Search Results (5,947)

The growing need for sustainable energy practices necessitates technology-driven interventions that can effectively bridge the disparity between consumer intentions and actual behavior. This paper formulates and empirically substantiates an IoT-enabled digital nudge architecture designed to promote sustainable energy behavior. The architecture provides goal-setting, social comparison, feedback, and informational nudges across multiple digital channels, utilizing linked devices, data processing layers, and a rule-based nudge engine. An 815-responder survey was analyzed using structural equation modeling with partial least squares (SEM-PLS) to identify the drivers of sustainable energy behavior and explore technology readiness as a moderating factor. The results show that nudges utilizing the Internet of Things (IoT) significantly enhance the alignment between intention and behavior. Goal-setting and feedback mechanisms have the highest effects. The findings also demonstrate that being ready for new technology improves nudge response, highlighting the importance of user-centered system design. This paper presents a scalable infrastructure for integrating IoT into sustainability projects, as well as theoretical contributions to technology adoption and behavioral intervention research. The study enhances the dialogue on environmental technology by illustrating the implementation of digital nudges through IoT infrastructures to expedite progress toward the Sustainable Development Goals (SDGs). Full article

This study examines how mindful consumption contributes to sustainable marketing and consumer engagement by influencing green purchase intention and life satisfaction among Generation Z, while also assessing the moderating role of social influence. Grounded in Self-Determination Theory, a survey of 1541 Thai consumers aged 18–24 was analyzed using a structural equation model and path analysis to test the mediation framework. The results show that mindful consumption significantly enhances sustainability values and purchase intentions, with sustainability values mediating the relationship between mindful consumption and both behavioral and psychological outcomes. Moreover, social influence strengthens the impact of sustainable consumption on purchase intentions, highlighting the role of peers, networks, and societal norms in promoting ethical and environmentally responsible consumer behavior. The findings extend sustainable marketing theory by highlighting mindful consumption as a driver of both behavioral (green purchase intention) and psychological (life satisfaction) outcomes. Beyond its theoretical contribution, the study offers practical insights for businesses, educators, and policymakers on fostering value-driven relationships with young consumers through mindful and socially reinforced sustainability initiatives. Promoting mindful consumption and leveraging social influence provides a pathway to engage Generation Z in sustainability-oriented lifestyles, supporting long-term consumer loyalty and achieving the United Nations’ Sustainable Development Goals. Full article

A hybrid energy system (HES) integrates various energy resources to attain synchronized energy output. However, HES faces significant challenges due to rising energy consumption, the expenses of using multiple sources, increased emissions due to non-renewable energy resources, etc. This study aims to develop an energy management strategy for distribution grids (DGs) by incorporating a hydrogen storage system (HSS) and demand-side management strategy (DSM), through the design of a multi-objective optimization technique. The primary focus is on optimizing operational costs and reducing pollution. These are approached as minimization problems, while also addressing the challenge of achieving a high penetration of renewable energy resources, framed as a maximization problem. The third objective function is introduced through the implementation of the demand-side management strategy, aiming to minimize the energy gap between initial demand and consumption. This DSM strategy is designed around consumers with three types of loads: sheddable loads, non-sheddable loads, and shiftable loads. To establish a bidirectional communication link between the grid and consumers by utilizing a distribution grid operator (DGO). Additionally, the uncertain behavior of wind, solar, and demand is modeled using probability distribution functions: Weibull for wind, PDF beta for solar, and Gaussian PDF for demand. To tackle this tri-objective optimization problem, this work proposes a hybrid approach that combines well-known techniques, namely, the non-dominated sorting genetic algorithm II and multi-objective particle swarm optimization (Hybrid-NSGA-II-MOPSO). Simulation results demonstrate the effectiveness of the proposed model in optimizing the tri-objective problem while considering various constraints. Full article

This study investigates the temperature-driven transmission switching behavior of our proposed smart glass, which utilizes a biphasic liquid crystal system under continuous application of a distinctive homeotropic (H) state voltage (V_H). By ascertaining V_H at temperatures near the phase transition point, the minimum voltage required to sustain the H state in the smectic A* (SmA*) phase is identified. Interestingly, this minimum V_H is unable to induce the H state in the chiral nematic (N*) phase, thereby maintaining a low-transmission scattering state; i.e., the focal conic (FC) state. This empowers passive, bidirectional optical switching between the transparent H state (in the SmA* phase) and the scattering FC state (in the N* phase) in an unaligned liquid crystal cell. This work employs two dissimilar chiral dopants, R811/S811 and CB7CB/R5011, both capable of inducing the SmA* phase. Neither resulting cell system underwent surface orientation treatment, and a black dye was incorporated to enhance the contrast ratio. The results indicate that the more efficacious CB7CB/R5011 system achieves a contrast ratio of 17 between the transparent and scattering states, with a corresponding haze level of 78%. To further reduce energy consumption, the experimental framework was transitioned from a continuous-voltage to a variable-voltage mode, giving rise to an increased haze level of 88%. The proposed switching scheme holds promise for diverse applications, notably in smart windows and light shutters. Full article

