Search Results (3,131)

Extracellular matrix (ECM) stiffening is a defining biophysical feature of solid tumors that reshape gene regulation through mechanotransduction. Increased collagen crosslinking and stromal remodeling enhance integrin engagement, focal-adhesion signaling and force transmission to the nucleus, where key hubs such as lysyl oxidase (LOX), focal adhesion kinase (FAK) and the Hippo co-activators YAP1 and TAZ (WWTR1) promote proliferation, invasion, stemness and therapy resistance. Here, we synthesize evidence that quantitative changes in matrix stiffness remodel the miRNome and lncRNome in both tumor and stromal compartments, including extracellular vesicle cargo that reprograms metastatic niches. To address heterogeneity in experimental support, we classify mechanosensitive ncRNAs into studies directly validated by stiffness manipulation (e.g., tunable hydrogels/AFM) versus indirect associations based on mechanosensitive signaling, and we summarize physiological versus pathophysiological stiffness ranges across tissues discussed. We further review competing endogenous RNA (ceRNA) networks converging on mechanotransduction nodes and ECM remodeling enzymes, and discuss translational opportunities and challenges, including targeting mechanosensitive ncRNAs, combining ncRNA modulation with anti-stiffening strategies, delivery barriers in dense tumors, and the potential of circulating/exosomal ncRNAs as biomarkers. Overall, integrating ECM mechanics with ncRNA regulatory circuits provides a framework to identify feed-forward loops sustaining aggressive phenotypes in rigid microenvironments and highlights priorities for validation in physiologically relevant models. Full article

Safe and reliable railway operation forms an integral part of autonomous transport systems and depends on accurate knowledge of the adhesion conditions. Both the underestimation and overestimation of adhesion can compromise real-time decision-making in traction and braking control, leading to accidents or excessive wear at the wheel–rail interface. Although limited research has explored the estimation of adhesion forces using data-driven algorithms, most existing approaches lack self-reliance and fail to adequately capture low adhesion levels, which are critical to identify. Moreover, obtaining labelled experimental data remains a significant challenge in adopting data-driven solutions for domain-specific problems. This study implements self-reliant deep learning (DL) models as perception modules for intelligent railway systems, enabling low adhesion identification by training on raw time sequences. In the second phase, to address the challenge of label acquisition, a semi-supervised generative adversarial network (SGAN) is developed. Compared to the supervised algorithms, the SGAN achieved superior performance, with 98.38% accuracy, 98.42% precision, and 98.28% F1-score in identifying seven different adhesion conditions. In contrast, the MLP and 1D-CNN models achieved accuracy of 91% and 93.88%, respectively. These findings demonstrate the potential of SGAN-based data-driven perception for enhancing autonomy, adaptability, and fault diagnosis in intelligent rail and robotic mobility systems. The proposed approach offers an efficient and scalable solution for real-time railway condition monitoring and fault identification, eliminating the overhead associated with manual data labelling. Full article

While advanced imaging techniques and ordered porous materials like MOFs have gained prominence, gas sorption remains the indispensable tool for characterizing the multiscale heterogeneity of industrially important disordered solids, such as catalysts and shales. This review examines recent developments in gas sorption methodologies specifically tailored for rigid, disordered porous media. We discuss experimental advances, including the choice of adsorbate and the utility of the overcondensation method for probing macroporosity and ensuring saturation. Furthermore, we critically evaluate theoretical approaches for determining pore size distributions (PSDs), contrasting classical methods with Density Functional Theory (DFT) and Grand Canonical Monte Carlo (GCMC) simulations. Special emphasis is placed on the impact of pore-to-pore cooperative effects, such as advanced condensation, cavitation, and pore-blocking, on the interpretation of sorption isotherms. We highlight how complementary techniques, including integrated mercury porosimetry, NMR, and computerized X-ray tomography (CXT), are essential for deconvolving these complex network effects and validating void space descriptors. We conclude that, while “brute force” molecular simulations on image-based reconstructions are progressing, “minimalist” pore network models, which incorporate cooperative mechanisms, currently offer the most empirically adequate approach. Ultimately, gas sorption remains unique in its ability to statistically characterize void spaces from Angstroms to millimeters in a single experiment. Full article

