Safety Resources Research Articles

Delineations for Police Patrolling on Street Network Segments with p-Median Location Models

Police patrolling intends to enhance traffic safety by mitigating the risks associated with vehicle crashes and accidents. From a view of operations, patrolling requires an effective distribution of resources and often involves area delineations for this distribution purpose. Given constraints such as budget and human resources for traffic safety, delineating geographic areas optimally for police patrol areas is an important agenda item. This paper considers two p-median location models using segments on a street network as observational units on which traffic issues such as vehicle crashes occur. It also uses two weight sets to construct an enhanced delineation of police patrol areas in the City of Plano, Texas. The first model for the standard p-median formulation gives attention to the cumulative number of motor vehicle crashes from 2011 to 2021 on the major transportation networks in Plano. The second model, an extension of this first p-median one, uses balancing constraints to achieve balanced spatial coverage across patrol areas. These two models are also solved with network kernel density count estimates (NKDCE) instead of crash counts. These smoothed densities on a network enable consideration of uncertainty affiliated with this aggregation. The analysis results of this paper suggest that the p-median models provide effective specifications, including their capability to define patrol areas that encompass the entire study region while minimizing distance costs. The inclusion of balancing constraints ensures a more equitable distribution of workloads among patrol areas, improving overall efficiency. Additionally, the model with NKDCE results in an improved workload balance among delineated areas for police patrolling activities, thus supporting more informed spatial decision-making processes for public safety.

Automated Water Quality Assessment Using Big Data Analytics

Water is one of the world's most precious resources, essential to life. Industrial waste, agricultural runoff, and urban discharge degrade water, rendering it unfit for consumption. Water quality monitoring and evaluation are more important than ever. Big Data analytics is used to examine water quality utilizing enormous datasets of pH, hardness, solids concentration, chloramine, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. This work classifies water potability, which is vital for human consumption, using strong machine learning on massive datasets. Classifiers were Random Forest, Gradient Boosting, and Support Vector Machine on 3,276 water bodies. The Random Forest classifier obtained the highest accuracy at 66.77% after significant data preparation and training, followed by Gradient Boosting at 66.01% and SVM at 62.80%. This shows that Big Data analytics and machine learning algorithms can interpret complex water quality data for public health and natural resource management. The Random Forest classifier and SVM in this study accurately calculate water potability. Prediction algorithms consider water cleanliness data and may aid public safety and water resource monitoring.

Effectiveness and Durability of a Workplace Sedentary Behavior Intervention Based on the Total Worker Health® Approach

We used the Total Worker Health® approach to develop a multi-component workplace sedentary behavior intervention and tested intervention effectiveness in a cluster randomized trial. Participants (n = 198; 75% female) were recruited from three call-centers (two intervention and one usual practice control). All worksites received pedal stand active workstations. The usual practice site received no additional support, while the intervention group completed a six-month program of activities including computer-based training, behavioral self-monitoring, health and safety discussions, and pedaling competitions. Data collection included a survey, a physical health assessment, and accelerometer measures of sedentary behavior, physical activity, and sitting/standing time. Primary analyses were generalized estimating equations comparing changes between intervention and usual practice conditions over time, along with analyses of changes in both groups combined over time. Six-month results revealed less prolonged sitting and reductions in musculoskeletal pain in both groups (all p < 0.05), while the intervention group showed additional improvements in moderate physical activity (p < 0.001) and use of pedal stands (p < 0.01). At 12-months, the additional physical activity and pedal stand use in the intervention group regressed to baseline levels, while reductions in prolonged sitting in both groups were durable (p < 0.01). This study adds to previous research showing the effectiveness of the Total Worker Health® approach for workplace health and safety. Results also show that, while providing employees with health and safety resources is beneficial, providing ongoing support for the adoption and use of resources is more effective.

Updated Results from the Retrospective CREST Study on the Safety and Effectiveness of 8-Week Glecaprevir/Pibrentasvir in HCV-Infected Treatment-Naïve Patients with Compensated Cirrhosis.

