A pore-scale study of fracture sealing through enzymatically-induced carbonate precipitation (EICP) method demonstrates its potential for CO2 storage management.

Geological fractures are mechanical breaks in subsurface rock volumes that provide important subsurface flow pathways. However, the presence of fractures can cause unwanted challenges, such as gas leakage through fractured caprocks, which must be addressed. In this study, the dynamics of enzymatically induced carbonate precipitation in rock fractures and their subsequent influence on CO2 leakage were investigated from a pore-scale perspective for the first time. This was achieved through real-time monitoring of the injection of the solution into a rock-microfluidic flow cell using optical and scanning electron microscopy. It was revealed that the main growth dynamics occur during the first three injection cycles, with growth continuing until the fracture aperture is fully closed in the 6th cycle. Based on the flow simulation, a significant reduction of up to 25% in the CO2 conductivity of the original fracture is expected even after the first treatment cycle. Future studies are suggested to explore different resolutions, testing conditions, and to conduct 3-dimensional investigations of the growth dynamics.

Resilience, job satisfaction, occupational stress, and occupational accidents among healthcare professionals: A Bayesian network analysis.

BACKGROUND: Occupational accidents remain a critical challenge for healthcare professionals. OBJECTIVE: In the present study, using the Bayesian network (BN) approach association among resilience, job satisfaction, stress, and occupational accidents among healthcare professionals is examined. METHODS: Data was gathered using several valid questionnaires. The BN approach was utilized to analyze the r5/31/2024ionships between the variables of the current study. The performance of BN analysis was evaluated using related indexes. RESULTS: In total, 300 healthcare professionals participated in this study. Results showed that almost 23% of healthcare professionals had experienced occupational accidents. Results of the sensitivity analysis demonstrated that job satisfaction had the most significant influence on occupational accidents in healthcare settings. The belief updating analysis results showed that by increasing job satisfaction and decreasing stress of healthcare professionals the occurrence of occupational accidents decreased 9.8% and 6.4%, respectively. Moreover, decreasing the stress of healthcare professionals can lead to an increase in the level of job satisfaction. Evaluation indexes showed that the performance of the developed BN was acceptable (error rate: 16.09). CONCLUSION: The Findings reveal that both job satisfaction and stress had a significant influence on occupational accidents in healthcare professionals. Moreover, by influencing job satisfaction and stress, resilience can indirectly affect occupational accidents.

Applications, Shortcomings, and New Advances of Job Safety Analysis (JSA): Findings from a Systematic Review.

Background: Job safety analysis (JSA) is a popular technique for hazard identification and risk assessment in workplaces that has been applied across a wide range of industries. This systematic review was conducted to answer four main questions regarding JSA: (1) which sectors and areas have used JSA? (2) What has been the aim of employing JSA? (3) What are the shortcomings of JSA? (4) What are the new advances in the field of JSA? Methods: Three main international databases were searched: SCOPUS, Web of Science, and PubMed. After screening and eligibility assessment, 49 articles were included. Results: Construction industries have used JSA the most, followed by process industries and healthcare settings. Hazard identification is the main aim of JSA, but it has been used for other purposes as well. Being time-consuming, the lack of an initial list of hazards, the lack of a universal risk assessment method, ignoring hazards from the surrounding activities, ambiguities regarding the team implementing JSA, and ignorance of the hierarchy of controls were the main shortcomings of JSA based on previous studies. Conclusion: In recent years, there have been interesting advances in JSA making attempts to solve shortcomings of the technique. A seven-step JSA was recommended to cover most shortcomings reported by studies.

Density-dependent relationship between changes in ultrasonic wave velocities, effective stress, and petrophysical-elastic properties of sandstone.

