Search Results (203)

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze and calibrate the key policy levers influencing owner participation timing (T*). The model, resolved using finite difference methods, captures the system’s non-linear threshold effects by simulating critical system parameters, including system instability (price volatility, ${\mathsf{\sigma }}_p$), internal friction (management fee, $w_g{g}_t$), and demand signals (transaction ratio, Q). Simulations reveal extreme non-linear system responses: a 100% increase in system instability (${\mathsf{\sigma }}_p$) delays participation by 325.5%. More critically, a 100% surge in internal friction (management fees) delays T* by 492% and triggers a 95% revenue collapse—demonstrating the risk of systemic collapse. Conversely, a 20% rise in the demand signal (Q) advances T* by 100% (immediate participation), indicating the system can be rapidly shifted to a new equilibrium by activating positive feedback loops. These findings support a sequenced calibration strategy: regulators must first manage instability via price stabilization, then counteract high friction with subsidies (e.g., 60%), and amplify demand loops. The LCC framework provides a novel dynamic decision support system for calibrating complex urban transportation systems, offering policymakers a tool for scenario testing to accelerate policy adoption and alleviate urban congestion. Full article

Research on users’ switching intentions in peer-to-peer (P2P) mobile payment systems, particularly in developing markets, remains limited. This study examines how two satisfaction dimensions, transaction-based satisfaction and experience-based satisfaction, influence switching intentions through two layers of trust: institution-based trust and disposition to trust. Grounded in Expectancy-Disconfirmation Theory, data from 529 users of Haiti’s leading P2P mobile payment platform were analyzed using structural equation modeling. Results show that while transaction-based satisfaction has minimal impact on switching intentions, experience-based satisfaction strengthens institution-based trust, which in turn significantly reduces switching intentions. These findings highlight the central role of institutional reliability in shaping post-adoption behavior in duopolistic and resource-constrained markets. The study extends satisfaction-trust theory to digital financial ecosystems and offers practical insights for improving user retention through sustained institutional credibility and long-term service reliability. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

This study specifies and validates a three-layer Structural Equation Model (SEM) that accounts for how tourists’ evaluations of destination attributes translate into loyalty; the model is based on UN Tourism’s sustainability pillars. Guided by service-science and Customer Experience (CX) logics, and adopting a Tourist Experience (TX) framework that treats Tourist Experience as a domain-specific case of CX, we define five first-order antecedents—Emotions (EMS), Local Culture (CTL), Authenticity (AUT), Entertainment (ENT), and Servicescape (SVS)—that load onto a higher-order appraisal, Global Perception (GEN), which in turn drives Destination Loyalty (LOY). Using ordinal indicators and a robust diagonally weighted least squares estimator (WLSMV), the model exhibits a good global fit (CFI/TLI = 0.970/0.968; SRMR = 0.049; RMSEA = 0.073 [90% CI = 0.070–0.076]). Standardized effects indicate that GEN is primarily explained by Emotions (β = 0.445, p < 0.001), Authenticity (β = 0.271, p < 0.001), and Servicescape (β = 0.241, p < 0.001), whereas CTL and ENT are not significant when competing with these other predictors. GEN strongly predicts LOY (β = 0.967, p < 0.001), mediating sizable indirect effects from EMS, AUT, and SVS to LOY. The findings corroborate a parsimonious mediational chain in which affective, meaning-related, and infrastructural inputs cohere into a single global appraisal that is proximal to loyalty. Our study provides a decision-focused blueprint for designing emotion-rich, authenticity-protecting, and well-orchestrated servicescapes to enhance GEN and, consequently, LOY; it adheres to established SEM reporting standards and articulates a holistic transactional conceptualization grounded in recent tourism literature. Improvements in GEN reflect not only better experiences but also designs consistent with long-run destination sustainability. Full article

