Human Factors and Ergonomics in Manufacturing & Service Industries最新文献

Sweat absorbed by clothing forms a moisture film on fabric surfaces, reducing fabric breathability and disrupting thermoregulation during exercise in a hot environment. We investigated whether T-shirts made from a newly developed fabric with hydrophobic and water-repellent fibers near the through holes could prevent sweat film formation, thereby enhancing intraclothing microclimate and thermoregulatory responses. Thirteen male runners completed 30-min treadmill sessions at moderate and high intensities while wearing either the new fabric T-shirt (Dry Aeroflow, DAF) or a polyester T-shirt (CONT) at an ambient temperature of 32°C and a relative humidity of 50% with an air velocity of 0.8 m/s. Compared with CONT, intraclothing humidity was decreased on the chest and back, and chest but not back skin temperature was decreased by 2°C in DAF. However, thermoregulatory key variables such as rectal temperature, mean skin temperature, sweat rates, and heart rate did not differ between the T-shirts. We concluded that the a T-shirt with enhanced breathability does not affect overall thermoregulatory response during exercise in the heat despite the partial improvements in intraclothing microclimate and in reducing local skin temperature.

Human error in manufacturing can have substantial consequences, including loss of life, injuries, productivity, and financial losses. Human reliability analysis (HRA) methods can be used to evaluate the likelihood of human error in manufacturing tasks and identify potential sources of error. Performance shaping factors (PSFs) are internal and external factors that influence human performance and can affect the likelihood of human reliability estimates in HRA methods. Understanding the impact of PSFs on human performance in manufacturing is essential for developing effective strategies to minimize the likelihood of human error and improve the safety and efficiency of manufacturing processes. This systematic review scrutinizes the literature on PSFs within manufacturing, highlighting HRA applications. Using the PRISMA protocol, studies from 2000 to 2024 across engineering and psychology were examined, culminating in the analysis of 35 pertinent works. The review identifies and contrasts various PSF taxonomies from established HRA methods like SPAR-H, HEART, CREAM, and THERP, revealing their diverse applications in different manufacturing settings. The review also uncovers a tendency to devise taxonomies through the lens of experts' domain knowledge, particularly tailored to discrete manufacturing contexts. A critical gap is observed in the lack of a uniform PSF framework, with the current literature reflecting a disparate understanding of PSFs' roles, definitions, and interrelations. This absence is further pronounced by the inadequate integration of human factors in the dialogue surrounding Industry 4.0. The analysis points to the necessity of harmonizing PSFs to better assess human reliability amid technological integration. The findings emphasize the need for an industry-specific PSF framework that aligns with the intricacies of manufacturing operations, thus enabling more accurate HRA outcomes and informing strategies for error reduction and process optimization.

This paper investigates the usage of a systemic neutralized taxonomy for understanding human factors to foster a restorative culture when studying adverse events. The available studies supporting accident analysis from a system-theoretic perspective with human factors-based methods are grounded on error taxonomies and do not reinforce the non-judgemental dimension that is encouraged by modern safety science. We propose a methodology to integrate the system-theoretic accident analysis technique, Causal Analysis based on System Theory, with a neutralized taxonomy of human explanatory factors. The proposed methodology has been applied to an aviation accident involving a military aircraft and a glider. This case revealed various critical interactions among system components, which require dedicated safety recommendations that go beyond the identification of single points of failures and root causes, leading to a deeper understanding of socio-technical orchestrations. Despite the use case in aviation, the proposed methodology remains suitable for various types of safety occurrences in diverse domains and industrial settings. It also represents the basis for supporting future proactive safety-related decision-making processes.

More than half of the population in impoverished areas around the world, which lack sufficient energy and water, do not have washing machines. Due to a lack of resources and economic reasons, the mechanical task of modern washing machines requiring electrical energy can only be done through hand washing. Additionally, water fetching and laundry work are predominately assigned to women due to socio-cultural dynamics in disadvantaged areas. The repetitive motions involved in washing over extended periods of time, coupled with awkward body positions, often lead to musculoskeletal disorders in women. This study aims to improve women's work safety and living conditions through a product design for laundry washing. First, the ergonomic risk associated with women's body postures while washing clothes on the floor was evaluated to better understand the impact of the intervention and rapid upper extremity assessment (RULA) method was used for this purpose. Subsequently, the design process for a manual washing product, which operates without electricity, can handle bulk laundry and facilitates easier and more ergonomic work instead of directly scrubbing clothes, was initiated as a remedial intervention. Recognizing that laundry work is a multidimensional experience in women's lives beyond its ergonomic aspects, a holistic design methodology was adopted, incorporating various design approaches to create concepts that better integrate with the lives of woman users. Insights gained from these design approaches were then combined with the conceptual design method to generate design proposals. At the end of the study, the developed manual washing machine design performed improved RULA analysis scores and biomechanical loading outcomes compared to hand washing.

Workers' health and safety, and equality between men and women are two of the main challenges currently facing the labor framework in Spain. Despite the fact that the different risks faced by men and women have been widely studied, the way in which gender can affect the working conditions and career development of occupational health and safety (OHS) technicians has hardly been investigated. To delve into this aspect, a study has been carried out by conducting semistructured interviews with six women who work as OHS technicians and a survey among OHS technicians in the Basque Country (Spain) with 124 responses. The results suggest that there are differences between men and women both in the conditions and in the career development of the OHS technician profession. These differences appear in various thematic blocks such as empowerment, recognition, or aspirations. Women tend to be more skeptical than men on gender equality issues such as the idea of equal promotion opportunities for men and women, or the notion that men's and women's ideas are equally valued, or the way in which having children affects men and women's aspirations. It is very important to reduce these gender differences to achieve a more efficient work environment, which can lead to more efficient OHS management.

