Hazardous Tasks Research Articles

A lean approach to comparing the mooring systems of Suezmax tankers

AbstractMooring Suezmax tankers is a process requiring skilled ship crews and terminal workers to accomplish the time-consuming, hazardous task of securing the ship to a jetty to load or discharge oil cargoes. The advent of new equipment technologies has enabled industry professionals to view the mooring process from a new perspective. A lean approach is applied to analysing alternative mooring processes of Suezmax tankers. A reduction in mooring time of about 30 min is noted when replacing steel wire with high modulus synthetic fibre (HMSF) ropes. Further, a potential time saving of 57 min is noted if automated mooring is used instead of steel wire, and 27 min compared with HMSF. The automated mooring concept reduces the manpower required to perform the mooring process, both on board and ashore. If HMSF lines replace steel wires, a significant reduction is noted in ship and crew time costs incurred with mooring maintenance onboard during sailing. A single case study addresses the two research questions: (1) What is the mooring process of Suezmax tankers? (2) How can the mooring process of Suezmax tankers be enhanced?

Efficient 3D robotic mapping and navigation method in complex construction environments

AbstractRecent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic mapping and navigation method specifically tailored for these environments. Utilizing light detection and ranging, simultaneous localization and mapping, and neural networks, this method generates precise 3D maps. It also combines grid‐based pathfinding with deep reinforcement learning to enhance navigation and obstacle avoidance in dynamic and complex construction settings. An evaluation conducted in a simulated attic environment—characterized by various truss structures and continuously changing obstacles—affirms the method's efficacy. Compared to established benchmarks, this method not only achieves over 95% mapping accuracy but also improves navigation accuracy by 10% and boosts both efficiency and safety margins by over 30%.

Ship recycling process in Bangladesh and a survey-based risk assessment with mitigation proposal

Ship recycling industries play a significant role in the economic development of South Asian countries, including Bangladesh. In the wake of entering the Hong Kong Convention (HKC) in June 2025, the industry aims to improve its overall safety standards cost-effectively. However, due to the restrictive nature of the industry, solutions based on field surveys are limited. This study successfully articulates the indigenous industry knowledge of the recycling process in Bangladesh after carefully studying the practices followed by the existing ship recycling yards in the country. Based on field surveys conducted across 18 ship recycling yards in Bangladesh, the risk patterns inherent in the ship recycling industry are studied; for example, highly hazardous tasks in the ship recycling process are found to be (i) explosion hazards during primary cutting operations at the intertidal zone, (ii) falling hazards during inspections at the outer anchorage, (iii) falling and crushing hazards during block cutting and handling heavy objects. Industry experts and scholars consider the perception of the risk pattern regarding the current practices of ship recycling activities to cover a broader perspective of this issue. The correlation of survey outcomes with actual accident cases is comparable. Finally, practical recommendations for minimising risks have been recommended, especially in high-risk workplace areas. The study adds knowledge to the existing literature by giving a clear picture of evidence-based findings for this sector and helps improve the industry's overall safety standard.

Robots as guardians: Industrial automation and workplace safety in China

Industrial robots can improve workplace safety by performing hazardous tasks on behalf of workers. This paper examines the impact of industrial robots on workplace safety in China. We find that a one-standard-deviation increase in robot exposure reduces annual workplace accidents and fatalities by 0.100 and 0.0133 cases per thousand population, compared to sample averages of 0.122 accidents and 0.0351 fatalities. These findings are robust to an instrumental variable strategy and various robustness checks. Our analysis of injuries in household surveys and Baidu search activities reinforces these results. Using an accounting framework, we show that the safety improvement is not driven by the mechanical effects of robot-induced employment reduction. Instead, within-occupation improvement in workplace safety plays a more crucial role.