Based on the “dual-driven” framework, this study uses data from A-share listed agricultural companies from 2012 to 2022 to empirically test the mechanism by which agricultural Supply Chain Finance (SCF) affects the performance of core enterprises via the dual paths of “efficiency” and “responsibility.” Drawing on transaction cost theory and corporate social responsibility perspectives, the analysis reveals several key findings. First, SCF significantly improves enterprise performance by reducing transaction costs (coefficient = 0.117, p < 0.01), with transaction costs playing a partial mediating role. Second, fulfilling the social responsibility of connecting with and leading farmers negatively affects enterprise performance due to increased transaction costs (interaction term coefficient = −0.423, p < 0.1), creating a sustainable efficiency–responsibility tension. Third, supply chain concentration strengthens the efficiency path of SCF (interaction term coefficient = 0.002, p < 0.1). Non-state-owned enterprises, large-scale enterprises, and enterprises with executives having a financial background are more sensitive to the dual-driven mechanism. This study provides policy-relevant implications for supply chain governance that coordinate economic and social benefits. Full article

Background: Anatomic pathology laboratories operate under conditions requiring high precision, strict documentation, biosafety protocols, and tight turnaround times. Evidence from Greece is limited, and joint assessment of burnout and leadership in this setting is rare. Objective: The aim of this study was to estimate burnout levels among anatomic pathology personnel in Attica and examine their association with perceived leadership style. Methods: A cross-sectional survey of public and private laboratories was carried out. The questionnaire included demographics and work characteristics, the Copenhagen Burnout Inventory (CBI), and the Multifactor Leadership Questionnaire Form 5X (MLQ-5X). Results: Burnout levels were moderate to low overall, with personal burnout highest, work-related intermediate, and colleague-related lowest. Women and employment type were associated with personal burnout (p < 0.05). Passive/avoidant leadership (including management by exception–passive and laissez-faire) showed positive associations with burnout, whereas transformational leadership and favorable leadership outcomes—particularly, perceived effectiveness and satisfaction with the leader—were inversely associated; transactional leadership followed the same direction but less robustly (p < 0.05 where supported). Conclusions: Burnout among anatomic pathology personnel in Attica is non-trivial and varies across domains. Leadership dimensions display differential links with burnout, indicating potentially modifiable organizational targets for intervention. Significance: To our knowledge, this is the first study in Greece and among the first in Europe to jointly apply CBI and MLQ-5X in anatomic pathology laboratories, offering practical evidence to inform leadership-oriented interventions. Full article

Food wanting in honeybees is closely associated with the neurotransmitter dopamine; however, the regulatory role of non-coding RNAs in this process remains unclear. In this study, using the honeybee (Apis mellifera) as a model organism, we systematically investigated the molecular network and functional mechanisms by which long non-coding RNAs (lncRNAs) regulate the dopaminergic signaling pathway to mediate food wanting. By establishing two appetite-state models, fed honeybees (FB) and starved honeybees (SB), and combining brain dopamine quantification with behavioral assays, we found that dopamine levels in the honeybee brain were significantly elevated during starvation. Using transcriptome sequencing, we identified 1146 lncRNAs in the honeybee brain, among which 174 were differentially expressed long noncoding RNAs (DElncRNAs) between the two states, predominantly upregulated. Cis- and trans-acting analyses revealed that these DElncRNAs could target multiple genes involved in neural signal transmission, synaptic function, and dopaminergic pathways. KEGG enrichment analysis showed that their target genes were significantly enriched in pathways such as taste transduction, dopaminergic synapse, and neuroactive ligand–receptor interaction. Furthermore, a ceRNA network revealed that several DElncRNAs may regulate dopamine synthesis genes, including DOPA decarboxylase (Ddc), by competing for dopamine-associated miRNAs such as miR-375-3p, influencing food wanting in honeybees. Overall, our findings provide a foundation for uncovering the potential regulatory mechanisms of DElncRNAs in honeybee food wanting and offer new insights into the connection between neural regulation and behavioral manifestation in insects. Full article