The present study aims to reveal the importance of density as a moderator variable in interpretation of possible relationships between variations in compressional and shear wave velocities (ΔVp and ΔVs), effective stress, and rock's petrophysical and elastic properties. Towards this end, fourteen subsurface sandstone samples were collected and analyzed through the measurement of ultrasonic wave velocities at standard and reservoir conditions within a Triaxial-testing cell. The results were interpreted for two groups, i.e., low density (LD) and high density (HD) samples, where greater ΔVp and ΔVs were observed for the HD group samples that have similar average porosity and permeability compared to samples from LD group. Effective stress exhibits better fit with ΔVp and ΔVs for the LD compared to HD group samples. Density was revealed to be well fitted with ΔVp of LD and ΔVs of HD samples. Porosity exhibits good fit with ΔVs of LD and permeability exhibits good fit with ΔVp of both LD and HD groups. Also, variations in estimated elastic limit (ΔEd) is well matched with ΔVs, while changes in estimated Poisson's ratio (Δν) manifests a good fitting with ΔVp. Finally, variation in deviatoric stresses from triaxial tests is in a good agreement with ΔVp. The results of this study provide convenient insights for conversion of wave velocities and elastic properties between standard and reservoir conditions.

A fuzzy Bayesian network DEMATEL model for predicting safety behavior.

Objectives. Safety behavior significantly affects safety performance in the workplace. This study aimed to develop a Bayesian network (BN) model for managing and improving safety behavior. Methods. This study was carried out in the chemical industries in Iran. The data were gathered by a questionnaire consisting of 13 variables including organization safety priority, systems design, safety communication, safety education, work strategy, human-system interaction, mental workload, environmental distractions, work pressure, fatigue, sleepiness, safety knowledge and locus of control. The BN structure was created using the fuzzy decision-making trial, evaluation laboratory method and expert opinions. Belief updating was used to determine variables with the strongest effect on safety behavior. Results. Locus of control, organization safety priority and safety knowledge were the best predictors of safety behavior. Moreover, it was found that improving organization safety priority and safety knowledge is the best intervention strategy to improve safety behavior significantly. Conclusions. BN is a powerful tool that can model causal relationships among variables. Improving organization safety priority and safety knowledge can lead to the maximum possible level of safety behavior.

Problematic internet use and safety behavior: The moderating role of safety climate.

Problematic internet use (PIU) can cause mental and physical harm to individuals and may be an emerging factor contributing to unsafe work behavior. In this study, the relationship between PIU and safety behavior was investigated. Moreover, it was hypothesized that safety climate can moderate the relationship between PIU and safety behavior. Participants were employees from a mining industry in Iran. Three validated questionnaires were distributed to gather the required data regarding PIU, safety climate, and safety behavior. Hierarchical regression analysis was used to assess the moderating effect of safety climate on the relationship between PIU and safety behavior. The size of moderation effect was assessed using f2 index. Four hundred eighty-five employees participated in this study. The results demonstrated a negative relationship between PIU and safety behavior. The moderating effect of safety climate on the relationship between PIU and safety behavior was supported. The f2 index was 0.027 which demonstrates a small moderation effect. In conclusion, PIU has a significant negative effect on the safety behavior of employees. Providing a strong safety climate can be a long-term solution for reducing the negative effect of PIU on safety behavior.

The mediating effect of workers' situation awareness on the relationship between work-related factors and human error: a path analysis approach.

Situation awareness is the main reason behind different patterns of unsafe behavior among workers and can play a mediating role in the relationship between predictive situational variables and human error. This questionnaire-based study carried out with Iranian workers investigated the direct and indirect effects of work pressure, mental workload, human-systems interaction and environmental distractions on three types of human error (i.e., slips, lapses and mistakes). The results of path analysis showed that, based on model fit indexes, the model is appropriately fit (χ2 / df = 3, comparative fit index = 099, Tucker-Lewis index = 0.99, root mean square error of approximation = 026). It was also observed that the effects of four independent variables on all three types of human error are indirect and mediated by situation awareness. Consequently, it is confirmed that situation awareness plays a key role in the occurrence of human error and is a sharp-end causal factor for human error in industrial workplaces.

Analysis of occupational accidents among nurses working in hospitals based on safety climate and safety performance: a Bayesian network analysis.

