Search Results (276)

Web applications increasingly handle sensitive data in diverse use cases, but conventional encryption implementations apply a uniform level of cryptographic protection to all traffic, regardless of the associated risk. This static approach results in either excessive computational overhead when applying maximum encryption universally, or inadequate protection when using lightweight encryption to preserve performance. This paper proposes an Adaptive Encryption Framework (AEF) designed to bridge the gap between performance and security in web applications. Rather than relying on a static protocol, AEF dynamically adjusts encryption algorithms based on a real-time composite risk score (0–100). This score is derived from six weighted variables: network risk (25%), authentication strength (20%), behavioral risk (20%), device trust (15%), data sensitivity (15%), and temporal risk (5%). Depending on the calculated risk, the system automatically transitions between three distinct security tiers: GREEN (utilizing ChaCha20-Poly1305), YELLOW (AES-256-GCM), or RED (AES-256-GCM with per-request HKDF key derivation for key isolation). All three profiles use exclusively standardized cryptographic primitives. The proposed weighting distribution was evaluated through sensitivity analysis on 27 framework-executed scenarios and further calibrated using 40,000 labeled application requests. Within these experimental conditions, it achieved complete agreement with the expected scenario classifications, and no alternative weight configuration produced better held-out performance. Additional validation on 61,065 HTTP requests from the CSIC 2010 dataset yielded an area under the ROC curve (ROC AUC) of 0.860, with no attack request assigned to the lightweight profile under the evaluated operating conditions. Across three hardware platforms and four payload sizes, all encryption profiles maintained sub-millisecond latency. Extended load testing showed that a four-worker Node.js cluster sustained 4948 requests per second at 2000 concurrent connections, a 7.1-fold improvement over a single process. When hardware cryptographic acceleration was disabled, ChaCha20-Poly1305 became up to 9.1 times faster than AES-256-GCM, supporting its use as the lightweight profile. The framework proposed in this paper operationalizes the qualitative risk assessment guidelines from NIST SP 800-30 and SP 800-63 into a quantitative, automated encryption selection mechanism for web applications, evaluated under the hardware platforms, concurrency levels and traffic assumptions described in this study. Full article

Traditional Earned Media Value (EMV) calculations suffer from methodological limitations by depending on oversimplified and outdated proxies like Advertising Value Equivalency (AVE). In service marketing, where offerings are intangible and consumers cannot inspect an offering before purchase, trust relies heavily on authentic influencer communication and peer advocacy; these measurement flaws are therefore especially problematic. This paper introduces two innovations tailored for the modern media ecosystem: (1) Mention Quality and Impact (MQI), a transparent scoring system (1–10 scale) that evaluates sentiment, engagement, credibility, and AI visibility, with a bounded adjustment for conversion intent; and (2) Mention Earned Media Value (mEMV), a CPM-based valuation model that integrates MQI with platform-specific benchmarks. By accounting for participation inequality, influencer dynamics, and the rise of virtual search environments, this framework advances media measurement for service brands toward greater accuracy, transparency, and scalability. Full article

Driven by rural energy decarbonization and the electrification of agricultural production, agricultural microgrids (agri-microgrids) have become a critical paradigm for accommodating distributed renewable generation and volatile farming loads. However, agri-microgrids usually face acute cost sensitivity. Coordinated operation of autonomous agri-microgrids offers a promising solution. Yet coordinating heterogeneous energy resources to exploit temporal complementarity while fairly allocating costs remains a key challenge. Information asymmetry, the lack of trusted node, and the computational complexity of revenue allocation impede cooperative dispatch of multi-microgrids.This paper proposes a decentralized game-theoretic framework for cooperative multi-microgrid dispatch. Firstly, a distributed node information collection algorithm is designed to acquire necessary data while preserving local privacy. Secondly, a trusted-node election method is proposed to enable reliable coalition profit calculation without external authorities or predefined centralized coordinators. Then, an enhanced Shapley value-based revenue allocation strategy, which reduces the computational complexity, is presented. Finally, case studies verify the effectiveness of the proposed algorithms in reducing operational costs and promoting green energy coordination among agri-microgrids. Full article

