Search Results (133)

Amid structural changes driven by the greening and digital transformation of the maritime industry, the demand for career transitions of seafarers with onboard experience to shore-based positions—particularly ship superintendents—is steadily increasing. However, the current lack of a systematic education and career development framework to support such transitions poses a critical challenge for shipping companies seeking to secure sustainable human resources. The aim of this study was to develop a competency-based training program that facilitates the effective transition of seafarers to shore-based ship superintendent roles. We integrated a developing a curriculum (DACUM) analysis with competency-based job analysis to achieve this aim. The core competencies required for ship superintendent duties were identified through three expert consultations. In addition, social network analysis (SNA) was used to quantitatively assess the structure and priority of the training content. The analysis revealed that convergent competencies, such as digital technology literacy, responsiveness to environmental regulations, multicultural organizational management, and interpretation of global maritime regulations, are essential for a successful career shift. Based on these findings, a modular training curriculum comprising both common foundational courses and specialized advanced modules tailored to job categories was designed. The proposed curriculum integrated theoretical instruction, practical training, and reflective learning to enhance both applied understanding and onsite implementation capabilities. Furthermore, the concept of a Seafarer Success Support Platform was proposed to support a lifecycle-based career development pathway that enables rotational mobility between sea and shore positions. This digital learning platform was designed to offer personalized success pathways aligned with the career stages and competency needs of maritime personnel. Its cyclical structure, comprising career transition, competency development, field application, and performance evaluation, enables seamless career integration between shipboard- and shore-based roles. Therefore, the platform has the potential to evolve into a practical educational model that integrates training, career development, and policies. This study contributes to maritime human resource development by integrating the DACUM method with a competency-based framework and applying social network analysis (SNA) to quantitatively prioritize training content. It further proposes the Seafarer Success Support Platform as an innovative model to support structured career transitions from shipboard roles to shore-based supervisory positions. Full article

Non-conformities—deviations from established standards or procedures—can significantly impact product quality and process performance. Although various tools and methodologies exist, current research lacks an integrated, deferred, and corrective approach to non-conformance management that bridges day-to-day operations with systematic quality control. The proposed tool aims to address this gap by providing a practical framework that combines batch data processing using the “Daily Challenge” tool with structured problem solving and corrective strategies. It serves as a comprehensive decision-making tool for systematically managing deviations. The methodology begins with identifying non-conformities through data collection and direct observation, followed by focused reporting and active discussion during departmental meetings. Issues are then categorized based on their frequency, operational impact, and resource requirements to determine the appropriate resolution path—whether through immediate correction or detailed analysis using structured tools such as the “Daily Challenge” sheet. It integrates well-established methodologies such as 5M and PDCA into a structured, daily workflow for resolving non-conformities. Implemented solutions are evaluated for effectiveness with ongoing monitoring to ensure continuous improvement. A key feature of this system is the use of the “Daily Challenge” form, which facilitates documentation, accountability, and knowledge retention—helping to reduce the recurrence of similar situations. The case studies illustrate the methodology through two examples: a labeling issue involving the omission of quantity information on product labels due to operator oversight and the management of production downtime caused by equipment and sensor failures. Although a standard existed, the errors revealed the need for reinforced procedures. Corrective actions included revising procedures, retraining personnel, repairing and recalibrating equipment, enhancing maintenance protocols, and using visual documentation to enhance process understanding. The “Daily Challenge” tool provides a replicable framework for managing non-conformities across various industries, aligning operational practices with quality assurance goals. By integrating structured analysis, clear documentation, and corrective strategies, it fosters a culture of continuous improvement and compliance. Full article

