Search Results (1,031)

In post-pandemic Peru, delivery platforms have become key market-access infrastructures for microenterprises, yet regional evidence remains limited. This study examines the extent to which—and under what conditions—the adoption of delivery services is associated with innovative business growth in La Libertad (2021–2025). A cross-sectional survey of 200 microentrepreneurs assessed delivery adoption, business model innovation (BMI), digital capabilities, and the institutional environment. Statistical analyses indicate that the use of delivery platforms is positively associated with business growth and that an indirect association operates through BMI. Likewise, higher levels of digital capabilities are associated with a steeper adoption–growth association, while more favorable institutional conditions are associated with higher odds of business formalization. These findings suggest that delivery platforms may function as catalysts for growth and formalization when adoption co-occurs with strategic redesign and digital skill development within supportive local ecosystems. The study contributes to sustainable entrepreneurship scholarship by providing regional-level evidence from a developing economy and underscoring that technological adoption alone is insufficient without complementary innovation and capability building. Although environmental metrics were not included, future research should incorporate verified indicators to assess the environmental dimension of delivery-based entrepreneurship comprehensively. Full article

Live-streaming e-commerce is a popular promotional method for attracting more customers in the era of the platform economy. This study investigates a better strategy for live-streaming sales for a manufacturer in an e-platform-based supply chain. Specifically, the manufacturer has two options—self-broadcasting or key opinion leader (KOL) live-streaming—to sell products on the e-platform, which acts as a reseller and agency seller in the dual-channel supply chain. We established three Stackelberg models for the supply chain and derived the equilibrium decisions and profits of supply-chain members under three scenarios: without live-streaming, KOL live-streaming, and manufacturer self-broadcasting. After a comparative analysis, we obtained the following key results: First, even if the influence level of the KOL is within a low range, the manufacturer should change the strategy from no live-streaming e-commerce to KOL live-streaming, with a decrease in the agency fee charged from the e-platform. However, with the continued decline in agency fee, the manufacturer should change the strategy from KOL live-streaming to self-broadcasting. Furthermore, we found a win–win zone such that both supply-chain members are better off under the KOL scenario when both the agency fee of the e-platform and the influence level of the KOL are in higher ranges. Our study not only provides a theoretical basis for the enterprise to choose the appropriate live-streaming mode but also helps it to further increase profits and reduce operational risks. In the future, we will further incorporate theoretical analyses into empirical study to discover more interesting results. Full article

Under the backdrop of the “dual carbon” strategy, the rapid increase in renewable energy penetration has exacerbated challenges such as widening peak–valley load gaps and insufficient grid regulation capacity, highlighting the urgent need to establish a market-oriented collaborative dispatching mechanism. This paper proposes a peak-shaving and valley-filling dispatching approach based on a multi-agent system (MAS) to enhance both the regulatory capability and economic efficiency of power grids. A multi-agent collaborative architecture is established on the generation side, where behavioral modeling and interaction simulations of generation, load, and energy storage agents are conducted using the NetLogo platform to emulate dynamic responses under market conditions. On the grid side, dynamic electricity pricing and energy storage control strategies are implemented. An integrated time-of-use electricity pricing mechanism is designed that incorporates environmental pollution factors, supply–demand state factors, and price-smoothing factors to dynamically adjust tariffs. A price-responsive load demand model and a dynamic threshold-based energy storage control strategy are developed to facilitate flexible regulation. On the load side, an optimized dispatch model is formulated with dual objectives of minimizing system operating costs and reducing the standard deviation of the net load profile. The Beetle Antennae Search (BAS) algorithm is employed to solve the model, striking a balance between economic efficiency and stability. Case study results demonstrate that, compared with traditional dispatch methods, the coordinated optimization of the BAS algorithm and the dynamic pricing mechanism proposed in this paper achieves a dual improvement in solution efficiency and economy. This ultimately reduces the system’s peak-to-valley difference by 10.92% and operating costs by 66.2%, proving its effectiveness and superiority in power grids with high renewable energy penetration. Full article