This brief report presents updated findings from the previously published CREST study evaluating the safety and effectiveness of 8-week glecaprevir/pibrentasvir (GLE/PIB) in treatment-naïve patients with chronic hepatitisC virus (HCV) infection and compensated cirrhosis. The current study includes an additional 51 patients, presents effectiveness data stratified by additional comorbidities and comedications, and offers insights into healthcare resource utilization. Analysis of treatment-naïve patients with HCV infection and compensated cirrhosis enrolled in the CREST study, a real-world, observational multicenter study. All enrolled patients were included in the full analysis set (FAS); the modified analysis set (MAS) excluded patients with missing SVR12 data, or who discontinued GLE/PIB for nonvirologic failure. The primary endpoint was sustained virologic response at posttreatment week12 (SVR12) in the MAS. Safety and healthcare resource utilization were also assessed. The FAS included 437 patients, and the MAS 375. Overall, the results were consistent with the previously published study, with 98.9% of patients in the MAS achieving SVR12. Patients with comorbidities such as alcoholism, diabetes, and hyperlipidemia achieved SVR12 rates > 94%. High SVR12 rates were also achieved by patients receiving comedications such as anxiolytics, antidepressants, and opioid agonists. Of the 26.8% of patients with an adverse event, 1.1% had a serious adverse event, none of which were deemed related to GLE/PIB. Healthcare resource utilization varied by employment status and history of drug use. Active drug users had more physician and nurse visits than specialist visits compared with former drug users. This study provides further evidence on the safety and effectiveness of 8-week GLE/PIB, supporting the use of shorter treatment in treatment-naïve patients with Child-PughA cirrhosis including subgroups of interest, regardless of comorbidities and comedications observed in this population. The variable healthcare resource utilization in different patient types can help plan and resource linkage to care better, thus supporting HCV elimination efforts.

EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection

Spam messages have emerged as a significant issue in digital communication, adversely affecting users’ mental health, personal safety, and network resources. Traditional spam detection methods often suffer from low detection rates and high false positives, underscoring the need for more effective solutions. This paper proposes the EGMA model, an ensemble learning-based hybrid approach for spam detection in SMS messages, which integrates gated recurrent unit (GRU), multilayer perceptron (MLP), and hybrid autoencoder models utilizing a majority voting algorithm. The EGMA model enhances performance by incorporating additional statistical features extracted from message content and employing text vectorization techniques, such as Term Frequency–Inverse Document Frequency (TF-IDF) and CountVectorizer. The proposed model achieved impressive classification accuracies of 99.28% on the SMS Spam Collection dataset, 99.24% on the Email Spam dataset, 99.00% on the Enron-Spam dataset, 98.71% on the Super SMS dataset, and 95.09% on UtkMl’s Twitter Spam dataset. These results demonstrate that the EGMA model outperforms individual models and existing methods in the literature, providing a robust solution for enhancing spam detection performance and effectively mitigating the threats that spam messages pose in digital communication.

Missing Medical Data in Neurological Emergency Care Compromise Patient Safety and Healthcare Resources.

Background: Acute care of patients in the emergency department (ED) can be very challenging when patients attend EDs without their important medical information. This is especially problematic for multimorbid patients under polypharmacy. The aim of this study was to assess systematically the frequency and clinical relevance of incomplete medical data upon ED admission. Methods: The study was conducted in the neurological ED of a German tertiary hospital. The availability and accuracy of medical data of all neurological patients in the ED were assessed upon arrival. Treating ED physicians were asked about the acute care of the patients to clarify whether missing data resulted in delays or complications in the emergency treatment. Additionally, doctors responsible for the inpatient care of patients who were admitted to a ward via the ED were questioned about the course of the inpatient stay to monitor how initially missing data might have influenced the hospital stay. Results: Medical data of 27% of the 272 included patients were missing or incomplete upon admission in the ED. The ED physicians had to make additional phone calls to gather information in 57% of these cases (vs. 22% in patients with complete data, p < 0.0001). Delays between 5 and 240 min were documented due to initially missing data. Unnecessary diagnostic procedures (e.g., lumbar puncture) were performed in 5% of these patients, thus compromising patient safety. Even the inpatient stay was complicated by initially missing data, as doctors still had to spend time (between 10 and 180 min) to gain relevant information. Retrospectively, 5% of hospitalizations could have been avoided if all medical information had been available upon ED admission. Conclusions: Missing medical data caused complications and delays in acute as well as inpatient care of patients admitted to the neurological ED. This compromised patient safety and led to a waste of medical resources and valuable time of the responsible medical team. Therefore, a comprehensive, digital data management system is urgently needed to improve patient safety and facilitate efficient patient care in the ED and beyond.

