Search Results (1,661)

Intelligent Transportation Systems (ITS) play a central role in the development of more efficient, adaptive, and resilient road networks. Traffic control strategies have progressively evolved from traditional approaches toward more intelligent and adaptive frameworks. This paper presents a taxonomy-based perspective on intelligent traffic control strategies for road networks, organizing existing approaches according to three complementary dimensions: control scope, decision-making mechanism, and control architecture. Based on this framework, the paper discusses representative methodologies, including rule-based control, model-based methods, simulation-based optimization, data-driven and artificial intelligence-based methods, and emerging cooperative strategies enabled by connected and automated vehicles (CAVs). The analysis also examines key application domains, such as traffic signal control, ramp metering, CAV-based traffic management, and simulation platforms, highlighting their operational principles, advantages, limitations, and implementation challenges. Particular attention is given to the transition from local and reactive control toward coordinated, predictive, and learning-based traffic management systems. The paper identifies major challenges related to scalability, robustness, interpretability, safety, real-world deployment, and the gap between simulation performance and practical implementation. The proposed taxonomy also supports practical comparison and preliminary selection of context-specific strategies. Future directions point toward integrated and hybrid frameworks combining data-driven adaptability, vehicle–infrastructure cooperation, and digital twin technologies. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains among the most therapeutically intractable malignancies, with a 5-year survival rate of approximately 10% and near-universal resistance to immune checkpoint inhibitor (ICI) therapy. This refractoriness arises from the convergence of pronounced intratumoral heterogeneity (ITH) and a profoundly immunosuppressive tumor microenvironment (TME), which together configure PDAC as a prototypical immune-excluded tumor. Beyond low tumor mutational burden, PDAC exhibits layered genetic, epigenetic, transcriptional, and metabolic heterogeneity that enables rapid adaptation and immune evasion under selective pressure, while dense desmoplastic stroma, cancer-associated fibroblasts (CAFs), and immunosuppressive immune populations collectively impose formidable physical and immunologic barriers to antitumor immunity. In this review, we synthesize multi-omics, spatial transcriptomic, and immunologic evidence to elucidate how ITH and the TME dynamically interact to reinforce immune resistance. We examine reciprocal crosstalk mechanisms—including immune-driven clonal selection, interclonal cooperation, metabolic niche specialization, and metabolic–epigenetic coupling—and discuss emerging platforms such as single-cell spatial omics, patient-derived organoid immune co-culture systems, and longitudinal circulating tumor DNA monitoring that enable high-resolution mapping of ITH–TME dynamics. Finally, we evaluate ITH–TME-guided combination therapeutic strategies targeting oncogenic drivers, stromal architecture, myeloid suppression, and metabolic checkpoints, and propose a prioritized framework for near-term and speculative clinical translation in PDAC. Full article

The Internet of Vehicles (IoV) is reshaping intelligent transportation through pervasive connectivity, real-time data exchange, cooperative perception, and vehicle–edge–cloud services, while also expanding cybersecurity and privacy risks across heterogeneous cyber–physical environments. This paper presents a PRISMA 2020-informed systematic review of IoV security and privacy protection research. A cross-layer and lifecycle-oriented analytical framework is developed by integrating a four-layer IoV architecture—sensing layer, network access layer, coordinative computing layer, and application layer—with a five-stage data lifecycle covering data collection, transmission, storage, usage, and disposal. Based on this framework, the paper examines representative threat surfaces, vehicle-to-everything (V2X) communication security, public key infrastructure (PKI) based authentication, trust management, privacy-preserving data sharing, intrusion detection, active defense, and AI-assisted security analytics. Privacy-preserving mechanisms, including differential privacy, federated learning, blockchain, homomorphic encryption, and secure multi-party computation, are further compared in terms of deployment layer, lifecycle stage, real-time suitability, and representative performance evidence. In addition, the review discusses the engineering relevance of UNECE WP.29 R155/R156, ISO/SAE 21434, and related national standards, with emphasis on compliance evidence, over-the-air (OTA) governance, supply-chain coordination, and lifecycle cybersecurity management. The review shows that no single protection mechanism can simultaneously satisfy the requirements of real-time performance, scalability, privacy preservation, trustworthiness, and regulatory compliance in dynamic IoV environments. Future research should emphasize lightweight and adaptive protection, cross-layer trust coordination, privacy–utility co-optimization, trustworthy AI-assisted security operations, and evidence-based lifecycle governance. This review provides a structured reference for researchers and a practical basis for secure and privacy-aware IoV system design. Full article

