Real Working Conditions Research Articles

Physical Demand Assessment of Volunteer Firefighters During Wildland Firefighting

Wildland firefighting is physically and mentally demanding. The aerobic capacity of firefighters is important due to the demands of the activity and the associated occupational risks. The main objectives of this study were to identify and characterise the physically demanding tasks undertaken by volunteer firefighters during wildland fires (real work conditions). A total of 125 firefighters replied to a survey about sociodemographic, biometric data, and work fitness assessment. A group of 23 was evaluated in a physical stress test using a VO2peak protocol to determine maximum oxygen consumption and ventilatory thresholds. The physical demands and physiological responses were collected during the operations at the firefront (n = 21). The results revealed that wildland firefighting entails physical demands that exceed established reference values, with maximum oxygen uptake exceeding 40%. The cardiovascular strain is particularly notable in tasks performed near the firefront, reflecting fatigue. The physical and cardiac demands associated with forest fire fighting have been demonstrated to contribute to occupational illnesses with prolonged exposure. This study underscores the imperative for interventions to enhance the identification and real-time monitoring of physiological parameters to enhance firefighters’ overall health and well-being.

Influence of a dynamic gas film on underwater drag reduction

The gas film at the liquid–solid interface, induced by hydrophobic microstructure, can achieve a high-efficiency underwater drag reduction. However, previous studies have rarely considered the impact of changes in gas structure morphology on drag performance, especially under turbulent conditions. We conducted numerical simulations to examine the dynamic process of gas on a hydrophobic spanwise grooved surface under turbulent conditions. Our findings indicate that the morphology of the gas phase structure at the liquid–solid interface undergoes continuous alterations due to fluid action, resulting in a dynamic state of drag performance. In addition, the gas morphology that completely covers the groove surface will reduce the turbulent kinetic energy on the groove surface, resulting in a better drag reduction effect. In the flow velocity range of 10–20 m/s, the drag reduction effect of the superhydrophobic grooved surface increases with the flow velocity. Finally, we conducted experiments to validate the effectiveness of this result. A mechanism for underwater drag reduction was proposed based on these simulation results. This study offers a novel perspective on the phenomenon of underwater gas drag reduction, which could significantly influence its practical applications, especially under real working conditions.

IMPACT OF APPRENTICESHIP ON THE EMPLOYMENT OF TECHNICAL VOCATIONAL EDUCATION GRADUATES

Over time, it has been proven that the most effective way to train specialists, especially for the 3-5 qualification level, is by involving the trainees in practical activities carried out within economic entities, giving them the opportunity to learn in real working conditions . In this article, we aim to address the apprenticeship and their impact on the students graduating from vocational technical education from the perspective of their employment after graduation. The research methods used were quantitative (1286 respondents) involving final year students from 13 educational institutions (5 - centers of excellence, 1 - college, 7 - vocational schools). The questioning of the respondents focused on the students' appreciation of the quality and relevance of internships within the economic entities, as well as topics aimed at their opportunity to engage in the labor field. Given the subject under investigation, we believe that the results included in this article will be of great interest to representatives of educational institutions, economic entities, as well as researchers in the field.

Performance Evaluation of Real-Time Kinematic Global Navigation Satellite System with Survey-Grade Receivers and Short Observation Times in Forested Areas

The Real-Time Kinematic (RTK) method is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSSs) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS currently being the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover. Unlike most previous research concerning this topic, the focus here is on employing a methodology that is as close as possible to real working conditions in the field of forestry. To achieve this, short observation times of 30 s were used, with corrections received directly in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. In total, 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. In terms of the overall horizontal accuracy, an RMSE of 2.03 m and MAE of 1.63 m are found. Meanwhile, the overall vertical accuracy is lower, as expected, with an RMSE of 4.85 m and MAE of 4.01 m. The variation in GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, which is application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier-phase-based, due to strong interference from the vegetation.

Exploring the Research Trends of Media Training Kit in Vocational Education through Bibliometric Analysis

This study aims to explore research trends on media training kits in vocational education, which have become crucial in preparing skilled workers to meet modern industry demands. Media training kits are essential tools that bridge the gap between theoretical knowledge and practical application, simulating real working conditions to enhance students' skills. Despite their growing use, research on their effectiveness, design, and innovation remains fragmented and poorly structured. A bibliometric analysis was conducted using the Scopus database to systematically map the landscape of media training kit research, identifying key publications, influential authors, major journals, and prevalent research topics. The analysis revealed significant gaps in the literature, underlining the need for continuous research to evaluate technological advancements, such as virtual simulations and augmented reality, in training kits. Results highlight the importance of integrating interactive and adaptive training kits to support evolving pedagogical and technological approaches in vocational education. This study provides valuable insights for researchers, educators, and policymakers, helping them make informed decisions to enhance the development and application of media training kits, ultimately improving the quality and relevance of vocational education in response to industry needs.

