Workload Conditions Research Articles

Risk Factors for Occupational Fatigue in Construction Workers: A Systematic Review

Occupational fatigue is a common issue in various industries, particularly in the construction sector, which is characterized by high occupational risks. This study aims to identify the risk factors contributing to occupational fatigue among construction workers and propose strategies to mitigate these risks. The research employs a systematic literature review method following PRISMA guidelines. The literature search was conducted using Google Scholar, PubMed, and ScienceDirect with keywords such as “Occupational Fatigue,” “Work Fatigue,” “Construction Worker,” “Risk Factor,” and “Construction,” covering publications from 2014 to 2024. Articles were selected based on inclusion and exclusion criteria to ensure relevance. The review of 12 selected articles identified several key risk factors associated with occupational fatigue, including job complexity, work stress, psychological health conditions, age, tenure, work climate, workload, and unsafe working conditions. Additionally, a negative work climate, excessive workload, and hazardous work environments were found to be major contributors to workplace fatigue. The findings indicate that addressing these risk factors is essential for improving worker safety and well-being. Implementing targeted interventions, such as workload management, stress reduction programs, and improvements in occupational health and safety standards, can significantly reduce fatigue levels among construction workers. These measures can also enhance productivity and overall job satisfaction in the construction industry.

Event-Driven Serverless Architectures for High-Scale Customer Support: An Empirical Analysis

Serverless computing has emerged as a transformative paradigm for building scalable cloud applications, yet its application in customer support systems remains largely unexplored. This article presents a comprehensive analysis of a cloud-native serverless architecture designed to address the scalability challenges inherent in modern customer support operations. The article proposes an event-driven framework that leverages Function-as-a-Service (FaaS) platforms across major cloud providers, demonstrating significant improvements in resource utilization and cost efficiency compared to traditional microservices architectures. Through empirical evaluation and real-world case studies, the article analyzes the performance characteristics of our proposed architecture under varying workload conditions, with particular attention to cold start latencies and event processing throughput. The findings reveal that serverless architectures, when properly implemented with event orchestration systems like Apache Kafka or AWS EventBridge, can provide superior scalability and cost benefits while maintaining system reliability. The article also identifies key architectural patterns and implementation strategies that organizations can adopt to optimize their customer support infrastructure, particularly during peak load periods.

Engine on/off management with consideration of thermal dynamics for HEVs.

An Internal Combustion Engine (ICE) suffers from extra gas injection and higher friction loss during operation in low temperatures, which provides the possibility for further improving the energy efficiency/economy with multiple power sources for a hybrid electric vehicle (HEV). However, it is challenging to provide a real-time efficient engine on/off policy that integrating the slow thermal dynamics with uncertain traffic and workload conditions. Existing solutions mainly focus on the non-linear optimization or complex learning-based methods, which unavoidably lead to high computation burden and hard to be online implemented. In this work, based on the most efficient operation point of engine, we transfer the energy optimization problem into the decision making of engine on/off, which greatly reduce the computation burden and is more practically applicable. Three rule-based engine on/off strategies, including the benchmark one that does not consider the thermal influences and two hysteresis strategies incorporating the thermal state, are compared through simulations under randomly generated driving profiles. Results verified that the proposed hysteresis strategies are feasible and the overall energy efficiency/economy can be enhanced by considering thermal influences in engine on/off decision.

Effects of Mental Workload Manipulation on Electroencephalography Spectrum Oscillation and Microstates in Multitasking Environments.

Multitasking during flights leads to a high mental workload, which is detrimental for maintaining task performance. Electroencephalography (EEG) power spectral analysis based on frequency-band oscillations and microstate analysis based on global brain network activation can be used to evaluate mental workload. This study explored the effects of a high mental workload during simulated flight multitasking on EEG frequency-band power and microstate parameters. Thirty-six participants performed multitasking with low and high mental workloads after 4 consecutive days of training. Two levels of mental workload were set by varying the number of subtasks. EEG signals were acquired during the task. Power spectral and microstate analyses were performed on the EEG. The indices of four frequency bands (delta, theta, alpha, and beta) and four microstate classes (A-D) were calculated, changes in the frequency-band power and microstate parameters under different mental workloads were compared, and the relationships between the two types of EEG indices were analyzed. The theta-, alpha-, and beta-band powers were higher under the high than under the low mental workload condition. Compared with the low mental workload condition, the high mental workload condition had a lower global explained variance and time parameters of microstate B but higher time parameters of microstate D. Less frequent transitions between microstates A and B and more frequent transitions between microstates C and D were observed during high mental workload conditions. The time parameters of microstate B were positively correlated with the delta-, theta-, and beta-band powers, whereas the duration of microstate C was negatively correlated with the beta-band power. EEG frequency-band power and microstate parameters can be used to detect a high mental workload. Power spectral analyses based on frequency-band oscillations and microstate analyses based on global brain network activation were not completely isolated during multitasking.

