Search Results (33)

The causal analysis of historical aviation accidents documented in investigation reports is important for the design, manufacture, operation, and maintenance of aircraft. However, given that most accident data are unstructured or semi-structured, identifying and extracting causal information remain labor intensive and inefficient. This gap is further deepened by tasks, such as system identification from component information, that require extensive domain-specific knowledge. In addition, there is a consequential demand for causation pattern analysis across multiple accidents and the extraction of critical causation chains. To bridge those gaps, this study proposes an aviation accident causation and relation analysis framework that integrates prompt engineering with a retrieval-augmented generation approach. A total of 343 real-world accident reports from the NTSB were analyzed to extract causation factors and their interrelations. An innovative causation classification schema was also developed to cluster the extracted causations. The clustering accuracy for the four main causation categories—Human, Aircraft, Environment, and Organization—reached 0.958, 0.865, 0.979, and 0.903, respectively. Based on the clustering results, a causation knowledge graph for aviation accidents was constructed, and by designing a set of safety evaluation indicators, “pilot—decision error” and “landing gear system malfunction” are identified as high-risk causations. For each high-risk causation, critical combinations of causation chains are identified and “Aircraft operator—policy or procedural deficiency/pilot—procedural violation/Runway contamination → pilot—decision error → pilot procedural violation/32 landing gear/57 wings” was identified as the critical causation combinations for “pilot—decision error”. Finally, safety recommendations for organizations and personnel were proposed based on the analysis results, which offer practical guidance for aviation risk prevention and mitigation. The proposed approach demonstrates the potential of combining AI techniques with domain knowledge to achieve scalable, data-driven causation analysis and strengthen proactive safety decision-making in aviation. Full article

This study addresses the critical challenge of designing retirement savings systems that effectively balance liquidity needs and long-term accumulation in contexts characterized by high unemployment and labor market instability, with a focus on South Africa. Traditional pension schemes often assume uninterrupted careers and stable incomes, assumptions frequently violated in low- and middle-income countries, leading to inadequate retirement security and consumption volatility during working life. Motivated by this gap, we develop a stochastic two-pot retirement savings model that explicitly integrates labor market uncertainty using a Markov chain-based Monte Carlo simulation. The model allocates annual contributions between an accessible savings pot and a locked retirement pot, with individuals optimizing consumption and withdrawal decisions to maximize expected lifetime utility under Constant Relative Risk Aversion (CRRA) preferences. Our findings, derived from calibration to South African labor data, reveal that high unemployment and career uncertainty significantly increase the welfare-maximizing preference for liquidity. This result challenges conventional policies prescribing fixed contribution allocations, such as the one-third/two-thirds split in the new two-pot system, and underscores the importance of flexible retirement savings designs. We conclude that tailoring pension design to labor market realities can enhance both retirement security and welfare in volatile economies. Full article

The paper proposes a method for assessing production risks related to potential exceedances of the agreed production lead time for batches of details in small and medium-sized enterprises. The study focuses on a linear production system composed of sequential technological operations, analyzed within the broader context of production and logistics processes. A stochastic model of the production flow has been developed, using dimensionless parameters to describe the state and trajectory of a product in a multidimensional technological space. The internal and external risk factors that affect the duration of operations are taken into account, including equipment failures, delays in material deliveries and labor availability. Analytical expressions enabling the quantitative assessment of the risk of production deadline violations and the resulting losses have been derived. The proposed method was validated on a production line for manufacturing wooden single-leaf windows. The results indicate that the presence of inter-operational reserves significantly reduces the probability of exceeding production deadlines and enhances the stability of the production process under stochastic disturbances. The use of inter-operational buffers in most cases ensured a reduction in the processing time of experimental batches of products by 18–25% and simultaneously led to a reduction in the level of production risk by several times, which confirms the effectiveness of the proposed approach and its practical significance for increasing the sustainability of production systems. Full article

