Search Results (597)

Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems. Full article

As data centers become major and schedulable loads of the new power system, connecting them to multiple microgrids offers a promising route to absorb local renewable energy through cross-domain coordination. However, when the microgrids belong to competing operators, coordinated scheduling forces each party to disclose its data-center load curve, storage state, and pricing strategy, which constitutes a core operational secret that no microgrid is willing to reveal. This paper develops a secure and privacy-preserving distributed scheduling scheme for data-center-integrated microgrids built on blockchain. A “data-stays-local, energy-crosses-centers” model is established that elevates privacy from an add-on feature to a first-order architectural constraint, defining a “three-no” principle and a two-layer architecture in which each microgrid optimizes its interior in plaintext and exposes only encrypted matchable factors. On this basis, a decentralized ciphertext scheduling-negotiation algorithm is designed on blockchain smart contracts, performing cross-microgrid matching under secure multi-party computation entirely in the encrypted domain, committing auditable encrypted digests on-chain, and dynamically allocating scheduling priority through an on-chain reputation mechanism. Case studies on a cluster of interconnected microgrids show that the proposed scheme attains cost and renewable accommodation within about three-tenths of a percent of the centralized optimum while reducing operational data-leakage risk from 96.7 percent to 3.8 percent, at the manageable expense of a few seconds of negotiation latency. Benchmarking against an exact mixed-integer solver on small-scale systems bounds the mean optimality gap of the decomposed scheme at 0.74 percent, with a worst case of 2.54 percent over sixty instances. Full article

Continuous inter-firm collaboration gives rise to technological innovation networks (TIN), which increasingly shape firms’ stakeholder perceptions and reputation. This study examines how TIN characteristics influence corporate reputation using Chinese A-share listed firms from 2018 to 2022. The results show that TIN scale, heterogeneity, and openness significantly enhance corporate reputation, while tie strength has no significant effect. Further analyses reveal that the employee income gap mediates the relationship between TIN scale and reputation with an inverted U-shaped effect. In addition, TIN heterogeneity improves reputation by enhancing investment efficiency, and TIN openness contributes to reputation enhancement by reducing customer concentration risk. These findings advance understanding of the reputational value of innovation networks and provide implications for network strategy and stakeholder management. Full article

Research on sharing economy platforms has expanded rapidly, yet the literature remains fragmented across studies on platform business models, gig work, algorithmic management, trust, reputation systems, artificial intelligence, and data-driven value creation. This article addresses this fragmentation through a bibliometric and systematic review of 660 documents retrieved from Scopus and Web of Science covering the period from 2010 to May 2026. A PRISMA-based protocol guided identification, deduplication, screening, eligibility assessment, and final corpus construction. The analysis combined performance indicators, co-citation analysis, keyword co-occurrence mapping, country collaboration analysis, longitudinal thematic evolution, strategic diagrams, and systematic content coding using Bibliometrix/Biblioshiny 5.4.1, VOSviewer 1.6.21, and SciMAT 1.1.04. The results show a marked acceleration of the field after 2020 and identify major research clusters around algorithmic labour and platform control, algorithmic management, trust and reputation, and dynamic pricing. The systematic coding further indicates that algorithmic management, reputation systems, dynamic pricing, surveillance, matching, and AI-enabled mechanisms recur across governance and value-creation processes. The study develops an integrative framework that interprets these patterns through four connected elements: data inputs, algorithmic mechanisms, governance functions, and value outcomes. This framework provides managers and regulators with a basis for assessing transparency, accountability, participant autonomy, value distribution, and the legitimacy of platform governance. Full article