Performance metrics outperform physiological indicators in robotic teleoperation workload assessment

Robotics holds the potential to streamline the execution of repetitive and dangerous tasks, which are difficult or impossible for a human operator. However, in complex scenarios, such as nuclear waste management or disaster response, full automation often proves unfeasible due to the diverse and intricate nature of tasks, coupled with the unpredictable hazards, and is typically prevented by stringent regulatory frameworks. Consequently, the predominant approach to managing activities in such settings remains human teleoperation. Teleoperation can be demanding, especially in high-stress situations, and involves a complex blend of both cognitive and physical workload. We present an experiment to explore a range of physiological and performance-related metrics for workload assessment during robotic teleoperation. Thirty-five participants performed a teleoperation task, during which we manipulated cognitive and physical workload conditions. We recorded multiple metrics, including brain activity using functional Near-Infrared Spectroscopy, galvanic skin responses, cardiovascular responses, subjective workload ratings, task and robot performance data. Our results suggest that robotic teleoperation performance may be the most robust metric for distinguishing between different levels of workload experienced during teleoperation, with most physiological measures becoming insignificant to distinguish high cognitive workload.

ARTIFICIAL INTELLIGENCE IN DIAGNOSING COVID-19 PNEUMONIA AND PULMONARY TUBERCULOSIS IN THE KYRGYZ REPUBLIC

Nowadays, the necessity to control lung diseases such as COVID-19 caused by the SARS-CoV-2 virus and tuberculosis is obvious. One of the most important areas of this work is rapid and accurate diagnostics, including lung imaging based on artificial intelligence (AI). Objective. The aim of the paper is to test AI for detecting COVID-19 pneumonia and pulmonary tuberculosis based on digital X-ray patterns. Materials and Methods. The study included several stages. 1. Development of an AI model for detecting COVID-19 pneumonia and pulmonary tuberculosis. 2. Creation of a test X-ray data base. 3. Interpretation of data by radiologists. 4. Use of AI for diagnosing COVID-19 pneumonia and pulmonary tuberculosis. Results. AI demonstrated good prognostic ability (sensitivity – 88.31 % and 83.33 %, specificity – 96.67 % and 97.78 % for pneumonia and pulmonary tuberculosis, respectively). AI effectively processes and analyzes big data, which saves doctors’ time. However, in order to ensure greater safety, healthcare professionals should bear responsibility for the final diagnosis. The collaboration between radiologists and AI seems to be desirable. AI can be an auxiliary tool in conditions of high workload or shortage of specialists, as it can improve the accuracy of radiological reports and ensure their timeliness.

A novel method of cognitive overload assessment based on a fusion feature selection using EEG signals.

