Search Results (1,838)

Factory announcements for battery production are increasing in number as European demand for battery cells grows. Using a Monte Carlo simulation (108 projects as of October 2025) with risk factors for individual projects, the predicted theoretical production capacity for lithium-ion batteries in Europe will rise to 1.1–1.5 TWh, enabling a real production output of 0.8–1.0 TWh by 2030. Our analysis suggests necessary cumulative investments in battery cell gigafactories of 36–139 billion euros by 2030. The industrial output of LIB cells in 2030 will have a value of 35–99 billion euros, of which the market size of battery production is around 6–17 billion euros. Furthermore, 43,000–174,000 direct jobs could be created, with the strongest impacts seen in Eastern Europe by the end of the decade. The raw material demand generated by this industry rises steeply: lithium will rise from 14 kt in 2025 to 47–133 kt, and nickel from 83 kt to 226–640 kt by 2030, implying continued import dependencies. The energy demand of European cell production will be 8.4–19.9 TWh in 2030. Furthermore, CO₂ emissions of cell production will be 1.6 to 3.7 Mt CO₂-eq in 2030. The volume of production scrap is estimated at 160–398 kt in 2030, creating near-term demand for recycling capacities. Full article

With the rapid transformation of the automotive industry towards electric vehicles, how to achieve efficient mixed-line production of electric vehicles and fuel vehicles has become a key challenge for modern assembly systems. This study investigated the balancing problem of a mixed-model multi-manned assembly line, considering workstation sharing (MMuALBP-WS), and developed a deterministic multi-objective model that integrates the heterogeneity of tasks and the coordination of shared workstations. An improved genetic algorithm was proposed, whose decoding mechanism enables different types of electric vehicle and fuel vehicle tasks to achieve dynamic collaboration within the shared workstations. A real case study from the chassis assembly line of Company W demonstrated the effectiveness of the proposed method, achieving a 25% reduction in the number of workstations, a 27% decrease in the total number of workers, and a 23.56% increase in average workstation utilization. The results confirmed that the workstation sharing mechanism significantly improved production balance, labor utilization, and flexibility, providing a practical and scalable optimization framework for the mixed-model assembly system in the era of the transition from electric vehicles to fuel vehicles. In addition to its practical significance, this study enhances the understanding of mixed-model multi-manned line balancing by incorporating workstation-sharing logic into both the mathematical modeling and optimization process, offering a theoretical basis for future extensions to more complex production environments. Full article

The mining industry places high priority on occupational safety, process quality and operational efficiency. Roadheaders are widely deployed in coal mines to support fully automated excavation, reducing workers’ physical strain and improving overall safety. This article examines an automatic control system for a roadheader cutting head designed to increase mining efficiency, reduce energy consumption and maintain stable performance under varying coal and rock conditions. The system integrates advanced control algorithms with geological strength index (GSI) analysis and asynchronous motor control strategies. GSI-based adaptive speed control conserves energy and increases cutting efficiency compared to manual control. By reducing dynamic load fluctuations, transitions between different cutting zones become smoother, which decreases equipment wear. The proposed control system incorporates speed feedback loops that use a proportional–integral (PI) controller with field-oriented control (FOC), as well as super-twisted sliding mode control (STSMC) with FOC. FOC with STSMC improves roadheader productivity by applying advanced control strategies, adaptive speed regulation and precise geological strength analysis. It is also better able to handle disturbances and sudden loads thanks to STSMC’s nonlinear control robustness. The result is safer, more efficient, and more cost-effective mining that can be implemented across a wide range of underground mining scenarios. Full article

The COVID-19 pandemic intensified labor shortages and safety challenges in Jordan’s construction sector, revealing systemic vulnerabilities in its reliance on migrant workers. This study evaluates an advanced robotic bricklaying system through simulation of 10 residential buildings (80 units) under Jordanian building codes (JSBC 2020) and strict pandemic constraints, including workforce absenteeism rates of 30% based on ILO data and Ministry of Health density protocols. The simulation-based analysis, which focuses specifically on standardized housing designs, demonstrates 84% faster bricklaying (6.75 vs. 43.2 days/unit), 94% productivity retention during absenteeism, 15% mortar waste reduction (advancing SDG 9), and 60% lower transmission risk versus manual methods. Despite higher rental costs (15,168 JD vs. 12,946 JD/unit), accelerated construction timelines substantially reduced overhead expenses, yielding a rapid <5-month payback period. Policy recommendations target vocational training programs and financial subsidies for small contractors, aligning with Jordan’s Economic Modernization Vision (2022–2024). Limitations involve architectural irregularities and supply chain dependencies; future work requires field validation to complement these simulation findings. Full article

