Search Results (1,831)

The article provides a comprehensive overview of the role of hydrogen as a key vector in the decarbonization of the global transport sector. The study situates hydrogen within the broader context of energy transition and climate neutrality targets, emphasizing its potential to replace fossil fuels in road, rail, maritime, and aviation applications. The analysis integrates a review of current technological, infrastructural, and policy developments, covering both combustion-based and fuel-cell hydrogen propulsion systems. Quantitative and qualitative data were assessed from international reports, scientific publications, and ongoing industrial projects to evaluate performance, efficiency, safety, and cost parameters such as Levelized Cost of Hydrogen (LCOH) and Total Cost of Ownership (TCO). The results indicate that while hydrogen remains economically challenging, technological progress in electrolysis, fuel cells, and refueling infrastructure significantly improves its competitiveness, particularly in heavy-duty and long-range transport. The paper highlights the critical role of international strategies, including the European Hydrogen Strategy and Fit for 55 package, in driving market adoption and regulatory alignment. The conclusions suggest that by 2050, hydrogen could contribute up to one-quarter of total transport energy demand, positioning it as a cornerstone of sustainable mobility and a bridge toward a fully decarbonized transport ecosystem. Full article

Sargassum filipendula is a widely distributed, edible brown alga that possesses a rich nutritional profile. Several studies have demonstrated that the components/extracts of S. filipendula (SFE) possess diverse pharmacological potential against both infectious and non-infectious diseases. These include antibacterial and antifungal properties, as well as antioxidant, anti-aging, anti-osteoporosis, antiviral, antiprotozoal, and immunomodulatory effects. Furthermore, SFE has shown significant anticancer activity across various malignant cell lines. The unique phytochemical profile of this species, characterized by the presence of sulfated polysaccharides (primarily fucoidan), carotenoids, phenols, glycolipids, and phlorotannins, serves as the foundation for these wide-ranging pharmacological activities. Studies have demonstrated that SFE can modulate key molecular targets, such as glycogen synthase kinase-3 beta, and activate the mitochondrial-dependent apoptosis pathway, providing a robust mechanistic basis for the observed pharmacological activities. Recent evaluations of nutritional parameters and techno-functional properties confirm the rich nutritional profile of S. filipendula, supporting its application in a diverse range of food products. Despite its diverse bioactive phytochemicals and broad efficacy against infectious and non-infectious diseases, research on S. filipendula remains largely restricted to in vitro preclinical studies. The lack of a comprehensive compilation of its pharmacological activities, phytochemical profiles, and molecular targets hinders its development as a therapeutic agent. This review aims to bridge this gap by compiling the existing knowledge, identifying research deficiencies, particularly the lack of in vivo data and safety assessments for high-dose therapeutic applications, while proposing suggestions for transitioning S. filipendula into a viable therapeutic or functional supplement. Full article

Background: Minimally invasive interventional radiology (IR) offers effective, uterus-preserving treatments for several gynecologic and obstetric conditions such as uterine fibroids, adenomyosis and postpartum hemorrhage. Despite their efficacy, these methods remain underused, partly to limited awareness among clinicians and patients. Large language models (LLMs) may help bridge this gap by providing accessible, reliable information. Objective: To evaluate how current LLMs address knowledge gaps and promote awareness of minimally invasive IR methods in gynecology and obstetrics. Methods: A structured ten-question instrument was used to query three publicly available LLMs (OpenEvidence, ChatGPT, and Google Gemini). Responses were analyzed for accuracy, completeness, safety considerations, and patient-centered communication. Results: All three models accurately identified a range of medical, minimally invasive, and surgical treatments for uterine fibroids, adenomyosis, and postpartum hemorrhage, with OpenEvidence and ChatGPT providing more detailed and clinically nuanced responses. OpenEvidence achieved the highest scores overall, closely followed by ChatGPT, while Google Gemini scored lower, particularly in completeness and patient-centered communication. In more complex scenarios, performance differences became more pronounced, with OpenEvidence again leading, ChatGPT performing strongly, and Google Gemini lagging behind. Overall, OpenEvidence and ChatGPT demonstrated higher accuracy, completeness, and safety considerations, whereas Google Gemini showed comparatively weaker and less consistent performance. Conclusions: LLMs may endorse the promotion of minimally invasive IR methods in gynecology and obstetrics, but their outputs vary considerably in quality. Ongoing refinement and integration of evidence-based sources are essential before routine use in clinical practice. Therefore, effective collaboration between artificial intelligence (AI) developers and medical professionals is essential to harness this technology’s full potential. Full article

