Search Results (990)

Resource allocation for cognitive Vehicle-to-Everything (V2X) networks is challenging due to dynamic spectrum sharing, strong interference coupling, and stringent latency constraints for safety-critical Vehicle-to-Vehicle (V2V) traffic. Although recent Multi-Agent Reinforcement Learning (MARL) approaches report promising gains, many evaluations are conducted at limited and fixed network scales, which restricts insights into scalability under dense spectrum reuse. This paper investigates cooperative multi-agent learning for interference-aware and deadline-constrained V2X resource management. We propose a Q-value Transformation (QTRAN)-based value decomposition framework under centralized training with decentralized execution (CTDE) for joint resource-block and power allocation among V2V agents. The proposed approach is implemented in a realistic V2V/V2I simulator incorporating Manhattan grid mobility, fast fading, explicit cross-tier and co-channel interference, and per-link payload/deadline dynamics. Beyond communication-level performance, improved timely delivery of V2V safety messages can support cooperative maneuvering, collision avoidance, platooning, and infrastructure-assisted traffic management. Extensive simulations across varying numbers of V2V agents benchmark QTRAN against independent learning baselines including MARL and centralized single-agent learning (SARL). Results show that QTRAN improves performance compared with the selected learning baselines and enhances the throughput–reliability trade-off under interference-coupled spectrum reuse. For instance, at $N_{\mathrm{V}2\mathrm{V}}=20$, QTRAN achieves a V2V rate of $0.194\pm 0.004$ and a V2I rate of $9.117\pm 0.213$, while reaching a V2V success rate of $0.812\pm 0.017$ with a low Deadline Miss Ratio of $0.001\pm 0.000$. At higher density ($N_{\mathrm{V}2\mathrm{V}}=50$), QTRAN sustains strong reliability (V2V success rate of $0.719\pm 0.006$ and Completion Ratio of $0.716\pm 0.006$) while maintaining competitive infrastructure throughput (V2I rate of $9.251\pm 0.114$). These results indicate that QTRAN effectively captures non-linear interference interactions, enabling coordinated decentralized spectrum and power decisions under the adopted density-based evaluation setting, thereby enhancing V2V reliability and throughput in cognitive Internet of Vehicles. Full article

Accurate fault detection is essential for the safe and reliable operation of grid-connected photovoltaic (PV) systems under complex and dynamically varying conditions. However, existing data-driven approaches are often hindered by the scarcity of labeled fault data and by their limited ability to model complex multivariate temporal dependencies. To address these challenges, this paper first develops a realistic simulation of a grid-connected PV system to generate a large volume of labeled multivariate time-series fault data spanning diverse fault scenarios under varying operating conditions. The simulated data augment the limited real-world measurements, improving fault coverage and model generalization. On this basis, a dual-channel enhanced Mamba model is proposed for PV fault detection. The model decouples temporal modeling and variable-wise modeling into two dedicated channels, enabling complementary extraction of global temporal dependencies and intra-variable dynamics. Extensive experiments show that the proposed approach consistently outperforms several mainstream time-series classification methods in accuracy, precision, recall, and F1-score, demonstrating that it provides an effective and scalable solution for data-driven fault detection in grid-connected PV systems. Full article

Geopolitical risks—including export controls, entity listings, and end-use restrictions—have become a major source of disruptions in semiconductor supply chains. The impact of such disruptions depends not only on the policy trigger itself but also on the vulnerability of cross-regional partnerships between supply chain partners. Specifically, under the same policy regime, firms with weak partnerships suffer far greater disruption than those with strong partnerships. Apart from risk propagation, this vulnerability also propagates through the supply chain: when an upstream supply channel has weak partnerships, its downstream stages also become more exposed to disruptions. We call this phenomenon vulnerability propagation. Existing Bayesian Network (BN) frameworks portray risk propagation through fixed parameters that do not reflect partnership vulnerability and cannot capture vulnerability propagation. To fill this gap, we propose a Dependency-Robust Bayesian Network (DeRBN) that conditions risk propagation parameters on the partnership vulnerability. A robust worst-case oriented evaluation method is developed to assess the disruption risk under data scarcity. Computational experiments on a typical semiconductor supply chain network show that (i) moving from all-strong to all-weak partnerships increases the worst-case risk by approximately 24%, (ii) the dependency-induced risk amplification is unevenly distributed across supply channels, with the most influential channel contributing approximately 2.2 times the marginal risk of the least influential one, and (iii) the relative ranking of vulnerability profiles remains perfectly stable under varying levels of data uncertainty. These results suggest that DeRBN has the potential to serve not only as a risk assessment tool but also as a diagnostic instrument for identifying and prioritizing the most vulnerable supply channels for targeted risk mitigation. Full article

