Impact Of Human Factors Research Articles

Human-Machine Interface Visual Communication Design Model of Electronic Equipment Using Machine Vision Technology

Electronic equipment has high precision, high reliability, high stability, and high security, as well as the ability to adapt to a variety of challenging environments. With the rapid advancement of mechanization, automation, and electronization, the impact of human factors in production is growing, and designers are increasingly concerned about the problem of man-machine coordination. It is critical to have a man-machine interface that is suitable for operators’ thinking and behavior and has a guiding function. The accuracy and timeliness of equipment control are linked to the human-computer interaction interface. The visual characteristics of human cone cells are used to divide the neural visual perception intensity grades. With the visual communication index as the optimization goal, a mathematical model of human-computer interaction interface optimization is established and solved using a genetic algorithm. This method is used to optimize the design of a human-computer interaction interface, and the results show that the visual communication index of the optimized human-computer interaction interface has improved significantly.

Microhabitat preferences of butterflies in urban parks: Both vegetation structure and resources are decisive

Urban parks comprise diverse microhabitats, such as vegetation units of lawn and arbour forests, with differing biodiversity potentials. However, the influences of microhabitats on butterfly diversity and the mechanisms involved remain unclear. This study used butterfly survey data from 112 plots in 27 urban parks in the central metropolitan area of Beijing, China, from June to September 2020. Based on the growth form of larval host plants, recorded butterfly species were classified into three functional groups: woody plant-feeding taxa (WF), herb-feeding taxa (HF), and feeding on multiple plant growth forms taxa (MF). We analysed the effects of 11 variables among three facets, namely, vegetation composition, vegetation structure, and human activity, on the butterfly diversity (species richness and abundance) of the whole community, three functional groups using generalised linear mixed models. Twenty-five butterfly species observed mainly feed on herbs rather than on woody plants. Our results demonstrated that vegetation community characteristics explain up to 24% and 43% variation in butterfly species richness and abundance, respectively. Of this, vegetation structure facets crucially affected butterfly species richness, and vegetation composition facets had the most significant influence on the abundance of the whole butterfly community. However, the impact of human activity factors was minimal. Light availability and herb height belonging to vegetation structure factors and nectar plant species richness and nectar abundance which belonged to vegetation composition factors showed the most important and positive effects on butterfly diversity. The positive impact of the above significant factors was found especially on herb-feeding butterfly diversity. In contrast, the diversity of butterflies feeding on woody plants was most positively influenced by herb height. We thus suggest that it is necessary to guarantee the presence of a well-developed herb layer, which provides abundant nectar sources and maintain specific open spaces to ensure light availability. In conclusion, our findings imply that the critical role of the spatial structure of vegetation community is conspicuous in the formation of suitable microhabitats for butterflies, and managers could combine vegetation management practices with the needs of specific functional groups.

Investigating the influence of big data analytics capabilities and human resource factors in achieving supply chain innovativeness

The current study aims to bridge the gap in understanding how big data analytics and human resource factors impact supply chain innovation and enhance supply chain sustainability. For empirical investigation, data were collected from employees working in manufacturing firms. For inferential analysis 341 valid responses were used. Results indicate that supply chain innovation is predicted by big data analytic capability, big data analytic staff capability, employee development, employee empowerment and employee involvement and explained large variance R2 0.538 % in measuring supply chain innovativeness. Practically, this research suggests that policy makers should focus on supply chain innovativeness, big data analytics, big data analytics staff capability and supply chain connectivity to enhance sustainable performance in supply chain operations. Theoretically, this research has synthesized literature and develops an integrated research model that combines technology and human resource factors altogether to investigate supply chain innovation and sustainability in supply chain performance.

Application of Deep Learning in Medical English Teaching Evaluation

Although the fact that the reform of college English teaching has yielded mixed results, medical colleges have yet to achieve a breakthrough. Deep learning (DL) is a type of education that emphasizes higher-order thinking skills. It encompasses the state of learning, the learning process, and the learning outcomes. It emphasizes critical knowledge understanding, focuses on the integration of old and new knowledge, confronts real-world problems, and pays attention to reflection. This paper establishes the EMET (evaluation of medical English teaching) model based on the improved BPNN algorithm and analyzes the performance of this model using the improved BPNN algorithm. The simulation results show that the improved BPNN algorithm proposed in this paper has fast convergence speed, strong optimization ability, and generalization ability, effectively reducing the impact of human factors on index weight determination.