Multi-threshold image segmentation plays an irreplaceable role in extracting discriminative structural information from complex images. It is one of the core technologies for achieving accurate target detection and regional analysis, and its segmentation accuracy directly affects the analysis quality and decision reliability in key fields such as medical imaging, remote sensing interpretation, and industrial inspection. However, most existing image segmentation algorithms suffer from slow convergence speeds and low solution accuracy. Therefore, this paper proposes an Adaptive Cooperative Pelican Optimization Algorithm (ACPOA), an improved version of the Pelican Optimization Algorithm (POA), and applies it to global optimization and multilevel threshold image segmentation tasks. ACPOA integrates three innovative strategies: the elite pool mutation strategy guides the population toward high-quality regions by constructing an elite pool composed of the three individuals with the best fitness, effectively preventing the premature loss of population diversity; the adaptive cooperative mechanism enhances search efficiency in high-dimensional spaces by dynamically allocating subgroups and dimensions and performing specialized updates to achieve division of labor and global information sharing; and the hybrid boundary handling technique adopts a probabilistic hybrid approach to deal with boundary violations, balancing exploitation, exploration, and diversity while retaining more useful search information. Comparative experiments with eight advanced algorithms on the CEC2017 and CEC2022 benchmark test suites validate the superior optimization performance of ACPOA. Moreover, when applied to multilevel threshold image segmentation tasks, ACPOA demonstrates better accuracy, stability, and efficiency in solving practical problems, providing an effective solution for complex optimization challenges. Full article

The increasing integration of autonomous driving technologies into heavy-duty road transport requires a clear understanding of how these systems affect professional drivers’ working time, vehicle utilization, and regulatory compliance. This study develops a model-based comparative analysis to assess the cooperation between human drivers and autonomous trucks at SAE Levels 3 and 4. Using EU Regulation (EC) No 561/2006 as a legal framework, single-driver, double-driver, and ego vehicle scenarios were simulated to evaluate changes in working time classification and vehicle movement. The results indicate that Level 3 automation enables up to 13.25 h of daily vehicle movement while complying with working time regulations, compared with the 10-h limit for conventional operation. Level 4 automation further extends the effective movement time to 14.25 h in double-crew configurations, offering opportunities for increased efficiency without violating labor codes. The novelty of this work lies in the quantitative modeling of human–machine collaboration in professional transport under real regulatory constraints. These findings provide a foundation for regulatory updates, tachograph adaptation to AI-driven vehicles, and the design of hybrid driver roles. Future research will focus on validating these models in real-world transport operations and assessing the implications of Level 5 autonomy for logistics networks and labor markets. Full article

The intersection of digitalization and sustainability is reshaping the tourism industry, with digital platforms playing a transformative role in optimizing travel experiences while simultaneously influencing economic inclusivity, labor dynamics, and environmental responsibility. This paper explores how Industry 4.0 technologies—such as artificial intelligence (AI), big data, blockchain, virtual reality (VR), and the Internet of Things (IoT)—are integrated into digital tourism platforms, assessing their dual impact on sustainability and market structures. The study develops a conceptual framework around five key dimensions: market power and digital dependency, AI-driven automation and workforce transformation, innovation and digital inclusion, sustainability innovations, and data security and governance. While digital platforms enhance personalization, operational efficiency, and eco-conscious travel, they also reinforce economic disparities, monopolization, and regulatory challenges, raising concerns related to SDGs such as SDG 1 (No Poverty), SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), SDG 10 (Reduced Inequalities), SDG 12 (Responsible Consumption, and Production), SDG 13 (Climate Action), and SDG 16 (Peace, Justice, and Strong Institutions). The study highlights the need for equitable governance frameworks to mitigate risks associated with AI-driven monopolization, algorithmic bias, and data privacy violations while ensuring digital accessibility for small and medium-sized enterprises (SMEs). The findings contribute to ongoing discussions on platform economics, digital governance, and sustainable tourism transformation, offering policy and managerial implications for fostering an inclusive and environmentally responsible tourism industry. Full article