Avelumab First-Line Maintenance for Locally Advanced or Metastatic Urothelial Carcinoma: Results From the Real-World US PATRIOT-II Study

IntroductionIn JAVELIN Bladder 100, avelumab first-line maintenance (1LM) improved overall survival (OS) and progression-free survival (PFS) in patients with locally advanced/metastatic urothelial carcinoma (la/mUC) without progression following 1L platinum-based chemotherapy (PBC) versus best supportive care. PATRIOT-II describes real-world outcomes with avelumab 1LM. Patients and MethodsThis observational, retrospective study of avelumab 1LM in US community/academic centers used medical record data collected from avelumab initiation for ≥12 months to assess survival, safety, and healthcare resource utilization; analyses are descriptive. ResultsThe study included 160 patients from 37 centers (median age, 70 years; 77% male). Avelumab 1LM was initiated at a median of 4 weeks (IQR 3-6) after PBC completion. Median follow-up from avelumab 1LM was 16 months (IQR 11-21). At study end, 19.4% of patients continued avelumab; 73.7% had discontinued due to progression, adverse events (AEs), or performance status deterioration. Median PFS and OS from avelumab initiation were 5.4 months (95% CI, 3.8-6.9) and 24.4 months (95% CI, 20.4-28.4), respectively. Grade ≥3 treatment-related AEs (TRAEs) occurred in 15 patients (9.4%); 35 (21.9%) had any-grade immune-related AEs, and 23 (14.3%) received high-dose systemic corticosteroids for AEs. Forty-four patients (27.5%) were hospitalized during the avelumab treatment period, of whom 13 (8.1%) were hospitalized due to TRAEs. Limitations of this study include a small sample size, potential selection bias, and missing/unknown data. ConclusionThese results align with the JAVELIN Bladder 100 clinical trial and other real-world studies, supporting avelumab 1LM use in patients with la/mUC without progression following 1L PBC.

Analytical Hierarchy Process for Construction Safety Management and Resource Allocation

The construction industry plays a crucial role in contributing to the economy and developing sustainable infrastructures. However, it is known as one of the most dangerous industrial domains. Over the years, special attention has been paid to developing models for managing and planning construction safety. Many research studies have been carried out to analyze the root causes of fatal accidents in construction sites to develop models for preventing them and mitigating their consequences. Root cause identification and analysis are essential for effective risk mitigation. However, implementing mitigation activities is usually limited to the project’s safety budget. The construction sector suffers from a lack of allocation of appropriate safety resources triggered by a dynamic and complex project environment. This study aims to address the gap in safety resource allocation through a comprehensive root cause analysis of construction work accidents. In this paper, we present a comprehensive review of work accident-related research, categorized according to the 5M model into five root factors: medium, mission, man, management, and machinery. A novel methodology for construction safety resource allocation is proposed to mitigate risks analyzed by the 5M model with the aid of advanced technological solutions. Safety resource allocation alternatives are formulated, and their priorities are established based on an analysis of structured criteria that integrate both risk and cost considerations. The Analytical Hierarchy Process (AHP) is employed to select the optimal alternative for safety resource allocation, with the objective of effective risk mitigation. The proposed model underwent validation through two different case studies. The findings indicate that risk aversion is a critical factor in the optimal allocation of safety resources. Furthermore, the results suggest that regulatory measures should prioritize the stimulation of risk motivation in the safety decision-making processes of construction firms.