For garment manufacturing, an efficient and precise assessment of ergonomics is vital to prevent work-related musculoskeletal disorders. This study creates a computer vision-based algorithm for fast and accurate risk analysis. Specifically, we introduced SE and CBAM attention mechanisms into the YOLO network and integrated the optimized modules into the HRNet architecture to improve the accuracy of human pose recognition. This approach effectively addresses common interferences in garment production environments, such as fabric accumulation, equipment occlusion, and complex hand movements, while significantly enhancing the accuracy of human detection. On the COCO dataset, it increased mAP and recall by 4.43% and 5.99%, respectively, over YOLOv8. Furthermore, by analyzing key postural features from worker videos of cutting, sewing, and pressing, we achieved a quantified ergonomic risk assessment. Experimental results indicate that the RULA scores calculated using this algorithm are highly consistent and stable with expert evaluations and accurately reflect the dynamic changes in ergonomic risk levels across different processes. It is important to note that the validation was based on a pilot study involving a limited number of workers and task types, meaning that the findings primarily demonstrate feasibility rather than full-scale generalizability. Even so, the algorithm outperforms existing lightweight solutions and can be deployed in real-time on edge devices within factories, providing a low-cost ergonomic monitoring tool for the garment manufacturing industry. This helps prevent and reduce musculoskeletal injuries among workers. Full article

Blossom-end enlargement (BEE) is a physiological disorder in cucumbers (Cucumis sativus L.) that affects postharvest quality and results in commercial loss due to reduced product value. Pre-cooling using modified atmosphere packaging (MAP) has been encouraged as a preventive method of BEE; however, BEE can still be observed under actual distribution conditions. This study reexamined the process from harvesting in midsummer to arriving at the market (550 km) and storage, while considering the impact of packaging materials, packaging methods, and human factors on BEE occurrence. More than 18 h were required from harvest to delivery at the pre-cooling warehouse at the common shipping site; however, despite using a refrigerated truck, the temperature inside the packaging increased again during transportation. The temperature then dropped during 24 h of pre-cooling; however, it did not reach 10 °C, the appropriate storage temperature for cucumbers. MAP suppressed the occurrence of BEE compared to conventional film packaging; however, the BEE index varied greatly between individuals who performed the packaging. We determined that tying both ends of the packaging film increases the degree of airtightness as individual differences decrease and is more effective at suppressing BEE. Porous mineral-containing film (PM) packaging, which generates a modified atmosphere (MA), significantly suppressed BEE compared to conventional perforated film (C). In 2019 transport trials, the BEE index at 6 DAH for C film was 77.3, while for PM film it was only 12.0. Furthermore, we found that the effectiveness of PM film was significantly affected by human-related operational factors. The novel packaging method of tying both ends of the film (PM-T) provided the most consistent BEE suppression and lowest BEE index regardless of the packaging worker, demonstrating its superior potential in standardizing airtightness and minimizing human-related operational variability. Full article

Human–robot collaboration (HRC) is a key focus of Industry 5.0, aiming to enhance worker productivity while ensuring well-being. The ability to perceive human psycho-physical states, such as stress and cognitive load, is crucial for adaptive and human-aware robotics. This paper introduces MultiPhysio-HRC, a multimodal dataset containing physiological, audio, and facial data collected during real-world HRC scenarios. The dataset includes electroencephalography (EEG), electrocardiography (ECG), electrodermal activity (EDA), respiration (RESP), electromyography (EMG), voice recordings, and facial action units. The dataset integrates controlled cognitive tasks, immersive virtual reality experiences, and industrial disassembly activities performed manually and with robotic assistance, to capture a holistic view of the participants’ mental states. Rich ground truth annotations were obtained using validated psychological self-assessment questionnaires. Baseline models were evaluated for stress and cognitive load classification, demonstrating the dataset’s potential for affective computing and human-aware robotics research. MultiPhysio-HRC is publicly available to support research in human-centered automation, workplace well-being, and intelligent robotic systems. Full article

