Search Results (1,042)

Efficient and financially sustainable social protection systems are essential under conditions of economic instability and increasing social demand. However, traditional administrative models are often characterized by high operational costs, procedural complexity, and delayed benefit delivery. This study examines the role of digitalization, process automation, and AI-driven administrative solutions in reducing administrative expenses while enhancing the sustainability and resilience of social protection systems. An integrated Automation Index is developed using standardized proxy indicators that reflect reductions in operational and transaction costs associated with digital and automated technologies. To assess future trajectories of administrative expenses, scenario-based modelling is applied under three digital transformation paths—baseline, moderate, and intensive. Administrative efficiency is estimated using a translog Stochastic Frontier Analysis (SFA) framework. The results indicate that digitalization and automation significantly reduce administrative costs only when supported by favorable institutional conditions, including decentralized governance, effective inter-agency coordination, and clearly regulated administrative procedures. Under the intensive digital transformation scenario, administrative expenses decline substantially relative to the baseline, while system responsiveness and beneficiary coverage improve. In contrast, weak institutional environments limit the efficiency gains of technological solutions. The study concludes that AI agents and automated systems should be viewed not as substitutes for human decision-making but as tools for optimizing administrative architectures. This transition from resource-intensive to technology-intensive models is particularly important for developing countries seeking sustainable social protection under constrained fiscal conditions. Full article

With the increasing penetration of renewable energy generation (REG) in novel distribution systems, active distribution networks (ADNs) integrated with microgrids (MGs) play a crucial role in enhancing the flexibility of regulation resources and promoting the accommodation of REG. To meet the operational requirements for efficient collaboration between ADNs and MGs under different dispatch time scales, this paper proposes a collaborative optimal dispatch strategy for multi-microgrid active distribution systems based on a hybrid game and variable time scales. Firstly, a transaction operation framework is constructed for the distribution network operator (DNO) and a multi-microgrid alliance (MMA), considering the peer-to-peer (P2P) transaction mode. On this basis, a day-ahead hybrid game model with a two-layer structure is constructed, the upper layer is a master–slave game with the DNO as the leader and the MMA as the follower, while the lower layer is a cooperative game for MGs within the MMA. An asymmetric Nash bargaining strategy based on contribution degree in P2P transactions is introduced to ensure equitable benefit allocation among cooperative MGs. Secondly, an intra-day rolling optimization model for reactive power and voltage based on variable time scales is proposed, which enhances the system’s responsiveness to real-time source–load power fluctuations by dynamically adjusting the dispatch time scale. Finally, the alternating direction method of multipliers (ADMM), integrated with a strategy separation mechanism, is adopted to efficiently solve the hybrid game model involving numerous 0–1 variables. The case study results indicate that, under the proposed strategy, the MMA’s power purchase cost from the DNO and ESS operational cost are decreased by 9.7% and 11.6%, respectively, while the system’s average deviation rate of node voltage decreases by 0.82%. Therefore, the proposed collaborative dispatch strategy can not only effectively reduce the system’s operational cost and ensure voltage stability but also significantly promote the accommodation of REG. Full article

With the widespread adoption of intelligent terminals, edge devices, and distributed information systems in the financial domain, financial security sensing data exhibit multisource heterogeneity, dynamic temporal patterns, and high privacy sensitivity. Traditional centralized anomaly detection methods are no longer able to simultaneously satisfy the requirements of cross-institutional or cross-node collaborative modeling, client data privacy protection, and robust monitoring of transaction and system anomalies. To address this challenge, a data-local federated deep anomaly detection framework has been proposed for distributed financial security sensing systems. Initially, a local deep financial security sensing representation module is constructed to perform temporal encoding and attention-based modeling on multisource financial signals, including terminal operation status, network transaction communication, backend server operation, identity authentication, and anomaly alerts, thereby extracting representations relevant to anomalous behaviors. Subsequently, a data-local federated optimization and personalized aggregation mechanism is developed to enable cross-node knowledge sharing without transmitting raw transaction or client data, while local personalized detection heads are employed to adapt to non-independent and identically distributed (non-IID) financial institution data. Furthermore, an adversarially robust security detection and trust-aware aggregation strategy is introduced to enhance model stability under input noise, feature masking, anomaly camouflage, and potential malicious client updates. Experimental results demonstrate that the proposed method achieves an Accuracy of $92.37\%$, a Precision of $89.41\%$, a Recall of $88.26\%$, an F1-score of $88.83\%$, an AUC of $93.06\%$, and a PR-AUC of $89.15\%$ in the primary financial anomaly detection task, significantly outperforming baseline methods such as Isolation Forest, Autoencoder, LSTM, Transformer, FedAvg, FedProx, SCAFFOLD, and MOON. In robustness experiments, the method attains F1-scores of $87.95\%$, $86.42\%$, $86.88\%$, $84.57\%$, $86.73\%$, and $83.91\%$ under Gaussian noise, feature masking, temporal shift, adversarial perturbation, and $20\%$ and $30\%$ malicious client scenarios, respectively. Ablation studies further confirm the effectiveness of local representation learning, personalized federated optimization, adversarial training, and trust-aware aggregation mechanisms. Overall, the proposed approach provides an efficient intelligent anomaly detection solution for financial AI security monitoring scenarios characterized by data localization requirements, node heterogeneity, and attack perturbations. Full article

