Marina Tumolo, V. Ancona, Domenico De Paola
et al.
Chromium is a potentially toxic metal occurring in water and groundwater as a result of natural and anthropogenic sources. Microbial interaction with mafic and ultramafic rocks together with geogenic processes release Cr (VI) in natural environment by chromite oxidation. Moreover, Cr (VI) pollution is largely related to several Cr (VI) industrial applications in the field of energy production, manufacturing of metals and chemicals, and subsequent waste and wastewater management. Chromium discharge in European Union (EU) waters is subjected to nationwide recommendations, which vary depending on the type of industry and receiving water body. Once in water, chromium mainly occurs in two oxidation states Cr (III) and Cr (VI) and related ion forms depending on pH values, redox potential, and presence of natural reducing agents. Public concerns with chromium are primarily related to hexavalent compounds owing to their toxic effects on humans, animals, plants, and microorganisms. Risks for human health range from skin irritation to DNA damages and cancer development, depending on dose, exposure level, and duration. Remediation strategies commonly used for Cr (VI) removal include physico-chemical and biological methods. This work critically presents their advantages and disadvantages, suggesting a site-specific and accurate evaluation for choosing the best available recovering technology.
T. Akinosho, Lukumon O. Oyedele, Muhammad Bilal
et al.
Abstract The construction industry is known to be overwhelmed with resource planning, risk management and logistic challenges which often result in design defects, project delivery delays, cost overruns and contractual disputes. These challenges have instigated research in the application of advanced machine learning algorithms such as deep learning to help with diagnostic and prescriptive analysis of causes and preventive measures. However, the publicity created by tech firms like Google, Facebook and Amazon about Artificial Intelligence and applications to unstructured data is not the end of the field. There abound many applications of deep learning, particularly within the construction sector in areas such as site planning and management, health and safety and construction cost prediction, which are yet to be explored. The overall aim of this article was to review existing studies that have applied deep learning to prevalent construction challenges like structural health monitoring, construction site safety, building occupancy modelling and energy demand prediction. To the best of our knowledge, there is currently no extensive survey of the applications of deep learning techniques within the construction industry. This review would inspire future research into how best to apply image processing, computer vision, natural language processing techniques of deep learning to numerous challenges in the industry. Limitations of deep learning such as the black box challenge, ethics and GDPR, cybersecurity and cost, that can be expected by construction researchers and practitioners when adopting some of these techniques were also discussed.
Sustainable risk and disaster management in the built environment has become a critical research focus amid escalating environmental challenges. Building Information Modeling (BIM) is recognized as a key digital tool for enhancing disaster resilience through simulation, data integration, and collaborative management. This study systematically reviews BIM applications in sustainable risk and disaster management from 2014 to 2024, employing the PRISMA framework, literature coding, and network analysis. Five primary research clusters are identified: (a) sustainable construction and life cycle assessment, (b) performance evaluation and implementation, (c) technology integration and digital innovation, (d) Historic Building Modeling (HBIM) and post-disaster reconstruction, and (e) project management and technology adoption. Despite increasing scholarly attention, the field remains dominated by conceptual studies, with limited empirical exploration of emerging technologies such as artificial intelligence (AI). Four key challenges are highlighted: weak foundational integration with structural risk research, technological bottlenecks in AI and digital applications, limited practical implementation, and insufficient linkage between sustainability and risk management. Future trends are expected to focus on achieving Industry 4.0 interoperability, advancing AI-driven intelligent disaster response, and adopting multi-objective optimization strategies balancing resilience, sustainability, and cost-effectiveness. This study provides a comprehensive overview of the field’s evolution and offers insights into strategic directions for future research and practical innovation.