Integrating Human Barriers in Human Reliability Analysis: A New Model for the Energy Sector.

Human reliability analysis (HRA) is a major concern for organizations. While various tools, methods, and instruments have been developed by the scientific community to assess human error probability, few of them actually consider human factors impact in their analysis. The active role that workers have in shaping their own performance should be taken into account in order to understand the causal factors that may lead to errors while performing a task and identifying which human factors may prevent errors from occurring. In line with this purpose, the aim of this study is to present a new methodology for the assessment of human reliability. The proposed model relies on well-known HRA methodologies (such as SPAR-H and HEART) and integrates them in a unified framework in which human factors assume the role of safety barriers against human error. A test case of the new method was carried out in a logistics hub of an energy company. Our results indicate that human factors play a significant role in preventing workers from making errors while performing tasks by reducing human error probability. The limits and implications of the study are discussed.

Proceedings of the 2021 Workshop on Laboratory Safety: Advancing Safety in Teaching and Research Laboratories

The University of California Center for Laboratory Safety held its fifth biennial Workshop on Laboratory Safety in May of 2021. The Workshops on Laboratory Safety provide a unique forum for researchers and safety professionals to exchange perspectives and ideas. The theme of this year’s workshop was Advancing Safety in Teaching and Research Laboratories. Speakers emphasized the importance of an enlightened leadership style, the significance of integrating risk assessments into the science curriculum, and the impact of human factors on risk minimization. Furthermore, speakers discussed innovative programs to engage Principal Investigators in organizational safety culture and computational approaches for defining the toxicity of chemical compounds. Panels discussed two topics: student-led safety initiatives and the long-term impact of COVID-19 on academic life, research, and lab safety. Finally, 11 workgroups examined current safety topics over the 3 day course of the workshop culminating in final presentations on their recommendations. This paper summarizes all presentations and lists key resources from each discussion.

Long Term Observation of Fractional Vegetation Cover in Qingyang of Gansu Province and Its Response to Climate Change

Vegetation is seen as a sensitive indicator of global change because of its crucial role in connecting the atmosphere, soil, and water. Fractional vegetation cover (FVC), in turn, is an important indicator of vegetation status. Qingyang is a typically ecologically sensitive region, with a range of changes in vegetation in the last decade as a result of climatic and non-climatic factors. However, the exact impact of climate change and human activities remains unclear. Satellite observations can help to clarify that impact, allowing us to assess trends in vegetation change in the last two decades (2000–2019). In this study, daily and composite time series vegetation variations were derived from moderate resolution imaging spectroradiometer (MODIS) data and the impact of climate and human activity factors was examined for different administrative districts. By deploying multiple regression models, the research revealed that human activity has contributed 46% to the FVC variation, while the remaining 54% was led by climate factors. In areas where FVC was increasing, human activity contributed 55.89% while climate factors contributed 44.11%. In areas where FVC was decreasing, human activity and climate factors contributed 24.58% and 75.42%, respectively. The study also looks at the impacts of El Nino/IOD events in FVC dynamics in the study site. The FVC inversion result from MODIS proved capable of capturing long-term and seasonal vegetation patterns and thus provide a valuable archive for decadal-scale vegetation dynamics in the study area. Moreover, the improvement in FVC was a dual effect of climatic and human activities, while the latter owns a higher contribution especially for the implementation of ecological construction projects.

Effects of Demographic Factors for Fatigue Detection in Manufacturing

Over the years, advancement in automation technology is allowed the increased integration of humans and machines in a manufacturing environment, these days fewer humans. The use of Knowledge-based Systems in improving and converting human overall performance has been restrained in truth because of a lack of expertise of the way an individual’s overall performance deteriorates with fatigue buildup, which may range from employees to the work environment. As a result, the performance benefits of increased automation in a manufacturing environment, as well as the impact of human factors, must be taken into account. To predict fatigue in physically demanding tasks, this study takes a data-driven strategy. The influence of demographic characteristics, their physical fatigue states, detected workloads, and reactivity to physiological changes are investigated through sensors (Inertial Measurement Unit; IMU and Heart Rate Variability; HRV) in this paper. A framework is established for the selection of key features, machine learning algorithms, and evaluating subjective measures. To attain that, specific application scenarios of the framework are shown, each for different sorts of manufacturing-related tasks.