Cognitive overload, as an overload state of cognitive workload, negatively impacts individuals' task performance and mental health. Cognitive overload assessment models based on Electroencephalography (EEG) can effectively prevent the occurrence of overload through early warning, thereby enhancing task execution efficiency and safeguarding individuals' mental health. Although existing EEG-based cognitive load assessment methods have achieved significant research outcomes, evaluating cognitive overload remains an ongoing challenge. Current research aims to develop an effective cognitive overload assessment model and enhance its efficacy through feature selection methods.
Approach. In the cognitive overload assessment model, we firstly employ Variational Mode Decomposition (VMD) to adaptively decompose the signal from each channel into four sub-band signals to capture valuable time-frequency information. Subsequently, frequency domain features are extracted from each sub-band, and an effective feature selection method based on Mutual Information (MI) and Neighborhood Component Analysis (NCA) was applied for feature selection, which optimizes the distribution of the feature space while considering feature correlations, making the selected features more representative. Finally, traditional machine learning methods are utilized for classification, and the effectiveness of the proposed method is tested using both offline and online classification results.
Main results. The average accuracy of offline cognitive overload assessment using the proposed method on local and open datasets is 82.44 ± 1.59% and 78.24 ± 1.43%, respectively. The average classification accuracy of its online cognitive overload assessment is about 79.90 ± 2.53%. This indicates that the proposed method can effectively assess cognitive overload under both offline and online conditions. Furthermore, we found that higher-frequency sub-bands are more advantageous for cognitive overload assessment.
Significance. EEG signals can be used for effectively cognitive overload assessment, and the integration of feature selection methods enhances the accuracy of the evaluation, providing reliable methodological support for future cognitive overload monitoring in human-computer interaction systems.&#xD.

Verification and Validation of Cognitive Workload Models for Adaptive Automation Tasks

Technological advances that seek to address future operational challenges abound. While advanced capabilities are being developed, there is an important space for human design considerations, including cognitive workload. One proposed solution to improve cognitive workload management is adaptive automation (AA). This research used a novel, model-based approach to assess the impacts of AA on cognitive workload. This assessment modeled the tasks in NASA’s Multi-attribute Task Battery-II (MATB-II) using the Improved Performance Research Integration Tool (IMPRINT). The effort sought to investigate the relationship of cognitive workload, situation awareness, and performance through three human-in-the-loop studies with 120 participants using MATB-II. The research also attempted to validate cognitive workload models from IMPRINT. The IMPRINT models were representative of the MATB tasks with statistically significant differences between workload conditions, which mirrored the models’ predictions. The results demonstrated that AA system task models can be developed using IMPRINT to provide design recommendations.

Investigation of the Actual State of Work Load in Large-scale Plants

Investigation of the Actual State of Work Load in Large-scale Plants

The Influence of Workload and Work Environment on the Turnover Intention of Employees in PT. XYZ

This research aims to investigate the impact of workload and work environment on employee turnover intention at PT. XYZ. High turnover intentions can disrupt workforce stability and result in significant costs for the company. Two factors often associated with turnover intention are workload and work environment. Excessive workload can cause stress and fatigue, while an unsupportive work environment can reduce employee satisfaction and commitment. The study uses a quantitative approach, distributing questionnaires to 107 employees and sampling using the saturated sample method. Data analysis was conducted using SEM-PLS software. The hypothesis test tested the influence of workload and work environment on employee turnover intention. The results showed that workload variable (X1) is in the high category, work environment variable (X2) is in the bad category, and turnover intention (Y) is in the medium category. Quantitative data analysis with SEM-PLS showed that workload variables do not have a significant influence on turnover intention, while work environment variables have a significant influence. The research aims to evaluate company management's attention to workload and work environment conditions, with one variable showing a significant positive influence on employee turnover intention

A performance-based mental workload identification method for special vehicle crews

Detecting the mental workload state of armored vehicle crews is of great significance for monitoring the driving state of the crew and improving comprehensive combat effectiveness. In this manuscript, we propose a performance-based mental workload identification method and carry out experimental validation to improve the accuracy of crew mental workload identification and realize the effective classification of mental workload. Based on the virtual simulation system of the special vehicle crew task, this manuscript selects 20 subjects for the mental workload experiment of special vehicle crews. The experiment collected NASA-TLX scale, EEG, eye-tracking data, and performance data. The results show that the mental workload of the crews fluctuates in the segmented tasks of complex operations in typical scenes of special vehicles. The method of determining mental workload using NASA-TLX generates label noise in classification, which is not suitable for special vehicle tasks. Performance-based mental workload identification method is able to recognize fluctuations in the crew's mental workload during segmented tasks. Performance-based and NASA-TXL-based methods were classified using linear discriminant analysis. The results show that the accuracy of the method based on performance is improved by 15.72 %. This manuscript found the NASA-TXL scale is not suitable for the complex tasks of special vehicles, and proposed a performance-based identification method that can help to categorize the mental workload states of special vehicle crews.

Sociodemographic, Occupational, and Health Profile of Brazilian Housekeepers and House Cleaners-A Hypothesis-Generating Study.

