Search Results (7,881)

Considering the escalating international geopolitical tensions and the ensuing great power maneuvers, China’s oil supply faced unprecedented threats. To safeguard against these risks and harness domestic resources more effectively, addressing the stability of refined oil supply had become an urgent imperative. The complex network theory is integrated into oil product delivery logistics, accounting for transportation volumes, distances, and node importance. Through simulation, we evaluated each scheme’s efficacy using a case study from a province in northwest China. The results demonstrate notable improvements in network robustness across all four strategies. The key node focuses on protection measures emerged as the most effective, followed by the oil depot resource optimization strategy and the network topology optimization strategy, in descending order. By mitigating the risks stemming from international uncertainties, our strategies ensured the timely supply of refined oil products, thereby upholding the stable functioning of the national economy. Full article

This study investigates how strategic foresight can enhance FinTech governance and policy resilience in emerging economies, using Kuwait as an illustrative case. It aims to identify which foresight interventions should be prioritized across alternative futures to strengthen innovation, security, and institutional adaptability within the digital finance ecosystem. A scenario-based Multi-Criteria Decision Analysis (MCDA) framework is applied, combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert evaluations were conducted to assess five foresight interventions against eight policy and performance criteria across three plausible scenarios: Optimistic Growth, Status Quo, and Crisis and Contraction. Sensitivity analyses were performed to validate the stability of intervention rankings. The results reveal distinct priorities under each scenario: SME-oriented digital finance platforms and talent development dominate under growth and stability, while cybersecurity investment becomes paramount during crisis conditions. Regulatory fast-tracking maintains a consistent, moderate influence across all contexts. These outcomes underscore the need for adaptive, context-sensitive policy design that accommodates uncertainty. The framework provides policymakers with a structured approach to align FinTech strategies with long-term national visions such as Kuwait’s Vision 2035, while offering transferable insights for other emerging economies. The study’s originality lies in integrating strategic foresight and MCDA for FinTech governance—a methodological and practical contribution to foresight-informed policymaking. Full article

Although carbon filters (CF) can exhibit limited adsorption/selectivity for certain emerging pollutants and operating conditions, incorporating carbon–metal-oxide composites provides a platform to study how surface chemistry, charge distribution and oxide dispersion influence adsorption behavior. This study investigates the incorporation of metal oxides (Fe₃O₄, TiO₂ and CaO) into a commercial carbon filter via mechanical milling, focusing on fundamental changes in surface properties and methylene blue (MB) adsorption mechanisms. The synthesized oxides were characterized by X-ray diffraction and scanning electron microscopy, confirming crystalline structures with crystalline sizes between 11 and 23 nm. Composite filters with varying oxide contents (10–30 wt%) were evaluated for point of zero charge (PZC), surface charge distribution and methylene blue (MB) adsorption. The kinetic experiments were adjusted to pseudo-second order (PSO). Although the maximum adsorption capacity (2.75 mg·g⁻¹ for CaO-modified filters) is lower than commercially activated carbons, this work clarifies how oxide type and dispersion control adsorption performance and interaction mechanisms. Langmuir and Freundlich models revealed monolayer adsorption with favorable dye-surface interactions. These models provide key insights into the role of oxide type and pH in the dye removal process. Full article

Time delays are intrinsic to mitotic regulation, particularly within the spindle assembly checkpoint (SAC) and the spindle position checkpoint (SPOC). These delays emerge from multi-step protein activation, molecular transport, force-dependent conformational transitions, and spatial redistribution of regulatory complexes. They span seconds to minutes and strongly influence checkpoint activation, maintenance, and silencing. Increasing evidence shows that such delayed processes shape mitotic timing, checkpoint robustness, and cell-fate decisions. While classical ordinary differential equation (ODE) models assume instantaneous biochemical responses, delay differential equations (DDEs) provide a natural framework for representing these finite timescales by explicitly incorporating system history. Recent DDE-based studies have revealed how delayed signaling contributes to bistability, oscillatory responses, prolonged mitotic arrest, and variability in checkpoint outputs. This review summarizes the biological origins of delays in SAC and SPOC, including Mad2 activation, MCC assembly and turnover, APC/C reactivation, tension maturation at kinetochores, and Bfa1–Bub2 regulation of Tem1. The article further discusses how mechanistic models with explicit delays improve our understanding of SAC–SPOC ordering, error-correction dynamics, and mitotic exit control. Finally, open challenges and future directions are outlined for integrative delay-aware modeling that unifies biochemical, mechanical, and spatial processes to better explain checkpoint function and chromosomal stability. Full article