With the in-depth construction of the new power system, the importance of demand-side resources is becoming more and more prominent. The virtual power plant (VPP) has become a powerful means to explore the potential value of distributed resources. However, the differentiated resources between different VPPs are not reasonably deployed, and the problem of realizing the sharing of resources and the distribution of revenues among multi-VPP needs to be urgently solved. A cooperative operation optimization strategy for multi-VPP to participate in the energy and reserve capacity markets is proposed, and the potential risks associated with uncertainty in distributed generators (DGs) output are quantitatively assessed using conditional value-at-risk (CVaR). Firstly, due to the good adjustable performance of electric vehicles (EVs) and thermostatically controlled loads (TCLs), their virtual energy storage (VES) models are established to participate in VPP scheduling. Secondly, based on the asymmetric Nash negotiation theory, a P2P trading method between VPPs in a multi-marketed environment is proposed, which is decomposed into a virtual power plant alliance (VPPA) benefit maximization subproblem and a cooperative revenue distribution subproblem. The alternating direction multiplier method is chosen to solve the model, which protects the privacy of each subject. Simulation results show that the proposed multi-VPP cooperative operation optimization strategy can effectively quantify the uncertainty risk, maximize the alliance benefit, and reasonably allocate the cooperative benefit based on the contribution size of each VPP. Full article

Benford’s Law, also known as the First-Digit Law, describes the non-uniform distribution of leading digits in many naturally occurring datasets. This phenomenon can be observed in data such as financial transactions, tax records, or demographic indicators, but the application of Benford’s Law to data from the field of transport infrastructure remains largely underexplored. As interest in using statistical distributions to identify spatial and regional patterns grows, this paper explores the applicability of Benford’s Law to anthropogenic geographic data, particularly whether the lengths of higher-level segments of the main road network across European Union member states follow Benford’s Law. To evaluate the conformity of the data from all European Union countries with Benford’s distribution, Pearson’s ${\chi }^2$ test of association, the p-value, and the Kolmogorov–Smirnov test were used. The results consistently show low ${\chi }^2$ values and high p-values, indicating a strong agreement between observed and expected distributions. The relationship between the distribution of higher-level segment lengths and the leading digits of these lengths was studied as well. The findings suggest that the length distribution of the main road networks’ higher-level segments closely follows Benford’s Law, emphasizing its potential as a simple yet effective tool for assessing the reliability and consistency of geographic and infrastructure datasets within the European context. Full article

Housing price indices (HPIs) are employed to assess the impact of the business cycle, monetary policy, housing policies, and local market dynamics. However, comparative empirical analysis of different HPI methodologies has not been conducted to measure why or when they may diverge and whether these differences are meaningful. Two leading US HPI choices, the repeat-sale transactional (S&P Case–Shiller) and characteristic-based hedonic (Zillow) indices, although highly correlated, generate different distributions and time-series properties primarily at the city level. The spread between these two HPI choices measures the difference between housing market transaction intensity and a willingness-to-pay characteristic valuation. We find that transactional indices are more volatile, with HPI spreads associated with both macro and local drivers. The transactional index will rise more rapidly in a market with increased buying (positive macro and local market conditions) and fall further in a market with increased selling (negative macro and local market conditions) relative to a hedonic index. A buyer- or seller-biased spread between a transactional and hedonic housing price index (HPI) may impact policy judgments during housing market extremes. Full article

