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In the realms of operations management (OM) and supply chain management (SCM), the significance of the human factor (HF) is increasingly recognised as a pivotal determinant of corporate performance. This burgeoning interest aligns with the recognition that individual characteristics—spanning personality traits, attitudes, and soft skills—play a critical role in enhancing organisational outcomes. Despite growing scrutiny, the discourse is hampered by terminological ambiguity and the conflation of critical human-centric concepts within the OSCM context. Addressing this gap, our study embarks on a mission to dissect and delineate the nuanced distinctions among “soft skills”, “attitudes”, and “personality traits”. By proposing a clear and actionable taxonomy, this paper aims to facilitate the practical application and understanding of these terms within organisational settings. Leveraging the “Knowledge Development Process-Constructivist” (ProKnow-C), we conducted a systematic examination of the existing scientific literature to unearth and critically review pertinent bibliometric and content analyses. Our work not only illuminates the path for future research but also underscores the necessity of clarity and precision in the conceptualisation and application of human-factor considerations in OM and SCM.

Artificial intelligence aims to be the solution to multiple engineering problems by trying to emulate the human learning process. In this sense, maritime transport standards have clearly evolved, which are based on two principal pillars: the International Convention for the Safety of Life at Sea Convention (SOLAS) and the International Convention for the Prevention of Pollution from Ships (MARPOL). Based on a formal safety assessment research process, these pillars try to solve most of the maritime transport accidents, which, in their final steps, are associated with human factors. In this research, an original methodology employing a deep learning process for image recognition during mooring line operation, a dangerous process on ships, is developed. The main results indicate that the proposed method is an excellent tool for advising ship officers on watch and, consequently, provides a new way to prevent human factors onboard from causing accidents, which in the future must be considered in international standards.

Academic research on the human element in phishing attacks is essential for developing effective prevention and detection strategies and guiding policymakers to protect individuals and organizations from cyber threats. This bibliometric study offers a comprehensive overview of international research on phishing and human factors from 2006 to 2024. Analysing 308 articles from the Web of Science database, a significant increase in publications since 2015 was identified, highlighting the growing importance of this field. The study revealed influential authors such as Vishwanath and Rao, leading journals like Computers & Security, and key contributing institutions including Carnegie Mellon University. The analysis uncovered strong collaborations between institutions and countries, with the USA being the most prolific and collaborative. Emerging research themes focus on psychological factors influencing phishing susceptibility, user-centric security measures, and the integration of technological solutions with human behaviour insights. The findings highlight the need for increased collaboration between academia and non-academic organizations and the exploration of industry-specific challenges. These insights offer valuable guidance for researchers, practitioners, and policymakers to advance their understanding of phishing attacks, human factors, and resource allocation in this critical aspect of digitalisation, which continues to have significant impacts across business and society at large.

In a labor-intensive cell production system, it is important to train operators effectively because their skills are essential for productivity. Our previous study proposed a method to classify these skills according to a “skill index” based on the time required for each task and the allocated operators based on this method. However, in actual workplaces, it is assumed that operators accumulate fatigue due to the repetition of work, which affects the assembly time. In this study, we propose an operator allocation method that considers the effect of fatigue and verify its effectiveness compared with the results of the previous study by computer experiments. In addition, an assembly experiment with a toy is conducted based on the operator allocation method derived from the computer experiments. The experimental results show that the proposed method is effective and indicate that appropriate parameter setting is crucial when applying it in real-world scenarios.

Artificial intelligence seems to be a new point of view to classical problems that, in the past, could not be understood in depth, leaving certain gaps in each knowledge area. As an example of this, maritime accidents are one of the most recognised international problems, with clear environmental and human life consequences. From the beginning, statistical studies have shown that not only the typical sampled variables must be considered but the accidents are related to human factors that, at the same time, are related to some variables like fatigue that cannot be easily sampled. In this research work, the use of machine learning algorithms on over 300 maritime accidents is proposed to identify the relationship between human factors and the main variables. The results showed that compliance with the minimum crew members and ship length are the two most relevant variables related to each accident for the Spanish Search and Rescue (SAR) region, as well as the characteristics of the ships. These accidents could be understood as three main groups of accidents related to the general tendency to not meet the minimum number of crew members and its difference in the year of construction of the ship. Finally, it was possible to use neural networks to model accidents with sufficient accuracy (determination factor higher than 0.60), which is particularly interesting in the context of a control system for maritime transport.