Fatal accidents in UK’s manufacturing sector are expected to remain the same or increase in coming years. This paper has tried to combat this issue by adapting and further developing a previously defined Safety Framework for the Paint Sector, to evaluate the safety performance of a metal manufacturing facility. To achieve this, the original Safety Framework was updated to align with the current British safety legislation outlined by the British Standards Institution. The framework was based on a three-level multi-attribute value theory (MAVT). Upon reviewing BSI 45001, the Safety Framework was founded upon the concept of Deming’s Plan, Do, Check, Act (PDCA) which is the foundation for the original framework, therefore, the first-level attributes remained consistent. The 13 attributes of the second level and 36 attributes of the third level were derived from the literature review and updated to relevant legislation. To develop the Safety Framework, the Delphi method was used. This included interviews that were conducted with employees and managers from either a Safety or Engineering background. The second part of the paper involved the improvement of the Safety Framework, based on the interview feedback. The main findings of the study revealed that the final Safety Framework has been deemed relevant for the Metal Manufacturing Sector by Industry Suitably Qualified and Experienced Personnel (SQEP). The majority of Interviewees deemed the Safety Framework to have a clear layout and easy to understand. The interviews and final Safety Framework suggested the importance of a company’s emphasis on employee welfare and health, in order to reduce accidents in the workplace. The originality of this paper lies in its application and validation of a sector-specific safety framework, contributing to the body of knowledge by offering a replicable methodology for adapting safety frameworks to other manufacturing sectors. Full article

Public safety personnel and frontline healthcare professionals are at increased risk of exposure to potentially psychologically traumatic events (PPTEs) and developing post-traumatic stress injuries (PTSIs, e.g., depression, anxiety) by the nature of their work. PTSI is also connected to increased absenteeism, suicidality, and performance decrements, which compromise occupational and public health and safety in trauma-exposed workers. There is limited evidence on the cost effectiveness of proactive “prevention” programs aimed at reducing the risk of PTSIs. The purpose of this meta-analysis is to measure the economic effectiveness of proactive PTSI mitigation programs among occupational groups exposed to frequent occupational PPTEs, focusing on the outcomes related to PTSI symptoms, absenteeism, and psychological wellness. Findings from 15 included studies demonstrate that proactive interventions can yield substantial economic and health benefits, with Return On Investment (ROI) values ranging widely from −20% to 3560%. Shorter interventions (≤6 months) often produced higher returns, while longer interventions (>12 months) showed more moderate or negative returns. Notably, the level at which an intervention is targeted significantly affects outcomes—programs aimed at managers, such as the 4 h RESPECT training course, demonstrated a high ROI and broad organizational impact by enhancing leadership support for employee mental health. Sensitivity analyses highlighted significant variability based on the organizational context, program design, and participant characteristics. The majority of proactive interventions successfully reduced psychological distress and enhanced workplace outcomes, although thoughtful consideration of program design and implementation context is essential. Full article

To address the issues of low efficiency and high omission rates in monitoring workers’ compliance with safety dress codes in intelligent factories, this paper proposes the SFA-YOLO network, an enhanced real-time detection model based on a Selective Feature Attention mechanism. This model enables inspection robots to automatically and accurately identify whether the workers’ attire meets the safety standards. First, this paper constructs a comprehensive dataset of safety attire, including images captured under various scenarios, personnel numbers, and operational conditions. All images are manually annotated to enhance the model’s generalization capability. The dataset contains 3966 images, covering four classes: vest, no-vest, helmet, and no-helmet. Second, the proposed model integrates the SFA mechanism to improve the YOLO architecture. This mechanism combines multi-scale feature fusion with a gated feature extraction module to improve detection accuracy, strengthening the model’s ability to detect occluded targets, partial images, and small objects. Additionally, a lightweight network structure is adopted to meet the inference speed requirements of real-time monitoring. The experimental results demonstrate that the SFA-YOLO model achieves a detection precision of 89.3% and a frame rate of 149 FPS in the safety attire detection task, effectively balancing precision and real-time performance. Compared to YOLOv5n, the proposed model achieves a 5.2% improvement in precision, an 11.5% increase in recall, a 13.1% gain in mAP@0.5, and a 12.5% improvement in mAP@0.5:0.95. Furthermore, the generalization experiment confirms the model’s robustness in various task environments. Compared with conventional YOLO models, the proposed method performs more stably in safety attire detection, offering a reliable technical foundation for safety management in intelligent factories. Full article