Background: In solid-organ transplant recipients, hypomagnesemia is a frequent and persistent complication, predominantly related to long-term use of calcineurin inhibitors. Oral magnesium supplementation is often ineffective due to poor adherence, gastrointestinal adverse effects, and high treatment costs. Objectives: This pilot study aimed to (i) develop a topical magnesium formulation for use in renal transplant recipients with persistent hypomagnesemia; (ii) evaluate the influence of formulation excipients on magnesium release from pharmaceutical preparations; and (iii) preliminarily assess the clinical outcomes, functional parameters, patient satisfaction, and tolerability associated with topical magnesium lipogel use in renal transplant recipients with persistent hypomagnesemia. Materials and Methods: Three lipogel formulations containing magnesium orotate, citrate, or sulfate were prepared and characterized in terms of organoleptic properties, pH, colloidal stability, and rheological behavior. In vitro magnesium release was evaluated using Franz diffusion cells. The formulation demonstrating the most favorable release profile, magnesium orotate lipogel, was administered twice daily for two months to 21 renal transplant recipients with persistent hypomagnesemia who had shown inadequate response, intolerance, or non-adherence to oral magnesium supplementation. Laboratory parameters were assessed at baseline and after treatment. Patient satisfaction was evaluated using the Lübeck questionnaire, while physical activity and exercise capacity were assessed using the International Physical Activity Questionnaire (IPAQ) and metabolic equivalent task (MET) calculations. Results: Franz cell studies demonstrated superior magnesium release from the magnesium orotate lipogel compared with the other formulations. After two months, serum magnesium levels were higher than baseline (1.554 ± 0.124 vs. 1.448 ± 0.111 mg/dL, p < 0.001), although they remained below the normal reference range. No significant changes were observed in eGFR or hsCRP. Moderate-intensity physical activity increased significantly (310.475 ± 92.505 vs. 279.286 ± 89.907 MET-min/week, p < 0.001), while vigorous-intensity activity did not change significantly. No relevant adverse effects were reported. Patient satisfaction was high across all evaluated domains, including practicability (86.6%), efficacy (96.4%), tolerability (97.1%), and trust in medical professionals (97.6%). Conclusions: In this pilot study, topical magnesium orotate lipogel was well tolerated and accepted by renal transplant recipients with persistent hypomagnesemia. Serum magnesium levels and moderate physical activity improved over the two-month observation period, although magnesium levels remained below the normal range. These preliminary findings warrant confirmation in larger, randomized, placebo-controlled studies. Full article

This paper presents an application of Zero-Knowledge Proofs (ZKPs) and blockchain for the monitoring and verification of Energy Performance Contracts (EPCs) within the FORTESIE project, addressing a gap that remains largely unexplored in the scientific literature. While existing research leverages ZKPs and blockchain for energy trading, no solution effectively automates the validation of contractual energy savings in EPCs. The FORTESIE platform provides an interoperable data collection and analysis framework with a Measurement and Verification (M&V) module that uses ZKPs to confirm energy savings without disclosing consumption data. Calculations are performed locally and verified through a smart contract, thereby supporting decentralized validation and secure automated incentives based on contract fulfillment. By merging cryptographic proofs with decentralized auditability, this framework establishes a new pilot model for trusted EPC execution, enhancing data sovereignty, stakeholder trust and unlocking the possibility of automatic financing models for energy efficiency. To the best of our knowledge, this is the first deployment of ZKP-driven EPC verification with blockchain transparency. Potential extensions include replicating the architecture at scale across residential and commercial buildings in Europe, integrating regulatory EPC registries, and supporting carbon credit programs or green bond issuance through credible, proof-based verification. Full article

This study analyzes the relationship between digital disclosure of information on Environmental, Social and Governance (ESG) factors, publicly verifiable environmental evidence, corporate responsibility, trust, and greenwashing risk in building construction in Bulgaria. The article applies a combined research design that integrates the Environmental Benchmarking Index for Building Construction Companies (EBI-C41), calculated at the company level for 12 construction companies, with a survey of 297 informed respondents. The results show that EBI-C41 has consistent positive associations with transparency, corporate responsibility, and trust, as well as a negative association with greenwashing risk. On this basis, the findings suggest that, in building construction, verifiable sustainability evidence is important for trust-related stakeholder evaluations and provides information beyond ESG communication visibility alone. Diagnostic indices are also proposed to assess the gap between communication, evidence, and trust. The results should be interpreted as exploratory associations within a purposive and non-representative sample, rather than as evidence of causal relationships or as an assessment of the full internal environmental performance of companies in the sector. Full article