Impact of climate change and human factors on water resources variations in reservoir dams (case study: Lighvan River, Iran)

In recent years, runoff of basins has changed due to climate change and human activities. Since river flow changes play a key role in reservoirs water resources planning purposes, the analysis of the role of climatic and human factors in river flow changes is of great importance in reservoirs development plans. In this study, the effect of climate change and human activities on the runoff responses was examined using hydrologic sensitivity analysis in the Lighvan basin located in the northwest of Iran. The Vanyar dam is located in downstream of the Lighvan basin. The Mann–Kendall test and the Pettitt test were applied to identify the monotonic and rapid trends respectively, in hydro-climatic data series. The results showed that there was negative trend in discharge data series. The effect percentages of the human factors and climatic factors on runoff reduction were 65%-84% and 16%-35%, respectively. Therefore, in addition to the role of climate change, there is a need to pay more attention to the impacts of human activities in the upstream of dams. Also, according to the rate of changes of climatic and human factors, the design and planning of water resources of reservoirs should be improved and adapted.

Evaluation of human factors on visual inspection skills in textiles and clothing: A statistical approach

In visual inspection, the impact of different human factors on inspection performance has been evaluated in past. However, there is a need to study the effect of relevant factors and their interrelation in a single framework to monitor and improve inspection performance. This study aims to identify and evaluate such human factors for proposing a framework that indicates their interrelationship. After reviewing the literature, five constructs with their respective observed variables are selected to propose the framework. The survey instrument is developed using the suggested framework to collect data from industry professionals where human labor perform the inspection for products such as textiles. Finally, data are collected through an online survey and analyzed using confirmatory factor analysis to test the hypotheses. The results verify that the selected constructs are good measures of inspection skill, however, some variables are excluded from the model for being insignificant. Results show that the factors related to the constructs – personal, physical/mental, and organizational – are found more influencing than system and inspection-task factors. This study identifies and models the inspection-related significant factors into a framework that will help monitor and improve the performance of an individual or whole inspection station.

Моделирование воздействия опасных факторов на систему "критически важный объект - экологическая среда"

Introduction. The article reveals the concept of "dangerous state", formulated the task of modeling the impact of hazardous factors on the system "Critical object – ecological environment", taking into account the interaction of elements in the subsystem "Potentially dangerous object – environment". Goals and objectives. On the basis of a systematic approach to the analysis of the elements of the system "Critical object – ecological environment" to substantiate the system of indicators of the protection of the subsystem "Potentially dangerous object – environment" and a system of indicators of the impact on the system under study and its subsystems of dangerous factors of the aggressive environment, taking into account the modeling of the impact of natural factors and human impact. Methods. To form a system of subsystem security indicators "Potentially dangerous object – environment" and systems of indicators of the impact on the system under study and its subsystems of dangerous factors of aggressive environment used methods of mathematical modeling of various states of subsystems, methods of probability theory, analytical methods for assessing the security and survivability of objects and complexes, methods of disaster theory. Results and discussion. As a result of a systematic approach to the analysis of the system "Critical object – ecological environment", the following conclusions are substantiated. Justification of subsystem security indicators "A potentially dangerous object – environment" is based on the security model of its subsystems. The system of indicators of the impact on the studied system and its subsystems of hazardous factors of an aggressive environment is based on models of the impact of natural factors and human factors. These include: - model of the impact of damaging elements on the elements of the subsystem "inventory storage facility – environment"; - model of shock-wave impact in case of an accident on the elements of the subsystem "object of storage of stocks – environment"; - model of the effect of polluting compounds in the air on the elements of the subsystem "storage facility – environment"; - model of the impact of a landscape fire on the elements of the subsystem "inventory storage facility – environment". - model of violators' access to stocks on potentially hazardous facility at their storage facilities; - model of countering terrorism in the system "object of storage of stocks – environment"; - model for predicting emergencies in case of violation of safety regulations by maintenance personnel in the subsystem "object of work with reserves environment". Conclusions. The concept of "dangerous state" is disclosed, the indicators of the protection of a potentially dangerous object are justified, the indicators of the impact of dangerous factors in the subsystem are justified "A potentially dangerous object is the environment". Keywords: dangerous condition, potentially dangerous object, system, environment, modeling, security indicators, exposure to dangerous factors. Funding: the article was prepared based on the results of research carried out at the expense of budgetary funds under the state assignment of the Financial University under the Government of the Russian Federation