Orthogonal direct sum masking (ODSM) protects embedded cryptographic implementations against side-channel attacks by splitting the ambient space into a source code C carrying sensitive data and a complementary masking code D carrying fresh randomness; when $D=C^{\perp }$, C must be a linear complementary dual (LCD) code. Much of the literature evaluates the masking code primarily through the minimum distance $d\left(D^{\perp }\right)=d\left(C\right)$ of its dual, treating this parameter as the quantitative summary of leakage resistance under a Hamming-weight leakage model. We show, first computationally and then via a general algebraic theorem, that this one parameter does not determine the variance of the masking code’s Hamming-weight distribution: cyclic codes with identical $d\left(C\right)$ can differ by close to an order of magnitude in this variance. We prove, for an arbitrary cyclic code $C\subseteq \mathrm{GF}{\left(q\right)}^n$ with nonzero dual $D=C^{\perp }$ and defining set T (the LCD property is not required for this algebraic result and is invoked only for the ODSM application), a closed-form theorem expressing ${\mathrm{Var}}_{c\in D}\left[\mathrm{wt}\left(c\right)\right]$ exactly as $(q-1)n^2/\left(q^{2}L\left(T\right)\right)$, where $L\left(T\right)$ is an intrinsically defined, representative-independent arithmetic invariant of T, computable via a single least-common-multiple of greatest-common-divisors and requiring no exponential-sum or Gauss-period evaluation. We verify the formula, with an explicit worked example, reproducible from the displayed defining sets, and zero discrepancies, against 64 independently constructed LCD cyclic codes spanning two finite fields and three code lengths. We are explicit that this variance is a second-order algebraic descriptor of leakage dispersion under an idealized leakage model, not a complete side-channel security metric; the connection to physical Hamming-weight leakage is direct for $q=2$; for $q>2$, the result stands as an exact coding-theoretic characterization whose relevance to physical bit-level leakage depends on an explicit bit-encoding model not developed here. We discuss its role as a design diagnostic for ODSM masking codes. This paper is, at its core, a contribution to the algebraic theory of cyclic codes: the reduction of the variance to a weight-two-codeword count is the classical Pless moment identity, and our closed-form arithmetic characterization of that count via $L\left(T\right)$ is algebraically equivalent, on its domain, to a 2024 result of Coelho and Brochero Martínez; we extend it to arbitrary cyclic length and arbitrary prime-power base fields, and position it as a complement to, not a replacement for, the more operational dual-distance/kissing-number methodology already used in the code-based masking literature. Full article

This study aimed to assess the role of ESG in management processes and strategic decision-making in enterprises operating in Poland, with particular emphasis on the relationship between the strategic use of ESG and its regulatory and reporting function. The research was based on an original survey questionnaire completed by 154 respondents representing enterprises with diverse business profiles. The analysis covered selected managerial dimensions of ESG, including ESG strategic integration, measurement and reporting, regulatory pressure, top management engagement, accountability structures, CSRD readiness, perceived ESG maturity, implementation benefits, and barriers. The results showed that ESG was often associated with strategy, reputation, and regulatory compliance; however, the level of formalization remained uneven. The greatest challenges concerned CSRD readiness, ESG indicator monitoring, accountability structures, and resource and competence constraints. The study contributes by showing a gap between declared ESG strategic integration and formalized ESG management mechanisms. CSRD readiness and ESG KPI monitoring were identified as key bottlenecks in this process. The findings indicate that the strategic role of ESG was associated not only with declarations, but also with management commitment, formal governance mechanisms, and the use of ESG data in decision-making processes. Full article

The credibility of corporate environmental claims is fundamental to achieving China’s dual-carbon goals, which aim to peak emissions by 2030 and achieve neutrality by 2060, yet widespread ESG decoupling, a phenomenon where disclosed ESG performance deviates from actual actions, directly undermines policy effectiveness. Guided by the Fraud Triangle Theory, this paper examines whether and how AI adoption curbs decoupling practices. Using data from Chinese A-share listed firms from 2009 to 2024 and mediation analysis, we find that AI adoption significantly reduces ESG decoupling. Specifically, we identify three distinct mediating pathways through which AI exerts its inhibitory effect: it reduces environmental uncertainty and ambiguity, thereby compressing opportunities for managers to engage in decoupling; it heightens media scrutiny and analyst attention, increasing deterrence pressure on firms; and it improves disclosure quality and transparency, undermining the rationalization of decoupling. Heterogeneity analyses reveal stronger effects in firms with R&D-experienced executives, higher reputation, and stronger governance, as well as in highly competitive industries, suggesting that complementary capabilities amplify AI’s governance. These findings provide novel evidence on how AI governs environmental information fraud and offer policy implications for using digital technologies to enhance ESG authenticity. Full article

