Search Results (12,496)

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies. Full article

In this study, we present a novel adaptive algorithm, social learning-enhanced deep reinforcement learning (SLDRL), which integrates social learning mechanisms into deep reinforcement learning (DRL) to improve agent performance in both discrete and continuous state-space environments. The proposed hybrid control architecture enables agents to autonomously decide when and how to exploit socially acquired behaviors, balancing social learning with individual exploration through an entropy-based intrinsic motivation mechanism. The framework incorporates online imitation and enactment mechanisms that allow agents to observe and selectively reuse behavioral sequences acquired from other agents during training. We evaluate SLDRL in a sparse-reward discrete grid-based foraging task and in the dense-reward continuous-state/discrete-action CartPole problem. In both domains, SLDRL agents outperform baseline DRL agents, achieving faster learning and higher cumulative rewards. The results show that socially acquired behaviors are utilized adaptively throughout training, with the balance between imitation and individual learning emerging dynamically according to the structure of the environment and the agent’s experience. Comparisons with a behavioral cloning baseline further indicate that selectively integrating observed behaviors can yield more robust long-term learning than direct imitation of demonstration trajectories. Overall, the results demonstrate that SLDRL can effectively leverage online social learning in diverse environments. Full article

In the intelligent transportation system of smart cities, object detection from UAV aerial imagery serves as the core technical support for traffic flow monitoring, violation detection, and emergency response. However, traffic objects captured from UAV perspectives typically exhibit extremely low pixel occupancy and are embedded in complex backgrounds, leading to three fundamental limitations in existing detection methods: insufficient utilization of global context information, inaccurate weak feature enhancement, and severe feature scale confusion. To address these challenges, this paper proposes ContextTiny-Net, an ultra-tiny object detection network built upon multi-dimensional symmetry design principles for urban UAV scenarios. Specifically, we first construct a global–local perception symmetric MetaFormer backbone and a hierarchical scale symmetric four-layer detection head, which achieves full-coverage detection from ultra-tiny to regular traffic objects with minimal computational overhead. Second, we design an information-theoretic and spatial-distribution-complementary symmetric-weak feature enhancement module, which accurately locates and strengthens weakly activated regions of small objects from two mutually complementary and symmetric dimensions. Finally, we propose a cross-scale decoupling symmetric feature fusion module and a symmetric Gaussian distribution-based normalized Wasserstein distance loss, which effectively eliminate scale confusion and significantly improve the robustness of small object bounding box regression. Extensive experiments on three mainstream benchmarks (AI-TOD, VisDrone, and COCO) demonstrate that ContextTiny-Net outperforms state-of-the-art methods in both overall detection accuracy and ultra-tiny object detection performance, verifying the effectiveness of the proposed symmetry-enhanced design paradigm. Full article

Background: Natriuretic peptides, including B-type natriuretic peptide (BNP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP), are established biomarkers of myocardial stress and circulatory overload. Although originally validated for diagnosis and exclusion of heart failure, their diagnostic and prognostic applications have expanded significantly in the context of critical illness. However, interpretation in critically ill patients is complicated by confounding factors such as systemic inflammation and renal dysfunction. Objective: This review synthesizes current evidence on the diagnostic, monitoring, and prognostic applications of natriuretic peptides in critically ill adults. It further outlines practical considerations, confounding variables, and emerging complementary biomarkers pertinent to clinical decision-making. Methods: A structured search of PubMed, Embase, and the Cochrane Library (January 2000 to October 2025) identified studies evaluating BNP, NT-proBNP, and atrial natriuretic peptide (ANP) in intensive care unit (ICU) patients. Eligible studies and review articles assessed diagnostic utility, volume status, hemodynamic monitoring, and prognostic performance. Narrative synthesis was employed using information obtained from eligible studies. Results: Twenty-four studies met the inclusion criteria. BNP and NT-proBNP facilitate differentiation between cardiogenic and noncardiogenic respiratory failure, identification of mixed shock states, and assessment of volume status when used in association with other modalities such as echocardiography and ultrasonography. Elevated natriuretic peptide concentrations consistently predict mortality, acute kidney injury, prolonged mechanical ventilation, and adverse outcomes in several disease states, including sepsis, acute respiratory distress syndrome [ARDS], postoperative cardiac dysfunction, and COVID-19-related critical illness. However, interpretation remains limited by confounders, including renal impairment, age, systemic inflammation, brain injury, mechanical ventilation, and right-ventricular strain/dysfunction. Conclusions: Natriuretic peptides serve as valuable adjuncts for diagnostic assessment, hemodynamic monitoring, and risk stratification in the ICU. When interpreted with attention to biological kinetics and clinical context, these biomarkers enhance multimodal monitoring and support individualized management. Future research should refine ICU-specific cutoffs and assess natriuretic peptide–guided therapeutic strategies in prospective multicenter trials. Full article