Smart city and smart region governance increasingly relies on data-informed decision-making, stakeholder participation, digital tools, and public feedback. However, these mechanisms do not automatically create institutional learning. This article examines sustainable smart region governance as an institutional learnability problem and asks whether participation, local knowledge, and feedback are converted into adaptive action and retained as institutional memory. The study applies a Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-informed structured evidence mapping combined with an embedded regional case diagnosis from the Tokaj Wine Region, Hungary. The analysis integrates the literature on smart governance, learning regions, higher education quality assurance, territorial resilience, and adaptive governance with regional governance observation materials, stakeholder survey data, coding tables, calculation workbooks, and analytical figures deposited in a public Figshare dataset. The results identify the Act/Remember gap as the central learning-cycle disruption. Planning, implementation, monitoring, and consultation may be present, but feedback often fails to become adaptive action, and action is weakly retained as institutional memory. The comparison with higher education quality assurance shows that structured feedback and continuous improvement principles are transferable only as learning logic, not as procedural models. The findings also show that single-profile territories are especially vulnerable to delayed learning, strategic lock-in, and weak community knowledge-building. The article contributes to smart governance research by proposing institutional learnability as a diagnostic capacity of sustainable smart regions. It argues that digital tools should function as learning infrastructure supporting traceability, feedback-to-action mechanisms, and institutional memory, rather than as substitutes for human deliberation, trust, and collective responsibility. Full article

Smart cities (SCs) use the Internet of Things (IoT) to collect and process data to communicate with their infrastructure and assets in real time. A great deal of techniques, such as encryption protocols, Random Forest-based AI-driven threat detection, and blockchain architectures, have been developed to address cybersecurity challenges in smart cities (SCs). These techniques, however, have limitations such as their scalability, high computational expenses, and energy inefficiency. Therefore, in this study, to overcome these challenges, we propose a blockchain-based infrastructure called BlockSafeNet. This uses artificial intelligence, big data, and blockchain to enhance cybersecurity in SCs. The effectiveness of the proposed BlockSafeNet framework was evaluated using responsiveness, computational time, encryption quality score, detection rate, false positive rate, latency, throughput, and energy consumption as the primary cybersecurity performance metrics. These metrics were selected to assess communication efficiency, threat detection capability, privacy preservation, scalability, and overall security performance within smart-city IoT environments. To ensure secure data transactions, robust threat detection, and efficient communication. The system’s high calculation speed and detection rate show potential for managing sensitive maternal health data collected by IoT devices. The platform also shows how IoT may be used by healthcare services to monitor public health in real time, allowing hospitals, emergency services, and public health agencies to securely share data. This aids in resource optimization, improving service delivery, and preserving data privacy and trust in SCs. Data was obtained from the UCI Machine Learning Repository on Kaggle to validate the developed framework. By evaluating the effectiveness of BlockSafeNet in tackling cybersecurity challenges, we establish its practical relevance and usability in SCs. The proposed BlockSafeNet framework achieved a responsiveness of 24 s, an encryption quality score of 0.89, computational time of 85 s, and a detection rate of 91%, demonstrating significant improvements in secure IoT communication, privacy preservation, and AI-driven cyber threat detection within smart city infrastructures. shows that SC IoT security has significantly improved through the adoption of new data protection methods and better measures of security, providing a positive impact on the SC ecosystem. Full article

