Search Results (2,520)

This study analyses the factors influencing renewable energy infrastructure public–private partnership (PPP) financing, using data from 28 countries covering the period from 1996 to 2024. A composite institutional quality index was constructed using Principal Component Analysis (PCA). The analysis employs a panel econometric framework: the autoregressive distributed lag (ARDL) model to capture short- and long-term dynamics. The results highlight the significance of the time dimension on renewable energy PPP financing. In the short term, none of the predictor variables are significant, reflecting the inherently long-term character of renewable energy PPP investments. However, in the long term, gross domestic product per capita, inflation dynamics, efficiency in energy transmission, and institutional quality are identified as key determinants of renewable energy investment. The findings suggest that strengthening sector-specific regulatory frameworks and improving various aspects of institutional quality as defined by the World Governance Indicators can be important to attract private capital in energy PPPs. These institutional reforms, complemented by growth-oriented macroeconomic policies, would contribute to making renewable energy markets more attractive while reducing exposure to macroeconomic and institutional risks. Full article

With the proliferation of the Internet of Things (IoT), gesture recognition has attracted attention as a core technology in human–computer interaction (HCI). In particular, mmWave frequency-modulated continuous-wave (FMCW) radar has emerged as an alternative to vision-based approaches due to its robustness to illumination changes and advantages in privacy. However, in real-world human–machine interface (HMI) environments, hand gestures are inevitably accompanied by torso- and arm-related reflections, which can also contain gesture-relevant variations. To effectively capture these variations without discarding them, we propose a preprocessing method called Adaptive Top-K Selection, which leverages vector entropy to summarize and preserve informative signals from both hand and body reflections. In addition, we present a Multi-Stream EfficientNetV2 architecture that jointly exploits temporal range and Doppler trajectories, together with radar-specific data augmentation and a training optimization strategy. In experiments on the publicly available FMCW gesture dataset released by the Karlsruhe Institute of Technology, the proposed method achieved an average accuracy of 99.5%. These results show that the proposed approach enables accurate and reliable gesture recognition even in realistic HMI environments with co-existing body reflections. Full article

Blockchain-based cold chain logistics (BCCL) systems establish a new logistics data-sharing mechanism with blockchain technology, which destroys the traditional data island problem and promotes cross-institutional data interoperability. However, security vulnerabilities, risks of data loss, exposure of private information, and particularly the emergence of quantum-based attacks pose heightened threats to the existing BCCL framework. This paper first introduces a transaction privacy preserving (TPP) model for BCCLS that aggregates the blockchain and ring signcryption scheme together to strengthen the security of the data exchange process. Then, a lattice-based ring signcryption (LRSC) scheme is proposed. This LRSC utilizes the lattice assumption to enhance resistance against quantum attacks while employing ring mechanisms to safeguard the anonymity and privacy of the actual signer. It also executes signature and encryption algorithms simultaneously to improve algorithm execution efficiency. Moreover, the formal security proof results show that this LRSC can capture the signer’s confidentiality and unforgeability. Experimental findings indicate that the LRSC scheme achieves higher efficiency compared with comparable approaches. The proposed TPP model and LRSC scheme effectively facilitate cross-institutional logistics data exchange and enhance the utilization of logistics information via the BCCL system. Full article

Background/Objectives: Umbilical cord blood (UCB) is a valuable source of hematopoietic stem cells used in treating blood and immune disorders. Despite its potential and the availability of public banking systems in Italy, donation rates remain low due to patient misinformation, emotional barriers, and organizational inefficiencies. This study aimed to evaluate the impact of integrating Narrative Medicine (NM) and Lean Management (LM) on UCB donation rates and operational effectiveness at the University Hospital of Alessandria. Methods: This prospective, single-center pre-post study ran from July 2022 to December 2024. Two interventions were introduced: NM training for healthcare staff to enhance empathetic communication, and LM-based reorganization of workflows to improve process efficiency. Outcomes included changes in UCB donation and adherence rates, transplant-eligible unit percentages, and patient satisfaction, assessed through institutional and project-specific surveys (PERLA–SIMeN). Results: Post-intervention, donation rates increased from 0% in early 2022 to 30.8% (2022), 25.8% (2023), and 30.6% (2024), with adherence rates near 40%, far exceeding the national average of ~3%. Patient satisfaction improved, resulting in PERLA certification in February 2025. Conclusions: The integration of NM and LM significantly improved both patient engagement and organizational efficiency. Empathetic communication fostered trust and reduced emotional barriers, while LM optimized workflows and resource use. These results suggest the model is applicable in other hospitals to enhance UCB donation outcomes and overall quality of maternal care. Full article