This study examined the extent to which corporate social responsibility (CSR) dimensions shaped hotel reputation (REP) through customer trust (TRU), and whether customer engagement (ENG) moderated this relationship in the Mekong Delta context. Although prior studies had acknowledged the importance of CSR in shaping customer perceptions, they had paid limited attention to the distinct effects of multidimensional CSR and the conditions under which TRU was transformed into REP. A structured questionnaire survey was administered to 874 hotel guests in the Mekong Delta, Vietnam. The measurement scales were adapted from previous studies and refined through a pilot test. Partial least squares structural equation modeling (PLS-SEM), together with bootstrapping, was used to test the direct, mediating, and moderating effects. CSR dimensions exhibited heterogeneous effects. Customer-oriented responsibility had the strongest impact on both TRU and REP, followed by environmental and economic responsibilities. Legal responsibility directly enhanced REP but did not significantly affect TRU, whereas community responsibility strengthened TRU without directly influencing REP. TRU significantly improved REP and mediated most CSR–REP relationships. ENG positively moderated the TRU–REP link, amplifying reputational outcomes. The study contributed to CSR and hospitality research by modeling CSR as a multidimensional construct and by identifying ENG as a boundary condition that strengthened the TRU–REP mechanism. Managers should prioritize customer-focused CSR and enhance engagement to maximize reputational gains. Full article

Strategic competition in health service markets requires managers to make pricing, marketing, and investment decisions under uncertainty, yet educational programs in healthcare management and dietetics lack experiential tools for teaching these competencies within a game-theoretic framework. This study develops and computationally validates SY142-Game-Theory-1, a computational simulation framework that models strategic competition between two asymmetric healthy living centers as a 36-month repeated Prisoner’s Dilemma, integrating demand decomposition, net present value analysis, employee satisfaction dynamics with burnout thresholds, reputation feedback, and stochastic shock events. The simulation produces a valid and distinctly asymmetric Prisoner’s Dilemma structure in which the established provider faces the classical temptation to defect while the new entrant’s rational incentive aligns with cooperation; Axelrod-style tournaments across 22 strategies (96,800 simulations) identify Forgiving Tit-for-Tat as the top-performing strategy; Monte Carlo validation (n = 1000) confirms a statistically significant cooperation premium of 24.1% over Nash equilibrium; and sensitivity analyses across four parameters demonstrate robustness of all qualitative findings. The open-source framework bridges game theory, simulation-based learning, and health service management education, providing a computationally validated foundation for teaching strategic decision-making in competitive healthcare environments, with empirical evaluation of learning outcomes reserved for future work. Full article

Public Relations (PR) is the strategic role of communication between an organization or public actor and the publics on whose recognition, trust and consent its operation depends. The field encompasses media relations, internal and employee communication, public affairs and government relations, crisis and reputation management, community relations and corporate social responsibility, investor relations, marketing communications, political communication, digital and platform engagement, and the communication of activist and non-governmental organizations. PR scholarship draws on sociology, political philosophy, organizational studies, rhetoric and media studies to examine how organizations construct, sustain and contest their legitimacy in the public sphere. The dominant theoretical framework of the late twentieth century, the Excellence Theory developed by James Grunig and colleagues, defined PR as the symmetrical management of relationships with strategic publics; the field has since broadened to include relational, dialogic, rhetorical, situational, contingent and critical approaches, alongside frameworks grounded in stakeholder theory, institutional analysis and emerging work on public legitimacy. Contemporary PR operates within a hybrid media environment shaped by digital platforms, algorithmic visibility, generative artificial intelligence and the structural erosion of institutional trust. It is simultaneously a professional industry of significant global economic scale and a contested civic function whose democratic role, ethical foundations and disciplinary boundaries remain the subject of active scholarly debate. Full article

