Effective Risk Assessment Research Articles

A Novel Framework for Data Assessment That Uses Edge Technology to Improve the Detection of Communicable Diseases.

Spreading quickly throughout populations, whether animal or human-borne, infectious illnesses provide serious risks and difficulties. Controlling their spread and averting disinformation requires effective risk assessment and epidemic identification. Technology-enabled data analysis on diseases allows for quick solutions to these problems. A Combinational Data Assessment Scheme intended to accelerate disease detection is presented in this paper. The suggested strategy avoids duplicate data replication by sharing data among edge devices. It uses indexed data gathering to improve early detection by using tree classifiers to discern between various kinds of information. Both data similarity and index measurements are considered throughout the data analysis stage to minimize assessment errors. Accurate risk detection and assessment based on information kind and sharing frequency are ensured by comparing non-linear accumulations with accurate shared edge data. The suggested system exhibits high accuracy, low mistakes, and decreased data repetition to improve overall effectiveness in illness detection and risk reduction.

Early Breast Cancer Risk Assessment: Integrating Histopathology with Artificial Intelligence.

Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes.

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocarcinogens (GHCs) in rats. Microarray gene expression data from the livers of rats administered a single dose of 58 compounds, including 5 GHCs, was obtained from the Open TG-GATEs database and used for the identification of marker genes and the construction of a predictive classifier to identify GHCs in rats. We identified 10 gene markers commonly responsive to all 5 GHCs and used them to construct a support vector machine-based predictive classifier. In the silico validation using the expression data of the Open TG-GATEs database indicates that this classifier distinguishes GHCs from other compounds with high accuracy. To further assess the model's effectiveness and reliability, we conducted multi-institutional 1-day single oral administration studies on rats. These studies examined 64 compounds, including 23 GHCs, with gene expression data of the marker genes obtained via quantitative PCR 24h after a single oral administration. Our results demonstrate that qPCR analysis is an effective alternative to microarray analysis. The GHC predictive model showed high accuracy and reliability, achieving a sensitivity of 91% (21/23) and a specificity of 93% (38/41) across multiple validation studies in three institutions. In conclusion, the present 1-day single oral administration model proves to be a reliable and highly sensitive tool for identifying GHCs and is anticipated to be a valuable tool in identifying and screening potential GHCs.

Analyzing the Effect of Liquidity on Financial Stability: Evidence from Kenyan Deposit-Taking Savings and Credit Cooperative Societies

Non-performing loans have been on the rise among DT SACCOs in Kenya over the past five years as evidenced by the increase in percentage of NPLs to gross loans in SACCO regulatory authority report of 2020. Consequently, if this trend is allowed to continue then this sector’s contribution to financial intermediation through provision of financial services will be negatively affected. In view of the above this study sought to investigate the effect of firm characteristics and financial stability of deposit taking savings and credit cooperative societies in Kenya. In view of the above this study sought to assess the effect of liquidity on financial stability of deposit taking savings and credit and cooperative societies in Kenya. The study was anchored on agency theory. Positivist research philosophy was adopted in this study. The study adopted explanatory research design. The target population for the study comprised 160 DT SACCOs which were fully operational in the period. A census approach was used for the study. This study utilized quantitative secondary data which was obtained from the society’s financial statements and supervision reports from the savings and credit cooperatives regulatory authority. The study utilized annual panel data for the period of 2017 to 2021. Multicollinearity test, normality tests, autocorrelation test, homoscedasticity, stationarity test and model specification test were carried out prior to panel data analysis. Data was analyzed using descriptive statistics, Pearson’s correlation analysis and panel regression analysis. STATA software was used for the analysis. The findings showed that liquidity had a strong, positive effect on NPLs ratio (β = 0.410056, p=0.003<0.05). In view of the findings, the study recommends that DT SACCOs with high liquidity levels should consider implementing rigorous lending practices to ensure that loans are extended to creditworthy borrowers. Additionally, effective credit risk assessment and continuous monitoring of borrower repayment behavior are essential to minimize NPLs. DT SACCOs should focus on improving management efficiency by implementing cost-effective operational processes. Keywords: Liquidity, Liquidity Ratio, Financial stability, Deposit-taking, Savings and Credit Cooperative Societies (SACCOs), Kenya.