Enhancing Large-Diameter Tunnel Construction Safety with Robust Optimization and Machine Learning Integrated into BIM

Aim This study aims to enhance safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). By acquiring and preprocessing various datasets, implementing feature engineering, and using algorithms like SVM, decision trees, ANN, and random forests, the study demonstrates the effectiveness of ML models in risk prediction and mitigation, ultimately advancing safety performance in civil engineering projects. Background Large diameter tunnel construction presents significant safety challenges. Traditional methods often fall short of effectively predicting and mitigating risks. This study addresses these gaps by integrating robust optimization and machine learning (ML) approaches with Building Information Modeling (BIM) technology. By acquiring and preprocessing diverse datasets, implementing feature engineering, and employing ML algorithms, the study aims to enhance risk prediction and safety measures in tunnel construction projects. Objective The objective of this study is to improve safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). This involves acquiring and preprocessing diverse datasets, using feature engineering to extract key parameters, and applying ML algorithms like SVM, decision trees, ANN, and random forests to predict and mitigate risks, ultimately enhancing safety performance in civil engineering projects. Methods The study's methods include acquiring and preprocessing various datasets (geological, structural, environmental, operational, historical, and simulation). Feature engineering techniques are used to extract key safety parameters for tunnels. Machine learning algorithms, such as decision trees, support vector machines (SVM), artificial neural networks, and random forests, are employed to analyze the data and predict construction risks. The SVM algorithm, with a 98.76% accuracy, is the most reliable predictor. Results The study found that the Support Vector Machine (SVM) algorithm was the most accurate predictor of risks in large diameter tunnel construction, achieving a 98.76% accuracy rate. Other models, such as decision trees, artificial neural networks, and random forests, also performed well, validating the effectiveness of ML-based solutions for risk assessment and mitigation. These predictive models enable stakeholders to monitor construction, allocate resources, and implement preventative measures effectively. Conclusion The study concludes that integrating machine learning (ML) approaches with Building Information Modeling (BIM) significantly improves safety in large diameter tunnel construction. The Support Vector Machine (SVM) algorithm, with 98.76% accuracy, is the most reliable predictor of risks. Other models, like decision trees, artificial neural networks, and random forests, also perform well, validating ML-based solutions for risk assessment. Adopting these ML approaches enhances safety performance and resource management in civil engineering projects.

Persistent pain, long-term opioids, and restoring trust in the patient-clinician relationship

The erosion of trust in the patient-clinician relationship is an underappreciated, and vital, component of the prescription opioid crisis. Drawing from lived experience of patients and clinicians, and a narrative evidence review, this report discusses how opioid use for persistent pain can impact the patient-clinician relationship from the vantage points of the patient and the family physician. For patients, the stress of dealing with persistent pain, misalignment with clinicians regarding goals of care, experiences of disrespect and stigma, fear of abrupt tapers, and frustration with a fragmented health system, all combine to breed a lack of trust. Clinicians, for their part, experience challenges due to inadequate resources for pain management and opioid safety, pressure to deprescribe opioids rapidly, inconsistent prescribing practices of colleagues, ‘policing’ opioid prescriptions when concern arises for opioid use disorder and adversarial relationships with frustrated patients wary of clinician intentions. As a result, many clinicians struggle to maintain a therapeutic relationship with patients in great need of empathy and healing. To support implementation of evidence-based guidelines and achieve public health goals of safer prescribing and reducing harm from prescription opioids, we recommend steps health systems and clinicians can take to rebuild trust in the patient-clinician relationship, enable patient-centered pain care, and embed patient perspectives into opioid safety processes. PerspectiveErosion of patient-clinician trust is a barrier to implementing evidence-based guidelines that aim to improve opioid safety. This paper explores lived patient and clinician experiences and recommends steps for health systems and clinicians to rebuild this trust as a strategy to actualize the benefits of adherence to these guidelines.

Electrolytes for Aluminum-Ion Batteries: Progress and Outlook.

Aluminum-ion batteries (AIBs) are promising electrochemical energy storage sources because of their high theoretical specific capacity, light weight, zero pollution, safety, inexpensiveness, and abundant resources. These theoretical advantages have recently made AIBs a research hotspot. However, electrolyte-related issues significantly limit their commercialization. The electrolyte choices for AIBs are significantly limited, and most of the available options do not facilitate the Al3+/Al three-electron transfer reaction. Thus, this review presents an overview of recent advances in electrolytes and modification strategies for AIBs to clarify the limitations of existing AIB electrolytes and offer guidance for improving their performance. Furthermore, herein, the advantages, limitations and possible solutions for each electrolyte are discussed, after which the future of AIB electrolytes is envisioned.