The Effectiveness of Using Simulation Technologies in the Study of Therapeutic Disciplines at the Faculty of Medicine: the Attitude of Students

To assess the effectiveness of simulation technologies during classes, an anonymous survey of 4th year students of the Faculty of Medicine using Google forms was conducted. The students noted the improvement of practical skills, the convenience of studying and memorizing the symptoms of diseases of internal organs, and the improvement of knowledge on providing assistance in emergency situations. The use of simulation technologies in teaching therapy brings students closer to real working conditions, which significantly increases their motivation to study and contributes to high-quality practical training.

A Machine-Learning-Based Method for Identifying the Failure Risk State of Fissured Sandstone under Water-Rock Interaction.

The mechanical properties of fissured sandstone will deteriorate under water-rock interaction. It is crucial to extract the precursor information of fissured sandstone instability under water-rock interaction. The potential of each acoustic emission (AE) parameter as a precursor for instability in the failure process of fissured sandstone was investigated in this study. An experimental dataset comprising 586 acoustic emission experiments was established, and subsequent classification training and testing were conducted using three machine learning (ML) models: AdaBoost, MLP, and Random Forest (RF). The primary parameters for identifying the instability risk state of fissured sandstone include acoustic emission ringing count, energy (mV·ms), centroid frequency, peak frequency, Rise Angle (RA), Average Frequency (AF), b value, and the natural/saturated state of fissured sandstone: state. To enhance data utilization, a 10-fold cross-validation method was employed during the model training process. The machine learning models were developed and designed to identify the instability risk of fissured sandstone under the natural and saturated states. The results demonstrated that the established RF model was capable of identifying fissured sandstone instability risks with an accuracy of 97.87%. Feature importance analysis revealed that state and b value exerted the most significant influence on identification results. The Spearman correlation coefficient was utilized to assess the correlation between input features. This study can provide technical support to identify the risk of instability of fissured sandstones under both natural and saturated water conditions. Based on the models developed in this study, it is possible to implement an early warning method for instability in fissured sandstone that meets realistic working conditions. Compared with the traditional empirical and formulaic methods, the machine learning method can more quickly process huge amounts of AE data and accurately identify the damage state of fissured sandstone.

Development of snow-blowing structures for protection roads from snow drifts

Introduction. The article discusses the use of the snow blowing method and the possibility of installing snowblowing structures on highways to protect against snow drifts. The problem of snow tolerance is identified due to the location of many highways in remote, hard-to-reach areas and difficult climatic conditions. Snow drifts significantly increase the cost of winter maintenance of roads, are a partial consequence of destruction and maintenance in the summer, and also pose a serious threat to safety, causing loss of control over the vehicle.Research methodology. The article discusses the application of the snow blowing method and the possibility of installing snow-blowing structures on roads to protect against snow drifts. The process of movement of snow particles in a snowstorm and the influence of these processes on snow blowing are considered.Results. The paper highlights the issues related to the analysis and assessment of the speed and composition of the air flow, weather conditions affecting the operation of structures. The main purpose and working conditions of snow-blowing structures on highways have been determined. The optimum speed at which the snow blowing effect is created is determined.Conclusion. The snow blowing process on the developed snow blowing structure in different conditions is modelled. Based on the considered approach, a comparative analysis of the operation of the snow blowing structure is performed. Based on the data obtained, the possibilities for the introduction of snow-blowing structures and refinement in real working conditions have been studied.

Experimental and theoretical evaluation of the effect of fluid resistance on the performance of a tubular indirect evaporative cooler

To evaluate the impact of fluid resistance on the performance of tubular indirect evaporative coolers (TIECs), a mathematical model of secondary air channel resistance was developed. An experimental setup was created to study the effects of factors such as secondary air flow rate and water spray flow rate on R2 under actual conditions. The model used the same conditions to analyze how factors like air flow, water flow, and droplet size influenced R2. The relationship between R2 and wet-bulb effectiveness (εwb) was also examined experimentally. The data from the experiments were compared to the model's predictions, showing a maximum relative error of 10 %, confirming the model's accuracy. It was found that R2 and secondary air flow rate (V2) are positively correlated, and εwb increases with R2. The experiments yielded secondary air resistances ranging from a minimum of 548.2 Pa to a maximum of 554.26 Pa. These results are valuable for selecting appropriate fans for the secondary air channel. Additionally, simulations were conducted to predict and calculate energy losses in the secondary air channel under real working conditions, providing a useful basis for future fan selection.