Visual inspection tasks often require humans to cooperate with artificial intelligence (AI)-based image classifiers. To enhance this cooperation, explainable artificial intelligence (XAI) can highlight those image areas that have contributed to an AI decision. However, the literature on visual cueing suggests that such XAI support might come with costs of its own. To better understand how the benefits and cost of XAI depend on the accuracy of AI classifications and XAI highlights, we conducted two experiments that simulated visual quality control in a chocolate factory. Participants had to decide whether chocolate molds contained faulty bars or not, and were always informed whether the AI had classified the mold as faulty or not. In half of the experiment, they saw additional XAI highlights that justified this classification. While XAI speeded up performance, its effects on error rates were highly dependent on (X)AI accuracy. XAI benefits were observed when the system correctly detected and highlighted the fault, but XAI costs were evident for misplaced highlights that marked an intact area while the actual fault was located elsewhere. Eye movement analyses indicated that participants spent less time searching the rest of the mold and thus looked at the fault less often. However, we also observed large interindividual differences. Taken together, the results suggest that despite its potentials, XAI can discourage people from investing effort into their own information analysis.

Studies have focused on the negative effects of anger on driving performance, but insufficient research has addressed intervention methods to reduce these emotional effects. This research investigated how music mitigates the deterioration of driving performance associated with angry emotions in a simulated car-following task. Forty-three licensed drivers participated in this study, and they were randomly separated into two groups: an intervention group and a control group. First, all the participants completed a car-following task involving neutral arousal. Then, both groups completed the car-following task after the anger arousal task. The intervention group drove while listening to relaxing music, but the control group did not. Driving performance and electrocardiographic data were recorded. The results showed that participants who listened to relaxing music had significantly shorter braking reaction times and greater heart rate variability (HRV) than did those who did not listen to music. Relaxing music can reduce driving anger and improve driving behavior.

In today's interconnected global economy, maritime trade is a pillar of prosperity, yet maritime accidents loom as a formidable challenge. The intricate nature of these accidents, coupled with rapid technological advancements, necessitates the evolution of systematic analysis methods. Conventional systemic approaches, while valuable, struggle to encapsulate the intricate web of mutual and dynamic dependencies inherent in these incidents. Furthermore, the call for more quantitative support in decision-making and the ability to account for emergent factors has become increasingly imperative. This study aims to analyze maritime accidents by introducing a quantitative and dynamic model. The endeavour begins with establishing an extended Accident Map-based model, a robust framework that unveils a sophisticated accident causation model. This preliminary action establishes the groundwork for integrating an innovative Spherical Fuzzy Set, navigating the complex landscape of knowledge acquisition. The subsequent phase charts a transformative course by mapping the model onto a dynamic Bayesian Network to conduct a forward and backward analysis. The essence of the model lies in its dynamic nature, allowing for real-time updates that reflect the evolving maritime accidents risk factors. The approach is validated through a partial benchmark exercise, a reality check, an independent peer review, and a sensitivity analysis. The model can explore emerging contributing factors, reduce uncertainty, and consider relationships between factors that yield designing more effective safety measures.

To improve the interaction between drivers and the in-vehicle information system (IVIS), various intelligent agents, such as robot agents, virtual agents, and voice-only agents, have been integrated into vehicles. However, it is not yet clear which type of in-vehicle agent is best suited to the driving context. This study aims to investigate the effect of in-vehicle agent embodiment on drivers' perceived usability and cognitive workload. In a within-subject simulated driving experiment, 22 participants interacted with three different in-vehicle agents (smartphone agent, robot agent, and virtual agent). Functional near-infrared spectroscopy and electrocardiogram (ECG) were used to record prefrontal cortex activation and electrical changes associated with cardiac activity during simulated driving, respectively. The results show that the smartphone agent had the lowest perceived usability scores, oxygenated hemoglobin concentration variation (ΔHbO), and maximum ECG signal variation compared to baseline. There were no statistical differences in cognitive workload, perceived usability scores, brain area activation, and ECG signals between the robot agent and the virtual agent. The research findings demonstrate the positive effects of the anthropomorphic appearance of in-vehicle agents on perceived usability and contribute to improving the design of in-vehicle intelligent agents.

Gas accidents represent a crucial domain of coal mine safety research, as they result in substantial property damage, environmental pollution, and even loss of life compared to other types of accidents. Particularly, human factors play a significant role in the majority of mining accidents. The objective of this paper is to enhance the quality of coal mine safety management, minimize the occurrence of adverse human factors in gas accidents, and analyze the factors influencing coal mine gas accidents using the Human Factors Analysis and Classification System (HFACS). To commence, this paper has devised a human factor influence index system based on the enhanced HFACS for coal mine gas accidents. Subsequently, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method has been employed to quantitatively delineate the causal relationships among these factors. Lastly, this paper utilized the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method to evaluate the importance of factors influencing coal mine gas accidents. The research findings indicate that through the utilization of the DEMATEL methodology for centrality and causal relationship calculations, the centrality and causality values associated with poor organizational management emerge as the foremost among all factors. This underscores the pivotal role that poor organizational management plays in the human factors influencing coal mine gas accidents. Furthermore, a meticulous examination using TOPSIS identified the top five indicators of influence capability: cognitive errors > habitual violations > operational management > management process > resource management. The analysis of human factors in coal mine gas accidents can provide enhanced theoretical support for the management of production safety in coal mines, as well as the prevention of gas accidents.