This study examined the complex interplay of demographic characteristics, behavioral patterns, and technological factors that contribute to digital fraud victimization within the context of a developing economy, focusing specifically on Thailand. Utilizing data collected from 1200 respondents and applying binary logistic regression analysis, the research identified key predictors of fraud exposure, including age, income, student status, use of portable devices, and social media engagement. A paradoxical finding emerged: stronger perceived digital security was associated with higher fraud risk, indicating that overconfidence in platform safeguards may unintentionally increase vulnerability. Interestingly, users’ perceptions of digital security—such as confidence in identity verification and password protocols—were positively associated with fraud victimization, indicating potential cognitive biases and overconfidence in digital environments. The findings revealed a high prevalence of fraud experiences among participants, highlighting the gap between perceived and actual digital safety. These results emphasized the urgent need for user-centered fraud prevention measures, enhanced digital literacy, and targeted public awareness campaigns. The study contributes to the broader understanding of cybersecurity challenges in emerging markets and offers policy-relevant insights for strengthening digital financial resilience. Full article

The rapid growth of electric vehicles (EVs) and energy storage systems (ESSs) has accelerated the generation of waste lithium-ion batteries, posing both environmental and industrial challenges. This study proposes and experimentally validates an AI-based Waste Battery and Plasma Convergence System (AI-WBPCS) designed to integrate residual energy recovery from retired EV batteries with adaptive plasma control. The system aims to establish a self-optimizing energy reuse framework that enhances real-time energy utilization, improves plasma process stability, and supports sustainable circular energy ecosystems. The AI-WBPCS consists of three key sub-models: D₁ for plasma output prediction, D₂ for battery health evaluation, and D₃ for adaptive energy-matching control. These models operate synergistically under a hybrid STM32–Jetson Nano platform, enabling predictive analysis and closed-loop optimization. Experimental validation using 2P6S retired EV modules demonstrated that the D₂ model achieved a 93.7% SOH prediction accuracy and a 2.3% mean absolute error (MAE) in DCIR estimation. The AI-controlled plasma subsystem maintained output stability within ±2.1%, compared to fluctuations exceeding 6% under conventional rule-based methods. The overall energy-matching efficiency (η) reached 96.5%, representing a 13% improvement in power coordination performance. Interpretability analysis using SHAP (SHapley Additive exPlanations) identified SOH (46%) and DCIR (29%) as the dominant features influencing AI-driven decisions, confirming the physical relevance and transparency of the model. The AI-WBPCS provides a practical pathway toward circular-economy-oriented energy reuse, enabling intelligent, autonomous plasma systems for applications such as smart agriculture, biomedical sterilization, and decentralized wastewater treatment. Overall, this research establishes a new paradigm for AI-empowered electrochemical–plasma systems, where artificial intelligence not only enhances operational efficiency but also redefines end-of-life batteries as adaptive energy resources for next-generation green technologies. Full article

Additive manufacturing (AM), commonly known as 3D printing, is revolutionizing modern dentistry, introducing high-precision, patient-specific, and digital-driven workflows across prosthodontics, orthodontics, implantology, and maxillofacial surgery. Extensive analysis explores the leading platforms in 3D printing such as stereolithography (SLA), fused deposition modeling (FDM), selective laser sintering (SLS), digital light processing (DLP), and PolyJet which all achieve superior performance across multiple areas including resolution capabilities, material compatibility options, clinical application readiness, and cost-effectiveness. Additionally, an extensive overview of common materials, including biocompatible polymers (PLA, PMMA, PEEK), metals (titanium, cobalt-chromium), and ceramics (zirconia, alumina, glass-ceramics), sheds light on the critical role of material selection for patient safety, durability, and functional performance. The review explores new advancements such as 4D printing with shape-adaptive smart biomaterials as well as artificial intelligence-enabled digital processes and prosthesis design for the transformation of regenerative dentistry and intraoral drug delivery operations into new domains and the automation of clinical planning. Equally groundbreaking are 3D printing applications in pediatric dentistry, surgical simulation, and dental education. However, full-scale adoption of AM technology is not without challenges, including material toxicity, regulatory hurdles for approval, high initial investments, and the need for extensive digital expertise training. Sustainability concerns are also being addressed, with recycled materials and circular economy models gaining traction. In conclusion, this article advocates for a future where dentistry is shaped by interdisciplinary collaboration, intelligent automation, and hyper-personalized biocompatible solutions, with 3D printing firmly established as the backbone of next-generation dental care. Full article

