The increasing concentration of atmospheric CO2 since the Industrial Revolution has driven research into subsurface storage as a viable solution. This study focuses on developing machine learning models to estimate net present value and carbon footprint in a combined gas production and CO2 sequestration scenario in shales. The dataset comprised a large set of numerical simulation scenarios, which were run using PSU SHALECOMP, a 3-dimensional, compositional and multiphase simulator, which incorporates an equation of state to capture the effects of pressure and temperature variations. A horizontal production/injection well with multiple hydraulic fractures was modeled using the stimulated reservoir volume approach which represents the volume impacted by hydraulic fractures as well as the induced fractures through the natural fracture network in the reservoir. The results of these scenarios were used to calculate net present value and carbon footprint associated with each scenario. Exploratory data analysis and feature engineering revealed that the net present value is primarily governed by stimulated reservoir volume’s fracture permeability, original gas in place within the stimulated reservoir volume, and injection constraints, whereas the carbon footprint is predominantly controlled by total production duration and injected CO2 volume. Machine learning models were trained to build robust forecasting tools for net present value and carbon footprint. These models revealed that the selected neural network model outperformed multiple linear regression and random forests models in predicting both net present value and carbon footprint, with R2 values of 0.99 and 0.96, respectively, for the testing sets. To further refine these estimates and improve the robustness of predictions, future research should focus on improving the certainty in deterministic and probabilistic estimations of net present value and carbon footprint by gathering more comprehensive data, and conducting detailed analyses of carbon emissions and operational costs. This research represents a significant step toward understanding the economic and environmental implications of CO2 sequestration in shale reservoirs, contributing valuable insights for future developments in this field.
Fishing, especially within industrial sectors, is widely recognized as one of the most dangerous occupations in the world, as fishers are exposed to adverse physical and environmental risks. While legal frameworks in Nigeria provide occupational safety and labor protections for industrial fishers, inconsistencies in enforcement and compliance persist. Voluntary sustainability standards (VSS) have been promoted as market-based mechanisms to reduce risks, increase safety compliance, and improve working conditions. Despite their relevance, evidence of their impacts on industrial fisheries remains unclear. This study examines the effects of the Friend of the Sea (FOS) certification program on the occupational safety and working conditions of industrial fishers in Nigeria's Gulf of Guinea, Atlantic Ocean. A mixed methods approach was employed, integrating quantitative surveys with semi-structured interviews with key informant to facilitate triangulation among participants recruited from FOS-certified and Business as Usual (BAU) fishing companies. Bivariate analyses revealed that FOS-certified fishers were significantly more likely to have adequate safety equipment (98.0% vs. 74.8%), access to health personnel and emergency services (100% vs. 17.4%), and training opportunities (98.5% vs. 46.8%). Multivariate logistic regression confirmed these associations such that absence of safety equipment was associated with a 0.364 decrease in the probability of FOS certification; no injury history with a 0.291 increase; no exposure to unsafe conditions with a 0.400 increase; and lack of training access with a 0.535 decrease (all p < 0.001). These findings suggest that, despite statutory legal protections in Nigeria, participation of fishing companies in the FOS certification programme may enhance the implementation of safety standards and improve occupational health outcomes among industrial fishers.
Generative artificial intelligence (GenAI) is fundamentally reshaping the planning, development, and execution of advertising campaigns. Despite the growing application of GenAI in advertising and scholarly interest, research findings on the effects of AI-generated ads on consumer-related outcomes are mixed and rapidly evolving. In particular, the extant literature does not provide sufficient insight into how consumers process cognitive (informativeness and credibility) and affective (creativity and entertainment) values delivered by AI-generated advertisements compared with human-generated advertisements. To fill this gap, the present research used two scenario-based experiments to collect data from 405 (study 1) and 602 (study 2) tourists by randomly exposing them to human and AI-generated advertisements. Data was analyzed in two stages. First, tourists' perceptions of advertising values were compared using multivariate analysis of variance (MANOVA), and then the impact of advertising values on outcome variables was measured using structural equation modeling (SEM). The findings indicate that tourists perceive human-generated advertisements as slightly more informative, credible, creative, and entertaining than AI-generated advertisements. However, the SEM results indicate that the effects of advertising values on tourists' self-expression, attitudes toward the destination, and destination evangelism do not differ significantly between the two advertising formats. These findings are particularly noteworthy, as they suggest that advertising values that emerge from AI-generated advertisements positively influence tourists' attitudes and self-expressive responses. Consequently, the results support tourism marketing professionals in strategically leveraging emerging GenAI tools to develop advertising content that fosters favorable destination attitudes, enhances tourists’ self-expression, and ultimately stimulates destination evangelism.
Fashion tourism is increasingly positioned as a driver of destination development, yet its governance remains poorly theorized with respect to power concentration, digital visibility, and sustainability. Drawing on Actor–Network Theory and in-depth interviews with 23 international stakeholders, this study examines how collaborative governance is assembled, contested, and maintained across fashion tourism destinations. The analysis identifies three interconnected dynamics: governance asymmetries, digital ecosystem reconfiguration, and emergent sustainability logics. The study employs Actor–Network Theory to show how unequal actor positions, platform-mediated visibility, and resource dependencies shape who is enrolled, who gains influence, and whose sustainability priorities count. It further traces how actor-networks are assembled through brand hierarchies, event infrastructures, and platform algorithms that mediate access to resources and visibility. The findings challenge assumptions of collaborative governance as inherently inclusive and reconceptualize fashion tourism destinations as asymmetrical, digitally mediated governance networks that shape power relations, value distribution, and adaptive capacity.
Stroke is the second leading cause of mortality worldwide, resulting from an interruption of blood flow to the brain, which subsequently diminishes oxygen supply. Although strokes can occur at any age, incidence rises significantly after 55. Vascular dementia, a progressive condition associated with cerebral infarction, is a long-term sequela of stroke that predominantly affects older populations. To date, there is no cure for this form of dementia. Recent experimental studies in rodent models, however, demonstrate promising outcomes in combating neuronal degeneration through gene treatments utilizing brain-derived neurotrophic factor, fibroblast growth factor-2, and vascular endothelial growth factor. These advancements suggest a potential breakthrough in vascular dementia treatment. This comprehensive review delves into the complexities of strokes, exploring their challenges and constraints. Furthermore, we explore the growing field of gene therapy, highlighting successful interventions that may revolutionize the landscape of vascular dementia treatment. This review aims to provide a refined understanding of stroke-related issues and the transformative potential of gene-based interventions in mitigating the burden of vascular dementia.
