks. In addition, recent proposed KD methods have utilized cross entropy (CE) loss function at student network and KD network. This research focuses on the quantifiable evaluation of teacher-student model, in which knowledge is not only distilled from training models that have the same CNN architecture but also from different architectures. Furthermore, we propose combination of CE, balance cross entropy (BCE), and focal loss functions to not only soften the value of loss function in transferring knowledge from large teacher model to small student model but also increase classification performance. The proposed solution is evaluated on four benchmark static image datasets, and the experimental results show that our proposed solution outperforms the state-of-the-art (SOTA) methods from 2.67% to 9.84% at top 1 accuracy.</p>

A Novel 3D-printed Educational Model for the Training of Laparoscopic Bile Duct Exploration:a pilot study for beginning trainees

BackgroundLaparoscopic common bile duct exploration (LCBDE) is a minimally invasive procedure for the removal of bile duct stones that is often performed by experienced hepatobiliary surgeons; beginners do not easily master this approach. AimTo investigate the effectiveness and practicality of a three-dimensional printed (3DP) anatomical model based on radiographic images in the training of LCBDE techniques and formulate standardized educational workflows. MethodsColored LCBDE training models were produced using 3DP technology. Twenty standardized training trainees were randomly divided into two groups: a 3DP model training group and a traditional laparoscopic simulation training group. Both groups received the same number of teaching hours. After a 4-weeks training course, the trainees’ subjective and objective progress in basic knowledge and manipulations were evaluated and compared. ResultsCompared with traditional laparoscopic simulation, 3DP model simulation training is of great significance in improving trainers' understanding of surgical procedures and cooperation during the operation. Trainees with 3DP models training demonstrated a significant improvement in their understanding of the key points of surgery (χ2 = 6.139, p = 0.013) and skills scores, especially in core procedural steps operation. More importantly, the trainees showed higher levels of satisfaction and self-confidence while assisting in the surgery. ConclusionWith the development of 3DP models, improvements in the learning effect for theoretical understanding and practical skills were significant. The present study is the initial educational experience with 3DP models to facilitate the operational capabilities of the trainees for LCBDE.

Guideline for Promote Basketball Club in Shaanxi China

Background and Aims: To further enrich the learning life of students and cultivate talents with all-round development in ethics, intellect, physicality, social skills, and aesthetics, the State issued the Opinions on Further Reducing the Burden of Homework on Students in Compulsory Education and the Burden of Out-of-School Training. The "double reduction" directly restricts the size of subject-based out-of-school training organizations and reduces the number of hours they conduct extracurricular training, which can greatly impact out-of-school training organizations. Based on the development of extracurricular training institutions in Xi'an, Shaanxi Province, this study examines the strength of the correlation between the policy context and the development of local sports training institutions. The "Double Reduction" policy refers to the state's efforts to reduce the extracurricular burden on students and the economic pressure on parents while promoting the all-round development of students. As the sports environment improves and schools pay more attention to physical education, the development of basketball training institutions in the policy context is also worth studying. Understanding the development of youth basketball training institutions in Xi'an, Shaanxi under the double reduction policy can provide reference and guidance for cultivating excellent basketball reserves in Xi'an and promote improving China's basketball level. Youth basketball training institutions are an important base for cultivating excellent basketball reserve talents, and understanding their development in the context of the double-reduced policy can help assess the effect of the existing training model on talent cultivation and put forward suggestions for improvement. Methodology: This paper focuses on the youth basketball training institutions in Xi'an, Shaanxi Province, and adopts the questionnaire survey method and interview method to conduct research. Using the 4M theory and POLC theory, 270 samples, including 20 managers, 50 coaches, and 100 students and parents each, were selected and studied by questionnaire survey method. Summarize the development status and give specific development measures. Finally, the results of the study were given to nine experts for evaluation to obtain a high-quality, scientific management guide for the sustainable development of basketball training organizations in Xi'an, Shaanxi Province. Results: The measures were screened and finally six specific management guidelines were identified after expert evaluation and recommendations, which were accepted by the experts after validation, and the feasibility of the specific measures was verified. Conclusion: The study finds that the release of policies has a significant impact on the development of organizations. The release of policies may trigger changes in the competitive landscape of the basketball training organization industry, and the changes in the market prompt basketball training organizations to improve their strengths to adapt to the new rules of competition. However, it can also provide guidance and direction for the development direction and focus of basketball training organizations, prompting them to transform and upgrade to grasp new development opportunities. Stricter regulatory standards and requirements have prompted basketball training institutions to strengthen compliance management and improve service quality and standardization.

