Plastics are essential in society as a widely available and inexpensive material. Mismanagement of personal protective equipment (PPE) during COVID-19 pandemic, with a monthly estimated use of 129 billion face masks and 65 billion gloves globally, is resulting in widespread environmental contamination. This poses a risk to public health as a vector for SARS-CoV-2 virus, which survives up to 3 days on plastics, as well as impacts to ecosystems and organisms more broadly functions. Concerns over the role of reusable plastics as vectors for SARS-CoV-2 virus contributed to the reversal of bans on single-use plastics, highly supported by the plastic industry. While not underestimating the importance of plastics in the prevention of COVID-19 transmission, it is imperative not to undermine recent progress made in the sustainable use of plastics. There is a need to assess alternatives that allow reductions of PPE and reinforce awareness on the proper public use and disposal. Finally, assessment of contamination and impacts of plastics driven by the pandemic will be required once the outbreak ends.
Securing mine sites is a challenging task due to the complexity of the infrastructure, the variety of physical and digital components, the distribution of assets and machineries, and the large number of stakeholders involved. Given the risks that are present in Tailings Storage Facilities (TSFs), mine operators are seeking technologies to accurately monitor the state of their dams. The latest developments implement evolutive monitoring and responsive risk management systems by adapting accurate Internet of Things technologies, automated mathematical model calculation to continually monitor the structural/geotechnical aspects of TSF, and a portfolio of innovative applications to support decision-making. Within this study, a comprehensive methodology is developed for assessing the environmental sustainability of a smart monitoring solution combining the life cycle assessment (LCA) method with the environmental risk assessment, which quantifies risk reduction potential. The use case scenario is identified based on real industrial data, also aligned with the common characteristics of tailing dams in Europe. Environmental sustainability of the smart monitoring solution is assessed through a cradle-to-grave LCA based on the ReCiPe 2016 (v1.1 Midpoint (H)) method. Monitoring impact alone is reduced primarily by the 40% reduction in monitoring visits, while the results show the environmental improvement of the TSF life cycle by 24% for CO<sub>2</sub>-eq., as a step in-line with the EU’s long-term strategy for total decarbonisation in 2050, and Sustainable Development Goal 9 for Industry by the United Nations. Additionally, the 27% freshwater ecotoxicity reduction, 20% human toxicity (cancer) decrease, and the rest of the studied categories indicate an overall footprint improvement for the monitoring solution application on TSFs. The findings demonstrate clearly theoretical, practical and policy implications, not only for the benefit of such solutions for environmental protection, but also for the necessity of integrating risk in sustainability analysis approaches.
As conventional realized third and fourth (co)moments estimated with sub-period returns are biased, Neuberger (2012) and Bae and Lee (2021) develop new unbiased realized (joint) cumulants using extended information. In this paper, we discuss practical issues in estimating the realized (joint) cumulants. In addition, we estimate (joint) cumulants through various methods using simulated prices and examine the characteristics of those estimators. The simulation results show that realized (joint) cumulants estimated from sub-period returns and option data serve as proxies when the true realized cumulants are not obtainable. Lastly, we estimate realized (joint) cumulants using financial data on the S&P 500, individual stocks, and their options, and investigate whether the realized (joint) cumulants are explained by other estimators. As a result, we find that realized coskewness and kurtosis are predicted by implied moments and other lagged ex post estimators.
Nazila Adabavazeh, Mehrdad Nikbakht, Atefeh Amindoust
et al.
Pipeline corrosion analysis is considered a challenging topic due to the complexity and uncertainty of the factors involved. The uncontrolled consequences of corrosion impact the ''natural ecosystem, society, and economy.'' Investigating corrosion plays a crucial role in managing incidents. This paper aims to provide an effective management tool for predicting corrosion using Bayesian modeling. This study illustrates how to integrate Bayesian modeling with ''incomplete data, scientific information in various formats, and expert knowledge,'' utilizing it effectively. Employing Netica software, a fault tree representing important elements affecting the natural gas transmission lines corrosion is converted into a cause-and-effect diagram of the Bayesian belief network. Natural gas transmission lines corrosion the model's output response is then analyzed after defining the relationships between these corrosion-affecting variables. The correlation between failures and corrosion is considered through a bivariate normal distribution as the likelihood function in the Bayesian update, and the model was validated using OpenBUGS software. In the next step, sensitivity analysis and scenario analysis were conducted in two industrial zones located in the central regions of the country. The final findings showed that the suggested model can produce accurate results for Corrosion monitoring systems in the Natural Gas Industry, providing an efficient approach to assess safety, quantitative risk analysis, and forming the basis for decisions aimed at averting pipeline episodes.