A New Approach to Prevent Injuries Related to Manual Handling of Carts: Correcting Resistive Forces between Floors and Wheels to Evaluate the Maximal Load Capacity

Test methods that use pushing forces to evaluate the maximal load capacities of carts in design standards require a flat, smooth and horizontal steel plate and thus do not take into account the real conditions of work. Resistive forces of a single wheel of a cart in a uniform rectilinear motion were measured using a unique test bench with five loads. Forty-four wheels were tested (varying diameters, treads and bearings) with one steel plate and four resilient floor coverings. Based on a linear mixed model, all the following results were significant (p < 0.05). Resistive forces were increased linearly with the load and depended on the characteristics of both the wheel and floor. These forces decreased as the diameter increased. They were important for wheels with sleeve bearings but decreased for cone ball bearings and precision ball bearings. Resistive forces depended on the material of the tread and were higher for solid rubber treads. In contrast, the hardness of the tread had little effect. Resistive forces strongly depended on the hardness of the base foam of resilient floor coverings: the softer the base foam, the higher the resistive forces. Test methods in design standards should be reviewed, using corrective forces based on these present results, to prevent musculoskeletal disorders.

Machine learning-based 3D scan coverage prediction for smart-control applications

Automatic control of a workpiece being manufactured is a requirement to ensure in-line correction and thus move towards a more intelligent manufacturing system. There is therefore a need to develop control strategies which are capable of taking precise account of real working conditions and enabling first-time-right control. As part of such a smart-control strategy, this paper introduces a machine learning-based approach capable of accurately predicting a priori the 3D coverage of a part according to a scan configuration given as input, i.e. predicting before scanning it which areas of the part will be acquired for real. This corresponds to a paradigm shift, where coverage estimation no longer relies on theoretical visibility criteria, but on rules learned from a large amount of data acquired in real-life conditions. The proposed 3D Scan Coverage Prediction Network (3DSCP-Net) is based on a 3D feature encoding and decoding module, which is capable of taking into account the specifics of the scan configuration whose impact on the 3D coverage is to be predicted. To take account of real working conditions, features are extracted at various levels, including geometric ones, but also features characterising the way structured-light projection behaves. The method is thus able to incorporate inter-reflection and overexposure issues into the prediction process. The database used for the training was built using an ad-hoc platform specially designed to enable the automatic acquisition and labelling of numerous point clouds from a wide variety of scan configurations. Experiments on several parts show that the method can efficiently predict the scan coverage, and that it outperforms conventional approaches based on purely theoretical visibility criteria.

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

Abstract To solve the problems of easy miss and false detection on rail surface defects caused by small size, dense target, and high similarity between features and background, this paper proposed an improved detection algorithm in complex background. First, the conventional convolution of YOLOv5 backbone network is replaced with omni-dimensional dynamic convolution (ODConv), which improves the feature extraction capability of the network without increasing the computational cost; second, to improve the model's performance in detecting tiny objects, a two-layer context augmentation module(CAM) is introduced into the path aggregation network(PAN) structure; finally, the traditional non-maximum suppression(NMS) algorithm is replaced by the Soft-NMS algorithm in the network post-processing to reduce the false-alarm and miss-rate. The experimental results on the Railway Track Fault Detection public dataset show that the OD-YOLO (OD stands for ODConv) and C-PAN(CAM module is introduced into PAN) structures could achieve better performance in the same type of improved algorithms; compared with the baseline algorithm YOLOv5, the ODCS-YOLO (OD stands for ODConv, C stands for CAM, and S stands for Soft-NMS) algorithm improves the precision by 12.4%, the recall by 3.6%, the map50 by 8.6% and the GFLOPs is reduced by 0.6. Compared with seven classical object detection algorithms, the ODCS-YOLO algorithm achieves the highest detection accuracy, which makes it able to meet the real-time detection requirements of rail surface defects in real working conditions. The ODCS-YOLO model provides certain technical support for the defects detection and a new method for the detection of dense small objects.

