Operator Safety Research Articles

Development of a Virtual Telehandler Model Using a Bond Graph

Recent technological advancements and evolving regulatory frameworks are catalysing the integration of renewable energy sources in construction equipment, with the objective of significantly reducing greenhouse gas emissions. The electrification of non-road mobile machinery (NRMM), particularly self-propelled Rough-Terrain Variable Reach Trucks (RTVRT) equipped with telescopic booms, presents notable stability challenges. The transition from diesel to electric propulsion systems alters, among other factors, the centre of gravity and the inertial matrix, necessitating precise load capacity determinations through detailed load charts to ensure operational safety. This paper introduces a virtual model constructed through multiphysics modelling utilising the bond graph methodology, incorporating both scalar and vector bonds to facilitate detailed interconnections between mechanical and hydraulic domains. The model encompasses critical components, including the chassis, rear axle, telescopic boom, attachment fork, and wheels, each requiring a comprehensive three-dimensional treatment to accurately resolve spatial dynamics. An illustrative case study, supported by empirical data, demonstrates the model’s capabilities, particularly in calculating ground wheel reaction forces and analysing the hydraulic self-levelling behaviour of the attachment fork. Notably, discrepancies within a 10% range are deemed acceptable, reflecting the inherent variability of field operating conditions. Experimental analyses validate the BG-3D simulation model of the telehandler implemented in 20-SIM establishing it as an effective tool for estimating stability limits with satisfactory precision and for predicting dynamic behaviour across diverse operating conditions. Additionally, the paper discusses prospective enhancements to the model, such as the integration of the virtual vehicle model with a variable inclination platform in future research phases, aimed at evaluating both longitudinal and lateral stability in accordance with ISO 22915 standards, promoting operator safety.

Multicenter Analysis of the Relationship Between Operative Team Familiarity and Safety and Efficiency Outcomes in Cardiac Surgery.

Safety in cardiac surgical procedures is predicated on effective team dynamics. This study associated operative team familiarity (ie, the extent of clinical collaboration among surgical team members) with procedural efficiency and Society of Thoracic Surgeons (STS) adjudicated patient outcomes. Institutional STS adult cardiac surgery registry and electronic health record data from 2014 to 2021 were evaluated across 3 quaternary hospitals. Team familiarity was defined as the mean number of cardiac operations performed by surgeon-anesthesiologist, surgeon-perfusionist, and anesthesiologist-perfusionist dyads within 1 year of the operation. The primary outcomes were (1) safety, measured by the STS' composite major morbidity and operative mortality measure, and (2) procedural efficiency, assessed by cardiopulmonary bypass duration. Team familiarity was stratified by terciles (low, moderate, and high) for crude analyses and analyzed continuously for adjusted analyses. Multivariable logistic and linear regression models were used to assess the association between team familiarity and outcomes. Team familiarity was calculated for 13 581 operations. The median (interquartile range) patient age was 64 (55-72) years, and 31.9% (4328/13 581) were women. Terciles of team familiarity were defined as low (<6.00 average shared operations), moderate (6.00-9.67), and high (>9.67). Teams in lower terciles had higher observed STS morbidity and mortality rates (low, 17.9%; moderate, 18.0%; high, 16.0%; P=0.02) and longer median cardiopulmonary bypass duration (low, 137 minutes; moderate, 131 minutes; high, 118 minutes; P<0.001). After risk adjustment, team familiarity was not significantly associated with STS morbidity and mortality (estimate, -0.001 [95% CI, -0.998 to 0.997]) but was inversely associated with cardiopulmonary bypass duration (estimate, -2.02 minutes per 1 unit increase in team familiarity [95% CI, -2.30 to -1.75]). Increased team familiarity was not associated with STS morbidity and mortality but was inversely correlated with cardiopulmonary bypass duration, demonstrating potential benefit. Interventions aimed at improving team familiarity among operative teams may increase procedural efficiency.