Concurrent/interleaved transcranial magnetic stimulation combined with functional MRI (TMS–fMRI) enables causal perturbation of targeted cortical regions while measuring whole-brain MR-based responses during stimulation. This perspective argues that the main translational value of concurrent/interleaved TMS–fMRI lies in operationalizing target engagement and network-level propagation as measurable endpoints, bridging stimulation “dose” to clinically meaningful effects. Rather than proposing a validated gold-standard protocol, we frame concurrent/interleaved TMS–fMRI as a measurement-driven translational approach in which MRI-informed targeting and MR-based readouts can be integrated to quantify target engagement under clearly specified methodological and quality-control conditions. This perspective specifically aims to make explicit an intermediate verification step that remains only partially formalized in current clinical neuromodulation workflows. We propose that MRI-based neuronavigation should move beyond template coordinates toward individualized anatomical and network-informed targeting, with the aim of improving precision, reproducibility, and safety. Building on the field’s evolution from technical feasibility to emerging clinical applications, we outline a staged framework from feasibility to biomarker potential, summarize representative protocol archetypes, and provide pragmatic recommendations for reporting and study design to improve comparability. This framework is intended to guide future concurrent/interleaved TMS–fMRI studies toward biomarker-ready designs and more clinically informative network neuromodulation. We further distinguish offline MRI-informed targeting from potential future real-time or closed-loop implementations, and we emphasize that current biomarker claims should remain proportional to the still heterogeneous evidence base. Full article

Modern performance-based bridge design seeks to control damage in specific failure modes in order to balance safety and economy, particularly in high-seismic regions where inelastic and ductile deformation is expected to occur, both in the structure and soil, allowing potential reduction in seismic demand through fuse elements. In short-span bridges, abutments strongly influence longitudinal response, whereas transverse performance depends largely on seismic components such as shear keys and other energy-dissipation devices. Thus, performance assessment requires explicit representation of their hysteretic behavior. This study presents a numerical evaluation of the damping provided by common elements in typical bridge systems, using as reference damage observations from bridges affected by recent interface earthquakes in Mexico. Three-dimensional finite-difference models were developed, and nonlinear response-history analyses were performed to simulate ductile behavior and energy dissipation. The Sig3 hysteretic model available in FLAC^3D was used for abutments and foundation soils, while shear keys were represented as nonlinear springs. The results established a relationship between plastic deformation and energy dissipation, showing that incorporating the hysteretic behavior of both soil and sacrificial structural components enhanced the seismic bridge performance assessment, and led to more reliable and cost-efficient designs when inelastic deformation capacity was explicitly included in the numerical simulations. Full article

Autonomous task assignment in multi-unmanned aerial vehicle (UAV) systems operating in dynamic and safety-critical airspace environments is highly challenging due to complex spatial interactions and rapidly changing relative geometries. This paper proposes a hierarchical decision-making framework that bridges individual maneuvering behaviors with cooperative task allocation in multi-agent aerial systems. First, a high-fidelity single-agent maneuver model is learned using a physics-consistent simulation environment, where spatial advantage is evaluated based on relative distance and angular relationships within a kinematically feasible interaction zone (KIZ). Subsequently, a Geometry-Aware Graph Attention Network (GA-GAT) is developed to address scalable multi-agent assignment problems. Unlike conventional approaches that rely on flat feature representations, the proposed method explicitly incorporates kinematic feasibility constraints into the attention mechanism via a novel gating module, enabling efficient relational reasoning under dynamic conditions. The proposed framework is applicable to a range of civilian and safety-oriented scenarios, including UAV swarm coordination, emergency response monitoring, infrastructure inspection, and autonomous airspace management. Simulation results demonstrate that the GA-GAT-based approach significantly outperforms heuristic baselines in terms of coordination efficiency and overall system performance in complex multi-agent environments. This study highlights that decoupling maneuver-level control from high-level coordination provides a scalable and computationally efficient solution for real-time multi-UAV decision-making in safety-critical applications. The proposed framework is designed for general multi-agent coordination problems in civilian aerial applications. Full article