Spontaneous movement analysis provides valuable information about the maturation of the central nervous system and the emergence of motor control strategies in very young babies. Nonlinear measures capture dynamic aspects of movement that cannot be represented by linear methods. However, their implementation in clinical practice faces challenges, including the lack of standardized protocols and accessible tools for routine use. This scoping review aimed to map and characterize the nonlinear measures used to analyze spontaneous infant movement, including assessment context, instruments, data collection protocols, and main variables. The review followed JBI methodology and PRISMA-ScR guidelines. Searches were conducted in PubMed^®, Web of Science™, IEEE Xplore^®, ScienceDirect^®, and Google Scholar for studies published from 1 January 2005 to 31 December 2025. Of 1166 records identified, 18 met the inclusion criteria. The nonlinear measures were grouped into five main methodological families: entropy-based measures (n = 10), state-space and dynamical systems measures (n = 4), recurrence-based analysis (n = 3), symbolic and discrete-state approaches (n = 3), and variance and frequency-based nonlinear descriptors (n = 1). Studies were conducted in laboratory settings (n = 6) and in hospital and/or home environments (n = 10). Two studies did not clearly specify the assessment context. Kinematic assessment was mainly performed using video-based systems (n = 7), accelerometers (n = 4), and wearable sensors (n = 2), with most studies focusing on the upper and lower limbs. Several investigations extended beyond single-joint analyses to examine inter-limb relationships and whole-body configurations, capturing spatial coordination patterns across multiple body segments. Kinetic assessment was conducted using pressure mats (n = 4) and force platforms (n = 1), with the center of pressure displacement as the primary outcome. Future research should prioritise methodological harmonisation and theoretical clarity. Consensus is needed regarding minimal data requirements, parameter selection, and reporting standards for commonly used nonlinear measures. Studies should also move beyond single-metric approaches and adopt multivariate frameworks that integrate complementary nonlinear metrics. The absence of standardised acquisition and analytical protocols currently limits cross-study comparability and hinders the clinical translation of nonlinear movement metrics as objective tools for early neurodevelopmental assessment. Full article

Background: Consumption of ultra-processed foods (UPFs) continues to rise alongside a growing body of epidemiological evidence linking high UPF intake to adverse health outcomes, including cardiovascular disease and type 2 diabetes, in the general population. However, the factors underlying these associations remain incompletely understood, underscoring the need to examine components beyond traditional nutrient composition. In particular, food-processing additives are increasingly recognized as defining features of industrially formulated UPFs. Objective/Methods: In this study, we used a large food label database to cross-sectionally examine the presence and co-occurrence of selected additives (sorbates, benzoates, phosphate additives, and non-nutritive sweeteners [NNSs]) in sweetened beverages sold by the 25 top-selling U.S. food and beverage manufacturers in 2020. Results: We found that sweetened beverages marketed in the U.S. frequently contain multiple additive classes concurrently, supporting the concept that these products represent complex chemical exposure mixtures rather than simple combinations of water and sweeteners. Formulations containing multiple additives were substantially more common than simpler formulations, with many beverages simultaneously containing combinations of sweeteners, preservatives, and phosphate additives. Products containing NNS exhibited higher additive clustering compared to products containing added sugar. Conclusions: Collectively, these findings support the need for broader consideration of beverage formulation complexity in nutrition research, dietary guidance, and policy regulation. Full article

The digital transformation has revolutionized document management, making Digital Transaction Management (DTM) systems essential for enhancing efficiency, security, and regulatory compliance in a wide range of organizations. This paper investigates the challenges, innovations, and state-of-the-art solutions in DTM platforms, with a focus on their integration with emerging technologies such as Artificial Intelligence (AI) and blockchain. In particular, the work introduces a reference architecture for next-generation DTM platforms, emphasizing blockchain-based security, smart-contract-based automation, and semantics-enhanced document retrieval and analysis. A case study in the utilities sector illustrates the benefits of the envisioned proposal, showcasing its suitability for managing complex, high-volume workflows. This research provides a foundation for future developments in resilient and interconnected DTM solutions, addressing the evolving demands of modern organizations within the broader digital ecosystem. Full article