A multivariate fusion collision detection method for dynamic operations of human-robot collaboration systems

Real-time human-robot collision detection is crucial for ensuring the safety of operators during human-robot collaboration(HRC) and for improving the efficiency of such collaboration. It plays an important role in promoting the development of intelligent manufacturing. To address this issue, our team developed a multi-faceted collision detection system using eXtended Reality (XR) technology, specifically designed for complex and dynamic human-robot collaborative operations. The system integrates three different methods: a Virtual Reality (VR) detection method that enables robots to better perceive and detect human operators. An Augmented Reality (AR) detection method that enhances the operator’s perception of the robot. And a fusion detection and evaluation method. This detection and evaluation method assesses the effectiveness of collaboration by analyzing key performance indicators, such as real-time distance between human and robot, changes in the operator’s Heart Rate(HR), and overall task completion time. Through empirical research on the human-robot collaborative assembly task of T-series spiral bevel gear reducers, the effectiveness of the innovative method is verified. The research results show that this method significantly improves safety and operational efficiency, providing a novel solution detection in industrial manufacturing environments.

Analysis and Effect of Cutting Parameters on The Surface Roughness of Type 4140 Steel in The Dry Milling Process

Currently in the world of metal cutting industry, wet machining is still carried out and machining experts continue to strive to reduce the use of coolant so that benefits can be obtained from economic, environmental and operator safety aspects. One of the criteria for determining machining quality is surface roughness. Surface roughness is influenced by machining parameters such as cutting speed, feed depth and feed rate as independent variables. The aim of the research is to obtain surface roughness values ​​for type 4140 steel workpieces due to spindle rotation speed and feed depth, which uses a carbide endmill as a cutting tool. Research activities were carried out with 9 specimens each for dry machining and wet machining using variations in machine spindle rotation of 1200 rpm, 1400 rpm and 1600 rpm with feed depths of 0.5 mm, 0.75 mm, 1 mm. Data processing uses the statics method with a normal distribution curve to obtain surface roughness data. To determine the roughness value of the machined surface, it is tested using a Surface Test measuring instrument. The surface roughness obtained from machining under optimum conditions for dry machining is 1.699 µm, 2.778 µm and 2.868 µm at a machine spindle rotation of 1600 rpm. while those carried out using wet machining obtained respective results of 1.602 µm, 2.654 µm and 2.815 µm at a machine spindle rotation of 1600 rpm. So optimum cutting/slicing is obtained at a spindle rotation speed of 1600 rpm with Ra = 1.699 µm for dry machining while for wet machining the surface roughness value Ra = 1.602 µm is obtained at a spindle rotation speed of 1600 rpm. The comparison of the results of dry machining (Ra= 1.699 µm) and wet machining (Ra= 1.602 µm) to obtain the most optimum Ra surface roughness value is not significant, so it is a good opportunity that dry machining technology can be applied to the metal cutting industry although Until now, many people still use wet machining processes using cutting fluids to cut steel meta

Research on automatic tool changing device for flexible material cutting machine

For the efficiency and operator safety issues caused by manual blade replacement in flexible material cutting machines. In order to improve the efficiency of tool changing, the automatic tool changing equipment for the cutting machine has been designed, which includes a clamping mechanism, tool magazine, tool feeding mechanism, and control system after studying the tool changing process and tool changing equipment of the automatic tool changing system in the existing machining center. A prototype and testing platform were built, and the prototype was tested and analyzed. The results show that the automatic tool change equipment of the cutting machine has achieved automation in the process of unloading, receiving, taking, feeding, and loading the cutting machine blade, and the success rate of tool change has achieved the expected effect.