Existing cross-chain bridges over-rely on static collateralization and post-event penalties, leaving them vulnerable to concealed on–off attacks and rational group collusion. To address these limitations, this paper proposes a Dynamic Trust Evaluation and Risk Control (DTERC) mechanism for heterogeneous cross-chain relay nodes. First, DTERC develops a multidimensional trust quantification model that combines temporal decay, robust multi-observer latency aggregation, verification accuracy, online stability, and an asymmetric one-strike penalty triggered only by cryptographic evidence. Second, DTERC constructs a threshold-aware N-player evolutionary game model to characterize the k-of-N signature structure of cross-chain relay consensus and introduces a dynamic staking function to reduce the economic incentive for collusion under bounded attack-value and parameter conditions. Third, DTERC designs a threshold-preserving FastPath mechanism to reduce redundant verification for low-risk transactions while retaining committee-level confirmation and challenge-based fallback. The empirical evaluation combines multi-agent simulation, smart-contract prototype testing, whitelist-compromise stress tests, malicious-oracle robustness analysis, network-jitter experiments, repeated trials, and parameter-sensitivity analysis. The results show that, under the tested settings, DTERC reduces the malicious transaction success rate to 0.15% under a 50% initial collusion scenario, lowers core contract Gas overhead by 35.7%, and reduces average end-to-end latency by approximately 10% in benign FastPath conditions. These findings indicate that DTERC improves the security–efficiency trade-off of heterogeneous cross-chain relay networks while making its assumptions and limitations explicit. Full article

Small- and medium-sized enterprises, especially in the agricultural food sector, struggle to implement end-to-end product traceability systems, such as enterprise resource planning (ERP), due to the high costs and complexity involved for businesses of this scale. As customer expectations and regulatory requirements place an increasing emphasis on traceability and transparency, the combined use of industrial Internet of things (IIoT) technologies and blockchain-based smart contracts offers a promising pathway to cost-effective automation of supply chain processes. This paper develops a conceptual, multi-layer architecture that integrates sensing, communication, integration and smart-contract layers to support affordable, automated and extensible traceability for agri-food supply chains. Building on information processing theory and transaction cost economics, the framework explains how such architecture can reduce information uncertainty, lower monitoring costs and strengthen the organisational trust in agri-food supply chains. The framework is empirically illustrated and tested through an implementation that links distributed sensing infrastructure with a blockchain-based smart contract in a real agricultural supply chain setting. The evaluation assesses operational performance, data integrity and cost-efficiency, demonstrating that the proposed architecture can serve as a viable alternative or most importantly complement to traditional ERP solutions for small- and medium-sized enterprises that seek end-to-end traceability, transparency and automation. Full article