Muhammad Fawad Afraz, S. Bhatti, Alberto Ferraris
et al.
Abstract The purpose of this study is to propose and validate a theoretical model to investigate the mediating effect of risk management capabilities (RMCs) on the relationship between supply chain innovation (SCI) and competitive advantage (CA). In addition, by utilizing contingent resource-based theory, we propose a moderating effect of the buyer-supplier relationship on the relationships between SCI and RMCs (i.e. robustness and resilience). We collected data through a survey with time-lagged observations in the construction industry in Pakistan. Structural Equation Modelling (PLS-SEM) was used in the study to investigate the theoretical framework. Our results show a positive impact of SCI on CA through the two risk management capabilities (mediating effect). Further, the findings provide evidence for the moderating effect of the buyer-supplier relationship on the SCI-CA relationship, with resilience capability as a mediator. However, the moderated mediation effect of the buyer-supplier relationship and robustness capability is not supported by our data. Our study addresses the question of the contribution of SCI to robustness and resilience capabilities and finally its impact on the competitive advantage of firms in the construction sector. Our study contributes to the empirical research on SCI and validates a model that links it to CA through the robustness and resilience capabilities of firms in the construction sector. Our study also provides insights for managerial decisions on investment in technology and process innovations and shows that SCI and RMCs are both necessary to achieve competitive advantage.
Creep deformation and failure of immersed sandstone under mining disturbances are critical factors driving water inrush and goaf collapse. This study employed a specialized creep-impact testing system capable of simultaneous water immersion and mechanical loading, conducting uniaxial compression and creep-impact tests on sandstone samples in three moisture states: dry, saturated, and freshly immersed. The results show that as moisture content increases, both compressive strength and elastic modulus decline significantly. The most notable reductions occurred within the first 2 hours of immersion, with strength and modulus dropping by 55.7% and 70.1%, respectively. Under creep-impact conditions, increasing the impact energy from 14.7 J to 24.5 J caused accelerated creep failure in saturated samples, shortening failure time by 25.2% and increasing the creep rate to 1.32–1.64 times the initial value. In contrast, immersed samples exhibited both accelerated creep and abrupt failure, shortening failure time by 20% but increasing the creep rate more sharply to 2.11–6.04 times the initial value. Post-failure analysis revealed more pronounced fragmentation and a more violent failure process in immersed samples compared to dry or saturated counterparts. These findings offer valuable insights for deep mining operations and the prevention of water inrush disasters.
The construction industry, being labor-intensive, prioritizes productivity to boost project performance, yet struggles to achieve expected levels despite increased focus by scholars and practitioners. This lagging causes significant losses in time, cost, and quality performance of construction projects but also broader implications for resource efficiency and environmental impacts. As a remedy to the multifaceted issue, this study aims to identify and evaluate life cycle risks of productivity management in construction projects in Türkiye. A comprehensive literature review identified risk factors affecting labor productivity, followed by a discussion session to finalize the decision framework, including life cycle phases of productivity management and risk factors in each phase. Then, the fuzzy analytical hierarchy (AHP) process revealed the most critical risk factors in each phase, followed by semi-structured interviews to reveal measures for addressing the most significant risks. The findings show that productivity management in construction projects contains nine phases. In addition, the most important factors were chiefly related to collaboration, information sharing, lack of supervision, work interruptions, and changes. Findings from semi-structured interviews emphasize regular employee training and open communication to enhance project outcomes, optimize workflows, and promote sustainability. The study’s key contribution is introducing a life cycle approach to construction productivity management, a previously unexplored perspective. This provides an effective framework that can be implemented in construction projects to manage and improve labor productivity as a whole-life cycle approach.
