Search Results (5,183)

Despite significant advances in bank regulation and the widespread adoption of macroprudential frameworks, emerging market economies remain persistently vulnerable to global financial shocks. Episodes such as the Global Financial Crisis, the COVID-19 market turmoil, and recent monetary tightening cycles reveal that financial stress originating in core markets continues to transmit rapidly and forcefully to emerging economies. This paper argues that such vulnerability reflects structural features of contemporary financial systems rather than deficiencies in domestic banking regulation alone. Adopting a conceptual and analytical approach, the article develops an integrated framework of macro-financial blind spots that links global financial cycles, non-bank financial intermediation, and regulatory fragmentation. The analysis highlights how funding liquidity, collateral valuation, margin dynamics, and market-based leverage amplify global shocks through channels that lie largely outside traditional, bank-centric macroprudential frameworks. As market-based finance expands, systemic risk increasingly originates in activities rather than institutions, limiting the effectiveness of entity-based regulation and reinforcing emerging markets’ role as price-takers in global portfolios. The paper contributes to the literature by synthesizing insights from macroprudential policy, market liquidity, and non-bank finance to explain the persistence of emerging market vulnerability in an era of globalized funding. It further derives policy implications for macro-financial governance, emphasizing the need for system-wide, activity-based approaches, improved data and transparency, and stronger domestic and international regulatory coordination. These findings are relevant for policymakers seeking to reconcile financial integration with systemic resilience in emerging markets. Full article

Oxidative stress-induced lipid peroxidation products (LPPs), particularly 4-hydroxy-nonenal (4-HNE) and 4-oxo-nonenal (4-ONE), have recently gained attention for their direct regulation of ion channels essential for pain signaling. In this study, we investigated how these two LPPs affect the electrophysiological properties of neurons, specifically voltage-gated sodium (Na_V) channels, thereby influencing sensory neuron excitability and pain pathways. Using human neuroblastoma (SH-SY5Y) and ND7/23 cells (a fusion cell line exhibiting partial sensory neuron properties), we measured changes in Na_V channel-mediated sodium currents following treatment with 4-HNE or 4-ONE. Whole-cell patch-clamp experiments showed that 4-ONE (10 µM) and 4-HNE (100 µM) did not significantly alter the peak sodium current amplitude in SH-SY5Y cells. However, in ND7/23 cells, both 4-HNE and 4-ONE induced a negative shift in Na_V channel activation voltage dependence, enabling sodium channel activation at lower membrane potentials. Furthermore, current-clamp recordings in primary mouse dorsal root ganglion neurons demonstrated that treatment with 4-ONE and 4-HNE reduced the current threshold required to elicit action potentials and significantly increased action potential firing frequency. These findings indicate that LPPs enhance pain sensitivity by modulating Na_V channels, which play a crucial role in pain transmission. In conclusion, 4-HNE and 4-ONE shift the voltage-dependent activation of sodium channels toward more negative potentials, thereby increasing the excitability of primary sensory neurons and amplifying pain signals. This study provides molecular insights into how oxidative stress-related lipid peroxidation contributes to sensory mechanisms and offers potential avenues for developing new treatments for oxidative stress- or inflammation-associated pain. Full article

Most transient receptor potential (TRP) channels are Ca²⁺-permeable non-selective cation channels that function as polymodal receptors activated by a wide variety of stimuli, including natural compounds such as pungent substances, physical stimuli, lipids, intracellular signaling molecules, and ions. Their physiological roles are diverse, including sensory perception, ion transport, and intracellular signaling. Similarly, Piezo channels, which are also Ca²⁺-permeable non-selective cation channels, are activated by mechanical stimuli such as membrane stretching and contribute to touch sensation, blood flow regulation, and bladder-filling sensation, among other functions. While research on non-selective cation channels in relation to energy metabolism has primarily focused on TRP channels expressed in primary afferent neurons, studies over the past decade have revealed the important roles of TRP and Piezo channels in brown adipocytes. In this review, we highlight evidence regarding the contributions of TRPV2 and Piezo1 to brown adipocyte differentiation and thermogenesis and briefly summarize recent advances regarding other TRP channels expressed in brown adipocytes. Furthermore, we propose a conceptual framework in which a “modal shift” in TRP/Piezo channels, defined as developmental stage-dependent changes in their functional properties, may contribute to the regulation of brown adipocytes’ functions. Full article