Systems Medicine aims to understand the dynamics of physiological systems and the differences between healthy and disease states, to then bring the latter back to health. To this aim, it is critical to identify the states that allow modifying the phenotype of a model system and are robust to perturbations. Indeed, for these changes to be sustained in time, the system’s robustness shall be investigated through various analyses and their emerging results. Lie symmetry analysis—a study of fixed variable relations in a differential equations model—uncovers the model’s hidden robustness through its conservation laws. The emerging conservation laws can then be used as a series of robust invariant characteristics of the system under a specific type of perturbations. Although it holds much predictive potential for robustness investigations, the application of Lie symmetry-based conservation law analysis to physiological systems is currently unexplored. Here, we propose a novel application of Lie symmetry-based conservation law analysis to identify the conservation laws—and their existence conditions—influencing the dynamics of a system towards robust remission or relapse. This methodology is used to analyse a minimal model of rheumatoid arthritis with the aim to: (i) investigate the existence and extent of robust disease characteristics as conservation laws of the model, (ii) clinically interpret their biological viability, and (iii) inform model plausibility, testing, and selection. This novel application of the Lie symmetry analysis can retrieve the robust characteristics of physiological conditions, thus providing a new analytical contribution to the Systems Medicine field. Full article

Resource allocation exhibits systematic divergence across heterogeneous time horizons, which aggregate measures fail to capture. This paper models allocation as a multi-component dynamic system composed of three coupled mechanisms: long-term accumulation, short-cycle response, and phase stabilization. A dual-horizon framework is developed to integrate finite-horizon local adjustment with infinite-horizon path evolution. The results show that system dynamics are governed by the coupling of these mechanisms under a shared resource constraint, which endogenously generates a unified adjustment signal and redistributes marginal resources across components. This process jointly determines short-term deviation correction, structural stabilization, and long-term equilibrium formation. Equilibrium is therefore characterized as an emergent system state shaped by intertemporal feedback and dynamic interactions. Numerical simulations further illustrate the dynamic adjustment paths and confirm the theoretical mechanisms. The framework provides a system-level explanation for divergent findings by showing how structural decomposition reveals heterogeneous mechanisms obscured under aggregate representations. Full article

Data has become a strategic production factor, but the institutional logic underlying data’s tradability, priceability, and governability remains insufficiently theorized. In response, this study develops a coevolutionary framework that connects conventional factor market theory with digital political economy, platform theory, and comparative institutional analysis. This study adopts a conceptual–analytical research design, integrating three research methods: theory synthesis, comparative institutional analysis, and policy-process interpretation. Through theoretical synthesis, institutional comparison, and policy-process interpretation, it analyzes the conditions under which data circulation becomes feasible, lawful, and economically sustainable. In addition, by combining transaction data, exchange listings, property rights registrations, network indicators, and regional policy variations, it formulates testable propositions and an empirical agenda. The study finds that data factor markets do not emerge automatically with digitalization; their formation requires three mutually reinforcing conditions: technologically reducing search, verification, privacy protection, and contract enforcement costs; institutionally realizing a modular definition of rights and establishing compliance boundaries; and market demand from firms, public agencies, and research organizations generating use-case-specific value. Meanwhile, this study revises the three-stage model of market evolution as a contingent and testable pathway—from administrative pilot allocation, through hybrid state–market professionalization, to ecosystem-based cross-domain circulation. It also clarifies a closed-loop dynamic mechanism consisting of external shocks, internal strategic feedback, and adaptive governance, which jointly shapes market boundaries, pricing rules, and competition patterns. Full article

Autonomous Underwater Vehicles (AUVs) are widely employed for deep sea exploration and underwater operations, but their navigation performance is often degraded in complex environments due to time-varying measurement noise, abnormal observations, and Doppler Velocity Log (DVL) outages. To address these challenges, this paper proposes an integrated SINS/DVL/PS navigation framework that combines an Adaptive Huber and Sage–Husa Extended Kalman Filter (AHR-EKF) with a Nonlinear AutoRegressive with eXogenous inputs (NARX)-based velocity prediction model. The AHR-EKF effectively suppresses outliers and adapts to time-varying noise, thereby enhancing filter stability and state estimation accuracy. During DVL outages, the NARX model predicts short-term AUV velocity using propeller speed, velocity increments from the navigation system, and attitude information as exogenous inputs. This data-driven approach compensates for lag and mismatch in propeller-based velocity measurements, while capturing both short-term fluctuations and overall velocity trends. Simulations and sea trials were conducted to validate the method. In the simulation experiment during DVL outages, the V-NARX method achieved east and north positioning of RMS errors of 8.397 m and 6.530 m, compared with 24.699 m and 10.218 m for the V-RPM method. In the sea trial, the V-NARX method achieved east and north RMS errors of 41.160 m and 28.023 m, respectively, compared with 52.820 m and 67.057 m for V-RPM, corresponding to reductions of 22.1% and 58.2%. The proposed method maintains trajectory continuity and effectively suppresses rapid INS error accumulation during DVL outages, significantly enhancing emergency navigation capability under DVL outages. Although its positioning accuracy does not match that of normal DVL operation, the method provides a practical and reliable engineering solution for continuous AUV navigation when DVL is unavailable. Full article