Concealed hazardous object detection for terahertz images with cross-feature fusion transformer

Terahertz (THz) waves can penetrate many non-metallic materials (such as paper, plastic, and clothing), making them highly valuable in security inspection areas. However, the low contrast between target objects and the background in THz images poses problems for object detection and recognition tasks. Though many schemes have been proposed to tackle the problems, the low detection accuracy and high computational complexity remain challenges. To address these challenges, we propose a concealed hazardous object detection method called Cross-Feature Fusion Transformer YOLO (CFT-YOLO) for THz images. We design a Multi-Channel Spatial Feature Module (MCSFM) to achieve cross-channel and cross-spatial information transfer between feature maps. MCSFM also realizes the feature parameter transfer between the backbone and neck of the network. Additionally, we develop a CrossFuse-Transformer (CF-Former) module that leverages self-attention to fully exploit the correlations and structural information between different feature maps. We conduct a series of experiments on public datasets. The results show that CFT-YOLO outperforms the other advanced methods in THz hazardous object detection tasks. Moreover, the CFT-YOLO achieves a better balance between model complexity and detection accuracy compared to YOLOv8s, making it more practical for real-world applications.

Cooperative defense of autonomous surface vessels with quantity disadvantage using behavior cloning and deep reinforcement learning

Autonomous Surface Vessels (ASVs) excel at undertaking hazardous tasks, garnering significant attention recently. Particularly, ASV cooperative defense is a crucial application for protecting harbors and combating smugglers. Here, ASVs intercept intruders from reaching a protected region. Unlike most research, which assumes defenders with numerical advantages, this work considers a more practical defense mission with fewer defenders, defender damages, and intruders employing evasion strategies. However, interception challenges are also introduced, including ASV underactuated dynamics, a limited interception time window, and environmental nonstationarity. Directly applying existing defense methods to such missions may not achieve success. To surmount the challenges, we propose an ASV decision-making framework by integrating supervised learning and deep reinforcement learning. Initially, supervised learning uses actions from a bi-level controller to train ASVs, addressing underactuated dynamics and aiding policy convergence. Subsequently, deep reinforcement learning explores more effective policies to enhance interception rates. Furthermore, hybrid rewards are meticulously designed to drive policy optimizations while mitigating environmental nonstationarity. Finally, numerical simulations are carried out to verify the effectiveness of our approach.

Industry 4.0 Technologies and Law in Enhancing Human Capacity Among Women in The Nigeria Construction Industry: A Systematic Review

Purpose: This study examines the impact of Industry 4.0 and the role of law in enhancing women's involvement in Nigeria's construction sector. Design/Methodology/Approach: The study adopted a systematic review of relevant literature from Scopus, IEEE Xplore, Science Direct, EBSCO, Emerald Insight, and Proquest Central between 2011 to 2023 on the various issues addressed in the research. A thematic analysis was adopted to discuss the findings from the reviewed articles in line with the study's objective. Findings: Industry 4.0 technologies enhance women's involvement in the construction industry by automating risky tasks, ensuring safety, and reducing working hours for family responsibilities. Limiting women's participation in construction results in unconstitutional gender-based discrimination and the law has not been fully explored in ensuring gender equality in the construction industry. Research Limitation/Implications: The study's reliance on a systematic literature review may limit capturing real-time industry dynamics. Practical Implication: Implementation of industry 4.0 technologies in the construction industry has the potential to create a safer, inclusive work environment by automating hazardous tasks and reducing working hours. Social Implication: Construction 4.0, if embraced, has the potential to challenge traditional gender norms, fostering a more equitable industry. Originality/Value: The study offers a novel approach that assesses the collective impact of technology and the law on enhancing women's participation and capacity-building initiatives, thereby contributing to gender equality and empowerment in the industry.