We study the problem of locally encoded secure distributed batch matrix multiplication (LESDBMM), where M pairs of sources each encode their respective batches of massive matrices and distribute the generated shares to a subset of N worker nodes. Each worker node computes a response from the received shares and sends the result to a sink node, which must be able to recover all M batches of pairwise matrix products in the presence of up to S stragglers. Additionally, any set of up to X colluding workers cannot learn any information about the matrices. Based on the idea of cross-subspace (CSA) codes and CSA null shaper, we propose the first LESDBMM scheme for batch processing. When the problem reduces to the coded distributed batch matrix multiplication (CDBMM) setting where $M=1,X=0$ and every source distributes its share to all worker nodes, the proposed scheme achieves performance matching that of the cross-subspace alignment (CSA) codes for CDBMM in terms of the maximum number of tolerable stragglers, communication cost, and computational complexity. Therefore, our scheme can be viewed as a generalization of CSA codes for CDBMM to the LESDBMM setting. Full article

The increasing adoption of Digital Construction Management (DCM) has introduced new ergonomic risks for construction professionals who now spend extended hours on computers in dynamic and often suboptimal work environments. While existing ergonomic research in construction has documented musculoskeletal disorders among both manual workers and office-based personnel, these studies have significant limitations: they primarily rely on subjective assessment methods (questionnaires and surveys) without validated ergonomic tools, and lack biomechanical validation of observational findings. This study addresses this critical gap by integrating Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and Digital Human Modeling (DHM) within a Six Sigma Define, Measure, Analyze, Improve, Control (DMAIC) framework to evaluate and mitigate musculoskeletal risks among construction professionals. A sample of 160 participants across 5 construction firms was observed and assessed through ergonomic scoring, biomechanical stress modeling using HumanCAD^®, and follow-up interventions. The results revealed that 87.5% of participants reported musculoskeletal symptoms, with neck and back being the most affected regions. Post-intervention evaluations showed significant reductions in ergonomic risk scores (RULA: 34%, REBA: 33.3%) and symptom prevalence (up to 46% reduction in neck discomfort). This study provides a validated, scalable framework for ergonomic risk management in digital construction roles and offers actionable design and policy recommendations to enhance occupational health and productivity. Full article

The queen, as the reproductive core of a honeybee colony, has declining reproductive capacity with age, making it necessary to rear new queens to replace older ones. Traditional artificial queen-rearing methods face challenges, such as difficulties in larval grafting, particularly for Apis cerana. To address these issues, we developed a queen-rearing technology by cutting the comb. This study compared queen-rearing technology using comb cutting (CC) with larval grafting in A. cerana, measuring egg traits (length, width, weight), capped brood number, worker offspring initial weight, forager honey sac weight, worker morphology traits, and colony foraging efficiency. Queens reared using comb-cutting technology exhibited superior egg quality compared with those reared by larval grafting. The CC group showed significant improvements in egg length, egg weight, and number of capped brood cells (p < 0.05). Worker offspring from the CC group demonstrated significantly superior morphological traits—including forewing length, hindwing width, and lengths of the third and fourth tergites—as well as higher daily colony foraging activity, compared with those from the grafting larvae group (p < 0.05). Queen-rearing technology using CC effectively enhances the reproductive capacity and productive performance of colonies, promising high-quality queen rearing in A. cerana and sustainable beekeeping optimization. Full article

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

Background: Chronic venous disease (CVD) substantially influences workers’ comfort, productivity, and capacity to remain employed, yet many occupational settings complicate the implementation of effective symptom management strategies. Temperature fluctuations, in particular, influence daily functioning: heat often worsens swelling, heaviness, pain, and fatigue, while cold may offer partial relief. This study examines how workplace thermal and organizational conditions affect adults with CVD, paying attention to the challenges they face in applying clinical recommendations. Methods: Fifty adults with CEAP C1–C6 disease were interviewed and observed in clinical settings. A qualitative descriptive approach was adopted to capture workers’ accounts rather than generate a new theory. Data were analyzed using Braun and Clarke’s reflexive thematic analysis within a qualitative descriptive framework. Results: Heat exposure consistently aggravated swelling, pain, and fatigue, whereas moderate cold often improved comfort and functional capacity. Participants highlighted numerous workplace barriers, including rigid schedules, restrictive uniforms, and difficulties maintaining compression in hot environments. Supportive supervisors, micro-breaks, access to hydration, and flexibility in posture facilitated better symptom control. Workers frequently described tensions between clinical advice and job demands, noting that instructions such as leg elevation or frequent breaks were often unrealistic in their occupational context. Conclusions: Aligning clinical guidance with workplace realities is essential for the well-being and long-term employability of individuals with CVD. Climate-sensitive and ergonomic job design represents an important strategy for supporting employees’ ability to manage symptoms and sustain productivity amid increasing thermal variability. Full article