This paper presents “Quantifying Airline Reputation from Multilingual Online Content: Artificial Intelligence and a Practical Application in a Lightweight Reputation-Intelligence Framework.” Airline reputation is increasingly shaped by multilingual digital narratives that evolve faster than conventional survey cycles, creating a need for timely and interpretable monitoring tools. This study develops and evaluates a lightweight reputation-intelligence framework that integrates brand-safe retrieval, multilingual transformer-based sentiment inference, zero-shot natural-language-inference aspect categorization, TF–IDF/KMeans topic induction, and short-horizon forecasting. The framework formalizes document-level outputs into managerial indicators, including a Net Sentiment Index, polarity shares, aspect scores, topic summaries, and projected sentiment trajectories. On a 3990-document annotated sentiment subset, the multilingual transformer achieved 0.9015 accuracy, 0.9048 macro-F1, 0.9050 weighted-F1, a Cohen’s kappa of 0.8492, and a Net Sentiment Index of 48.5%, while errors were concentrated between adjacent polarity classes. Aspect evaluation showed that supervised in-domain learning substantially outperformed zero-shot inference, clarifying the trade-off between cold-start portability and benchmark accuracy. The results support the framework as a pilot decision-support architecture for airline reputation sensing rather than as a definitive large-scale benchmark. The approach offers scalable and CPU-friendly monitoring capability for airlines, airports, consultants, and public-sector users, while future work should expand multilingual annotation and domain adaptation. Full article

The last five years have seen considerable growth in the topic of peer-to-peer (P2P) federated learning (FL). This framework removes the central coordinating server used in conventional federated learning and instead requires participating nodes to manage model training, peer selection, communication, aggregation, and trust directly. This provides a promising route for privacy-preserving and decentralised machine learning, but it also introduces unresolved challenges in topology selection, participant incentivisation, communication efficiency, security, and evaluation. Existing studies frequently evaluate proposed methods under narrow assumptions, such as static network membership, homogeneous devices, fixed bandwidth, limited topology choices, and public benchmark datasets. Existing surveys also tend to present taxonomies of decentralised federated learning rather than synthesising how topology, incentives, and communication algorithms jointly affect deployability. This paper reviews recent work on peer-to-peer federated learning across three connected dimensions: network topology, incentive mechanisms, and communication algorithms. We compare the topologies, datasets, experimental assumptions, incentive designs, communication strategies, and open issues reported in the literature. The review shows that highly connected topologies tend to improve convergence but increase communication overhead and vulnerability to bottlenecks; sparse and dynamic topologies improve efficiency but create challenges for convergence, reliability, and node drop-out. Incentive mechanisms increasingly combine reward, reputation, validation, and punishment but remain weakly validated under realistic churn, heterogeneous resources, and adversarial behaviour. Communication algorithms reduce bandwidth through gossip, sparsification, prediction, routing, and multi-step aggregation but often trade communication savings against accuracy, robustness, and generalisability. Across all three areas, the field lacks standardised benchmarks, reproducible experimental settings, and realistic evaluation under unstable peer-to-peer conditions. We conclude by identifying cross-cutting research gaps and recommending future work on dynamic topologies, heterogeneous devices, real-world datasets, incentive robustness, and comparable benchmarking. Full article

Objectives: Polycystic ovary syndrome (PCOS) is a widespread and complex endocrine disorder affecting women of childbearing potential, characterized by reproductive dysfunction, hyperandrogenism, and metabolic disorders, including insulin resistance. Insulin resistance is a key pathogenetic factor contributing to ovarian dysfunction and reduced fertility. Myo-inositol (MI), a ubiquitous polyol, has earned a reputation as a promising dietary supplement due to its vital role in insulin signaling pathways. This scoping review aimed to map the available scientific literature on the effects of MI supplementation in women with PCOS, with particular emphasis on fertility and ovarian function, and to identify gaps in the current evidence base. Methods: This scoping review was conducted in accordance with the methodology developed by the Joanna Briggs Institute (JBI) and presented in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines. A literature search was conducted against six electronic databases: PubMed, Scopus, Web of Science, EBSCO (MEDLINE Complete), Cochrane Library and Google Scholar. Searches were conducted between 10 January and 20 February 2026. Eligibility sources included original articles (observational and randomized controlled trials), meta-analyses, systematic and narrative reviews, published in English with full text available, focusing on adult women with PCOS. Data extraction was performed independently by two reviewers using the Population–Concept–Context (PCC) framework. In accordance with the scope review methodology, no formal critical appraisal of study quality and no quantitative synthesis were performed. This is consistent with JBI methodology, which does not require critical appraisal for scoping reviews unless explicitly justified. Results: Of the 77 records initially identified, 13 studies were included in the review, and no duplicates were found. These potential benefits should be interpreted cautiously, as the available evidence is heterogeneous and varies across study designs. Potential benefits were also reported for hormonal and metabolic parameters, including reductions in hyperandrogenism and the improvement of insulin sensitivity. Some studies suggest benefits for oocyte and embryo quality, but results remain inconsistent. Conclusions: MI supplementation may support PCOS management, particularly in fertility-related outcomes. Its ability to improve ovulation, increase pregnancy rates, optimize ART outcomes, and mitigate the risk of OHSS highlights its clinical utility. However, the evidence remains heterogeneous, and some outcomes, particularly oocyte and embryo quality, remain inconclusive. Full article

