This study evaluated the development and validation of an integrated operational model for the Underground Logistics System (ULS) in South Korea’s metropolitan area, aiming to address challenges in urban logistics and freight transportation by highlighting the potential of innovative logistics systems that utilize underground spaces. This study used conceptual modeling to define the core concepts of ULS and explored the system architecture, including cargo handling, transportation, operations and control systems, as well as the roles of cargo crews and train drivers. The ULS operational scenarios were verified through model simulation, incorporating both logical and temporal analyses. The simulation outcomes affirm the model’s logical coherence and precision, emphasizing ULS’s pivotal role in boosting logistics efficiency. Thus, ULS systems in Korea offer prospects for elevating national competitiveness and spurring urban growth, underscoring the merits of ULS in navigating contemporary urban challenges and championing sustainability.

This investigation extends into the intricate fabric of customer-based corporate reputation within the banking industry, applying advanced analytics to decipher the nuances of customer perceptions. By integrating structural equation modeling, particularly through SmartPLS4, we thoroughly examine the interrelations of perceived quality, competence, likeability, and trust, and how they culminate in customer satisfaction and loyalty. Our comprehensive dataset is drawn from a varied demographic of banking consumers, ensuring a holistic view of the sector’s reputation dynamics. The research reveals the profound influence of these constructs on customer decision-making, with likeability emerging as a critical driver of satisfaction and allegiance to the bank. We also rigorously test our model’s internal consistency and convergent validity, establishing its reliability and robustness. While the direct involvement of Business Intelligence (BI) tools in the research design may not be overtly articulated, the analytical techniques and data-driven approach at the core of our methodology are synonymous with BI’s capabilities. The insights garnered from our analysis have direct implications for data-driven decision-making in banking. They inform strategies that could include enhancing service personalization, refining reputation management, and improving customer retention efforts. We acknowledge the need to more explicitly detail the role of BI within the research process. BI’s latent presence is inherent in the analytical processes employed to interpret complex data and generate actionable insights, which are crucial for crafting targeted marketing strategies. In summary, our research not only contributes to academic discourse on marketing and customer perception but also implicitly demonstrates the value that BI methodologies bring to understanding and influencing consumer behavior in the banking sector. It is this blend of analytics and marketing intelligence that equips banks with the strategic leverage necessary to thrive in today’s competitive financial landscape.

Project risk management in the mining industry is necessary to identify, analyze and reduce uncertainty. The engineering features of mining enterprises, by their nature, require improved risk management tools. This article proves the relevance of creating a simulation model of the production process to reduce uncertainty when making investment decisions. The purpose of the study is to develop an algorithm for deciding on the economic feasibility of creating a simulation experiment. At the same time, the features and patterns of the cases for which the simulation experiment was carried out were studied. Criteria for feasibility assessment of the model introduction based on a qualitative parameters became the central idea for algorithm. The relevance of the formulated algorithm was verified by creating a simulation model of a potassium salt deposit with subsequent optimization of the production process parameters. According to the results of the experiment, the damage from the occurrence of a risk situations was estimated as a decrease in conveyor productivity by 32.6%. The proposed methods made it possible to minimize this risk of stops in the conveyor network and assess the lack of income due to the risk occurrences.

Flood risk analysis is the instrument by which floodplain and stormwater utility managers create strategic adaptation plans to reduce the likelihood of flood damages in their communities, but there is a need to develop a screening tool to analyze watersheds and identify areas that should be targeted and prioritized for mitigation measures. The authors developed a screening tool that combines readily available data on topography, groundwater, surface water, tidal information for coastal communities, soils, land use, and precipitation data. Using the outputs of the screening tool for various design storms, a means to identify and prioritize improvements to be funded with scarce capital funds was developed, which combines the likelihood of flooding from the screening tool with a consequence of flooding assessment based on land use and parcel size. This framework appears to be viable across cities that may be inundated with water due to sea-level rise, rainfall, runoff upstream, and other natural events. The framework was applied to two communities using the 1-day 100-year storm event: one in southeast Broward County with an existing capital plan and one inland community with no capital plan.

E-learning has become an integral part of higher education, significantly influencing the teaching and learning landscape. This study investigates the impact of student characteristics such as gender, grade, and major on E-learning satisfaction. Utilizing Structural Equation Modeling (SEM) and collecting data through 527 valid questionnaires from Nanjing Normal University students, this research reveals the nuanced relationships between these variables and E-learning satisfaction. The findings indicate that gender, grade, and major significantly and positively impact student satisfaction with E-learning, highlighting the need for tailored E-learning resources to meet diverse student needs. The study underscores the importance of continuous improvement in E-learning resources and platforms to enhance student satisfaction. This research contributes to the understanding of effective E-learning strategies in higher education institutions.

Reusable bags have been introduced as an alternative to single-use plastic bags (SUPB). While beneficial, this alternative is economically and environmentally viable only if utilized multiple times. This study aims to identify the determinants influencing the use of reusable bags (RB) over single-use plastic bags (SUPB) within the framework of ecological impact reduction, employing the Theory of Planned Behavior (TPB). The focus is on understanding how attitudes (AT), subjective norms (SN), and perceived behavioral control (PBC) collectively guide consumers towards adopting reusable bags as a pro-environmental choice. The focus is on understanding how attitudes (AT), subjective norms (SN), and perceived behavioral control (PBC) collectively guide consumers towards the adoption of reusable bags as a pro-environmental choice. Data were collected through a survey administered to 814 consumers in Lahore, employing both regression analysis and Structural Equation Modeling (SEM) to assess the impact of AT, SN, and PBC on reusable bag consumption (RBC). The TPB framework underpins the hypothesis that these three psychological factors significantly influence the decision to use RBs. Both regression and SEM analyses demonstrated that AT, SN, and PBC positively affect RBC, with significant estimates indicating the strength of each predictor. Specifically, PBC emerged as the strongest predictor of RBC (PBC2, β = 0.533, p < 0.001), highlighting the paramount importance of control perceptions in influencing bag use. This was followed by AT (β = 0.211, p < 0.001) and SN (β = 0.173, p < 0.001), confirming the hypothesized positive relationships. The congruence of findings from both analytical approaches underlines the robustness of these techniques in validating the TPB within the context of sustainable consumer behaviors. The investigation corroborates the TPB’s applicability in predicting RBC, with a clear hierarchy of influence among the model’s constructs. PBC’s prominence underscores the necessity of enhancing consumers’ control over using RBs to foster sustainable consumption patterns. Practical implications include the development of policies and marketing strategies that target the identified determinants, especially emphasizing the critical role of PBC, to promote broader adoption of RBs and contribute to significant reductions in plastic waste.