The role of air traffic controllers is to direct and manage highly dynamic flights. Their work requires both efficiency and accuracy. Previous studies have shown that fatigue in air traffic controllers can impair their work ability and even threaten flight safety, which makes it necessary to carry out research into how to optimally detect fatigue in controllers. Compared with single-modality fatigue detection methods, multi-modal detection methods can fully utilize the complementarity between diverse types of information. Considering the negative impacts of contact-based fatigue detection methods on the work performed by air traffic controllers, this paper proposes a novel AF dual-stream convolutional neural network (CNN) architecture that simultaneously extracts controller radio telephony fatigue features and facial fatigue features and performs two-class feature-fusion discrimination. This study designed two independent convolutional processes for facial images and radio telephony data and performed feature-level fusion of the extracted radio telephony and facial image features in the fully connected layer, with the fused features transmitted to the classifier for fatigue state discrimination. The experimental results show that the detection accuracy of radio telephony features under a single modality was 62.88%, the detection accuracy of facial images was 96.0%, and the detection accuracy of the proposed AF dual-stream CNN network architecture reached 98.03% and also converged faster. In summary, a dual-stream network architecture based on facial data and radio telephony data is proposed for fatigue detection that is faster and more accurate than the other methods assessed in this study.

The development of unmanned aerial vehicles (UAVs) and the increasing air traffic of these devices make it necessary to pay attention to the issue of the human factor in UAV operations. In this article, tests were conducted in real conditions on the unmanned aerial vehicle operator’s (UAVO) psychophysical state during training. The parameters of the human cardiovascular system, and more specifically the heart rate variability (HRV), were used to conduct research and analysis. The purpose of this research is to elaborate the typical HRV parameters for student operators during UAVO training. These reference values could be used during UAVO training to assess candidates’ psychophysical state objectively and could allow for the monitoring of operators’ state and management of their cognitive load. Monitoring operators’ state may have a positive impact on increasing training effectiveness. Research confirmed the thesis that HRV parameters are significantly different during performed tasks with cognitive load and can be used to assess candidates’ psychophysical state objectively. This can help flight instructors perform student assessment, meaning that they would not have to rely only on their subjective feelings.

This paper presents the system development of a co-simulation platform aimed at studying driving behavior with multiple participants. The objective of this study was to create an immersive and interactive environment where different driving scenarios could be simulated and driver behavior could be recorded and analyzed. The platform integrated the Unity game engine with the VISSIM microscopic traffic simulator to create a hybrid simulation environment that combined the advantages of both tools. A virtual reality massive multiplayer online (VRMMO) module was developed to capture the interactions of the participants during the simulation experiments. The external control devices of this co-simulation platform were calibrated using the empirical data of a Controller Area Network (CAN-BUS) from actual driving behaviors. The main contributions of this study are the demonstration of the Unity–VISSIM co-simulation platform in simulating interactive driver behavior and the potential for its use in various research areas, such as intelligent transportation systems, human factors, driving education, and traffic safety analyses. The platform could be a valuable tool for evaluating the effectiveness of collective intelligence countermeasures in improving traffic systems, with relatively lower costs and risks.

The software development process (SDP) is complex because it uses specific knowledge, skills, tools, and techniques to deliver value to people, and the user’s needs are always growing and becoming more complex. Practitioners and academics have proposed different approaches for improving the successful implementation of the SDP. This remains a non-trivial task, and the number of successful software development projects increases only slightly each year. This paper proposes a new approach for adapting the classical SDP to a dynamic, knowledge-intensive, and context-dependent model by applying the case-handling and fuzzy inference approaches for SDP modeling and simulation. The advantages and novelties of this approach consist in: SDP dynamicity modeling using the case-handling approach; the inclusion of human factor uncertainty into the execution of SDP activities through fuzzification; and agent-based simulation, which allows for the inclusion of human actors in SDP simulations. Modeling and simulation of Agile SDPs were carried out using this prototype. Experiments show the correspondence of the proposed approach to the needs of dynamically changing and human-factor-dependent SDPs, as well as the possibilities for modeling and simulating the SDP, predicting SDP execution, and observing possible risks.

