Comprehensive Risk Assessment Model Research Articles

Climate risk analysis of low-altitude tea gardens in central Taiwan using a Bayesian network.

Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future.

Enhancing Environmental Sustainability: Risk Assessment and Management Strategies for Urban Light Pollution

Light pollution imposes significant and far-reaching adverse effects on human society, necessitating its stringent regulation. However, intervention policies could be customized to suit the unique characteristics of each region, taking into account local conditions. To address this challenge, we have developed a comprehensive light pollution risk assessment model using a combination of objective and subjective weighting methods, including analytic hierarchy process (AHP), independent weighting method (IWM), entropy weight method (EWM), coefficient of variation (CV), criteria importance through intercriteria correlation (CRITIC), and principal component analysis (PCA). This model facilitates a systematic evaluation of light pollution risk levels across diverse regions in China. Subsequently, we have proposed intervention policies targeting light pollution risk reduction and assessed their efficacy using the synthetic control method. Our findings reveal elevated light pollution risk levels in coastal and mountainous regions with heightened concentrations closer to urban centers. Strategies focused on enhancing lighting hardware, optimizing lighting schedules, and upgrading light sources demonstrated the impact on reducing light pollution risk levels (LPRL). This study not only lays a solid theoretical foundation for assessing urban light pollution risks but furnishes empirical evidence to aid relevant authorities in formulating effective light pollution control strategies.

Comprehensive Risk Assessment of Farmer Households in Flood and Earthquake Prone Areas Based on Multidimensional Information Diffusion Model

In recent years, the global practice of regional natural disaster reduction and comprehensive risk prevention has promoted the integration of multiple disciplines, and the theory and technology of comprehensive risk assessment for multiple natural disaster chains urgently need innovation. This article is based on multiple natural disaster chain scenarios and sets secondary indicators for multiple influencing factors based on the first level indicators composed of basic risk factors such as hazard factors, vulnerability of disaster bearing bodies, and disaster prevention and reduction capabilities. By using multidimensional information diffusion technology, multiple hazard factor hazard combination factors, vulnerability combination factors of disaster bearing bodies, and comprehensive disaster prevention and reduction capability combination factors reflecting various natural disaster scenarios are constructed, A household comprehensive risk assessment index system for multi-dimensional information diffusion assessment of natural disaster comprehensive risk was designed by quantifying risk factors such as the intensity of comprehensive disaster causing factors, vulnerability of disaster bearing bodies, and comprehensive disaster prevention and reduction capabilities in disaster scenarios. This paper expands the traditional information diffusion comprehensive risk assessment model, makes up for the lack of information on geographical units, establishes a multi impact factor multi dimension natural disaster risk comprehensive assessment model for multi risk factors of complex disaster scenarios with small samples, obtains the theoretical value of multi-dimensional information Diffusion model comprehensive risk assessment for complex disaster scenarios, breaks through the sample size limit of natural disaster risks, and makes up for the lack of information on small samples of small probability events, Improved the reliability and accuracy of comprehensive risk assessment for multiple natural disaster chains. Then, take the disaster scenario formed by the comprehensive risk of earthquake and flood in a place in the upper reaches of the Fujiang River basin as the analysis sample, calculate the comprehensive risk assessment value of flood and earthquake damage in the study area's micro samples (rural households in villages and towns). By comparison, it is found that the comprehensive risk value assessed by the multi-dimensional information Diffusion model has good reliability and high accuracy, which is significantly better than the traditional Kriging interpolation method and geographical weighted regression method. This article provides a comprehensive risk assessment of natural disasters at the micro household level, which can better reflect the diversity and regionality of risks, help clarify the hidden risks of rural natural disasters, and provide scientific basis for government emergency management decision-making.