The digital integration of existing industrial equipment (retrofitting) is a central element of the Industry 4.0 paradigm, wherein the energy efficiency of Internet of Things (IoT) gateways is a decisive design consideration. This research aims to experimentally compare various wireless and wired communication protocols—ESP-NOW, Bluetooth Low Energy (BLE), Bluetooth Classic (Serial Port Profile, SPP), Message Queuing Telemetry Transport (MQTT), and S7 Protocol—within a legacy Programmable Logic Controller (PLC)-based environment. A dedicated testbed was developed using Siemens S7-300 PLCs and ESP32-based gateway devices to ensure measurement reproducibility. Energy consumption was determined using a high-precision power profiler with payloads ranging from 50 to 15,000 bytes, applying the trapezoidal rule while considering both active transaction and standby states. The specific energy consumption metric (μJ/byte) introduced in this study highlights the distinct scaling limitations of the protocols. While ESP-NOW proved highly efficient for small telemetry packets, Bluetooth Classic exhibited superior scalability for bulk data volumes. Furthermore, a critical energetic crossover point was identified for ESP-NOW due to hardware fragmentation limits, whereas MQTT demonstrated massive energetic overhead for small payloads. Standby measurements confirmed that the continuous baseline consumption of the wired Ethernet interface significantly dominates the energy budget compared to wireless alternatives. These empirical findings are synthesized into a formal Qualitative Decision Matrix to help engineers optimize protocol selection based on the expected duty cycle, facilitating the development of sustainable industrial digitalization solutions. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Foreign-exchange decisions rest on hierarchically organized evidence whose latent structure is inadequately captured by Euclidean representations. Reinforcement-learning agents trained on flat embeddings inherit stability guarantees that do not transfer to the manifold supporting the latent state. We address both limitations through a hybrid architecture in which a schema-constrained structured chain-of-thought is embedded into a Poincaré ball, transported to a qubit register via angle encoding, and processed by an L-layer hardware-efficient variational ansatz on a state-vector backend. The circuit exposes two read-outs to the policy, namely, a scalar Pauli-Z observable and a projected quantum kernel inducing a fidelity-based similarity over magnet-price attractors, the latter identified via kernel-weighted recurrence density and finite-time Lyapunov statistics. The Lipschitz constraint on the action-value function is lifted from the hyperbolic geodesic distance to a joint metric on ${\mathbb{B}}_{\kappa }^n\times \mathbb{P}\left(\mathcal{H}\right)$. A stability theorem yields an explicit bound depending on the read-out operator norm, on the depth–width product of the ansatz, and on the curvature–Hilbert balance. The pipeline is evaluated on nine major FX crosses over a 2015–2025 out-of-sample window, with rolling-origin walk-forward retraining and broker-published transaction costs. The system attains $2.55\%$ pair-averaged non-compounded monthly P&L and $8.83\%$ maximum drawdown, with Sharpe $1.78$, Calmar $3.43$, and Probabilistic Sharpe Ratio exceeding $0.95$ on every cross. The gain remains significant under a deflated-Sharpe-ratio test with $N_{\mathrm{trials}}=42$ correction. Block-wise ablations exhibit strictly monotone degradation: removing the projected kernel costs $-4.15$ p.p. on annualized P&L, the joint Lipschitz penalty $-6.42$ p.p., the attractor module $-7.64$ p.p., and the hyperbolic embedding $-8.40$ p.p. The quantum block thereby instantiates a structurally non-classical, geometry-aware regularizer identifiable through ablation rather than asymptotically advantageous. Full article

This study employs sample data of Chinese A-share listed companies from 2008 to 2023, takes investment efficiency as a core indicator to measure corporate sustainable development, and adopts the difference-in-differences (DID) model to examine the impact of free trade zone policies on enterprise sustainable development. The research results show that the establishment of FTZs has significantly improved the level of enterprise sustainable development, and this conclusion has passed a series of robustness tests. In the mechanism analysis, the main paths of action include alleviating financing constraints, optimizing internal control, and reducing transaction costs. The heterogeneity analysis finds that there are different regional effects in FTZs, and the policy effects are more obvious in the eastern, western, provincial capital, sub-provincial, and non-coastal areas. This study deepens the understanding of the sustainable impact of FTZs on enterprises, presents how the interaction between institutions and the market promotes the efficiency improvement of different regions, and also provides key suggestions for enterprises to make wise investment decisions by using the policies of FTZs and find a sustainable development path. Full article

