Search Results (74)

Waste electrical and electronic equipment (WEEE) is one of the fastest-growing waste streams globally. Disposable e-cigarettes are among the products that have gained popularity in recent years. Their complex construction and embedded lithium-ion batteries (LIBs) present environmental, safety, and resource recovery challenges. Despite growing research interest, integrated analyses linking material composition with user disposal behavior remain limited. This study is the first to incorporate device-level mass balance, material contamination assessment, battery residual charge measurements, and user behavior to evaluate the waste management challenges of disposable e-cigarettes. A mass balance of twelve types of devices on the Polish market was performed. Plastics dominated in five devices, while non-ferrous metals prevailed in the others, depending on casing design. Materials contaminated with e-liquid residues accounted for 4.4–10.7% of device mass. Battery voltage measurements revealed that 25.6% of recovered LIBs retained a residual charge (greater than 2.5 V), posing a direct fire hazard during waste handling and treatment. Moreover, it was estimated that 7 to 12 tons of lithium are introduced annually into the Polish market via disposable e-cigarettes, highlighting substantial resource potential. Survey results showed that 46% of users disposed of devices in mixed municipal waste, revealing a knowledge–practice gap largely independent of gender or education. Integrating technical and social findings demonstrates that improper handling is a systemic issue. The findings support the relevance of eco-design requirements, such as modular casings for battery removal, alongside the enforcement of Extended Producer Responsibility (EPR) schemes. Current product fees (0.01–0.03 EUR/unit) remain insufficient to establish an effective collection infrastructure, highlighting a key systemic barrier. Full article

In this paper, we propose a novel sparse clustering method designed for high-dimensional mixed-type data, integrating Azzalini’s score-based encoding for ordinal variables. Our approach aims to retain the inherent nature of each variable type—continuous, ordinal, and nominal—while enhancing clustering quality and interpretability. To this end, we extend classical distance metrics and adapt the Davies–Bouldin Index (DBI) to better reflect the structure of mixed data. We also introduce a weighted formulation that accounts for the distinct contributions of variable types in the clustering process. Empirical results on simulated and real-world datasets demonstrate that our method consistently achieves better separation and coherence of clusters compared to traditional techniques, while effectively identifying the most informative variables. This work opens promising directions for clustering in complex, high-dimensional settings such as marketing analytics and customer segmentation. Full article

Sub-hourly operational optimization of Power-to-X (PtX) hydrogen systems remains largely unexplored, despite their growing importance as flexible assets in renewable-dominated energy systems. Existing models typically assume hourly market resolution and linear process behavior, overlooking how intra-hour price volatility and non-linear electrolyzer efficiencies shape operational costs, flexibility, and emissions. This study pioneers a data-driven optimization framework that integrates synthetic 15 min electricity-price generation, agent-based simulation, and mixed-integer quadratically constrained programming (MIQCP) to evaluate hydrogen-production strategies under the forthcoming European 15 min market regime. Using a Danish PtX facility with on-site wind and solar generation as a case study, the framework quantifies how adaptive scheduling compares with non-adaptive baselines across multiple volatility scenarios. The results show that dynamic 15 min optimization reduces hydrogen-production costs by up to 40% relative to hourly scheduling, and that extending the objective function to include electricity-sales revenue improves net profitability by approximately 11%. Although adaptive scheduling slightly increases CO₂ intensity due to altered renewable utilization, it substantially enhances flexibility and cost efficiency. Scientifically, this study introduces the first reproducible synthetic-data approach for sub-hourly optimization of non-linear electrolyzer systems, bridging a critical gap in the demand-side-management and sector-coupling literature. Practically, it provides evidence-based guidance for PtX operators and regulators on designing adaptive, volatility-responsive control strategies aligned with Europe’s transition to high-frequency electricity markets and net-zero objectives. Full article

This study examines the emergence of cryptocurrency-enabled democracy (c-democracy) through an analysis of blockchain-based prediction markets during South Korea’s 2024 presidential impeachment crisis. Using a mixed-methods approach, namely network analysis, discourse analysis, and statistical validation, we identify transnational communities engaging in Korean politics beyond citizenship boundaries. Findings reveal a discourse–betting disconnect, where expressive, playful discourse coexists with serious financial stakes, reflecting hybrid motivations for participation. We also observe playful activism and transnational community formation that transcend geographical limits. These results highlight c-democracy as a novel form of political engagement that extends, but also complicates, traditional e-democracy frameworks. Full article