Observer-Based Fault-Tolerant Control for Uncertain Robot Manipulators without Velocity Measurements

In recent years, robot manipulator arms have become increasingly prevalent and are playing pivotal roles across various industries. Their ability to replace human labor in arduous and hazardous tasks has positioned them as indispensable assets. Consequently, there has been a surge in research efforts aimed at enhancing their operational performance. The imperative to improve their efficiency and effectiveness has garnered significant attention within the research community. In this study, a novel fault-tolerant control (FTC) scheme for robot manipulators to handle the effects of the unknown input is proposed to aid robots in achieving good tracking performance. In the first step, an extended state observer (ESO) is constructed to approximate both velocities and the unknown input in the robot system. The observer offers estimation information with good accuracy and quick convergence. The estimated signals are then combined with computed torque control (CTC), which is a useful control technique for trajectory tracking of robot manipulator systems, to construct an active FTC to decrease the influences of the unknown input. The proposed algorithm does not require velocity measurement in the design process. In addition, with a novel design approach, the combination of controller and observer provides a novel control signal that delivers higher tracking performance compared to the traditional design approach. The global and asymptotic stability of the suggested technique is proved through the Lyapunov theory. Finally, simulations are implemented on a 2-degree-of-freedom (DOF) robot manipulator to validate the efficiency of the proposed controller–observer method.

Secure pharmaceutical supply chain using blockchain in IoT cloud systems

Supply Chain Management (SCM) systems require time sequencing, coordination and tracking the movement of goods and processes. Internet of Things (IoT) and Blockchain technologies are useful to develop a secure automated SCM. IoT devices with in-built sensors and actuators help to keep track of the state, location, temperature or other parameters of an entity, and control the automation of routine as well as hazardous tasks. Blockchain technology supports time-stamping, authentication, process coordination, non-repudiation, commercial transactions, and also provides security for transactions and storage. The pharmaceutical SCM demands accurate, immediate and secure control system. Additionally, the supply chain process data from IoTs is stored and processed in Cloud by Analytics applications, for business planning and improvement. An efficient and secure IoT-Cloud-Blockchain based system for both SCM automation and analytics has been proposed in this work. It leverages a hierarchical IoT, Mist, Edge, Fog, Cloud computing (IMEFC) architecture to enhance Communication-Response-Compute-Security-Storage (CRCSS) in the system. Blockchain technology provides security for the SCM transactions and data. The efficiency of the Blockchain is measured in terms of upload time, download time and transaction fees for Bitcoin, Ethereum and Filecoin platforms. The Filecoin blockchain platform is quicker and cost-effective for larger file sizes, compared to Ethereum and Bitcoin, making it suitable for Pharmaceutical SCM systems.

Comprehensive Approach to Workplace Injury Prevention: Strategies and Technological Solutions

Workplace injuries pose significant risks to employees and organizations across industries. This article explores integrating emerging technologies into comprehensive workplace injury prevention strategies to forge a safer future. It examines traditional approaches, such as hazard assessment, engineering controls, and personal protective equipment (PPE). It delves into cutting-edge technologies like wearable devices, artificial intelligence (AI), virtual reality (VR), and robotics. These innovations offer new avenues for monitoring workers, predicting risks, providing immersive training, and automating hazardous tasks. The article discusses strategies for seamlessly integrating these technologies, highlighting potential benefits, challenges, and best practices for successful implementation. With a focus on continuous improvement, the article presents a vision for leveraging emerging technologies to enhance workplace safety and drive positive outcomes.

Robotics in Manufacturing: A Review of Advances in Automation and Workforce Implications

The integration of robotics in manufacturing processes has undergone significant advancements, reshaping the landscape of industrial production and introducing transformative changes. This review explores the latest developments in robotics within the manufacturing sector, shedding light on the technological breakthroughs and their implications on the workforce. The recent surge in robotics adoption in manufacturing has been driven by the pursuit of increased efficiency, precision, and cost-effectiveness. From traditional robotic arms to cutting-edge collaborative robots (cobots) that work alongside human operators, the manufacturing industry has witnessed a paradigm shift. Advanced robotic systems equipped with artificial intelligence (AI) and machine learning algorithms enable adaptive and autonomous decision-making, further enhancing their capabilities. Automation in manufacturing, fueled by robotics, has yielded numerous benefits such as improved product quality, reduced production cycle times, and increased overall productivity. The review delves into case studies and real-world applications where robotics have proven instrumental in optimizing manufacturing processes across diverse industries. However, as the manufacturing landscape evolves, the implications for the workforce cannot be overlooked. The integration of robotics poses challenges and opportunities for human workers. While automation has the potential to eliminate routine and hazardous tasks, it also necessitates upskilling and reskilling of the workforce to operate, program, and maintain robotic systems. The review examines the socio-economic impacts of increased automation, discussing potential shifts in employment patterns and the need for a strategic approach to workforce development. This review provides a comprehensive analysis of the advances in robotics within manufacturing and their profound implications on the workforce. Striking a balance between automation and human collaboration is crucial for the future of manufacturing, emphasizing the importance of proactive strategies to harness the benefits of robotics while ensuring a resilient and adaptable workforce.