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance. Full article

This study addresses the question of how augmented reality (AR) technologies can be designed and integrated into maritime navigation systems to meet the needs of young navigators within contemporary socio-technical bridge environments. The article is based on a qualitative, literature-based research methodology involving a structured analysis and synthesis of peer-reviewed journal articles and conference proceedings related to AR interfaces, human performance, decision support, and maritime training. The reviewed studies indicate that AR can enhance perceptual and situational awareness by overlaying navigational information directly into the navigator’s field of view, thereby reducing head-down time, improving spatial alignment of information, and supporting performance in low-visibility and high-traffic conditions. The literature also shows that AR-enabled visualizations and shared displays can support individual and team-based decision-making by facilitating real-time, context-aware information exchange on the ship’s bridge. Safety-related benefits are identified as indirect outcomes of improved perception and cognitive support rather than as isolated technological effects. Simultaneously, the findings highlight that these benefits depend strongly on human-centered interface design and appropriate training. The study concludes that AR has significant potential to enhance maritime navigation for young navigators when integrated as part of a balanced socio-technical system combining technology, human factors, and structured education. Full article

As modern society grows increasingly complex, access to essential services such as healthcare, legal aid, tailored education, and psychological support remains heavily gated by socio-economic, neurological, and systemic barriers. This paper explores the transformative potential of Large Language Models (LLMs) and Generative Artificial Intelligence not merely as industrial productivity enhancers, but as vital “social scaffolds” capable of fostering a more inclusive society. Crucially, we propose a paradigm shift in the concept of Ethical Computing—moving from a passive defensive framework of non-maleficence (“do no harm”) to an active mandate of beneficence, where AI systems are explicitly developed to serve marginalized and un(der)served populations. Through this expanded ethical lens, we systematically analyze the democratizing impact of AI across four primary axes of inclusivity: socio-economic (providing zero-cost medical triage and legal translation for undocumented populations), neurospicy (acting as a non-judgmental communicative bridge for individuals with Autism Spectrum Disorder), pedagogical (delivering hyper-personalized executive function support for Special Educational Needs), and psychological (serving as an accessible, first-level triage system for mental health crises). By framing LLMs as a modern social safety net, we outline a clear trajectory for future research, advocating for an “ethical-by-design” development paradigm that explicitly prioritizes equity, accessibility, and the active dismantling of historical barriers for the digitally and socially disenfranchised. Full article

Aviation safety depends critically on pilots’ mental and cognitive states, particularly in high-stakes and complex operational environments where human errors cause most safety events today. This paper reviews current advances in real-time monitoring of commercial pilots’ cognitive states through physiological and neurophysiological signals and identifies methods applicable to enhance aviation safety and efficiency. In an increasingly complex and congested system, it is essential to investigate the relationships between pilots’ mental workload, stress, startle effect, and physiological parameters to highlight cognitive overload or deficiencies in real time. This systematic literature review was conducted according to PRISMA 2020 guidelines, using Google Scholar, Scopus, and PubMed, and identified 26 eligible studies. A targeted backward citation search screened 17 additional records, and two studies were added to the initial set. Twenty-eight records were therefore included and the review highlights a range of biometric indicators of pilots’ mental states with varying degrees of validity and operational applicability. Collectively, these studies offer a clear overview of state-of-the-art approaches, while also evidencing constraints related to intrusiveness and real-world feasibility. Physiological monitoring holds strong promise for enhancing pilot performance and safety by detecting early signs of overload and stress. However, its integration into operational aviation remains limited. Future research should prioritise longitudinal, in situ evaluations, multimodal data fusion, and pilot-centred design to ensure practical applicability, non-intrusiveness, and regulatory compliance, ultimately bridging the gap between academic research and cockpit reality. Full article