This article investigates how Web3 decentralization unfolds in practice and asks two guiding questions: (i) How democratic are decentralized governance systems in practice? (ii) Under what institutional conditions can technological decentralization translate into social inclusion? Based on multi-year ethnographic fieldwork (2022–2025) across Silicon Valley, Washington, D.C., Europe, and the Global South, this study draws on participant observation, semi-structured interviews, and comparative analysis of seven ecosystems—Ethereum, MakerDAO, Uniswap, Mastodon, Celo, Grassroots Economics, and GoodDollar. The findings show that participation asymmetries are structural: token-based governance is dominated by a small group of technically skilled or capital-rich actors, while voter turnout often remains below ten percent. Intermediaries such as foundations, developers, NGOs, and cooperatives are indispensable for coordination, contradicting the idea of hierarchy-free decentralization. In contrast, projects that institutionalize clear membership, monitoring, and accountability—particularly in cooperative and federated settings—display stronger democratic resilience. Comparative evidence also reveals oligarchic consolidation in Global North ecosystems and infrastructural exclusion in the Global South. These results substantiate what Richard R. Nelson termed “the Moon and the Ghetto” paradox: extraordinary technical innovation without corresponding social progress. Interpreted through innovation systems theory, the study concludes that advancing decentralized technologies requires parallel investment in mission-oriented institutions that ensure participation, equity, and accountability in digital infrastructures. Full article

How individuals and organizations interface with the digital economy has been largely influenced by transformations ushered in on the global financial map by the rapidly expanding Financial Technology (FinTech). This paper seeks to shed light on the successes of FinTech, namely on how it contributed to sustainability through financial inclusion, reduction in reliance on cash and the promotion of an innovation-driven economy known for being paperless. Based on contributions from students at Taif University in Saudi Arabia, determinants of FinTech adoption intentions are analyzed using data from n = 544. Our study focuses on evaluating the effects of financial, technical and external factors on adoption behavior by using a two-prong approach: first, we use the DeLone and McLean IS Success Model; then we employ a Second-Order Construct using Structural Equation Modelling (SEM). The results indicated that the strongest effects on attitudes stem from technical factors—information, system and service quality. Additionally, they also show that adoption intention is considerably shaped by financial as well as external dimensions. The Saudi Vision 2030 has set national goals of digital transformation, financial inclusion and human capital empowerment. This study provides a modest contribution to those goals by fostering FinTech adoption among the youth. Furthermore, its findings also offer educators, policymakers and Fintech providers a platform to enhance literacy, strengthen trust and develop sustainable digital finance ecosystems in line with the Kingdom’s Vision 2030 objectives. Full article

The intensifying global warming and the increasing frequency of extreme weather events have created an urgent need for targeted resilience building in mountainous villages. This study focuses on three typical villages in the Hengduan Mountains region. From the perspective of individual villagers, a disaster resilience evaluation index system was constructed, encompassing four dimensions: disaster prevention capacity, disaster resistance capacity, disaster relief capacity, and recovery capacity. Using the entropy method and a village disaster resilience assessment model, the disaster resilience levels of each village were quantitatively evaluated. The results indicate the following: (1) Disaster resistance capacity is the key factor constraining the disaster resilience level of mountain villages. (2) The overall disaster resilience of mountain villages is at a medium level, with minor differences among villages. (3) Significant disparities exist in capacity dimensions across villages: Qina Village demonstrates the strongest disaster resistance capacity, while Xiamachang Village excels in disaster prevention capacity but shows relative weakness in recovery capacity. (4) Household material endowment has a significant positive impact on disaster prevention, resistance, relief, and recovery capacities, while individual self-rescue capability and individual–government collaboration capacity also significantly enhance disaster prevention, resistance, and relief capacities. We propose the following: Leveraging the rural revitalization strategy as a pivotal point, this approach promotes the diversified development of the village economy. It facilitates the increase in villagers’ income through the implementation of employment skill training programs, thereby strengthening household material foundations to enhance individual disaster resilience. By relying on the mass monitoring and mass prevention mechanism and a disaster information sharing platform, real-time exchange of disaster situation information is achieved, which enhances communication and collaboration between villagers and the government, consequently improving the synergistic efficiency between individuals and governmental bodies. Simultaneously, a villager-centered disaster prevention system is constructed. Through measures such as disaster prevention publicity and practical disaster response drills, villagers’ awareness of disasters and their capabilities for self and mutual rescue are elevated, ultimately strengthening the overall disaster resilience of rural areas in the Hengduan Mountains region. Full article