A novel regional ship collision risk assessment framework for multi-ship encounters in complex waters

To address the issue of precisely quantifying ship collision risk and identifying collision hotspots in complex waters, this study proposed a regional ship collision risk assessment framework that includes a multi-ship encounter recognition model, a multi-ship comprehensive collision risk assessment model, and a collision hotspot recognition model. Firstly, the directed distance density-based spatial clustering of applications with noise (DBSCAN) algorithm was proposed. Based on this algorithm, a multi-ship encounter recognition model was established to cluster ship positions and obtain ship encounter clusters. Subsequently, the multi-ship comprehensive collision risk assessment model was constructed for multi-ship encounter situations based on the hierarchical classification method, enabling the determination of each ship's Comprehensive Collision Risk Value (CCRV). Finally, the collision hotspot recognition model was built using the grid analysis method, which can identify areas with a higher risk of ship collision. The automatic identification system (AIS) data of the Yangtze River estuary were used to validate the effectiveness of our framework. The results show that our framework can accurately measure ship collisions risks and identify areas with high collision risks, offering valuable insights to maritime authorities, providing actionable insights for real-time ship collision risk monitoring.

Distribution and risk assessment of microplastics in water, sediment and brine shrimps in a remote salt lake on the Tibetan Plateau, China

Microplastics (MPs) are pervasive environmental contaminants that have infiltrated even the most remote ecosystems. Despite their widespread distribution, the transfer patterns and impacts of MPs in remote lakes remain poorly understood. This study aimed to address the knowledge gap regarding the pathways and consequences of MP pollution in these isolated environments. Focusing on Kyêbxang Co, a remote salt lake in Tibet, this study investigated the transfer patterns, sources and ecological impacts of MPs, providing insights into their mobility and fate in pristine ecosystems. Water, sediment and biota (brine shrimp) samples from Kyêbxang Co, collected during the summer of 2020, were analyzed using µ-Raman spectroscopy to determine MP abundances, polymer types and potential sources. Findings indicated significant MP contamination in all examined media, with concentrations highlighting the role of runoff in transporting MPs to remote locations. The majority of detected MPs were small fragments (<0.5 mm), constituting over 93 %, with polypropylene being the predominant polymer type. The presence of a halocline may slow the descent of MPs, potentially increasing the exposure and ingestion risk to brine shrimp. Despite the currently low ecological risk estimated for MPs, this study underscores the need for long-term monitoring and development of a comprehensive ecological risk assessment model for MPs.

Comprehensive risk assessment of flammable refrigerants in HVAC systems based on Bayesian networks and consequence modeling

Promoting environmentally friendly refrigerants is a global imperative. However, their excellent thermal properties often come with flammability risks, raising concerns about HVAC safety. Existing studies focus on basic flammable characteristics and concentration distribution, neglecting comprehensive risk assessment for refrigerant leakage, diffusion, and fire. This paper fills this gap by constructing a comprehensive risk assessment model. The model integrates Bayesian networks to analyze causes of refrigerant leakage and employs CFD to evaluate distribution and residence time of flammable clouds, ignition sources, and consequences of ignition. Validation using real incident events quantitatively assesses risks associated with leaks, flammable cloud migration, ignition probability, and fire hazard. Critical failure points like improper welding and compressor exhaust are identified. Increasing ventilation speed from 0 to 1 m/s and 2 m/s reduces fire probability from 1.65 × 10−9 to 1.0 × 10−9 and 2.5 × 10−10, respectively. Effective measures to reduce leakage and fire risk are proposed based on accident experience and risk acceptability. The model is applicable not only to R290 chillers but also to other vapor compression cycles, aiding safety measures and emergency response plans for accidents involving flammable refrigerants in HVAC systems.

Construction risk of highway bridges in plateau and mountainous areas based on safety risk assessment

The study designed a risk assessment scheme to reduce the risk of highway bridge construction in highland mountainous areas, and optimised the existing hierarchical analysis method used for risk weight calculation, using entropy weight and fuzzy numbers for improvement, and designed an optimised fuzzy hierarchical entropy weight comprehensive risk assessment model. The results found that the maximum affiliation degree of site safety management risk is 0.39, which is a low-level risk; the maximum affiliation degree of personnel safety and operation quality category is 0.42, which is an intermediate risk; the maximum affiliation degree of machinery and equipment is 0.40, which is a high-level risk; the maximum affiliation degree of construction materials is 0.69, which is a low-level risk; and the maximum affiliation degree of environment category is 0.51, which is an intermediate risk. The maximum affiliation of the overall construction risk is 0.369, which indicates that the fuzzy comprehensive evaluation of the project is an intermediate risk. The results of the study show that the proposed construction risk assessment scheme for highway bridges in highland mountainous areas can provide certain reference for the construction of highland mountainous areas and avoid the corresponding safety risks.

