Search Results (2,850)

Expressway unmanned aerial vehicle (UAV) pre-deployment is a geospatial decision-support task that requires reliable road-segment-level traffic flow prediction based on spatial sensing networks. However, existing spatiotemporal forecasting models remain limited in characterizing cross-segment propagation relationships, long-lag causal dependencies, and atypical traffic evolution patterns. In addition, complex models often fail to meet the computational requirements of edge-device deployment. Based on electronic toll collection (ETC) gantry data, this study proposes a causal–semantic spatiotemporal forecasting framework for long-term traffic flow prediction with a 24 h forecasting horizon. First, conditional Granger causality analysis is used to construct a directed causal prior graph that characterizes traffic propagation relationships among expressway segments. Second, scenario-semantic priors generated by a large language model are introduced to describe atypical traffic conditions. Then, causal structural priors and scenario-semantic priors are integrated into a teacher model and transferred to a lightweight student model through response-level and feature-level knowledge distillation. Experiments using expressway data from Hubei Province, China, show that the proposed model achieves the best overall performance in the typical scenario and competitive performance in the atypical scenario. The results indicate that the proposed framework can provide day-scale decision support for expressway law-enforcement UAV pre-deployment and enhance the spatial intelligence of traffic emergency management. Full article

Metabolic reprogramming constitutes a fundamental hallmark of malignancy, orchestrating a hostile tumor microenvironment (TME) that severely compromises anti-tumor immunity. Despite the transformative success of immune checkpoint blockade and adoptive cell therapies, clinical efficacy is frequently curtailed by the metabolic barriers imposed by the TME. This review systematically elucidates the complex metabolic interplay between tumor cells and infiltrating T cells, highlighting two defining mechanisms driving immune evasion: the competitive sequestration of essential nutrients and the accumulation of immunosuppressive oncometabolites. We detail how the depletion of glucose and critical amino acids (glutamine, arginine, methionine, etc.) imposes a state of “metabolic siege” on T cells, impairing their bioenergetics and effector functions. Concurrently, we explore how accumulated metabolites—such as lactate, succinate, 2-hydroxyglutarate, kynurenine, and lipids—function as non-canonical signaling molecules to subvert immune surveillance via epigenetic remodeling and oxidative stress. Furthermore, we synthesize emerging therapeutic strategies designed to dismantle this metabolic barrier, including targeting metabolic enzymes (IDO1 and FASN) and transporters, repurposing metabolic waste, and genetically engineering T cells with enhanced metabolic fitness and resilience. By integrating the latest insights into the “metabolism–epigenetics–immunity” axis, this review provides a theoretical foundation for developing next-generation immunotherapies that target metabolic vulnerabilities to overcome resistance in cancer treatment. Full article

The walking cycle is biomechanically considered a fundamental unit of biomedical analysis, a neuromuscular-controlled repetitive process and an energy-optimized system. As defined in the literature, the walking cycle is represented by the interval between two successive contacts of the same foot with the ground, having two main phases of stance and swing. Current theoretical and experimental research seeks to build complex models of analysis of the parameters of the walking cycle to highlight different behaviors at the level of the human body (biomechanical, energetic, sensory, etc.). In this paper, the authors develop a model of theoretical analysis by simulating the walking cycle in Matlab code to highlight energy levels. The theoretical model is proposed to be applied to a sample of subjects with simulations of locomotor pathologies to confirm hypotheses. Full article

Sustainability certification schemes (SCSs) such as LEED, BREEAM, etc., are widely accepted as an effective tool for the promotion of sustainable design principles and lifecycle carbon reduction in building projects. Greece, despite having a relatively small real estate market, presents solid activity in this field, but the related research remains extremely limited. Following a comprehensive quantitative data analysis regarding the use of SCSs in Greece and Europe, this paper examines adoption determinants, challenges, and prospects in the Greek construction sector, drawing on semi-structured interviews. The findings show that LEED dominates the Greek market, and although the SCS benefits are well documented in the literature, the overall market demand is largely confined to office and commercial developments and constrained by affordability concerns, low market awareness, supply chain constraints, administrative complexity, and project coordination challenges. Nevertheless, as future prospects are positive overall, the research provides recommendations towards targeted actions for policy-makers, industry professionals, and other stakeholders. This can encourage supply chain development and accelerate certification uptake, thereby supporting national sustainability objectives and the implementation of European climate and energy policies. Full article

