Work Efficiency Research Articles

The Research of Collaborative System of Remote Sensing Monitoring Based on Bimodal Cloud

Abstract. Cloud service is based on cloud computing, Offering a On-Demand service to every terminal equipment of computing resource pool. This paper designed and developed a coordinated operating system based on bimodal cloud. This system is taken mutual scheduling mechanism into account, which is capable of storing massive amounts of heterogeneous remote sensing data and provides fast indexing of data based on various characteristics, integrated Satellite transit forecast, DOM Produce, coordinated change information extraction and results sharing based on Nginx load balancing, in addition, the system designed two layer security system to ensure the safety of data results.The "YunYao" geographic information service rendering engine built on the dual-state cloud platform significantly outperforms mainstream platforms in the same testing environment. Its rendering speed surpasses ArcGIS Desktop by more than two times, exceeds GeoServer by more than four times, and is over seven times faster than ArcGIS Server. Remote sensing practitioners can quickly and conveniently utilize this system, while providing convenient functionalities that enable remote sensing scientists to independently conduct scientific research and development using this system. Experimentation and practice shows that this system simplified routine work flow, improved work efficiency, has a important reference meaning to remote sensing monitoring.

An examination of the usefulness of a quantitative appraisal method in nursing human resource management in primary hospital operating rooms: An example of integrated collaborative scheduling.

In recent years, China medical and health services have made great development. However, the management of nursing human resources in operating room of primary hospitals still faces a series of challenges. In the nursing work of operating room, high-quality nursing human resource management is important for improving the efficiency of operating room and ensuring the safety of patients. From January 2022 to December 2022, comprehensive collaborative scheduling and quantitative scoring evaluation methods were carried out in our hospital, and relevant data were collected. The flexible scheduling combined quantitative scoring performance appraisal system and the traditional scheduling plus average distribution performance appraisal system were statistically analyzed and compared in terms of annual surgical cases, annual overtime hours, annual back work hours, annual compensatory rest hours, and average daily working hours. This study was based on 30 medical staff (27 females and 3 males) in the operating room of a primary hospital. The annual operation volume increased by 387 cases compared with before, and the attitudes of patients to the service attitude and preoperative waiting time were significantly improved, reaching more than 95%. In addition, in the survey of surgeons, it was found that their satisfaction with preoperative preparation and operation time was significantly higher than that of the traditional scheduling method, and reached more than 95%. In the survey of nursing staff, it was found that the satisfaction with the traditional scheduling method was about 80%, and the satisfaction directly reached 100% after the comprehensive collaborative scheduling system. Based on the above survey, the satisfaction of nurses, doctors and patients with the new comprehensive collaborative scheduling system has improved compared with before. After the implementation of the comprehensive collaborative scheduling system, the annual surgical volume has increased significantly, and the average daily working hours of nursing staff have decreased. Comprehensive collaborative scheduling is an effective method of nursing human resource management in operating room, which can effectively improve the work efficiency of nurses and the satisfaction of patients, doctors and nurses. In practice, this method needs to be continuously explored and refined to adapt to different application scenarios and requirements.

RESEARCH OF THE TECHNOLOGICAL FEATURES OF THE PAVING TILES MANUFACTURE FOR THE IMPROVEMENT OF INDUSTRIAL AND CIVIL FACILITIES