Agriculture and its allied activities contribute to the primary sector in India and act as the basis for the country’s economy. Available agricultural landholdings are scattered as multiple plots across the country. Land fragmentation has led to problems achieving economies of scale and economies of scope; lower productivity, efficiency, and modernization; loss of biodiversity; and little scope for mechanization and technology. FPOs are small clusters of farmers who collaborate to enhance their bargaining strength through collective procurement, processing, and marketing efforts. To enhance the performance of FPOs at the grassroots level, the engagement of cluster-based business organizations (CBBOs) is vital. Millet FPOs are similar to voluntary farmer groups that are involved in the cultivation and promotion of millets. IIMR-promoted millet FPOs were selected purposively for the present study as they are involved in millet cultivation and farming. A total of 450 millet farmers from 15 FPOs and 3 states were randomly chosen for this action research study. The present research identified 10 key factors and collected farmers’ opinions toward member participation in millet FPOs using interpretive structural modeling. The ISM approach provided a clear understanding of how the selected factors interconnect hierarchically with each other as foundational drivers and dependent outcomes. The results from the MICMAC analysis demonstrated that foundational interventions, such as post-harvest technology availability (V2) and knowledge transfer by KVKs (V5), directly support higher-level objectives. Intermediate factors like economies of scale (V1) and market and credit linkages (V3) transform these services into operational advantages, while the outcome factors of business planning (V8), FPO branding (V7), and bargaining power (V9) emerge as dependent variables. The model demonstrates that V2 catalyzes improvements across the production, market, and institutional domains, cascading through intermediate enablers (V1, V4, V5, V6) to strengthen outcomes (V3, V7, V8, V9, V10). This hierarchy demonstrates that investing in post-harvest technology and complementary extension services is critical for building resilient millet FPOs and enhancing member participation. Full article

Existing point cloud registration methods can effectively handle large-scale and partially overlapping point cloud pairs. However, registering unbalanced point cloud pairs with significant disparities in spatial extent and point density remains a challenging problem that has received limited research attention. This challenge primarily arises from the difficulty in achieving accurate local registration when the point clouds exhibit substantial scale variations and uneven density distributions. This paper presents a novel registration method for unbalanced point cloud pairs that utilizes the local point cluster structure feature for effective outlier rejection. The fundamental principle underlying our method is that the internal structure of a local cluster comprising a point and its K-nearest neighbors maintains rigidity-preserved invariance across different point clouds. The proposed pipeline operates through four sequential stages. First, keypoints are detected in both the source and target point clouds. Second, local feature descriptors are employed to establish initial one-to-many correspondences, which is a strategy that increases correspondences redundancy to enhance the pool of potential inliers. Third, the proposed Local Point Cluster Structure Feature is applied to filter outliers from the initial correspondences. Finally, the transformation hypothesis is generated and evaluated through the RANSAC method. To validate the efficacy of the proposed method, we construct a carefully designed benchmark named KITTI-UPP (KITTI-Unbalanced Point cloud Pairs) based on the KITTI odometry dataset. We further evaluate our method on the real-world TIESY Dataset which is a LiDAR-scanned dataset collected by the Third Railway Survey and Design Institute Group Co. Extensive experiments demonstrate that our method significantly outperforms the state-of-the-art methods in terms of both registration success rate and computational efficiency on the KITTI-UPP benchmark. Moreover, it achieves competitive results on the real-world TIESY dataset, confirming its applicability and generalizability across diverse real-world scenarios. Full article

Background: The digitalization of medical services is promoted as a solution for improving access, quality, and efficiency within healthcare systems. In this context, the study investigates the extent to which digitalization in Romania meets the current needs of patients through a convergent analysis of user perceptions and managerial perspectives. Based on the specialized literature, the research tests two hypotheses: (H1) the implementation of digital technologies significantly contributes to improving the quality of medical services and operational efficiency; (H2) digitalization has a positive impact on patient satisfaction by facilitating access to care and improving communication with medical personnel. Methods: The study adopted methodology is cross-sectional and mixed, including an online mixed-methods questionnaire for patients, distributed between 6 and 14 May 2025, and a qualitative questionnaire with open-ended questions distributed via e-mail to managers from public hospitals through The Administration of Hospitals and Medical Services of Bucharest, between 3 and 24 March 2025. Results: In total, 125 patients and 15 hospital managers participated in the study. Statistical analysis (χ², ordinal regression) and data triangulation highlight a predominantly positive, yet heterogeneous, patient perception of digitalization, with Hypothesis H1 only partially supported (weak, inconsistent, and in some cases negative associations between technology use and perceived service quality). By contrast, H2 was robustly validated, with patient satisfaction strongly linked to tangible benefits, particularly easier access and online appointment scheduling. However, use remains limited to administrative functions, while advanced technologies such as telemedicine or electronic health records are poorly adopted. From an institutional perspective, hospitals predominantly use IT systems for internal purposes, without real patient access to their own data, no interoperability between medical units, and marginal implementation of telemedicine. This reveals a significant gap between user perception and organizational realities, emphasizing the lack of a patient-oriented digital infrastructure. Conclusions: The results highlight the potential of digitalization to enhance patient experience and service efficiency, while also pointing out structural limitations that hinder the full realization of this potential. Patient satisfaction is strongly associated with tangible benefits, particularly easier access and online scheduling, whereas the effect on perceived quality is weaker and sometimes inconsistent. There are significant disparities in digitalization levels between healthcare providers, perceived by patients as public–private differences, and gaps among public hospitals are also confirmed by managerial data. These findings suggest that a successful digital transformation of the medical system in Romania must address both technological infrastructure gaps and organizational barriers, within a coordinated national strategy that ensures interoperability, patient-centered design, and sustainable implementation. Full article