Risk Assessment of Landfalling Tropical Cyclones in China Based on Hazard Risk Theory

As a frequent hazard, tropical cyclones have a great impact on the social and economic development of China, which is close to the origin of tropical cyclones in the western Pacific Ocean. The primary objective of this study was to construct a comprehensive risk assessment model for tropical cyclone hazards based on natural influencing factors, informing recommendations for hazard prevention and mitigation in affected regions. This research focused on tropical cyclones that made landfall in mainland China and Hainan from 1949 to 2023, utilizing hazard risk theory and classical extreme value theory. The wind speed and rainfall data during the peak cyclone periods (June to October) from 1997 to 2021 gathered from various meteorological stations, as well as altitude and vegetation cover data, were examined. Hierarchical analysis and ArcGIS spatial analysis methods were employed to study the characteristics of the spatiotemporal distribution of landfalling tropical cyclones and the comprehensive risk of tropical cyclone hazards, and the regions of China were delineated according to these methods. The results showed that, during the period from 1949 to 2023, the overall number of landfalling tropical cyclones decreased in a fluctuating manner, while the intensity of the cyclones increased. Furthermore, severe typhoons tended to occur more frequently in the summer than autumn with time, intensifying the challenge to resist short-term hazards. Moreover, the hazard-causing factors in areas affected by tropical cyclones displayed an increasing trend from north to south and from west to east. In detail, the regions sensitive to natural hazards were primarily located in the central part of Liaoning province, Tianjin, central and eastern Hebei province, Shandong province, eastern Henan province, central and northern Anhui province, Jiangsu province, and Shanghai, which are characterized by flat terrain and relatively low vegetation cover. Overall, the comprehensive risk of tropical cyclone hazards showed a geographical distribution that decreases from south to north and from east to west, with coastal cities in provinces such as Hainan, Guangdong, Guangxi, Fujian, and Zhejiang—including Haikou, Zhanjiang, Xiamen, Beihai, and Taizhou—exhibiting the highest levels of risk.

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Integrating Disaster Risks: A Comprehensive Catastrophe Risk Assessment Model and Its Empirical Validation

Integrating Disaster Risks: A Comprehensive Catastrophe Risk Assessment Model and Its Empirical Validation

Proposal of a comprehensive risk assessment model for cut slope considering long-term deterioration characteristics based on the concept of disaster immunity

In Japan, the stability of cut slopes alongside expressways has deteriorated over time due to factors such as weathering, rendering them susceptible to significant collapses, especially during heavy rainfall. This study presents an evaluation model to assess the stability of cut slopes, with the aim of capturing their long-term deterioration characteristics using the logistic regression method. Additionally, to explore variations in deterioration patterns among cut slopes in diverse regions with varying geological compositions, a hierarchical Bayesian model is utilized to estimate parameters in the logistic regression model. This methodology enables the creation of a novel stability probability evaluation model for cut slopes that is adaptable to regions with different geological structures. Moreover, to comprehensively evaluate changes in the disaster resistance capabilities of extensively used cut slopes, the post-disaster recovery capabilities in the slope region are assessed through a comprehensive weighted evaluation method (EWM-TOPSIS). Building on this assessment, a novel, simplified, and more generalized resilience evaluation method is proposed. By integrating stability probability and resilience, an evaluation metric named "disaster immunity" is introduced to measure alterations in the disaster risk resistance of extensively utilized cut slopes. A case study is presented to illustrate the application of the evaluation model on selected cut slopes from various regions. This evaluation framework can assist road managers in predicting the disaster risk status of extensively used cut slopes and identifying priority slopes for implementing preventive measures to mitigate potential losses in the event of slope disasters.