Oblivious Transfer (OT) extension, in particular, the construction of Ishai, Kilian, Nissim, and Petrank (CRYPTO 2003) requires a hash function H that is correlation-robust(CR). All practical instantiations model H as a random oracle or an ideal cipher, leaving CR with no quantifiable reduction to a structural property of the deployed hash. It is natural to ask whether the most nonlinear balanced Boolean functions available on an odd number of variables, the near-bent functions of the Maiorana–McFarland (MM) class, furnish an algebraic, standard-model CR candidate. We prove that they do not, and we identify precisely why. First, we keep a correct spectral fact: a balanced $H:{\{0,1\}}^n\to \{0,1\}$ is $\epsilon$-CR if and only if ${\max }_{\Delta \ne 0}\left|{\mathcal{A}}_f(\Delta )\right|\le 4\epsilon ·2^n$, reducing CR to an autocorrelation bound. Against this criterion we establish three obstructions: (i) The MM-doubling family ${\mathcal{NB}}_k$ on $n=2k+1$ variables has autocorrelation supported only on the directions $(a,0,1)$, where it equals $2^{k+1}{W}_a$ with ${\sum }_{a\ne 0}{W}_a^2=2^{2k}$; hence $\epsilon \ge \frac{1}{4}{(2^k-1)}^{-1/2}$, a factor $\ge 2^{k/2}$ above the value one would need, and an exhaustive search over all balanced members for $k\le 2$ returns the maximal $\epsilon =\frac{1}{4}$ in every case. (ii) Near-bentness controls the Walsh maximum (nonlinearity), not autocorrelation: every near-bent function satisfies ${\sum }_{\Delta \ne 0}{\mathcal{A}}_f{(\Delta )}^2=2^{2n}$, so ${\max }_{\Delta \ne 0}\left|{\mathcal{A}}_f(\Delta )\right|\ge 2^n{(2^n-1)}^{-1/2}$ and no near-bent function is even approximately CR. (iii) A deterministic $\mathbf{H}:{\{0,1\}}^{\kappa }\to {\{0,1\}}^{\mathcal{\ell }}$ admits the support bound $\mathbf{SD}\left((\mathbf{H}\left(x\right),\mathbf{H}(x\oplus \Delta )),(U_{\mathcal{\ell }},U_{\mathcal{\ell }})\right)\ge 1-2^{\kappa -2\mathcal{\ell }}$, so statistical multi-output CR is impossible for $\mathcal{\ell }>\kappa /2$ and in particular at the IKNP regime $\mathcal{\ell }\approx \kappa$. Together, these results close the near-bent route to standard-model CR and clarify which design objective (low absolute indicator, not high nonlinearity) and which parameter regime ($\mathcal{\ell }\le \kappa /2$) a viable algebraic candidate would have to target. Full article

Business influence increasingly unfolds in platform environments where visibility is ranked, repeated, measured, and monetized. At the same time, many platform interactions have shifted from reciprocal sociality toward creator-centered attention, mediated familiarity, and one-sided attachment. This perspective article develops the concept of parasocial business to explain how business actors convert platform visibility into credibility, trust, advocacy, and commercial value. Drawing on an integrative reading of research on parasociality, platformization, influencer labor, organizational reputation, marketing communication, and creator economies, this paper identifies three linked mechanisms: algorithmic visibility, persona design, and parasocial conversion. The conceptual analysis shows that parasocial business is not simply influencer marketing, personal branding, brand community, or consumer–brand relationship management. Its distinctive feature is the platformed conversion of repeated, persona-based familiarity into business-relevant authority and value. This paper develops the related concept of platformed authority and illustrates it through LinkedIn thought leadership, podcast-based business influence, and B2B expert visibility. This paper contributes to business administration and management research by showing how leadership communication, reputation governance, expert visibility, and organizational influence are increasingly shaped by platform infrastructures and public-facing personae. Full article