Purpose. The purpose of the scientific article is to substantiate the parameters of the production of high-quality paving slabs and to determine the basic principles of paving technology of road surfaces using paving slabs. The work is based on the experience gained in the field of paving stone production, specifies the basic principles of its production, as well as methods that allow to increase its strength and resistance to the effects of weather conditions, mechanical influences and loads. Methodology. The research was carried out in accordance with the current regulatory documents that regulate the research of construction composites, raw materials for their manufacture and finished products, which were considered in the submitted work. A number of experimental studies of fine-grained concrete have been conducted from the point of view of their physical and mechanical properties and the parameters of the construction of road surfaces from manufactured elements. Findings. When installing paving slabs, it is necessary to take into account not only the quality of the products themselves, but also observe the technology of laying the soil base for paving. It was established that not only the main composition of fine-grained concrete is important. It is determined from which raw materials and under which manufacturing conditions it is possible to obtain high-quality, durable products. An analysis of the compositions that are most widely used in the conditions of modern production of paving stones is given. The most rational dosage of each component, which affects the properties of the working mixture and the finished construction coating, is also given. Originality. The relationship between the combination of components of fine-grained concrete, which allows obtaining high-quality small-sized products for the installation of a road surface, has been established. The list of the most common paving defects that occur during the manufacturing process has been defined and the technological requirements for the preparation of the soil base for the road surface have been established. Practical value. The obtained research results allow to product high-quality paving slabs, which have the necessary indicators for the installation of a durable road surface. The conditions for the preparation of the ground base for the paving of road surfaces are determined and the importance of their observance is substantiated. This will increase the efficiency of work and ensure the comfort of the population.

Prevalences and Interrelationships of Post COVID-19 Fatigue, Sleep Disturbances, and Depression in Healthy Young and Middle-Aged Adults

Background: An evaluation of the persistence of symptoms following COVID-19 in economically active young and middle-aged adults is crucial due to its significant socioeconomic impact resulting from compromised work performance. Methods: A prospective, multicenter study at 12 South Korean hospitals from January to December 2022 involved telephone interviews along with validated questionnaires. Results: Among 696 participants with a median age of 32 and no prior diagnoses, 30% of participants experienced persistent fatigue, while 21.4% suffered from sleep disturbance at 6 months following infection. Additionally, approximately 25% of the participants exhibited depression that endured for up to 6 months. Symptomatic individuals at 3 months exhibited a significantly higher prevalence of persistent fatigue, sleep disturbances, and depression at 6 months compared to those who remained asymptomatic. Notably, sleep disturbance and persistent fatigue at 3 months emerged as significant independent predictors of the presence of depression at 6 months. Conclusions: Even among young and middle-aged healthy adults, prolonged fatigue, sleep disturbance, and depression exhibit a significant prevalence and persisted for up to 6 months. Therefore, implementing a workplace management protocol for these symptoms is essential to mitigate the socioeconomic burden caused by the impairment of work efficiency.

Workplace violence, work-related exhaustion, and workplace cognitive failure among nurses: A cross-sectional study.

To examine the relationships between nurses' exposure to workplace violence and self-reports of workplace cognitive failure. A cross-sectional study. An online questionnaire was administered in April 2023 to nurses in Michigan, US. Structural equation modelling was used to examine effects of physical and non-physical workplace violence (occupational stressors) and work efficiency and competence development (occupational protective factors) on workplace cognitive failure. Physical violence was a significant predictor of the action subscale of cognitive failure. There were no direct effects of non-physical violence, workplace efficiency, or competence development on any of the workplace cognitive failure dimensions. Both types of violence and efficiency had significant indirect effects on workplace cognitive failure via work-related exhaustion. Work-related exhaustion predicted significantly higher scores for workplace cognitive failure. Workplace violence and work efficiency exhibited primarily indirect effects on workplace cognitive failure among nurses via work-related exhaustion. Nurses experiencing workplace violence may be at increased risk for workplace cognitive failure, especially if they are also experiencing work-related exhaustion. Workplaces that nurses perceive as more efficient can help to mitigate the effects of violence on nurses' cognitive failure. This study addressed the possible effects of workplace violence as well as work efficiency and competence development on nurses' cognitive failure at work. Analyses revealed primarily indirect effects of workplace violence, and indirect protective effects of work efficiency, on nurses' cognitive failure via work-related exhaustion. This research has implications for healthcare organizations and suggests that efforts made by healthcare workplaces to prevent violence and work-related exhaustion, and to enhance work efficiency, may help to mitigate workplace cognitive failure among nurses. We have followed the STROBE checklist in reporting this study. No Patient or public contribution.