Comprehensive risk assessment of typhoon disasters in China's coastal areas based on multi-source geographic big data

Typhoons can bring substantial casualties and economic ramifications, and effective prevention strategies necessitate a comprehensive risk assessment. Nevertheless, existing studies on its comprehensive risk assessment are characterized by coarse spatial scales, limited incorporation of geographic big data, and rarely considering disaster mitigation capacity. To address these problems, this study combined multi-source geographic big data to develop the Comprehensive Risk Assessment Model (CRAM). The model integrated 17 indicators from 4 categories of factors, including exposure, vulnerability, hazard, and mitigation capacity. A subjective-objective combination weighting method was introduced to generate the indicator weights, and comprehensive risk index of typhoon disasters was calculated for 987 counties along China's coastal regions. Results revealed a pronounced spatial heterogeneity of the comprehensive typhoon risk, which exhibited an overall decreasing trend from the southeast coastal areas toward the northwest inland territories. 61.7 % of the counties exhibited a medium-to-high level of comprehensive risk, and counties with very-high risks are predominantly concentrated in the Shandong Peninsula, Yangtze River Delta, Hokkien Golden Triangle, Greater Bay Area, Leizhou Peninsula, and Hainan Province, mainly due to high exposure and hazard factors. The correlation coefficient between the risk assessment results and typhoon-induced direct economic losses reached 0.702, indicating the effectiveness and reliability of the CRAM. Meanwhile, indicators from intrinsic attributes of typhoons and geographic big data had pronounced importance, and regional mitigation capacity should be improved. Our proposed method can help to scientifically understand spatial patterns of comprehensive risk and mitigate the effects of typhoon disasters in China's coastal regions.

An Integrated Risk Assessment Methodology of In-Service Hydrogen Storage Tanks Based on Connection Coefficient Algorithms and Quintuple Subtraction Set Pair Potential

At present, there have been a number of hydrogen storage tank explosions in hydrogen filling stations, causing casualties and property losses, and having a bad social impact. This has made people realize that the risk assessment and preventive maintenance of hydrogen storage tanks are crucial. Therefore, this paper innovatively proposes a comprehensive risk assessment model based on connection coefficient algorithms and quintuple subtractive set pair potential. First of all, the constructed index system contains five aspects of corrosion factors, material factors, environmental factors, institutional factors and human factors. Secondly, a combined weighting analysis method based on FAHP and CRITIC is proposed to determine the weight of each indicator. The basic indicators influencing hydrogen storage tanks are analyzed via the quintuple subtraction set pair potential and full partial connection coefficient. Finally, the risk level and development trend of hydrogen storage tanks in hydrogen filling stations are determined by a combination of the three-category connection coefficient algorithms and the risk level eigenvalue method. The results of our case analysis show that the proposed risk assessment model can identify the main weak indicators affecting the safety of hydrogen storage tanks, including installation quality, misoperation and material quality. At the same time, it is found that the risk of high-pressure hydrogen storage tanks is at the basic safety level, and the development trend of safety conditions holds a critical value. The evaluation results can help establish targeted countermeasures for the prevention and maintenance of hydrogen storage tanks.

Integrated flood risk assessment in Hunza-Nagar, Pakistan: unifying big climate data analytics and multi-criteria decision-making with GIS

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.

Integration of ESG Factors in China's Coffee Industry and Identification of Financial Risks

With the increasing concern for sustainable development in the world, ESG (environmental, social and corporate governance) factors are playing a more and more important role in enterprise management. The purpose of this study is to explore the influence of the integration of ESG factors in China's coffee industry on financial risk identification. By systematically integrating ESG factors such as environmental friendliness, social responsibility and corporate governance, we build a comprehensive financial risk assessment model to identify the potential risks faced by coffee enterprises more comprehensively and accurately. In the method, CatBoost algorithm is used to identify financial risks, and ADASYN algorithm is used to optimize the training set to improve the sensitivity of the model to default risks. The research results show that through the integration of ESG factors, our model has achieved remarkable results in financial risk identification. The Accuracy value reached 0.9671, 12 out of 15 default samples were accurately predicted, and the identification of non-default companies also had strong accuracy, only 2 of them were misjudged. This shows that ESG factors not only have an important impact on sustainable management in the coffee industry, but also can provide strong support for enterprises in identifying financial risks.

Construction and Validation of a Nomogram to Predict the Postoperative Venous Thromboembolism Risk in Patients with HGSOC.