Disassembly line balancing with hazardous task failures – Model based solution approaches

In this study, we consider a disassembly line balancing problem that involves a fixed number of workstations and the possibility of failures in hazardous tasks. We assume that each hazardous task may fail with a pre-specified probability and once it fails, the disassembly line stops, and all tasks assigned to subsequent workstations are interrupted. Our problem is to select the set of tasks for processing and assign them to the workstations to maximize the total expected net revenue.We develop a heuristic procedure that uses the linear programming relaxation of the recently reported stochastic mixed-integer program in the literature. To assess the performance of the heuristic procedure, we use two upper bounds that use some properties of the optimal solutions as valid cuts.Our extensive experiments have shown the excellent performance of our heuristic algorithm and upper bounds.

Gait Learning for Hexapod Robot Facing Rough Terrain Based on Dueling-DQN Algorithm

In the handling of dangerous goods in explosive environments, robots are increasingly being used instead of human operators. Robots designed for operation in explosive environments are mostly equipped with tracked structures, which, due to their limited terrain adaptability, struggle to movement rugged landscapes. Hexapod robots, with their excellent maneuverability and adaptability, possess advantages in completing hazardous material handling tasks in such rugged terrains. One current challenge lies in enabling hexapod robots to autonomously adjust their gaits to cope with rugged terrain. This paper proposes a gait learning method based on the Dueling Deep Q-Network (Dueling-DQN) algorithm to address the gait adjustment problem of hexapod robots in sloped, terraced, and rugged terrain. The method combines lidar data and reinforcement learning to extract features from the lidar data to determine terrain types and foot coordinates. Finally, the Dueling-DQN algorithm and redundant phase strategy are employed to facilitate the motion of hexapod robots in these three types of rugged terrain. Simulation and prototype experiments are conducted to evaluate the Dueling-DQN algorithm's performance in terms of rewards and stability margins for the three types of terrain. During algorithm training on sloped, terraced, and rugged terrain, stable rewards and positive stability margins are achieved after approximately 490 iterations. The effectiveness and feasibility of the proposed method are further validated through Gazebo simulation and prototype experiments. In the context of movement in rugged terrain within explosive environments, the gait learning method based on the Dueling-DQN algorithm offers valuable insights into the control of hexapod robots.

Selection of Industry 4.0 technologies for Lean Six Sigma integration using fuzzy DEMATEL approach