Symmetry and asymmetry are foundational design principles in artificial intelligence, defining the balance between invariance and adaptability in multimodal learning systems. In audio-visual speaker verification, where speech and lip-motion features are jointly modeled to determine whether two utterances belong to the same individual, these principles govern both fairness and discriminative power. In this work, we analyze how symmetry and asymmetry emerge within a gated-fusion architecture that integrates Time-Delay Neural Networks and Bidirectional Long Short-Term Memory encoders for speech, ResNet-based visual lip encoders, and a shared Conformer-based temporal backbone. Structural symmetry is preserved through weight-sharing across paired utterances and symmetric cosine-based scoring, ensuring verification consistency regardless of input order. In contrast, asymmetry is intentionally introduced through modality-dependent temporal encoding, multi-head attention pooling, and a learnable gating mechanism that dynamically re-weights the contribution of audio and visual streams at each timestep. This controlled asymmetry allows the model to rely on visual cues when speech is noisy, and conversely on speech when lip visibility is degraded, yielding adaptive robustness under cross-modal degradation. Experimental results demonstrate that combining symmetric embedding space design with adaptive asymmetric fusion significantly improves generalization, reducing Equal Error Rate (EER) to 3.419% on VoxCeleb-2 test dataset without sacrificing interpretability. The findings show that symmetry ensures stable and fair decision-making, while learnable asymmetry enables modality awareness together forming a principled foundation for next-generation audio-visual speaker verification systems. Full article

The importance of GIS models to monitor the spread of infectious diseases and support emergency response has been underlined by a large body of literature and strengthened by the COVID-19 pandemic to identify possible solutions able to recognise spatio-temporal clusters and patterns, evaluate the presence of acceleration factors and define specific actions. In the field of applied research on health geography and geography of safety, this work briefly displays the main aims of the project “Integrated revealing GIS models to monitor, understand and foresee the spread of diseases and support emergency response” and shows some illustrative applications. The basic assumption of the project is to test revealing models regarding key objectives of social utility, and one of its main aims is to elaborate GIS applications able to understand the spread of COVID-19, relating the geocalisations of the cases with specific variables. In order to provide targeted evidence able to better highlight local differences, a number of elaborations derived from (Arc)GIS models and based on data regarding COVID-19 according to sex, age and healthcare facilities in the Rome municipality (Italy) are presented and contextualised as examples, also replicable for precision preparedness. Full article

The integration of Educational Robotics (ER) into teaching and learning has increased in recent years. While several studies have described the design and use of simulators, no content analysis has systematically documented the features and contribution of simulators in ER. Therefore, a systematic review of eight databases was conducted. From 1200 retrieved articles, 89 met the inclusion criteria. The emerged articles comprised two distinct categories: (a) simulator framework studies—describing tools or platforms (54 articles)—and (b) simulator-based intervention studies—reporting empirical implementations (35 articles). Each article was analyzed by two independent researchers who recorded the design features of simulators, their domain of use, the educational level at which the implementation occurred, intervention characteristics, teacher involvement in the studies, and the skills they tried to promote. Findings showed that simulators were primarily designed for STEM education. Most operated in coding environments, used 3D visualization, and were freely available. Interventions were more frequent at the tertiary level, with fewer at primary and secondary levels. Many empirical studies that used simulators employed small samples in short durations, limiting the generalizability of the findings. Simulator-based practices were mainly linked to programming, problem solving, and computational thinking. Higher-order competences such as collaboration or metacognition were rarely addressed. Finally, most intervention studies reported either no or only moderate teacher involvement. This article aims to be a basis for researchers who study ER implementation and simultaneously serve as a basis for choosing, designing, and adopting ER simulators as teaching tools. Full article