Venous thromboembolism (VTE) is a common complication in patients with high-grade serous ovarian cancer (HGSOC) after surgery. This study aims to establish a comprehensive risk assessment model to better identify the potential risk of postoperative VTE in HGSOC. Clinical data from 587 HGSOC patients who underwent surgical treatment were retrospectively collected. Univariate and multivariate logistic regression analyses were performed to identify independent factors influencing the occurrence of postoperative VTE in HGSOC. A nomogram model was constructed in the training set and further validated in the verification set. Logistic regression identified age (odds ratio [OR] = 1.063, P = .002), tumor size (OR = 3.815, P < .001), postoperative transfusion (OR = 5.646, P = .001), and postoperative D-dimer (OR = 1.246, P = .003) as independent risk factors for postoperative VTE in HGSOC patients. A nomogram was constructed using these factors. The receiver operating characteristic curve showed an area under the curve (AUC) of 0.840 (95% confidence interval [CI]: 0.782, 0.898) in the training set and 0.793 (95% CI: 0.704, 0.882) in the validation set. The calibration curve demonstrated a good consistency between model predictions and actual results. The decision curve analysis indicated the model benefits at a threshold probability of less than 70%. A nomogram predicting postoperative VTE in HGSOC was established and validated. This model will assist clinicians in the early identification of high-risk patients, enabling the implementation of appropriate preventive measures.

Elucidating the Influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma.

Clear cell renal carcinoma (ccRCC) stands as the prevailing subtype among kidney cancers, making it one of the most prevalent malignancies characterized by significant mortality rates. Notably,mitochondrial permeability transition drives necrosis (MPT-Driven Necrosis) emerges as a form of cell death triggered by alterations in the intracellular microenvironment. MPT-Driven Necrosis, recognized as a distinctive type of programmed cell death. Despite the association of MPT-Driven Necrosis programmed-cell-death-related lncRNAs (MPTDNLs) with ccRCC, their precise functions within the tumor microenvironment and prognostic implications remain poorly understood. Therefore, this study aimed to develop a novel prognostic model that enhances prognostic predictions for ccRCC. Employing both univariate Cox proportional hazards and Lasso regression methodologies, this investigation distinguished genes with differential expression that are intimately linked to prognosis.Furthermore, a comprehensive prognostic risk assessment model was established using multiple Cox proportional hazards regression. Additionally, a thorough evaluation was conducted to explore the associations between the characteristics of MPTDNLs and clinicopathological features, tumor microenvironment, and chemotherapy sensitivity, thereby providing insights into their interconnectedness.The model constructed based on the signatures of MPTDNLs was verified to exhibit excellent prediction performance by Cell Culture and Transient Transfection, Transwell and other experiments. By analyzing relevant studies, we identified risk scores derived from MPTDNLs as an independent prognostic determinant for ccRCC, and subsequently we developed a Nomogram prediction model that combines clinical features and associated risk assessment. Finally, the application of experimental techniques such as qRT-PCR helped to compare the expression of MPTDNLs in healthy tissues and tumor samples, as well as their role in the proliferation and migration of renal clear cell carcinoma cells. It was found that there was a significant correlation between CDK6-AS1 and ccRCC results, and CDK6-AS1 plays a key role in the proliferation and migration of ccRCC cells. Impressive predictive results were generated using marker constructs based on these MPTDNLs. In this research, we formulated a new prognostic framework for ccRCC, integrating mitochondrial permeability transition-induced necrosis. This model holds significant potential for enhancing prognostic predictions in ccRCC patients and establishing a foundation for optimizing therapeutic strategies.

A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or ‘fracking’. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study’s results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations.

A Comprehensive Model for Predicting 1-Year Survival after Allogeneic Hematopoietic Cell Transplantation in Older Patients