The psychological contract is a fundamental construct for understanding the relationships established between employee and employer. However, the current literature states that there is no validated instrument to measure this construct. The present research aims to validate the psychological contract questionnaire developed by the PSYCONES team, providing empirical evidence on its reliability and validity in the current labor context. A sample of 1764 workers in the Spanish labor market was used, and a cross-validation process was applied, in which exploratory factor analysis was performed and various models (CFA, ESEM, CFA bifactor and ESEM bifactor) were tested to evaluate the structure of the questionnaire. In addition, multigroup invariance analyses were performed to examine the stability of the model according to gender and job level. The results indicated that the four-factor ESEM model showed the best fit and representation of the following dimensions of the psychological contract: fulfillment of company promises, fulfillment of employee promises, psychological contract violation, and perception of justice and trust. Likewise, the scale showed significant relationships with job satisfaction and organizational commitment, reinforcing its concurrent validity. This study provides a psychometrically robust instrument to assess the psychological contract in the workplace, offering a basis for future research and practical applications in different organizational contexts. Full article

Background: Migrant construction workers involved in building infrastructure for mega-sporting events face elevated risks of illness and death. However, specific health outcomes for these workers have not been systematically reviewed, limiting opportunities to identify and address their challenges. Methods: This study systematically reviewed health outcomes among migrant construction workers involved in mega-sporting events. Results: 89 eligible studies involving 23,307 workers were identified. Of these, only 11 directly addressed specific health outcomes, including heat stress, occupational fatalities, and sexually transmitted infections. Notably, increased heat exposure during peak construction phases and the proximity of deadlines for mega-sporting events were correlated with elevated rates of occupational fatalities. Other key adverse factors impacting migrant construction workers’ health included an observed correlation between the timing of mega-sporting events and increased occupational fatalities, the involvement of labor recruiters, and shifting health and safety responsibilities among stakeholders (e.g., host states, event organizers, contractors, and recruitment agencies). Positive outcomes were observed when workers voluntarily engaged in non-mandatory safety activities, such as safety training programs and awareness meetings. Conclusions: There is a critical need for longitudinal and comparative studies to comprehensively examine the health of migrant workers throughout all stages of their journey, from pre-migration to return. This review underscores the urgency of prioritizing evidence-based policies that address unique health risks in this population, including mitigation of heat stress and enforcement of occupational safety standards, particularly amid construction spikes preceding mega-sporting events. Recommendations: Future research should prioritize understanding the unique health challenges faced by migrant workers to inform policy making, develop effective interventions, and implement best practices to improve their health and well-being. Full article

The article discusses Pope Francis’s visit to Bahrain in 2022, which, although religiously and politically important, has not yet been exposed to science. This is the second visit of the head of the Catholic Church to the countries of the Persian Gulf, which is part of the refreshing interreligious dialogue between Christians and Muslims. This article analyzes changes in the Catholic Church’s understanding of dialogue with Islam, as illustrated by Pope Francis’s visit to Bahrain. Using comparative analysis, the article highlights the evolving nature of interreligious dialogue and its role in strengthening Muslim–Christian connections. The papal visit to Bahrain caused much controversy among human rights activists. Bahrain is seen, on one hand, as a tolerant and religiously inclusive country, but on the other, as a nation that frequently violates human rights, particularly in cases involving differences among its members. Pope Francis, as a diplomat, was not afraid to talk about some social issues in Bahrain (death penalty, discrimination, labor law) from the beginning of his visit. Pope Francis’s attitude and the benefits of interreligious dialogue that he has generated may not only confirm the presence of Christians in Bahrain but, above all, must change their destiny in the countries of the region that have so far treated this religion as an enemy. This article is an analysis of sources and their systematic review. The authors have focused on the interpretation of Pope Francis’s statements and their reception in the world. Interreligious and intercultural dialogue, as well as interpersonal fraternities, are extremely important in the international policy of the Holy See. Full article