The ecosystem approach to fisheries management (EAFM) is considered one of the key management approaches for addressing global resource decline and promoting the health and resilience of ecosystems. This paper explores how the Western and Central Pacific Fisheries Commission (WCPFC), which manages tuna fisheries, has incorporated the ecosystem approach into its management and decision-making system. This study finds that (1) the WCPFC lacks incentives to adopt EAFM as a whole due to its management priorities on target species and some key bycatch species; (2) inadequate scientific information on associated species and the environment hinders ecosystem risk assessments, leading to delays in EAFM-related decisions; and (3) the organization has given little consideration to human factors. The authors suggest developing an EAFM vision with a clear roadmap to guide the implementation of EAFM and applying area-based management tools in cases where there is limited data and scientific information. The proposed coordination mechanism aims to address growing concerns about labor issues by involving multiple stakeholders in the development of a management measure on labor standards.

This paper investigates the usability of touch screens used in mass production road vehicles. Our goal is to provide a detailed comparison of conventional physical buttons and capacitive touch screens taking the human factor into account. The pilot test focuses on a specific Non-driving Related Task (NDRT): the control of the on-board climate system using a touch screen panel versus rotating knobs and push buttons. Psychological parameters, functionality, usability and, the ergonomics of In-Vehicle Information Systems (IVIS) were evaluated using a specific questionnaire, a system usability scale (SUS), workload assessment (NASA-TLX), and a physiological sensor system. The measurements are based on a wearable eye-tracker that provides fixation points of the driver’s gaze in order to detect distraction. The closed road used for the naturalistic driving study was provided by the ZalaZONE Test Track, Zalaegerszeg, Hungary. Objective and subjective results of the pilot study indicate that the control of touch screen panels causes higher visual, manual, and cognitive distraction than the use of physical buttons. The statistical analysis demonstrated that conventional techniques need to be complemented in order to better represent human behavior differences.

Human-related issues have become a popular topic in maritime safety research, with an increasing number of relevant research articles being published annually. However, a persistent problem in this field is that three terms, namely “human element”, “human factor”, and “human error” are used interchangeably in the literature. This issue poses questions regarding the characteristics of their usage; do these three terms have the same meaning? Herein, we conducted systematic research on the three terms by analyzing official information and published research using a collecting–classifying–summarizing policy. The results show that “human error” is easier to identify than “human element” and “human factor”, while the latter two terms have intersecting contents. These contents prompt the user to decide which term to choose depending on the situation. Herein, we aim to help scholars accurately distinguish these terms.

The human–machine relationship was dictated by human needs and what technology was available at the time. Changes within this relationship are illustrated by successive industrial revolutions as well as changes in manufacturing paradigms. The change in the relationship occurred in line with advances in technology. Machines in each successive century have gained new functions, capabilities, and even abilities that are only appropriate for humans—vision, inference, or classification. Therefore, the human–machine relationship is evolving, but the question is what the perspective of these changes is and what developmental path accompanies them. This question represents a research gap that the following article aims to fill. The article aims to identify the status of change and to indicate the direction of change in the human–machine relationship. Within the framework of the article, a literature review has been carried out on the issue of the human–machine relationship from the perspective of Industry 5.0. The fifth industrial revolution is restoring the importance of the human aspect in production, and this is in addition to the developments in the field of technology developed within Industry 4.0. Therefore, a broad spectrum of publications has been analyzed within the framework of this paper, considering both specialist articles and review articles presenting the overall issue under consideration. To demonstrate the relationships between the issues that formed the basis for the formulation of the development path.

Metasilicate-rich groundwater could meet the high demand of the international community for high-quality water. In order to comprehensively analyze the genetic mechanism of metasilicate-rich groundwater, and help human communities effectively exploit and utilize high-quality water resources, taking the Ji’nan rock mass area as an example, this study carried out systematic research on the spatial distribution and genetic mechanism of the metasilicate-rich groundwater Based on the regional hydrogeological conditions, the influencing factors on the spatial distribution characteristics of the metasilicate-rich groundwater in the study area were systematically sorted out by means of petrogeochemistry, hydrochemistry, and chemical weathering index analysis, and the accumulation mechanism of the metasilicate-rich groundwater was discussed from the perspective of water-rock interaction. The results show that: (1) On the northwest side and part of the northeast side of Ji’nan rock mass, the metasilicate content of the groundwater samples exceeded 25 mg/L; the metasilicate content on the south, west, and east sides were relatively low; (2) Ji’nan rock mass is mainly composed of gabbro easily weathered, with high SiO2 content and high weathering degree showing obvious characteristics of desilication. FeS2 developing along the contact zones between the rock mass and surrounding rocks was easily oxidized to form H2SO4, which enhanced the solubility of silicate minerals in the groundwater. Ji’nan rock mass was located in the low-lying position of the monocline structure, which presented better water conservation and recharges conditions. The above factors resulted in the metasilicate-rich groundwater accumulating in the area of Ji’nan rock mass and showed different spatial distribution characteristics.