Lithium-ion batteries (LIBs) are essential for current technological infrastructure, driving the development of portable electronics, electric vehicles or grid-scale energy storage. The performance and sustainability of LIBs are critically dependent on their anode materials. This comprehensive review analyzes the evolution and characteristics of key anode materials, highlighting the specific properties they confer to the final battery products. Beyond material properties, the synthesis methods employed for these materials, from conventional techniques (such as solid-state reactions, sol–gel, hydrothermal/solvothermal, co-precipitation, etc.) to innovative and greener approaches (like electrospinning and a novel induction furnace-oxidation hybrid method for complex oxides), are a crucial part in the development of sustainable materials. While these methods offer different advantages, the challenges in achieving optimal electrochemical performance, including issues related to material stability, capacity retention and scalability, remain significant for both research and manufacturing industries. Furthermore, a significant focus is placed on strategies for mitigating the environmental impact associated with anode material production, emphasizing the importance of unconventional and sustainable synthesis routes. Ultimately, the sustainable evolution of LIB technology to achieve future energy demands hinges on overcoming existing limitations. This necessitates integrated research combining advanced material modeling and design, scalable and environmentally conscious synthesis techniques and in-depth electrochemical characterization. Full article

Hollow glass microspheres, as demonstrated in recent studies, have shown significant importance in the field of composite materials and have emerged as transformative materials in biomedical applications. This is necessitated by their ability to provide a physicochemical gradient, a desirable tool for complex tissues and biological interfaces, through the spatiotemporal release of bioactive factors and nanophase ceramics. HGMs are structures with diameters ranging from 1 to 1000 µm that can be used as support for cell growth, either in the form of a scaffold or a drug delivery system. In this review, we describe the various methods for HGM fabrications, synthesis (e.g., flame spraying, sol-gel processes, spray drying, etc.), structural characterizations, and chemical and physical properties (e.g., densities ranging from 0.1 to 0.6 g/cm³ and compressive strength ranging from 10 MPa to 30 MPa for low and high densities, respectively), highlighting how these methods influence their drug delivery, tissue engineering, bone implants, and nanocarrier abilities. Furthermore, a comprehensive list of other materials and their various biomedical uses is reported. Some of the limitations of existing techniques and future investigations into how HGM can perform as a biomedical material are discussed. Full article

Micro-expression recognition (MER) has a lot of applications in lie detection, education, healthcare, etc., as involuntary micro-expressions (MEs) may provide subtle facial cues associated with affective responses. With the development of deep learning, many studies have recently employed Vision Transformers (ViTs) to investigate MER, since ViTs show promising performance in various visual domains due to their excellent local–global modeling ability. However, such methods confront two fundamental challenges: First, fine-grained visual features are needed to capture the subtle facial movements of MEs, which ViTs relatively fall short on due to coarse patch resolution constrained by their quadratic complexity. Second, the data-intensive nature of ViTs impedes effective learning given the limited scale of ME data. To overcome the aforementioned limitations of using ViTs for MER, we propose the Prior-aware Sparse Transformer (PAST), a novel Transformer-based architecture integrating spatial and semantic prior knowledge synergistically into a sparse attention mechanism, enabling linear-complexity processing of large amounts of fine-grained features. Specifically, we first designed an extraction algorithm to generate a representative set of motion-intensive Principal Anchors, which are used to guide the model’s focus on biologically critical regions during sampling. Second, we introduced the Semantic Dictionary, which was trained with a carefully designed self-contrastive loss to embed task-invariant discriminative semantics of the anchors. Such global semantics further modulate patch sampling and attention weighting in the sparse attention procedure, achieving better training performance with limited ME data. Extensive evaluations on MEGC and CD6ME protocols demonstrate state-of-the-art performance, validating PAST’s efficacy for MER. Full article