Metalloporphyrin-based covalent organic frameworks (MPor-COFs) are emerging porous crystalline materials that combine the optoelectronic properties of metalloporphyrins with the highly ordered structure of COFs. Such a combination not only extends the light absorption spectrum of COFs by incorporating porphyrins but also improves the separation and transport capabilities of photo-generated electrons and holes by leveraging the structural advantages of organic frameworks. At the same time, the metal ions embedded in the porphyrin ring provide abundant active sites and optimize charge transfer channels, showing particular advantages in photocatalysis. The molecular design, construction, and photocatalytic application of MPor-COFs were reviewed in this paper. The intrinsic relationship among the structure, optoelectronic properties, and specific photocatalytic application received special attention. First, the role of the metal center in regulating the electronic structure and photophysical property of porphyrin monomers was introduced, as well as the impact of bond type on framework stability and charge transport efficiency. Then, the synthesis strategies for MPor-COFs were summarized. Finally, the applications of these materials in photocatalysis were critically reviewed, and their prospects and challenges in energy conversion and environmental remediation were also discussed. Full article

The worldwide population is anticipated to reach 10.12 billion by the year 2100, thereby amplifying the necessity for sustainable agricultural methodologies to secure food availability while reducing ecological consequences. Conventional synthetic pesticides, while capable of increasing crop yields by as much as 50%, present considerable hazards such as toxicity, the emergence of resistance, and environmental pollution. This review examines biopesticides, originating from microbial (e.g., Bacillus thuringiensis, Trichoderma spp.), plant, or animal sources, as environmentally sustainable alternatives which address pest control through mechanisms including antibiosis, hyperparasitism, and competition. Biopesticides provide advantages such as biodegradability, minimal toxicity to non-target organisms, and a lower likelihood of resistance development. The global market for biopesticides is projected to be valued between USD 8 and 10 billion by 2025, accounting for 3–4% of the overall pesticide sector, and is expected to grow at a compound annual growth rate (CAGR) of 12–16%. To mitigate production costs, agro-industrial byproducts such as rice husk and starch wastewater can be utilized as economical substrates in both solid-state and submerged fermentation processes, which may lead to a reduction in expenses ranging from 35% to 59%. Strategies for process intensification, such as the implementation of intensified bioreactors, continuous cultivation methods, and artificial intelligence (AI)-driven monitoring systems, significantly improve the upstream stages (including strain development and fermentation), downstream processes (such as purification and drying), and formulation phases. These advancements result in enhanced productivity, reduced energy consumption, and greater product stability. Patent activity, exemplified by 2371 documents from 1982 to 2021, highlights advancements in formulations and microbial strains. The integration of circular economy principles in biopesticide production through process intensification enhances the safety, quality, and sustainability of food systems. Projections suggest that by the 2040s to 2050s, biopesticides may achieve market parity with synthetic alternatives. Obstacles encompass the alignment of regulations and the ability to scale in order to completely achieve these benefits. Full article

Background: Tick-borne encephalitis (TBE) virus is transmitted to humans via tick bites and occasionally via the consumption of unpasteurized milk products. According to the literature, the most important driver of TBE emergence and increase in incidence in humans is changes in human behaviour/activities. Method and principal findings: To compensate for the lack of data, expert opinions were gathered to identify the risk factors for exposure to tick bites linked to twenty-eight human activities (professional or recreational) in forests and to target prevention messages at the populations most at risk. Opinions were elicited from a total of twenty-five European experts. Seven criteria were included in the analysis for each activity: frequency, seasonality, duration of exposure, distance covered, degree of contact with vegetation, speed and average level of protection against tick bites. The activities considered to be the most at risk of exposure to tick bites are, in descending order: three occupational activities (forest monitoring activities, forestry and wood industry activities and scientific and/or analytical activities), five recreational activities and one hunting activity (mushroom picking, spending the night in the forest, hunting, naturalist activities, orienteering, and berry or fruit picking). Conclusions and significance: Prevention messages regarding tick bites could be targeted at people who engage in activities considered in this analysis to be at highest risk of exposure to tick bites. Full article