Assessing the performance of a robust multiparametric wearable patch integrating silicon-based sensors for real-time continuous monitoring of sweat biomarkers

The development of wearable devices for sweat analysis has experienced significant growth in the last two decades, being the main focus the monitoring of athletes health during workouts. One of the main challenges of these approaches has been to attain the continuous monitoring of sweat for time periods over 1 h. This is the main challenge addressed in this work by designing an analytical platform that combines the high performance of potentiometric sensors and a fluidic structure made of a plastic fabric into a multiplexed wearable device. The platform comprises Ion-Sensitive Field-Effect Transistors (ISFETs) manufactured on silicon, a tailor-made solid-state reference electrode, and a temperature sensor integrated into a patch-like polymeric substrate, together with the component that easily collects and drives samples under continuous capillary flow to the sensor areas. ISFET sensors for measuring pH, sodium, and potassium ions were fully characterized in artificial sweat solutions, providing reproducible and stable responses. Then, the real-time and continuous monitoring of the biomarkers in sweat with the wearable platform was assessed by comparing the ISFETs responses recorded during an 85-min continuous exercise session with the concentration values measured using commercial Ion-Selective Electrodes (ISEs) in samples collected at certain times during the session. The developed sensing platform enables the continuous monitoring of biomarkers and facilitates the study of the effects of various real working conditions, such as cycling power and skin temperature, on the target biomarker concentration levels.

Unlocking Single Particle Anisotropy in Real‐Time for Photoelectrochemistry Processes at the Nanoscale

AbstractThe key to rationally and rapidly designing high‐performance materials is the monitoring and comprehension of dynamic processes within individual particles in real‐time, particularly to gain insight into the anisotropy of nanoparticles. The intrinsic property of nanoparticles typically varies from one crystal facet to the next under realistic working conditions. Here, we introduce the operando collision electrochemistry to resolve the single silver nanoprisms (Ag NPs) anisotropy in photoelectrochemistry. We directly identify the effect of anisotropy on the plasmonic‐assisted electrochemistry at the single NP/electrolyte interface. The statistical collision frequency shows that heterogeneous diffusion coefficients among crystal facets facilitate Ag NPs to undergo direction‐dependent mass transfer toward the gold ultramicroelectrode. Subsequently, the current amplitudes of transient events indicate that the anisotropy enables variations in dynamic interfacial electron transfer behaviors during photothermal processes. The results presented here demonstrate that the measurement precision of collision electrochemistry can be extended to the sub‐nanoparticle level, highlighting the potential for high‐throughput material screening with comprehensive kinetics information at the nanoscale.

Unlocking Single Particle Anisotropy in Real-Time for Photoelectrochemistry Processes at the Nanoscale.

The key to rationally and rapidly designing high-performance materials is the monitoring and comprehension of dynamic processes within individual particles in real-time, particularly to gain insight into the anisotropy of nanoparticles. The intrinsic property of nanoparticles typically varies from one crystal facet to the next under realistic working conditions. Here, we introduce the operando collision electrochemistry to resolve the single silver nanoprisms (Ag NPs) anisotropy in photoelectrochemistry. We directly identify the effect of anisotropy on the plasmonic-assisted electrochemistry at the single NP/electrolyte interface. The statistical collision frequency shows that heterogeneous diffusion coefficients among crystal facets facilitate Ag NPs to undergo direction-dependent mass transfer toward the gold ultramicroelectrode. Subsequently, the current amplitudes of transient events indicate that the anisotropy enables variations in dynamic interfacial electron transfer behaviors during photothermal processes. The results presented here demonstrate that the measurement precision of collision electrochemistry can be extended to the sub-nanoparticle level, highlighting the potential for high-throughput material screening with comprehensive kinetics information at the nanoscale.

Rancang Bangun Prototipe Pengisian Air Expansion Tank Berbasis IoT

The shipping industry faces challenges in reducing environmental impact and increasing operational efficiency. Among them are the expansion tanks on ship engines which play an important role in maintaining the pressure and water quality of the engine cooling system, which is still run manually on some ships. This can result in the risk of engine damage due to overheating or unstable pressure. Manual water filling that requires crew supervision can cause operational instability. This research designed a prototype of an automatic water filling system for expansion tanks, using microcontroller-based automation technology and an Internet of things system to make monitoring easier. This system aims to increase efficiency and reduce the risk of engine damage, thereby supporting the sustainability of ship operations in the future. In the trials that have been carried out, this tool is able to operate the expansion tank filling automatically and efficiently. The distance sensor used has high accuracy with a largest error of 1.6%, and an average error of 0.01%. Integration with the Internet of Things system can run well and reliably with 100% success and latency of 0.5 – 1 second. The system's overall success rate is 80%. So the system can be used in real working conditions

Guided Wave Behavior in Type IV Composite Overwrapped Pressure Vessels Under Fatigue Loading

