This study introduces a novel Groundwater Flooding Risk Assessment (GFRA) model to evaluate risks associated with groundwater flooding (GF), a globally significant hazard often overshadowed by surface water flooding. GFRA utilizes a conditional probability function considering critical factors, including topography, ground slope, and land use-recharge to generate a risk assessment map. Additionally, the study evaluates the return period of GF events (GFRP) by fitting annual maxima of groundwater levels to probability distribution functions (PDFs). Approximately 57% of the pilot area falls within high and critical GF risk categories, encompassing residential and recreational areas. Urban sectors in the north and east, containing private buildings, public centers, and industrial structures, exhibit high risk, while developing areas and agricultural lands show low to moderate risk. This serves as an early warning for urban development policies. The Generalized Extreme Value (GEV) distribution effectively captures groundwater level fluctuations. According to the GFRP model, about 21% of the area, predominantly in the city’s northeast, has over 50% probability of GF exceedance (1 to 2-year return period). Urban outskirts show higher return values (> 10 years). The model’s predictions align with recorded flood events (90% correspondence). This approach offers valuable insights into GF threats for vulnerable locations and aids proactive planning and management to enhance urban resilience and sustainability.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

Polymer waste drilling fluid has extremely high stability, and it is difficult to separate solid from liquid, which has become a key bottleneck problem restricting its resource recycling. This study aims to reveal the stability mechanism of polymer waste drilling fluid and explore the destabilization effect and mechanism of ultrasonic waste drilling fluid. Surface analysis techniques such as X-ray energy spectrum and infrared spectrum were used in combination with colloidal chemical methods to study the spatial molecular structure, stability mechanism, and ultrasonic destabilization mechanism of drilling fluid. The results show that the particles in the drilling fluid exist in two forms: uncoated particles and particles coated by polymers, forming a high molecular stable particle system. Among them, rock particles not coated by polymer follow the vacancy stability and Derjaguin-Landau-Verwey-Overbeek (DLVO) stability mechanism, and the weighting material coated by the polymer surface follows the space stability and DLVO stability mechanism. The results of ultrasonic destabilization experiments show that after ultrasonic treatment at 1000 W power for 5 min, coupled with the addition of 0.02% cationic polyacrylamide, the dehydration rate is as high as 81.0%, and the moisture content of the mud cake is as low as 29.3%, achieving an excellent solid-liquid separation effect. Ultrasound destabilizes polymer waste drilling fluid by destroying the long-chain structure of the polymer. This study provides theoretical support and research direction for the research and development of polymer waste drilling fluid destabilization technology.

High-quality implementation of cross-border mergers and acquisitions (cross-border M&As) is an important pathway for emerging-market multinational enterprises (EMNEs) to enhance their international competitiveness. However, in comparison to developed countries, cross-border M&As by EMNEs are often prohibited by the liability of origin caused by negative political coverage. How and why negative political coverage affect the completion of cross-border M&As by EMNEs? What are the contextual constraints that moderate the impact of negative political coverage on cross-border M&As completion? Based on the “liability of origin” theory, this paper addresses these questions using data from the Zephyr database on cross-border M&As by EMNEs in the United States from 2016 to June 2021 and employing a logit model for estimation. The research findings are as follows: (1) Negative political coverage leads to negative perceptions of emerging market countries by host country stakeholders, creating the liability of origin and stigmatizing the corporate nationality, thereby reducing the success rate of cross-border M&As by EMNEs. (2) Increasing geographical distance leads to information asymmetry, reinforcing the negative impact of negative political coverage on the completion of cross-border M&As by EMNEs. (3) Relevant mergers and acquisitions exacerbate the negative effect of negative political coverage on the success rate of cross-border M&As by EMNEs. (4) Being a publicly traded firm and having successful experience in cross-border M&As both intensify the negative impact of negative political coverage on the success rate of cross-border M&As by EMNEs.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

The food industry progressively requires innovative and environmentally safe packaging materials with increased physical, mechanical, and barrier properties. Due to its unique properties, cellulose has several potential applications in the food industry as a packaging material, stabilizing agent, and functional food ingredient. A coffee pod is a filter of cellulosic, non-rigid, ready-made material containing ground portions and pressed coffee prepared in dedicated machines. In our study, we obtained, with homogenization and sonication, cellulose micro/nanoparticles from three different coffee pods. It is known that nanoparticulate systems can enter live cells and, if ingested, could exert alterations in gastrointestinal tract cells. Our work aims to investigate the response of HT-29 cells to cellulose nanoparticles from coffee pods. In particular, the subcellular effects between coffee-embedded nanocellulose (CENC) and cellulose nanoparticles (NC) were compared. Finally, we analysed the pathologic condition (Cytolethal Distending Toxin (CDT) from Campylobacter jejuni) on the same cells conditioned by NC and CENC. We evidenced that, for the cellular functional features analysed, NC and CENC pre-treatments do not worsen cell response to the C. jejuni CDT, also pointing out an improvement of the autophagic flux, particularly for CENC preconditioning.