An Evaluation Method of Dental Treatment Quality Combined with Deep Learning and Multi-index Decomposition

ABSTRACT Dentists judge that the quality of dental treatment for each patient is very time-consuming and inefficient, lacks quantitative evaluation criteria, and is easy to cause errors. At the same time, the traditional method of extracting tooth and root canal image features based on experience is difficult to accurately extract the tooth area and root canal filling area, resulting in low accuracy of tooth and root canal segmentation, which in turn affects the accuracy of tooth treatment quality evaluation. In this paper, a deep learning convolutional neural network is used to segment the root canal filling area, tooth boundary, and the boundary between tooth and soft tissue for the real patient ‘s root canal treatment and filling image. Finally, the segmented image is quantitatively evaluated according to the multi-evaluation index of professional doctors. The experimental results show that the intelligent evaluation method of dental treatment quality combined with deep learning and multi-index decomposition proposed in this paper not only unifies the evaluation criteria of dental treatment quality but also the therapeutic effect of quantitative scoring can effectively improve the work efficiency of doctors, which has reference significance for the application of artificial intelligence in the medical field.

Ethical climate and its relationship with attitude to teamwork in operating room nurses: A cross-sectional study

IntroductionIn the nursing profession, ethical climate is considered to be an important element of the work environment. Nurses are exposed to moral tension due to the volume and high work pressure; on the other hand, the stressful nature of this profession can have many negative effects on nurses and, as a result, the quality of care. Therefore, improving the conditions and ethical climate becomes very important. In the operating room, due to the special working conditions and the variety of available specialties, team work is of great importance among the operating room staff. Improving teamwork in the operating room reduces stress and anxiety, increases work efficiency, improves the quality of care, and increases the patient safety. ObjectiveThis study was conducted with the aim of determining the relationship between the ethical climate and the attitude towards teamwork of operating room nurses in hospitals affiliated with Shiraz University of Medical Sciences. MethodThis research was a cross-sectional study conducted in 2022. The study involved a sample of 310 operating room nurses from hospitals affiliated with Shiraz University of Medical Sciences, selected using convenient and purpose-based sampling methods. In order to collect data, the questionnaires of demographic information, Olsan's ethical climate and attitude towards teamwork baker were used. The collected data were analyzed by SPSS V20. FindingsThe average score of the operating room staff from the ethical climate was 94.12±14.96, and that of the overall attitude towards teamwork was 121.92±11.74, which indicates a favorable attitude towards teamwork. There was a significant relationship between the scores of the ethical climate and the attitude toward teamwork among the operating room personnel (r = 0.339,p < 0.0001). ConclusionThe results showed that there is a significant relationship between the attitude towards teamwork and the ethical climate of operating room nurses. Managers of hospitals and operating rooms can strengthen the spirit of teamwork and create a suitable atmosphere in the operating room by holding training courses and increase safety and productivity there.

Work Environment and Job Performance: The Mediating Role of Office Ergonomics

This study aimed to address the influence of the Work Environment on employees’ health and safety, comfort, and task accomplishment by investigating office ergonomics as the possible underlying mechanism. The study was conducted in Dodoma City Council, specifically in the ward and Mitaa offices. Data were collected from 79 respondents through a questionnaire. Data were analyzed quantitatively using the Pearson correlation coefficient and regression analysis, and the Sobel test was performed to assess if the mediator variable was significant enough to mediate the relationship between variables. The study has found that work environment and ergonomics have a linear and positive relationship (r = 0.5433, p &lt; .0001). The results have also indicated that ergonomics is related to employee health and safety, comfort, and task accomplishment. The study revealed that most offices in lower local government authorities in Dodoma City Council need ergonomically designed chairs and tables, adequate quality air, and lighting. Inadequacy of these elements risks the office bearers' health and safety, comfort, and performance. Therefore, ergonomics should be a strategic aspect in enhancing work efficiency and productivity in lower local government in Tanzania.

Usability of myocardial work parameters in demonstrating myocardial involvement in INOCA patients.