Innovative Ultrasonic Guided Wave Approach for Structural Health Monitoring of Type IV Composite Overwrapped Pressure Vessels Seyedreza Hashemi, Jan Heimann, Amir Charmi, Eric Duffner, Jens Prager Abstract: The digitalization of quality control processes and the use of digital data infrastructures is a novel idea that can be applied for ensuring the operational safety and reliability of pressure vessels, particularly in the context of hydrogen storage at high pressure. Despite the critical role these pressure vessels play, current safety regulations lack an established concept for Structural Health Monitoring (SHM). This research addresses this gap by presenting a study on the application of ultrasonic guided waves (GWs) for SHM of Type IV Composite Overwrapped Pressure Vessels (COPVs). The study focuses on the development of a reliable measurement system to transition from conventional periodic inspections to SHM and predictive maintenance, prolonging the remaining lifetime of the vessels. A sensor network is employed, consisting of fifteen piezoelectric wafers arranged in three rings, which are mounted on the outer surface of the COPV. Deploying GWs, known for their long-distance propagation and ability to cover complex structures, the study explores GW behavior under different environmental and operational conditions, including periodic pressure fluctuations and temperature loadings. Meticulous analysis of GW signals by utilizing various features and damage indices, underscores their suitability for an effective SHM under realistic working conditions. The project aims to localize defects by considering temperature, and internal pressure. Mimicking the continuous monitoring of Type IV COPVs in H2 refueling gas stations under authentic operational conditions, the COPV underwent thousands of pressure load cycles in our special test facility . The implemented methodology facilitates early damage detection, showcasing the efficacy of the designed method in effective safety assurance.

STUDYING THE IMPACT OF DUST EXPOSURE AND ASSESSING THE RISK TO EMPLOYEE HEALTH

Purpose. This article is dedicated to the experimental determination of the level of dust pollution during ore crushing and the study of the long-term impact of dust exposure on workers, as well as the assessment of their health risk. Design/methodology/approach. To achieve this goal, we carried out full-scale measurements of dust pollution in real working conditions, performed mathematical modelling to analyse the data and predict the dust load over a certain period, and calculated the conditional risk to employees. Conclusions. As a result of the experiment, it was found that during the operation of the crusher, the concentration of dust in the air significantly exceeds the maximum permissible concentration (2 mg/m3). A mathematical dependence describing the dynamics of changes in the concentration of dust in the air was obtained. The average dust load per worker during a work shift and the average daily dust load, the conditional risk of an inhalation occupational disease in workers, the dust load per worker for different periods of time depending on the use/non-use of a respirator are calculated and it is proved that the use of PPE significantly reduces the impact of dust load on the worker. Limitations/consequences of research. The proposed methods for calculating conditional risk and predicting dust exposure for workers are universal for those professions in which there is significant dust pollution in the workplace. Practical consequences. The results of the study can be used to calculate and assess the dust exposure of employees during different periods of work, which will help to select the optimal personal protective equipment for employees. Originality/meaning. For the first time, an integrated method has been applied, which includes field studies of dust pollution in the workshop of a business entity, namely a mining and processing plant, mathematical modelling with modern applied software tools and, accordingly, a reasonable prediction of the consequences of long-term exposure to dust load, taking into account the conditional risk of respiratory disease.

Revealing the structure of the active sites for the electrocatalytic CO2 reduction to CO over Co single atom catalysts using operando XANES and machine learning.

Transition-metal nitrogen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co-N-C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co-N-C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co-N-C catalysts, and further optimization of this class of electrocatalytic systems.

Welfare and stress assessment of tourism carriage horses under real working conditions in Sicily

Animal welfare has become an increasingly important concern regarding equids working as carriage animals. In the present study, the changes in the markers of stress and inflammatory responses as a result of the work performed by tourism carriage horses under real working conditions in Sicily (Italy) were investigated. Twenty-two Standardbreds performed a normal working day in the carriage tourism business during the months of May, June and July 2022, consisting of one day of work for each month. Blood samples were collected in the stables at rest before the tour route (Pre; 07.00 AM) and within 10 min after the end of the workday (Post; 05.00 PM). Haematological parameters, serum concentration of cortisol, total proteins together with the globulin fractions were investigated before and after the carriage work. Environmental temperature, relative humidity and temperature humidity index (THI) were also assessed. The direct erythrocyte indices increased after work compared to rest condition (P < 0.05). The values of cortisol, total proteins and globulins were not affected by carriage work (P > 0.05), while, higher cortisol, total proteins, α1- and α2-globulins values were observed in July compared to May and June (P < 0.05). These changes are probably due to the increase in THI values which showed mild stress in June and high stress in July. This study suggests that the tourism carriage horses herein investigated have adapted to their work activity, however, avoiding working horses during the hottest hours of the day in the summer months is advocated.