From Surgical Procedures to Glovebox Processing: Enhancing Safety and Efficiency in Glovebox Operations for High-Consequence Materials

High-consequence materials, such as nuclear waste, pose significant risks if improperly handled. Gloveboxes provide a controlled environment to manage these materials safely, yet they present challenges in operator safety, efficiency, and ergonomics. This paper addresses these challenges by presenting a proof-of-concept framework for Semi-Autonomous Robot Teleportation for Nuclear Glovebox Processing (SART-NGP). Unlike the existing manual systems already in use at nuclear facilities, the SART-NGP framework incorporates adjustable autonomy capabilities, drawing inspiration from advancements in surgical robotics. By integrating high-level human inputs, semi-autonomous execution, robust feedback mechanisms, and a simulator-based interface, this framework significantly enhances safety and efficiency while alleviating ergonomic concerns associated with glovebox processing in nuclear facilities. Although we have not fully solved the challenges associated with nuclear glovebox processing, our research provides promising insights and methodologies that may significantly improve the management of high-consequence materials and augments existing efforts in self-driving labs for glovebox operations. The findings suggest that the SART-NGP framework holds substantial potential for enhancing glovebox processing in nuclear facilities, with future work focusing on its real-world implementation and evaluation.

The role of fluorescent cholangiography to improve operative safety in different severity degrees of acute cholecystitis during emergency laparoscopic cholecystectomy: a prospective cohort study

The role of fluorescent cholangiography to improve operative safety in different severity degrees of acute cholecystitis during emergency laparoscopic cholecystectomy: a prospective cohort study

Food and Drug Administration Database Secondary Analysis: Difference in Operative Hysteroscopy Device Safety Profiles

Food and Drug Administration Database Secondary Analysis: Difference in Operative Hysteroscopy Device Safety Profiles

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator

The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision–recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety.

Experimental analysis of wheel and bogie frame transfer functions for on-board measurements of rail roughness

Acceleration measurements on in-service rail vehicles give many valuable insights into track condition. These measurements are favourable in terms of measurement frequency, no need for additional track possession for measurements, operator safety, etc. The collected acceleration data however needs to be further processed to extract more detailed information on rail surface defects. To derive rail roughness from bogie frame acceleration, a complex signal processing approach is required, as well as determining the dynamic parameters of the track, vehicle, and operational conditions. In this paper, a method to evaluate the influence of resilient wheel and bogie frame on the acceleration data received from bogie frame-mounted accelerometers is described. The method is based on a standstill measurement of frequency response functions on wheel and bogie frame positions using an impact hammer and accelerometers. Following the preliminary results of bogie acceleration measurements, in-situ wheel receptance measurements were carried out on the same tramway vehicle in a standstill position, to determine the transfer function between the wheel-rail contact point and the position of the accelerometer mounted on the wheel frame. After the experimental study, a discussion of their potential application in the method for deriving rail roughness from vibration data is presented.

Design Development and Performance Evaluation of Solar Operated Pigeon Pea (Cajanus cajan L. Millsp.) Pruner

Pigeon pea (Cajanus cajan L. Millsp.) is a vital grain legume known for its contribution to food security and soil fertility. However, pruning, which enhances plant architecture and yield, remains a significant challenge in its cultivation, especially in large-scale operations. Manual pruning is labor-intensive, time-consuming, and costly, which has led to the need for mechanized alternatives, a solar-operated pruner was developed at IGKV, Raipur, to reduce labour, time, and costs. This study focuses on the design, development, and performance evaluation of a solar-operated pruner tailored specifically for pigeon pea. The pruner integrates key components such as a cutting blade, DC motor, solar panel, battery, and charge controller. A 12 V DC motor, generating 10.53 N of force and operating at 8500 RPM, ensures efficient pruning. The motor is powered by a 7.2 Ah battery, which can be charged in 4 hours using an AC adaptor or 8 hours via a 10 W solar panel. The battery allows for 4-6 hours of continuous operation. The pruner's lightweight PVC frame makes it portable and user-friendly, while the protective blade guard ensures operator safety. Performance evaluation of the pruner indicated a field efficiency of 81% and pruning efficiency of 76%. The actual field capacity was measured at 0.097 ha/h, with a walking speed of 1.2 km/h. Losses due to half-cut stems were calculated at 9.04%. The pruner demonstrated its ability to reduce labor costs, improve pruning efficiency, and promote sustainable agriculture through its solar-powered operation. This study highlights the economic and environmental benefits of adopting solar-operated pruners for pigeon pea cultivation.