Convolutional Neural Network (CNN) accelerators are widely deployed in edge Artificial Intelligence (AI), embedded vision, and object detection systems, but their hardware designs often incur significant power consumption due to intensive multiply–accumulate (MAC) operations, frequent register toggling, memory transactions, and persistent signal switching. This study examines Register Transfer Level (RTL)-level power optimization of a CNN accelerator on a Field-Programmable Gate Array (FPGA) using three design approaches: a baseline, a Local Explicit Clock Gating (LECG) + Memory Split scheme, and a sparsity-aware scheme. The LECG + Memory Split approach reduces redundant sequential and memory-switching operations, while the sparsity-aware scheme further minimizes arithmetic operations on zero-valued operands. FPGA power measurements on a Xilinx ZCU102 platform reveal a total power decrease from 3.644 W in the baseline to 2.775 W with LECG + Memory Split and 2.442 W with sparsity-aware optimization. This achieves up to a 32.99% reduction in total power without increasing Digital Signal Processing (DSP) block or Block Random Access Memory (BRAM) usage. The findings confirm that integrating control-based, memory-aware, and data-aware RTL methods enhances the power efficiency of CNN accelerators while maintaining the main compute and memory architectures. Full article

Quantum machine learning on near-term noisy quantum devices has generated substantial theoretical interest, but rigorous empirical comparisons under realistic noise on practically relevant data remain scarce. This paper compares two paradigmatic quantum learning models, a Quantum Support Vector Machine (QSVM) built on the ZZFeatureMap quantum kernel and a Variational Quantum Classifier (VQC) with an EfficientSU2/RealAmplitudes ansatz, against tuned classical baselines (SVM with four kernels, Random Forest, XGBoost, LightGBM and CatBoost) on the ULB Credit Card Fraud dataset (284,807 transactions, 0.17% fraud). All models share an identical 4-qubit PCA-reduced feature space, evaluated on the full unbalanced test fold over 15 fits (3 folds × 5 seeds) and reported as mean ± standard deviation with bootstrap confidence intervals, AUPRC as the primary metric. Noise robustness is assessed under depolarizing noise $p\in \{0,0.001,0.01,0.05\}$, with ranking preservation measured directly through Spearman $\rho$ and Kendall $\tau$ between the noisy and noiseless decision scores rather than read off AUPRC, alongside the per-paradigm computational cost. At four qubits the classical baselines lead (AUPRC 0.60 to 0.74, CatBoost best), above the VQC (0.494) and the QSVM (0.240); the controlled QSVM-versus-RBF–SVM comparison puts the cost of the quantum kernel at about 0.45 AUPRC. Under noise the QSVM keeps its score ranking ($\rho =0.998$ at $p=0.001$, 0.906 at $p=0.01$) and an operational decision threshold (recall 0.87 to 0.89, stable calibration), while the VQC AUPRC peaks non-monotonically at $p=0.01$ (0.494 rising to 0.654, then easing to 0.569 at $p=0.05$) even as its ranking decays monotonically ($\rho$ from 0.72 to near zero), so average precision on its own misrepresents how noise affects it. The quantum models do not surpass the tuned classical reference at four qubits; the contribution is methodological: under noise, AUPRC has to be read together with a genuine rank statistic, because the two can move in opposite directions. Full article

This paper develops a real-time rolling-horizon optimization framework based on semidefinite programming (SDP) for vehicle-to-grid (V2G)-enabled electric vehicle (EV) fleets in distribution networks. The model coordinates time-varying EV availability, departure energy requirements, and distribution-network operating constraints under alternating-current (AC) power flow. The objective integrates voltage-dependent network loss cost, load-dependent EV energy transaction cost, and throughput-based battery degradation cost, while asymmetric charging/discharging efficiencies, EV implementation errors, and load forecast errors are also considered. To address the nonconvexity caused by AC power-flow equations and voltage-dependent losses, Hermitian lifting is used to reformulate the problem into a rank-constrained SDP model, followed by a convex SDP relaxation. Numerical studies on IEEE 33-bus and IEEE 69-bus systems show that the proposed rolling SDP method reduces EV-induced load peaks, improves load-smoothing performance, satisfies network and EV-side constraints, and yields numerically rank-one solutions in the tested cases. Further tests on time-slot lengths, look-ahead horizons, EV penetration levels, benchmark methods, EV implementation errors, and load forecast errors further verify the effectiveness and practical robustness of the proposed framework. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly being integrated into dental diagnostics, promising improved detection, efficiency, and patient communication. While these developments offer potential clinical benefits, emerging commercial applications raise important ethical concerns. This study explores how providers of diagnostic AI systems frame their technologies in marketing materials, with particular attention to features designed to influence patient acceptance and increase revenue. Methods: An exploratory qualitative thematic analysis was conducted on publicly available promotional content from leading dental AI companies between September and October 2025. Materials were analyzed for recurring rhetorical strategies related to commercialization, persuasion, technological authority, and representations of objectivity. Ethical interpretation was guided by principlism, professional ethics, and virtue-based perspectives. Results: The findings show that AI is frequently marketed not only as a diagnostic aid but also as a tool for boosting case acceptance, return on investment, and practice expansion. Visualizations and performance metrics are used rhetorically to position AI as authoritative and objective, encouraging patient compliance while downplaying uncertainty and potential harm. These practices risk undermining patient autonomy, promoting diagnostic inflation and overtreatment, and compromising professional integrity by shifting attention from patient welfare toward commercial outcomes. Conclusion: Pervasive marketing of diagnostic AI amplifies existing tensions between professional integrity and commercial incentives in dentistry. Without appropriate safeguards, AI risks reinforcing a transactional model of care in which patients are treated as consumers and diagnostics become instruments of persuasion. To preserve trust and ethical practice, dentists and professional organizations must ensure that AI remains a supportive clinical tool rather than a commercial device, prioritizing transparency, informed consent, and patient-centered care. Full article