The aim of this study is to establish an analysis of certification indicators for the Pavement Condition Index (PCI) to address previously unaccredited issues. In the management of road infrastructure, the PCI serves as a crucial indicator for assessing road pavement conditions. However, the lack of standardized detection and accreditation mechanisms in the past may have led to variations in the reliability of detection results among different personnel. This study observes the accuracy and precision of the data obtained by different inspectors. The analysis is based on repeated manual inspections conducted by four inspectors across 12 road units on Zhongda Road, using the ASTM D6433-23 (2023) standard. To ensure the reliability of the inspection results, the Coefficient of Variation (CV) is used to measure repeatability, while the Mean Relative Error (MRE) is used to evaluate accuracy. Training was introduced between inspection phases to assess performance improvement. The results showed that the average CV decreased from 27.2% to 7.5% and the average MRE reduced from 45.3% to 5.9% after training. These findings demonstrate that targeted training significantly enhances the repeatability and accuracy of manual PCI assessments. This study concludes with recommendations to set a CV threshold of ≤0.1 and an MRE threshold of ≤10% as practical benchmarks for manual PCI Quality Control. These standards can serve as validation criteria for future automated inspection systems. Full article

Effective project management depends on a deep understanding of the human attributes that influence project success. This study aims to quantify the causal relationships between human resource variables in order to establish a prioritisation criterion for workforce allocation in industrial projects. Traditional statistical models often overlook the multidimensional nature of these factors, limiting their effectiveness in complex planning contexts. To address this, the Decision-Making Trial and Evaluation Laboratory method is used to assess and prioritise the key competencies required of project personnel. The analysis is based on an extensive literature review of management and industrial project studies, combined with data collected from experienced managers through structured questionnaires. Respondents assessed how different human resource attributes interact and influence each other. The results show that personal motivation, innovation, education, work–life balance, flexibility and adaptability are dominant causal factors. Stakeholder relations, conflict management, negotiation skills, objectivity and impartiality are more reactive. This study is differentiated in that it analyses a complex network of 400 influence relationships, providing a more comprehensive perspective than previous research. By integrating a structured decision-making approach, the results contribute to both the academic literature and practical applications, supporting more effective workforce planning and improved performance in industrial projects. Full article

Landslide risk assessment can quantify the potential damage caused by landslides to disaster-bearing bodies, which can help to reduce casualties and economic losses. It is not only a tool for disaster prevention and mitigation, but also a key step to achieve the coordinated development of the environment, economy, and society, and it provides important support for the realization of the global sustainable development goals (SDGs). In this study, a risk assessment method is proposed for an individual landslide based on the non-probabilistic reliability theory. The method represents an improvement to and innovation in existing risk assessment methods, which can obtain more accurate assessment results with fewer sample data points, refines the methods and steps of landslide risk assessment, and fully considers the destabilization mechanism of the landslide and the interaction with disaster-bearing bodies. A non-probabilistic reliability analysis of the slope was conducted, and the possibility of landslide occurrence was characterized by the failure value of the slope. Moreover, the influence range of the landslide was predicted using empirical formulas; space–time distribution probabilities of the disaster-bearing bodies were estimated by combining their location and activity patterns; and the vulnerability of the disaster-bearing bodies was calculated according to the landslide intensity and the resistance or susceptibility index of the disaster-bearing bodies. The method’s feasibility was verified through its application to the Xiatudiling landslide as a case study. In the process of performing slope stability calculations, it was found that the calculation results of the Monte Carlo method were consistent with those of the non-probabilistic reliability approach proposed in this paper, which was able to obtain more accurate results with less sample data. The personnel life and economic risks were 1.8499 persons/year and CNY 184,858/year (USD 25,448/year), respectively, under heavy rainfall conditions. The results were compared with the risk judgment criteria for geological disasters, and both risk values were unacceptable. After landslide treatment, the possibility of landslide occurrence was reduced, and the personnel life risk and economic risk of the landslide were also reduced. Both risk values then became acceptable. The effect of landslide treatment was obvious. The proposed method provides a new technique for assessing landslide risks and can help in designing mitigation strategies. This method can be applied to landslide risk surveys conducted by geological disaster prevention institutions, demonstrating enhanced applicability in data-scarce regions to improve risk assessment efficiency. It is particularly suitable for emergency management authorities, enabling rapid and comprehensive assessment of landslide risk levels to support informed decision making during critical response scenarios. Full article

