Search Results (238)

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study utilizes a sample comprising Chinese A-share listed enterprises over the period from 2012 to 2024 to construct an enterprise climate transition risk index using text analysis methods. It empirically investigates this index’s impact on enterprise green innovation by adopting panel data analysis method to construct a fixed effects model and further examines the moderating roles of institutional investors’ shareholding and enterprise environmental uncertainties in response to climate transition risks. The research findings indicate the following: First, climate transition risks significantly enhance enterprise green innovation. The validity of this conclusion persists following a series of robustness and endogeneity tests, including replacing the explained variable, lagging the explanatory variable, controlling for city-level fixed effects, and applying instrumental variable methods. Second, both institutional investors’ shareholding and enterprise environmental uncertainties exert a significant positive regulatory effect on the relationship between climate transition risk and green innovation, indicating that external monitoring and heightened risk perception jointly enhance enterprises’ responsiveness in driving green innovation. Thirdly, heterogeneity analysis indicates that the positive impact of climate transition risks on green innovation is notably amplified within non-state-owned enterprises and manufacturing enterprises. By examining the dual regulatory mechanisms of ‘external monitoring’ and ‘risk perception’, this study broadens the study framework on the relationship between climate risks and enterprise green innovation, offering new empirical evidence supporting the applicability of the ‘Porter Hypothesis’ within the context of climate-related challenges. Furthermore, it provides valuable implications for policymakers in refining climate information disclosure policies and assists enterprises in developing forward-looking green innovation strategies. Full article

This paper develops a quantitative framework for climate–financial risk measurement that combines a spatially explicit jump–diffusion asset–loss model with prudentially aligned risk metrics. The approach connects regional physical hazards and transition variables derived from climate-consistent pathways to asset returns and credit parameters through the use of climate-adjusted volatilities and jump intensities. Fat tails and geographic heterogeneity are captured by it, which conventional diffusion-based or purely narrative stress tests fail to reflect. The framework delivers portfolio-level Spatial Climate Value-at-Risk (SCVaR) and Expected Shortfall (ES) across scenario–horizon matrices and incorporates an explicit robustness layer (block bootstrap confidence intervals, unconditional/conditional coverage backtests, and structural-stability tests). All ES measures are understood as Conditional Expected Shortfall (CES), i.e., tail expectations evaluated conditional on climate stress scenarios. Applications to bank loan books, pension portfolios, and sovereign exposures show how climate shocks reprice assets, alter default and recovery dynamics, and amplify tail losses in a region- and sector-dependent manner. The resulting, statistically validated outputs are designed to be decision-useful for Internal Capital Adequacy Assessment Process (ICAAP) and Pillar 2: climate-adjusted capital buffers, scenario-based stress calibration, and disclosure bridges that complement alignment metrics such as the Green Asset Ratio (GAR). Overall, the framework operationalises a move from exposure tallies to forward-looking, risk-sensitive, and auditable measures suitable for supervisory dialogue and internal risk appetite. Full article

Artificial intelligence (AI) has played a pivotal role in enhancing the efficiency of financial technology (Fintech), ultimately contributing to the stability of the banking sector. The advancements in Fintech driven by AI tools are significantly improving risk management within the banking industry. This paper investigates the mediating role of AI in the relationship between Fintech and financial stability in the context of Islamic and conventional banks across selected countries in the Organization of Islamic Cooperation (OIC). It employs structural equation modeling (SEM) to explore the causal linkages across time domains. The results of this research identify that AI is a significant mediator, playing a critical role between Fintech and stability. It either mitigates or amplifies risks, depending on the regulatory framework and implementation practices in place. The analysis indicates that AI has a weak mediating effect in the short run, but a strong mediating effect in the long run between Fintech and stability. This research paper emphasizes the importance of developing robust, forward-thinking policies to leverage the benefits of AI. It also addresses the risks to financial stability in both Islamic and conventional banking systems. Full article