Victor Andre Ariza Flores, Fernanda Oliveira de Sousa, Sandra Oda
This study examines the integration of epistemological principles into road infrastructure risk management, emphasizing the need for adaptive strategies in the face of inherent climate uncertainties, particularly flash floods. A systematic review of peer-reviewed articles, industry reports, and case studies from the past two decades was conducted, focusing on the application of epistemological approaches within the infrastructure sector. The research employs a mixed methods approach. Quantitatively, the risk of pavement failure is measured by analyzing the relationship between pavement serviceability rates and Intensity–Duration–Frequency (IDF) data in areas frequently affected by flash floods. For example, rainfall intensities during flood events on the BR-324 highway in Brazil were significantly higher than monthly averages, with maximum values reaching 235.73 mm for a 5 min duration over a 50-year return period. These intensities showed an increase of approximately 15% over 5 to 10 years and 8% over 50 to 75 years. Qualitatively, traditional risk management methods are combined with epistemological concepts. This integrated approach fosters reflective practice, encourages the use of both quantitative and qualitative data, promotes a dynamic management environment, and supports sustainable development goals by aligning risk management with environmental and social sustainability. This study finds that incorporating epistemological insights can lead to more fluid and continuously improving risk management practices in construction, design, and maintenance. It concludes with a call for future research to explore the integration of emerging technologies such as artificial intelligence to further refine these approaches and more effectively manage complexity and uncertainty.
Introduction With the growing prevalence of AI-based systems and the development of specific regulations and standardizations in response, accountability for consequences resulting from the development or use of these technologies becomes increasingly important. However, concrete strategies and approaches of solving related challenges seem to not have been suitably developed for or communicated with AI practitioners. Methods Studying how risk governance methods can be (re)used to administer AI accountability, we aim at contributing to closing this gap. We chose an exploratory workshop-based methodology to investigate current challenges for accountability and risk management approaches raised by AI practitioners from academia and industry. Results and Discussion Our interactive study design revealed various insights on which aspects do or do not work for handling risks of AI in practice. From the gathered perspectives, we derived 5 required characteristics for AI risk management methodologies (balance, extendability, representation, transparency and long-term orientation) and determined demands for clarification and action (e.g., for the definition of risk and accountabilities or standardization of risk governance and management) in the effort to move AI accountability from a conceptual stage to industry practice.
The banking industry is rapidly adopting artificial intelligence (AI)-enabled technologies to improve efficiency, reduce costs, and enhance customer experience. The paper utilizes case studies and data from various research papers to analyze the use of AI in different areas of the banking industry. The paper highlights that AI-enabled technologies can be applied in various areas of the banking industry, with significant potential for improving decision making, reducing the risk of fraud, and enhancing customer experience. The study also provides examples of AI implementations in various banking domains, such as risk assessment, credit approval process, investment/portfolio management, and others. The use of AI in fraud detection, personalized financial advisory services, and automated customer support is discussed in detail, including examples from major financial institutions. Additionally, the paper discusses how AI can be used in claims management, wealth management, and loan and credit management. Several research studies have been reviewed that propose AI-based credit scoring models and loan underwriting systems to enhance the accuracy and efficiency of loan management processes. Overall, this paper provides a comprehensive survey of the opportunities and challenges associated with the use of AI in the banking industry, highlighting the potential for AI to transform the way banks operate and serve their customers.
Traditional agricultural industries in protected areas (PAs) provide opportunities for both nature conservation and the wellbeing of local residents. However, knowledge about the synergies between nature conservation and traditional agricultural industries is still limited. This research takes the traditional tea industry in Wuyishan National Park as a case to identify and examine the synergistic mechanism between forest conservation and industrial development, why traditional agricultural industries are necessary to the regional economy, and how they secure local livelihoods as well as achieve conservation goals. We conducted literature research and semi-structured interviews with Wuyishan National Park Authority, local government administrations, enterprises and small-scale farmers. The results were obtained through a two-stage mixed method of grounded theory and system dynamics. The findings revealed that: (1) Traditional agricultural industries in PAs were resilient and adaptable in the face of external changes, in which traditional culture and ecological awareness played an important role. (2) Small-scale agri-industries were vulnerable to external shocks, but they also have advantages in terms of moderate agglomeration and standardization, risk perception and response, and market-based diversification of production. (3) The synergetic process of traditional agricultural industries and conservation is mainstreaming biodiversity by cooperation among the government, the park agency, and local people. Thus, we suggested that the local government should work together with the PA management agencies to re-evaluate the existence of traditional agricultural industries for their necessity in the regional economy and the feasibility of improving local livelihoods.