Deep reinforcement learning based proactive dynamic obstacle avoidance for safe human-robot collaboration

Ensuring the health and safety of human operators is paramount in manufacturing, particularly in human-robot collaborative environments. In this paper, we present a deep reinforcement learning-based trajectory planning method for a robotic manipulator designed to avoid collisions with human body parts in real-time while achieving its goal. We modelled the human arm as a freely moving cylinder in 3D space and formulated the dynamic obstacle avoidance problem as a Markov decision process. The algorithm was tested in a simulated environment that closely mimics our laboratory environment, with the goal of training a deep reinforcement learning model for autonomous task completion. A composite reward function was developed to balance the effects of different environmental variables, and the soft-actor critic algorithm was employed. The trained model demonstrated a 93% success rate in avoiding dynamic obstacles while achieving its goals when tested on a generated data set.

Recent innovations in crop protection research.

As the world's population continues to grow and demand for food increases, the agricultural industry faces the challenge of producing higher yields while ensuring the safety and quality of harvests, operators, and consumers. The emergence of resistance, pest shifts, and stricter regulatory requirements also urgently calls for further advances in crop protection and the discovery of new innovative products for sustainable crop protection. This study reviews recent highlights in innovation as presented at the 15th IUPAC International Congress of Crop Protection Chemistry held in New Delhi, in 2023. The following new products are discussed: the insecticides Indazapyroxamet, Dimpropyridaz and Fenmezoditiaz, the fungicides Mefentrifluconazole and Pyridachlomethyl, the nematicide Cyclobutrifluram, the herbicides Rimisoxafen, Dimesulfazet, and Epyrifenacil as well as the abiotic stress management product Anisiflupurin. In addition, the latest innovative research areas and discovery highlights in all areas of crop protection will be presented, including insecticidal alkyl sulfones and 1,3,4-trisubstituted pyrazoles, fungicidal picolinamides, herbicidal ketoenols, and trifluoromethylpyrazoles, as well as the latest advances in crop enhancement and green pest control research. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Post-approval changes in Labelling regulations in the United States, European Union and Canada

The review “Post-approval changes in Labelling Regulations in the United States, Europe and Canada” goes into the details and consequences of post-approval changes in labeling regulations for pharmaceutical products. It covers the concept of post-approval changes and outlines the key areas of changes, such as various elements of product lifecycle management, market access, innovation, risk control, and legal compliance for manufacturers in the United States, Europe, and Canada. Apart from that, the abstract discusses the similarities and differences in labeling regulations across the regions in authority, structure, and the affected changes. In summary, the abstract outlines the complexities and effects of post-approval-related changes in labeling regulations concerning the pharmaceutical industry in multiple jurisdictions and the challenges encountered in implementing them. More specifically, the issues include differing regulatory frameworks and varying interpretations; operator compliance, safety evaluation and management; consolidation approaches; and the influence of digitalization and automation as a pivotal and minor player.

A mixed-methods examination of fixed-object crashes among electric utility company fleet vehicles

IntroductionOne of the most common and serious types of crashes among all motor-vehicle users involves collisions with fixed objects. This type of crash occurs frequently among utility vehicle workers while driving for work. The overarching objective of this research was to improve the safety of electric utility company vehicle operators by determining the circumstances under which utility vehicles are involved in crashes with fixed objects and to provide recommendations to help drivers and utility fleet company management prevent these types of crashes. MethodsThe study incorporated a mixed methods approach and gathered information from structured interviews with safety managers at electric power companies and an analysis of utility vehicle crashes using data supplied by electric power companies and a statewide database in Michigan. ResultsMany factors were found to contribute to fixed-object crashes including engagement in secondary tasks and job productivity pressure. Information was organized and evaluated to develop 11 recommendations for countermeasure strategies that are directed at various levels within the system in which the drivers operate. DiscussionThis study was the first to explore fixed-object crashes among electric utility fleet vehicles. The findings are unique in that they provide insight into the safety practices in an industry that has received limited academic attention and highlight the need to place greater emphasis on road safety practices in the workplace. Practical applicationCountermeasures generated for this study are applicable to companies in which fleet driver safety is a primary concern.