In biomedical research and clinical practices, Graph Neural Networks (GNNs) are playing an increasingly important role and have been applied to the problems of disease pathway detection, gene–disease relation prediction, etc. They show great potential for biomedical predictions; however, there are interpretability issues when used on complex datasets like gene expression data. Current explainability methods such as GNNExplainer are designed to explain individual instances, not the whole network. The absence of transparency hinders trust and limits the clinical/biomedical implementation of GNNs. Additionally, more interpretable models like GNN-SubNet and XGDAG do not fulfill the expectation of a clear picture for the entire network. This research addresses the limitation of the network-wide explainability of GNNs by introducing a GNN-based BioGraphEX model that incorporates interpretable methods at two levels, instance-level and network-wide level, such as gradient-based methods and SHAP (Shapley Additive Explanations). Using the GSE25097 biomedical dataset, the model achieves an accuracy of 85% and an F1 Score of 0.82, surpassing baseline methods in both predictive performance and interpretability. These results address the limitations of existing models like GNN-SubNet and XGDAG by providing both instance-level and network-wide insights. Metrics like Explanation Fidelity (83%) further validated the robustness of the explanations. Full article

Polymers based on tricyclic decane skeleton in the role of high-performance polycarbon, polyester, polyacrylate, etc., are used in optical equipment, dental restoration, photoresist, and other fields because of their rigid ring structure and corresponding excellent heat/weather/impact/scratch resistance. The preparation process of monomer tricyclodidecane dimethanol is complex and has engineering safety problems. Also, it has been monopolized by a few enterprises for a long time, and the price is expensive. There is a lack of systematic reviews on the synthesis of tricyclodecane dimethanol. In this paper, focusing on the preparation process of tricyclic decane dimethanol, the preparation process of bicyclic decane dimethanol to be prepared by dicyclopentadiene is summarized, including the reaction path, catalytic system and separation method, and the homogeneous catalysis, aqueous/organic two-phase catalysis and heterogeneous catalysis in the hydroformylation of high-carbon olefins are discussed, as well as the difference between stripping, extraction, membrane separation and other methods in the separation methods of catalyst and product. Then, the current research status at home and abroad is summarized, and the advantages and disadvantages of the above reaction methods are analyzed according to the reaction system, catalyst used, solvent, reaction conditions, and final reaction level. Finally, the downstream application and market of tricyclic decane dimethanol are analyzed. It provides a reference for the design and optimization of the preparation process of tricyclodecane dimethanol. Full article

Mitochondria coordinate cellular energy production, metabolism, and signalling through the organization of the electron transport chain (ETC) and formation of respiratory supercomplexes. These structures facilitate efficient electron transfer and enable coenzyme Q (CoQ) channelling, allowing differential regulation of NADH- and succinate-driven respiration while modulating reactive oxygen species (ROS) production. Beyond their damaging potential, ROS act as key signalling molecules that regulate mitochondrial function through redox-sensitive modifications. Mitochondrial protein kinases add an additional layer of control, with Src-family kinases playing a central role. In particular, the mitochondrial tyrosine-kinase Fgr is activated by H₂O₂ and promotes phosphorylation of succinate dehydrogenase, boosting complex II activity, delivering more electrons to CoQ and inducing reverse electron transfer (RET) through CI, in a ROS-induced ROS generation amplification cycle. This induces a metabolic rewiring aimed at supporting stress adaptation, immune cell activation, and macrophage polarization. Overall, the interplay between supercomplex organization, ROS signalling, and kinase activity is critical for metabolic flexibility and represents a promising target for therapeutic intervention. Full article

