Process Approach Research Articles

“I was angry that I fell” all the way to “if I fall, I fall”: a qualitative study of the spectrum of behavioral fall risk factors for veterans with dysvascular lower-limb amputation

Purpose To explore behavioral risk factors contributing to fall and near-fall scenarios for Veterans with dysvascular lower-limb amputation. Materials and methods Participants were a convenience sample of Veterans with unilateral dysvascular lower-limb amputation, receiving care at a single Veterans Administration Regional Amputation Center, who participated in an interview (12/2021–04/2023). We used phenomenological research and directed content analysis to explore participant perspectives on fall scenarios. Two frameworks informed a priori codes (The Health Action Process Approach, and the Falls-Type Classification Framework); however, analysis was also inductive, with additional codes emerging. Following an independent and iterative coding process, codes were categorized, and patterns identified to create final themes. Results Twenty-one veterans completed interviews. Five themes were identified that were salient across a behavioral fall risk factor spectrum from low to high: self-efficacy, trust in the prosthesis, dual-task awareness, participation, and emotional regulation, and one theme pertaining to context surrounding the index fall. Conclusion Different levels of behavioral fall risk factors exist for Veterans with dysvascular lower-limb amputation. Understanding individuals’ perceptions of these behavioral risk factors is important as a first step in developing comprehensive fall-risk management rehabilitation interventions for Veterans with amputation.

ФІНАНСОВІ ІНСТРУМЕНТИ ЯК ВАЖЛИВА ДОМІНАНТА ЗАБЕЗПЕЧЕННЯ ІНКЛЮЗИВНОГО РОЗВИТКУ ТЕРИТОРІАЛЬНИХ ГРОМАД

Introduction. In the context of the latest transformational challenges, the problem of ensuring inclusive development of territorial communities is becoming increasingly relevant, and it is advisable to develop innovative approaches to the use of financial instruments in managing socio-economic development at the local level. The purpose of the article is to develop the conceptual framework for the use of financial instruments by public authorities as a means of management to ensure the inclusive development of territorial communities. Methods. The methodology of the study is based on the use of dialectical and process approaches, methods of induction and deduction, analysis and synthesis - when considering the content and specification of the essence of the concept of “inclusive development of territorial communities” and determining the imperative of this type of socio-economic development; structural and functional approach and methods of theoretical generalization, scientific abstraction, systematization, argumentation - to develop a conceptual model of the use of financial instruments as a dominant factor in ensuring inclusive development of territorial communities. Results. Based on the generalization of scientific views and the use of the process approach, the author's own definition of the concept of “inclusive development of territorial communities” is formed. The study of the key aspects of the concepts of inclusive development, barrier-free, integrated and sustainable development, and current global challenges and threats made it possible to outline the imperatives of inclusive development of territorial communities that should be taken into account by public authorities when choosing financial instruments. The use of the structural-functional approach made it possible to consider financial instruments for the inclusive development of territorial communities as a means of managing socio-economic phenomena and processes, which result in the implementation of the principles of financial policy aimed at the inclusive development of territorial communities. The article reveals the dualistic nature of the impact of financial instruments for inclusive development of territorial communities on the object of management from the standpoint of their provisioning, allocative and regulatory effects. Conclusions. Financial instruments are an important dominant of the inclusive development of territorial communities. It is through the dualistic nature of their impact on the object of management, taking into account the imperative of inclusive development, the priorities of the State regional policy and strategies for the development of territorial communities, that the goal of managing socio-economic development at the local level is achieved.

Single Dimension Attention and Mixed Attention

Due to its ability to effectively improve model performance, the attention mechanism has gained widespread adoption across a variety of fields in recent years as neural network models have been developed. Compared to traditional single attention mechanisms, mixed attention offers more diverse prediction dimensions, enabling a more comprehensive data processing approach. This, in turn, significantly improves both prediction accuracy and overall model performance. The main body of this article begins by introducing the single attention mechanism, followed by a detailed explanation of the mixed attention mechanism. It then addresses the current limitations of mixed attention and outlines potential future directions for research. The article seeks to advance the understanding of mixed attention by giving a broad overview of the state of research in this area. In conclusion, the exploration of mixed attention mechanisms holds considerable promise for advancing model performance across a wide range of applications. Future research will likely focus on addressing existing challenges, further unlocking its potential and broadening its scope of use.