In this work, a layout-level design methodology is presented for Low-Noise Amplifiers (LNAs), targeting a wide frequency spectrum from RF to millimeter-wave (mmWave) bands, and implemented using a 22 nmFDSOI CMOS process. A nested inductor structure is introduced at RF frequencies to reduce silicon footprint, with magnetic crosstalk effects characterized through electromagnetic (EM) simulations using Ansys^® RaptorX, Release 2024 R2, ANSYS, Inc. and integrated into the design process. Single-ended LNA architectures are employed for RF bands, while at mmWave frequencies, a differential topology is adopted to enhance linearity and enable simultaneous input and output impedance matching. An EM-based verification flow is applied across all designs to ensure RF/mmWave design flow compatibility, simulation accuracy, and enhanced performance. The proposed designs are evaluated using key metrics including input/output matching, reverse isolation, forward gain, noise figure, linearity ($I{P}_1,I{P}_3$), stability factor, power consumption, and total chip area to quantify the efficiency of the proposed methodology. The simulation results demonstrate that nested inductors are highly effective for area reduction in RF LNAs, while differential topologies are more suitable for mmWave designs, providing a unified framework for area-efficient and high performance LNA implementation. Full article

Telecommunication infrastructures rely on cryptographic protocols designed for long-term confidentiality, yet data exchanged today faces future exposure when adversaries acquire quantum or large-scale computational capabilities. This harvest-now, decrypt-later (HNDL) threat transforms persistent communication records into time-dependent vulnerabilities. We model HNDL as a temporal cybersecurity risk, formalizing the adversarial process of deferred decryption and quantifying its impact across sectors with varying confidentiality requirements. Our framework evaluates how delayed post-quantum cryptography (PQC) migration amplifies exposure and how hybrid key exchange and forward-secure mechanisms mitigate it. Results show that high-retention sectors such as satellite and health networks face exposure windows extending decades under delayed PQC adoption, while hybrid and forward-secure approaches reduce this risk horizon by over two-thirds. We demonstrate that temporal exposure is a measurable function of data longevity and migration readiness, introducing a network-centric model linking quantum vulnerability to communication performance and governance. Our findings underscore the urgent need for crypto-agile infrastructures that maintain confidentiality as a continuous assurance process throughout the quantum transition. Full article

Human life expectancy has risen dramatically in the last century, but this demographic triumph has come at the cost of an explosion of non-communicable diseases (NCDs), threatening the sustainability of healthcare systems in aging, low-fertility societies. Evolutionary medicine provides a framework to understand, at least in part, this paradox. Many vulnerabilities to disease are not failures of design but the predictable outcomes of evolutionary trade-offs, constraints, and mismatches. Evolutionary mismatch theory explains how traits once advantageous in ancestral environments become maladaptive in modern contexts of abundance, sedentarism, and urbanization. The developmental origins of health and disease (DOHaD) concept describes how epigenetic plasticity in early life can buffer or amplify these mismatches, depending on whether adult environments align with developmental forecasts. Transgenerational epigenetic inheritance, even if still debated in humans, may further influence phenotypic plasticity, increasing or mitigating the mismatch. In evolutionary terms, the theories of mutation accumulation, antagonistic pleiotropy, and the disposable soma explain why longer lifespans, and ecological and social conditions profoundly different from those in which we developed, increase the likelihood that these costs are expressed clinically. Because most NCDs can be prevented and effectively controlled but not cured, efforts should prioritize quality of life for people, families, and communities. At the individual level, aligning lifestyles with evolved biology can mitigate risk, but the greatest leverage lies in population-level interventions. Urban health strategies represent a forward-looking attempt to realign modern environments with human biology. In this way, the concept of the evolutionary misfit becomes not just a diagnosis of maladaptation, but a guide for building healthier, more sustainable societies. Full article

We present an experimental demonstration of a daylight-capable Optical Amplify-and-Forward (OAF) relaying system designed to support flexible and high-capacity network topologies. The proposed architecture integrates fiber-based infrastructure with OAF Free Space Optics (FSO) relaying, enabling bidirectional optical communication over 460 m (x2) using SFP-compatible schemes, while addressing Non-Line-of-Sight (NLOS) constraints and fiber disruptions. This work achieves a Bit Error Rate (BER) below the Hard-Decision Forward Error Correction (HD-FEC) limit, validating the feasibility of high-speed urban FSO links. By leveraging low-cost fiber-coupled optical terminals, the system transmits single-carrier 120 Gbps Intensity Modulation/Direct Detection (IM/DD) signals using NRZ (Non-Return-to-Zero) and PAM4 (4-Pulse Amplitude Modulation) modulation formats. Operating entirely in the optical C-Band domain, this approach ensures compatibility with existing infrastructure, supporting scalable mesh FSO deployments and seamless integration with hybrid Radio Frequency (RF)/FSO systems. Full article