Abstract The degree of safe drinking water scarcity worldwide is increasing due to climate change, droughts, population growth, and inadequate management, affecting 2.2 billion people worldwide and causing 829,000 deaths annually, necessitating effective systems and regulatory frameworks. Understanding the knowledge, attitudes, perceptions and behaviors of industry workers is crucial for upholding food safety systems. This study aimed to investigate the effects of FBOs’ knowledge and attitudes on their perceptions and intended behavior during the implementation of food safety programs in bottled water factories. The quantitative data were collected through laboratory testing, online surveys, interviews, and document reviews. Qualitative data were obtained through observation and employee interviews. Smart PLS modeling was used to illustrate relationships and assess model fit. The evaluation process involved assessing the impact of knowledge and attitudes on perceptions and their subsequent influence on intended behavior. The significance level was set at p ≤ 0.05. The assessment of the measurement model revealed no common method bias or multicollinearity problems, confirming the validity and reliability of the construct. The research identified a positive relationship between knowledge, attitudes, and risk perception, as well as between risk perception and intended behavior. Seventy percent of the workers had good health, but the swab test results indicated the presence of numerous microorganisms on their hands both before and after washing. A considerable number of participants exhibited good knowledge, perceptions, and attitudes, while a significant portion displayed moderate intended behavior. Understanding safety regulations, positive attitudes, and heightened risk perceptions impacts the workplace culture of food manufacturing workers. Ongoing training and reinforcement improve the manufacturing process and consumer confidence. More research is necessary to understand workers' lack of enthusiasm for safety programs.
With the advent of digitization, digital twin technology is gradually becoming one of the core technologies of the Industry 4.0 era, highlighting the increasing importance of digital twin project management. Despite its potential, DT projects face significant risks during implementation, stemming from technical, managerial, and operational complexities. To address these challenges, this study proposes an improved failure mode and effect analysis (FMEA) framework by integrating double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This framework converts qualitative assessments into quantitative metrics and calculates weights using a hybrid approach, enabling more precise risk prioritisation. Application of the model to an automotive manufacturing company’s DT project identified key risks, particularly in the iteration and upgrade phase, emphasising the importance of cross-departmental collaboration and robust digital infrastructure. The proposed model provides a systematic framework for enterprises to assess and mitigate risks, ensuring the successful deployment of DT projects.
<b>Background</b>: The construction industry faces significant safety hazards, frequent accidents, and inadequate management. Studies identify unsafe worker behaviors as the primary cause of construction accidents. However, most research overlooks the psychological state, particularly emotions, of construction workers. <b>Methods</b>: This study designed a behavioral experiment integrating social cognitive neuroscience, collecting real-time EEG data to classify and recognize fear, anger, and neutral emotions. Variance analysis explored differences in safety hazard identification and risk assessment under these emotional states. A total of 22 male participants were involved, with data collection lasting three days. The role of psychological capital in mediating the effects of emotions on unsafe behaviors was also examined. <b>Results</b>: Emotional classification using EEG signals achieved 79% accuracy by combining frequency domain and nonlinear feature extraction. Fear significantly enhanced safety hazard identification accuracy compared to neutral and anger emotions (F = 0.027, <i>p</i> = 0.03). Risk assessment values under fear and anger were higher than under neutral emotion (F = 0.121, <i>p</i> = 0.023). Psychological capital interacted significantly with emotions in hazard identification accuracy (F = 0.68, <i>p</i> = 0.034), response time (F = 2.562, <i>p</i> = 0.003), and risk assessment response time (F = 1.415, <i>p</i> = 0.026). Safety hazard identification correlated with the number of safety trainings (<i>p</i> = 0.002) and safety knowledge lectures attended (<i>p</i> = 0.025). Risk assessment was significantly associated with smoking (<i>p</i> = 0.023), alcohol consumption (<i>p</i> = 0.004), sleep duration (<i>p</i> = 0.017), and safety training (<i>p</i> = 0.024). <b>Conclusions</b>: The findings provide insights into how emotions affect safety hazard identification and risk assessment, offering a foundation for improving emotional regulation, reducing accidents, and enhancing safety management in construction.
Changki Park, Christos Kontovas, Zaili Yang
et al.
Cybersecurity risks are becoming a major concern in the maritime industry due to the increasing reliance on information technology and operational technology systems. This paper aims to develop a new methodology to evaluate the effectiveness of risk control measures (RCMs). Six criteria influencing the choice of cybersecurity RCMs are identified through literature review. Expert opinions are used to assess major cybersecurity RCMs using the fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The methodology prioritises the most viable RCMs using primary data collected from 100 experts. The findings indicate that the most effective cybersecurity control measures based on stakeholders’ opinions are “Effective Antivirus software management,” “Management of network devices,” and “Developing a cybersecurity strategy.” This paper contributes to maritime cybersecurity policy guidance by providing experimental evidence and offers a new decision tool to aid stakeholders in selecting the most suitable measures to address the relevant risks.