For many women, house cleaning is an important way to participate in the labor market. In Brazil, there are 2 types of domestic workers: housekeepers have relatively secure employment and house cleaners are day laborers. The aim of this hypothesis-generating study was to describe the sociodemographic, occupational and health profile of a sample of domestic workers in Brazil. House cleaners received lower wages, had longer daily working hours and worked in a larger number of homes each week in comparison to housekeepers. About 51% of the domestic workers in this sample reported the use of pain medication and 34% reported spinal problems. Musculoskeletal symptoms were frequent in the lower back and upper limbs. Forty-seven percent reported high blood pressure. This study highlights the vulnerability of domestic workers, especially house cleaners, regarding workload, salary, and health conditions. Level of education is a contributing factor to this vulnerability.

PENGARUH PERCEIVED ORGANIZATIONAL SUPPORT, JOB DEMANDS, DAN BURNOUT TERHADAP KEPUASAN KERJA KARYAWAN

This study aims to analyze the effect of perceived organizational support, job demand, and burnout on employee job satisfaction at the Regional Office of the Directorate General of Taxes in Central Java II. This study uses a quantitative approach with data collection techniques through online questionnaires using Google Form. The research sample is determined by the purposive sampling method, and the data are analyzed using the SmartPLS 3.3 software. The results showed that perceived organizational support had a positive and significant influence on employee job satisfaction with a T-Statistic value of 6,754 (p <0.05). Job demand also has a positive and significant influence on employee job satisfaction with a T-Statistic value of 3,687 (p <0.05). In addition, burnout has a positive and significant influence on employee job satisfaction with a T-Statistic value of 3,713 (p <0.05). This study provides new insights on the importance of managing organizational support, workload, and burnout conditions to increase employee job satisfaction.

Collaborative Decision Making with Responsible AI: Establishing Trust and Load Models for Probabilistic Transparency

In responsible AI development, the construction of AI systems with well-designed transparency and the capability to achieve transparency-adaptive adjustments necessitates a clear and quantified understanding of user states during the interaction process. Among these, trust and load are two important states of the user’s internal psychology, albeit often challenging to directly ascertain. Thus, this study employs transparency experiments involving multiple probabilistic indicators to capture users’ compliance and reaction times during the interactive collaboration process of receiving real-time feedback. Subsequently, estimations of trust and load states are established, leading to the further development of a state transition matrix. Through the establishment of a trust–workload model, probabilistic estimations of user states under varying levels of transparency are obtained, quantitatively delineating the evolution of states and transparency within interaction sequences. This research lays the groundwork for subsequent endeavors in optimal strategy formulation and the development of transparency dynamically adaptive adjustment strategies based on the trust–workload state model constraints.

Adolescent girls and young women’s (AGYW) access to and use of contraception services in Cape Town: perspectives from AGYW and health care providers

BackgroundAccess and use of contraception services by adolescent girls and young women (AGYW) remains suboptimal, exposing AGYW to early and often unexpected pregnancy. Unexpected pregnancies are a public health concern, associated with poor neonatal and maternal health outcomes, as well as school dropout, which may result in economic hardships. This study aimed to explore (a) AGYW perceptions and experiences of receiving contraception services from health care providers and (b) health care providers’ perceptions and experiences of providing contraception services to AGYW.MethodsData were collected through semi-structured individual interviews with AGYW aged 15–24 years old and health care providers working in eight health care facilities around the Cape Town metropolitan area, in South Africa’s Western Cape Province. Thematic analysis was used to analyse the data.ResultsAGYW and health care providers voiced varying, and often contrasting, perceptions of some of the barriers that hinder AGYW’s access to contraception services. AGYW indicated that provider-imposed rules about when to access contraceptive services hindered access, while health care providers felt that these rules were necessary for coordinating their work. In addition, AGYW highlighted health care providers’ hostile attitudes towards them as an important factor discouraging access. On the contrary, health care providers did not think that their attitudes hampered AGYW’s access to and use of contraception services, instead they emphasised that challenges at the health system level were a major issue, which they feel they have little control over. Such challenges made health care providers’ work unpleasant and frustrating, impacting on their work approach and how they receive and offer services to AGYW.ConclusionThe expectation of negative attitudes from health care providers continues to be at the centre of AGYW discouragement towards accessing contraception services. System challenges are among some of the key drivers of health care provider’s hostile attitudes, posing challenges to the efficient provision of services. In order to improve AGYW’s access to and use of contraception services, and subsequently achieve the country’s SDGs, conscious efforts need to be directed towards improving the workload and working conditions of health care providers.