The rapid convergence of the Internet of Things (IoT), quantum computing, and artificial intelligence (AI) has amplified the urgency for lightweight yet resilient data protection mechanisms, particularly within unmanned aerial vehicles (UAV). Traditional cryptographic approaches, while mathematically secure, often fail to reconcile the competing requirements of robustness, computational efficiency, and energy sustainability when deployed on resource-constrained platforms such as drones. To address this gap, this paper proposes a novel hybrid lightweight cryptographic model that strategically integrates symmetric and asymmetric primitives in a dual-layer design. The model leverages the efficiency of lightweight authenticated encryption for high-throughput data protection, while incorporating elliptic-curve and lattice-based key exchange mechanisms to ensure both forward secrecy and post-quantum resilience. Experimental evaluation demonstrates that the proposed scheme achieves superior performance compared to conventional methods, offering reduced computational overhead, lower energy consumption, and enhanced resistance to cyber threats. Crucially, the model maintains high levels of confidentiality, integrity, and authenticity while extending operational endurance, making it particularly well-suited for next-generation UAV operating within the broader IoT ecosystem. Full article

The presence of fling step and forward directivity, as distinctive features of near-fault ground motions, can lead to substantial alterations in the seismic performance of reinforced concrete bridges. This study examines the seismic performance of reinforced concrete bridges with various deck slabs subjected to two distinct sets of earthquake events. One set is of forward-directivity records, and the other set is of fling-step records. Three-dimensional finite element models for the analyzed reinforced concrete bridges are constructed using the CSI-BRIDGE v26 software package, incorporating appropriate material and geometric nonlinearities. The developed bridge models are of three spans and have different deck slab systems, namely, box girder, RC girder, and hollow core slab bridges. Extensive nonlinear response time-history analyses of various configurations representing the examined RC bridges are performed to elucidate the impact of seismic loads, including forward-directivity and fling-step records, on the seismic response of supporting columns and deck slabs in the longitudinal direction. The numerical simulations indicate that ground vibrations with fling step significantly amplify the seismic response demands in both substructure and superstructure elements. Moreover, bridge type substantially influences the induced seismic responses, particularly supporting columns and deck slabs. Full article

The increasing market demand for high-power and high-frequency applications necessitates the development of highly reliable Gallium Nitride (GaN) High-Electron-Mobility Transistors (HEMTs). While GaN offers superior performance and efficiency over traditional silicon, gate-related defects pose a significant reliability challenge, often leading to premature device failure under stress. Traditional High-Temperature Gate Bias (HTGB) testing is effective but time-consuming and costly, particularly when defects are only identified post-packaging. This study focuses on developing an effective wafer-level screening methodology to mitigate the financial burden and reputational risk associated with late-stage defect discovery. Failure analysis of an HTGB premature failure revealed a gate metal deposition defect characterized by identical elemental composition to the bulk metal, suggesting a small-volume structural anomaly. Crucially, a comparative analysis showed that Forward Gate Current (I_GON) is an insensitive screening metric due to high inherent gate leakage through the passivation layer. In contrast, the Reverse Gate Current (I_GOFF) exhibited sensitivity, particularly under the tensile stress induced by package molding, which is attributed to the piezoelectric effect altering the depletion region width beneath the p-GaN gate. Based on this observation, a multi-pulse I_DSS test was developed as a wafer-level screen. This method successfully amplified the subtle electrical field perturbations caused by the gate defect. After screening 231 dies using the new methodology, zero failures were recorded after 1000 h of HTGB stress, a significant improvement over the initial failure rate of 0.43% (1 out of 231). This work demonstrates that early, sensitive wafer-level screening of gate defects is indispensable for optimizing manufacturing yield and enhancing long-term device reliability. Full article