Background: Energy drink [ED] consumption is common among young adults and has been linked to adverse health effects and risky behaviors. This study compared medical and non-medical university students to assess whether health education influences knowledge, consumption, and attitudes toward EDs. Although medical and non-medical students are not minors, their opinions on the national ban on EDs sales to individuals under 18 provide valuable insight into attitudes toward regulation. Material and Methods: A cross-sectional online survey was conducted among 871 students (42.1% medical, 57.9% non-medical). The questionnaire assessed demographics, ED consumption, knowledge, motivations, and regulatory attitudes. It was pilot-tested on 30 students to ensure clarity, and internal consistency was confirmed (Cronbach’s α = 0.78 for knowledge; α = 0.81 for attitudes). Non-parametric tests (Mann–Whitney U, Kruskal–Wallis) and chi-square analyses compared groups. Results: Participants’ mean age was 22.1 years; most were female (73.2%). Medical students demonstrated significantly better knowledge of ED ingredients (simple sugars, B vitamins, L-carnitine, electrolytes; p < 0.01) and adverse effects (e.g., irritability, dizziness, nausea; p < 0.05). However, ED consumption frequency did not differ between medical and non-medical students. The main reasons for ED use were energy and concentration; social motives were less frequent. Female students more often supported the ban on ED sales to minors and additional advertising restrictions (p < 0.001), while overall confidence in enforcement was low. Conclusions: Despite greater awareness, medical students consume EDs at rates comparable to non-medical students. Educating medical students on safe caffeine use is crucial, since shift work may promote stimulant intake. Combining targeted education with stronger enforcement could enhance the impact of regulatory policies and reduce risky consumption among young adults. Full article

Objective: This study aimed to explore the direct relationships between social determinants and behavioral clusters, as well as their potential indirect associations mediated by edentulism. Methods: Information on social variables (collected in Wave 3, 2006/07), edentulism (Wave 5, 2010/11), and health-related behaviors (Wave 7, 2014/15) was drawn from the English Longitudinal Study of Ageing (ELSA). Baseline sociodemographic characteristics, including age, gender, ethnicity, education, and wealth, were accounted for. Latent class analysis (LCA) was applied to four behavioral indicators—smoking status, alcohol consumption, fruit and vegetable intake, and physical activity—to identify behavioral clusters. A confirmatory factor analysis (CFA) was then used to construct a latent variable representing social support and social networks. Two structural equation models (SEM) were developed to examine both the direct associations between social support/network and behavioral clusters, and the indirect associations mediated by edentulism. Results: In LCA, the two-class model was the best fit for the data. Class 1 (risky behaviors) had 7%, while Class 2 (healthy behaviors) had 93%. In SEM Model 1, higher social support/network levels predicted being in the healthy cluster directly (SC = 0.147) and indirectly (SC = 0.009). In Model 2, accounting for wealth and education, higher levels of social support/network maintained the direct association with the healthy cluster (SC = 0.132), but the indirect path lost significance. Conclusions: This study found that greater social support was associated with healthier behaviors, and this relationship may be mediated by edentulism. Health policies that encourage social interaction could therefore improve both general and oral health. Full article

Understanding luxury tourists required a more comprehensive approach than traditional expenditure-based segmentation, which often overlooked travelers’ financial capacity. This study therefore aimed to develop and validate a new typology of luxury tourists by jointly analyzing income and expenditure patterns using the International Visitor Survey of South Korea. The study addressed the need to capture both tourists’ economic capability and consumption behavior to enhance the precision of market segmentation and support sustainable destination management. Using the Jenks natural breaks classification and logistic regression, four distinct tourist types were identified: economy, spurious, scrooge, and premier, each reflecting unique combinations of income and expenditure. The results revealed that age, nationality, occupation, and trip purpose significantly influenced tourists’ classification. Younger and middle-aged professionals from East Asia were more likely to belong to high-income and high-expenditure groups, whereas Western tourists tended to spend more relative to their income. This income–expenditure typology advanced theoretical understanding of luxury tourism segmentation and provided practical insights for destination marketing organizations. The findings offered new insights for understanding how the alignment between tourists’ financial capacity and spending behavior can redefine strategies for sustainable and inclusive tourism development. Full article