Nonflammable localized high-concentration deep eutectic electrolytes for safe and stable rechargeable aluminum batteries

Rechargeable aluminum batteries (RABs) are regarded as a promising next-generation energy storage system due to the high theoretical capacity, good safety, low cost, and abundant resources of aluminum. However, the practical development of RABs is impeded by the commonly used AlCl3-based electrolytes, which exhibit strong corrosion, high cost, and extreme sensitivity to air. Herein, we propose a novel deep eutectic electrolyte composed of hydrated aluminum perchlorate, N,N-dimethylacetamide (DMA) ligand, and 1,1,2,2-tetrafluoroethyl-2,2,3,3-tetrafluoropropylether (TFE) diluent (named as ADT45) for low-cost, intrinsically safe, and stable RABs. Various spectroscopic analyses combined with theoretical calculations reveal that TFE establishes a localized high-concentration environment in the electrolyte, which can not only boost ionic conductivity but also suppress side reactions through forming an AlF3-rich solid electrolyte interface layer. Evaluation of Al||Al symmetric cells and CuHCF||Al full cells demonstrates the ADT45 electrolyte can accelerate reaction kinetics and enhance cycling stability. Furthermore, the energy storage mechanism of the RAB with ADT45 is revealed through various in-situ and ex-situ characterizations. This study presents a novel approach for developing cost-effective and safe electrolytes for RABs for large-scale energy storage.

Impacts of illegal mining activities on water quality for irrigation and implications for public health: A case study of the Oda River in the Ashanti Region of Ghana

ABSTRACT This study evaluated the biological and physicochemical parameters of the Oda River, including the detection of heavy metals. To achieve this purpose, a quantitative experimental research design was employed. Twenty-four water samples were taken along the Oda River from Obeng ne Obeng, Abuakwaa, and Odaso communities. The samples were analysed in a laboratory, and the results were compared to the irrigation water quality standards from the Food and Agricultural Organization (FAO). The study found that the concentration of turbidity and total suspended solids exceeded the recommended standards of FAO, while the river's pH, electrical conductivity, and total dissolved solids concentrations were within permissible boundaries. Lead, cadmium, mercury, arsenic, and fluoride were present at concentrations lower than the recommended guidelines, whereas copper and cyanide were not discovered. However, iron concentrations exceeded the FAO guidelines. Escherichia coli concentrations in the Obeng ne Obeng were lower than the FAO irrigation standards but were higher in the Abuakwaa and Odaso. The Pearson correlation coefficient highlighted significant correlations between the physicochemical parameters. This paper concludes that unregulated mining activities may endanger vital water resources for irrigation, public health, food safety, ecosystems, and livelihoods.

Higher Flower Hydraulic Safety, Drought Tolerance and Structural Resource Allocation Provide Drought Adaptation to Low Mean Annual Precipitation in Caragana Species.

Determining the differences in flower hydraulic traits and structural resource allocation among closely related species adapted to low mean annual precipitation (MAP) can provide insight into plant adaptation to arid environments. Here, we measured the maximum flower hydraulic conductance (Kmax-flower), water potential at induction 50% loss of Kmax-flower (P50-flower), flower pressure-volume parameters, dry mass of individual flowers and structural components (vexillum, wings, keels, stamens and sepals) of six Caragana species growing in regions ranging from 110 to 1400 mm MAP. Compared with species from high-MAP environments, those from low-MAP environments presented lower Kmax-flower, more negative P50-flower, osmotic potential at full turgor (πo) and turgor loss points (πtlp), and a greater bulk modulus of elasticity (ε). Consequently, a negative correlation between Kmax-flower (hydraulic efficiency) and P50-flower (hydraulic safety) was observed across Caragana species. Furthermore, the dry masses of individual flowers and structural components (vexillum, wings, keels, stamens and sepals) were greater in the species from the low-MAP environment than in those from the high-MAP environment. These findings suggest that greater flower hydraulic safety and drought tolerance combined with greater structural resource allocation promote drought adaptation in Caragana species to low-MAP environments.

Parliamentarily speaking: A thematic analysis of Kerala Legislative Assembly questions relating to health from 2016 to 2021.