In this paper, we provide a new property of value at risk (VaR), which is a standard risk measure that is widely used in quantitative financial risk management. We show that the subadditivity of VaR for given loss random variables holds for any confidence level if and only if those are comonotonic. This result also gives a new equivalent condition for the comonotonicity of random vectors.
As the pharmaceutical manufacturing industry embraces digital transformation, innovative technologies are being adopted to improve risk management and regulatory compliance. The present study investigates the application of artificial intelligence, machine learning, the Internet of Things, and advanced data analytics in this sector. Integrating these technologies is essential for tackling product quality, patient safety, and operational efficiency challenges. Although the potential benefits are significant, there is a scarcity of comprehensive studies on the implementation and impact of digital technologies, specifically in the context of risk management and regulatory compliance within pharmaceutical manufacturing. The current study aims to address this gap by exploring how digital technologies can be utilized to enable real-time monitoring, predictive maintenance, proactive quality control, and streamlined regulatory compliance processes. Through a systematic literature review and analysis of case studies, best practices, challenges, and strategies for successful digital transformation in the pharmaceutical manufacturing industry are examined. The results emphasize the remarkable potential of digital technologies to enhance risk management, ensure regulatory compliance, and drive operational excellence. The central message conveyed is that embracing digital transformation is crucial for pharmaceutical companies to maintain competitiveness, mitigate risks, and deliver high-quality, safe medicines to patients. Furthermore, the necessity for collaboration between industry stakeholders and regulators to promote innovation while upholding rigorous quality and safety standards is highlighted.
Stella Emeka-Okoli, Ekene Ezinwa Nwankwo, Tochukwu Chinwuba Nwankwo
et al.
Non-technical risks play a critical role in the oil and gas industry, influencing operational efficiency, financial performance, and stakeholder trust. This review explores key insights and frameworks for understanding and managing non-technical risks in the oil and gas sector. The review begins by defining non-technical risks and highlighting their significance in the industry. It then discusses various types of non-technical risks, including regulatory, environmental, social, and geopolitical risks, and their impact on oil and gas operations. The review also examines the interconnected nature of non-technical risks and how they can escalate into larger crises if not managed effectively. It emphasizes the importance of adopting a holistic approach to risk management that considers the interplay between different risk factors. Furthermore, the review identifies several key frameworks and methodologies for assessing and managing non-technical risks in the oil and gas industry. These include risk assessment tools, scenario planning, and stakeholder engagement strategies. The review concludes by highlighting the need for oil and gas companies to proactively identify, assess, and mitigate non-technical risks. It emphasizes the importance of integrating risk management into overall business strategies and decision-making processes. Overall, this review provides valuable insights and frameworks for navigating non-technical risks in the oil and gas industry. It offers practical recommendations for industry practitioners and policymakers to enhance risk management practices and ensure the long-term sustainability of the sector. Keywords: Oil and Gas, Insights, Frameworks, Navigating, Non- Technical Risks
This study investigates the impact of management efficiency on credit risk in the ASEAN-6 banking industry, comprising Singapore, Malaysia, Indonesia, Thailand, Vietnam, and the Philippines. We analyze a sample of 122 banks from 2009 to 2022, utilizing panel data analysis with a random effect model (REM). The study addresses four key research questions: (1) What is the impact of management efficiency on credit risk? (2) Does bank size moderate the relationship between management efficiency and credit risk? (3) Does the corruption index moderate the relationship between management efficiency and credit risk? (4) Does Islamic banking perform better than conventional banks? Our findings reveal a negative and significant relationship between management efficiency and credit risk, indicating that higher management efficiency leads to increased credit risk. The study also finds that Islamic banks exhibit higher credit risk compared to conventional banks. Interaction effects show that larger banks and those in less corrupt countries tend to have lower credit risk. These results suggest that while efficient management practices are crucial, they may lead to higher risk if not accompanied by adequate risk management strategies. Additionally, the regulatory environment and bank size play a significant role in mitigating credit risk. The implications of these findings are vital for bank managers and policymakers. Bank managers should balance efficiency with robust risk management practices, and policymakers should consider the regulatory environment and corruption levels when formulating policies for the banking sector.