Distinct detection and discrimination sensitivities in visual processing of real versus unreal optic flow.

We examined the intricate mechanisms underlying visual processing of complex motion stimuli by measuring the detection sensitivity to contraction and expansion patterns and the discrimination sensitivityto the location of the center of motion (CoM) in various real and unreal optic flow stimuli. We conducted two experiments (N = 20 each) and compared responses to both "real" optic flow stimuli containing information about self-movement in a three-dimensional scene and "unreal" optic flow stimuli lacking such information. We found that detection sensitivity to contraction surpassed that to expansion patterns for unreal optic flow stimuli, whereas this trend was reversed for real optic flow stimuli. Furthermore, while discrimination sensitivity to the CoM location was not affected by stimulus duration for unreal optic flow stimuli, it showed a significant improvement when stimulus duration increased from 100 to 400 ms for real optic flow stimuli. These findings provide compelling evidence that the visual system employs distinct processing approaches for real versus unreal optic flow even when they are perfectly matched for two-dimensional global features and local motion signals. These differences reveal influences of self-movement in natural environments, enabling the visual system to uniquely process stimuli with significant survival implications.

Microstructure, microhardness and wear properties Investigation in Friction Stir Processing of Stir Cast Mg/B4C metal matrix composite

Abstract Friction Stir Processing (FSP) has emerged as an excellent processing approach to tailor the microstructural and other properties of cast alloys and composite materials. In the current investigation, a stir-cast magnesium metal matrix composite (MMMC) reinforced with 12 wt% B4C particles was processed using single-pass FSP. FSP was carried out with a simple cylindrical tool pin using different tool rotational speeds of 800, 1000, 1200 rpm and transverse speed of 60 mm/min. Severe deformation occurred during FSP which helped to refine the distribution of the B4C particles and grain refinement of as-cast composites. Microstructural examination showed an appreciable grain refinement from 98 to 5μm because of dynamic recrystallization during FSP. The maximum microhardness of the friction stir processed (FSPed) composites increased from 73 Hv to 105.4 Hv. Pin-on-disk wear tests were conducted under dry sliding conditions in the load range of 1 kg to 5 kg at a 1 m/s sliding velocity. The wear results indicated an enhancement of wear properties of FSPed composites in comparison to as-cast magnesium metal matrix composites. The improved hardness and wear resistance results were pertaining to the remarkable refinement in grain size coupled with refined B4C particulates and uniform distribution. The best results for hardness and wear resistance of the FSPed composite were obtained for a sample processed at a tool rotation speed of 1000 rpm. SEM/EDS of the worn samples was carried out. The wear mechanism was found as slight abrasion and oxidation. After single pass FSP, the surface of Mg/12wt% B4C cast composite showed remarkable changes in worn surface morphology.

A Review on the Impact of a Blended Process-oriented Approach on the English Writing Skills

This study examines the application and efficacy of the blended process-oriented learning method in improving English writing skills among ESL/EFL learners. Data were gathered via an extensive literature analysis employing the key search phrases "blended," "process approach," and "English writing" across three academic databases: Web of Science, Scopus, and Google Scholar. After evaluating 34 preliminary records and implementing rigorous inclusion and exclusion criteria, 10 articles were chosen for an in-depth bibliometric analysis. The results indicate that this pedagogical method has been adopted in diverse educational environments throughout Asia and Africa, markedly enhancing students' writing abilities. The paper delineates several technologies employed in these implementations, such as Schoology, MOOCs, Google Classroom, and WeChat. The integrated process-oriented approach enhances writing performance, boosts student enthusiasm, fosters interaction, and solves writing anxiety. Despite the positive outcomes, the study acknowledges limitations, such as the restricted number of articles reviewed and the focus on students' perspectives. Future research should broaden the scope to include more articles and explore teachers' perspectives. The study concludes with recommendations for seamlessly integrating technology into traditional classrooms and emphasizing the indispensable role of instructors in blended learning environments. These insights aim to guide ESL/EFL educators in effectively incorporating blended process-oriented learning into their teaching practices.