Big Data Analytics is vital for power grids, as it empowers informed decision-making, anticipates potential operational and maintenance issues, optimizes grid management, supports renewable energy integration, ultimately reduces costs, improves customer service, monitors consumer behavior, and offers new services. This paper describes the AI–Big Data Analytics Architecture based on a data lake architecture that uses a reduced and customized set of Hadoop and Spark as a cost-effective, on-premises alternative for advanced data analytics in power systems. As a case study, a comparative analysis of electricity price forecasting models in the day-ahead market for nodes of the Mexican national electrical system using statistical, machine learning, and deep learning models, is presented. To build and select the best forecasting model, a data science and machine learning methodology is used. The results show that the Gradient Boosting and Support Vector Regression models presented the best performance, with a Mean Absolute Percentage Error (MAPE) between 1% and 4% for five-day-ahead electricity price forecasting. The implementation of the best forecasting model into the Big Data Analytics Platform allows the automation of the calculation of the local electricity price forecast per node (every 24, 72, or 120 h) and its display in a comparative dashboard with actual and forecasted data for decision-making on demand. The proposed architecture is a valuable tool that allows the future implementation of intelligent energy forecasting models in power grids, such as load demand, fuel prices, power generation, and consumption, among others. Full article

This paper investigates the risk determinants and spatial patterns of tax revenue loss due to illicit tobacco consumption across the 27 EU Member States from 2017 to 2022. Using a panel dataset covering economic, demographic, social, political, and behavioral dimensions, we apply principal component analysis to identify key factors associated with revenue loss, and hierarchical clustering to group countries with similar risk profiles. Geographic Information Systems visualize the spatial heterogeneity of fiscal vulnerabilities. Findings reveal that institutional and economic stability, international trade and market share, socio-economic inequality and tax burdens, health and well-being, demographic aging and social dynamics, tobacco taxation policy, and labor dynamics and shadow consumption structure the patterns of tax loss risk. Findings also highlight significant differences among Member States, emphasizing the multidimensional nature of fiscal risks. Full article

With the rapid advancement and widespread adoption of artificial intelligence (AI), AI-powered personalized product recommendation (AI-PPR) has become a core tool for enhancing user experience and driving monetization on short-form video platforms, fundamentally reshaping consumer behavior. While prior research has largely focused on impulse buying intention (I-BI)—purchases triggered by emotional and sensory stimuli—there remains a lack of systematic exploration of spontaneous buying intention (S-BI), which emphasizes rational and cognitively driven decisions formed in unplanned contexts. Addressing this gap, this study integrates the Technology Acceptance Model (TAM) with a perceived evaluation perspective to propose and validate a dual-mediation framework: “AI-PPR → Perceived Usefulness/Perceived Trust → S-BI”. Using a large-scale survey of Generation Z users in mainland China (N = 754), data were analyzed via SPSS 26.0, including reliability and validity tests, regression analysis, and Bootstrap-based mediation analysis. The results indicate that AI-PPR not only has a significant positive direct effect on S-BI but also exerts strong indirect effects through perceived usefulness and perceived trust. Specifically, perceived usefulness accounts for 35.17% and perceived trust for 31.18% of the mediation, jointly constituting 66.35% of the total effect. The findings contribute theoretically by extending the boundary of purchase intention research, differentiating rational S-BI from emotion-driven impulse buying, and enriching the application of TAM in consumption contexts. Practically, the study highlights the importance for short-form video platforms and brand managers to enhance recommendation transparency, interpretability, and trust-building while pursuing algorithmic precision, thereby fostering rational spontaneous buying and achieving a balance between short-term conversions and long-term user value. Full article