Self-organizing systems arise in complex biomechanical structures, human locomotion, and neural control hierarchies, yet quantitative methods for describing order formation and loss of stability remain limited. This study develops a mathematical framework for analyzing self-organization using entropy-based measures, indicators of chaotic dynamics, and network-theoretic structure. The approach (the LET framework) combines Lyapunov exponents with entropy families and graph metrics (algebraic connectivity, Load-Path Heterogeneity Index) to: (i) examine transitions between ordered and disordered states, (ii) assess sensitivity to perturbations, and (iii) characterize structural coherence in evolving cervical spine kinematics. Analytical models and computational validations are presented for cervical stability and post-operative Adjacent Segment Disease (ASD) using the Branney–Breen dataset. The findings indicate that entropy and chaos measures identify regime shifts and the emergence of a “stability corridor” more clearly than task-oriented indices, and provide finer resolution of dynamical variability within self-organizing processes. Network metrics complement these results by linking local segmental interactions to global structural fragility transfer. The study shows that entropy, chaos indicators, and network structure together form a consistent basis for describing self-organization in biomechanical systems, enabling quantitative comparison of dynamical regimes and improved interpretation of emergent pathological behavior. The approach utilizes a hybrid kinematic surrogate model to resolve passive and active components, bypassing direct force measurements by employing viscoelastic mechanotransduction principles. Full article

Solar drying is a sustainable and energy-efficient method for preserving agricultural products; however, its performance is strongly influenced by fluctuating environmental conditions. This study presents an artificial neural network (ANN)-based predictive temperature control system for an indirect forced-convection solar dryer. A data-driven dynamic model of the drying process was developed using experimental measurements and implemented in MATLAB R2014a (MathWorks, Natick, MA, USA). The proposed ANN-based controller was evaluated against a conventional PID controller under identical operating conditions. The results show that the ANN-based approach reduced the settling time by approximately 36% (160 s compared to 250 s for PID) and maintained drying chamber temperature stability within ±1.2 °C. These improvements demonstrate the effectiveness of neural predictive control for enhancing dynamic response and temperature regulation accuracy in solar drying systems. The study is limited to a prototype-scale dryer and short-term experimental data; therefore, further validation under varying climatic conditions and larger-scale systems is required. Full article

Recently, due to the expansion of telecommunication and power networks, as well as other structures, the demand for designing efficient and durable tall supporting columns has increased. Efficient steel columns are well known, including prestressed stayed beam–column systems. However, because of their relatively high cost, designers often turn to reinforced concrete structures, which are not only relatively cheaper but also sufficiently strong and resistant to aggressive external influences. Nevertheless, the large self-weight of reinforced concrete structures and considerable material consumption encourage the search for new efficient solutions. One such solution is the use of spun reinforced concrete structures. Compared to conventional reinforced concrete structures, these solutions not only reduce material consumption but also increase durability. This study examines an innovative prestressed stayed beam–column structure consisting of a spun reinforced concrete core and supporting prestressed steel tension ties. The behavior of such a composite structure is analyzed, and calculations of internal forces and displacements are presented. The rational parameters of the composing elements of this new prestressed stayed beam–column structure are discussed, and their influence on the stress–strain state of the structure is evaluated. Expressions are provided for calculating the rational bending moments of the spun reinforced concrete core. The obtained solutions make it possible to select rational cross-sections of the core and ties, as well as the required prestressing of the tension ties, without iterative calculations. Full article