Disaster risk perception in informal urban settlements remains insufficiently understood despite its importance for climate adaptation and urban resilience policies. This study evaluated disaster risk perception among residents of nine informal settlements in Greater Santo Domingo, Dominican Republic, focusing on the relationship between the subjective risk of disaster, poverty, and socio-environmental vulnerability. A mixed-methods approach was applied using the RISKPERCEP algorithm and a structured survey composed of 98 questions grouped into key perception variables. A total of 198 surveys were conducted across four municipalities, supported by expert consultation, probabilistic sample-size calculation, and statistical analyses including chi-square tests and Cramér’s V coefficients. The results revealed a generalized moderate underestimation of climate-related risks, mainly associated with limited understanding of hazards, weak institutional trust, low personal involvement, poor memory of past disasters, and reduced concern about consequences. Socioeconomic deprivation, particularly among women, retirees, and low-income groups, was strongly associated with higher perceived vulnerability. Statistical analyses demonstrated a dense and multidimensional network of interdependent social, economic, and spatial variables shaping vulnerability. The study concludes that low disaster risk perception constitutes a major barrier to climate adaptation in informal settlements and highlights the need for integrated public policies combining social protection, climate education, urban resilience, and community-based adaptation strategies. The implications of this study point to the use of a disaster risk perception inquiry method at the community level, structured around specific variables that allow the identification of underlying causes of the widespread underestimation of disaster risk. This approach enables the design of adaptive policies—both structural and non-structural—that explicitly incorporate these aspects when formulating resilience measures. Future research should aim to generalize the study to other communities across the country, preceded by addressing the limitations identified. Full article

Public responses during health crises are shaped by interacting risk perceptions, affect, trust, information needs, overload, misinformation, and protective behavior. Existing applications of the Risk Information Seeking and Processing (RISP) model are largely static and therefore cannot represent stochastic multichannel exposure, delayed correction, or policy feedback. We develop the Stochastic Probabilistic Dynamic RISP (SP-D-RISP) model, which recasts RISP as a bounded stochastic state-space system. Its symmetry structure is explicit: the channel-allocation mechanism is equivariant under simultaneous relabeling of channels and their parameter blocks, while the multi-agent dynamics are invariant to agent relabeling under exchangeable sampling and a label-independent policy. Channel-specific effects, heterogeneous traits, rumor shocks, and interventions generate symmetry breaking. The model combines softmax–multinomial channel competition, discounted Bayesian trust updating, and policy-coupled state transitions. Projection guarantees feasible states by construction, whereas stronger stochastic stability is conditional on a coefficient-level small-gain criterion. For the stationary bounded-memory specification, this criterion is sufficient for Wasserstein contraction, uniqueness of the invariant distribution, and geometric forgetting of initial conditions. The criterion is formulated at the coefficient level and is kept distinct from finite-horizon simulation diagnostics. For the fully disclosed semi-synthetic coefficient vector, the scenario-specific gain matrices have spectral radii between $0.852765$ and $0.857123$; the worst-case column-sum norm is $0.983948$. Thus, the fixed-policy kernels satisfy the stated contraction certificate. For deterministic time-varying paths, the calculation is used only as a common-path one-step certificate, and for the threshold-adaptive rule, it is used only mode by mode rather than as a stationary invariant-law claim. While concentration bounds and Monte Carlo inference quantify population and replication uncertainty, a semi-synthetic experiment with 2500 heterogeneous agents over 90 days examines trust and literacy heterogeneity, clarification delays, communication volume, and intervention portfolios. Within the calibrated SP-D-RISP scenarios, the simulations suggest that higher communication volume may reduce modeled protective behavior when overload effects dominate knowledge gains, delayed clarification may increase transient misinformation, and an integrated portfolio can yield a more favorable simulated outcome profile than the evaluated single-lever strategies. Full article

This study examines the challenges facing intercultural competence education in higher education during times of intense conflict. Most of the intercultural competence programs in higher education are based on Western liberal multicultural logic that strives to build trust between groups in conflict to promote peacemaking. The common conception of trust is described as a developmental cognitive–emotional effort that is anchored in a reflective and multi-perspective calculation of events and procedures that require a willingness to give up parts of positions and standpoints. The process is supposed to be mediated by a professional and unbiased facilitator who creates a “dialogic space” based on self-reflection and perspective-taking of the “other”. Previous findings suggest that these programs were relatively efficient before the 7 October war broke out in Israel. Thus, a joint Palestinian and Jewish program that was conducted at a teacher training college after 7 October challenged these findings. Examining the discursive practices used by the participants during the meetings revealed that the basic liberal multicultural assumptions of the intercultural competence model were rejected by students from both groups. The students interpreted the model as a way to contextualize the term peace in a specific ideological–political context. Understanding peace as a concept with a layered meaning contradicts the intercultural competence model’s perception of it as a universal–humanistic–independent position. Thus, the findings further problematize the universal perception of intercultural competence models in order to adjust them to teacher trainees working outside the West during difficult times. Full article