Objective: To identify factors influencing the structure and functioning of long-term residential care facilities for older adults worldwide, in order to uncover practices and support evidence-based improvements in care delivery. Method: An overview of systematic reviews was performed according to the PRISMA protocol and registered on the PROSPERO platform (no. CRD42023486204). Research was carried out on 21 September 2023, using the following databases: PubMed (via MedLine), EMBASE, Web of Science, Scopus, Virtual Health Library (VHL), and Epistemonikos. Results: The search yielded 12,040 articles, including 61 systematic reviews. Analyzing the primary outcomes, personnel structure, and risk management were the most-studied outcomes of the systematic reviews, followed by pharmaceuticals, food services, mobility/accessibility, and technological and physical structures. In terms of primary outcomes of the systematic reviews, the personnel structure was the most highlighted (in 39.34%), followed by risk management (in 32.79%), while the least highlighted was physical structure (in 9.84%). Conclusions: Personnel are critical to the safe and effective functioning of Long-Term Care Facility (LTCF) operations. Future research is needed to identify associations between models of care and structural concerns, including physical environment, as they relate to quality of care in LTCFs, particularly in low and middle-income countries (LMIC). Full article

Magnetic Resonance (MR) technology is extensively used in academic and industrial research laboratories and represents one of the most significant methodologies in clinical radiology. Although MR does not use ionizing radiation, it cannot be considered risk-free due to the strong static magnetic fields and time-varying electromagnetic fields employed in the technology. To mitigate risks for MR operators, the European Community and ICNIRP have established safety limits based on the existing literature, primarily related to diagnostic MR. However, the literature on occupational exposure in non-clinical nuclear magnetic resonance (NMR) spectroscopy is limited. Due to their specificity, non-medical NMR environments present unique challenges from the point of view of operator exposure. NMR spectrometers are characterized by extremely high static magnetic fields, reaching up to 28 T in commercial systems; moreover, routine activities performed near the magnet, where field gradients are highest, increase operator exposure. Such environments are not typically perceived as hazardous and are frequented by various types of personnel, often without specific training. This study aims to highlight the critical issues in managing a preclinical MR laboratory equipped with a high-field NMR spectrometer, discussing operator safety challenges and presenting risk assessment data. Full article

This study investigates the job competencies and training needs of net-zero sustainability management personnel in Taiwan, in response to increasing climate change and ESG-related challenges. Using the Importance-Performance Analysis (IPA) method, the research surveyed 26 experts and 395 corporate sustainability professionals based on official competency standards issued by Taiwan’s Workforce Development Agency (WDA) and the Industrial Development Administration (IDA). The results indicate that training is most urgently needed in seven knowledge competencies (e.g., sustainable development concepts, GHG inventory reporting, and international sustainability standards), eight skill competencies (e.g., project planning, cross-departmental communication, cost budgeting, and benefit analysis), and one key attitude competency—proactiveness. The study also identifies discrepancies between expert evaluations and personnel self-assessments. While limited by its reliance on self-reported data, cross-sectional design, and constraints in identifying all net-zero engaged organizations, this study contributes original insights into sector-specific training priorities. The findings provide a guide for organizations to develop targeted, competency-based training programs that can enhance sustainability performance and support Taiwan’s transition toward net-zero emissions. Full article

To evaluate the operation modes of AC/DC hybrid power grids, a comprehensive evaluation index system is established based on the principles of index system construction, with a focus on mode development personnel in practical engineering. This system includes four aspects: static security, transient stability, DC security, and economic performance of the operation mode, with detailed explanations of the statistical or computational methods for each index. This index system can objectively and comprehensively reflect the rationality of the operation mode. Then, the Decision-making Trial and Evaluation Laboratory—Criteria Importance Through Intercriteria Correlation (DEMATEL-CRITIC) method is used for combining subjective and objective weights. Considering the scheduling decisions of power grid operators, the method is used to analyze the relationships among indexes, as well as their conflicts and variations. Gray relational analysis is used as the evaluation method to form a comprehensive evaluation model for the operation modes, achieving a scientific and effective comprehensive assessment of AC/DC hybrid power grids. Finally, a case study of an AC/DC hybrid power grid is used to validate the index system and the evaluation model. A comparative analysis demonstrates the effectiveness of the evaluation method and provides a practical tool for the planning, modification, and safe operational management of grids. Full article