Particulate matter in necropsy activities: experience from a health operators’ exposure monitoring campaign

BackgroundOperators in the obituary and necropsy sectors are exposed to various environmental hazards during specific tasks. Despite this exposure, occupational risks have often been underestimated, resulting in a lack of substantial evidence. The primary objectives of this study were to identify sources of chemical risk, establish procedures for monitoring and quantifying exposure during necropsy activities, and recommend adjustments to regulatory guidelines to protect the health of the operators. The study was conducted at the Legal Medicine Unit of the Umberto I General Hospital in Rome, focusing on the quantitative measurement of particulate matter (PM) exposure among at-risk operators during necropsy activities. Environmental levels of total suspended particles, PM10, PM4, PM2.5, and PM1 were assessed by evaluating the average, minimum, and maximum instantaneous indoor concentrations using an airborne analyzer.ResultsThe monitoring activities revealed that the PM concentrations were significantly lower than the recognized reference values. However, bone sawing, body removal, and cleaning were identified as high-risk maneuvers for dust suspension.ConclusionsOur study highlighted specific risks associated with necropsy activities, particularly concerning timing and certain maneuvers. These results may lead to interventions for improving current prevention procedures, implementing good practices, and developing specific guidelines to enhance operator safety.

Lessons Learned From Haul Truck Operator Near-Miss Events: Use of the Critical Decision Method to Identify Strategies to Improve Operator Safety in Mining

Lessons Learned From Haul Truck Operator Near-Miss Events: Use of the Critical Decision Method to Identify Strategies to Improve Operator Safety in Mining

Using Semi-supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance.

Mental workload (MWL) assessment is critical for accident prevention and operator safety. However, achieving cross-task generalization of MWL classification models is a significant challenge for real-world applications. Classifiers trained on labeled samples from one task often experience a notable performance drop when directly applied to samples from other tasks, limiting its use cases. To address this issue, we propose a semi-supervised cross-task domain adaptation (SCDA) method using power spectral density (PSD) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance on our data and COG-BCI public dataset, with accuracies of 90.98% ± 9.36% and 96.61% ± 4.35%, respectively. Furthermore, in the cross-task classification of cross-subject scenarios, SCDA showed the highest average accuracy (75.39% ± 9.56% on our data, 90.98% ± 9.36% on the COG-BCI public dataset). The findings indicate that the semi-supervised transfer learning approach using PSD features is feasible and effective for cross-task MWL assessment.

Effect of Spectral Filtering and Segmental X-ray Tube Current Switch-Off on Interventionalist's Scatter Exposure during CT Fluoroscopy.

Dose optimization in computed tomography (CT) is crucial, especially in CT fluoroscopy (fluoro-CT) used for real-time navigation, affecting both patient and operator safety. This study evaluated the impact of spectral X-ray filtering using a tin filter (Sn filter), and a method called partial-angle computed tomography (PACT), which involves segmentally switching off the X-ray tube current at the ambient dose rate H˙*(10) at the interventional radiologist's (IR) position. Measurements were taken at two body regions (upper body: head/neck; lower body: lower legs/feet) using a 120 kV X-ray tube voltage, 3 × 5.0 mm CT collimation, 0.5 s rotation speed, and X-ray tube currents of 43 Eff.mAs (without Sn filter) and 165 Eff.mAs (with Sn filter). The study found significant dose reductions in both body regions when using the Sn filter and PACT together. For instance, in the upper body region, the combination protocol reduced H˙*(10) from 11.8 µSv/s to 6.1 µSv/s (p < 0.0001) compared to the protocol without using these features. Around 8% of the reduction (about 0.5 µSv/s) is attributed to the Sn filter (p = 0.0005). This approach demonstrates that using the Sn filter along with PACT effectively minimizes radiation exposure for the IR, particularly protecting areas like the head/neck, which can only be insufficiently covered by (standard) radiation protection material.