Listeria monocytogenes from Food Products and Food Associated Environments: Antimicrobial Resistance, Genetic Clustering and Biofilm Insights.

Listeria monocytogenes, a foodborne pathogen, exhibits high adaptability to adverse environmental conditions and is common in the food industry, especially in ready-to-eat foods. L. monocytogenes strains pose food safety challenges due to their ability to form biofilms, increased resistance to disinfectants, and long-term persistence in the environment. The aim of this study was to evaluate the presence and genetic diversity of L. monocytogenes in food and related environmental products collected from 2014 to 2022 and assess antibiotic susceptibility and biofilm formation abilities. L. monocytogenes was identified in 13 out of the 227 (6%) of samples, 7 from food products (meat preparation, cheeses, and raw milk) and 6 from food-processing environments (slaughterhouse-floor and catering establishments). All isolates exhibited high biofilm-forming capacity and antibiotic susceptibility testing showed resistance to several classes of antibiotics, especially trimethoprim-sulfamethoxazole and erythromycin. Genotyping and core-genome clustering identified eight sequence types and a cluster of three very closely related ST3 isolates (all from food), suggesting a common contamination source. Whole-genome sequencing (WGS) analysis revealed resistance genes conferring resistance to fosfomycin (fosX), lincosamides (lin), fluoroquinolones (norB), and tetracycline (tetM). In addition, the qacJ gene was also detected, conferring resistance to disinfecting agents and antiseptics. Virulence gene profiling revealed the presence of 92 associated genes associated with pathogenicity, adherence, and persistence. These findings underscore the presence of L. monocytogenes strains in food products and food-associated environments, demonstrating a high virulence of these strains associated with resistance genes to antibiotics, but also to disinfectants and antiseptics. Moreover, they emphasize the need for continuous surveillance, effective risk assessment, and rigorous control measures to minimize the public health risks associated to severe infections, particularly listeriosis outbreaks. A better understanding of the complex dynamics of pathogens in food products and their associated environments can help improve overall food safety and develop more effective strategies to prevent severe health consequences and economic losses.

Assessment of radiation exposure in the phosphate mining area of Gafsa (Southeastern Tunisia)

ABSTRACT This study investigates the radiological health implications of sixty-three (63) samples of soil, food, and drinking water collected from the phosphate mining area of Gafsa in Southeastern Tunisia. Additionally, it assesses the exposure to radon concentrations in houses located around the phosphate mining sites. The activity concentrations in soil samples were found to be 382.1, 36.1 and 16.3 Bq kg−1 respectively for 40K, 232Th and 226Ra. The average activity of 232Th was higher than worldwide average value of 30 Bq kg−1. Eleven (11) radiological hazard parameters were computed based on various exposure scenarios including external gamma dose rate (GDR), annual effective dose (AED) and risk assessment (RA). The calculated GDR values were of 3.2 nGy h−1 in food and 45.4 nGy h−1 in soil samples, while the average AED values were of 3.9 µSv y−1 in food and 83.5 µSv y−1 in soil samples. Internal AED due to ingestion of radionuclides was found to have a mean value of 0.1 µSv y−1 in soil, 264.4 µSv y−1 in food and 10 µSv y−1 in drinking water samples. The AED values due to inhalation of soil-dust and radon were assessed to be 2.1 and 1.2 µSv y−1, respectively. The RA average values in food and drinking water samples were found to be 3 × 10−5 and 2.4 × 10−4, respectively. Based on the exposure pathway, inhalation of radioactivity through soil and radon resulted in highest total AED values, which remained below the reference limit. This was followed by internal exposure, with total AED values of 274.5 µSv y−1.