Microstructure, microhardness and wear properties Investigation in Friction Stir Processing of Stir Cast Mg/B4C metal matrix composite

Abstract Friction Stir Processing (FSP) has emerged as an excellent processing approach to tailor the microstructural and other properties of cast alloys and composite materials. In the current investigation, a stir-cast magnesium metal matrix composite (MMMC) reinforced with 12 wt% B4C particles was processed using single-pass FSP. FSP was carried out with a simple cylindrical tool pin using different tool rotational speeds of 800, 1000, 1200 rpm and transverse speed of 60 mm/min. Severe deformation occurred during FSP which helped to refine the distribution of the B4C particles and grain refinement of as-cast composites. Microstructural examination showed an appreciable grain refinement from 98 to 5μm because of dynamic recrystallization during FSP. The maximum microhardness of the friction stir processed (FSPed) composites increased from 73 Hv to 105.4 Hv. Pin-on-disk wear tests were conducted under dry sliding conditions in the load range of 1 kg to 5 kg at a 1 m/s sliding velocity. The wear results indicated an enhancement of wear properties of FSPed composites in comparison to as-cast magnesium metal matrix composites. The improved hardness and wear resistance results were pertaining to the remarkable refinement in grain size coupled with refined B4C particulates and uniform distribution. The best results for hardness and wear resistance of the FSPed composite were obtained for a sample processed at a tool rotation speed of 1000 rpm. SEM/EDS of the worn samples was carried out. The wear mechanism was found as slight abrasion and oxidation. After single pass FSP, the surface of Mg/12wt% B4C cast composite showed remarkable changes in worn surface morphology.

Enhancing Civic Education and University Quality Management: An Innovative Approach

In modern socio-economic conditions, the problem of civic education of the population of the country acquires special significance. The multifaceted nature of this problem requires different approaches to its consideration and is associated both with increasing requirements for the formation of a person - a citizen, for the process of civic education, for the civic qualities of an individual, and with a change in value guidelines, when universal human values and the legal culture of people become priorities. Studying the problem of civic education at different stages of its evolution allows us to critically comprehend the process of its formation and development and use the most advanced ideas and experience for the further development of this problem. A historiographic review of scientific and pedagogical literature on the topic indicates that researchers of the problems of civic education have most fully studied the issues of civic education and the formation of a citizen, various aspects of its organization in the educational process of a comprehensive school. The article deals with the problems of the didactic system of improving the quality management of higher education by means of IT technologies from the point of view of the process approach. Innovative technologies make it possible to increase the efficiency of the university's quality management system by optimizing the structure of all main and auxiliary processes. The article is devoted to the problems of implementing automation of the process of assessing the quality of the educational process, the quality of teaching individual disciplines at the university, the creation of an effective management system based on the principles of quality management. Special attention was paid to the management structures of the higher education system of Kazakhstan, where information systems based on computer technologies are used. Specified for the purpose of implementation as well as operation Attention is drawn to the contradiction between the requirements of the international standard ISO 9000 series, which regulate the conduct and study of the opinions of consumers of educational services in the monitoring mode, and the lack of a properly developed information system that allows collecting, processing and presenting information about customer satisfaction

Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading

The increasing demand for advanced materials capable of withstanding extreme loading conditions, such as those encountered during impact or blast events, underscores the need for innovative approaches in material processing. This study focuses on leveraging machine learning (ML) to enhance predictive accuracy in the continuous extrusion of CP-Titanium Grade 2, a material vital for structural resilience in critical applications. Specifically, an Artificial Neural Network (ANN) model optimized using Stochastic Gradient Descent (SGD) was introduced to forecast power requirements with high precision. The analysis utilized a published dataset that comprises theoretical, numerical, and experimental power calculations as a robust foundation for validation and comparison. A visualization highlighted the influence of process parameters, such as feedstock temperature and extrusion wheel velocity, on structural performance to align with the thematic focus of resilient material design. The ANN-SGD model achieved an RMSE of 0.9954 and a CVRMSE of 11.53% which demonstrated significant improvements in prediction accuracy compared to traditional approaches. By achieving superior alignment with experimental results, the model validated its efficacy as a reliable and efficient tool for understanding and optimizing complex manufacturing processes. This research emphasizes the potential of ML to revolutionize material processing for extreme conditions and contribute to the broader goals of structural resilience and sustainable manufacturing.