Accurately predicting molten steel carbon content plays a crucial role in improving productivity and energy efficiency during the Basic Oxygen Furnace (BOF) steelmaking process. However, current data-driven methods primarily focus on endpoint carbon content prediction, while lacking sufficient investigation into real-time curve forecasting during the blowing process, which hinders real-time closed-loop BOF control. In this article, a novel Transformer-based framework is presented for real-time carbon content prediction. The contributions include three main aspects. First, the prediction paradigm is reconstructed by converting the regression task into a sequence classification task, which demonstrates superior robustness and accuracy compared to traditional regression methods. Second, the focus is shifted from traditional endpoint-only forecasting to long-term prediction by introducing a Transformer-based model for continuous, real-time prediction of carbon content. Last, spatial–temporal feature representation is enhanced by integrating an optical flow channel with the original RGB channels, and the resulting four-channel input tensor effectively captures the dynamic characteristics of the converter mouth flame. Experimental results on an independent test dataset demonstrate favorable performance of the proposed framework in predicting carbon content trajectories. The model achieves high accuracy, reaching 84% during the critical decarburization endpoint phase where carbon content decreases from 0.0829 to 0.0440, and delivers predictions with approximately 75% of errors within ±0.05. Such performance demonstrates the practical potential for supporting intelligent BOF steelmaking. Full article

Financial regulation assumes that parent firms reliably support distressed subsidiaries during crises. We test this assumption with evidence from the 2007–2009 financial crisis and find that parent support was selective rather than reliable. Using novel measures of sibling distress and granular parent-affiliate funding flows, our findings reveal that capital allocation within bank holding companies (BHCs) disproportionately favored stronger affiliates. The results show that BHCs channeled capital toward more liquid and resilient subsidiaries while limiting support to weaker ones. Profitable parents became increasingly selective under stress, and nonbank subsidiaries emerged as critical internal liquidity providers when external markets froze. This selective reallocation highlights a gap between regulatory doctrine and actual behavior: intra-group capital allocation mechanisms can amplify systemic stress rather than mitigate it. By examining overlooked internal market dynamics during this major financial crisis, the study offers insights for strengthening financial stability against future systemic shocks. Assessing parent firm strength alone appears insufficient. Effective crisis prevention requires supervisory frameworks that monitor sibling fragility across conglomerates, evaluate the liquidity roles of nonbank affiliates, and stress test intra-group capital flows. Full article

The efficient extraction of natural gas from water-drive reservoirs is often hindered by premature water breakthrough and the consequent trapping of significant residual gas, which collectively result in suboptimal recovery and economic returns. Traditional production methods have proven inadequate in mitigating water influx and mobilizing this trapped gas, underscoring the need for advanced enhanced gas recovery (EGR) strategies. This research specifically examines the potential of nitrogen injection as a tertiary recovery technique in such reservoirs, with a focus on its mechanistic role and displacement efficiency. Utilizing high-pressure core flooding experiments and complementary numerical simulations, the process of nitrogen injection following water flooding was systematically investigated. Experimental findings at 30 MPa indicate that while water flooding left a substantial residual gas saturation of 28.1%, subsequent nitrogen injection reduced this to 20.8% at breakthrough and ultimately to 7.99%, achieving a final recovery of 88.9%. Simulation results further elucidate that in fractured systems, water preferentially channels through high-permeability fractures, while capillary imbibition leads to gas entrapment within the matrix. Nitrogen injection effectively targets and reduces this trapped gas saturation by 30-50%, demonstrating its efficacy as a viable EGR method. The study thus provides critical theoretical and practical insights for improving recovery in challenging water-drive gas reservoirs. Full article

Droplet-based microfluidics has emerged as a powerful platform for precise fluid manipulation in biomedical, chemical, and material science applications. Among various geometries, T-junction microchannels are widely utilized for droplet generation and splitting due to their simplicity and reliability. This review provides a comprehensive overview of droplet splitting mechanisms in T-junction microfluidic systems, with particular emphasis on the role of actuation methods in enhancing control and functionality. We first discuss the fundamental physics governing droplet behavior, including the influence of capillary and viscous forces, flow regimes, and geometric parameters. Passive strategies based on flow rate tuning and channel design are outlined, followed by an in-depth examination of active actuation techniques: thermal, electrical, magnetic, acoustic, and pneumatic and their effects on droplet dynamics. In addition, the review highlights computational modeling approaches and experimental tools used to characterize and predict splitting behavior. Finally, we explore the current challenges and future directions in integrating multifunctional actuation systems for real-time, programmable droplet control in lab-on-a-chip platforms. This article serves as a foundational resource for researchers aiming to advance microfluidic droplet manipulation through actuator-enabled strategies. Full article