Unlocking the complete blood count as a risk stratification tool for breast cancer using machine learning: a large scale retrospective study

Optimizing early breast cancer (BC) detection requires effective risk assessment tools. This retrospective study from Brazil showcases the efficacy of machine learning in discerning complex patterns within routine blood tests, presenting a globally accessible and cost-effective approach for risk evaluation. We analyzed complete blood count (CBC) tests from 396,848 women aged 40–70, who underwent breast imaging or biopsies within six months after their CBC test. Of these, 2861 (0.72%) were identified as cases: 1882 with BC confirmed by anatomopathological tests, and 979 with highly suspicious imaging (BI-RADS 5). The remaining 393,987 participants (99.28%), with BI-RADS 1 or 2 results, were classified as controls. The database was divided into modeling (including training and validation) and testing sets based on diagnostic certainty. The testing set comprised cases confirmed by anatomopathology and controls cancer-free for 4.5–6.5 years post-CBC. Our ridge regression model, incorporating neutrophil–lymphocyte ratio, red blood cells, and age, achieved an AUC of 0.64 (95% CI 0.64–0.65). We also demonstrate that these results are slightly better than those from a boosting machine learning model, LightGBM, plus having the benefit of being fully interpretable. Using the probabilistic output from this model, we divided the study population into four risk groups: high, moderate, average, and low risk, which obtained relative ratios of BC of 1.99, 1.32, 1.02, and 0.42, respectively. The aim of this stratification was to streamline prioritization, potentially improving the early detection of breast cancer, particularly in resource-limited environments. As a risk stratification tool, this model offers the potential for personalized breast cancer screening by prioritizing women based on their individual risk, thereby indicating a shift from a broad population strategy.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Prediction of flood sensitivity based on Logistic Regression, eXtreme Gradient Boosting, and Random Forest modeling methods.

Floods are one of the most destructive disasters that cause loss of life and property worldwide every year. In this study, the aim was to find the best-performing model in flood sensitivity assessment and analyze key characteristic factors, the spatial pattern of flood sensitivity was evaluated using three machine learning (ML) models: Logistic Regression (LR), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). Suqian City in Jiangsu Province was selected as the study area, and a random sample dataset of historical flood points was constructed. Fifteen different meteorological, hydrological, and geographical spatial variables were considered in the flood sensitivity assessment, 12 variables were selected based on the multi-collinearity study. Among the results of comparing the selected ML models, the RF method had the highest AUC value, accuracy, and comprehensive evaluation effect, and is a reliable and effective flood risk assessment model. As the main output of this study, the flood sensitivity map is divided into five categories, ranging from very low to very high sensitivity. Using the RF model (i.e., the highest accuracy of the model), the high-risk area covers about 44% of the study area, mainly concentrated in the central, eastern, and southern parts of the old city area.

Assessment of the Developmental Neurotoxicity of Environmental Chemicals: Investigating the Mechanisms & Identifying Potential Biomarkers

Background: Developmental neurotoxicity resulting from exposure to environmental chemicals is a significant public health concern. Understanding the mechanisms underlying such neurotoxicity and identifying potential biomarkers are essential for early detection and effective risk assessment. Objective: This study aimed to assess the developmental neurotoxicity of selected environmental chemicals and investigate the underlying mechanisms. Additionally, the study sought to identify potential biomarkers for early detection and monitoring of developmental neurotoxicity. Methods: In vitro neurodevelopmental models were established, including neuronal cell cultures and organoid systems, to evaluate the neurotoxic effects of environmental chemicals. Key endpoints, such as neuronal viability, morphology, neurite outgrowth, synaptogenesis, and synaptic activity, were assessed using appropriate assays and imaging techniques. Mechanistic investigations involved exploring oxidative stress, inflammation, disruption of neurotransmitter systems, and interference with neurodevelopmental processes through gene expression analysis, protein profiling, and signaling pathway investigations. Results: Exposure of the in vitro models to selected environmental chemicals resulted in significant neurotoxic effects, including impaired neuronal viability, disrupted morphological development, and altered synaptic activity. Mechanistic investigations revealed the involvement of oxidative stress and disruption of neurotransmitter systems in the observed neurotoxicity. Furthermore, several potential biomarkers, including gene expression changes and epigenetic modifications, showed significant correlations with developmental neurotoxicity. Conclusion: This study demonstrates the developmental neurotoxicity of environmental chemicals in in vitro neurodevelopmental models. The findings highlight the role of oxidative stress and neurotransmitter disruption as key mechanisms underlying neurotoxic effects. Moreover, the identification of potential biomarkers provides promising avenues for early detection and monitoring of developmental neurotoxicity. These results contribute to a better understanding of the risks associated with environmental chemical exposure during neurodevelopment and have implications for risk assessment and regulatory guidelines.