Background: The impact of technologies on workers has been a recurring theme in occupational health psychology. In particular, the sudden digital transformation of the last two decades, accelerated by the COVID-19 pandemic, has stressed the urgency to investigate new ways of working that are characterized by flexibility and a constant increase of autonomy. In this perspective, this study aims to investigate the state of the art of the innovation process in Italian factories, explore whether and how digitalization can be seen as an opportunity, and imagine a new way of working characterized by adaptability, resilience, and openness to change. Methods: Thirty in-depth interviews of Italian experts in HR management were collected and analyzed using a mix-method approach. Results: The findings underline the Italian HR experts’ perceptions of the risks associated with rapid changes required by technological progress in terms of workers’ wellbeing and satisfaction and suggest how important it is that organizations rapidly set up learning and training programs to guide workers to the acquisition of new skills required by Industry 4.0. Conclusions: Future workplaces will be characterized by extreme versatility, which requires workers to increasingly have both technical and soft skills as well as the ability to collaborate and build functional relationships.

As the development of science and technology has reached the point where the desire to travel to Mars has become a tangible reality, the physical limits of human movement are also part of the systematic research based on the space environment. The critical issues of radiation, altered gravity, hostile environment, isolation or confinement, and distance from Earth (travel time) are the five major hazards for astronauts during spaceflight. The prepared technology of space medicine is significant for physical health. However, how would the lone space exploration (2.5 to three years) affect the mental conditions of the astronauts? How can the space community keep astronauts safe from psychological obstacles, such as depression, conflict, resentment, bipolar disorder, obsession, and addiction? This paper explores the environmental factors of a healthy lifestyle (well-being) of the spacecraft. It presumes that a successful mission often relies on positive interactions between crew members and between the crew and ground personnel. The paper considers the mental sustainability from stress, emotions, and perceptions to improve human tonicity or vitality and argues a new mental strategy in space exploration policy that the role of an astronautical religion beyond human intelligence and artificial intelligence (AI) could be a psychiatric anchor (in a moral, ethical, and self-sacrificial context) of each astronaut and leadership of the space team as a psychoanalytical countermeasure, along with physical exercise, hobbies, pets, and virtual and augmented reality (VR/AR) entertainment, especially in the case of unexpected crises where science and technology fail its general function.

The specific professions of aviation personnel include the professions of the pilot and air traffic controller. These occupations are specific in that while performing their work, they must be able to simultaneously operate the devices in the handling area and in the pedipulation area, supplemented by acoustic sensations in the form of correspondence between flying and ground stations. The performance requirements of pilots and air traffic controllers place high demands on their health, psychological condition, attention, and concentration, due to being in constant pursuit of minimization of erroneous decisions, otherwise defined as the human factor in aviation. This article is focused on the development and testing of a technical device for measuring the relative error rate of students in multitasking tasks in preparation for employment. The main result is a designed technical device with hardware (HW) and software (SW) parts. An experimental method was used to measure the qualitative and quantitative performance indicators of the test subjects. The results of the experiment were observed and evaluated based on the analytical-synthetic method based on critical thinking. By comparing and abstracting the measured data, the reference values of the performance indicators of the tested subjects were determined. The selection of the final sample of subjects consisted of two phases. In the first phase, questionnaires were evaluated, and in the second phase, reaction time measurements during multitasking tasks using technical equipment were evaluated. Based on the measurements, an error ratio was defined, which could be graphically represented. The testing proved the full functionality of the designed technical equipment for these purposes in aviation education.