Chromium (Cr) is a widespread heavy metal contaminant in aquatic environments, posing serious risks to phytoplankton due to its persistence, biotoxicity, and mutagenic potential. Microalgae have emerged as promising biological agents for Cr remediation. In this study, the Cr removal potential of living Dunaliella salina (D. salina) was evaluated by examining the toxic effects and adsorption behavior of trivalent Cr(III) and hexavalent Cr(VI) through short-term exposure experiments. This study elucidated the mechanisms by which Cr disrupts key photosynthetic metabolic pathways, quantified the short-term toxicity thresholds of Cr(III) and Cr(VI) to D. salina, and characterized the saturation adsorption capacity and adsorption kinetics of Cr on algal cells. The results showed that Cr(VI) at concentrations of 5–20 mg/L inhibited the growth of D. salina in a dose-dependent manner throughout the culture period, with inhibition rates ranging from 22.8% to 70.9%. After 72 h of exposure, the maximum growth inhibition rates caused by Cr(III) and Cr(VI) reached 42.5% and 52%, respectively. Interestingly, low concentrations of Cr(VI) (0.1–1 mg/L) slightly enhanced the growth of D. salina. However, Cr(VI) exhibited stronger biotoxicity than Cr(III). Exposure to both Cr species significantly reduced the levels of chlorophyll a (Chl a), chlorophyll b (Chl b), and carotenoids (Car), resulting in damage to the photosynthetic reaction centers and suppression of the photosynthetic electron transport system. The adsorption of Cr(VI) by D. salina followed a pseudo-second-order kinetic model, with a maximum adsorption capacity of 38.09 mg/g. The process was primarily governed by monolayer chemisorption. These findings elucidate the toxic mechanisms of Cr in D. salina and highlight its potential application as an effective bioremediation agent for heavy metal pollution, particularly Cr(VI), in marine environments. Full article

The recognition of targets in ship remote sensing images is crucial for ship collision avoidance, military reconnaissance, and emergency rescue. However, climatic factors such as clouds and fog can obscure and blur remote sensing image targets, leading to missed and false detections in target detection. Therefore, it is necessary to study ship remote sensing target detection that considers the impact of cloud and fog occlusion. Due to the large scale and vast amount of information in remote sensing images, in order to achieve high-precision target detection based on limited resource platforms, a comparison of the detection accuracy and parameter quantity of the YOLO series algorithms was first conducted. Based on the analysis results, the YOLOv8s network model with the least number of parameters while ensuring detection accuracy was selected for lightweight network model improvement. The FasterNet was utilized to replace the backbone feature extraction network of YOLOv8s, and the detection accuracy and lightweight level of the resulting FN-YOLOv8s network model were both improved. Furthermore, structural improvements were made to the AOD-Net dehazing network. By introducing a smoothness loss function, the halo artifacts often generated during the image dehazing process were addressed. Meanwhile, by integrating the atmospheric light value and transmittance, the accumulation error was effectively reduced, significantly enhancing the dehazing effect of remote sensing images. Full article

Microtubules are cylindrical protein polymers that organize the cytoskeleton and play essential roles in intracellular transport, cell division, and possibly cognition. Their highly ordered, quasi-crystalline lattice of tubulin dimers, notably tryptophan residues, endows them with a rich topological and arithmetic structure, making them natural candidates for supporting coherent excitations at optical and terahertz frequencies. The Penrose–Hameroff Orch OR theory proposes that such coherences could couple to gravitationally induced state reduction, forming the quantum substrate of conscious events. Although controversial, recent analyses of dipolar coupling, stochastic resonance, and structured noise in biological media suggest that microtubular assemblies may indeed host transient quantum correlations that persist over biologically relevant timescales. In this work, we build upon two complementary approaches: the parametric resonance model of Nishiyama et al. and our arithmetic–geometric framework, both recently developed in Quantum Reports. We unify these perspectives by describing microtubules as rectangular lattices governed by the imaginary quadratic field $\mathbb{Q}\left(i\right)$, within which nonlinear dipolar oscillations undergo stochastic parametric amplification. Quantization of the resonant modes follows Gaussian norms $N=p^2+q^2$, linking the optical and geometric properties of microtubules to the arithmetic structure of $\mathbb{Q}\left(i\right)$. We further connect these discrete resonances to the derivative of the elliptic L-function, $L\prime (E,1)$, which acts as an arithmetic free energy and defines the scaling between modular invariants and measurable biological ratios. In the appended adelic extension, this framework is shown to merge naturally with the Bost–Connes and Connes–Marcolli systems, where the norm character on the ideles couples to the Hecke character of an elliptic curve to form a unified adelic partition function. The resulting arithmetic–elliptic resonance model provides a coherent bridge between number theory, topological quantum phases, and biological structure, suggesting that consciousness, as envisioned in the Orch OR theory, may emerge from resonant processes organized by deep arithmetic symmetries of space, time, and matter. Full article