Current functional safety mechanisms mainly control the access points and perimeters of manufacturing cells without guaranteeing the integrity of their internal components or the absence of unauthorized humans or objects. In this work, we present a novel deep learning (DL)-based safety system that enhances the safety circuit designed according to functional safety principles, detecting, with great reliability, the presence of persons within the cell and, with high precision, anomalous elements of any kind. Our approach follows a two-stage DL methodology that combines contrastive learning with Bayesian clustering. First, a supervised contrastive scheme learns the characteristics of safe scenarios and distinguishes them from unsafe ones caused by workers remaining inside the cell. Next, a Bayesian mixture models the latent space of safe scenarios, quantifying deviations and enabling the detection of previously unseen anomalous objects without any specific fine-tuning. To further improve robustness, we introduce an ensemble-based hybrid latent-space methodology that maximizes performance regardless of the underlying encoders’ characteristics. The experiments are conducted on a real dataset captured in a belt-picking cell in production. The proposed system achieves 100% accuracy in distinguishing safe scenarios from those with the presence of workers, even in partially occluded cases, and an average area-under-the-curve of 0.9984 across seven types of anomalous objects commonly found in manufacturing environments. Finally, for interpretability analysis, we design a patch-based feature-ablation framework that demonstrates the model’s reliability under uncertainty and the absence of learning biases. The proposed technique enables the deployment of an innovative high-performance safety system that, to our knowledge, does not exist in the industry. Full article

BTEX compounds (benzene, toluene, ethylbenzene, and xylene isomers) are aromatic hydrocarbons widely used in various industries. Due to their volatility, they become persistent pollutants in workplace air, posing serious risks to worker health. The aim of this study is to systematically map academic publications on BTEX exposure and health effects and to evaluate the impact of exposure levels in industrial settings on worker health. Publications obtained from the Web of Science database between 2010 and 2025 were bibliometrically analyzed in terms of productivity, collaboration networks, thematic trends, and analysis methods. In addition, the sources of BTEX compound dispersion, analysis methods, and industrial hazard classifications were evaluated through content analysis. According to the findings, Iran and China stood out as the most active countries, with publication intensity peaking in 2023. BTEX exposure was observed to be particularly high in the petrochemical sector. However, there is a lack of studies that systematically address the direct effects on worker health. This study aims to contribute to the more effective management of BTEX-related exposure risks by providing decision-makers with scientifically based and interpretable analyses. Full article

Background: Small-scale food animal production is common worldwide but often underestimated as a source of antimicrobial resistance. This study aimed to determine the prevalence of MRSA and VRE-E. faecium, and ESBL-E. coli bacteria among workers and within the production environment of low-capacity slaughterhouses, as well as to analyze the antimicrobial resistance patterns of these bacteria and their ability to form biofilms. Methods: The measurements were carried out in three low-capacity slaughterhouses in Poland. Bioaerosol samples, swabs from the production environment fomite and carcasses, meat samples, and swabs from workers’ hands and nostrils were taken. The strains’ susceptibility to antibiotics was assessed using the disk diffusion method, and their biofilm-forming potential was assessed using the microplate method. Isolates were also tested for the presence of genes related to biofilm formation and resistance to antiseptics. Results: In this study, 13.8%, 20.5%, and 14.9% of the samples (n = 268) were positive for MRSA, ESBL-E. coli, and VRE-E. faecium, respectively, with the highest detection rates on pork carcasses and surfaces. MRSA and ESBL-E. coli bacteria were also detected in swabs from workers’ hands and nasal swabs, and in bioaerosol samples. Most isolates revealed multidrug resistance, including 89% of MRSA, 76% of ESBL-E. coli, and 83% of VRE-E. faecium. The majority of them were also capable of biofilm formation—81%, 65%, and 75%, respectively—emphasizing their survival capabilities in slaughterhouse environments. Conclusions: The slaughterhouse workers are regularly exposed to antibiotic-resistant bacteria such as MRSA, ESBL-E. coli, and VRE-E. faecium. To reduce these risks, it is essential for small slaughterhouses to strictly follow hygiene protocols, enhance the separation between clean and contaminated areas, improve ventilation, and ensure the use of protective measures. Full article