Literature Review: the Relationship Between Internal Control System of the Government (SPIP), Good Government Governance (GGG), And Fraud Prevention

The aim of this article is to clarify the connection between the government's internal control system, good government governance, and endeavors to thwart fraud, alongside the influencing factors. The methodology utilized encompasses a review of literature utilizing data drawn from international and national journals, with a total of 23 journals as the sample. From the analysis, it was determined that good government governance serves as an intermediary between the internal control system of government and the action of fraud prevention. In simpler terms, the more robust the internal control, the more adept it is at fostering effective and high-quality good governance, ultimately bolstering efforts to prevent fraud. Elements that must be taken into account for effective fraud prevention encompass: (1) Enhancing internal government supervision involves documenting and reinforcing the organization's dedication to the relevant code of ethics and conduct; (2) Ensuring a distinct division of roles and responsibilities; (3) Interpreting regulations comprehensively and ensuring consistent, unambiguous, and clear meanings; (4) It is essential to have qualified personnel matching their fields and expertise, whether through recruitment or competency development like education and training; (5) Utilizing information technology; (6) Conducting effective risk assessment and monitoring is crucial for overseeing organizational and identifying potential fraud risks.

МАТЕМАТИЧЕСКИЕ МОДЕЛИ И ЧИСЛЕННЫЕ МЕТОДЫ В КАРДИОЛОГИИ: СОБСТВЕННЫЙ ОПЫТ

Вackground. In the era of digital transformation in medicine, which is radically changing the way patient information is stored, processed and used, medical professionals need new tools and approaches that can efficiently process and interpret data for fast and accurate decision making. These methods also enable the creation of personalized treatment approaches, taking into account individual patient characteristics and risks. In this article, the authors share their own experience in applying numerical methods in cardiology. Purpose of the study. To develop methods of effective risk assessment of cardiovascular outcomes in different categories of patients. Characteristics of patients and methods of the study. Three variants of outcome prediction were included in the study. The 1st - assessment of the total risk of one-year cardiovascular outcomes after myocardial infarction with ST-segment elevation, the 2nd - assessment of the risk of postinfarction chronic heart failure, the 3rd – assessment of the risk of progressive course of atherosclerosis. Statistical analysis methods included the free cross-platform visual programming system Orange, the SPSS 22.0 package, and the C programming language console. Results and Conclusion. The conducted study has shown that the application of different approaches: regression analysis, machine learning, Excel programming capabilities, C programming language and console interface allows to develop models of cardiovascular outcomes prediction with high accuracy.

Theoretical Aspects of Organizational Risk Management

Risk is multifaceted and complex, shaped by the industry-specific factors and inherent uncertainties. Risk assessment is a dynamic process that involves identifying, analyzing, and evaluating potential hazards to make informed decisions about risk mitigation. In industrial settings, this process is necessary to create a safe working environment and protect the overall well-being of the organization. Effective risk assessment involves the use of multiple factors and methods to address different types of risks. The article discusses the means, ways and methods used by enterprises to effectively assess risks. In addition, the strategies are studied - financial, operational, organizational, economic, credit and technical risks, which are used to assess different types of risks. Keywords: Risk, risk assessment, interest rate, financial, operational, organizational, credit risk.

A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.