Background: As the number of elderly patients referred to and undergoing allogeneic hematopoietic cell transplantation (alloHCT) continues to rise, there is a growing need for a comprehensive risk assessment model to accurately predict transplantation outcomes in this specific patient population. The widely used Hematopoietic Cell Transplantation-specific Comorbidity Index (HCT-CI) does not fully capture factors influencing transplant outcomes in elderly patients. This study aims to enhance risk stratification for elderly patients undergoing alloHCT by integrating additional functional, nutritional, and clinical indicators. Methods: This is a retrospective study utilizing the data registry of HCT patients who received their treatment at UF Health Cancer Center. The inclusion criteria included all patients age &amp;gt; 65 years, who underwent their first alloHSCT between January 2012 and December 2022, regardless of graft type, conditioning regimen, donor type, or graft source. Patient data were extracted through chart review. The primary endpoint was 1-year Overall Survival (OS). Prognostic significance of each variable was assessed using Cox proportional hazard models. We subsequently used all variables to build a comprehensive prediction model to assess 1-year survival outcomes in elderly patients undergoing alloHCT. The predictive model's performance was measured by the Concordance-statistic (c-statistic). Results: A total of 69 patients satisfied the inclusion criteria and were included in the analysis. Median age was 69 years (range 66-81) and 65.2% were male. Unrelated donors were used in 66.6% of patients, while the remainder received grafts from related donors. Most patients received peripheral blood stem cell grafts (91%). Myeloid malignancies comprised the most common indication for alloHCT in this group of patients (89.8%). 59.4% had abnormal cytogenetics. 35 variables were considered in the analysis. In the univariate analysis, variables with impact on 1-year OS post-transplant included creatinine (HR 3.35, CI 1.62-6.91, p=0.001), blood urea nitrogen (HR 1.03, CI 1.01-1.06, p=0.007), estimated glomerular filtration rate (HR 0.93, CI 0.89-0.93, p=0.001), hemoglobin (HR 0.8, CI 0.66-0.97, p=0.02), abnormal cytogenetics (HR 2.27, CI 1.20-4.31, p=0.012) and high and very high disease risk index (HR 0.51, CI 0.27-0.98, p=0.04). Notably, both hemoglobin and cytogenetics demonstrated a c-statistic greater than 0.6 in the univariate analysis. In the multivariate analysis, variables impacting 1-year OS included hemoglobin (HR 0.69, CI 0.48-0.99, p=0.04), remission status at the time of transplant i.e., not reached (NR) vs complete remission 1 (CR1) (HR 0.05, CI 0.00-0.71, p=0.027), and abnormal cytogenetics (HR 7.55, CI 2.32-24.58, p=0.001). Interestingly, the HCT-CI did not significantly impact 1-year OS in the univariate analysis (c-statistic 0.51, HR 0.99, CI 0.85-1.15, p=0.896). Our prediction model showed good performance with a mean area under the receiver operating characteristic curve of 0.82. Conclusion: We developed a comprehensive prediction model to assess 1-year OS in elderly patients undergoing alloHCT to overcome some of the limitations of the HCT-CI in this unique age group. This model holds potential to enhance decision-making regarding candidacy for elderly patients undergoing alloHCT.

Exploring the Mutual Influence Relationships of International Airport Resilience Factors from the Perspective of Aviation Safety: Using Fermatean Fuzzy DEMATEL Approach

International airports are responding to the threat of climate change and various man-made hazards by proposing impact protection measures. Airport managers and risk controllers should develop a comprehensive risk assessment model to measure the mutual influence relationships of resilience factors. In this paper, the problem of treating resilience factors as independent ones in previous studies is overcome. In this study, we not only develop a framework for assessing resilience factors in international airports based on an aviation safety perspective, but also develop the Fermatean fuzzy decision-making trial and evaluation laboratory (FF-DEMATEL) to identify the mutual influence relationships of resilience factors. Fermatean fuzzy sets are incorporated in DEMATEL to reflect information incompleteness and uncertainty. The critical resilience factors of international airports were identified through real-case analysis. In terms of importance, the results show that rescue capability is a core capability that is important for airport resilience. In addition, “security management system (SeMS) integrity”, “education and training of ground staff on airport safety awareness”, “first aid mechanism for the injured”, and “adequate maintenance equipment for rapid restoration tasks” are identified as key factors that are given more weights. On the other hand, in terms of influence strength, the detection capability has the highest total influence and significantly influenced the other resilience capabilities. Finally, the influential network relation map (INRM) is utilized to assist decisionmakers in swiftly comprehending the impact of factors and formulating viable strategies to enhance airport resilience. This enables airport managers and risk controllers to make informed decisions and allocate resources efficiently.