We detail a procedure to transform the current empirical stage in the study of complex systems into a predictive phenomenological one. Our approach starts with the statistical-mechanical Landau-Ginzburg equation for dissipative processes, such as kinetics of phase change. Then, it imposes discrete time evolution to explicit back feeding, and adopts a power-law driving force to incorporate the onset of chaos, or, alternatively, criticality, the guiding principles of complexity. One obtains, in closed analytical form, a nonlinear renormalization-group (RG) fixed-point map descriptive of any of the three known (one-dimensional) transitions to or out of chaos. Furthermore, its Lyapunov function is shown to be the thermodynamic potential in q-statistics, because the regular or multifractal attractors at the transitions to chaos impose a severe impediment to access the system’s built-in configurations, leaving only a subset of vanishing measure available. To test the pertinence of our approach, we refer to the following complex systems issues: (i) Basic questions, such as demonstration of paradigms equivalence, illustration of self-organization, thermodynamic viewpoint of diversity, biological or other. (ii) Derivation of empirical laws, e.g., ranked data distributions (Zipf law), biological regularities (Kleiber law), river and cosmological structures (Hack law). (iii) Complex systems methods, for example, evolutionary game theory, self-similar networks, central-limit theorem questions. (iv) Condensed-matter physics complex problems (and their analogs in other disciplines), like, critical fluctuations (catastrophes), glass formation (traffic jams), localization transition (foraging, collective motion). Full article

The Internet of Things (IoT) has transformed modern life through interconnected devices enabling automation across diverse environments. However, its reliance on legacy network architectures has introduced significant security vulnerabilities and efficiency challenges—for example, when Datagram Transport Layer Security (DTLS) encrypts transport-layer communications to protect IoT traffic, it simultaneously blinds intermediate proxies that need to inspect message contents for protocol translation and caching, forcing a fundamental trade-off between security and functionality. This paper presents an architectural solution based on the Recursive InterNetwork Architecture (RINA) to address these issues. We analyze current IoT network stacks, highlighting their inherent limitations—particularly how adding security at one layer often disrupts functionality at others, forcing a detrimental trade-off between security and performance. A central principle underlying our approach is the role of structural symmetry in RINA’s design. Unlike the heterogeneous, protocol-specific layers of TCP/IP, RINA exhibits recursive self-similarity: every Distributed IPC Facility (DIF), regardless of its position in the network hierarchy, instantiates identical mechanisms and offers the same interface to layers above. This architectural symmetry ensures predictable, auditable behavior while enabling policy-driven asymmetry for context-specific security enforcement. By embedding security within each layer and allowing flexible layer arrangement, RINA mitigates common IoT attacks and resolves persistent issues such as the inability of Performance Enhancing Proxies to operate on encrypted connections. We demonstrate RINA’s applicability through use cases spanning smart homes, healthcare monitoring, autonomous vehicles, and industrial edge computing, showcasing its adaptability to both RINA-native and legacy device integration. Our mixed-methods evaluation combines qualitative architectural analysis with quantitative experimental validation, providing both theoretical foundations and empirical evidence for RINA’s effectiveness. We also address emerging trends including AI-driven security and massive IoT scalability. This work establishes a conceptual foundation for leveraging recursive symmetry principles to achieve secure, efficient, and scalable IoT ecosystems. Full article

This paper presents a comprehensive framework for evaluating the robustness and adaptability of human–robot collaboration (HRC) controllers under a spectrum of dynamic and unpredictable human intentions. Building upon variable admittance controller (VAC) frameworks augmented with Radial Basis Function Neural Network (RBFNN) online adaptation, we introduce two key innovations: (1) an intent-aware human force generator capable of simulating aggressive, hesitant, oscillatory, conflicting, and nominal behaviors, through the modulation of force gains and the introduction of stochastic noise, and (2) the extension of VAC to incorporate variable stiffness as an adaptive control parameter alongside damping and inertia. The adaptive parameters are jointly tuned online using a self-supervised learning (SSL) mechanism driven by motion error metrics and interaction dynamics. The framework is simulated in a dual-arm collaborative manipulation scenario involving two 7-DoF Franka Emika Panda robots transporting a shared object in a high-fidelity simulation environment. Simulation results demonstrate the system’s capability to maintain stable behavior and minimize tracking error despite abrupt changes in human intent. This work provides a novel and systematic tool for stress-testing adaptive controllers in HRC, with implications for the design of resilient, safe, and reliable robotic systems in real-world collaborative environments. Full article

