Search Results (16,222)

Background: Digital transformation is reshaping public organizations worldwide, yet in low-resource contexts, its success is constrained by weak infrastructure and governance. In India, programs such as the Ayushman Bharat Digital Mission and the Aspirational Districts Programme rely on meso-level officers who act as key managerial intermediaries, but their organizational challenges remain understudied. Aim: This study examines sub-district health and nutrition officers’ experiences, organizational barriers, and adaptive strategies in implementing digital reforms. Methods: Eight in-depth interviews were conducted with Medical Officers in Charge (MOICs) and Child Development Project Officers (CDPOs) across urban, semi-urban, rural, and flood-prone blocks of Muzaffarpur, Bihar. Data were transcribed, translated, and thematically analyzed using Braun and Clarke’s approach, informed by organizational and technology adoption theories. Results: Officers valued digital tools for transparency and real-time monitoring but faced systemic barriers, including hardware decay, poor connectivity, fragmented platforms, and limited fiscal autonomy. Despite these, they displayed managerial agency through informal infrastructures such as WhatsApp, peer mentoring, and parallel records. COVID-19 accelerated digital use while widening inequities. Conclusions: Meso-level officers are critical enablers of organizational resilience. Their experiences highlight how leadership, governance, and adaptive management shape digital transformation in resource-constrained settings. Full article

High-purity quartz (HPQ) is a critical raw material for advanced technologies including semiconductors, photovoltaic cells, and optical fibers. This study reviews the geological occurrence, beneficiation routes, and strategic significance of HPQ within the European context. Quartz processing follows a sequential flowsheet of comminution, magnetic separation, flotation, acid leaching, and thermal treatment, designed to remove mineral impurities such as Fe, Al, Ti, and mica. The resulting ultra-high-purity quartz (UHPQ) achieves the chemical and physical specifications required for high-tech industries. Quartz, which is the most common mineral on Earth, can be found in a variety of geological locations such as granitic rocks and pegmatites in the Variscan Belt, metamorphic quartzites, hydrothermal veins, and Pleistocene periglacial and aeolian sediments. Case studies of European deposits demonstrate that geological origin directly influences processing requirements, and that tailored beneficiation strategies are essential to unlock viable resources. To our knowledge, this is the first Europe-focused synthesis that links these findings with the EU Critical Raw Materials Act, the work that emphasizes the potential for domestic HPQ development to strengthen European supply chain resilience, reduce dependence on imports, and support the transition to a green and digital economy. Full article

Driven by the rapid evolution of AI technology, compatible management mechanisms have become a systematic project involving the participation of multiple stakeholders. However, constrained by the rigidity and lag of traditional laws, the “one-size-fits-all” regulatory model will exacerbate the vulnerability of the complex system of AI governance, hinder the sustainable evolution of the AI ecosystem that relies on the dynamic balance between innovation and responsibility, and ultimately fall into the dilemma of “chaos when laissez-faire, stagnation when over-regulated”. To address this challenge, this study takes the multi-stakeholder collaborative mechanism co-established by governments, enterprises, and third-party technical audit institutions as its research object and centers on the issue of “strategic fluctuations” caused by key factor disturbances. From the perspective of the full life cycle of technological development, the study integrates the historical compliance performance of stakeholders and develops a nonlinear dynamic reward and punishment mechanism based on Credit Entropy. Through evolutionary game simulation, it further examines this mechanism as a realization path to promote the transformation from passive campaign-style AI supervision to agile governance of AI, which is characterized by rapid response and minimal intervention, thereby laying a foundation for the sustainable development of AI technology that aligns with long-term social well-being, resource efficiency, and inclusive growth. Finally, the study puts forward specific governance suggestions, such as setting access thresholds for third-party institutions and strengthening their independence and professionalism, to ensure that the iterative development of AI makes positive contributions to the sustainability of socio-technical systems. Full article