(1) Background: Chronic endometritis (CE) is a chronic disease of the endometrium characterized by inflammatory infiltration into the endometrial stroma. The diagnosis of CE is complex. Clinical examination and transvaginal ultrasound are not specific for CE. Biopsy is an indispensable tool. Two specific populations can be analyzed at the endometrial level: CD138 positive plasma cells and CD56 positive Natural Killer cells. (2) Methods: Our narrative review was based on an online search with a focus on the last ten years of literature in the English language. (3) Results: Twenty studies on CD138 positive plasma cells and 16 articles on CD56 positive Natural Killer cells highlighted the correlations of CE with diagnostic possibilities and reproductive outcomes. (4) Conclusions: The etiological pathway and management of CE require further scientific studies and investigations. Hysteroscopy is the diagnostic gold standard, but other blind biopsy techniques (i.e., Perma, Pipelle, Novak, etc.) and molecular procedures may help in the diagnosis of chronic endometritis. Full article

Die forging is a widely used production method for manufacturing high-strength components with high accuracy and good mechanical properties. Since the die forging process involves many complicated thermo-mechanical coupled field physical problems, such as large plastic deformation, high temperature, friction, and heat transfer, etc., experimental studies are difficult and expensive to perform. The numerical simulation method has become the main method of study and optimal design for the die forging process. This paper reviews the published papers on numerical simulation of the die forging process from 2016 to 2026, in a structured literature review of computational simulations dealing with die forging processes. The literature search was conducted using the Scopus and Web of Science databases. After screening and full-text assessment, 24 relevant journal articles were selected for this paper. The articles studied were analyzed in terms of finite element modelling strategies, constitutive and material models used, friction and thermal boundary conditions considered, and process parameters. Typical results obtained from the studies discussed in the paper include stress, strain, temperature, forging load, and metal flow. The current state-of-the-art research has evolved from simple metal-flow predictions to more complex thermo-mechanical models, and even optimization-based models. Most of the current studies are based on experimentally derived constitutive equations, as well as more complex friction and heat-transfer models. Furthermore, studies applying optimization methods (Taguchi methods, design of experiments, machine learning, artificial intelligence) are increasingly common. The growing interest in the digital twin concept and real-time process control is observed. However, experimental validation, thermal contact modelling, and simulation of stress, strain, temperature, and microstructure in one simulation remain key challenges. Full article

In the context of global climate change and China’s “Dual Carbon” target, the misallocation of carbon emission reduction responsibilities and low regulatory efficiency urgently require analysis and resolution. Based on China’s 2020 MRIO and carbon emission inventory data, this study integrates multi-regional input–output models and complex network theory to construct an embodied carbon emission (ECE) transfer network at the provincial-industrial level and analyze its structural characteristics. Drawing on complex network control theory, this paper proposes a heuristic node-ranking strategy to identify driver nodes for full controllability of the ECE transfer network and compare its regulatory effect with other topological indicators. The findings reveal: (1) At the provincial level, embodied carbon emissions show a distinct transfer pattern from central provinces to southeast coastal or economically developed regions. Jiangxi, Anhui, Shandong, etc., are net outflow provinces, while Jiangsu, Beijing, Guangdong, etc., are net inflow provinces. (2) At the industrial level, secondary industry is the main net inflow industry, and primary industry is the main net outflow industry. The secondary industries in Guangdong, Henan, etc., have high betweenness centrality, acting as “hub” nodes for carbon transmission. Community detection shows that the largest community in China is centered on the secondary and tertiary industries of Jiangsu, Henan, Guangdong, etc., and the network overall exhibits small-world characteristics. (3) Compared with other control strategies, the designed algorithm achieves the best control effect: it realizes full network controllability with the minimum number of control nodes (26), and the shortest reachable paths from the control node set to non-control nodes, meaning policy signals imposed on control nodes transmit at the fastest speed. (4) Among the control node set, 22 key control nodes are mostly secondary and tertiary industries, located at the center of the transfer network and ranking high in net outflow or inflow, belonging to the core nodes of the ECE transfer network. This study provides a scientific basis and methodological support for clarifying the attribution of carbon transfer responsibilities and formulating differentiated collaborative regulatory policies. This paper establishes a qualitative matching mechanism between network control inputs and carbon tax, emission quotas and industrial regulation to connect controllability theory and practical carbon governance. Full article