As renewable energy sources (RESs) become increasingly prevalent, limitations on connecting new sources arise due to insufficient suitable locations and grid constraints. Existing RES installations introduce challenges such as generation variability, the necessity for costly reserves, and overproduction, which can lead to forced outages. In response, grid operators have adopted more flexible connection policies, notably “cable pooling”, which only restricts the power injected at a given node rather than the total capacity of the connected sources. This article proposes a method for optimal sizing of diverse RES combinations connected to high-voltage networks under cable pooling conditions from an investor’s perspective. The most prominent findings show the existence of a strong relationship between optimal RES sizing and composition on financial objectives, revenue sources, and market prices. Subsequent achievements involve demonstrating that the profitability of energy storage without subsidies is essentially limited to participation in the capacity market and that the reduction of RES generation depends on the investor’s financial objective, not on the market type. Full article

Addressing the challenge of balancing high-precision positioning with strict safety constraints for underactuated bridge cranes subject to model parameter mismatch and stochastic wind disturbances, an adaptive hybrid control framework is presented integrating a Safety-Aware Dynamic Gain Sliding Mode Controller (DG-SMC) with a TD3-based residual deep reinforcement learning network. By designing a gain scheduling mechanism based on swing angle amplitude, the proposed method physically limits trolley acceleration to strictly constrain the payload swing angle within a safe range ($\pm 7°$). Simultaneously, a TD3 agent is introduced as a residual compensator to adaptively learn system dynamics through environmental interaction, generating real-time compensatory control forces to counteract unmodeled dynamics arising from system parameter deviations and continuous wind resistance. Numerical simulations demonstrate that, under conditions involving payload mass deviations of up to 25% and stochastic wind disturbances, the proposed control method effectively reduces steady-state positioning errors, suppresses payload swing during operation, and significantly enhances the system’s energy dissipation efficiency and global robustness in uncertain environments. Full article

Connected and Autonomous Vehicles (CAVs) are exposed to increasingly sophisticated cyber threats hidden within high-dimensional, heterogeneous network traffic. A critical bottleneck in existing Intrusion Detection Systems (IDS) is the feature heterogeneity gap: discrete protocol signatures (e.g., flags, services) and continuous traffic statistics (e.g., flow duration, packet rates) reside in disjoint latent spaces. Traditional deep learning approaches typically rely on naive feature concatenation, which fails to capture the intricate, non-linear semantic dependencies between these modalities, leading to suboptimal performance on long-tail, minority attack classes. This paper proposes HCA-IDS, a novel framework centered on Semantics-Aware Cross-Modal Alignment. Unlike heavy-weight models, HCA-IDS adopts a streamlined Multi-Layer Perceptron (MLP) backbone optimized for edge deployment. We introduce a dedicated Multi-Head Cross-Attention mechanism that explicitly utilizes static “Pattern” features to dynamically query and re-weight relevant dynamic “State” behaviors. This architecture forces the model to learn a unified semantic manifold where protocol anomalies are automatically aligned with their corresponding statistical footprints. Empirical assessments on the NSL-KDD and CICIDS2018 datasets, validated through rigorous 5-Fold Cross-Validation, substantiate the robustness of this approach. The model achieves a Macro-F1 score of over 94% on 7 consolidated attack categories, exhibiting exceptional sensitivity to minority attacks (e.g., Web Attacks and Infiltration). Crucially, HCA-IDS is ultra-lightweight, with a model size of approximately 1.00 MB and an inference latency of 0.0037 ms per sample. These results confirm that explicit semantic alignment combined with a lightweight architecture is key to robust, real-time intrusion detection in resource-constrained CAVs. Full article

Urban pluvial flooding in highly developed basins is challenging to forecast in real time because detailed 1D–2D hydraulic models are computationally expensive, while purely data-driven approaches often lack physical consistency. This study aims to enable operational urban flood nowcasting by proposing a model-informed AI framework for short-term stream-stage and urban inundation prediction in the Bisan-dong district of Anyang, South Korea, where the Anyang and Hagui Streams frequently overflow. A gated recurrent unit (GRU) network was trained on 10 min rainfall and stream-stage observations from 2011 to 2018 and independently validated on 2019–2022 data at four gauges to forecast stream stage at lead times of 10–60 min. In parallel, an ANN–CNN inundation surrogate was trained on 864 XP-SWMM 1D–2D simulation scenarios, forced by design storms and downstream water-level boundary conditions, to produce 256 × 256 maps of maximum inundation depth. The GRU model achieved ${\mathrm{R}}^2$ and Nash–Sutcliffe efficiency values generally above 0.95, with a mean absolute percentage error (MAPE) below approximately 5% for 10–30-min lead times; performance decreased but remained useful at 60 min. The inundation surrogate reproduced XP-SWMM results with an MAPE of 8.89% for inundation area and 19.49% for grid-based depth. Together, the ANN–CNN system enables rapid generation of high-resolution flood maps and provides a practical basis for AI-assisted urban flood nowcasting and risk management. Full article