This study uses provincial panel data from China (2014–2022) to examine the effect of agricultural outward foreign direct investment (OFDI) on agricultural imports. Employing panel regression, mediation effect, and spatial Durbin models, it explores the underlying mechanisms, spatial spillover effects, and regional heterogeneity, while emphasizing the role of OFDI in building sustainable agricultural supply chains. The results show that: (1) OFDI significantly promotes agricultural imports, enhancing the stability and diversity of the domestic supply, supporting food security, and facilitating the sustainable allocation of resources. (2) Mechanism analysis reveals that OFDI affects imports through reverse technology spillovers and improved international relations. (3) Heterogeneity analysis indicates that OFDI exerts stronger influence in major grain-marketing areas, production–marketing balance regions, and provinces along the Belt and Road, compared with grain-producing areas and non-Belt and Road provinces. (4) Spatial analysis based on the 0–1 adjacency matrix reveals that agricultural imports across Chinese provinces exhibit significant positive spatial autocorrelation. Furthermore, OFDI not only directly promotes agricultural imports within a given province but also generates notable positive spatial spillover effects, whereby OFDI in neighboring provinces likewise exert a positive influence on the province’s agricultural imports. To enhance the import effect of agricultural OFDI and stabilize the domestic supply of agricultural products, policy implications suggest that the government should adhere to the agricultural “going global” strategy, enhance enterprises’ capacity to absorb reverse technology spillovers, and explore regionally differentiated pathways for agricultural OFDI, among other policy recommendations. Full article

The emergence of large language models (LLMs) has redefined the potential of artificial intelligence in clinical domains. In this context, retrieval-augmented generation (RAG) systems provide a promising approach to enhance traceability, timeliness, and accuracy in tasks such as biomedical question answering (QA). This article presents a narrative and thematic review of the evolution of these technologies in maternal health, structured across five axes: technical foundations of RAG, advancements in biomedical LLMs, conversational agents in healthcare, clinical validation frameworks, and specific applications in obstetric telehealth. Through a systematic search in scientific databases covering the period from 2022 to 2025, 148 relevant studies were identified. Notable developments include architectures such as BiomedRAG and MedGraphRAG, which integrate semantic retrieval with controlled generation, achieving up to 18% improvement in accuracy compared to pure generative models. The review also highlights domain-specific models like PMC-LLaMA and Med-PaLM 2, while addressing persistent challenges in bias mitigation, hallucination reduction, and clinical validation. In the maternal care context, the review outlines applications in prenatal monitoring, the automatic generation of clinically validated QA pairs, and low-resource deployment using techniques such as QLoRA. The article concludes with a proposed research agenda emphasizing federated evaluation, participatory co-design with patients and healthcare professionals, and the ethical design of adaptable systems for diverse clinical settings. Full article

Sustainable innovation represents both a strategic priority and survival imperative for small- and medium-sized enterprises in resource-constrained economies. While digital transformation offers potential solutions, the synergistic effects of digital leadership, employee behaviors, and emerging technologies remain poorly understood. This study bridges this gap by developing and testing a behavioral-tech leadership framework grounded in the Job Demands-Resources (JD-R) model and Technology Acceptance Model. Analyzing survey data from 400 Iraqi SME employees using partial least squares structural equation modeling, we demonstrate that digital leadership directly enhances sustainable innovation while reducing counterproductive cyberloafing. Crucially, social cyberloafing, when properly managed, emerges as a positive mediator, improving employee well-being and creativity, particularly among mid-career and educated workers. Artificial Intelligence’s integration further amplifies these effects by optimizing operational efficiency and reducing human-resource strain. These findings challenge conventional perspectives by repositioning cyberloafing as a conditional resource within the JD-R framework and provide actionable insights for achieving sustainable innovation even in challenging environments. Practical implications include gender-inclusive digital leadership programs, ethical AI implementation guidelines and restorative cyberloafing policies. The study contributes to United Nations Sustainable Development Goals 8 (decent work), 9 (industry innovation) and 12 (responsible consumption) while highlighting the transformative potential of human-centric digital strategies in resource-constrained contexts. Full article