PurposeThis study aims to explore the integration of Industry 4.0 technologies with lean six sigma practices in the manufacturing sector for enhanced process improvement.Design/methodology/approachThis study used a fuzzy decision-making trial and evaluation laboratory approach to identify critical Industry 4.0 technologies that can be harmonized with Lean Six Sigma methodologies for achieving improved processes in manufacturing.FindingsThe research reveals that key technologies such as modeling and simulation, artificial intelligence (AI) and machine learning, big data analytics, automation and industrial robots and smart sensors are paramount for achieving operational excellence when integrated with Lean Six Sigma.Research limitations/implicationsThe study is limited to the identification of pivotal Industry 4.0 technologies for Lean Six Sigma integration in manufacturing. Further studies can explore the implementation challenges and the quantifiable benefits of such integrations.Practical implicationsIntegrating Industry 4.0 technologies with Lean Six Sigma enhances manufacturing efficiency. This approach leverages AI for predictive analysis, uses smart sensors for energy efficiency and adaptable robots for flexible production. It is vital for competitive advantage, significantly improving decision-making, reducing costs and streamlining operations in the manufacturing sector.Social implicationsThe integration of Industry 4.0 technologies with Lean Six Sigma in manufacturing has significant social implications. It promotes job creation in high-tech sectors, necessitating advanced skill development and continuous learning among the workforce. This shift fosters an innovative, knowledge-based economy, potentially reducing the skills gap. Additionally, it enhances workplace safety through automation, reduces hazardous tasks for workers and contributes to environmental sustainability by optimizing resource use and reducing waste in manufacturing processes.Originality/valueThis study offers a novel perspective on synergizing advanced Industry 4.0 technologies with established Lean Six Sigma practices for enhanced process improvement in manufacturing. The findings can guide industries in prioritizing their technological adoptions for continuous improvement.

Exploratory` study on driving ability of people with schizophrenia: Relationships among cognitive function, symptoms, and brain activity

BackgroundThis study aimed to examine the relationships among cognitive function, symptoms, prefrontal activation, basic driving skills, and collision risk factors using a hazard prediction task in simulated driving. MethodsParticipants included 42 people with schizophrenia aged 20–50 years who had actual experience of driving. The Trail making test (TMT) A and TMTB, Wechsler Memory Test–Revised (WMS-R), and Zoo Map test (ZMT) were used to evaluate cognitive function. Positive and negative syndrome scale was used to assess symptoms, and brain activity was assessed by evaluating cerebral blood flow during a visual working memory task using functional near-infrared spectroscopy. Driving tasks that tested basic skills, such as brake reaction, steering wheel skills, and standard deviation of lateral position, were analyzed using multiple regression analysis. Three hazard prediction tasks were performed using discriminant analysis. ResultsBrake reaction associated with cerebral blood flow and TMT-A. Steering wheel skills associated with WMS-R, driving experience and depression. Significant differences were found between the collision and noncollision groups in the hazard prediction task, as shown by the ZMT, driving experience, and brake reaction. ConclusionsBrain activity in the frontal lobe during a desk task may be useful data for driving assessment. Assessment of processing speed and learning ability may be particularly important in the evaluation of basic skills for safe driving. In addition, for people with schizophrenia, foresight, as represented by proactive planning, experience, and quick braking may be an essential characteristic to anticipate danger and react quickly enough to avoid collisions.

Lightweight Tunnel Obstacle Detection Based on Improved YOLOv5.

Considering the high incidence of accidents at tunnel construction sites, using robots to replace humans in hazardous tasks can effectively safeguard their lives. However, most robots currently used in this field require manual control and lack autonomous obstacle avoidance capability. To address these issues, we propose a lightweight model based on an improved version of YOLOv5 for obstacle detection. Firstly, to enhance detection speed and reduce computational load, we modify the backbone network to the lightweight Shufflenet v2. Secondly, we introduce a coordinate attention mechanism to enhance the network's ability to learn feature representations. Subsequently, we replace the neck convolution block with GSConv to improve the model's efficiency. Finally, we modify the model's upsampling method to further enhance detection accuracy. Through comparative experiments on the model, the results demonstrate that our approach achieves an approximately 37% increase in detection speed with a minimal accuracy reduction of 1.5%. The frame rate has improved by about 54%, the parameter count has decreased by approximately 74%, and the model size has decreased by 2.5 MB. The experimental results indicate that our method can reduce hardware requirements for the model, striking a balance between detection speed and accuracy.