Military decision-making is inherently complex and highly critical, requiring commanders to assess multiple variables in real-time, anticipate second-order effects, and adapt strategies based on continuously evolving battlefield conditions. Traditional approaches rely on domain expertise, experience, and intuition, often supported by decision-support systems designed by military experts. With the rapid advancement of Large Language Models (LLMs) such as ChatGPT, Claude, and DeepSeek, a new research question emerges: can LLMs perform causal reasoning at a level that could meaningfully replace human decision-makers, or should they remain human-led decision-support tools in high-stakes environments? This paper explores the causal reasoning capabilities of LLMs for operational and strategic military decisions. Unlike conventional AI models that rely primarily on correlation-based predictions, LLMs are now able to engage in multi-perspective reasoning, intervention analysis, and scenario-based assessments. We introduce a structured empirical evaluation framework to assess LLM performance through 10 de-identified real-world-inspired battle scenarios, ensuring models reason over provided inputs rather than memorized data. Critically, LLM outputs are systematically compared against a human expert baseline, composed of military officers across multiple ranks and years of operational experience. The evaluation focuses on precision, recall, causal reasoning depth, adaptability, and decision soundness. Our findings provide a rigorous comparative assessment of whether carefully prompted LLMs can assist, complement, or approach expert-level performance in military planning. While fully autonomous AI-led command remains premature, the results suggest that LLMs can offer valuable support in complex decision processes when integrated as part of hybrid human-AI decision-support frameworks. Since our evaluation directly tests this capability, this paradigm shift raises fundamental question: Is there a possibility to fully replace high-ranking officers/commanders in leading critical military operations, or should AI-driven tools remain as decision-support systems enhancing human-driven battlefield strategies? Full article

Overweight and obesity represent a significant global health challenge, requiring comprehensive, long-term approaches. Digital weight-loss services (DWLSs) have emerged as promising obesity care models, as they facilitate access to continuous multidisciplinary care. This study aimed to evaluate 12-month weight-loss and adherence patterns in a large unsubsidized DWLS in the UK, which combined lifestyle therapy with semaglutide treatment. A retrospective cohort design was used to analyze data from 7279 patients who initiated treatment between 1 January 2023, and 1 May 2024. Of these patients, 1678 (23.05%) met all criteria for inclusion in the efficacy estimand, which included receiving a minimum of 8 medication orders and submitting weight data within 341–379 days after program initiation. The efficacy estimand achieved a mean weight loss of 15.67%, with 92.49% losing a clinically meaningful amount of weight (≥5%). A strong positive association was found between weight tracking frequency and weight loss, to the extent that a percentage discrepancy of 8.41 points was observed between patients who tracked on less than 20 occasions (Median = 11.83%) and those who tracked at least 100 times (Median = 20.24%). A significant association between weight loss and semaglutide orders was also observed, with a clear distinction existing between patients who received less than 12 orders, and those who received 12 or more orders. Patients whose DWLS experience was supplemented with Wegovy recorded significantly higher mean weight loss than those who were treated with Ozempic (17.68% vs. 14.72%). The findings highlight the importance of program engagement in DWLS outcomes and suggest the need for a comparative analysis of unsubsidized and subsidized services. The study is limited by its real-world observational design and reliance on self-reported data; future research should compare outcomes between unsubsidized and subsidized DWLS cohorts. Full article