In the construction industry, despite the development of technology and the efforts of companies, safety accidents are frequent, and the types of accidents are also diversified. In particular, when looking at the accident rates of the construction industry, the number of deaths from fall accidents accounts for a very high proportion. To resolve this, various measures to prevent fall, such as installation of safety railings and safety nets, have been proposed at the national level, but the effect is very insignificant. Therefore, it is necessary to establish measures for safety management and to propose prevention techniques by in-depth analysis of the causes of fall accidents through actual accident cases at the construction sites. The purpose of this study is to assess the risk of the cause of fall accidents for sustainable safety management at construction sites. To this end, data collection of fall accident cases at domestic construction sites, risk assessment by cause, and fall accident prevention techniques are conducted in order. This study was conducted on fall accident cases that occurred at a height of more than 2m. The results of this study will contribute to substantially reducing fall accidents at construction sites in South Korea. Additionally, it is used as basic data for improving Korea's construction safety management system.
Mursalim Nohong, Sabir, Muhammad Try Dharsana
et al.
With increasing environmental and strategic challenges, achieving sustainable competitive advantage is crucial for businesses. This study aims to examine the impact of strategic risk and green financial management on sustainable competitive advantage, focusing on the mediating role of sustainable business resilience and the moderating effect of government policy. A quantitative approach was utilized, applying the SMART-PLS methodology to analyze data gathered through a survey of 316 small and medium-sized enterprise (SME) owners in Indonesia, selected for their direct involvement in daily operations and strategic decision-making. The response rate was 63.2%, representing various industry sectors. The results indicate that strategic risk significantly enhances sustainable business resilience (β = 0.796 and p-value < 0.01), which is strongly associated with sustainable competitive advantage (β = 0.458 and p-value < 0.01). Green financial management, however, does not significantly impact resilience (β = 0.008 and p-value = 0.89). Both strategic risk and green financial management, nonetheless, indirectly influence competitive advantage through resilience, reflecting partial mediation (β = 0.112, p-value = 0.02 and β = 0.053, p-value = 0.04, respectively). Additionally, government policy strengthens the effect of green financial management on resilience (β = 0.556 and p-value < 0.01). These findings underscore the importance of firms managing strategic risks proactively and providing supportive regulations to encourage sustainable business practices by governments. The study provides practical insights for businesses and policymakers aiming to foster corporate resilience and enhance sustainable competitive positioning.
Michael C. Okika, Andre Vermeulen, Jan-Harm C. Pretorius
Interface risks are inherent in every construction project from start to finish. Identifying and managing these risks effectively in every project phase is crucial for actualising project objectives. This paper shows a comprehensive framework showing several relationships between project stakeholders and how the interface risks between them that influence project execution are identified and managed for the overall construction project success. Firstly, a literature review on interfaces and interface risks and a discussion on how organisations managed interface risks were carried out, and secondly, the collection of quantitative data was conducted by means of structured online questionnaires. The sample consisted of 205 construction project professionals who were selected randomly. This group included individuals with various roles in the construction industry. The data were analysed using descriptive statistical methods, including factor analysis, reliability assessment, and calculations of frequencies and percentages. The results showed all the factors, work cultures, and organisational approaches that influence interface risk management and ways to identify and manage interface risks effectively. Effective stakeholder management is crucial for effective interface risk management since many interface risks are created by the numerous stakeholders involved in the project and the proposed frameworks will effectively mitigate the consequences and causes of interface risks. Effectively mitigating these risks involves effective stakeholder management, building information modelling volume strategy, and creating a virtual construction model during the construction phase; in addition, construction supply chain risks must be carefully identified during the interfaces establishment stages; interface risks must be carefully identified during the conceptualisation; and the planning, construction, and execution stages and standard methods and procedures must be defined to effectively identify and manage interface risks as the occur in the project lifecycle plus implementing the proposed risk mitigation frameworks.