Digital signal processing plays a central role in intelligent railway communications under high-mobility, strong-multipath, and time-varying-channel conditions. This review surveys representative techniques for multi-carrier modulation, precoding, index modulation, and chaos-inspired physical layer security and highlights their mathematical foundations. Core themes include transform-domain representations typified by time–frequency analysis, linear-algebraic formulations of precoding and equalization, combinatorial structures underlying index mapping and spectral efficiency gains, and nonlinear dynamical systems theory of chaotic encryption. The methods are compared in terms of bit error performance, peak-to-average power ratio, spectral efficiency, computational complexity, and information security, with emphasis on railway-specific deployment constraints. The synergistic application of these methods with intelligent railway transportation systems is expected to enhance the overall performance of railway transportation systems in terms of transmission efficiency, reliability, and security. It provides critical technological support for the efficient and secure operation of next-generation intelligent transportation systems. Full article

In meandering river floodplain systems, remote sensing is a valuable tool for assessing connectivity processes relevant to fish ecological functions. This study used the Google Earth Engine platform and multispectral Landsat 7 imagery. A random forest classifier was used to evaluate water types and area changes in oxbow lakes of the Beni River in Bolivia. Water type dynamics were mainly associated with lake age and distance from the main channel. Seasonal variations highlighted the role of wind-driven sediment resuspension and overflow during high discharge conditions. Long-term lake area changes reflected typical oxbow lake evolution as well as alterations caused by the main channel. Multiannual changes showed a notable area decrease during years of low discharge. Relationships between discharge and lake area dynamics allowed the classification of three lake groups with different levels of connectivity and overbank flow influence. The ecological relevance of these groups was evaluated based on fish habitat preferences and migration patterns. Results emphasize the importance of preserving natural hydrologic variability, with flooding associated with increased habitat availability. Overall, this study demonstrates the usefulness of satellite remote sensing for detecting ecohydrological processes and offers insights to preserve ecological functions in data-scarce regions. Full article

Autonomous Underwater Vehicles (AUVs) play a central role in marine observation, inspection, and monitoring missions, where effective trajectory planning is essential for ensuring safe operation, reliable sensing, and efficient data transfer. In realistic underwater environments, uneven seafloor geometry, limited acoustic communication, navigation uncertainty, and sensing visibility constraints significantly influence mission performance and challenge classical planar planning formulations. This survey reviews trajectory planning methods for AUVs operating in uneven environments, with a focus on two major classes of underwater sensing missions: underwater area coverage using onboard sensors and underwater sensor data collection within underwater acoustic sensor networks (UASNs) supporting the Internet of Underwater Things (IoUT). For area coverage, the survey examines the progression from classical planar coverage strategies to terrain-aware, occlusion-aware, multi-AUV, and online planning frameworks designed to address uneven terrain and sensing visibility. For underwater sensor data collection, it reviews mobile sink-based trajectory planning strategies, including energy-aware, channel-aware, and information-based formulations based on metrics such as Age of Information (AoI) and Value of Information (VoI), as well as cooperative architectures involving unmanned surface vehicles (USVs). By synthesizing these two bodies of literature, the survey clarifies current capabilities and limitations of trajectory planning methods for AUVs operating in uneven underwater environments. Full article

Building extraction from remote sensing imagery plays a pivotal role in applications such as smart cities, urban planning, and disaster assessment. Although deep learning has significantly advanced this task, existing methods still struggle to strike an effective balance among global semantic understanding, local detail recovery, and multi-scale contextual awareness—particularly when confronted with challenges including extreme scale variations, complex spatial distributions, occlusions, and ambiguous boundaries. To address these issues, we propose TriadFlow-Net, an efficient end-to-end network architecture. First, we introduce the Multi-scale Attention Feature Enhancement Module (MAFEM), which employs parallel attention branches with varying neighborhood radii to adaptively capture multi-scale contextual information, thereby alleviating the problem of imbalanced receptive field coverage. Second, to enhance robustness under severe occlusion scenarios, we innovatively integrate a Non-Causal State Space Model (NC-SSD) with a Densely Connected Dynamic Fusion (DCDF) mechanism, enabling linear-complexity modeling of global long-range dependencies. Finally, we incorporate a Multi-scale High-Frequency Detail Extractor (MHFE) along with a channel–spatial attention mechanism to precisely refine boundary details while suppressing noise. Extensive experiments conducted on three publicly available building segmentation benchmarks demonstrate that the proposed TriadFlow-Net achieves state-of-the-art performance across multiple evaluation metrics, while maintaining computational efficiency—offering a novel and effective solution for high-resolution remote sensing building extraction. Full article