Accurate real-time monitoring of underground conditions in coal mines is crucial for effective production management. However, limited computational resources and complex environmental conditions in mine shafts significantly impact the recognition and computational capabilities of detection models. This study utilizes a comprehensive dataset containing 117,887 images from five common underground mining tasks: mine personnel detection, large coal lump identification, conveyor chain monitoring, miner behavior recognition, and hydraulic support shield inspection. We propose the ESFENet backbone network, incorporating a Global Response Normalization (GRN) module to enhance feature capture stability while employing depthwise separable convolutions and HGRNBlock modules to reduce parameter volume and computational complexity. Building upon this foundation, we propose UCM-Net, a detection model based on the YOLO architecture. Furthermore, a self-supervised pre-training method is introduced to generate mine-specific pre-trained weights, providing the model with more semantic features. We propose utilizing the combined backbone and neck portions of the detection model as the encoder of an image-masking pre-training structure to strengthen feature acquisition and improve the performance of small models in self-supervised learning. Experimental results demonstrate that UCM-Net outperforms both baseline models and the state-of-the-art YOLOv12 model in terms of accuracy and parameter efficiency across the five mine datasets. The proposed architecture achieves 21.5% parameter reduction and 14.8% computational load decrease compared to baseline models while showing notable performance improvements of 1.3% (mAP_50:95) and 0.8% (mAP₅₀) in miner behavior recognition. The self-supervised pre-training framework effectively enhances training efficiency, enabling UCM-Net to attain an average mAP₅₀ of 94.4% across all five datasets. The research outcomes can provide key technical support for coal mine safety monitoring and offer valuable technological insights for the public safety sector. Full article

Background/Objectives: Medical research institutions are increasingly leveraging artificial intelligence (AI) to enhance the processing and analysis of medical imaging data. However, scaling AI-driven medical image analysis often requires specialized expertise and infrastructure that individual labs may lack. A centralized solution is to establish a core facility—a shared institutional resource—dedicated to Automated Medical Image Processing and Analysis (AMIPA). Methods: This technical note offers a practical roadmap for institutions to create an AI-based core facility for AMIPA, drawing on our experience in building such a resource. Results: We outline the key components for replicating a successful AMIPA core facility, including high-performance computing resources, robust AI software pipelines, data management strategies, and dedicated support personnel. Emphasis is placed on workflow automation and reproducibility, ensuring researchers can efficiently and consistently process large imaging datasets. Conclusions: By following this roadmap, institutions can accelerate AI adoption in imaging workflows and foster a shared resource that enhances the quality and productivity of medical imaging research. Full article

Enhancing rural teacher agency to foster more proactive and enthusiastic teaching practices is crucial for addressing the underdeveloped state of rural education. In recent years, the introduction of performance-based personnel management, such as performance-based pay, has been a significant reform initiative. This study focuses on the first county in Beijing to implement performance-based personnel management, conducting a three-year longitudinal investigation to explore how established Western management policies perform when confronted with guanxi (Chinese-style relationships) that emphasize familiarity and personal ties. Using Hierarchical Linear Modeling, the study analyzes three waves of longitudinal data from 516 teachers in a Beijing county, examining the impact of performance-based personnel management on teacher agency and the mediating and moderating roles of guanxi. The findings reveal that (1) Performance-based personnel management did not enhance teacher agency as anticipated. In fact, the stronger the initial guanxi among teacher groups, the more negative the policy’s impact on teacher agency. (2) Guanxi culture exerted a suppressing effect on the policy. In rural China, the indigenous guanxi culture hindered the implementation of performance-based personnel management, eroding collaborative networks among teachers and obscuring the policy’s intended positive effects. Performance-based personnel management weakened work connections among strong-guanxi groups while directly boosting the agency of weak-guanxi groups. (3) Instrumental guanxi demonstrated greater adaptability to policy changes compared to affective guanxi. Overall, externally imposed performance-based personnel management faces significant adaptive challenges within China’s deeply rooted guanxi culture. The effectiveness of such policies is constrained by their degree of alignment with local cultural and social structures. Therefore, policymakers should consider local cultural characteristics in educational policy design to enhance the adaptability and effectiveness of these policies. Full article