Myocardial work (MW) is a novel echocardiographic modality, which has been shown to have diagnostic and prognostic values in patients with cardiovascular diseases, patients with obstructive coronary artery disease, in particular. However, only a handful of studies have examined the MW analysis in ischemia with nonobstructive coronary artery (INOCA) disease. This study, therefore, aimed to detect the early left ventricular involvement in INOCA patients diagnosed by an invasive coronary angiography performing the MW analysis. This study included a total of 119 patients with nonobstructive coronary artery disease diagnosed by invasive coronary angiography, who were checked for prior ischemia tests performing myocardial perfusion scintigraphy. Out of these 119 patients, 49 patients developed ischemia (i.e., ischemic group) diagnosed using cardiac single-photon emission computed tomography, whereas 70 patients did not (i.e., nonischemic group). The subjects were divided into three groups based on the global MW tertiles. The groups were compared in terms of the conventional, longitudinal strain, and MW findings by conducting echocardiographic examinations. The study subjects were divided into three groups based on the global constrictive work (GCW) value. The three groups were not statistically different in terms of the mean age of the patients (53.0 ± 12 vs. 52.4 ± 13.3 vs. 52.1 ± 12.3; p = 0.96). Furthermore, the three groups were not statistically different regarding the gender, height, weight, and laboratory parameters of the patients except albumin. There was no statistically difference among the tertiles of GCW groups in the measurements of cardiac chambers, LA diameter, interventricular septum, E wave, and A wave. Also, there was no statistical difference in tissue Doppler recordings. The parameters associated with MW were examined, three groups were not statistically different in terms of the global waste work (GWW) (116 ± 92, 122 ± 73, 135 ± 62, p = 0.52, respectively). In contrast, the three groups were different regarding the Global work index (GWI) (1716 ± 300, 1999 ± 130, 2253 ± 195, p < 0.001, respectively), GCW(1888 ± 206, 2298 ± 75, 2614 ± 155, p < 0.001, respectively), and Global work efficiency parameters (92.8 ± 3.6, 94.4 ± 3.2, 95.1 ± 1.8 p = 0.004, respectively). It was concluded that the MW parameters GCW and GWI may have been used for predicting INOCA in patients.

Research on Fault Diagnosis Method of CNC Machine Tools Based on Integrated MPA Optimised Random Forests

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production.OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods.METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis.RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy.CONCLUSION: Solve the problem of poor accuracy of fault diagnosis of CNC machine tools and unsound feature system. 

Performance of the left ventricular myocardium in patients with a negative test result during exercise stress echocardiography

Aim. To determine the possibility of identifying patients with significant coronary artery disease (CAD) by evaluation of the left ventricular (LV) myocardial work indicators by constructing pressure-strain loops despite a negative test result during exercise stress echocardiography. Materials and methods. The study included 79 patients with suspected or previously confirmed CAD, of which 47 (59%) men, who had a negative test result during exercise stress echocardiography on the treadmill. Global work index (GWI), global constructive work (GCW), global wasted work (GWW) and global work efficiency (GWE) were evaluated using the technique of constructing pressure-strain loops at rest and at peak exercise. Results. With similar systolic blood pressure and LV ejection fraction at peak exercise between the control group and the group of patients with single-vessel CAD, there was revealed significant difference in GWI, GCW and GWE at peak exercise. In patients with multi-vessel CAD, GWI and GCW also significantly differed from the control group at peak exercise. Conclusion. The evaluation of LV myocardial work indicators may be recommended for a more accurate diagnosis of CAD in negative or unreliable test results during exercise stress echocardiography.