Multi-angular remote sensing plays a critical role in the study domains of ecological monitoring, climate change, and energy balance. The successful retrieval of the surface Bidirectional Reflectance Distribution Function (BRDF) and albedo from multi-angular remote sensing observations for various applications relies on the sensitivity of an appropriate BRDF model to both the number and the sampling distribution of multi-angular observations. In this study, based on selected high-quality multi-angular datasets, we designed three representative angular sampling schemes to systematically analyze different illuminating–viewing configurations of the retrieval results in a kernel-driven BRDF model framework. We first proposed an angular information index (AII) by incorporating a weighting mechanism and information effectiveness to quantify the angular information content for the angular sampling distribution schemes. In accordance with the principle that observations on the principal plane (PP) provide the most representative anisotropic scattering features, the assigned weight gradually decreases from the PP towards the cross-principal plane (CPP). The information effectiveness is determined based on the cosine similarity between the observations, effectively reducing the information redundancy. With such a method, we assess the AII of the different sampling schemes and further analyze the impact of angular distribution on both BRDF inversion and the estimation of snow surface albedo, including White-Sky Albedo (WSA) and Black-Sky Albedo (BSA) based on the RossThick-LiSparseReciprocal-Snow (RTLSRS) BRDF model. The main conclusions are as follows: (1) The AII approach can serve as a robust indicator of the efficiency of different sampling schemes in BRDF retrieval, which indicates that the RTLSRS model can provide a robust inversion when the AII value exceeds a threshold of −2. (2) When the AII value reaches such a reliable level, different sampling schemes can reproduce the BRDF shapes of snow across different bands to somehow varying degrees. Specifically, observations with smaller view zenith angle (VZA) ranges can reconstruct a BRDF shape that amplifies the anisotropic effect of snow; in addition, the forward scattering tends to be more pronounced at larger solar zenith angles (SZAs), while the variations in BRDF shape reconstructed from off-PP observations depend on both wavelength and SZAs. (3) The relative differences in both BSA and WSA grow with increasing wavelength for all these sampling schemes, mostly within 5% for short bands but up to 30% for longer wavelengths. With this novel AII method to quantify the information contribution of multi-angular sampling distributions, this study offers valuable insights into several main multi-angular BRDF sampling strategies in satellite sensor missions, which relate to most of the fields of multi-angular remote sensing applications in engineering. Full article

As aviation operations extend into complex natural environments, dust particles present significant challenges to flight stability and safety, particularly in dust-prone regions like southern Xinjiang. This study employs high-fidelity computational fluid dynamics (CFD) simulations, combined with the SST turbulence model and the Lagrangian discrete phase model, to analyze the aerodynamic response of the NACA 0012 airfoil at varying wind speeds (5, 15, and 30 m/s) and angles of attack (3°, 8°, and 12°). The results indicate that, at low speeds and moderate to high angles of attack, dust particles reduce lift by over 70%, primarily due to boundary layer instability, weakened suction-side pressure, and premature flow separation. Higher wind speeds slightly delay flow separation, but cannot counteract the disturbances caused by the particles. At higher angles of attack, drag increases by more than 60%, driven by wake expansion, shear dissipation, and delayed pressure recovery. Pitching moment frequently reverses from negative to positive, reflecting a forward shift in the aerodynamic center and a loss of pitching stability. An increase in dust concentration amplifies these effects, leading to earlier moment reversal and more abrupt stall behavior. These findings underscore the urgent need to improve aircraft design, control, and safety strategies for operations in dusty environments. Full article