Amid rising geopolitical fragmentation and growing uncertainty in global financial systems, Central Bank Digital Currencies (CBDCs) are increasingly viewed as a potential innovation in cross-border digital payments. This paper provides a systematic review of the literature on CBDCs, with a particular focus on their role in cross-border payment systems, while also considering broader implications for monetary power and geopolitical realignment. Using a PRISMA-based review approach, complemented by bibliometric mapping, the study synthesizes existing research across economic, technological, institutional, and geopolitical dimensions. Unlike prior studies that primarily examine technical design features or domestic monetary implications, this review develops an integrated framework that situates CBDCs within the evolving architecture of cross-border digital payment systems in a fragmented global environment. The evidence suggests that CBDCs can enhance cross-border payment efficiency by reducing transaction costs, shortening settlement times, and enabling more direct transfer mechanisms that bypass traditional correspondent banking networks. At the same time, the literature highlights several critical challenges, including interoperability constraints, regulatory divergence, privacy concerns, and cybersecurity risks. Importantly, the findings also point to the potential emergence of parallel digital currency ecosystems, which may reinforce existing financial fragmentation rather than fully resolve it. Overall, CBDCs should be understood not only as technological innovations in digital payments but also as strategic instruments with implications for monetary sovereignty and global economic influence. Their long-term impact on cross-border payment systems will depend on the development of interoperable standards, coordinated regulatory frameworks, and sustained international cooperation. By bringing together fragmented strands of research, this study contributes to a more comprehensive understanding of how CBDCs are reshaping both digital payment infrastructures and the broader global financial order. Full article

The real estate sector continues to face challenges such as inefficiencies, fraud risks, and high transaction costs stemming from opaque processes and heavy reliance on intermediaries. These challenges highlight the need for transparent and efficient solutions to support secure real estate transactions and management. While a growing body of literature has examined blockchain applications in real estate, existing studies are often fragmented and predominantly descriptive, with limited systematic synthesis of evidence and insufficient attention to governance and institutional contexts. This study aims to systematically examine and synthesise the benefits and challenges of blockchain technology in real estate, providing evidence-based insights for practitioners and policymakers. Using a Systematic Literature Review (SLR) approach, peer-reviewed publications from 2016 to 2025 were analysed to identify blockchain applications, reported outcomes, and implementation barriers. The findings reveal that blockchain has been applied in land registration (e.g., Sweden, India, Serbia), valuation systems, decentralised housing finance, and tokenised investment platforms (e.g., Exporo, RealT). The reported benefits include reduced fraud, enhanced transaction efficiency, transparency, and expanded investment access through fractional ownership. However, regulatory uncertainty, scalability limitations, data privacy risks, and low stakeholder awareness remain key barriers. Ethical issues such as digital exclusion and data exposure also require further consideration. Compared with the more advanced adoption observed in Europe and North America, supported by established regulatory frameworks and digital land governance initiatives, this review identifies relatively slower uptake in parts of the Asia-Pacific region, particularly in Australia and Malaysia. It highlights a critical need for future research on legal recognition, privacy-enhancing technologies, and governance frameworks, particularly regarding blockchain applications in property development and urban planning processes. By integrating technological and governance perspectives, this study provides a more comprehensive and structured understanding of blockchain adoption in real estate systems. Full article