In an increasingly volatile global economy, countries are scaling up financial investment in innovation to sustain their competitive advantages. Financial institutions, specifically banks, are facing the challenge of aligning more radical financial policies for innovation with their own business sustainability, and innovation has thus become a key issue confronting banking. Existing studies mostly explain financial innovation from an external incentive perspective, leaving theoretical divergences and empirical paradoxes regarding core innovation dynamics unclear. Adopting a novel perspective of constraint-induced innovation theory, which challenges the dominant external pull view in the existing literature, this paper takes China’s tech–finance integration pilots as a quasi-natural experiment and uses a double/debiased machine learning model to identify causal effects by using panel data from 142 Chinese regional banks from 2008 to 2024. We find that tech–finance integration policies induce regional bank financial innovation through the dual channels of profitability and safety constraints. Banks must innovate to cope with the pressure exerted by new policies, and constraint-induced innovation is the main dynamic mechanism. In addition, financial innovation amplifies the policy’s impact on banks’ business sustainability. This study bridges the gap between existing theory and empirical evidence, extending the boundaries of financial innovation dynamic research. Full article

Vulnerabilities in conventional agri-food supply chains (CAFSCs) necessitate a shift toward resilient, localized production models. Within the Agri-Food 4.0 landscape, urban Controlled-Environment Hydroponic Agriculture (CEHA) systems address these challenges by shortening supply chains and mitigating climate-induced breakdowns. However, structurally aligning Triple Bottom Line (TBL)-oriented stakeholder needs with complex technical specifications remains a critical challenge in sustainable CEHA system design. To address this challenge, the present study proposes a novel framework integrating the Full Consistency Method (FUCOM) and Quality Function Deployment (QFD) within a Finite Interval Type-2 (FIT2) Gaussian fuzzy environment. This approach systematically translates TBL-oriented priorities into precise engineering specifications, mapping 17 stakeholder needs (SNs) to 30 technical design requirements (TDRs) while capturing linguistic uncertainty and hesitation. The findings reveal a clear strategic focus on environmental and social sustainability. Specifically, high product quality, food safety and traceability, consumer acceptance, and minimization of environmental impacts emerge as the primary drivers of CEHA adoption. The QFD translation identifies scalable IoT infrastructure, sensor maintenance and calibration, and AI-enabled decision support as the most critical TDRs. The framework’s reliability and structural robustness were rigorously validated through comprehensive analyses, including Kendall’s W test to confirm expert consensus, alongside a Leave-One-Out (LOO) approach, weight perturbations, and a structural evaluation of TDR intercorrelations. These findings provide a scientifically grounded roadmap for designing sustainable, intelligent urban agricultural systems. Ultimately, this framework offers actionable managerial implications for agribusiness stakeholders to bridge strategic TBL-oriented goals with practical engineering, significantly enhancing Agri-Food 4.0 supply chain resilience. Full article

Background/Objectives: Lung cancer (LC) remains the leading cause of cancer-related death worldwide, despite advances in systemic and targeted therapies. A mechanism of survival of tumor cells is metabolic reprogramming, characterized by increased glucose uptake, aerobic glycolysis, and alterations in mitochondrial function. These adaptations seem to support tumor growth, immune evasion, and therapeutic resistance. In parallel, supportive care and specifically nutritional interventions have become essential components of modern oncology. The interplay between metabolic reprogramming and targeted nutritional strategies represents a promising area of investigation that bridges tumor biology with supportive care, aiming to enhance both therapeutic efficacy and patient quality of life. Methods: This narrative review explores the biological and pathophysiological rationale for the ketogenic diet (KD) as a possible complementary intervention in LC management and summarizes the published preclinical and clinical data supporting this rationale. Results: We discuss key aspects of tumor metabolism, including the Warburg effect, glucose dependency, oxidative stress regulation, fatty acid metabolism, lactate cycling and tumor microenvironment interactions, with particular emphasis on how carbohydrate restriction and ketosis may exacerbate mitochondrial dysfunction in cancer cells and modulate inflammatory pathways. Furthermore, we summarize available preclinical and clinical evidence evaluating the KD in oncology and, more specifically, in LC, focusing on feasibility, safety, metabolic effects, and potential synergy with chemotherapy, radiotherapy, and immunotherapy. Conclusions: While preclinical models suggest enhanced treatment efficacy, clinical data remain limited and heterogeneous, with patient adherence representing a major challenge. Further well-designed longitudinal studies are required to clarify the therapeutic role of the ketogenic diet in lung cancer. Full article