Self-testing Algorithm of Metro One-button Switch Station Equipment Based on a Neural Network

According to the operation and management requirements of subway stations, equipment managed by the station, such as lighting, escalators, broadcasting, etc., must be operated with one-button switch according to a certain process every day. The first step in one-button switch station operation is to perform routine self-testing on these devices. Traditional self-testing operations mainly rely on manual handling by operators, which greatly affects their work efficiency. To optimize the operational processes, it is necessary to establish intelligent self-testing algorithms. An AC-CNN (Attention Causal Convolutional Neural Network) model to increase the self-testing efficiency in subway switching stations was established in this paper. AC-CNN model is a deep learning prediction network based on attention mechanism, which can capture the causal convolutional layer of the time before and after relationship as the basic network structure, optimize the convergence process through residual connections, and fuse global and local attention representations through matrix multiplication in the attention mechanism module to reduce useful information loss. The algorithm used the one-hot encoding and sliding window feature engineering operations to preprocess the historical self-testing operation data and completed the prediction of the self-testing information of metro one-button switch station equipment through the deep analysis of AC-CNN. The on-site application results indicated that AC-CNN had greater advantages in terms of accuracy and operation time compared to those of existing BP neural networks, GBDT, and LSTM algorithms, which can significantly improve the work efficiency of subway staff on one-button switch station.

Time Composition, Efficiency, Workload, and Noise Exposure during Tree Felling and Processing with Petrol and Battery-Powered Chainsaws in Mixed High Forest Stands

This study presents the effects of using a battery-powered chainsaw on work efficiency and ergonomics under real conditions during timber harvesting. The study was conducted during the felling and processing of coniferous and deciduous trees with a diameter at breast height (DBH) of 13 cm to 78 cm using both a petrol-powered and battery-powered chainsaw. The results include comparisons of time composition, work efficiency, psychophysical workload, and noise exposure. Heart rate and noise exposure were measured over ten days as part of a time study using the Husqvarna 543 XP petrol-powered chainsaw and the Husqvarna 540i HP battery-powered chainsaw. The comparison of the time composition between the chainsaws used showed 3%–4% differences in the duration of productive time operations and 16% in service time. The difference in work efficiency during the productive time between the two chainsaws was statistically insignificant, but generally higher when working with the battery-powered chainsaw than with the petrol-powered chainsaw. During the main productive time, the work efficiency was 9.89 min/t for the petrol-powered chainsaw and 9.44 min/t for the battery-powered chainsaw. The psychophysical workload of the feller was lower when using the battery-powered chainsaw than when using the petrol-powered chainsaw as the relative working heart rates during the entire productive time was 32.5% for the battery-powered chainsaw and 35.0% for the petrol-powered chainsaw. The noise exposure of the workers was lower when using a battery-powered chainsaw, namely 6.0 dB(A) and 0.4 dB(C) compared to the use of a petrol-powered chainsaw. The results of this paper indicate that battery-powered chainsaws can compete with petrol chainsaws in harvesting conditions that are currently considered unsuitable due to the large volume of trees.

SMART Irrigation Robot

Abstract: Smart irrigation robots are solutions for today's agriculture that solve water management, work efficiency and crop quality problems. This autonomous robotic system combines advanced technology, sensors, artificial intelligence and precision robots to revolutionize the irrigation process. This content highlights the key features and advantages of smart water users. The robot uses a network of soil moisture sensors and weather data to make real-time, data-driven decisions about when and how much to harvest. Using smart algorithms, irrigation strategies can be tailored to specific crop types and growth stages to ensure efficient use. Operating costs. It also promotes sustainability and environmental protection by reducing water waste by providing the required amount of water where and when it is needed.

Estimation of Maize Residue Cover Using Remote Sensing Based on Adaptive Threshold Segmentation and CatBoost Algorithm