A study on delay of Local Government Development Projects Completion and Its Impact in Bangladesh

Successful project outcomes rely on various well-coordinated actions from start to finish. This study aims to identify the issues affecting local government projects in Bangladesh and their significant impacts. Delays in projects are often caused by the conventional administrative system and poor coordination among stakeholders. Ministries and departments Develop Project Proposals (DPPs) without thorough feasibility studies or appropriate measures, leading to delays in approval and project execution. Although the Local Government Division undertakes numerous projects with substantial funding, several issues prevent timely completion, including insufficient funds, untrained personnel, inadequate planning, flawed feasibility studies, lack of modern technology, frequent natural disasters, and fluctuating project material costs. Additionally, the protracted administrative process and top-down planning approach do not align with optimal outcomes. This outdated planning system complicates project implementation, whereas a bottom-up approach could be more effective. It is noted that although Local Government Division undertake many projects, gaps in execution often lead to deteriorated project quality, time and cost overruns, and eventual project failure. In addition to assessing corrective measures like structured planning, thorough feasibility studies, compliance enforcement, project prioritization, efficient monitoring, and land acquisition challenges, this study will look into delays that result in misappropriation of public funds and setbacks in ADP progress. This research seeks to address these issues by analyzing all available information, including primary and secondary data, respondents' opinions, and other evidence through a qualitative approach.

Facilitators and Barriers of Health Behaviors in Patients With Type 2 Diabetes: A Qualitative Study.

The purpose of the study was to explore the facilitators and barriers of health behaviors in patients with type 2 diabetes (T2D), providing a reference for the development of health behavior interventions programs. A qualitative descriptive research design was adopted, and interviews were conducted with 25 patients with T2D. The interview guide was developed based on the health action process approach theory. The interviews were audio-recorded and transcribed verbatim. Data analysis was performed using thematic analysis. Through the analysis of interview data, 2 main themes were identified: facilitators and barriers of health behaviors, comprising 18 subthemes. The facilitators included self-efficacy, outcome expectations, risk perception, intention, action planning, coping planning, emotional regulation, proactive and sustained sense of responsibility, and multidimensional social support. The barriers included insufficient disease awareness, insufficient self-control, impact of social activities, low self-efficacy, accessibility of personal conditions, lack of problem-solving skills, lack of disease management knowledge, lack of social support, and cognitive decline. When designing health behavior intervention programs for patients with T2D, it is crucial to consider both facilitators and barriers to enhance the effectiveness of the interventions to encourage patients to adopt healthy lifestyles, improve their quality of life, and reduce the occurrence of complications.

Review of Intelligent Modeling for Sintering Process Under Variable Operating Conditions

The steel industry serves as a cornerstone of a nation’s industrial system, with sintering playing a pivotal role in the steelmaking process. In an effort to enhance the intelligence of the sintering process and improve production efficiency, numerous scholars have carried out extensive research on data analysis and intelligent modeling techniques. These studies have made significant contributions to expanding production capacity, optimizing cost efficiency, and enhancing the quality of products, and supporting the sustainable development of the steel industry. This paper begins with an analysis of the sintering production process, explores the distinctive characteristics of the sintering process, and discusses the methods for identifying the operating conditions of sintering. It also provides an overview of the current state of research on both mechanism modeling and data-driven modeling approaches for the sintering process. Finally, the paper summarizes the existing challenges in sintering process modeling and offers perspectives on the future direction of research in this field.

Does internet-based climate information improve non-fishing employment opportunities? Insights from smallholder fishermen in Indonesia

ABSTRACT In the face of climate change, smallholder fishermen increasingly turn to non-fishing employment to sustain their livelihoods. Diversifying sources of income becomes paramount to mitigate the impact of unpredictable weather patterns on fish populations and, hence, household livelihoods. However, fishermen need convenient climate information access to navigate this transition effectively. With information about weather trends and forecasts, they can make informed decisions about when to fish and when to focus on alternative sources of income. This study offers novelty by investigating the impact of Internet-based climate information on non-fishing employment among smallholder fishermen in Indonesia. The study employs cross-sectional data collected from 503 smallholder fishermen in Indonesia, analyzed using a conditional mixed process (CMP) approach to tackle the endogeneity issue that may arise in the estimation. The results highlight that adopting Internet-based climate information among smallholder fishermen was positively and significantly associated with education, distance to fishing ports, social activity, fishing location, adoption of multiple gears, and access to the Internet among family members and relatives. However, the age of fishermen shows otherwise. Furthermore, our empirical findings confirm that Internet-based climate information significantly increases opportunities for non-fishing employment among fishermen. Disaggregate estimation by fishing boat size category strengthens this finding by showing a positive and significant coefficient in all categories. Therefore, this finding implies a need to improve and develop Internet-based climate information platforms in fisheries.