Factors Contributing to the Road Traffic Accidents in Sri Lanka

The impact of environmental and human factors has significant importance on the Road Traffic Accidents (RTAs) in Sri Lanka. Past studies have shown that neither the expansion of roads horizontally or vertically nor the reduction in the vehicle density is a solution to reduce RTAs. Therefore, this study is to determine the significant human factors associated with RTAs in Sri Lanka using data on RTAs during 2005 – 2019 obtained from the Department of Police, Sri Lanka, and suggest some solutions to reduce RTAs. The six reasons for major RTAs are overtaking, diversion, speed driving, alcohol consumption of driver, negligence of pedestrians, and mechanical fault of vehicles. About 85% of major RTAs were due to overtaking (32%), diversion (27%), and speed driving (25%). The percentage of RTAs under each of these variables is significantly higher (p < 0.05) than that of RTAs when the drivers have alcohol more than the minimum level (9%). The Exploratory factor analysis (EFA) and Conformity factor analysis (CFA) confirmed that reasons for RTAs can be classified into two latent factors, namely, ‘lack of attention of the drivers’ and ‘negligence of the drivers. The two factors are invariant on the type of extraction method as well the type of orthogonal rotation. The fitted binary logistic model revealed that the significant variables on RTAs are negligence of the road conditions by the drivers, lack of attention of the driver, age of driver less than 18 years, and status of alcohol by the drivers. The odds of fatal accidents happening when the driver has a valid driving license are 4.3 times higher than that of a driver without a valid license. The inferences derived from this study can be easily used by Department of Police in Sri Lanka to reduce the RTAs in Sri Lanka. Drivers should be motivated to become well-disciplined drivers. The fines for the who do not adhere road rules need to be increased.

Digital Twin-Enabled Decision Support in Mission Engineering and Route Planning

This article presents a Model-Based Systems Engineering (MBSE) methodology for the development of a Digital Twin (DT) for an Unmanned Aerial System (UAS) with the ability to demonstrate route selection capability with a Mission Engineering (ME) focus. It reviews the concept of ME and integrates ME with a MBSE framework for the development of the DT. The methodology is demonstrated through a case study where the UAS is deployed for a Last Mile Delivery (LMD) mission in a military context where adversaries are present, and a route optimization module recommends an optimal route to the user based on a variety of inputs including potential damage or destruction of the UAS by adversary action. The optimization module is based on Multiple Attribute Utility Theory (MAUT) which analyzes predefined criteria which the user assessed would enable the successful conduct of the UAS mission. The article demonstrates that the methodology can execute a ME analysis for route selection to support a user’s decision-making process. The discussion section highlights the key MBSE artifacts and also highlights the benefits of the methodology which standardizes the decision-making process thereby reducing the negative impact of human factors which may deviate from the predefined criteria.

The impact of green organizational and human resource factors on developing countries' small business firms tendency toward green innovation: A natural resource‐based view approach

Developing countries' small business firms are deemed insensitive towards the environment. However, little is known regarding what can reshape their attitude towards environmental management and uplift their abilities to counter environmental issues. Based on the extant literature review, we suggest that small business firms necessitate direction, motivation, structural capabilities, and ingenuity to tackle environmental issues through green innovation. We postulate that green organizational and human resource factors such as green business strategy (direction), green competencies (structural capabilities), green organizational culture (impetus), and green absorptive capacity (ingenuity) can reshape small businesses firms' tendency towards environmental preservation and green innovation. We tested our hypotheses on the data collected from small manufacturing firms located in different regions of Pakistan. Our findings suggest that developing countries' small business firms lag in environmental preservation due to their inability to comprehend and handle environmental problems. Introducing relevant green organizational and human resource factors can significantly improve small business firms' attitudes towards environmental management and green innovation. Moreover, our findings also reveal that small business firms lean towards green process innovation rather than green product innovation, depicting small business firms' behavioural tendencies towards innovation.