Introduction: AQUArestore is a three-year project focused on promoting adaptive ecological restoration strategies for river ecosystems in the vulnerable cross-border region of Portugal. The project responds to pressing environmental challenges across the territory, including severe habitat degradation, climate vulnerability, declining water security, and biodiversity loss, with particular concern for freshwater fish communities, making river restoration essential to preserve native species and freshwater ecosystem services. Objective: The project aims to develop a replicable framework for restoration of Mediterranean transboundary riverine habitats, supporting the objectives of the EU Nature Restoration Law (NRL, Regulation 2024/1991). The consortium AQUArestore will develop (1) robust restoration indicators, (2) implement living labs for restoration experimentation, and (3) establish capacity-building and training programs for technicians and citizens. Methodology: The project kick-off meeting was used to operationalize project tasks, detail the implementation calendar and milestones, and clarify responsibilities of each project member and partner institutions within the different work tasks. The meeting gathered consortium members from the coordinating institution CEF-ISA (researchers at the Instituto Superior de Agronomia) and partners WWF Portugal (an environmental NGO) and Mushmore Cooperative, each one contributing according to their respective expertise and institutional objectives. Results: The AQUArestore project kick-off meeting took place in January 2026 at ISA, Lisbon, and included a presentation of the NRL and a detailed discussion of project task development. In detail, the activities will begin with the compilation of information on previously restored sites (Task 1). This will support the development and validation of environmental and biodiversity indicators of restoration outcomes, including those linked to freshwater fish assemblages and riparian vegetation (Task 2). The project will then establish two living labs as platforms to test nature-based solutions in collaboration with stakeholders and local communities (Task 3). In parallel, AQUArestore will strengthen technical capacity through training for practitioners and public authorities (Task 4). Finally, dissemination will be supported through citizen science, communication activities, and stakeholder engagement, fostering a broader impact (Task 5). Together, these tasks provide an integrated, science-based, and participatory framework aiming to support adaptive river restoration under climate and environmental changes. Conclusions: By integrating ecological restoration, biodiversity and environmental monitoring, and stakeholder engagement, AQUArestore is expected to contribute to the recovery of Mediterranean freshwater ecosystems and improve habitat quality and connectivity for native fish communities, enhancing resilience to climate change and other anthropogenic pressures. Full article

The larger research problem is to get combustion engines more effective and flexible and reduce or even eliminate greenhouse gas emissions. Here we concentrate more on a smaller-scale and focused research problem about the significance of air feeding in engine operation. Therefore, the need for modeling a charge air system is obvious. The interaction and co-operation between the charge air systems and combustion engines is a central issue in this article. A literature review was carried out on related topics, and it reveals a research gap in this area. A simulation model of a charge air system based on first principles is developed. It is based on physical and systemic modeling, and it is constructed including control loops reducing and controlling the pressures in the charge air chain. The simulation models of this auxiliary system and engine are successfully combined, and functioning together is demonstrated. The composed models represent real research laboratory equipment in the University of Vaasa Energy Laboratory under construction. The research laboratory equipment and the whole research environment are described. Simulation scenarios are presented both with the charge air system alone and with the combined model, including also the engine part. The significance of the developed models is discussed, and the path towards a digital twin experiment environment is outlined. As a conclusion, we can claim that the combined simulation model is successfully constructed and shown to operate in a stable and physically plausible manner. The digital twin concept can be tested completely only when the research laboratory is constructed and ready and the test runs begin to produce measurement data for the digital part. Then also the simulation models can be tuned to a better accuracy level, and the operation as a digital twin will be verified. Full article