378 Workload Assessment of Suturing Tasks and AI-Task Classification Using Electromyography

Abstract Aim It is well established that excess surgeon workload results in poorer performance. Workload can be divided into physical workload (PWL) and cognitive workload (CWL). The study aims are twofold: 1. Assess how the subjective PWL of suturing is affected by varying PWL and CWL 2. Develop an AI-classification algorithm for task detection using electromyography (EMG). Method Novices were trained in interrupted suturing and asked to suture under four conditions of varied workload. Tasks included were: 1. Control, 2. Different suture material (Silk), 3. Simultaneously undertaking a neurocognitive task and 4. Suturing at depth. Subjective workload was recorded using a validated workload questionnaire (SURG-TLX). EMG sensors recorded physiological data from the biceps brachii and extensor ulnar carpi. A convolutional neural network (CNN) was created for AI-task classification using EMG data. Results The depth task had the highest subjective workload. Subjective depth raw workload was significantly higher than Control (p=<0.01) and Silk (p=<0.001) but comparable to the neurocognitive task. Subjective depth weighted workload was significantly higher than the neurocognitive task (p=<0.05). Subjective depth adjusted workload was significantly higher than the neurocognitive and silk tasks (both p=<0.05). The CNN training accuracy was 76.4% (95%CI 75.5-77.4) and had a classification accuracy of 76.5% (95%CI 75.7-77.2). Conclusions The relationship between PWL and CWL on subjective PWL is complex. Despite this, the CNN was able to differentiate between tasks using objective EMG data with good accuracy. This CNN has potential to characterise CWL which can be further developed to detect excess workload to improve surgical training through objective benchmarks.

Ist informelle Telearbeit eine Gefahr für die Vereinbarkeit von Erwerbsarbeit und Familie? Ein Vergleich der Implikationen von formeller und informeller Telearbeit für Vereinbarkeitskonflikte

AbstractThe study analyses the relationship between formal and informal telework and work-family conflicts using the job-demands and resources (JD-R) model. Previously, the model has more frequently been applied to understand the implications of telework without distinguishing between formal telework and informal telework outside contracted working hours. We conducted cross-sectional OLS-regressions on pilot data from the European Social Survey compiled in 2019. The results show that formal telework is associated with fewer, and informal telework with more work-family conflicts. While formal telework appears as a resource, informal telework rather functions as a demand. Additional analyses report that formal teleworkers are nevertheless at risk of work-family conflicts because formal telework often goes hand in hand with informal telework and, thus, work-family conflicts.Practical Relevance: While formal telework can facilitate the reconciliation of work and family life, employees are at risk of counteracting this dynamic when telework is conducted informally during paid or unpaid overtime. The findings suggest that it is important for managers, HR managers and employees to be aware of the potentials and risks of frequent teleworking that come with the ability to work anytime and anywhere in order to avoid unintended consequences. In this context, an organisational culture that values adherence to schedules and ‘switching off’ at the end of the workday, even under conditions of high workloads and low personnel resources, can be beneficial.

The effect of COVID-19 anxiety on nurses' productivity determinants through perceived workload and individual job performance: A Bayesian mediation analysis