Gendered criticism of Eve and general criticism of Eve and Adam are rooted in the idea of their moral failing when they disobey Yahweh. Two lenses bring a more ancient understanding of the text into focus. The first lens is reading the story in the context of the J source of the Pentateuch. The second lens is that of natural law as understood by Greco-Roman philosophers. These lenses provide new clarity, showing how Eve and Adam’s decision to eat from the tree of knowledge violates a non-moral norm: they transgress the boundary between humanity and divinity. It is this ontological transgression to which Yahweh responds. Mortality, many labors, and many pregnancies reflect the natural consequences of this ontological violation, not an arbitrary punishment for a moral failing. This alternative understanding of Genesis 2–3 allows us to understand that Eve and Adam are neither cursed nor punished. Full article

This study explains the Venezuelan migration involving unaccompanied or separated adolescents (UASA) on the Colombian–Venezuelan border, specifically in Norte de Santander-Táchira. This explanation is framed within the concept of the social moratorium, highlighting three subcategories that contribute to the early abandonment of childhood: 1. the violation of rights, 2. working life, and 3. confrontation of dangers. These subcategories compel UASA to transition prematurely into youth, assuming social, labor, or family responsibilities. Methodologically, we adopt a narrative approach, conducting group interviews with 24 immigrant children and adolescents. Furthermore, 14 interviews are conducted in 2 local markets, and the remaining 10 on 2 central avenues in the city of Cúcuta, Colombia. We conduct a theoretical analysis drawing upon key concepts, including the social moratorium, social constructionism, interaction, and moral and cognitive development. This theoretical framework helps us understand the consequences for the life prospects of this generation. They arise from factors such as school dropout, exposure to health risks, and the absence of free leisure time. These indicators reflect socioeconomic problems, including poverty, abuse, and violence. Full article

The Belt and Road Initiative (BRI) has become a household name for developing countries, especially in Africa. The BRI proposal by Chinese President Xi Jinping was positively received by many countries, including policymakers in China. In response, the Chinese Government committed to investing USD 1 trillion over ten years from 2013 to 2023. As a result, 152 countries signed a cooperation agreement with China to work under the BRI framework. The BRI has played a vital role in addressing the global infrastructures gap through the construction of modern highways, airports, high-speed railways, bridges, power generation (hydropower), and industrial parks. As a result, this has enhanced connectivity and economic growth between Asia, Europe, and Africa. Despite the BRI’s significant role in strengthening trade, infrastructure and investment links between China and other countries, there is limited literature on specific countries’ experience with the initiative. This study, therefore, will advance our understanding of the BRI, especially on the conceptualization of the term; comparative analysis of Africa–China relationships before and after the BRI; the benefits in relation to the “Five Connectivities” and the challenges the BRI is facing in Africa. The article is based on a literature review and case study as research methodologies mainly used the Policies, Projects, Initiatives, and Strategies (PPIS) as a data source. The study focuses on five African countries; Uganda, Kenya, Egypt, Djibouti, and Mozambique. These countries were selected purposefully for analysis because of their experience, long-term relationships with China, and strategic locations. The findings revealed that the BRI lacked a clear description and that it was difficult to distinguish between BRI projects and other regular economic or diplomatic relations. The study also identified four differences between Africa–China relationships before and after the BRI. Furthermore, the findings revealed that the BRI has positively contributed to all five connectivity pillars. However, the major challenges reported concerning the initiative from the various countries were: procurement corruption, low/lack of involvement of stakeholders, high compensation prices, labor violations, increasing debts, and environmental hazards. In conclusion, while the BRI has brought about significant infrastructure development and economic benefits, the project has also experienced some challenges. This study, therefore, contributes to the body of knowledge on China’s Belt and Road Initiative and its impact on African countries, specifically in Uganda, Kenya, Djibouti, Mozambique, and Egypt. The paper then provides conclusions and policy implications as well as future research opportunities in the current body of the literature. Full article