Natural fibre meshes have emerged as a promising class of sustainable geotechnical reinforcement materials; however, no calibrated and validated directional constitutive model currently exists in the scientific literature for Furcraea macrophylla (fique) fabric under contrasting hygroscopic states. This knowledge gap prevents the adoption of performance-based, reliability-centred design approaches and limits the broader use of this Andean biotextile in bioengineering practice. The present study develops, calibrates, and validates a nonlinear constitutive model integrating warp/weft fabric anisotropy, a scalar damage law (Dk), and a hygroscopic reduction factor (φh). A multiscale experimental programme—comprising SEM-EDX, FTIR-ATR, TGA, CO₂ physisorption, wide-strip tensile testing (n ≥ 5 per condition; 4 conditions × 2 directions), large-scale direct shear, and RL-CBR tests—provided all model parameters. Statistical analysis (two-way ANOVA; Shapiro–Wilk normality test; Levene homogeneity test) confirmed the significance of both configuration and moisture states on mechanical response (p < 0.001). Model validation by cross-validation (n = 12 retained series), Sobol global sensitivity analysis, and Monte Carlo uncertainty propagation (N = 1000 iterations, Latin Hypercube Sampling) yielded R² = 0.94 ± 0.03 and a normalised root mean square error (NRMSE) < 8.5% across all series; 98% of all individual experimental data points (≥60 points across 12 test series) fell within the 95% Monte Carlo confidence interval. The hygroscopic reduction factor dominated mechanical uncertainty with a Sobol first-order index of Si(φh) = 0.82, confirming that moisture-induced plasticisation governs reinforcement stability in tropical service conditions (φh = 0.62–0.78; CV = 6.4%). A technical comparison with Colombian road construction standards confirmed that fique mesh meets minimum tensile requirements for erosion control and slope reinforcement. All raw data and the constitutive model code are available upon reasonable request to ensure full reproducibility. Full article

Insect antimicrobial peptides (AMPs) are classically viewed as terminal effectors of innate immunity, but emerging evidence suggests that some can also shape defined neural states. In this Review, we argue that insect systems provide a powerful framework for resolving immune–brain communication at the level of individual peptide effectors, because genetically tractable innate-immune pathways allow pathway activation to be distinguished from peptide-specific effector function. Rather than surveying AMP families exhaustively, we focus on representative cases in which peptide identity, source, and timing can be linked to sleep, memory-related plasticity, and responses to acute injury. These studies show that the neural consequences of AMP induction cannot be inferred from pathway activation alone, but require peptide-level analysis of effector identity, cellular context, and exposure logic. This perspective also raises the question of translational potential. At present, direct biomedical development of endogenous insect AMPs in neural contexts remains limited, whereas more tangible applied interest has centered on insect venom peptides that share AMP-like physicochemical features. We therefore discuss insect venoms separately from endogenous AMP physiology. Venom peptides are not physiological equivalents of endogenous insect AMPs, but represent evolutionarily diversified AMP-like templates for scaffold discovery, mechanistic probing, and therapeutic engineering. Together, this review develops a peptide-level perspective on insect neuroimmune biology while highlighting insect venoms as a valuable, but highly constrained, source of templates for biomedical discovery. Full article

This paper presents a comprehensive review of state-of-the-art developments in electric vehicle (EV) charging technologies, charging stations, and charging protocols, with particular emphasis on their integration with renewable energy sources (RESs). EV chargers are generally classified into on-board and off-board configurations. This study examines recent designs and advanced control strategies for both AC/DC and DC/DC power conversion stages, highlighting key technical aspects, recent innovations, and existing challenges. Furthermore, it provides an in-depth discussion of emerging multiport EV charger architectures that integrate photovoltaic (PV) systems, energy storage units, EVs, and the power grid within a unified framework. A comparative analysis is also presented to evaluate various converter topologies and energy management strategies used in the AC/DC and DC/DC stages of EV charging systems. Critical performance indicators such as power rating, output voltage level, efficiency, economic feasibility, and system complexity are also discussed. A comprehensive comparison is conducted among 13 review papers between 2015 and 2026, identifying key trends, methodological differences, and common findings. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article