This study examines the challenges of implementing educational technologies for sustainability education in diverse, real-world settings. While such tools are often designed for universal applications, a multitude of contextual factors, particularly in low-resource scenarios, can impede their full implementation. Through a series of in situ experiments conducted across three educational settings in Greece, Romania, and Italy, the research revealed that field deployment yields critical insights into organisational and technical limitations that are not evident in controlled experiments. The key findings underscore the importance of incorporating a broad range of socio-technical factors into design research protocols. The research also reveals a significant trade-off between the readiness of a tool and the need for its contextualisation, underscoring that effective implementation requires iterative adaptation and tailored training. Ultimately, the work concludes that real-world deployment blurs the distinction between a prototype and a product, necessitating a flexible approach to ensure equitable and prosperous adoption. Full article

Forest and grassland fire behavior prediction is increasingly critical under climate change, as rising fire frequency and intensity threaten ecosystems and human societies worldwide. This paper reviews the status and future development trends of wildfire behavior modeling and prediction technologies. It provides a comprehensive overview of the evolution of models from empirical to physical and then to data-driven approaches, emphasizing the integration of multidisciplinary techniques such as machine learning and deep learning. While conventional physical models offer mechanistic insights, recent advancements in data-driven models have enabled the analysis of big data to uncover intricate nonlinear relationships. We underscore the necessity of integrating multiple models via complementary, weighted fusion and hybrid methods to bolster robustness across diverse situations. Ultimately, we advocate for the creation of intelligent forecast systems that leverage data from space, air and ground sources to provide multifaceted fire behavior predictions in regions and globally. Such systems would more effectively transform fire management from a reactive approach to a proactive strategy, thereby safeguarding global forest carbon sinks and promoting sustainable development in the years to come. By offering forward-looking insights and highlighting the importance of multidisciplinary approaches, this review serves as a valuable resource for researchers, practitioners, and policymakers, supporting informed decision-making and fostering interdisciplinary collaboration. Full article

Background: Three-dimensional (3D) printing technologies such as Stereolithography (SLA) and Digital Light Processing (DLP) are widely used in dental implantology for the fabrication of surgical guides. While both methods offer clinical viability, their comparative accuracy, efficiency, and material consumption remain subjects of debate. Objectives: To compare the dimensional accuracy, printing time, and material consumption of dental surgical guides fabricated using an SLA printer (Formlabs Form 3B) and a DLP printer (NextDent 5100) at various printing orientations. Methods: A standardized surgical guide was designed and printed on both printers across seven orientations (0–90°). Five guides per angle were fabricated per technology (n = 35 per printer), scanned, and compared with the CAD reference to evaluate dimensional accuracy. Printing time and resin consumption were recorded. Statistical analyses included the Shapiro–Wilk test and Mann–Whitney U test (α = 0.05). Results: Within the evaluated printers and resins, SLA-printed guides demonstrated slightly lower Root Mean Square (RMS) values in most regions, especially in occlusal and drill hole surfaces, while DLP guides tended to undersize Optimal accuracy was observed at 45° for SLA and 60° for DLP. Material consumption was lower for the SLA printer compared with the DLP printer, but SLA required longer printing time (90–200 min vs. 25–75 min for DLP). Conclusions: Both technologies produced clinically acceptable guides under the tested conditions. The tested SLA printer tended to offer slightly higher accuracy and material efficiency, whereas the DLP printer achieved shorter printing times, supporting its use in high-throughput workflows. Printing orientation significantly influenced accuracy and resource use. Full article