TRAINING FOR LECTURERS AT PTKK IN PRODUCING SCIENTIFIC RESEARCH AND PUBLICATIONS

This training aimed to enhance the functional position of lecturers by improving their research and publication skills, which in turn contributes significantly to the overall quality of higher education. The program focused on building independent learning capabilities and offering new experiences and knowledge through a qualitative approach utilizing the Research and Development (R&D) model. The primary beneficiaries of this program were 24 lecturers from two higher education institutions (Perguruan Tinggi Keagamaan Kristen - PTKK). The activities included structured training, workshops, and mentoring sessions, employing the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The results of this program were highly positive: 1) The training model employed proved to be more engaging and effective, significantly enhancing participant interest compared to previous trainings; 2) The research and scientific publications produced by the lecturers saw a substantial increase, indicating the effectiveness of the ADDIE-based training model; 3) Participants demonstrated improved understanding of the critical importance of research and publication in advancing their academic careers, which directly impacts their ability to progress in their functional positions.

SMALNet: Segment Anything Model Aided Lightweight Network for infrared image segmentation

Infrared based visual perception is important for night vision of autonomous vehicles, unmanned aerial vehicles (UAVs), etc. Semantic segmentation based on deep learning is one of the key techniques for infrared vision-based perception systems. Currently, most of the advanced methods are based on Transformers, which can achieve favorable segmentation accuracy. However, the high complexity of Transformers prevents them from meeting the real-time requirement of inference speed in resource constrained applications. In view of this, we suggest several lightweight designs that significantly reduce existing computational complexity. In order to maintain the segmentation accuracy, we further introduce the recent vision big model – Segment Anything Model (SAM) to supply auxiliary supervisory signals while training models. Based on these designs, we propose a lightweight segmentation network termed SMALNet (Segment Anything Model Aided Lightweight Network). Compared to existing state-of-the-art method, SegFormer, it reduces 64% FLOPs while maintaining the accuracy to a large extent on two commonly-used benchmarks. The proposed SMALNet can be used in various infrared based vision perception systems with limited hardware resources.

Development and External Validation of Motor-intention Integrated Prediction Model for Upper Extremity Motor Recovery After Intention-driven Robotic-hand Training for Chronic Stroke.

ObjectiveTo derive and validate a prediction model for minimal clinically important differences (MCID) in upper extremity (UE) motor function after intention-driven robotic-hand training using residual voluntary EMG signals from affected UE. DesignA prospective longitudinal multicenter cohort study. We collected pre-intervention candidate predictors: demographics, clinical characteristics, Fugl-Meyer assessment of UE (FMAUE), Action Research Arm Test scores, and motor-intention of flexor digitorum and extensor digitorum (ED) measured by EMG during maximal voluntary contraction (MVC). For EMG measures, recognizing challenges for stroke survivors to move paralyzed hand, peak signals were extracted from eight time-windows during MVC-EMG (0.1s-5s) to identify subjects’ motor-intention. Classification And Regression Tree algorithm was employed to predict survivors with MCID of FMAUE. Relationship between predictors and motor-improvements was further investigated. SettingNine rehabilitation centers. ParticipantsChronic stroke survivors (N=131), including 87 for Derivation-sample, and 44 for Validation-sample. InterventionsAll participants underwent 20-session robotic-hand training (40min/session, 3-5sessions/week). Main Outcome measuresPrediction efficacies of models were assessed by area under the receiver operating characteristic curve (AUC). The best effective model was final model and validated using AUC and overall accuracy. ResultsThe best model comprised FMAUE (cut-off score: 46) and peak activity of ED from one-second MVC-EMG (MVC-EMG 4.604 times higher than resting-EMG), which demonstrated significantly higher prediction accuracy (AUC: 0.807) than other time-windows or solely using clinical-scores (AUC: 0.595). In external validation, this model displayed robust prediction (AUC: 0.916). Significant quadratic relationship was observed between ED-EMG and FMAUE increases. ConclusionsThis study presents a prediction model for intention-driven robotic-hand training in chronic stroke survivors. It highlights significance of capturing motor-intention through 1-second EMG-window as a predictor for MCID improvement in UE motor-function after 20-session robotic-training. Survivors in two conditions showed high percentage of clinical motor-improvement: moderate-to-high motor-intention and low-to-moderate function; as well as high-intention and high-function.

Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study.

Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations. We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors. In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

Enhanced marine fish small sample image recognition with RVFL in Faster R-CNN model

Addressing the challenge of insufficient underwater marine life images leading to diminished accuracy in existing target recognition models, this paper introduces Random Vector Functional Link (RVFL), a suitable model for small sample training, to reduce the dependency of traditional visual recognition models on data volume by replacing the Softmax classifier in the Faster Region-Based Convolutional Neural Network (R-CNN) model. The RVFL network assigns random weights to enhance the model's adaptability to diverse data distributions, thereby improving its generalization performance. Furthermore, the RVFL model incorporates multiple nonlinear activation functions, thereby enhancing the neural network's representational capacity for high-dimensional data. The application of the proposed model to the task of marine fish small sample target recognition demonstrates its effectiveness in mitigating the low accuracy issue caused by data scarcity. It significantly improved recognition accuracy and offered new insights into addressing underwater small sample image recognition challenges.

The Impact of Technology Induced Professional Development Model on Coaching and Mentoring of Teachers

The paper assessed the impact of technology-enhanced coaching and mentoring on the Continuous Professional Development (CPD) of teachers. It examined leveraging technology to build mentors’ capacity, support mentees, and improve learning outcomes even with limited resources. The study explored the feasibility and effectiveness of a technology-enhanced training model in promoting CPD for mentors and enhancing teacher competencies in low-performing schools. A cross-sectional survey design was used, with questionnaires collecting data from 500 school improvement support officers (SISOs) in Ghana, selected through quota sampling. The data was analyzed using descriptive statistics. The results indicated that technology stimulates teachers’ curiosity, intrinsic motivation, learning quality, and creative thinking. The study also found that technology-induced CPD is cheaper in the long term compared to traditional face-to-face models. However, many teachers face issues with internet connectivity and high data consumption, hindering access to online content despite having smart devices. The study recommends deliberate capacity building for teachers to improve their skills in accessing technology-enhanced CPD. It also suggests collaboration between governments, educational institutions, telecommunication companies, and internet service providers to make internet connectivity accessible and affordable. Exploring zero-rated data for teachers to access relevant content is also recommended. Keywords: Technology, motor learning outcome, teacher professional development, model

Региональная модель сопровождения профессионального развития педагогов среднего профессионального образования в условиях новой образовательной технологии «Профессионалитет»

The aim of the work is to describe the directions and growth zones for the professional development of teachers in secondary vocational education from the perspective of the new educational technology “Professionalitet” in the context of the implemented regional model of organizational and methodological support for the professional development of teachers in secondary vocational education. The paper summarizes the experience of the Sverdlovsk Region from 2019 to 2024. During that time research was conducted, a model of a unified managed system of network organizational and methodological support for the professional development of teaching and management personnel of vocational educational organizations at the regional level was proposed and tested. The successful implementation of the main components of the model, the accumulated experience in training qualified personnel based on practice-oriented training, and the dissemination of the sectoral model of personnel training within the framework of the implementation of the federal project “Professionalitet” have made it possible to build an algorithm for regional interaction. The effectiveness and quality of the result of the algorithm actualize the directions of support and growth zones for the professional development of teachers in the Sverdlovsk Region. The scientific novelty of the research lies in the approach to determining the directions and growth zones for the professional development of teachers in secondary vocational education from the perspective of the new educational technology “Professionalitet”. As a result, an algorithm for regional interaction of subjects in the system of secondary vocational education and a possible sequence for implementing the principles of the new educational technology “Professionalitet” are proposed.