Latino/a/x workers perform labor-intensive forestry and fire stewardship work in the U.S. Pacific Northwest, but are not well recognized in research and practice about wildfire governance. This industry has pervasive issues of unsafe working conditions, inequitable wage practices, violations of worker rights, limited opportunity for advancement, and a lack of recognition and inclusion of workers in decision making. We draw on a literature review and practice-based knowledge to make this workforce’s history more visible, from its origins in lumber production and reforestation to expansion into forest and fire stewardship. We suggest a new conceptual framing of “multiple marginalities” that situates this workforce as simultaneously crucial to our future with wildfire and subject to structural, distributional, recognitional, and procedural inequities. We recommend new approaches to research and practice that can better examine and address these inequities, while also acknowledging the persistent and systemic nature of these challenges. These include participatory action research, lessons learned from research and advocacy related to farmworkers and incarcerated workers, and Cooperative Extension and education programs that are learner-centered and culturally appropriate. Multiple interventions of offering education and outreach, enforcing or reforming law, and changing policy and practice must all occur at multiple scales given the many drivers of these marginalities. Study and practice can contribute new knowledge to inform this and expand current conceptions of equity and environmental justice in the wildfire governance literature to become more inclusive of the forest and fire stewardship workforce. Full article

The major problem in Thailand related to parking is time violation. Vehicles are not allowed to park for more than a specified amount of time. Implementation of closed-circuit television (CCTV) surveillance cameras along with human labor is the present remedy. However, this paper presents an approach that can introduce a low-cost time violation tracking system using CCTV, Deep Learning models, and object tracking algorithms. This approach is fairly new because of its appliance of the SOTA detection technique, object tracking approach, and time boundary implementations. YOLOv8, along with the DeepSORT/OC-SORT algorithm, is utilized for the detection and tracking that allows us to set a timer and track the time violation. Using the same apparatus along with Deep Learning models and algorithms has produced a better system with better performance. The performance of both tracking algorithms was well depicted in the results, obtaining MOTA scores of (1.0, 1.0, 0.96, 0.90) and (1, 0.76, 0.90, 0.83) in four different surveillance data for DeepSORT and OC-SORT, respectively. Full article

With the rapid development of artificial intelligence technology, the exploration and application in the field of intelligent education has become a research hotspot of increasing concern. In the actual classroom scenarios, students’ classroom behavior is an important factor that directly affects their learning performance. Specifically, students with poor self-management abilities, particularly specific developmental disorders, may face educational and academic difficulties owing to physical or psychological factors. Therefore, the intelligent perception and identification of school-aged children’s classroom behaviors are extremely valuable and significant. The traditional method for identifying students’ classroom behavior relies on statistical surveys conducted by teachers, which incurs problems such as being time-consuming, labor-intensive, privacy-violating, and an inaccurate manual intervention. To address the above-mentioned issues, we constructed a motion sensor-based intelligent system to realize the perception and identification of classroom behavior in the current study. For the acquired sensor signal, we proposed a Voting-Based Dynamic Time Warping algorithm (VB-DTW) in which a voting mechanism is used to compare the similarities between adjacent clips and extract valid action segments. Subsequent experiments have verified that effective signal segments can help improve the accuracy of behavior identification. Furthermore, upon combining with the classroom motion data acquisition system, through the powerful feature extraction ability of the deep learning algorithms, the effectiveness and feasibility are verified from the perspectives of the dimensional signal characteristics and time series separately so as to realize the accurate, non-invasive and intelligent children’s behavior detection. To verify the feasibility of the proposed method, a self-constructed dataset (SCB-13) was collected. Thirteen participants were invited to perform 14 common class behaviors, wearing motion sensors whose data were recorded by a program. In SCB-13, the proposed method achieved 100% identification accuracy. Based on the proposed algorithms, it is possible to provide immediate feedback on students’ classroom performance and help them improve their learning performance while providing an essential reference basis and data support for constructing an intelligent digital education platform. Full article