Multi-unmanned aerial vehicle (UAV)-assisted communication is a critical technology for the low-altitude economy, supporting applications from logistics to emergency response. Semantic communication effectively enhances transmission efficiency and improves the communication performance of multi-UAV-assisted systems. Existing research on multi-UAV semantic communication networks predominantly assumes static ground devices, overlooking computation offloading and resource allocation challenges when ground devices are mobile. This overlooks the critical challenge of dynamically managing computation offloading and resources for mobile users, whose varying channel conditions and semantic compression needs directly impact system performance. To address this gap, this paper proposes a multi-UAV-assisted semantic communication model that novelly integrates user mobility with adaptive semantic compression, formulating a joint optimization problem for computation offloading and resource allocation. The objective is to minimize the maximum task processing latency through the joint optimization of UAV–device association, UAV trajectories, transmission power, task offloading ratios, and semantic compression depth. To solve this problem, we design a MAPPO-APSO algorithm integrating alternating iteration, multi-agent proximal policy optimization (MAPPO), and adaptive particle swarm optimization (APSO). Simulation results demonstrate that the proposed algorithm reduces the maximum task latency and system energy consumption by up to 20.7% and 16.1%, respectively, while maintaining transmission performance and outperforming benchmark approaches. Full article

The growing global demand for secure, transparent, and efficient electoral systems has highlighted the limitations of traditional voting methods, which remain susceptible to voter impersonation, ballot tampering, long queues, logistical challenges, and delayed result processing. To address these issues, this study presents the design and implementation of a Radio Frequency Identification (RFID)-based electronic voting framework that integrates robust voter authentication, encrypted vote processing, and decentralized real-time monitoring. The system is developed as a scalable, cost-effective solution suitable for both urban and resource-constrained environments, especially those with limited infrastructure or inconsistent internet connectivity. It employs RFID-enabled smart voter cards containing encrypted unique identifiers, with each voter authenticated via an RC522 reader that validates their UID against an encrypted whitelist stored locally. Upon successful verification, the voter selects a candidate via a digital interface, and the vote is encrypted using AES-128 before being stored either locally on an SD card or transmitted through GSM to a secure backend. To ensure operability in offline settings, the system supports batch synchronization, where encrypted votes and metadata are uploaded once connectivity is restored. A tamper-proof monitoring mechanism logs each session with device ID, timestamps, and cryptographic checksums to maintain integrity and prevent duplication or external manipulation. Simulated deployments under real-world constraints tested the system’s performance against common threats such as duplicate voting, tag cloning, and data interception. Results demonstrated reduced authentication time, improved voter throughput, and strong resistance to security breaches—validating the system’s resilience and practicality. This work offers a hybrid RFID-based voting framework that bridges the gap between technical feasibility and real-world deployment, contributing a secure, transparent, and credible model for modernizing democratic processes in diverse political and technological landscapes. Full article

The rapid advancement of digital finance profoundly impacts urban development by expanding and deepening financial services for the real economy, with significant ecological and economic implications. This study hypothesizes that digital finance significantly enhances urban green development by simultaneously promoting ecological and economic objectives. To test this hypothesis, we investigate the influence of digital finance on urban green development from theoretical and empirical perspectives. Utilizing panel data from 265 prefecture-level and above cities in China (2011–2023), we comprehensively analyze the impact, underlying mechanisms, and the moderating role of environmental policies. Empirical results confirm our main hypothesis: digital finance significantly enhances urban green development. Robustness checks, including variable substitution, difference-in-differences, and instrumental variable estimations, confirm the results’ stability. Heterogeneity analysis reveals that the positive effect is more pronounced in peripheral cities (vs. core cities), central and western regions (vs. eastern region), and resource-based cities (vs. non-resource-based), highlighting digital finance’s role in mitigating regional development imbalances. Mechanism analysis indicates that green technology innovation is the primary channel through which digital finance fosters green development. Furthermore, the beneficial impact of digital finance is significantly amplified in cities with stringent environmental regulations, underscoring the critical importance of well-designed environmental policy. Overall, the evidence robustly supports the hypothesis that digital finance promotes urban green development. This research provides robust empirical evidence and valuable policy insights for leveraging digital finance to advance sustainable urban development. Full article