The Proposal of the “Classroom-Workplace” Model in University-Enterprise Joint Training in Vietnam

The purpose of this article is to propose a solution for training oriented towards developing professional competence for the students, using the “Classroom - Workplace” model. The theoretical research is based on the theory of teaching for the development of professional competence. It utilizes modern teaching methods to provide a scientific basis for the innovation of teaching methods at the university. Practical research is conducted using a mixed method, combining qualitative and quantitative approaches to assess the current state of training quality through training products and propose a teaching model that combines the university with the business environment. Based on the research results, the article suggests the “Classroom - Workplace” model to implement teaching aimed at developing professional competence for students to meet the current labor market requirements.

Something has sort of opened up for me: psychology students' reflections after participating in an apprenticeship training model.

Typically, psychotherapy training comprises of didactic approaches and clinical practice under supervision, with students rarely having the opportunity to observe other therapists' work in real time. Many trades and professions employ apprenticeship to teach new skills. However, it is rarely employed in psychotherapist training. This qualitative study was part of a pilot study that developed and tested the feasibility of an apprenticeship model to be used in psychotherapy training, and investigated how students experienced such training. Ten first-year clinical psychology students joined experienced therapists as observers and/or co-therapists. Each student attended up to 8 therapy sessions with different therapists/patients. The students wrote reflective log entries after each session. In sum, 66 log entries were collected and analyzed with reflective thematic analysis. Five themes were generated, reflecting how the students changed their perspectives from an internal focus to an increasingly external focus: Being informed by emotions, What sort of therapist will I become? Shifting focus from me to the other, The unpredictable nature of therapy, and Growing confidence in therapeutic change. The students gained insights into the dynamic nature of therapy, therapists' responsiveness, and how internal and external foci of attention inform the therapeutic work. Such tacit knowledge is difficult to convey via didactic methods and might receive limited attention in clinical programs. Apprenticeship training is a promising supplement to traditional training.

Innovative Intrauterine Device Training Model as Dynamic New Teaching Tool

Innovative Intrauterine Device Training Model as Dynamic New Teaching Tool

A code change‐oriented approach to just‐in‐time defect prediction with multiple input semantic fusion

AbstractRecent research found that fine‐tuning pre‐trained models is superior to training models from scratch in just‐in‐time (JIT) defect prediction. However, existing approaches using pre‐trained models have their limitations. First, the input length is constrained by the pre‐trained models.Secondly, the inputs are change‐agnostic.To address these limitations, we propose JIT‐Block, a JIT defect prediction method that combines multiple input semantics using changed block as the fundamental unit. We restructure the JIT‐Defects4J dataset used in previous research. We then conducted a comprehensive comparison using eleven performance metrics, including both effort‐aware and effort‐agnostic measures, against six state‐of‐the‐art baseline models. The results demonstrate that on the JIT defect prediction task, our approach outperforms the baseline models in all six metrics, showing improvements ranging from 1.5% to 800% in effort‐agnostic metrics and 0.3% to 57% in effort‐aware metrics. For the JIT defect code line localization task, our approach outperforms the baseline models in three out of five metrics, showing improvements of 11% to 140%.

Exploration of the Talent Cultivation Model for the Integration of Industry and Education in University Asset Evaluation

During the 14th Five Year Plan period, the main task of talent team construction in China’s asset appraisal industry was to develop innovative talent training models. Therefore, this article focuses on the talent cultivation model of integrating industry and education in asset evaluation in universities, systematically summarizes the theoretical and practical significance of research on asset evaluation talent cultivation models in universities, and explores the construction measures of asset evaluation talent cultivation models based on the integration of industry and education that meet social needs and the development of the times [1]. At the same time, the strategy of constructing a deep integration talent training system was explored, guided by the integration of industry and education, to cultivate asset evaluation composite talents with strong practical skills. The aim is to provide a reference for improving the quality of asset evaluation professionals in China and promoting the development of asset evaluation talent training models.