Improper design of the geometric elements and facilities of bikeway systems could endanger cyclists’ safety and comfort, resulting in an increased risk of bicycle accidents; such accidents sometimes have severe consequences, namely casualties. The method of expression for cyclists’ safety and comfort and the question of how the correlation of these factors with bikeway characteristics—such as the design of geometry and facilities—can be quantitatively described are the key problems facing a reduction in accident risk. Cycling workload can be employed to assess cyclists’ safety and comfort. However, there has been little quantitative expression research on this topic, with no clear definition of cycling workload. The quantitative expression of cycling workload is important for developing guidance for the safe design and operational management of bikeways; this is necessary for controlling conditions that might induce overworking and discomfort among users. In this paper, the concept of cycling workload is clearly defined based on cyclists’ comfort and safety formation mechanisms. Through a literature review and a comparative analysis, it is inferred that heart rate variability (HRV) can be used as a quantitative measure and the low-frequency–high-frequency ratio (LF/HF) can be used as a physiological signal to quantify cycling workload. A subjective scale was found to effectively express cyclists’ feelings of safety and comfort, with the performance assessed according to a human factor engineering research paradigm that classified cycling status into three qualitative levels—comfortable; a little stressful; and stressful. In order to form various cycling workload states and to obtain the relationship between LF/HF data and various bikeway characteristics, we designed a field cycling experiment. This was conducted by 24 participants who wore a physiological measuring apparatus under three different bikeway characteristic scenario types including variations in cycling width, direction, and bikeway edges at four cycling speeds in the 10–25 km/h range. Statistical analysis was used to address the collected LF/HF values and the subjective scale results, and a quantitative model for assessing cycling workload was established. By adopting a classification and regression tree (CART) algorithm as a data-mining method, the classification threshold values (ΔHRV) of three cycling workload levels were obtained: 19 indicated a level between comfortable and a little stressful; and 79 indicated a level between a little stressful and stressful.

The human factor is an essential aspect of the operability and safety of many technical systems. This paper focuses on the analysis of human errors in the railway domain. The subject of human reliability analysis is the behavior of operators of station-signaling systems responsible for rail traffic management. We use a technique for human-error rate prediction as the 1st generation human reliability analysis to deal with task analyses, error identification and representation, and the quantification of human error probabilities. The paper contributes to the comparison of three technologically different railway traffic control systems, having different degrees of automation—from the manually operated (electro-mechanical), through semi-automated (relay-based) to almost fully automated (computer-based) station-signaling systems. We observe the frequency of individual operations performed in time intervals and calculate human error probability and human success probability values for each operation. Thus, we can analyze human reliability and compare the workload of operators working with control systems of different degrees of automation.

The overwhelming majority of methodologies for the flexible flow shop scheduling problem proposed so far have a common feature, which is the assumption of constant time and cost for the execution of individual technological operations (ignoring an optimal selecting combination of individual employees and tools). Even if the existence of the influence of the selection of production resources on the course of operations is signaled in the available works, the research so far has not focused on the measurable effect of such a solution that takes into account this phenomenon in scheduling. The proposed production scheduling methodology, including the influence of employees and tools, turned out to be more effective in terms of minimizing the maximum completion time and the cost of the production process compared to existing solutions. The efficiency of the new proposed scheduling methodology was assessed using examples of four technological processes. The research was carried out on the basis of a dedicated adaptation of the Monte Carlo optimization algorithm in order to determine the actual effect of the new solution. The algorithm itself is not an integral part of the proposed solution, and the universal methodology developed will ensure significant profit for any optimization algorithm correctly implemented.

Historically, researchers and managers have often failed to consider organizations as a sum of functions leading to a set of capabilities that produce a product that can serve society’s needs. Furthermore, functions have increased with the development of industrial revolutions, however, many manufacturing organizations have not realized their full potential. As a result, many industrial organizations do not know why, where, and when the existing functions and projects for implementing new functions fail where tactical and strategic functions of a manufacturing organization are commonly over-seen. Thus, the aim of this research was to propose a holistic approach for manufacturing organizations in order to model their functions enabling the assessment, design, management, and control of operations and performance as well as to identify improvement potentials. For this purpose, a conceptual model was developed based on the evolution of functions along with the industrial revolutions. Moreover, using the conceptual model, manufacturing organizations can be modeled, considering common organizational functions in the respective areas of production, maintenance, and quality, etc., in the three planning horizons—strategic, tactical, and operative. As a result, the model serves as a basis for the integral management and control of manufacturing organizations. Moreover, it can be also used as a basis framework for a digital twin model for organizations. Thus, a system dynamics simulation model based on the conceptual model was developed for a generic organization. The goal of the simulation model is to provide an exemplary digital model of a manufacturing organization in which the different functions are applied with different methods, systems, and/or individuals along the development phases.

Innovative sociotechnical change is forthcoming because of autonomous driving; however, only a few studies have focused on the acceptance of this technology, which is not up to social expectation. In this study, we present and validate a research framework on the basis of the Kano model to identify the effective acceptance elements for autonomous driving technology. By collecting and analyzing the survey data of 187 people, it was confirmed that the elements of acceptance for autonomous driving technology can be classified according to the Kano attributes. This means that these acceptance elements should be resolved with priority in order to secure the acceptance. Legal policies and ethical guidelines are identified as top priorities for ensuring the acceptance of autonomous driving. Traffic congestion, situational awareness, malfunction prevention, and fatigue/stress relief must be addressed as utmost priorities. The framework and results from this study can be used to establish efficient strategies for developing autonomous driving technologies according to the user requirement levels.