Analytical Modelling of Fault Seal Effectiveness in Hydrocarbon Reservoirs: A Multi-Criteria Decision Analysis and Analytical Hierarchy Process Approach

Analytical Modelling of Fault Seal Effectiveness in Hydrocarbon Reservoirs: A Multi-Criteria Decision Analysis and Analytical Hierarchy Process Approach

CoGraphNet for enhanced text classification using word-sentence heterogeneous graph representations and improved interpretability

Text Graph Representation Learning through Graph Neural Networks (TG-GNN) is a powerful approach in natural language processing and information retrieval. However, it faces challenges in computational complexity and interpretability. In this work, we propose CoGraphNet, a novel graph-based model for text classification, addressing key issues. To overcome information loss, we construct separate heterogeneous graphs for words and sentences, capturing multi-tiered contextual information. We enhance interpretability by incorporating positional bias weights, improving model clarity. CoGraphNet provides precise analysis, highlighting important words or sentences. We achieve enhanced contextual comprehension and accuracy through novel graph structures and the SwiGLU activation function. Experiments on Ohsumed, MR, R52, and 20NG datasets confirm CoGraphNet’s effectiveness in complex classification tasks, demonstrating its superiority.

Designing criteria of interactive educational environment at medical school to teach students of information technology field at professionally-oriented foreign language classes

Introduction. Applying lifelong learning approach in educational process requires to sustain this procedure with engaging, practice-oriented, active teaching methods, to develop critical thinking skills, practical skills, cognitive skills, cognitive education, social-emotional education, to make a model of interactive educational environment. The aim is to determine the criteria to make an interactive educational environment model at medical school to develop future IT students’ foreign language communicative competence. Materials and methods. The study to determine the criteria to make a model of interactive educational environment was being conducted at First Moscow State Medical University from 01.09.2022 to 08.04.2024. "Bioengineering and Bioinformatics" students as well as “Information Systems and Technologies” students were target of this study. The experimental part of this study consisted of theoretical review, observation, online testing, survey ‘needs analysis’ and ‘final survey in the form of cooperative metacognitive discussion, analysis of students’ reviews (essays on the topic “your attitude to the organization of your education in an interactive way”). Results. Online testing of indicating foreign language proficiency (English) showed diverse results among students withing one group. Besides, it refers to all courses and specialties. The needs analysis has demonstrated that students have strong motivation to learn a foreign language at a professional communicative level. The interactive classes observation and essays analysis (“your attitude to the organization of your education in an interactive way”) and the results of the final survey in the form of cooperative metacognitive discussion have illustrated that 90% of students are satisfied with educational process in an interactive way, others 10% either they are not used to work under such a regime or they believe that to build a process of education based only on the interactive methods is incorrect, or they are true introverts. Furthermore, due to these results we could single out some criteria for making a model of interactive educational environment. Conclusion. Obtained research data have theoretical and practical importance, because the goal of this study – to distinguish criteria for making an interactive educational environment model for development of future IT students’ foreign language communicative competence at medical school – was successfully achieved.

New Advances of the P-SBAS Approach for an Efficient Parallel Processing of Large Volumes of Full-Resolution Multitemporal DInSAR Interferograms

New Advances of the P-SBAS Approach for an Efficient Parallel Processing of Large Volumes of Full-Resolution Multitemporal DInSAR Interferograms