Maize residue cover (MRC) is an important parameter to quantify the degree of crop residue cover in the field and its spatial distribution characteristics. It is also a key indicator of conservation tillage. Rapid and accurate estimation of maize residue cover (MRC) and spatial mapping are of great significance to increasing soil organic carbon, reducing wind and water erosion, and maintaining soil and water. Currently, the estimation of maize residue cover in large areas suffers from low modeling accuracy and poor working efficiency. Therefore, how to improve the accuracy and efficiency of maize residue cover estimation has become a research hotspot. In this study, adaptive threshold segmentation (Yen) and the CatBoost algorithm are integrated and fused to construct a residue coverage estimation method based on multispectral remote sensing images. The maize planting areas in and around Sihe Town in Jilin Province, China, were selected as typical experimental regions, and the unmanned aerial vehicle (UAV) was employed to capture maize residue cover images of sample plots within the area. The Yen algorithm was applied to calculate and analyze maize residue cover. The successive projections algorithm (SPA) was used to extract spectral feature indices from Sentinel-2A multispectral images. Subsequently, the CatBoost algorithm was used to construct a maize residue cover estimation model based on spectral feature indices, thereby plotting the spatial distribution map of maize residue cover in the experimental area. The results show that the image segmentation based on the Yen algorithm outperforms traditional segmentation methods, with the highest Dice coefficient reaching 81.71%, effectively improving the accuracy of maize residue cover recognition in sample plots. By combining the spectral index calculation with the SPA algorithm, the spectral features of the images are effectively extracted, and the spectral feature indices such as NDTI and STI are determined. These indices are significantly correlated with maize residue cover. The accuracy of the maize residue cover estimation model built using the CatBoost model surpasses that of traditional machine learning models, with a maximum determination coefficient (R2) of 0.83 in the validation set. The maize residue cover estimation model constructed based on the Yen and CatBoost algorithms effectively enhances the accuracy and reliability of estimating maize residue cover in large areas using multispectral imagery, providing accurate and reliable data support and services for precision agriculture and conservation tillage.

The Influence of Cultural Background on Employment Behavior

Cultural background plays an important role in the organizational environment and has a wide and profound impact on employee behavior. Based on the cultural dimension theory proposed by Hofstede, this article attempts to explain more clearly from a theoretical perspective the predictable effects that different cultural backgrounds may have on cooperation in cross-cultural teams. This article also refers to a research report that investigated the cross-cultural after-sales team of "DM Company". It attempts to use this study as an example to explore the impact of different cultural backgrounds on sales work in cross-cultural teams, and to explore the impact of other cultural backgrounds on work. This paper aims to explore the impact of cultural background on employee behavior, including communication style, decision-making style, work attitude and so on. By analyzing how different cultural backgrounds shape employee behavior, it can help organizations better manage diverse employee groups and improve work efficiency and employee satisfaction.

Research on a machine learning-based adaptive and efficient screening model for psychological symptoms of community correctional prisoners.

Community correction institutions in China frequently employ the Symptom Checklist-90 (SCL-90) and the health survey brief (SF-12) as primary tools for psychological assessment of community correctional prisoners. However, in practical application, the SCL-90 Checklist faces issues such as complex item numbers, overall low cultural level of the subjects, and insufficient professional level of the administrators. The SF-12 health survey brief, as a preliminary screening tool, although only has 12 questions, to some extent simplifies the evaluation process and improves work efficiency, it is prone to missed screening. The research team collected 17-dimensional basic characteristic data and corresponding SCL-90 and SF-12 data from 25,480 samples of community correctional prisoners in Zhejiang Province, China. This study explored the application of multi-label multi-classification algorithms and oversampling techniques in building machine learning models to delve into the correlation between the psychological health risks of community correctional prisoners and their characteristic data. Inspired by computerized adaptive testing (CAT), we constructed an adaptive and efficient screening model for community correctional prisoners through experimental comparisons, based on the binary relevance algorithm with sample oversampling. This screening model personalize the assessment process by dynamically matching participants with the most relevant subset (s) of the nine dimensions of the SCL-90 based on their individual characteristics. Thus, adaptive dynamic simplification and personalized recommendation of the SCL-90 scale between question groups were achieved for the specific group of community correctional prisoners. As a screening tool for psychological symptoms of community correctional prisoners, this model significantly simplifies the number of questions compared to SCL-90, with a simplification rate of up to 65%. However, it achieves this simplification while maintaining excellent performance. The accuracy reached 0.66, with a sensitivity of 0.754, and an F1 score of 0.649. This innovation simplified the assessment process, reduced the assessment time, improved work efficiency, and enhanced the ability to judge the specificity of community correctional prisoners population. Compared to the SF-12, although the simplification rate and accuracy of the model are slightly lower than those of the SF-12, the sensitivity increased by 42.26%, and the F1 score improved by 15.28%. This means the model greatly reduces the possibility of missed screening, effectively preventing prisoners with abnormal psychological or mental states from losing control due to missed screening, and even committing suicide, self injury, or injuring others.