Human factor plays an important role in the implementation of the fourth industrial revolution (Industry 4.0). The concept of Industry 4.0 is poorly researched, particularly the social aspect. The authors have conducted a study to determine the level of preparation of employees for the introduction of technological changes. This study involved conducting a survey on a sample of 453 employees based in four organizations within the automotive industry. The respondents were thereafter divided into groups based on age and positions held. The employees’ knowledge of the Industry 4.0 technology was examined, and their openness to change and readiness to increase competence was determined. A causal relationship was found between knowledge and trust in technology. Employees’ fears associated with production automation were discussed. A group of production workers was found to be the least prepared to implement technology changes. Actions to improve the situation and potential consequences of inadequate implementation of this technology were indicated. The findings from this study were compared with the results obtained in other countries. Special attention was paid to the limitations of this study, and we identified the necessity to conduct international comparative research on small, medium and large enterprises.

This study investigated the nature and causes of unsafe driving behavior at roundabouts through an on-road study. Four urban double-lane roundabouts with different layouts were selected for an on-road study. Sixty-six drivers (41 males and 25 females) aged 18–65 years took part in the study. Unsafe behaviors observed during the in situ survey were divided into three different categories: entry unsafe behaviors, circulation unsafe behaviors, and exit unsafe behaviors. Three chi-square automatic interaction detection (CHAID) analyses were developed in order to analyze the influence of roundabout characteristics and maneuvers on unsafe behaviors at double-lane roundabouts. The results confirmed the awareness that double-lane roundabouts are unsafe and inadvisable. More than half of unsafe driving behaviors were found to be entry unsafe behaviors. Moreover, the entry radius was found to be the geometric variable most influencing unsafe driving behaviors.

A common interest of all shipping industry stakeholders is safe and accident-free shipping. To reach that goal, one of the most important actions that can be done is to analyze previous marine accidents. It means finding causes of accidents and, based on the analysis results, implementing effective corrective measures that can help reduce such undesired events in the future and improve safety efforts in shipping. Since it is widely accepted that human error accounts for 80–85% of all marine accidents, the research was focused on the human factor analysis in marine accidents. In this paper, 135 marine accident reports recorded in the UK Marine Accident Investigation Branch (MAIB) database from 2010 to 2019 were analyzed. The analysis aimed to categorize causal factors and discover the ones that are the most common. The Human Factor Analysis and Classification System for Maritime Accidents (HFACS-MA) method was used to be able to do so. Furthermore, multiple linear regression was used to determine the relationship between the number of accidents and the most common HFACS-MA causal factors. The research revealed that the causes of marine accidents are primarily dependent on two human factor categories and confirmed that by influencing those human factors categories, the number of marine accidents could be reduced and shipping safety improved in general.

With numerous efforts undertaken by both industry and academia to develop and implement autonomous merchant vessels, their safety remains an utmost priority. One of the modes of their operation which is expected to be used is a remote control. Therein, some, if not all, decisions will be made remotely by human operators and executed locally by a vessel control system. This arrangement incorporates a possibility of a human factor occurrence. To this end, a variety of factors are known in the literature along with a complex network of mutual relationships between them. In order to study their potential influence on the safety of remotely-controlled merchant vessels, an expert study has been conducted using the Human Factors Analysis and Classification System-Maritime Accidents (HFACS–MA) framework. The results indicate that the most relevant for the safety of this prospective system is to ensure that known problems are properly and timely rectified and that remote operators maintain their psycho- and physiological conditions. The experts elicited have also assigned higher significance to the causal factors of active failures than latent failures, thus indicating a general belief that operators’ actions represent the final and the most important barrier against accident occurrence.

This paper is intended to give an overview of the experiments to evaluate the cognitive load of the officer on watch (OOW) during a collision avoidance maneuver in a full-mission simulator. The main goal is to investigate the possibilities of recording the biometric parameters of an OOW during a simulated collision avoidance maneuver. Potentially dangerous navigation errors known as human erroneous action (HEA) are induced by excessive cognitive load. Despite modern navigational aids on the ship’s bridge, investigators of maritime incidents typically link the reason for incidents at sea with human factors, including high cognitive load. During the experimental tasks on the bridge, the biometric parameters of the OOW are recorded. Statistical tools are used to visualize the data and evaluate the cognitive load of the OOW. Biometric peaks of the OOW typically occur either during the collision avoidance maneuver or when the OOW has been exposed to disturbing factors that increase reaction time and cause potentially dangerous navigation. Assessing the cognitive load of OOWs in the simulator is challenging for several reasons: e.g., the environmental conditions of the simulator, the type of task to be simulated, and even the type of sensor used. After careful study of the available literature, an original experimental design using non-invasive biometric sensors is proposed.