Legislative assemblies often provide a platform for legislators to question the government during question hours, which are crucial for governance However, question hours remain understudied, especially when addressing health policy and systems related issues in lower- and middle-income countries. This study assesses the 14th Kerala Legislative Assembly questions, focusing on health-related areas to provide insights for health policy formulation and decision-making processes. We sourced and transcribed all starred questions (346) related to health that were answered by the health minister in the 14th Legislative Assembly between 2016 and 2021 from the archives of the assembly website. We conducted a thematic analysis of these questions and mapped them into various themes, guided by the World Health Organization building blocks framework. About 7.8% of all questions (N = 4404) were related to health (N = 346). Of these questions, the majority were directly related to service delivery (43.4%), followed by health information (16.5%). Health financing, food safety, and human resources were the least discussed topics throughout the assessed period within the state. The legislators primarily focussed on health services and health information, with less attention given to health financing, food safety, and human resources regardless of constituency or political affiliation. This study underscores the need for a balanced approach to public health issues, highlighting the importance of legislators to priortizing health services and information, while also addressing health financing, food safety, and human resources. This would enable a robust and resilient public health system to effectively address diverse health concerns.

Planar Deposition via In Situ Conversion Engineering for Dendrite-Free Zinc Batteries.

Owing to the considerable capacity, high safety, and abundant zinc resources, zinc-ion batteries (ZIBs) have been garnering much attention. Nonetheless, the unsatisfactory cyclic lifespan and poor reversibility originate from side reactions and dendrite obstacles to their practical applications. In addition to inhibiting the corrosion of aqueous electrolytes, regulating planar deposition is a key strategy to enhance their long-term stability. Herein, an in situ conversion strategy is reported to construct a protective "dual-layer" structure (VZSe/V@Zn) on zinc metal, consisting of the VSe2-ZnSe outer layer with low lattice mismatch to Zn (002) plane, and corrosion-resistant nanometallic V inner layer. Such design integrates superior interfacial ionic/electronic transfer, corrosion resistance, and unique planar deposition regulation capability. The as-prepared VZSe/V@Zn demonstrates remarkable durability of 238h at 50mA cm-2 with a high depth of discharge (68.3% DOD) in the Zn||Zn symmetric cell. Even in the anode-free system, the as-prepared protective layer can extend the cycle life up to 2000 cycles, with an outstanding capacity retention of 93.1% and ultra-high average coulombic efficiency of 99.998%. This work delineates an effective strategy for fabricating lattice-matching protective layers, with profound implications for elucidating zinc deposition mechanisms and paving the way for the development of high-performance zinc batteries.

Expression of a novel hydrolase MhpC in Brevibacillus parabrevis BCP-09 and its characteristics for degrading synthetic pyrethroids

Synthetic pyrethroids are widely used insecticides which may cause chronic diseases in non-target organisms upon long-term exposure. Microbial degradation offers a reliable method to remove them from the environment. This study focused on Brevibacillus parabrevis BCP-09 and its enzymes for degrading pyrethroids. The predicted deltamethrin-degrading genes phnA and mhpC were used to construct recombinant plasmids. These plasmids, introduced into Escherichia coli BL21(DE3) cells and induced with L-arabinose. The results indicated that the intracellular crude enzyme efficiently degraded deltamethrin by 98.8 %, β-cypermethrin by 94.84 %, and cyfluthrin by 73.52 % within 24 h. The hydrolytic enzyme MhpC possesses a catalytic triad Ser/His/Asp and a typical “Gly-X-Ser-X-Gly” conservative sequence of the esterase family. Co-cultivation of induced E. coli PhnA and E. coli MhpC resulted in degradation rates of 41.44 ± 3.55 % and 60.30 ± 4.55 %, respectively, for deltamethrin after 7 d. This study states that the degrading enzymes from B. parabrevis BCP-09 are an effective method for the degradation of pyrethroids, providing available enzyme resources for food safety and environmental protection.