MUSCULOSKELETAL DISORDERS AFFECTING WORK PERFORMANCE AMONG GROUND-HANDLING PERSONNEL: A SYSTEMATIC LITERATURE REVIEW

Ground-handling personnel are critical for airline operations and ensuring safety during flights. Yet, these workers are at risk of developing MSDs due to highly physically demanding tasks. which can impact their health and work performance. This review examines common risk factors affecting MSDs and their impact on work performance, while also exploring prevention and management strategies for this occupational group. A total of 342 articles were identified through an explorative search of the PubMed database, Direct Scopus, and Google scholar with the limitation of the year from to 2015-2024. Only 16 articles were considered and classified based on their operation category, such as ramp operation, flight maintenance, cabin crew and baggage handler articles. The results reveal that musculoskeletal disorders (MSDs) in ground handling personnel can be influenced not only by physical factors but also by organizational, psychological, and social aspects, which can negatively impact work efficiency and performance. ground handling workers are exposed to various musculoskeletal risks, including physical exertion, unnatural postures, Standing for extended periods., repetitive tasks, tight deadlines, and heavy lifting, as well as organizational and psychological concerns. It is crucial to mitigate these risks to safeguard and enhance musculoskeletal health of the workers.

COVID-19로 인한 정신간호사의 코호트 격리 경험

Objectives This study aimed to explore the experiences of psychiatric nurses who were quarantined in cohorts to prevent the spread of infection during the COVID-19 pandemic. Methods Data were collected from July 19 to December 28, 2021 through in-depth interviews with psychiatric nurses who were in cohort isolation for more than 3 weeks until the data was saturated through the snowball sampling method. Through one-on-one in-depth interviews with psychiatric nurses, the meaning of experiences in cohort isolation was analyzed according into themes, sub-themes, and categorization stages according to Colaizzi's phenomenological method. Results The experience of nurses in cohort isolation, such as patients in psychiatric hospitals, was derived into six themes. The six themes were ‘anxious due to unpredictable situations’, ‘re-awareness of the ward environment’, ‘need a support system’, ‘do activities to cope with anxiety’, ‘think about changing jobs’, ‘ self-esteem improved as a psychiatric nurse.’ These results showed that cohort isolation was a problem under unilateral hospital instructions, without having any prior explanation or opportunity to judge cohort isolation. During cohort isolation, psychiatric nurses experienced the conflict of turnover and the meaning of their professional role as mental nurses while analyzing the causes of cohort isolation and working to cope with them. Conclusions Based on the results of psychiatricl nurses' cohort isolation experience, it can be used as basic data for developing strategies to support psychiatric nurses who are cohort- isolated in the event of a new infectious disease in the future, developing manuals for work efficiency, and maintenance education.

PENERAPAN LEAN SIX SIGMA DALAM PENGENDALIAN KUALITAS PRODUK: STUDI KASUS PERUSAHAAN TEKSTIL

The increasing number of fabric defects over the past three years has caused the company's performance to decline in terms of production. The data obtained is defect data in the period December 2019 to January 2023 from internal company data, while waste data is obtained by waste assessment questionaire and waste relationship matrix. The DMAIC method is the main method in this study, starting from defining problems and measuring process capabilities based on defects, continued with WRM and WAQ for waste type analysis, and the root of the problem in defects and waste is analyzed with fishbone diagrams. The results of this study show that the most common types of defects are holes, broken threads, and broken needles, while the types of waste that occur are waiting, overprocessing, defects, and overproduction. The root of the problem is that there are no QC procedures for raw materials, machine operating procedures, and periodic inspection procedures. By providing improvement proposals and implementing improvements, it was concluded that there was a reduction in production costs of up to 4%, an increase in work efficiency of up to 2.48%, and a manufacturing lead time of up to 35.98%.