A dynamic risk assessment model based on multidimensional and quantitative inference theory

With the continuous increase in petroleum consumption and the trend towards more refined management, crude oil storage tanks are playing an increasingly crucial role in the petrochemical industry. However, the corrosion issue of crude oil storage tanks has consistently been a major factor limiting their service life, and leaks in crude oil tanks are often attributed to corrosion of the tank bottom. Due to the high cost and complexity associated with tank opening inspections, in this paper, directly observable information and physical quantities are utilized as indirect evidence, combined with Bayesian network, to indirectly determine the probability of tank corrosion. This approach allows for selective internal tank inspections, enabling effective risk assessment while also facilitating cost savings. Some parameters in the Bayesian network model are obtained through a probability estimation model combining expert experience and fuzzy set theory. Finally, the feasibility of the method is demonstrated by citing examples. The current model can offer a relatively reliable reference for evaluating the risks associated with tanks and their ancillary facilities, while also reducing the costs of tank inspections to some extent.

Reducing Rockfall Hazards in Tunnel Construction: A Case Study Using Root Cause Analysis Methodology

Abstract 
 
 Assessing the conditions that create a rockfall hazard is a realistic goal in efforts to reduce worker injuries related to rockfall incidents. If tunnel workers can properly assess the risks of rockfall, with appropriate technical and administrative controls, they can better mitigate the risks. To achieve this goal, the Root Cause Analysis (RCA) methodology can be considered as a risk assessment method. An effective risk assessment method should include the ability to observe variable ceiling conditions and assess how much potential they represent for injuring workers. RCA's ability to prioritize the risks associated with changing conditions provides significant benefits to anyone responsible for designing, approving, or installing controls that are reasonably repeatable and stabilize the ceiling or reduce the risk of material falling from the roof. Using the RCA methodology, this study presents a case analysis of the risks and causes of rockfall incidents in a metro construction project. This study explains the use of an RCA methodology that can help improve system-level failures and weaknesses, such as rocks falling from the ceiling of a tunnel. It can be seen that personnel problems, management failures and information systems problems are now replacing technical failures as the focus of most accident analyses.

Application of Machine Learning to Customer Churn Risk Prediction

Accompanied by technological globalization and upswing of telecommunication industry in the 21st century, the number of operators is springing up like mushrooms after rain in the market and that intensifies the industry competition environment due to the unprecedented growth trend and the challenges. As a research hotspot in the field of business analysis, prediction of customer attrition risk possesses some extensive range pertaining to applications within global marketing, telecommunications and other fields. Due to the complex relationship between customer information and the products used, it is difficult for merchants to conduct effective risk assessments. Nevertheless, customer churn risk prediction has made such rapid progress over the past few years with the development within computer science and machine learning so that the researchers would be able to establish more potential connections and achieve more accurate predictions about customer churn. This article summarizes customer churn risk prediction in the order of time and technology iteration, and mainly introduces 9 classic prediction methods based on machine learning. Additionally, four relevant performance measurement metrics and one data set are explored. Eventually, in view of the problems existing in the current customer churn risk prediction methods, prospects for future research are proposed.

Assessing fragility functions of unreinforced masonry buildings

The seismic vulnerability entrenched within the historical urban landscapes of Algeria serves as a poignant reminder of the pressing need for comprehensive risk management strategies, particularly concerning low and medium height unreinforced masonry (URM) structures. These architectural gems, rich in cultural significance, stand as tangible embodiments of Algeria's storied past. However, their susceptibility to seismic disturbances presents an immediate threat, demanding meticulous attention and innovative methodologies for effective risk assessment and mitigation. In a pioneering scholarly pursuit, this research embarks on an ambitious exploration, melding fragility functions and a sophisticated vulnerability index method to unravel the intricate tapestry of seismic risk assessment. Through the lens of fragility functions, the study delves into a nuanced analysis, probabilistically delineating the vulnerabilities entrenched within the complex framework of URM buildings. Furthermore, the meticulous derivation of vulnerability functions via the vulnerability index method enhances the granularity of risk assessment, providing a multifaceted perspective to discern vulnerabilities accurately. Central to this investigation is the Belouizdad district, a microcosm of historical significance nestled within the vibrant metropolis of Algiers. By scrutinizing seismic scenarios spanning a spectrum of intensities (ranging from VII to X), the study orchestrates a symphony of empirical data and analytical prowess, unraveling the potential seismic reverberations poised to impact the district's architectural heritage. In essence, this scholarly endeavor transcends the confines of mere academic pursuit, serving as a guiding light illuminating the path towards tailored seismic reduction policies. It stands as a clarion call to safeguard Algeria's architectural patrimony, fostering resilience amidst the tremors of uncertainty and ensuring the preservation of its cultural legacy for generations to come.