In response to the persistent failures of traditional election polling, this study introduces the Decentralized Prediction Market Voter Framework (DPMVF), a novel tool to empirically test and quantify the predictive capabilities of Decentralized Prediction Markets (DPMs). We apply the DPMVF to Polymarket, analysing over 11 million on-chain transactions from 1 September to 5 November 2024 against aggregated polling in the 2024 U.S. Presidential Election across seven key swing states. By employing Cross-Correlation Function (CCF) for linear analysis and Dynamic Time Warping (DTW) for non-linear pattern similarity, the framework provides a robust, multi-faceted measure of the lead-lag relationship between market sentiment and public opinion. Results reveal a striking divergence in predictive clarity across different electoral contexts. In highly contested states like Arizona, Nevada, and Pennsylvania, the DPMVF identified statistically significant early signals. Using a non-parametric Permutation Test to validate the observed alignments, we found that Polymarket’s price trends preceded polling shifts by up to 14 days, a finding confirmed as non-spurious with a high confidence (p < 0.01) and with an exceptionally high correlation (up to 0.988) and shape similarity. At the same time, in states with low polling volatility like North Carolina, the framework correctly diagnosed a weak signal, identifying a “low-signal environment” where the market had no significant polling trend to predict. This study’s primary contribution is a validated, descriptive tool for contextualizing DPM signals. The DPMVF moves beyond a simple “pass/fail” verdict on prediction markets, offering a systematic approach to differentiate between genuine early signals and market noise. It provides a foundational tool for researchers, journalists, and campaigns to understand not only if DPMs are predictive but when and why, thereby offering a more nuanced and reliable path forward in the future of election analysis. Full article

Background/Objective: This study examined whether a brief measure combining subjective cognitive concerns and financial interference, termed Subjective Cognitive Decline-Financial (SCD-F), is associated with excess spending behavior in older adults. Methods: Community-dwelling older adults, some with early memory loss and some with no cognitive complaints (N = 150, M age = 72.6), provided 12 months of checking account statements and participated in interviews to clarify aspects of their personal financial behaviors. SCD-F was defined by asking if memory decline was interfering with financial decision-making or transactions. A 3-point SCD-F measure was created. Excess spending was determined by checking whether account expenditures exceeded all sources of income. Nonparametric tests (Kruskal–Wallis and Mann–Whitney U) and multiple regression models assessed group differences and predictors. Results: Group differences in excess spending were pronounced (H(2) = 15.75, p < 0.001). Those in the high SCD-F group had a significantly greater likelihood of excess spending (Z = −4.11; r = 0.43) and higher excess spending percentages (Z = −4.11; r = 0.43) compared to those with no memory loss. Regression analyses indicated that SCD-F was the strongest predictor of excessive spending (β = 0.40, t = 5.43, p < 0.001), even after controlling for age, gender, race, and education (R² = 0.235, F(5,144) = 8.86, p < 0.001). Conclusions: A brief self-report measure, SCD-F, effectively identifies older adults at risk of financial mismanagement, even absent formal cognitive impairment. Monitoring subjective cognitive concerns together with financial interference could enable early intervention. This brief measure may be useful in clinical settings as a screening tool, and in large national surveys. Full article

As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids, a critical function for integrating Renewable Energy Sources (RESs). Utilising a high-resolution dataset of over 240,000 charging transactions in China, the research classifies charging volumes into “inputs” (charging during peak grid load periods) and “outputs” (charging during off-peak, low-price periods). The Vector Autoregression (VAR) model is used to analyse interrelationships between charging periods. The methodology employs a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model to calculate overall efficiency, incorporating charging variance as an undesirable output. A Malmquist index is also used to analyse temporal changes between charging periods. Key findings indicate that efficiency varies significantly by charging pile type. Bus Stations (BS) and Expressway Service Districts (ESD) demonstrated the highest efficiency, often achieving optimal performance. In contrast, piles at Government Agencies (GA), Parks (P), and Shopping Malls (SM) showed lower efficiency and were identified as key targets for optimisation due to input redundancy and output shortfall. Scenario analysis revealed that increasing off-peak charging volume could significantly improve efficiency, particularly for Industrial Parks (IP) and Tourist Attractions (TA). The study concludes that a categorised approach to the deployment and management of charging infrastructure is essential to fully leverage electric vehicles for grid balancing and renewable energy integration. Full article

Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks. Full article