Soil-borne diseases pose a severe threat to global agricultural production and food security. Traditional chemical control methods face significant challenges, including environmental pressure, pathogen resistance, and food safety concerns. The rhizosphere microbial community, often termed the plant’s ‘second genome’, plays a pivotal role in maintaining plant health and defending against pathogen invasion. Recent advances in multi-omics technologies, synthetic microbial communities (SynComs) construction, and rhizosphere metabolomics have significantly advanced our understanding of the mechanisms by which rhizosphere microbiomes suppress soil-borne diseases. This review systematically summarizes the following: 1. key drivers of rhizosphere microbial community assembly, particularly plant “cry for help” signaling; 2. core beneficial microbial taxa and their disease-suppressive mechanisms; 3. the critical role of microbial interaction networks; 4. microbiome-based management strategies and their application progress; and 5. current challenges and future research directions. Compared with previous reviews that separately discussed rhizosphere microbiota, disease-suppressive soils, synthetic microbial communities (SynComs), or prebiotics, this review uniquely integrates multiple levels of regulation, from plant genetic determinants (‘M genes’) and root exudate-mediated ‘crying for help’ to microbiome engineering (SynComs and prebiotics) and cross-kingdom interactions (bacteria–fungi–protists–phages). A central conceptual axis of ‘M genes → microbiome engineering → breeding’ is proposed, bridging plant genetics, microbial ecology, and crop improvement for durable disease suppression. Ultimately, this work aims to provide a theoretical foundation for developing efficient and sustainable green control technologies against soil-borne diseases. Full article

Purpose: To evaluate the safety, technical feasibility, and clinical outcomes of a strict two-step protocol—CT/fluoroscopic-guided thermal ablation followed by Balloon-Assisted Acetabuloplasty (BAA)—for the treatment of painful acetabular metastases, utilized as an immediate mechanical stabilization bridge prior to radiotherapy. Materials and Methods: A retrospective study was conducted on 16 consecutive patients treated for severe mechanical pain (VAS ≥ 6) and impaired mobility due to osteolytic acetabular metastases. The physiological rationale mandated a strict procedural sequence: (1) preliminary thermal devitalization using radiofrequency or microwave ablation to reduce tumor pressure and vascularity, followed subsequently by (2) balloon-assisted cavity compaction and polymethylmethacrylate (PMMA) cement injection. Clinical outcomes included VAS for pain and the Functional Mobility Scale (FMS) assessed before treatment and up to 6 months post-procedure. Results: Technical success was 100% with a mean procedural time of 58 ± 14 min. No major complications occurred. At a mean follow-up of 8.2 months, all 16 patients were alive. The procedure yielded dramatic acute pain relief: mean baseline VAS dropped from 7.4 ± 0.8 to 2.3 ± 1.0 at 1 week, and to 0.9 ± 0.9 at 1 month (p < 0.001), remaining stable at 6 months. Functional mobility was rapidly restored, with mean FMS improving from 2.9 ± 0.7 pre-procedure to 1.1 ± 0.3 at 1 month (p < 0.001), allowing independent ambulation in 87.5% of patients. Conclusion: The strict “ablation-first” BAA strategy is safe and highly effective. It abolishes load-bearing pain and restores biomechanical stability immediately, allowing previously immobilized patients to rapidly regain independent ambulation and seamlessly transition to necessary consolidative radiotherapy. Full article