Background/Objectives: India’s private healthcare sector remains fragmented, with weak primary care, uneven secondary services, and tertiary care accessible to few. Fee-for-service payments and indemnity-style insurance distort prices and fragment accountability. This paper develops a conceptual, theory-driven framework for integrating financing and delivery so that prices reflect social opportunity costs and competition rewards value rather than volume. Methods: A comparative synthesis of international integration models covering Israel, the United States, Spain, Brazil, and the United Kingdom was undertaken. Each exemplar was analysed for ownership form, market maturity, and regulatory capacity, and interpreted using four strategic management theories: Contingency theory, the Resource-based view, Dynamic capabilities, and Institutional theory. These perspectives were combined to construct a contingency-based typology tailored to India’s mixed health system. Results: Two state-contingent integration pathways emerged. Hospital-first vertical integration suits hospital-dense, high-growth states such as Tamil Nadu and Delhi, where capital and regulatory depth permit managed-care scaling. Primary-care-first reverse integration is preferable in resource-constrained contexts such as Bihar and Chhattisgarh, leveraging community trust and lower capital intensity. Conclusions: Achieving universal health coverage in India requires regulatory conditions, such as ownership flexibility, solvency oversight, risk adjustment, and transparent outcomes reporting, to enable accountable payer–provider organisations to form. The framework extends contingency theory to mixed health systems and offers a transferable blueprint for emerging markets seeking sustainable, integrated managed care. Full article

Background: South Africa’s pome fruit industry serves over 60 international markets, competing with Chile, New Zealand, and the United States. Inefficiencies in the beginning stages of South Africa’s pome fruit supply chain compromise competitiveness as global quality standards rise and consumers demand premium fruit with an extended shelf life. This research identifies operational bottlenecks in the post-harvest handling and processing of pome fruit, focusing on temperature control, lead times, and infrastructure constraints. Methods: A mixed-methods case study approach of Company X, combining on-site observations, semi-structured interviews, and analysis of Company X’s processing data. Findings were triangulated with Hortgro and PPECB sources for validity. Results: Prolonged ambient temperature exposure from packhouse processing bottlenecks resulted in increased fruit pulp temperatures, with congestion, inefficient practices, and poor communication exacerbating problems. Pre-cooling proved most inefficient, with pulp temperatures averaging 1.9 °C (peak season: 3.2–3.5 °C), far exceeding the −0.5 °C industry standard required for international markets and resulting in a downgrade from Class 1 to Class 2 fruit. Conclusions: This research identifies cold chain bottlenecks affecting South Africa’s global competitiveness. Recommended solutions include hydrocooling, infrastructure upgrades, and enhanced stakeholder coordination to strengthen the country’s position in international pome fruit markets. Full article

This study investigates the psychological and emotional mechanisms underlying women’s reactions to breast cancer awareness advertisements through the dual lens of Protection Motivation Theory (PMT) and neuromarketing methods, addressing a gap in empirical research on the integration of biometric and cognitive approaches in health marketing. Utilizing a lab-based experiment with 78 women aged 40 and older, we integrated Facial Expression Analysis using Noldus FaceReader 9.0 with semi-structured post-exposure interviews. Six manipulated health messages were embedded within a 15 min audiovisual sequence, with each message displayed for 5 s. Quantitative analysis revealed that Ads 2 and 5 elicited the highest mean fear scores (0.45 and 0.42) and surprise scores (0.35 and 0.33), while Ad 4 generated the highest happiness score (0.31) linked to coping appraisal. Emotional expressions—including fear, sadness, surprise, and neutrality—were recorded in real time and analyzed quantitatively. The facial analysis data were triangulated with thematic insights from interviews, targeting perceptions of threat severity, vulnerability, response efficacy, and self-efficacy. The findings confirm that fear-based appeals are only effective when paired with actionable coping strategies, providing empirical support for PMT’s dual-process model. By applying mixed-methods analysis to the evaluation of health messages, this study makes three contributions: (1) it extends PMT by validating the emotional–cognitive integration framework through biometric–qualitative convergence; (2) it offers practical sequencing principles for combining threat and coping cues; and (3) it proposes cross-modal methodology guidelines for future health campaigns. Full article