Purpose. This study aimed to analyze causal relationships among safety climate dimensions, safety performance dimensions and occupational accidents for nurses working in hospitals. Methods. Data were gathered from questionnaires filled in by nurses from three public hospitals. To analyze interactions among variables, a Bayesian network (BN) analysis was conducted. Using the function of BN 'belief updating', variables with the highest influences on occupational accidents were determined. Results. A total of 211 nurses participated in this study. They were mainly female and married. The results showed that 39.3% of nurses experienced occupational accidents during the last 12 months before the study. Less than half of the nurses had an acceptable safety performance. Safety participation had the highest influence on occupational accidents, followed by safety compliance. Reporting of errors had the highest score among the safety climate dimensions. Training of nurses was necessary for improving both safety compliance and safety participation, and thereby reducing occupational accidents. Conclusion. The effect of safety participation on occupational accidents among nurses was higher than that of safety compliance. Among the safety climate dimensions, supervisors' attitude to safety and safety training had the highest effects on both nurses' safety performance and occupational accidents.

Human reliability analysis in de-energization of power line using HEART in the context of Z-numbers.

Investigation reveals that a high percentage of incident causes are ascribed to some forms of human error. To effectively prevent incidents from happening, Human Reliability Analysis (HRA), as a structured way to represent unintentional operator contribution to system reliability, is a critical issue. Human Error Reduction and Assessment Technique (HEART) as a famous HRA technique, provides a straightforward method to estimate probabilities of human error based on the analysis of tasks. However, it faces varying levels of uncertainty in assigning of weights to each error producing condition (EPC), denoted as assessed proportion of affect (APOA), by experts. To overcome this limitation and consider the confidence level (reliability or credibility) of the experts, the current study aimed at proposing a composite HEART methodology for human error probability (HEP) assessment, which integrates HEART and Z-numbers short for, Z-HEART. The applicability and effectiveness of the Z-HEART has been illustrated in the de-energization power line as a case study. Furthermore, a sensitivity analysis is fulfilled to investigate the validity of the proposed methodology. It can be concluded that Z-HEART is feasible for assessing human error, and despite the methodological contributions, it offers many advantages for electricity distribution companies.

Pore-scale analysis of formation damage; A review of existing digital and analytical approaches.

Formation damage is one of the most challenging problems that occurs during the lifetime of a well. Despite numerous previous studies, an organized review of the literature that introduces and describes the digital and analytical approaches developed for formation damage analysis is lacking. This study aims to fill this gap through briefly describing the main mechanisms behind formation damage in porous media as well as investigating the main related experimental methods with an emphasis on novel imaging techniques. Specifically, there will be a focus on a number of modern and nondestructive analytical methods, such as dry/cryogenic Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), CT-scanning (both using adapted medical scanners and the use of high-resolution micro-CT instruments) and Nuclear Magnetic Resonance (NMR), which obtain outstanding results for the identification of formation damage mechanisms. These approaches when used in combination provide a robust identification of damage processes, while they reduce the risk of operational mistakes for decision makers through visualization of the distribution, severity, and nature of the damage mechanisms.

The Relationships Among Occupational Safety Climate, Patient Safety Climate, and Safety Performance Based on Structural Equation Modeling.

OBJECTIVES: The aim of the present study was to investigate the relationships among hospital safety climate, patient safety climate, and safety outcomes among nurses. METHODS: In the current cross-sectional study, the occupational safety climate, patient safety climate, and safety performance of nurses were measured using several questionnaires. Structural equation modeling was applied to test the relationships among occupational safety climate, patient safety climate, and safety performance. RESULTS: A total of 211 nurses participated in this study. Over half of them were female (57.0%). The age of the participants tended to be between 20 years and 30 years old (55.5%), and slightly more than half had less than 5 years of work experience (51.5%). The maximum and minimum scores of occupational safety climate dimensions were found for reporting of errors and cumulative fatigue, respectively. Among the dimensions of patient safety climate, non-punitive response to errors had the highest mean score, and manager expectations and actions promoting patient safety had the lowest mean score. The correlation coefficient for the relationship between occupational safety climate and patient safety climate was 0.63 (p<0.05). Occupational safety climate and patient safety climate also showed significant correlations with safety performance. CONCLUSIONS: Close correlations were found among occupational safety climate, patient safety climate, and nurses' safety performance. Therefore, improving both the occupational and patient safety climate can improve nurses' safety performance, consequently decreasing occupational and patient-related adverse outcomes in healthcare units.