968 Delivering Human Factors and Non-Technical Skills Training Using Interactive Online Platforms in the COVID Era

Abstract Aim To evaluate the impact and effectiveness of an interactive online human factors (HF) and non-technical skills (NTS) course delivered to Core Surgical Trainees during the COVID-19 pandemic Method A 1-day HF and NTS course was conducted online, using the Zoom platform, to Core Surgical Trainees in the North West. The course consisted of interactive lectures, small group teaching sessions, and self-directed learning with written reflections. Pre- and post-course surveys were administered, evaluating the participants’ awareness, knowledge and skills using a 5-part Likert scale, along with a multiple-choice assessment of knowledge. Statistical analysis was undertaken with significance considered at p < 0.05 Results The course was attended by 63 CT1/2 participants, representing all surgical specialties. In the post-course evaluation, participants’ self-rating of awareness and knowledge for both HF and patient safety increased by between 10-20%. There was a significant increase in the mean post-course test score from 7.54 (SD ± 1.7) to 8.65 (SD ± 1.2) out of 10 (p < 0.0001). The course overall was rated relevant and useful (weighted averages 4.4 and 4.5 respectively) Conclusions To our knowledge, this is the first time a video conferencing platform has been used to deliver a live HF/NTS course. This study provides evidence supporting the use of such interactive online platforms in postgraduate surgical education. Training and professional development must continue, despite the evolving pressures from COVID-19. Embracing new methods of education delivery is required, with ongoing reporting and evaluation of education practice, sharing lessons learned and informing the evidence base in postgraduate surgical training during this time

A theoretical framework for data-driven artificial intelligence decision making for enhancing the asset integrity management system in the oil & gas sector

Asset integrity and reliability is one of the 20 elements of Process Safety Management (PSM) as defined by the Center for Chemical Process Safety (CCPS). We combine expert knowledge and data analytics (Artificial Intelligence, Machine Learning, and Keyword Analysis) to create a reaction network for Asset Integrity Management (AIM) and provide a theoretical and practical basis for handling uncertainty in large data sets such as company incident databases. The purpose of the current study is to control and minimize the total number of incidents that occur within an oil and gas operation by applying a multidisciplinary approach to explore and develop AIM. This systematic approach can improve AIM to better understand PSM as a whole and the underlying dynamics ever-present in the system. In this study, AIM is divided into 2 major groups – asset and human factors – and then, in order to get more detailed results, each group is divided into 9 and 5 subcategories, respectively. To analyze the relationships between the different factors of AIM, two score-based (Tabu and Hill Climbing) and one hybrid (Max-Min Hill-Climbing) Bayesian networks are used to develop one final viable solution. The findings of these techniques point towards the same results for reducing incident rates. Four factors related to assets, including construction, testing, inspection, and maintenance, account for more than half the incidents (54.78%). Additionally, there must be a greater emphasis placed on the impact of human factors as they are directly (23.58%) and indirectly (11.10%) responsible for accidents as well as other technological malfunctions. By focusing on AIM which is a key element of PSM, it will be possible to gain a better understanding of one of the most significant and problematic sources of risk in process safety.

Research on prediction of rockburst microseismic parameters based on CNN-LSTM hybrid model

Time series prediction of parameters is a method of learning existing observational data and predicting its future evolution, which has been applied in many fields. In this paper, the key microseismic indexes are mined to describe the rockburst development, and a hybrid prediction model of CNN-LSTM neural network combining convolutional neural network (CNN) and long-short-term memory neural network (LSTM) is proposed. This model uses the good feature extraction capabilities of CNN and the special memory prediction function of LSTM neural network to achieve accurate prediction of microseismic indexes. The model can avoid the problem of determining index weights and is completely driven by data. It effectively combines qualitative and quantitative analysis to reduce the impact of human factors. The model is tested with the microseismic monitoring data of the E-han Expressway Grand Canyon Tunnel. The results show that when predicting cumulative energy, cumulative apparent volume, and energy index, the root mean square error (RMSE) of the CNN-LSTM hybrid model is reduced by 9.5%, 7.2%, and 8.3%, respectively, compared with the single LSTM neural network model. The model can effectively provide a scientific basis for the prediction of rockburst microseismic parameters in similar projects.