Continuous-variable quantum secret sharing (CVQSS) distributes a secret key among multiple players while requiring their cooperation for reconstruction. Its performance deteriorates rapidly in sequential multiparty links because optical loss, excess noise, and receiver noise accumulate with the number of players. We investigate a dealer-side heralded hybrid linear amplifier (HHLA), formed by measurement-based noiseless linear amplification and trusted deterministic preamplification, for mitigating the relative receiver-noise penalty. The accepted data are described by an equivalent Gaussian channel and evaluated with an asymptotic reverse-reconciliation key-rate model against collective Gaussian attacks. We explicitly condition parameter estimation and Eve’s Holevo information on successful heralding, include a cutoff-dependent Gaussian-input estimate of the heralding probability in the rate per transmitted pulse, apply a single calibrated output-quadrature rescaling, and provide parallel heterodyne and homodyne calculations. The homodyne simulation includes detector noise consistently in the mutual information and Holevo bound and enforces the Gaussian-equivalent NLA feasibility constraint. For an excess-noise variance of $0.001$ shot-noise units per player, the constrained homodyne optimization gives maximum reported distances of approximately 108, 42, and $25.5$ km for 5, 50, and 100 players, respectively, at a reporting floor of ${10}^{-6}$ bit/pulse. Composable finite-size security, finite-cutoff non-Gaussian corrections, and active-insider verifiability are left for future work. Full article

The use of liquid hydrogen (LH₂) in rocket propulsion systems is among the most critical technologies enabling highly efficient space exploration. Fuel-oxidizer combinations directly impact mission performance, safety, and sustainability. In the literature, determining which oxidizer to use to obtain an efficient combination in experimental studies is a valuable area of research. However, evaluating different alternatives according to various criteria in experimental studies is expensive, time-consuming, and quite dangerous. This study aims to provide decision-makers with analytical support through a novel multi-criteria decision-making methodology. In this context, the simple weight calculation (SIWEC) method is integrated to determine the weights of the criteria, and the mulTi-noRmalization mUlti-distance aSsessmenT (TRUST) method is integrated to evaluate the oxidizers. The results show that the most important criterion is combustion, followed by environmental sustainability, applicability, cost, and operational safety. The foremost oxidizer, according to the analyses, parametric sensitivity analysis scenarios, and comparative analyses, is overwhelmingly LH₂-liquid oxygen (LOX). In all analyses, fluorine (F₂) ranked second, followed by the FLOX mixture in third place, and ozone (O₃) in last place. In addition, sensitivity analyses based on α and β parameter variations and comparative analyses were conducted to evaluate the robustness and stability of the proposed decision-making framework. Full article

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

Universities compete for funding, and their positions depend on the results of national assessments and rankings, which are expensive to produce and based on difficult-to-predict expert opinions. Assessment results have a significant impact on a university’s reputation, funding levels, attractiveness to faculty and staff, and success in recruiting top-tier students. Expert assessments and forecasts are widely used, but additional support from trusted AI tools is desirable. Several attempts have been made to use various machine learning methods, but confidence in such solutions is limited due to perceived difficulties in clearly and reliably justifying the resulting predictions. This research aims to present a proposal for using neural network models, accompanied by explanations of their predictions, to support trustworthy and sustainable assessment of university competitiveness. This methodological contribution enhances the transparency and interpretability of the assessment process and is further supported by empirical studies based on data from selected universities. A Fully Connected Neural Network (FCNN) is used for the calculations, and the local interpretable model-agnostic explanations (LIME) method is applied to explain the prediction results. The results confirm the usefulness of the proposed model and provide a solid foundation for improving evaluation systems and building trust in AI applications for assessing universities’ competitive position and the benefits of scientific research for society. Full article