Implementing a vegetation-based risk index to support management actions in Mediterranean coastal dunes

Coastal dunes play a crucial role in mitigating sea-related impacts and safeguarding coastlines. However, increasing human influence and natural factors such as sea-level rise underscore the need for effective coastal risk assessment methodologies. This study introduces a comprehensive coastal risk index covering 24 km of the Italian coastline within the protected area of San Rossore Park (Tuscany, Italy). The study area, distinguished by its notable coastal dune ecosystems, holds naturalistic, cultural, and economic importance. Nevertheless, diverse uses, zoning, and human impact variables pose challenges.By incorporating geological, socioeconomic, cultural, and ecological parameters, the index integrates a range of data sources and field observations. This research focused on developing and applying a vegetation-based risk index (VRI) within a geographic information system (GIS) framework, recognizing the ecological importance of dune vegetation in mitigating coastal erosion. Analysisrevealed varying risk levels within the study area. Half of the San Rossore Park coastline exhibited low risk values, 37.5% had moderate risk values, and 12.5% had high risk values. The publicly accessible northernmost section displays excellent preservation of dune habitats but faces heightened risk due to anthropogenic impacts. Conversely, the central-southern portion, inaccessible to the public, registers high-risk levels linked to variables associated with coastal erosion.Furthermore, the results highlight areas with heightened cultural and ecological vulnerabilities aligned with elevated risk levels. The index facilitates clear and intuitive cartographic representations of coastal risk, identifying variable categories that substantially influence on risk determination. Tailored strategies, including mitigating human pressure in the northern sector and implementing erosion management in the central-southern region, are recommended.In summary, this study not only provides a practical tool for assessing and managing coastal areas and directing attention to specific threats but also supports stakeholders in informed decision-making. The VRI enhances global sustainable coastal conservation, deepening our understanding of coastal risks and providing valuable insights for effective management strategies.

Risk Prioritization in A Manufacturing Project with Fuzzy SWARA and Fuzzy MOORA Methods

Businesses must constantly change and develop to keep up with customer needs and changes in the market. Not being able to adapt to change or not being ready enough can bring risks. These changes and risks can affect both the process and the outputs of the process. It is essential to be prepared for the possible risks experienced in the operations, especially in project-based, production-to-order enterprises, to meet the customer's demands and to comply with the delivery date. Businesses must implement an effective risk assessment process to achieve this. This study implemented a risk prioritization for a new business project that produces energy storage systems by having to order. Changes must be made in the production process for an additional feature to be added to the product in line with the customer's request. The risks posed by the change and the short deadline were analyzed and prioritized. A new model was developed by developing the classical Decision Matrix Risk Assessment (DMRA) method, one of the risk assessment methods to evaluate the ten identified risks. The "severity" component of the classical DMRA method is divided into three sub-components (the severity of delivery time, the severity of cost, and the severity of quality) to be evaluated more specifically. Moreover, since the weights of these sub-components on the risks are different, the components are weighted with the Fuzzy SWARA method. The weights obtained from the Fuzzy SWARA method are used in the Fuzzy MOORA method, and the risks are prioritized. Thus, the effectiveness of the classical risk assessment approach has increased by detailing the "severity" component of the risk assessment components and weighting and prioritizing the components using multi-criteria decision-making (MCDM) techniques.