Occupational Health Assessment and Risks in Automotive and Wood Manufacturing Industries: A Retrospective Study

Objective: This study examines workers' health status and occupational risks in the automotive manufacturing and wood manufacturing sectors based on medical examinations conducted between January 2019 and January 2020. Materials and Methods: It involves 27 individuals with an average age of 36.26. Younger workers, especially those aged 20 to 35, show a higher propensity for workplace accidents due to their engagement in hazardous tasks and limited experience. The research emphasizes prevalent health issues such as metabolic syndrome, diabetes, obesity, and cardiovascular diseases among these workers, influenced by sedentary work routines and inadequate physical activity. Workers employed for 1-10 years exhibit the highest incidence of workplace accidents, warranting careful monitoring in this demographic. Key factors observed include tobacco consumption, blood cell counts, hemoglobin, glucose, liver function indicators, and exposure to hazardous substances. The study highlights prevalent hearing impairment and lung abnormalities in these workers, aligning with global trends in occupational health. Results: It recommends regular health screenings, lung film examinations during initial employment, and periodic check-ups for early detection and treatment. While acknowledging limitations, such as study specificity and resource constraints, it underscores the role of such studies in shaping policies, guiding preventive measures, and enhancing worker health and safety in high-risk industries. Conclusion: This study underscores the need for proactive health monitoring and regular screenings to safeguard workers' health, even in industries where symptoms might not be evident.

Simulation Based Study Of Ground Mobile Robots For Inspection And Control Of The Bridge Assembly Process

Nowadays, the inspection of bridges poses a significant challenge for safeguarding critical infrastructures. Recent advancements have seen the integration of robots to replace human personnel in hazardous tasks. These robots, whether operating autonomously or through tele-operation, play a crucial role in monitoring structures and infrastructure, ensuring secure access to areas such as manholes on decks and box girder bridges. Specifically designed for environments deemed dangerous, difficult, or inaccessible to humans, ground mobile robots equipped with appropriate sensors are increasingly deployed for inspection purposes. This paper addresses simulation tests conducted by a hybrid mobile robot. Its compact design and maneuverability enable it to navigate through obstacles, making it suitable for inspecting railway or highway bridge decks as well as confined spaces within box girder bridges. The study includes a survey of existing bridges highlighting key issues to be addressed and presents simulation results for the hybrid rover, which has been developed and equipped with sensors tailored to fulfill inspection tasks within a selected case study.

Improved Brain-Storm Optimizer for Disassembly Line Balancing Problems Considering Hazardous Components and Task Switching Time

Disassembling discarded electrical products plays a crucial role in product recycling, contributing to resource conservation and environmental protection. While disassembly lines are progressively transitioning to automation, manual or human–robot collaborative approaches still involve numerous workers dealing with hazardous disassembly tasks. In such scenarios, achieving a balance between low risk and high revenue becomes pivotal in decision making for disassembly line balancing, determining the optimal assignment of tasks to workstations. This paper tackles a new disassembly line balancing problem under the limitations of quantified penalties for hazardous component disassembly and the switching time between adjacent tasks. The objective function is to maximize the overall profit, which is equal to the disassembly revenue minus the total cost. A mixed-integer linear program is formulated to precisely describe and optimally solve the problem. Recognizing its NP-hard nature, a metaheuristic algorithm, inspired by human idea generation and population evolution processes, is devised to achieve near-optimal solutions. The exceptional performance of the proposed algorithm on practical test cases is demonstrated through a comprehensive comparison involving its solutions, exact solutions obtained using CPLEX to solve the proposed mixed-integer linear program, and those of competitive peer algorithms. It significantly outperforms its competitors and thus implies its great potential to be used in practice. As computing power increases, the effectiveness of the proposed methods is expected to increase further.

Research on Bionic Climbing Robot Based on Gait Analysis

In order to replace humans performing hazardous tasks in complex high-altitude environments, we have designed a two-armed modular bionic robot with clawing, climbing and mobility capabilities for high-altitude tasks in agriculture, forestry and construction. In this paper, a five-degree-of-freedom climbing modular robot with the ability to maneuver between poles in space is presented and analyzed. Three types of climbing gaits is designed and compared in the ADAMS simulation environment based on the torques required by the main joints and the energy consumed by the robot to climb the poles in different directions. The simulation results are the basis for planning the gait of the robot during climbing.