Higher education institutions play a critical role in the transition to a low-carbon future due to their research capacity and societal influence. Accordingly, the calculation of greenhouse gas (GHG) emissions and the prioritization of mitigation strategies are of particular importance. In this study, a comprehensive campus-level GHG inventory was prepared for a public university in Türkiye in alignment with the ISO 14064-1:2018 standard, and mitigation strategies were evaluated. To prioritize these strategies, both the classical Policy Modeling Consistency (PMC) index and, for the first time in the literature, a fuzzy extension of the PMC model was applied. The results reveal that the total GHG emissions for 2023 amounted to 4888.63 tCO₂e (1.19 tCO₂e per capita), with the largest shares originating from investments (31%) and purchased electricity (28.38%). While the classical PMC identified only two high-priority actions, the fuzzy PMC reduced score dispersion, resolved ranking ties, and expanded the number of high-priority actions to seven. The top strategies include awareness programs, energy-efficiency measures, virtual meeting practices, advanced electricity monitoring, and improved data management systems. By comparing the classical and fuzzy approaches, the study demonstrates that integrating fuzzy logic enhances the transparency, reproducibility, and robustness of strategy prioritization, thereby offering a practical roadmap for campus decarbonization and sustainability policy in higher education institutions. Full article

Accurate estimation of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing the reliability and efficiency of energy storage systems. This study explores custom deep learning models to predict RUL using a dataset from the Hawaii Natural Energy Institute (HNEI). Three approaches are investigated: an Encoder-only Transformer model, its enhancement with SimSiam transfer learning, and a CNN–Encoder hybrid model. These models leverage advanced mechanisms such as multi-head attention, robust feedforward networks, and self-supervised learning to capture complex degradation patterns in the data. Rigorous preprocessing and optimisation ensure optimal performance, reducing key metrics such as mean squared error (MSE) and mean absolute error (MAE). Experimental results demonstrated that Transformer–CNN with Noise Augmentation outperforms other methods, highlighting its potential for battery health monitoring and predictive maintenance. Full article

This study takes the environmental protection tax (EPT) implemented in China in 2018 as the policy background and systematically examines the impact mechanism and boundary conditions of EPT on the market value of listed companies in the polluting industries. The results indicate that EPT significantly inhibits Tobin’s Q of polluting companies. A one-unit increase in EPT leads to a 0.274-unit decrease in Tobin’s Q. The heterogeneity test reveals that the EPT shock exhibits a spatial gradient effect of “Eastern > Central > Western > Northeastern”. The rigidity of the tax system is stronger than that of the pollution discharge fee, and the effect on non-heavily polluting industries is stronger than that on heavily polluting industries. Mechanism analysis shows that while corporate financial flexibility can buffer against short-term EPT shocks, R&D investment and patent quality expose an “innovation trap” characterized by high investment but low conversion efficiency, largely determined by the type of innovation pursued. By elucidating the multiple moderating and mediating mechanisms at play, this study constructs an integrated “institutional pressure-resource constraints-market feedback” model, thereby providing a new analytical framework for environmental economics in emerging markets. Full article

Current medical informatics systems face critical challenges, including limited scalability across distributed institutions, insufficient real-time AI-driven decision support, and lack of standardized interoperability for heterogeneous medical data exchange. To address these challenges, this paper proposes a novel distributed web system architecture for medical informatics, integrating artificial intelligence techniques and cloud-based services. The system ensures interoperability via HL7 FHIR standards and preserves data privacy and fault tolerance across interconnected medical institutions. A hybrid AI pipeline combining principal component analysis (PCA), K-Means clustering, and convolutional neural networks (CNNs) is applied to diffusion tensor imaging (DTI) data for early detection of neurological anomalies. The architecture leverages containerized microservices orchestrated with Docker Swarm, enabling adaptive resource management and high availability. Experimental validation confirms reduced latency, improved system reliability, and enhanced compliance with medical data exchange protocols. Results demonstrate superior performance with an average latency of 94 ms, a diagnostic accuracy of 91.3%, and enhanced clinical workflow efficiency compared to traditional monolithic architectures. The proposed solution successfully addresses scalability limitations while maintaining data security and regulatory compliance across multi-institutional deployments. This work contributes to the advancement of intelligent, interoperable, and scalable e-health infrastructures aligned with the evolution of digital healthcare ecosystems. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