Wind turbine blade damage detection is crucial for advancing wind energy as a sustainable alternative to fossil fuels. Existing methods based on image processing technologies face challenges such as limited adaptability to complex environments, trade-offs between model accuracy and computational efficiency, and inadequate real-time inference capabilities. In response to these limitations, we put forward MSWindD-YOLO, a lightweight real-time detection model for wind turbine blade damage. Building upon YOLOv5s, our work introduces three key improvements: (1) the replacement of the Focus module with the Stem module to enhance computational efficiency and multi-scale feature fusion, integrating EfficientNetV2 structures for improved feature extraction and lightweight design, while retaining the SPPF module for multi-scale context awareness; (2) the substitution of the C3 module with the GBC3-FEA module to reduce computational redundancy, coupled with the incorporation of the CBAM attention mechanism at the neck network’s terminus to amplify critical features; and (3) the adoption of Shape-IoU loss function instead of CIoU loss function to facilitate faster model convergence and enhance localization accuracy. Evaluated on the Wind Turbine Blade Damage Visual Analysis Dataset (WTBDVA), MSWindD-YOLO achieves a precision of 95.9%, a recall of 96.3%, an mAP@0.5 of 93.7%, and an mAP@0.5:0.95 of 87.5%. With a compact size of 3.12 MB and 22.4 GFLOPs inference cost, it maintains high efficiency. After TensorRT acceleration on Jetson Orin NX, the model attains 43 FPS under FP16 quantization for real-time damage detection. Consequently, the proposed MSWindD-YOLO model not only elevates detection accuracy and inference efficiency but also achieves significant model compression. Its deployment-compatible performance in edge environments fulfills stringent industrial demands, ultimately advancing sustainable wind energy operations through lightweight lifecycle maintenance solutions for wind farms. Full article

The Strait of Messina poses unique challenges for small vessels due to strong currents and complex wave conditions, which critically affect structural integrity and operational safety. This study proposes an integrated methodology that combines seakeeping analysis, a comparison with classification society rules, and fatigue life assessment within a unified and computationally efficient framework. A panel-based approach was used to compute vessel motions and vertical bending moments at different speeds and wave directions. Hydrodynamic loads derived from Response Amplitude Operators (RAOs) were compared with regulatory limits and applied to fatigue analysis. A further innovative aspect is the use of high-resolution bathymetric data from the Strait of Messina, enabling a realistic representation of local currents and sea states and providing a more accurate assessment than studies based on idealized conditions. The results show that forward speed amplifies bending moments, reducing safe wave heights from 2 m at rest to about 0.5 m at 16 knots. Fatigue analysis indicates that aluminum hulls are highly vulnerable to 2–3 m waves, while steel and titanium show no significant damage. The proposed workflow is transferable to other vessel types and supports safer design and operation. The case study of the Strait of Messina, the busiest and most challenging maritime corridor in Italy, confirms the validity and practical importance of the approach. By combining hydrodynamic and structural analyses into a single workflow, this study establishes the foundation for predictive maintenance and real-time structural health monitoring, with significant implications for navigation safety in complex sea environments. Full article

Integrated sensing and communication (ISAC) has emerged as a transformative technology for intelligent transportation systems. Index modulation (IM), recognized for its high robustness and energy efficiency (EE), has been successfully incorporated into ISAC systems. However, most existing IM-based ISAC schemes overlook the spatial multiplexing potential of millimeter-wave channels and remain confined to single-hop vehicle-to-vehicle (V2V) setups, failing to address the challenges of energy consumption and noise accumulation in real-world multi-hop V2V networks with complex road topologies. To bridge this gap, we propose a spatial scattering modulation-based ISAC (ISAC-SSM) scheme and introduce it to multi-hop V2V networks. The proposed scheme leverages the sensed positioning information to select maximum signal-to-noise ratio relay vehicles and employs a detect-amplify-and-forward (DAF) protocol to mitigate noise propagation, while utilizing sensed angle data for Doppler compensation to enhance communication reliability. At each hop, the transmitter modulates index bits on the angular-domain spatial directions of scattering clusters, achieving higher EE. We initially derive a closed-form bit error rate expression and Chernoff upper bound for the proposed DAF ISAC-SSM under multi-hop V2V networks. Both theoretical analyses and Monte Carlo simulations have been made and demonstrate the superiority of DAF ISAC-SSM over existing alternatives in terms of EE and error performance. Specifically, in a two-hop network with 12 scattering clusters, compared with DAF ISAC-conventional spatial multiplexing, DAF ISAC-maximum beamforming, and DAF ISAC-random beamforming, the proposed DAF ISAC-SSM scheme can achieve a coding gain of 1.5 dB, 2 dB, and 4 dB, respectively. Moreover, it shows robust performance with less than a 1.5 dB error degradation under 0.018 Doppler shifts, thereby verifying its superiority in practical vehicular environments. Full article