A Comparative Study of Deep Learning Approaches for Arabic Language Processing

Arabic is a difficult language for natural language processing (NLP) because of its complicated morphology, dialectal differences and the limited annotated resources. Although deep learning algorithms have reached state-of-the-art results in many NLP tasks, comprehensive comparative studies for Arabic remains scarce. This paper addresses this gap by systematically evaluating three prominent deep learning architectures - namely Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs) and Transformers - across five essential Arabic NLP tasks: i) mention of sentiment analysis, ii) named entity recognition, iii) machine translation, iv) text classification and v) dialect identification. We differ the performance of models trained from scratch with fine-tuned versions of AraBERT, a powerful Transformer-based model pre-trained on a large Arabic corpus. Our experiments employ the Arabic datasets already existing in literature and utilizes accuracy, F1-score and BLEU as the evaluation metrics. The results are indicative of the supremacy of Transformer-based models with regard to AraBERT that shows the highest scores in each task. Notably, AraBERT attains 95. 2% accuracy on sentiment analysis, which is higher than the accuracies of RNNs and CNNs. These improvements also become apparent in other tasks, with AraBERT ending up as the best among RNN, CNN and others. A 3-point difference for 3 BLEU in machine translation and 2. 3% F1-score on dialect recognition. This extensive assessment, in turn, highlights the advantages and disadvantages of deep learning architectures for Arabic NLP. The excellent AraBERT representation also demonstrates how transfer learning and synergy between Transformer architectures and large-scale pre-training can significantly help Arabic language technology development.

Evaluating and prioritizing strategies to reduce carbon emissions in the circular economy for environmental sustainability.

The conversion of economic system into a circular economy (CE) is indispensable, as it is the key strategy in combating climate change and reducing carbon dioxide (CO2) emissions in China. However, difficulties are inevitable during this process. It is essential for the long-term operation of a CE to adequately address and overcome any challenges it faces. The present research used the Fuzzy Analytical Hierarchy Process (FAHP) approach to determine the main challenges and sub-challenges in the context of CE and its ability to help reduce the CO2 emission levels for carbon-free economy. Next, the study used the Fuzzy VIekriterijumsko KOmpromisno Rangiranje (FVIKOR) method to measure the strategizing solutions, which are hindering the effective use of CE towards the green future. The findings of the FAHP indicated that waste management disposal (CECER2) is the most crucial challenge. The regulatory and policy complexity (CECER5) and financial investment and cost considerations (CECER4) are the next key challenges. The results of the FVIKOR revealed that policy and regulatory support (CES6), waste reduction and zero waste goals (CES4), and recycling and upcycling (CES2) are most significant mitigation strategies for lowering carbon emissions through CE for environmental sustainability in China. The implications reveal the essential need for tailored policy frameworks and financial incentives to promote implementation of CE strategies, leading to substantial carbon emissions reduction in China.

Enhancing suicidal behavior detection in EHRs: A multi-label NLP framework with transformer models and semantic retrieval-based annotation

Background:Suicide is a leading cause of death worldwide, making early identification of suicidal behaviors crucial for clinicians. Current Natural Language Processing (NLP) approaches for identifying suicidal behaviors in Electronic Health Records (EHRs) rely on keyword searches, rule-based methods, and binary classification, which may not fully capture the complexity and spectrum of suicidal behaviors. This study aims to create a multi-class labeled dataset with annotation guidelines and develop a novel NLP approach for fine-grained, multi-label classification of suicidal behaviors, improving the efficiency of the annotation process and accuracy of the NLP methods. Methods:We develop a multi-class labeling system based on guidelines from FDA, CDC, and WHO, distinguishing between six categories of suicidal behaviors and allowing for multiple labels per data sample. To efficiently create an annotated dataset, we use an MPNet-based semantic retrieval framework to extract relevant sentences from a large EHR dataset, reducing annotation space while capturing diverse expressions. Experts annotate the extracted sentences using the multi-class system. We then formulate the task as a multi-label classification problem and fine-tune transformer-based models on the curated dataset to accurately classify suicidal behaviors in EHRs. Results:Lexical analysis revealed key themes in assessing suicide risk, considering an individual’s history, mental health, substance use, and family background. Fine-tuned transformer-based models effectively identified suicidal behaviors from EHRs, with Bio_ClinicalBERT, BioBERT, and XLNet achieving the F1 scores (0.81), outperforming BERT and RoBERTa. The proposed approach, based on a multi-label classification system, captures the complexity of suicidal behaviors effectively particularly “Suicide Attempt” and “Family History” instances. The proposed approach, using task-specific NLP models and a multi-label classification system, captures the complexity of suicidal behaviors more effectively than traditional binary classification. However, direct comparisons with existing studies are difficult due to varying metrics and label definitions. Conclusion:This study presents a robust NLP framework for detecting suicidal behaviors in EHRs, leveraging task-specific fine-tuning of transformer-based models and a semi-automated pipeline. Despite limitations, the approach demonstrates the potential of advanced NLP techniques in enhancing the identification of suicidal behaviors. Future work should focus on model expansion and integration to further improve patient care and clinical decision-making.