This study aims to predict how COVID-19 anxiety affects productivity determinants by examining the role of perceived workload and individual job performance as mediators using Bayesian mediation analysis. To achieve the study's objectives, all nurses working at Imam Reza Hospital were recruited to participate. To gather data, all eligible and voluntary nurses were asked to complete questionnaires that included the Corona Disease Anxiety Scale (CDAS), Individual Job Performance Questionnaire (IJPQ), NASA Task Load Index (NASA-TLX), and Workforce Productivity Questionnaire. Of the total number of nurses employed, 222 (126 women and 96 men) nurses fully completed the questionnaires. Out of these, 140 individuals were married and 82 were single. The mean ± standard deviation (SD) of the age and experience were 28.56 ± 5.78 and 8.49 ± 6.50, respectively. GeNIe software version 2.0 utilized to analyze the Bayesian network. The results showed that for the high COVID-19 anxiety and high workload states, with a probability of 100 %, the probability of high evaluation and high environment increased by 16.6 % and 16.3 %, respectively, but low evaluation and low environment decreased by 22.4 % and 22 %, respectively. In the high COVID-19 anxiety and low IJP states with a probability of 100 %, most change was related to the high ability (6 % increase), low evaluation (3.9 % increase), high incentive (3.8 % increase), low ability (4.6 % decrease) and low support (2 % decrease). Regarding COVID-19 anxiety (high 100 %), workload (high 100 %) and IJP (low 100 %), most of the increase was related to high environment and high evaluation by an increase of 17.3 and 17.2 %, respectively. Also, the value of low evaluation and low environment was reduced by 24 and 23.4 %, respectively. The study's findings confirm that nurses have been more productive during the COVID-19 pandemic, despite facing high workloads and the COVID-19 anxiety.

The dual protective role of accountability: Mitigating missed nursing care and nurse moral distress in a nested diary study design.

To examine a novel moderated-mediation model, investigating whether personal accountability moderates the link between nurse workload and missed nursing care and whether missed nursing care mediates the association between workload and moral distress. Nested diary study. Data spanning from February 2019 to February 2023 were collected from 137 nurses working in various inpatient wards in two medium-sized hospitals. Nurses reported care given to specific patients on three to five occasions across different shifts, establishing nurse-patient dyads. Validated measures of missed nursing care, personal accountability, moral distress and workload were analyzed using mixed linear models to test the nested moderated-mediation model. Under high workload conditions, nurses with higher personal accountability reported lower frequencies of missed nursing care compared to those with lower personal accountability. In contrast, under low workload conditions, personal accountability did not significantly influence missed nursing care occurrences. Furthermore, the interaction between workload and personal accountability indirectly affected nurses' moral distress through missed nursing care. Specifically, higher personal accountability combined with lower missed nursing care contributed to reduced levels of moral distress among nurses. The study highlights accountability's dual role-safeguarding against care omissions and influencing nurses' moral distress amid rising workload pressures. Cultivating a culture of accountability within healthcare settings can serve as a protective factor against the negative effects of workload on patient care quality and nurse psychological distress, highlighting the need for organizational interventions to promote accountability among nursing staff. By recognizing accountability's pivotal role, organizations can implement targeted interventions fostering accountability among nurses, including training programs focused on enhancing responsibility/ownership in care delivery and creating supportive environments prioritizing accountability to achieve positive patient outcomes. The study has adhered to STROBE guidelines. No patient or public contribution.

Analysis of Drivers’ Workload States on Highways in High Elevation Regions

The low-pressure (and low-oxygen) environment in plateau regions affects drivers’ psychophysiological states and, thus, driving behavior and road safety. Six scenarios of expressway exit ramps were constructed based on the UC/win-road simulator to quantitatively analyze the effect of the plateau environment on workload state. A total of 50 participants in Nanjing (altitude 50 m) and 50 participants in Lhasa (altitude 3,650 m) were recruited for the simulation experiments. Based on the principal component analysis, a sample entropy index (SEI) was constructed to reflect the drivers’ psychophysiological levels by combining the sample entropy of electroencephalogram (EEG) signals β%, β/(α+θ), β/α, and heart rate (HR). Meanwhile, the pupil area change rate and the percentage of fixation time and duration within the region of interest were analyzed to characterize the drivers’ visual workload. Based on the psychophysiological and visual load, the differences in workload states among plain and plateau drivers were analyzed using a regression model and a one-way analysis of variance (ANOVA) test. At the same time, the changing trends of the drivers’ workload states in the curved section of the exit ramp under different scenarios were analyzed. The plateau drivers experienced a higher level of mental stress and greater psychological load. Furthermore, the higher radius of horizontal curves reduced the visual load level for drivers when traveling on the highway exit ramp sections. These findings should serve as a valuable reference for refining the design of plateau expressway exit ramps and providing a theoretical basis for improving driving safety in plateau areas.