With the evolution of Reconfigurable Intelligent Surface (RIS) technology, its potential for dynamically optimizing wireless channels has garnered significant attention. However, existing methods still face challenges in real-time control in complex environments due to high computational complexity. To address this, this paper proposes a reconfigurable wireless channel optimization framework based on Intelligent Metasurfaces 2.0 and designs a low-complexity control strategy. The strategy integrates an adaptive adjustment mechanism and multi-dimensional feedback, aiming to reduce system computational load. Experimental results show that compared to traditional methods (such as MRC and MMSE), the proposed method improves signal transmission quality (SNR improvement of 3.8 dB) and system stability (exponential increase to 0.92). When compared to advanced deep reinforcement learning (DRL) and graph neural network (GNN) methods, it achieves similar signal quality while reducing computational overhead by 20.0% and energy consumption by approximately 32.4%. Ablation experiments further verify the effectiveness and synergistic role of the proposed core modules. This study provides a feasible approach toward high-efficiency, low-complexity dynamic channel optimization in 5G and future communication networks. Full article

As a key mechanism for guiding capital flows into green and low-carbon sectors, green finance plays a significant role in supporting China’s “dual carbon” strategy and driving the high-quality development of its energy system. This research investigates the mechanisms and effects through which green finance influences new-quality productivity in the energy sector. Based on Chinese provincial panel data spanning 2012–2022, we apply fixed-effects models, mediation effect tests, and a threshold regression model to empirically analyze the transmission channels, nonlinear features, and regional heterogeneities of this influence. The findings indicate the following: (1) Green finance significantly drives the development of new-quality productivity in the energy sector, and this conclusion holds robust after a series of robustness tests. (2) The primary transmission channels operate by promoting technological innovation and enhancing the level of foreign trade openness. (3) The impact presents nonlinear characteristics: the positive effect of green finance weakens when the level of technological innovation is excessively low or when foreign trade openness is overly high. (4) Significant heterogeneity is observed, with a more pronounced promotional effect in regions with weaker financial endowment, while the effect is relatively limited in regions with stronger financial endowment. Accordingly, this research proposes optimizing the green financial system and formulating differentiated regional policies to synergistically advance the development of new-quality productive forces in energy. Full article

During public health emergencies, individuals typically obtain epidemic-related information through mass media channels and personal social media platforms. This information enables them to monitor epidemic progression and adjust their preventive behaviors accordingly to mitigate infection risks. To capture these processes, this paper proposes a three-layer coupled metapopulation network model that investigates the effects of regional mass media and social information propagation on the spatial spread of epidemic. The mass media layer represents regional outlets that propagate epidemic-related information to individuals within corresponding patches. Migrant individuals not only follow mass media information of the residential patch, but also continue to follow mass media information from their destination patch. The information layer captures the dynamics of information exchange on social media platforms. The epidemic layer depicts the spread of the epidemic within the metapopulation network and simulates the reaction-diffusion dynamics of migrating individuals across different patches through a Migration-Interaction-Return (MIR) mechanism; the coupling between the information layer and the epidemic layer is asymmetric. Theoretical analysis using the Microscopic Markov Chain Approach (MMCA) derives the evolution equation and determines the epidemic thresholds, while Monte Carlo (MC) simulations validate the model and explore factors influencing propagation dynamics. Our research indicates that when migrants simultaneously receive mass media information from both residential and destination patches, it significantly enhances information coverage and promotes protective behaviors, thereby effectively suppressing epidemic spread. Furthermore, promoting information propagation—particularly the communication among individuals within a patch—significantly increases the proportion of aware individuals, reduces the infection scale, and raises the epidemic threshold. Notably, population migration would originally lead to an increase in infection scale, but as the intensity of information propagation strengthens, migration instead has a good effect on controlling epidemic spread. These results provide deeper insights into the role of awareness propagation and human mobility in epidemic containment. Full article

Exposure to microgravity results in cardiovascular deconditioning, with endothelial cell apoptosis recognized as a pivotal initiating event. However, the mechanosensitive mechanisms underlying this process remain poorly understood. Here, we demonstrate that the expression of mechanosensitive ion channel protein PIEZO1 is upregulated in human umbilical vein endothelial cells (HUVECs) under simulated microgravity. Functional studies revealed that PIEZO1 activation promotes endothelial apoptosis under simulated microgravity conditions. Proteomic analysis following PIEZO1 knockdown revealed extensive alterations in biological processes associated with apoptosis. Furthermore, we found that PIEZO1 activation triggers calcium influx, leading to elevated expression of the HMGA2. Moreover, we identify that PIEZO1 activation induces calcium influx, which subsequently elevates the expression of HMGA2. The knockdown of HMGA2 significantly mitigated microgravity-induced endothelial apoptosis, indicating its role in PIEZO1-mediated apoptosis. These findings reveal a novel PIEZO1–Ca²⁺–HMGA2 axis critical for microgravity-induced endothelial apoptosis, providing mechanistic insight into cardiovascular adaptation to spaceflight and potential therapeutic targets for countermeasure development. Full article