Gold deposits of the Iron Quadrangle are highly heterogeneous, requiring integrated studies to optimize processing. This study presents a geometallurgical assessment of the Lamego orogenic gold deposit, located in the Iron Quadrangle, Brazil. Eleven composite samples representing four lithotypes, namely metandesite, banded iron formation (BIF), smoky quartz, and carbonaceous phyllite, were analyzed through QEMSCAN, fire assay, and Leco methods. Samples underwent gravity separation and flotation tests to evaluate mineralogical variability and its metallurgical implications. The results show that sulfide-rich lithotypes, particularly those containing pyrite and arsenopyrite, achieved higher gold and sulfur recoveries, especially in flotation. In contrast, samples with high concentrations of muscovite or reactive carbonates such as ankerite and dolomite showed reduced selectivity due to reagent competition and flotation interference. Grinding behavior varied among lithologies, with smoky quartz requiring the highest energy input (10.32 kWh/t) and displaying the lowest breakage parameter (K = 0.120), reflecting its high hardness and fine mineral intergrowths. Strong correlations were established between ore mineralogy and process performance; for instance, sulfide abundance directly predicted flotation recovery, while quartz content correlated with higher grinding energy consumption. These findings underscore the importance of incorporating detailed mineralogical characterization into process design. Geometallurgical tools enable more accurate prediction of metallurgical performance and support the development of lithotype-specific flowsheets for improved recovery, reduced energy consumption, and more efficient gold processing in complex ore systems such as Lamego. Full article

The intensification of industrialization and increasing energy consumption have led to elevated emissions of hazardous gases such as NO, NO₂, and SO₂, making their efficient capture and removal crucial for environmental remediation. In this work, first-principles calculations were employed to systematically investigate the adsorption behavior of these gases on single-atom-decorated (Sc, Ti, and V) 1T-ZrS₂ monolayers. The results indicate that the transition metal atoms preferentially occupy the hexagonal hollow sites of ZrS₂, forming an approximately octahedral coordination field and exhibiting characteristic d-orbital splitting. During gas adsorption, the decorated systems exhibit pronounced metal-to-adsorbate charge donation and strong d-p hybridization, indicative of strong chemisorption. Notably, Ti-ZrS₂ exhibits the strongest adsorption toward NO₂, inducing partial molecular dissociation and suggesting catalytic activity, whereas Sc- and V-decorated systems predominantly maintain molecular adsorption. Recovery time calculations indicate that the adsorption processes are comparatively stable, making these systems suitable for gas capture and pollution abatement. Overall, single-atom decoration provides an effective strategy to modulate the electronic structure and gas interactions of ZrS₂, highlighting its potential as an efficient gas scavenger for NO, NO₂, and SO₂. Full article

Sustainable consumption has become a global priority, yet the factors that encourage people to adopt environmentally friendly purchasing behavior differ across cultures and technologies. This study explores how environmental knowledge, environmental attitude, and the perception of AI-driven personalization influence green purchasing intention and actual purchasing behavior among young consumers in Java, Indonesia. A survey of 517 university students was conducted, and the relationships among these factors were analyzed using structural equation modeling. The findings reveal that environmental knowledge strongly shapes environmental attitudes, which in turn enhance the intention and behavior to purchase green products. Perception of AI-driven personalization also strengthens green purchasing intention, although its direct effect on behavior is limited. These results suggest that digital platforms and marketers can promote sustainable consumption by combining environmental education with transparent and value-based AI personalization. The study contributes to understanding how psychological readiness and technological engagement together encourage greener consumption among youth in emerging economies. Full article

Public understanding of complex sustainability concepts like life cycle assessment (LCA) is crucial for promoting environmentally responsible consumption yet remains a significant educational challenge. This study introduces and evaluates Paper Goes Green, a competitive board game designed to make abstract LCA principles tangible and personally relevant. The game simulates the paper production chain, compelling players to make strategic decisions about resource allocation, production pathways (conventional vs. green), and waste management to fulfill paper orders. Through a single-group pre-test/post-test design with 85 participants (25 environmental educators and 60 public members), the game’s efficacy was assessed. Paired-sample t-tests revealed significant improvements in participants’ perceived knowledge of green chemistry/LCA (pre-game mean 2.05, post-game 3.24 on a 5-point scale, p < 0.001), pro-environmental attitudes (3.38 to 4.22, p < 0.001), and behavioral intentions toward green consumption (3.97 to 4.44, p < 0.001). These gains correspond to medium-to-large effect sizes (Cohen’s d = 0.94 for knowledge, 0.70 for attitude, 0.71 for behavior), indicating substantial practical impact. Qualitative feedback further highlighted the game’s engaging and thought-provoking nature. Notably, specific design features—such as immediate feedback, player control, and interactivity—were identified as key contributors to learning, fostering systems thinking in players. These findings suggest that Paper Goes Green is a promising educational tool for translating complex environmental science into an engaging, impactful learning experience. The game effectively bridges the gap between abstract concepts and real-world consumer choices, fostering life cycle thinking and empowering players to make greener choices in their daily lives. Full article

The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities. Full article