Hydrogen is a promising clean-energy carrier, but its low ignition energy, high diffusivity, and wide flammability range demand reliable leak detection. Chemiresistive sensors based on n-type metal oxide semiconductors are attractive owing to their simple architecture, low cost, large resistance modulation, thermal robustness, and compatibility with miniaturized devices. This review focuses on n-type metal oxide semiconductor nanomaterials for hydrogen sensing, particularly ZnO, SnO₂, In₂O₃, WO₃, TiO₂, and related mixed oxides. The fundamental sensing mechanisms are examined, including oxygen chemisorption, electron-depletion-layer modulation, grain-boundary barrier control, catalytic hydrogen spillover, and hydrogen-induced surface reduction or metallization, together with the way these mechanisms compete and cooperate under different operating conditions. Recent performance-enhancement strategies are organized around morphology and porosity control, noble-metal sensitization, defect and dopant engineering, n–n heterojunctions, molecular sieving, and low-temperature activation. Density functional theory is discussed as a design tool for evaluating adsorption energetics, vacancy formation, work-function shifts, band alignment, and interfacial charge transfer, along with its current limitations for modeling humid surfaces. Finally, key challenges and future directions, including humidity tolerance, standardized reporting, device integration, and emerging materials, are summarized to guide the development of high-performance hydrogen sensors. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

Janus kinase 2 (JAK2) occupies a central position in cytokine signaling and plays essential roles in hematopoiesis, immune regulation, and cancer. Although recent advances in structural biology, cryo-EM, receptor modeling, and biophysical analysis have substantially expanded current views of JAK2 function, key mechanistic questions remain regarding how receptor geometry, JH2-mediated autoinhibition, and disease-associated mutations are structurally integrated. In this review, we discuss the multidomain organization of JAK2 and examine how the FERM–SH2 module, the pseudokinase domain (JH2), and the catalytic kinase domain (JH1) cooperate to govern receptor specificity, allosteric control, and cytokine-induced activation. We further analyze how pathogenic mutations rewire this regulatory system by weakening autoinhibitory contacts, altering linker-mediated communication, or stabilizing active dimeric conformations. Finally, we assess current and emerging therapeutic strategies, from ATP-competitive inhibitors to macrocyclic and JH2-selective allosteric modulators, with emphasis on how structural insight can guide next-generation drug design. These advances support a more integrated view of JAK2 regulation and define new opportunities for selective therapeutic intervention. Full article

The rapid growth of the sharing economy has improved resource utilization in car-sharing, yet it has also sharpened market competition and diversified user demand. A persistent obstacle is the low coordination efficiency between asset-heavy operating companies and traffic-driven platforms, whose misaligned objectives waste social resources. This paper uses differential game theory to analyze their dynamic coordination strategies and benefit allocation mechanisms. The Nerlove–Arrow model captures the evolution of brand goodwill, while the company’s decisions on station layout, vehicle dispatch, and pricing, together with the platform’s advertising investment, form the core decision variables in a two-party game framework linking the asset side and the traffic side. Compared with the non-cooperative Nash equilibrium, the cooperative mode removes the double marginalization effect, strengthens the investment incentives of both parties, and raises the system’s steady-state goodwill and total profit, achieving a Pareto improvement. To ground the cooperative framework in rigorous theory, we supply a verification theorem confirming that the linear candidate value functions satisfy the Hamilton–Jacobi–Bellman equations over the entire admissible state space. A formal proof of instantaneous rationality ensures that neither party falls into a cooperation trap on the horizon $[0,T]$, and the asymptotic stability of the steady-state goodwill trajectory is established. We further endogenize the revenue-sharing coefficient through a generalized Nash bargaining model that admits asymmetric bargaining structures, and introduce a Stackelberg leadership benchmark as a third comparative regime. Sensitivity analyses with respect to the discount rate and user heterogeneity confirm the robustness of the findings. A dedicated discussion section bridges the gap between idealized parameterization and data-driven calibration, describing practical pathways via A/B testing, user churn metrics, and econometric estimation of demand parameters. The results offer a scientific decision-making reference for strategic cooperation in the car-sharing industry. Full article