This research investigates the dynamic spillover effects among green bond markets and the impact of investor sentiment on these spillovers. We employ different research methods, including a time-varying parameter vector autoregression, an exponential general autoregressive conditional heteroscedasticity, and a generalized autoregressive conditional heteroskedasticity-mixed data sampling model. Our sample is for twelve international green bond markets from 3 January 2022 to 31 December 2024. Our results evidence the strong correlation between twelve green bond markets, with the United States and China being the net risk receivers and Sweden being the largest net shock transmitter. We also find the varied impact of direct and indirect investor sentiment on the net total directional spillovers. Our research offers fresh contributions to the existing literature in different ways. On the one hand, it adds to the green finance literature by clarifying the dynamic spillovers among leading international green bond markets. On the other hand, it extends behavioral finance research by including direct and indirect investor sentiment in the spillovers of domestic and foreign green bond markets. Our study is also significant to related stakeholders, including investors in their portfolio rebalancing and policymakers in stabilizing green bond markets. Full article

The global shift toward sustainable transportation has gained increasing interest, promoting the use of electric vehicles (EVs) as an environmentally friendly alternative to conventional vehicles as a result of a complex interaction between economic incentives, social dynamics, and environmental imperatives. This study is based on the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) to understand the key factors influencing consumers’ intentions in the Sultanate of Oman toward adopting electric vehicles. It is based on a mixed methodology combining quantitative data from a questionnaire of 448 participants, analyzed using ordinal logistic regression, with qualitative thematic analysis of in-depth interviews with 18 EV owners. Its results reveal that performance expectations, trust in EV technology, and social influence are the strongest predictors of EV adoption intentions in Oman. These findings suggest that some issues related to charging infrastructure, access to maintenance services, and cost-benefit ratio are key considerations that influence consumers’ intention to accept and use EVs. Conversely, recreational motivation is not a statistically significant factor, which suggests that consumers focus on practical and economic motivations when deciding to adopt EVs rather than on their enjoyment of driving the vehicle. The findings of this study provide valuable insights for decision-makers and practitioners to understand public perceptions of electric vehicles, enabling them to design effective strategies to promote the adoption of these vehicles in the emerging sustainable transportation market of the future. Full article

Urban green spaces are increasingly recognized as important habitats for pollinators, and wildflower seed mixes marketed as pollinator-friendly are gaining popularity, though their actual conservation value remains poorly understood. This study provides the first systematic screening of commercially available seed mixes in Portugal, evaluating their taxonomic composition, origin, life cycle traits, and potential to support pollinator communities. A total of 229 seed mixes were identified. Although these have a predominance of native species (median 86%), the taxonomic diversity was limited, with 91% of mixes comprising species from only one or two families, predominantly Poaceae and Fabaceae, potentially restricting the range of floral resources available to pollinators. Only 21 seed mixes met the criteria for being pollinator-friendly, based on a three-step decision tree prioritizing native species, extended flowering periods, and visual diversity. These showed the highest percentage of native species (median 87%) and a greater representation of flowering plants. However, 76% of all mixes still included at least one non-native species, although none is considered invasive. Perennial species dominated all seed mix types, indicating the potential for the long-term persistence of wildflower meadows in urban spaces. Despite their promise, the ecological quality and transparency of the seed mix composition remain inconsistent, with limited certification or information on species origin. This highlights the need for clearer labeling, regulatory guidance, and ecologically informed formulations. Seed mixes, if properly designed and implemented, represent a largely untapped yet cost-effective tool for enhancing the pollinator habitats and biodiversity within urban landscapes. Full article