Nutrition. Foods and food supply, Food processing and manufacture
Grain security guarantees national security. China has many widely distributed grain depots to supervise grain storage security. However, this has led to a lack of regulatory capacity and manpower. Amid the development of reserve-level information technology, big data supervision of grain storage security should be improved. This study proposes big data research architecture and an analysis model for grain storage security; as an example, it illustrates the supervision of the grain loss problem in storage security. The statistical analysis model and the prediction and clustering-based model for grain loss supervision were used to mine abnormal data. A combination of feature extraction and feature selection reduction methods were chosen for dimensionality. A comparative analysis showed that the nonlinear prediction model performed better on the grain loss data set, with R2 of 87.21%, 87.83%, 91.97%, and 89.40% for Gradient Boosting Regressor (GBR), Random Forest, Decision Tree, XGBoost regression on test sets, respectively. Nineteen abnormal data were filtered out by GBR combined with residuals as an example. The deep learning model had the best performance on the mean absolute error, with an R2 of 85.14% on the test set and only one abnormal data identified. This is contrary to the original intention of finding as many anomalies as possible for supervisory purposes. Five classes were generated using principal component analysis dimensionality reduction combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering, with 11 anomalous data points screened by adding the amount of normalized grain loss. Based on the existing grain information system, this paper provides a supervision model for grain storage that can help mine abnormal data. Unlike the current post-event supervision model, this study proposes a pre-event supervision model. This study provides a framework of ideas for subsequent scholarly research; the addition of big data technology will help improve efficient supervisory capacity in the field of grain supervision.
The financial and economic crisis has had an adverse impact on the Lithuania’s economy and construction industry. The GDP of Lithuania grew slightly in 2010, in contrast to a decrease of 14.7% in 2009. Lithuania’s GDP increased from 1.3% in 2010 to 4.6% in 2011. Annual GDP growth decreased from its highest point of 6.7%, reached in the third quarter, to 4.4% in the last quarter of 2011 [1,2]. Some industries, such as construction; trade, transport and communications; and the industry sectors were most affected by the crisis. In 2010, the gross value added within the construction sector decreased by 43.3%, and in the trade, transport and communications sector – by 16.6%. In 2011, a positive change in the gross value added was observed in all groups of economic activities. The largest growth in the gross value added was observed in enterprises engaging in construction (by 15%) and trade, transport and communication services (7.3%) [1,3]. The construction sector, one of the engines of economic growth in Lithuania over the last decade, is now facing with serious challenges as companies’ closures, rising unemployment, and postponed or even cancelled investments. These events also have changed the clients’ and construction companies’ behaviour. A reduced demand and shortage of orders dramatically increased a competition between companies of the construction sector. This increased pressure to improve quality, productivity and reduce costs, and the need for project strategies and management that can appropriately and effectively manage project risk.
This study presents geomorphic analysis of Xiadian buried fault in eastern Beijing plain (China), based on the analysis of a Satellite Pour l’Observation de la Terre (SPOT-5) image, a high-resolution digital elevation model (DEM) derived from an unmanned aerial vehicle (UAV) system, SRTM DEM and field investigation. Interpretations of the SPOT-5 image show that the pits always distribute between fault scarp segments or shallow grooves. The geomorphic features near the fault show echelon arrangements caused by dextral strike-slip activities of the fault. Based on this, the characteristics of stress field in this area have been clearly inferred. At centimeter-level accuracy, UAV-derived DEM profiles can clearly show micro tectonic landforms such as fault scarps, shallow grooves, steep slopes, and pits. Combined with previous research and field measurements, the evolution rates in length and height of the fault scarps are analysed. Furthermore, the deflection analysis of the drainage system also shows the characteristics of the continuous strike slip activity of the Xiadian fault. The study can provide valuable insight into geomorphic analysis of buried and semi-buried active faults in plain areas with increasingly frequent human activities.
Transportation network vulnerability analysis has developed increasingly in the last decade with the goal to identify the most critical locations against incidences. In this domain, many of the previous researches have focused on congested urban networks; however, there is still a need to consider regional and interurban sparse rail networks, specifically those networks in developing countries. In such sparse rail networks, there are limited possibilities to redirect trains if a link is disrupted, there might be less possibility of finding redundant alternative routes, and network failures are usually accompanied by a phenomenon called ‘unsatisfied demand.’ The study reported in this paper stemmed from research aimed to design precautionary actions for a developing country's sparse railway system. Our study framework deemed to find the most vulnerable part of an inter-urban sparse rail network using a network scan approach, which found the consequences of network disruptions. A number of criteria were defined to determine the total cost including unsatisfied demand and additional transportation costs due to disruptions. The results showed that how well the process of the vulnerability analysis, considering the features of both supply and demand sides, can be a guide for railway authorities in applying system safety measures.