Fostering healthy outcomes in hotels: The effects of healthy organizational resources on employees’ stress reduction, resilience, and task performance

This study aims to learn from past global health crises, encouraging industry leaders worldwide to take proactive steps in implementing extra health and safety measures for future potential crises. The research employs a quantitative approach to investigate a proposed model suggesting that hotel employees’ adherence to health and safety regulations is contingent on the availability of supportive organizational resources. An electronic questionnaire was administered to hotel employees in Egypt, resulting in 213 valid responses. The data was analyzed using SPSS 25 and AMOS 24. The findings indicate a strong connection between health and safety training, health and safety resources, and health and safety compliance. Additionally, the results reveal direct and significant links between health and safety compliance, stress reduction, improved employee resilience, and enhanced task performance. Moreover, significant indirect effects were found between health and safety training, health and safety resources, and healthy organizational outcomes, with health and safety compliance serving as a mediator. The current study provides a valuable theoretical and practical contribution on employee motivation to comply with health standards, employee-organization interactions, resilience, and crisis management to better prepare for future health crises.

Dynamic risk assessment of natural gas transmission pipelines with LSTM networks and historical failure data

In the realm of energy infrastructure, ensuring the security of gas transmission pipelines is critical. This research introduces an advanced dynamic risk assessment framework that leverages the predictive capabilities of LSTM networks, presenting an improvement over conventional failure prediction models. Unlike traditional approaches that rely on averaging historical failure records, this framework dynamically processes historical pipeline failure incidents into sequential time series analysis, facilitating and improving the accuracy of the current failure rate. The model refines the failure rate estimation for individual pipelines by incorporating unique characteristics and modification factors, resulting in a highly precise failure likelihood estimation. Additionally, this study introduces a quantifiable linkage between mortality risk and the fatality probit value across various accident scenarios, enhancing consequence evaluation. A sensitivity analysis is then performed to assess the impact of various input parameters on the model's performance. The practical application of the model on a U.S. pipeline confirms its effectiveness. This proposed methodology substantially enhances the understanding of incident causation in gas pipeline systems, paving the way for superior safety management strategies. It is instrumental in enhancing pipeline safety, refining infrastructure planning, and optimizing safety resource allocation. The methodology offers benefits for pipeline operators, industry professionals, and regulatory agencies, contributing to improved operational safety and resource management in the pipeline industry.

Oceanographic Characteristics in the Three International Indonesian Archipelago Sea Lanes (IASLs) Region: Implications for Underwater Acoustics System

Graphical Abstract Highlight Research The IASL-1 entry portal in the southern and northern regions shows the emergent SOFAR channels. The shadow zone and the existence of a SOFAR deep sound channel in the IASL-2 and IASL-3 routes can be triggered by the emergent “saddle” SVP pattern. The variability seasonally and interannually due to variations in seawater properties stratification plays an important role in SOFAR channel appearances in IASLs. The diverse oceanographic characteristics of IASLs necessitate the implementation of sustainable marine geospatial data. Abstract The Indonesian Maritime Continent (IMC) provides three international sea lanes, known as the Indonesian Archipelago Sea Lanes (IASLs), allowing ships to navigate across territorial waters between the Pacific and Indian Oceans and vice versa. Gaining knowledge about the distinct oceanographic characteristics of the three IASLs can offer valuable insight into maritime safety and sustainable marine resource management. This study aims to review oceanographic characteristics in IASL regions from available past studies to provide a comprehensive insight into the processes and dynamical oceanography in the IASL region and its implications for underwater acoustic patterns. The results of research found that the IASL-1 route is characterized by a shallow shelf passage with homogeneous sound velocity profile (SVP) but a deep and narrow entry portal in the southern and northern SOFAR channels. Seasonal reversal monsoonal wind-driven current dominates the circulation. The IASL-2 and IASL-3 routes convey a deep and narrow passage with complexity of sea-air interactions; they vary on seasonal and interannual time scales. These IASLs emerge with the “saddle” SVP, which can trigger the shadow zone and the existence of a SOFAR deep sound channel that varies seasonally and interannually due to variations in seawater properties stratification. The diverse oceanographic characteristics discussed above significantly influence the underwater object detection equipment, the planning time, and the strategies for underwater defense systems. Due to these implications, it is necessary to utilize marine geospatial database. Subsequently, these data may be utilized to facilitate policy-making and provide approximations for marine activities and management along the IASLs.