The effects of population aging, life expectancy, unemployment rate, population density, per capita GDP, urbanization on per capita carbon emissions

Human-induced carbon dioxide emissions (CO 2 ) is the main source of global warming. This work is aimed to systematically explore the effects of human factors on CO 2 . To this end, this paper investigates the effects of population aging, life expectancy, population density, unemployment rate, per capita GDP, urbanization on per capita CO 2 using linear panel data analysis and panel threshold regression approach for 154 countries. The results show that there is no threshold value between unemployment rate and per capita CO 2 , and between urbanization and per capita CO 2 , indicating that the effects of unemployment rate and urbanization on per capita CO 2 is linear. Urbanization contributes to the increase in per capita CO 2 , which is offset by the unemployment rate. In addition, there are threshold values between aging and per capita CO 2 , between life expectancy and per capita CO 2 , between population density and per capita CO 2 , and between per capita GDP and per capita CO 2 . This means the effects of aging, life expectancy, population density, per capita GDP on per capita CO 2 are nonlinear. The contribution effect of per capita GDP on per capita CO 2 decreases with the increase in per capita GDP. The inhibitory effects of population aging, life expectancy on per capita CO 2 increase with the increase in the degree of aging and life expectancy, respectively. Whereas the inhibitory effect of population density on per capita CO 2 decreases with increase in the population density. The empirical analysis of 154 countries further confirms the complexity and heterogeneity of the impact of human factors on carbon emissions.

Towards teleoperation performance and psychophysiological state assessment in the SIRIUS-19 analog campaign

Future lunar bases and long duration space travel have increased the scientific interest in the potentially negative effects that confinement and isolation can have on a crew’s interpersonal behavior, cognitive state and performance. Given the hazardousness of these missions, it seems crucial to analyze the impact of Human Factors on the daily parameters of operators in order to enhance both their safety and performance. In order to progress towards a more global assessment of a confined operator’s cognitive state we propose to analyze subjective and objective measures regarding a professional task. Hence, this study evaluates the correlation between the participants’ performance in a teleoperation task with their reported mood, motivation and their measured cardiac activity during a ground-based analog space mission. The facility considered was the NEK at IBMP in Moscow, Russia. Six participants (3 females; Mean Age=33.4, σ=6.656; 2 Americans and 4 Russians) were confined during 4 months in this facility. Over the period of confinement, they undertook seventeen teleoperation sessions. The teleoperation task consisted in the remote control of a digital rover that was used to collect a sample on the lunar surface. Completion time and accuracy were measured for each session to evaluate the participants’ performance. Training sessions were run before and after the mission. Also, the participants filled shortened versions of the PANAS and the IMI questionnaires to assess their motivation and mood before the task. Additionally, their cardiac activity was measured during the task. The main results are significant Spearman correlations between the reported feeling of confinement and task completion time (ρ=0.375,p<.01) and the reported feeling of confinement with the positive affect component of the mood (ρ=−0.547,p=<0.01). In addition to that, a general decrease of motivation was observed along the mission with the exception of a booster created by the Moon landing phase. The results confirm a link between confinement stressors and the crew’s performance during this professional task as found in other analog missions such as the Lunares and MDRS ones. The project is in continuous expansion, as more data are required to confirm the obtained results and to pursue their analysis. This work was intended to lay the foundations for a neuroergonomic approach of space operators’ assessment.

Conceptual Design of Mechatronics System Based on Intelligent Control

With the development of society, the research on product concept design has become more and more mature, and the conceptual design of mechatronics system has gradually become the key research and design direction in the field of mechatronics. The rapid development of modern industrial production not only provides strong support for the application and popularization of mechatronics system, but also promotes the overall improvement of industrial production efficiency and quality. Electromechanical integration is a comprehensive mechanical system in essence. Because the traditional industrial control technology can no longer meet the requirements of industrial production on the function of mechatronics system. Intensify the research and application of intelligent control technology, minimize the impact of human factors on industrial production, and promote the effective improvement of the operation performance of mechatronics system. This paper analyzes the application of intelligent control in mechatronics system, and discusses the conceptual design method of mechatronics system based on intelligent control.