Within the circular economy, repair is increasingly recognised as a crucial yet underexplored strategy that extends product lifespans and reduces waste. Service design offers approaches to support this transition by addressing technical, social, and systemic dimensions. This review aimed to synthesise how service design contributes to repair practices and identify research gaps. Following PRISMA 2020 guidelines, we systematically searched Scopus and Web of Science, applied inclusion criteria focusing on service design and repair within the circular economy, and conducted multi-step screening and snowballing. From 132 initial records, 73 studies were included (journal articles, conference papers, book chapters). Thematic synthesis identified three areas: micro-level interactions between producers, products, and users (e.g., motivations, trust, communication); meso-level tools, frameworks, and platforms enhancing accessibility and efficiency; and macro-level societal transformation through regulations, standards, and communities. Results highlight service design’s potential to foster systemic change by integrating environmental, social, and economic aspects, while also revealing notable research gaps related to the limited engagement of repairers, policymakers, and cross-level collaboration. Compared to previous studies, this review contributes a novel integrated framework linking micro-, meso-, and macro-level dimensions of repair within the circular economy, offering both conceptual insights and actionable directions for practitioners and policymakers. The study is limited by language constraints and the lack of a formal bias evaluation. All reviewed materials are publicly accessible on OSF. This research was conducted without external financial support. Full article

High-throughput screening (HTS) is an essential process in drug discovery, requiring platforms that ensure reagent economy, high efficiency, and resistance to cross-contamination. Click chemistry is well suited for HTS because of its biocompatibility, high selectivity, and quantitative fluorescent readout. We focus on droplet-array sandwiching technology (DAST), in which two droplet microarrays (DMAs) are vertically opposed to achieve solute transport and reagent mixing by controlled contact and separation. Herein, we integrate click chemistry with DAST and evaluate its feasibility as a HTS platform. In DAST, DMAs are formed on wettability-patterned (WP; hydrophilic/hydrophobic) substrates, preserving resistance to cross-contamination. First, we immobilized dibenzocyclooctyne (DBCO) on a WP substrate and verified the occurrence of DBCO–azide reaction using an azide-functional fluorescent dye. The fluorescence intensity increased with concentration and reached a plateau at higher concentrations, indicating saturation behavior in the DBCO–azide click reaction. Second, acoustic mixing with repeated droplet contact–separation was applied to generate concentration gradients on a single substrate while maintaining droplet independence. Third, we qualitatively reproduced the expected concentration dependence of manual handling by combining DAST-based gradient formation with click reaction fluorescence readout. These results reveal that DAST enables a reagent-efficient, cross-contamination-resistant, and low-instrument-dependent HTS foundation for click-chemistry-based assays. Full article

As living standards rise, the proliferation of discarded apparel contributes significantly to environmental pollution through traditional disposal methods, underscoring the importance of sustainable recycling practices in promoting a green lifestyle. This research examines factors influencing waste clothing recycling among Chinese residents, based on 1220 survey responses. It employs the entropy weight method to quantify dependent and moderating variables, followed by ordinary least squares regression analysis. The results indicate that (1) socio-demographic characteristics (notably gender, with females demonstrating higher recycling rates) and clothing disposal behaviors (including recycling knowledge, frequency of new clothing acquisition, usage duration, and disposal practices) markedly impact recycling activity, whereas age, geographic region, and income levels exhibit no significant effects. (2) Objective environmental conditions (such as recycling infrastructure and policy incentives) and recycling mode (formal outlets and donation platforms) positively moderate the relationship between key factors and recycling volume. These findings suggest that enhancing waste apparel recycling necessitates comprehensive strategies encompassing knowledge dissemination, environmental optimization, and diversification of recycling modalities—providing valuable insights for advancing circular economy initiatives and sustainable waste management within the apparel industry. Full article