Thematic analysis of large-scale political discourse remains a challenge due to semantic complexity and overlapping policy areas and changing diplomatic vocabulary. Although large language models (LLMs) offer promise for scalable thematic classification, their reliability in politically sensitive contexts requires systematic validation against expert human annotations. We evaluate LLM-based thematic classification of United Nations General Assembly (UNGA) speeches across a decade (2014–2023), using 7680 human-annotated themes mapped into 12 policy domains. Our results show that DeepSeek R1 achieves the highest accuracy 77% (F1 = 0.73), followed by ChatGPT, Gemini and LLaMA, with strong performance in lexically stable domains but substantial degradation in semantically overlapping categories such as governance and international cooperation. A unique dimension of our work is timeline analysis, which shows that the performance of LLMs over the years varies strongly and the precision decreases during times of rhetorical transformation, including pandemic-related discussions and the discourses of cooperation determined by the Russia–Ukraine conflict. By linking domain-level ambiguity and geopolitical shifts to temporal instability, this study introduces a dynamic robustness perspective for evaluating LLMs in computational political discourse analysis. Full article

We present a review on the application of the mathematical models for research on social processes, social structures, and actors in social systems. The scope of the review is not restricted to the classical applications of mathematics such as theory of probability, statistics, stochastic processes, differential equations, and game theory. We also discuss applications of the theory of networks for social network analysis and the numerical research on dynamics of social systems. The number of these applications has increased very fast in recent years. Special attention is given to the results from the area of sociophysics, where mathematical methodology is used to analyze social systems in cooperation with the models and concepts of physics. Another special topic in his review is connected to the results from econophysics, where the mathematical methodology and theories and methods of physics are used in the studies on the dynamics of economic systems. In addition, we give several examples for the application of mathematical methods to social systems: (a) application of difference equations to model the flow of substances in channels of networks; (b) analytical solution of nonlinear equations connected to the model of waves of popularity; (c) numerical results of the waves of popularity in a model that accounts for the change in the opinion of the supporters of the ideas for positive or negative popularity of a person, material item, or a piece of information (idea, theory, ideology, etc.) In the last case, we illustrate the effectiveness of the numerical analysis to discover new effects on the studied social system. The review ends with a large list of references. These references can be used as a guide of the way of new researchers to the large field of mathematical social dynamics. Full article

Metabolic dysfunction-associated fatty liver disease (MAFLD) is now recognized as a leading form of chronic liver disease globally and is strongly associated with metabolic abnormalities. Traditionally, the pathogenesis of MAFLD has mainly been attributed to genetic susceptibility and unhealthy lifestyles (such as high-calorie diets and sedentary behavior). However, in recent years, environmental factors, especially air pollution, have been confirmed as independent risk factors and important promoting factors for MAFLD development and further disease progression. This review summarizes current epidemiological findings on the link between air pollution exposure and MAFLD, while exploring its potential biological mechanisms involving systemic inflammation, oxidative stress, immune alteration, genetic risk, and epigenetic regulation underlying the relationship between air pollution and hepatic steatosis. It also reviews the additive interaction between air pollution and lifestyle or socioeconomic factors in MAFLD. Finally, we also discuss multilevel strategies spanning individual-, community-, national-, and global-level cooperation to address the increasing public health burden caused by air pollution. Therefore, incorporating the assessment and control of air pollution into the comprehensive strategies for MAFLD prevention and treatment has important scientific value and public health significance. Full article