Air transport networks function as strategic infrastructure whose structural evolution reflects broader geopolitical and economic forces. This study introduces a network-based interpretive framework for Geoeconomics in Air Transport by integrating complex network analysis with geoeconomic perspectives. It conceptualizes air transport networks as strategic economic infrastructure in which network topology encodes market access, power asymmetries, and resilience under geopolitical uncertainty. Using global civil aviation data, this paper constructs air transport networks at both the global level and across major regions—including the United States, Europe, the Middle East, ASEAN, China, and Japan—and compares passenger and cargo connectivity before (2019) and after (2023) the COVID-19 pandemic. Standard network metrics, such as centrality, topology, and connectivity, are used to quantify structural changes, which are subsequently interpreted through a geoeconomic lens. Global connectivity increased by approximately 8% in the post-pandemic period. In contrast, the United States—maintaining the most structurally resilient national air transport network—expanded by about 12%, while connectivity across Asian countries contracted, either domestically, internationally, or both. These patterns reflect a combination of intentional strategic responses and unintended structural adjustments. North American and European networks remain large-scale, meshed, and structurally resilient, whereas regions outside these core areas exhibit stronger hub-and-spoke dependence, both internally and in their connections with core regions. Such dependence signals persistent geoeconomic asymmetries and increased exposure to external shocks, despite higher traffic volumes per route. Betweenness centrality shifted markedly from European and North American hubs toward the Middle East, indicating the emergence of a geoeconomic intermediary region capable of sustaining connectivity across increasingly fragmented markets. The findings further demonstrate that, despite the United Kingdom’s withdrawal from the European Union, institutional and strategic realignments can enhance air transport network resilience in ways not anticipated by conventional geoeconomic interpretations of regional integration. By linking quantitative network outcomes with geoeconomic interpretation, this study provides reproducible insights into the strategic reconfiguration of global air transport systems under rising geopolitical uncertainty. Full article

Foods for special medical purposes play a critical role in clinical nutritional support, especially oil-in-water emulsions characterized as having high energy density, which could provide efficient energy for patients with insufficient intake or those requiring fluid restriction. The included oil types are the critical determinants of emulsion stability, which, in turn, governs digestive behavior, absorption efficiency, and ultimate bioavailability of the delivered nutrients. However, such emulsions face stability challenges during storage and application. In the present study, high-energy-density fat emulsions formulated with six typical oils, which contained 50% oil content, were prepared and systematically analyzed in terms of their particle size, zeta potential, microstructure, centrifugal stability, multiple light scattering, and rheological properties. The results indicated that oils with medium-chain fatty acids, due to their compact molecular structure and low viscosity, facilitated the formation of finer droplets and promoted the orderly arrangement of phospholipids at the interface of the emulsion system, leading to the formation of a dense, elastic interfacial layer and a gel network structure. Its marked shear-thinning characteristic and lowest frequency dependence contributed to desirable processing and storage stabilities. In contrast, long-chain triacylglycerols, especially those enriched with monounsaturated and saturated fatty acids, tended to form rigid but insufficiently elastic interfacial layers, which were unfavorable for resisting coalescence and phase separation induced by external forces. Highly unsaturated oils, on the contrary, exhibited medium levels for emulsion stability. Further analysis of the relationship between the physicochemical properties of oils and the characteristics of emulsions revealed that fatty acid species in the oil phase were the key determinants of emulsification behavior. It was therefore speculated that oils rich in medium-chain fatty acids with a moderate degree of unsaturation, especially including selected ω-3 and ω-6 fatty acids, could improve emulsion stability and fatty acid balance synchronously. This study provides a theoretical basis and technical support for the formulation design and stability control of high-energy-density fat emulsions. Full article