Plastic pollution, driven by the durability and widespread use of polyolefins such as polypropylene (PP) and high-density polyethylene (HDPE), poses a formidable environmental challenge. To address this issue, we have developed an integrated multiscale framework that combines thermocatalytic experimentation, process-scale simulation, and molecular-level modeling to optimize the catalytic pyrolysis of PP and HDPE waste. Under the identified optimal conditions (300 °C, 10 wt % HMOR zeolite), liquid-oil yields of 60.8% for PP and 87.3% for HDPE were achieved, accompanied by high energy densities (44.2 MJ/kg, RON 97.5 for PP; 43.7 MJ/kg, RON 115.2 for HDPE). These values significantly surpass those typically reported for uncatalyzed pyrolysis, demonstrating the efficacy of HMOR in directing product selectivity toward valuable liquids. Above 400 °C, the process undergoes a pronounced shift toward gas generation, with gas fractions exceeding 50 wt % by 441 °C, underscoring the critical influence of temperature on product distribution. Gas-phase analysis revealed that PP-derived syngas contains primarily methane (20%) and ethylene (19.5%), whereas HDPE-derived gas features propylene (1.9%) and hydrogen (1.5%), highlighting intrinsic differences in bond-scission pathways governed by polymer architectures. Aspen Plus process simulations, calibrated against experimental data, reliably predict product distributions with deviations below 20%, offering a rapid, cost-effective tool for reactor design and scale-up. Complementary density functional theory (DFT) calculations elucidate the temperature-dependent energetics of C–C bond cleavage and radical formation, revealing that system entropy increases sharply at 500–550 °C, favoring the generation of both liquid and gaseous intermediates. By directly correlating catalyst acidity, molecular reaction mechanisms, and process-scale performance, this study fills a critical gap in plastic-waste valorization research. The resulting predictive platform enables rational design of catalysts and operating conditions for circular economy applications, paving the way for scalable, efficient recovery of fuels and chemicals from mixed polyolefin waste. Full article

Cities around the world are facing growing challenges related to climate change, urban sprawl, infrastructure strain, and digital transformation. In response, smart and sustainable urban development has become a global focus, aiming to integrate technology and environmental stewardship to improve the quality of life. The smart and sustainable city concept is typically applied at the city scale; however, its impact is most tangible at the neighborhood level, where residents interact directly with infrastructure, services, and community spaces. A variety of global frameworks have been developed to assess sustainability and technological integration. However, these models often fall short in addressing localized needs, particularly in regions with distinct environmental and cultural contexts. In Saudi Arabia, Vision 2030 emphasizes livability, sustainability, and digital transformation, yet there remains a lack of tailored tools to evaluate smart and sustainable progress at the neighborhood scale. This study develops HayyScore, a localized evaluation framework and prototype digital platform developed to assess neighborhood performance across five core categories: (i) Environment and Urban Resilience, (ii) Smart Infrastructure and Governance, (iii) Mobility and Accessibility, (iv) Quality of Life and Social Inclusion, and (v) Economy and Innovation. The HayyScore platform operationalizes this framework through an interactive web-based tool that allows users to input data through structured forms, calculate scores, receive category-based and overall certification levels, and view results through visual dashboards. The methodology involved a comprehensive review of global frameworks, expert input to define localized indicators, and iterative prototyping of the platform using Python 3.13.5 and Streamlit 1.45.1. To demonstrate its practical application, the prototype was tested on two Saudi neighborhoods: King Abdullah Petroleum Studies and Research Center (KAPSARC) and King Fahd University of Petroleum and Minerals (KFUPM). Key platform features include automated scoring logic, category weighting, certification generation, dynamic performance charts, and a rankings page for comparing multiple neighborhoods. The platform is designed to be scalable, with the ability to add new indicators, support multilingual access, and integrate with real-time data systems in future iterations. Full article

The rapid rise of fashion resale platforms has created new pathways for sustainable consumption, yet little research has compared how different governance models, company-controlled versus seller-controlled, shape consumer trust and purchasing behavior. This study addresses that gap by applying the Stimulus–Organism–Response (SOR) framework to examine how information precision, authenticity, and risk aversion influence consumer trust and purchase intention within circular fashion markets. Drawing on an experimental design with 524 U.S. consumers randomly assigned to each platform type, multi-group structural equation modeling reveals that the three stimuli significantly enhance trust, which in turn drives purchase intention. Risk aversion exerted stronger effects in company-controlled contexts, whereas trust translated more directly into purchase intention on seller-controlled platforms. Theoretically, the research extends SOR applications to sustainability by identifying trust as the psychological bridge linking platform design to circular consumption. Practically, it offers actionable guidance for brands and peer-to-peer platforms on authentication, information transparency, and risk-reduction strategies that strengthen consumer confidence and promote environmentally responsible resale participation. The findings advance understanding of how governance structures can accelerate sustainable fashion retailing and contribute to the circular economy. Full article