A password is considered to be the first line of defence in protecting online accounts, but there are problems when people handle their own passwords, for example, password reuse and difficult to memorize. Password managers appear to be a promising solution to help people handle their passwords. However, there is low adoption of password managers, even though they are widely available, and there are fewer studies on users of password managers. Therefore, the issues that cause people not to use password managers must be investigated and, more generally, what users think about them and the user interfaces of password managers. In this paper, we report three studies that we conducted: on user interfaces and the functions of three password managers; a usability test and an interview study; and an online questionnaire study about users and non-users of password managers, which also compares experts and non-experts regarding their use (or non-use) of password managers. Our findings show that usability is not a major problem, rather lack of trust and transparency are the main reasons for the low adoption of password managers. Users of password managers have trust and security concerns, while there are a few issues with the user interfaces and functions of password managers.

This paper presents an approach to conduct risk assessments of complex incentive systems, using a case study of the normative Peer Review Process (PRP). This research centers on appliances and adaptations of the Conflicting Incentives Risk Analysis (CIRA). First as an approach to Root Cause Analysis of a known incident, and then for a full assessment of the incentives in the PRP together with possible risk treatments. CIRA uses an alternative notion of risk, where risk modeling is in terms of conflicting incentives between the risk owner and the stakeholders concerning the execution of actions. Compared to traditional risk assessment approaches, CIRA provides an insight into the underlying incentives behind a risk, and not just the technical vulnerability, likelihood and consequence. The main contributions of this work are an approach to obtain insight into incentives as root causes, and an approach to detecting and analyzing risks from incentives in the normative PRP. This paper also discusses risk treatments in terms of incentives to make the PRP more robust, together with a discussion of how to approach risk analysis of incentives.

Modern general system theory proposed a holistic integrative approach based on input-state-output dynamics as opposed to the traditional reductionist detail based approach. Information complexity and uncertainty required a fuzzy system theory, based on fuzzy sets and fuzzy logic. While successful in dealing with analysis, synthesis and control of technical engineering systems, general system theory and fuzzy system theory could not fully deal with humanistic and human-like intelligent systems which combine technical engineering components with human or human-like components characterized by their cognitive, emotional/motivational and behavioral/action levels of operation. Such humanistic systems are essential in artificial intelligence, cognitive and behavioral science applications, organization management and social systems, man-machine systems or human factor systems, behavioral knowledge based economics and finance applications. We are introducing here a “postmodern fuzzy system theory” for controlled state dynamics and output fuzzy systems and fuzzy rule based systems using our earlier postmodern fuzzy set theory and a Kabbalah possible worlds model of modal logic and semantics type. In order to create a postmodern fuzzy system theory, we “deconstruct” a fuzzy system in order to incorporate in it the cognitive, emotional and behavioral actions and expressions levels characteristic for humanistic systems. Kabbalah offers a structural, fractal and hierarchic model for integrating cognition, emotions and behavior. We obtain a canonic deconstruction for a fuzzy system into its cognitive, emotional and behavioral fuzzy subsystems.

The aim of this study was to predict chronic diseases in individual patients using a character-recurrent neural network (Char-RNN), which is a deep learning model that treats data in each class as a word when a large portion of its input values is missing. An advantage of Char-RNN is that it does not require any additional imputation method because it implicitly infers missing values considering the relationship with nearby data points. We applied Char-RNN to classify cases in the Korea National Health and Nutrition Examination Survey (KNHANES) VI as normal status and five chronic diseases: hypertension, stroke, angina pectoris, myocardial infarction, and diabetes mellitus. We also employed a multilayer perceptron network for the same task for comparison. The results show higher accuracy for Char-RNN than for the conventional multilayer perceptron model. Char-RNN showed remarkable performance in finding patients with hypertension and stroke. The present study utilized the KNHANES VI data to demonstrate a practical approach to predicting and managing chronic diseases with partially observed information.