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development. Full article

With the rapid development of the new energy vehicle market, the demand for efficient, low-noise, low-energy consumption, high-strength, and durable gear transmission systems is continuously increasing. Therefore, it has become imperative to conduct in-depth research into the fluid heat transfer and lubrication dynamics within gearboxes. In gear systems, the interaction between fluids and solids leads to complex nonlinear heat transfer characteristics between gears and lubricants, making the development and resolution of gearbox thermodynamic models highly challenging. This paper proposes a gear lubrication heat transfer dynamics model based on LBM-LES coupling to study the dynamic laws and heat transfer characteristics of the gear lubrication process. The research results indicate that the interaction between gears and the intense shear effects caused by high speeds generate vortices, which are particularly pronounced on larger gears. The fluid mixing effect in these high vortex regions is better, achieving a more uniform heat dissipation effect. Furthermore, the flow characteristics of the lubricant are closely related to speed and temperature. Under high-temperature conditions (such as 100 °C), the diffusion range of the lubricant increases, forming a wider oil film, but its viscosity significantly decreases, leading to greater stirring losses. By optimizing the selection of lubricants and stirring parameters, the efficiency and reliability of the gear transmission system can be further improved, extending its service life. This study provides a comprehensive analytical framework for the thermodynamic characteristics of multi-stage transmission systems, clarifying the heat transfer mechanisms within the gearbox and offering new insights and theoretical foundations for future research and engineering applications in this field. Full article

The agricultural equipment market has the characteristics of rapid demand changes and high demand for machine models, etc., so multi-variety, small-batch, and customized production methods have become the mainstream of agricultural machinery enterprises. The flexible job shop scheduling problem (FJSP) in the context of agricultural machinery and equipment manufacturing is addressed, which involves multiple resources including machines, workers, and automated guided vehicles (AGVs). The aim is to optimize two objectives: makespan and the maximum continuous working hours of all workers. To tackle this complex problem, a Multi-Objective Discrete Grey Wolf Optimization (MODGWO) algorithm is proposed. The MODGWO algorithm integrates a hybrid initialization strategy and a multi-neighborhood local search to effectively balance the exploration and exploitation capabilities. An encoding/decoding method and a method for initializing a mixed population are introduced, which includes an operation sequence vector, machine selection vector, worker selection vector, and AGV selection vector. The solution-updating mechanism is also designed to be discrete. The performance of the MODGWO algorithm is evaluated through comprehensive experiments using an extended version of the classic Brandimarte test case by randomly adding worker and AGV information. The experimental results demonstrate that MODGWO achieves better performance in identifying high-quality solutions compared to other competitive algorithms, especially for medium- and large-scale cases. The proposed algorithm contributes to the research on flexible job shop scheduling under multi-resource constraints, providing a novel solution approach that comprehensively considers both workers and AGVs. The research findings have practical implications for improving production efficiency and balancing multiple objectives in agricultural machinery and equipment manufacturing enterprises. Full article

The transformation of traditional food markets into urban tourism destinations has garnered increasing attention, yet the mechanisms driving tourist motivations remain underexplored. This study addresses this gap by proposing a host–guest sharing model grounded in the Theory of Self-Regulation (TSR). Employing a mixed-methods approach, we first conducted grounded theory analysis on 358,700 words of travelogues, identifying six TSR-based constructs: host–guest sharing, sense of place, behavior attitude, desire, subjective norms, and behavioral intention. These constructs were then validated through structural equation modeling (SEM) using survey data from 416 tourists. Results indicate that host–guest sharing (β = 0.925) and sense of place (β = 0.947) are the primary drivers of tourist intention, mediated by behavior attitude (β = 0.662) and desire (β = 0.861). Subjective norms (β = 0.724) further reinforce intention formation. The findings highlight the centrality of authentic cultural experiences and resident–tourist interactions in shaping food market tourism. This research extends the TSR framework by integrating geographical and psychological perspectives and offering actionable insights for urban planners to enhance food markets as sustainable tourism attractions through improved service quality, cultural storytelling, and equitable space design. Full article

Traditional numerical association rule mining optimization algorithms have limitations in handling discrete attributes, and they are susceptible to becoming trapped in local optima, uneven population distribution, and poor convergence. To address these challenges, we propose a multi-objective nutcracker optimization algorithm based on a cubic chaotic map (C-MONOA), specifically designed for mining association rules from mixed data (continuous and discrete). Unlike existing models, C-MONOA leverages a chaotic map for population initialization, alongside Michigan rule encoding, to dynamically optimize feature intervals during the optimization process. This algorithm integrates continuous and discrete data more effectively and efficiently. This article uses support, confidence, Kulc metric, and comprehensibility as evaluation indicators for multi-objective optimization. The experimental results show that C-MONOA performs well in rule scoring and can generate frequent, simple, and accurate rule sets. This study extends the association rule mining method for mixed data, demonstrating high performance and robustness and providing new technical tools for application fields such as market analysis and disease prediction. Full article