The integration of blockchain technology into healthcare systems has emerged as a technical solution for enhancing data security, protecting privacy, and improving interoperability. Blockchain-based smart contracts offer reliability, transparency, and efficiency in healthcare services, making them a focal point of many studies. However, challenges such as scalability, regulatory compliance, and interoperability continue to limit their widespread adoption. This study conducts a comprehensive literature review to assess blockchain-driven health data management, focusing on the classification of blockchain-based smart contracts in health policy and the health protocols and standards applicable to blockchain-based smart contracts. This review includes 80 core studies published between 2019 and 2025, identified through searches in PubMed, Scopus, and Web of Science using the PRISMA method. Risk of bias and methodological quality were assessed using the Joanna Briggs Institute tool. The findings highlight the potential of blockchain-enabled smart contracts in health policy management, emphasizing their advantages, limitations, and implementation challenges. Additionally, the research underscores their transformative impact on digital health policies in ensuring data integrity, enhancing patient autonomy, and fostering a more resilient healthcare ecosystem. Recent advancements in quantum technologies are also considered as they present both novel opportunities and emerging threats to the future security and design of healthcare blockchain systems. Full article

This paper presents a digital approach for the identification of apple varieties bred in Kazakhstan using deep learning methods and transfer learning. The main objective of this study is to develop and evaluate an algorithm for automatic varietal classification of apples based on color images obtained under controlled conditions. Five representative cultivars were selected as research objects: Aport Alexander, Ainur, Sinap Almaty, Nursat, and Kazakhskij Yubilejnyj. The fruit samples were collected in the pomological garden of the Kazakh Research Institute of Fruit and Vegetable Growing, ensuring representativeness and taking into account the natural variability of the cultivars. Two convolutional neural network (CNN) architectures—GoogLeNet and SqueezeNet—were fine-tuned using transfer learning with different optimization settings. The data processing pipeline included preprocessing, training and validation set formation, and augmentation techniques to improve model generalization. Network performance was assessed using standard evaluation metrics such as accuracy, precision, and recall, complemented by confusion matrix analysis to reveal potential misclassifications. The results demonstrated high recognition efficiency: the classification accuracy exceeded 95% for most cultivars, while the Ainur variety achieved 100% recognition when tested with GoogLeNet. Interestingly, the Nursat variety achieved the best results with SqueezeNet, which highlights the importance of model selection for specific apple types. These findings confirm the applicability of CNN-based deep learning for varietal recognition of Kazakhstan apple cultivars. The novelty of this study lies in applying neural network models to local Kazakhstan apple varieties for the first time, which is of both scientific and practical importance. The practical contribution of the research is the potential integration of the developed method into industrial fruit-sorting systems, thereby increasing productivity, objectivity, and precision in post-harvest processing. The main limitation of this study is the relatively small dataset and the use of controlled laboratory image acquisition conditions. Future research will focus on expanding the dataset, testing the models under real production environments, and exploring more advanced deep learning architectures to further improve recognition performance. Full article

Sprayers for vineyards with solution recovery represent an important innovation, offering several advantages, the most important being the efficient use of pesticides and environmental protection. This paper presents the experimental equipment designed to study the treatment process of grapevine foliage, the applied research methods, and the results of optimizing key technological parameters (hydraulic pressure p of the working solution, speed V of the airflow at the nozzle outlet) and design parameters (surface area S of the central orifice of the diffuser) in different growth stages of grapevines with varying foliar density ρ, the response function being the recovery rate of the working solution. The construction of the SVE 1500 (Experimental model, manufactured at the Institute of Agricultural Technology “Mecagro”, Chisinau, Republic of Moldova) vineyard sprayer with solution recovery is presented, along with test results obtained in field conditions, which demonstrated that the experimental model of our machine ensures a 38% reduction in working solution consumption during the active vegetation phase while maintaining treatment quality in compliance with agrotechnical requirements. The SVE 1500 machine can be towed with a sufficient turning radius for use in modern vineyard plantations. Construction documentation has been developed for the production and delivery of the experimental batch of SVE 1500 machines to agricultural enterprises. Full article