Analysis of human and organizational factors that influence mining accidents based on Bayesian network.

Purpose. The present study aimed to analyze human and organizational factors involved in mining accidents and determine the relationships among these factors. Materials and methods. In this study, the human factors analysis and classification system (HFACS) was combined with Bayesian network (BN) in order to analyze contributing factors in mining accidents. The BN was constructed based on the hierarchical structure of HFACS. The required data were collected from a total of 295 cases of Iranian mining accidents and analyzed using HFACS. Afterward, prior probability of contributing factors was computed using the expectation-maximization algorithm. Sensitivity analysis was applied to determine which contributing factor had a higher influence on unsafe acts to select the best intervention strategy. Results. The analyses showed that skill-based errors, routine violations, environmental factors and planned inappropriate operation had higher relative importance in the accidents. Moreover, sensitivity analysis revealed that environmental factors, failed to correct known problem and personnel factors had a higher influence on unsafe acts. Conclusion. The results of the present study could provide guidance to help safety and health management by adopting proper intervention strategies to reduce mining accidents.

Analysis of the severity of occupational injuries in the mining industry using a Bayesian network.

OBJECTIVES: Occupational injuries are known to be the main adverse outcome of occupational accidents. The purpose of the current study was to identify control strategies to reduce the severity of occupational injuries in the mining industry using Bayesian network (BN) analysis. METHODS: The BN structure was created using a focus group technique. Data on 425 mining accidents was collected, and the required information was extracted. The expectation-maximization algorithm was used to estimate the conditional probability tables. Belief updating was used to determine which factors had the greatest effect on severity of accidents. RESULTS: Based on sensitivity analyses of the BN, training, type of accident, and activity type of workers were the most important factors influencing the severity of accidents. Of individual factors, workers' experience had the strongest influence on the severity of accidents. CONCLUSIONS: Among the examined factors, safety training was the most important factor influencing the severity of accidents. Organizations may be able to reduce the severity of occupational injuries by holding safety training courses prepared based on the activity type of workers.

Effects of human and organizational deficiencies on workers' safety behavior at a mining site in Iran.

OBJECTIVES: Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents. METHODS: In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers' unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies. RESULTS: Organizational deficiencies had a direct positive effect on workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.23). The effect of unsafe supervision on workers' violations and workers' errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers' unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers' unsafe acts. CONCLUSIONS: Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.

Health consequences of shift-work: the case of iranian hospital security personnel.

BACKGROUND: Shift-work, which is an ergonomics issue in workplaces, can negatively affect workers. The security personnel of medical centers in Iran have multiple responsibilities and consequently are exposed to such unwanted situations as observing patients, disputing with patient's attendants, unwanted shift schedules, and being away from family for long periods. OBJECTIVE: This study assessed health problems of Iranian hospital security personnel (shift-worker personnel) using the Survey of Shift-workers (SOS) questionnaire (Persian version). METHODS: This cross-sectional study was conducted in seven medical centers (4 hospitals and 3 clinics). A total of 416 workers were surveyed: shift-workers (exposed group) (n=209) and non-shift-workers (unexposed group) (n=207). RESULTS: The prevalence of adverse health effects was higher in shift-workers than day-workers. The level of education and mean Body Mass Index (BMI) in shift-workers were significantly higher compared with day-workers. The prevalence of gastrointestinal disorders, cardiovascular and psychological problems were also significantly higher in shift-workers compared with day-workers. CONCLUSIONS: Overall, the prevalence of health problems among the security personnel of medical centers was high. Hence, it is recommended that personnel be put under periodic monitoring and receive medical counseling and treatment if there is any disorder.