Accurately defining, measuring and mitigating risk is a cornerstone of financial risk management, especially in the presence of financial contagion. Traditional correlation-based risk assessment methods often struggle under volatile market conditions, particularly in the face of external shocks, highlighting the need for a more robust and invariant predictive approach. This paper introduces the Causal Network Contagion Value at Risk (Causal-NECO VaR), a novel methodology that significantly advances causal inference in financial risk analysis. Embracing a causal network framework, this method adeptly captures and analyses volatility and spillover effects, effectively setting it apart from conventional contagion-based VaR models. Causal-NECO VaR's key innovation lies in its ability to derive directional influences among assets from observational data, thereby offering robust risk predictions that remain invariant to market shocks and systemic changes. A comprehensive simulation study and the application to the Forex market show the robustness of the method. Causal-NECO VaR not only demonstrates predictive accuracy, but also maintains its reliability in unstable financial environments, offering clearer risk assessments even amidst unforeseen market disturbances. This research makes a significant contribution to the field of risk management and financial stability, presenting a causal approach to the computation of VaR. It emphasises the model's superior resilience and invariant predictive power, essential for navigating the complexities of today's ever-evolving financial markets.
I propose a functional on the space of spectral risk measures that quantifies their ``degree of risk aversion''. This quantification formalizes the idea that some risk measures are ``more risk-averse'' than others. I construct the functional using two axioms: a normalization on the space of CVaRs and a linearity axiom. I present two formulas for the functional and discuss several properties and interpretations.
Computer systems process, store and transfer sensitive information which makes them a valuable asset. Despite the existence of standards such as ISO 27005 for managing information risk, cyber threats are increasing, exposing such systems to security breaches, and at the same time, compromising users' privacy. However, threat modelling has also emerged as an alternative to identify and analyze them, reducing the attack landscape by discarding low-risk attack vectors, and mitigating high-risk ones. In this work, we introduce a novel threat-modelling-based approach for risk management, using ISO 27005 as a baseline for integrating ISO 27001/27002 security controls with privacy regulations outlined in the European General Data Protection Regulation (GDPR). In our proposal, risk estimation and mitigation is enhanced by combining STRIDE and attack trees as a threat modelling strategy. Our approach is applied to an IoT case study, where different attacks are analyzed to determine their risk levels and potential countermeasures.
I. Lobos-Ortega, N. Pizarro-Aránguiz, N.L. Urrutia
et al.
Bovine milk is one of the most complete foods that exist. During the last decades, milk FA have shown to improve human health due to the reduction in risk of cardiovascular disease and related pathologies. The aim of this study was to evaluate the feasibility of near infrared spectroscopy (NIRS) reflectance analysis to predict the nutritional value, fatty acid (FA) composition, and health index of fresh milk from dairy cows of pastoral systems. The prediction of Atherogenicity and Thrombogenicity indexes, along with other FA ratios in fresh milk samples by NIRS were precise and accurate. In addition, the calibration model obtained by NIRS provides an opportunity for the routine quantification of milk’s healthy FA such as omega-3 and conjugated linoleic acid (CLA), with applications in the dairy industry for food labeling, and at the farm level for management of the dairy cow’s diet.
As a result of the rapid development of the construction industry, in recent years, research has increasingly used system dynamics (SD) modelling to determine the positive and negative causal feedback in various management processes. Given SD diverse approach to the management of construction companies and projects, it would be necessary to develop a framework for a system dynamics model (SDM) incorporating the main processes of the Baltic States by identifying and providing a systematic understanding of their distribution. Using literature analysis, this study provides the results of 79 selected scientific literature sources from 1991 to 2020. The obtained information is structured according to the annual volume of publications, country of affiliation, the most successful authors and the most popular scientific journals on the topic of the SD. With the assistance of bibliometric analysis and co-occurrence of keywords, system dynamics management processes in construction companies were structured, choosing the following separate elements: (1) project planning, (2) project management, (3) risk management, (4) project performance, (5) project productivity, (6) sustainability. Using a systematic approach, according to the framework of the model, the characteristics of each management process were classified and identified, dividing them into subgroups. The results of the analysis show that the overview of some SD components or processes is mostly provided, which emphasises its aspects and usability in company management, thus indicating the need to identify the framework of the process module. The introduction of the SD process framework in the company would gradually create a competitive advantage in market conditions.
Real estate business, Regional economics. Space in economics