Many causal factors to marine traffic accidents (MTAs) influence each other and have associated effects. It is necessary to quantify the correlation path mode of these factors to improve accident prevention measures and their effects. In the application of human factors to accident mechanisms, the complex structural chains on causes to MTA systems were analyzed by combining the human failure analysis and classification system (HFACS) with theoretical structural equation modeling (SEM). First, the accident causation model was established as a human error analysis classification in sight of a MTA, and the constituent elements of the causes of the accident were conducted. Second, a hypothetical model of human factors classification was proposed by applying the practice of the structural model. Third, with the data resources from ship accident cases, this hypothetical model was discussed and simulated, and as a result, the relationship path dependency mode between the latent independent variable of the accident was quantitatively analyzed based on the observed dependent variable of human behavior. Application examples show that relationships in the HFACS are verified and in line with the path developing mode, and resource management factors have a pronounced influence and a strong relevance to the causal chain of the accidents. Appropriate algorithms for the theoretical model can be used to numerically understand the safety performance of marine traffic systems under different parameters through mathematical analysis. Hierarchical assumptions in the HFACS model are quantitatively verified.

(1) Objective: This study was conducted with the objective of characterizing the variability of a force on a simulated skin surface using the Semmes⁻Weinstein monofilament test (SWMT). (2) Research Design and Methods: Two distinct experiments were performed to determine the effects of human hand motion variability on the monofilament buckling force, and to determine the monofilament’s mechanical properties using a positioning stage. (3) Results: In manual operation (by human hand motion), the buckling force over the ten compressions decreased by over 10%, and the human hand motion variations during the SWMT may have impacted the buckling force. When the SWMT was performed under manual control, the buckling force was closely correlated with the number of compressions. In automated operation (by positioning stage), the buckling force was affected not only by the number of compressions but also by both the velocity and the contact angle between the monofilament and the skin surface. (4) Conclusions: The buckling force decreased in ten successive compressions, independent of the hand motion. Hence, medical staff need to consider not only the operator’s hand motion but also the effect of repeated trials.

The eye blink rate, a major human physiological response, directly affects ocular diseases, such as keratitis and dry eye syndrome. It has been shown that the eye blink rate in normal eyes has a certain frequency for individuals, from 6–30 times/min. It was suggested in a previous study that the eye blink rate can be decreased during the viewing of high-intensity and realistic content. Therefore, in this paper, we examine the change of the eye blink rate during the HMD (head-mounted display) viewing of VR (virtual reality) contents; accordingly, we propose an algorithm to measure the eye blink rate as well as compare and analyze this rate in three different environments (natural, monitor, and HMD). We confirmed that IPD (interpupillary distance) and phoria affected the eye blink rate in each environment. In this experiment, 21 subjects (28.38 ± 6.87 years) were selected, and a paired t-test was performed for changes in the eye blink rate over 1 min for each environment. The IPD and phoria effects on the eye blink rate were confirmed using the Spearman’s correlation coefficient. In this experiment, the eye blink rate was decreased in the monitor and HMD environments compared with the natural environment, while that in the HMD environment was decreased compared with the monitor environment. The results of the correlation analysis of far IPD and the eye blink rate show no statistical significance or correlation. The correlation analysis of near IPD and the eye blink rate showed a strong positive correlation of the eye blink rate in the monitor environment. The correlation analysis of distance phoria and the eye blink rate showed a strong negative correlation of the eye blink rate in the HMD environment. The correlation analysis of near-field phoria and the eye blink rate showed a strong negative correlation of the eye blink rate in the HMD environment. It is expected that the results of this study will be used as a VR-viewing recommendation.

Cybersecurity (CS) is a contemporary field for research and applied study of a range of aspects from across multiple disciplines. A cybersecurity expert has an in-depth knowledge of technology but is often also recognized for the ability to view technology in a non-standard way. This paper explores how CS specialists are both a combination of professional computing-based skills and genetically encoded traits. Almost every human behavioral trait is a result of many genome variants in action altogether with environmental factors. The review focuses on contextualizing the behavior genetics aspects in the application of cybersecurity. It reconsiders methods that help to identify aspects of human behavior from the genetic information. And stress is an illustrative factor to start the discussion within the community on what methodology should be used in an ethical way to approach those questions. CS positions are considered stressful due to the complexity of the domain and the social impact it can have in cases of failure. An individual risk profile could be created combining known genome variants linked to a trait of particular behavior using a special biostatistical approach such as a polygenic score. These revised advancements bring challenging possibilities in the applications of human behavior genetics and CS.