Mitochondria-targeting metal complexes (MTMCs) are a mechanistically distinct class of metallopharmaceuticals. Unlike first-generation platinum drugs that form nuclear DNA adducts, MTMCs exploit organelle-specific vulnerabilities such as hyperpolarised mitochondrial membrane potential (ΔΨm), elevated reactive oxygen species (ROS), limited mitochondrial DNA (mtDNA) repair capacity, and redox-dependent enzymes such as thioredoxin reductase (TrxR). We systematically searched PubMed, Web of Science, Scopus, and Google Scholar databases for studies published between 2016 and 2026, applying predefined inclusion criteria that included subcellular localization evidence and functional bioenergetic endpoints. The search identified 147 studies covering Pt(II/IV), Ru(II/III), Au(I/III), Ir(III), Os(II), Re(I), and V(IV/V) complexes and metal–organic framework nanoplatforms. Mechanistic evidence converges on four intramitochondrial target categories: inhibition of ETC (Electron Transport Chain) Complexes I/III with consequent ATP depletion; ROS overproduction, coupled with glutathione and TrxR depletion; outer mitochondrial membrane permeabilization and intrinsic apoptotic cascade activation; and mtDNA damage within a compartment limited to base excision repair. Multi-modal cell death—the co-occurrence of apoptosis, ferroptosis, necroptosis, and autophagic cell death—was a recurrent finding across the reviewed studies. This review thoroughly surveys the latest trends in MTMC drug design (metals, ligand structures, and mechanisms of action) and summarises analytical techniques for speciation, pharmacokinetics, safe monitoring, and resistance, while critically analysing translational barriers and clinical failures. To address the field’s inconsistent terminology, we introduce an explicit localization evidence hierarchy that distinguishes mitochondria-targeting complexes (through quantitative ICP-MS fractionation or co-localization with defined Pearson/Manders coefficients) from simply mitochondria-localising or mitochondria-perturbing agents, and we apply it throughout. We also point out that the idea of selectivity being purely driven by membrane voltage (ΔΨm) and thermodynamics is constrained by membrane and protein binding, as well as the transmembrane pH gradient, kinetic limitations, and demonstrated heterogeneity of cancer-cell membrane potential, and, as such, the functional mitochondrial effects must not be equated with mitochondrial accumulation. Since elemental quantification cannot distinguish intact complex from protein adducts and decomposition products, speciation-aware pharmacokinetics emerges as a prerequisite for a credible exposure–response interpretation. The translational progress will depend less on new chemotypes than on this analytical and pharmacokinetic rigour, together with organelle-level safety monitoring and biomarker-guided patient selection. Full article

The next generation of agriculture mechanisation lies in the integration of autonomy, connectivity, and intelligence. This systematic review presents a conceptual, integrated engineering framework for safe and intelligent autonomy in agriculture tractors based on a systematic survey that covers major technical advances between January 2020 and May 2026, including research trends, key authors, conceptual clusters, etc. mapped by AI-assisted tools like Bibliometrix, Litmaps, and Elicit. We have a particular focus on the integration among three key research domains related to swarm robotics, secure telematics, and navigation through artificial intelligence. Important technical trends were identified, including but not limited to decentralised consensus algorithms for multi-vehicle coordination, lightweight telemetry secure protocols (CoAp/Oscore, DTLS), neural networks, and a fuzzy logic hybrid approach that adapts complex data to unstructured field constraints. Through the research reviewed, it was found that safety, decision-making, and collaboration are not designed and evaluated together as part of the overall proposed structure, which inhibits future expandability and safety. Based on current research in both areas and its shortcomings, an integrated concept was created that combines elements of decision-making, in-field telemetry, and safe decision-making from farm to cloud using security as the binding approach. At the end of the paper, three practical engineering guidelines are presented. Full article