Background/Objectives: Inflammatory bowel diseases (IBD) including ulcerative colitis and Crohn’s disease can be associated with other immune-mediated inflammatory diseases (IMIDs) and extraintestinal manifestations (EIM) including dermatological manifestations, ophthalmologic manifestations, musculoskeletal manifestations and neurological manifestations. The aim of this narrative review is to discuss the optimal management and treatment strategy of IBD with immune-mediated inflammatory disease and extraintestinal manifestations. Methods: This review is based on published studies searched in PubMed until 31 December 2025. Our search focused on systemic reviews, review articles, randomized trials, cohort studies, guidelines and case series. Results: In IBD, the presence of additional immune-mediated inflammatory diseases (IMIDs) should be considered a poor prognostic factor, prompting closer disease monitoring and earlier escalation to or optimization of advanced therapy. Therapeutic management requires careful consideration including a multidisciplinary approach and a selection of the most appropriate treatment option(s) based on the presence, severity of IBD and/or IMIDs and/or EIM. For patients with refractory disease affecting multiple organs, emerging strategies include dual biologic therapies. Conclusions: The optimal management of IBD with associated IMIDs/EIM should be multidisciplinary in close cooperation among specialists to align treatment goals and management plans. Full article

Temperature is a physical quantity that represents the degree of heat or cold of an object and has significant application value across various fields. Traditional contact temperature measurement technologies, such as thermocouples and infrared thermometers, suffer from limitations like poor environmental adaptability and low spatial resolution, which makes it difficult to meet the temperature measurement requirements for micro-/nano-devices and extreme environments. In recent years, non-contact optical temperature measurement technology based on the luminescence characteristics of rare earth ions has garnered widespread attention due to its high sensitivity, strong interference resistance, and good environmental adaptability. In addition to inorganic luminescent materials, lanthanide-based molecular and coordination-complex thermometers have also become an important branch of this field; however, this paper focuses on inorganic rare earth luminescence thermometry. This paper provides a systematic review of the mechanisms of temperature measurement using rare earth ion luminescence, including single-energy-level luminescence intensity measurement and luminescence intensity ratio measurement based on thermally coupled levels (TCLs) and non-thermally coupled levels (NTCLs). It analyzes the principles of various technologies, performance parameters (such as absolute sensitivity S_a, relative sensitivity S_r, and temperature resolution δT), and their application progress in fields such as biomedical imaging, high-temperature aerospace environments, and the integration of micro-/nano-devices. Special attention is paid to emerging research directions, including Stark sublevel engineering for enhanced sensitivity, negative thermal expansion (NTE) host design for anti-thermal quenching, multi-modal collaborative thermometry, and artificial intelligence (AI)-assisted material design and data processing. The article also discusses the challenges currently faced by the technology, such as high-temperature fluorescence quenching and signal interference, and looks forward to future development directions, including artificial intelligence-assisted material design and multi-modal cooperative temperature measurement, aiming to provide a reference for the research and application of rare earth luminescence temperature sensing technology. Full article

This article discusses a novel aircraft coordination algorithm for automated vertiport operation. New applications of Innovative Air Mobility (IAM) including inspection, logistics and security UAVs, Urban Air Mobility (UAM) or Regional Air Mobility (RAM) present a coordination challenge, especially near vertiports, as large numbers of vehicles with different characteristics share the airspace, and so avoiding collisions, optimizing resource usage and operating with low human intervention is important.In this paper, this problem is addressed by proposing a new formulation of the aircraft coordination problem that makes use of a discretized airspace to detect potential conflicts and collisions between cooperative and non-cooperative aircraft in the surroundings of a vertiport. The proposed algorithm not only considers the cells traversed by the aircraft, but also the set of adjacent cells, making the algorithm more conservative and robust than other algorithms found in the literature, and achieving a 100% conflict-detection rate. A mathematical model of aircraft dynamics is employed to turn high-level flight plans into detailed aircraft trajectories, using those trajectories to detect potential collisions. The deconfliction problem is formulated as a mixed-integer optimization program that computes orders of pass for every conflict while minimizing the divergence between requested time of arrival (RTA) and estimated time of arrival (ETA). This problem is implemented in OR-Tools to be solved by means of the CP-SAT solver. The validity of the solution is tested by extensive simulation, showing tactical coordination of up to 25 aircraft landing on a vertiport. Full article