With the rapid advancement of mobile edge computing and Internet of Things (IoT) technologies, device-to-device (D2D) cooperative computing has garnered significant attention due to its low latency and high resource utilization efficiency. However, workflow task scheduling in D2D networks poses considerable challenges, such as severe heterogeneity in device resources and complex inter-task dependencies, which may result in low resource utilization and inefficient scheduling, ultimately breaking the computational symmetry—a balanced state of computational resource allocation among terminal devices and load balance across the network. To address these challenges and restore system-level symmetry, a novel workflow task scheduling method tailored for D2D cooperative environments is proposed. First, a Non-dominated Sorting Genetic Algorithm (NSGA) is employed to optimize the allocation of computational resources across terminal devices, maximizing the overall computing capacity while achieving a symmetrical and balanced resource distribution. A scoring mechanism and a normalization strategy are introduced to accurately assess the compatibility between tasks and processors, thereby enhancing resource utilization during scheduling. Subsequently, task priorities are determined based on the calculation of each task’s Shapley value, ensuring that critical tasks are scheduled preferentially. Finally, a hybrid algorithm integrating Q-learning with Asynchronous Advantage Actor–Critic (A3C) is developed to perform precise and adaptive task scheduling, improving system load balancing and execution efficiency. Extensive simulation results demonstrate that the proposed method outperforms state-of-art methods in both energy consumption and response time, with improvements of 26.34% and 29.98%, respectively, underscoring the robustness and superiority of the proposed method. Full article

The dynamic growth in the dependence of numerous industrial sectors, businesses, and critical infrastructure on infocommunication technologies necessitates the enhancement of their resilience to cyberattacks and radio-frequency threats. This article addresses a relevant scientific and applied issue, which is to formulate prospective directions for improving the effectiveness of cybersecurity approaches for infocommunication networks through a comparative analysis and logical synthesis of the state-of-the-art of applied research on cyber threats to the information security of mobile and satellite networks, including those related to the rapid development of quantum computing technologies. The article presents results on the systematisation of cyberattacks at the physical, signalling and cryptographic levels, as well as threats to cryptographic protocols and authentication systems. Particular attention is given to the prospects for implementing post-quantum cryptography, hybrid cryptographic models and the integration of threat detection mechanisms based on machine learning and artificial intelligence algorithms. The article proposes a classification of current threats according to architectural levels, analyses typical protocol vulnerabilities in next-generation mobile networks and satellite communications, and identifies key research gaps in existing cybersecurity approaches. Based on a critical analysis of scientific and applied literature, this article identifies key areas for future research. These include developing lightweight cryptographic algorithms, standardising post-quantum cryptographic models, creating adaptive cybersecurity frameworks and optimising protection mechanisms for resource-constrained devices within information and digital networks. Full article

The topic proposal concerns the axes of climate operation and modification, the consequences and/or benefits of the flow of the economy, as well as the risks to social security, amidst the evolution of human interventions, which the Anthropocene highlights. Atmospheric data demonstrates the interaction of gaseous pollutants and aerosols, with the contribution of different emission and pollution sources to its chemical composition. At the same time, satellite remote sensing of precipitation and the water cycle reveal an imbalance in components and effects, in an environment of rapid rates of commercial production and human mobility in the developed world. How does mobility prevent the full observation and modeling of the elements involved (in atmospheric and hydrological data)? What is the role of multi-sensor technologies for detecting gases and what are their applications in decontamination? With sources from bibliographic reviews, data were collected from the detection of point sources of gases and dynamic analyses of the extent of the water surface, in order to highlight the descriptive characteristics of the meteorological phenomena and their activity. The scientific approach to analyzing the individual data is based on the techno-scientific Actor-Network Theory, in order to test their connection and contribution to the overall problematic result. The aim of this study is to build an interdisciplinary analysis with documentation of vulnerabilities in the expression of weather phenomena, of the present geological time. The ambition of the study is to propose principles of regulation and precaution, related to the sustainable development of geo-resources and ways to reduce vulnerability. Full article