In past decades, the macroeconomic stability of India has been tested repeatedly by major global disruptions, including oil price shocks, the 2008 global financial crisis and the COVID-19 pandemic. Analysing how macroeconomic variables respond to these shocks is essential for evaluating external vulnerability and policy resilience in emerging economies. Our study provides a comprehensive empirical investigation of the dynamic responses of wholesale price inflation, industrial output, oil prices and exchange rates in India by employing monthly data from January 1993 to December 2024. To examine long-run equilibrium relationships along with short-run adjustment dynamics, the present study employs co-integration analysis within a Vector Error Correction Model (VECM) framework. Further, we applied impulse response functions and forecast error variance decomposition to track volatility spillover mechanisms. Quantile regression and ARCH–GARCH models were further estimated to account for distributional heterogeneity and time-varying volatility. The findings of our study suggested stable long-run linkages among the selected variables, where oil price shocks emerged as a key external source of macroeconomic fluctuations. Short-run dynamics suggested that shocks in oil prices are transmitted primarily through inflation and exchange rate channels and then affect industrial output. Distributional estimates revealed the effects were stronger during stress periods, indicating tail risks that were not captured by the mean-based models. Lastly, volatility analysis confirmed persistent clustering, especially during phases of crisis. Overall, the findings suggest that India’s macroeconomic system remains externally sensitive, with adjustment mechanisms that operate gradually but come under strain during global disruptions. These results underscore the importance of energy risk management and crisis-responsive macroeconomic stabilisation policies. Full article

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

Accurate and reliable groundwater-level monitoring in deep observation wells remains difficult for conventional non-contact ultrasonic systems because narrow tubular geometries intensify multipath reflections, signal attenuation, and echo ambiguity. This study proposes a dual-signal direct time-of-flight (ToF) method that combines radiofrequency (RF) synchronization with one-way airborne ultrasonic propagation to a floating receiver located at the groundwater surface. In the proposed architecture, the RF signal provides a near-instantaneous time reference, whereas the ultrasonic signal defines the propagation delay, thereby eliminating dependence on echo-based ranging. The system integrates a wellhead surface unit for synchronized transmission and control, a floating unit for ToF acquisition and embedded processing, and an optional reference channel for in situ estimation of the effective sound speed. A duty-cycled power architecture is used to support low-power long-term deployment, while a multi-shot acquisition strategy with a median-like estimator improves robustness against startup transients, timing jitters, and false detections. Field validation was conducted over a 12-month period under actual groundwater-monitoring conditions, during which the groundwater depth varied between 14 m and 30 m below the wellhead datum. Within this field-validation interval, the proposed system achieved a mean absolute error of 0.048 m, a maximum absolute error of 0.050 m, and an overall valid detection rate of 99.4% over 358 valid cycles out of 360 scheduled cycles. In addition, a separate range-dependent confined-tubular propagation test was conducted to evaluate the extended detection capability of the RF-synchronized one-way ultrasonic ToF architecture. This test demonstrated stable acoustic-link ToF detection up to 300 m inside the tested 170 mm confined plastic pipeline. Therefore, the 300 m result should be interpreted as a range-dependent valid-detection result rather than as a 12-month groundwater-depth validation over the full 300 m interval. These results demonstrate that the proposed direct-ToF method provides an RF-synchronized one-way ultrasonic ToF framework with a floating receiver for groundwater-level monitoring in deep observation wells, while remaining compatible with low-power and IoT-based environmental monitoring systems. Full article

The rapid expansion of housing financialization (REF) has profoundly reshaped China’s subnational fiscal landscape, yet the dynamic nature of this relationship remains under-explored. This study investigates how the impact of REF on fiscal deficits (DB) evolves over time and identifies the specific transmission channels mediating this influence. First, we construct a multidimensional REF index by integrating enterprise, household, market, financial, and industry indicators via the fuzzy-TOPSIS method. A Markov Regime Switching model identifies three distinct volatility regimes, revealing that REF dynamics are highly sensitive to policy shifts and exhibit significant path dependency. Second, using a Time-Varying Parameter Vector Autoregression model, we find that REF initially functioned as a fiscal stabilizer providing short-term revenue relief; however, as financialization deepened, REF transformed into a procyclical driver of deficit expansion. Third, we further decompose this mechanism, demonstrating that land finance (LAND) and local government debt (UID) amplify systemic fiscal fragility as dynamic mediating channels. Finally, due to the unsustainability of the current “real estate-land-debt” model, we propose policy interventions including the institutionalization of fiscal-debt firewalls, the formation of counter-cyclical fiscal risk reserve funds, and an accelerated transition toward a stable, tax-oriented revenue structure to mitigate systemic risks. Full article

As the offshore wind industry scales toward 15 MW capacity, the reliability of Direct-Drive Permanent Magnet Synchronous Generators (DD-PMSGs) becomes critical. However, real-world run-to-failure data for these massive, multi-pole machines is virtually non-existent, creating a barrier for developing effective data-driven diagnostic systems. This study proposes a high-fidelity framework for detecting permanent magnet faults in the International Energy Agency (IEA) 15 MW Reference Wind Turbine. Using Finite Element Analysis (FEA), a dataset (magnetic flux and back electromotive-force (EMF)) capturing the electromagnetic signatures of healthy and faulty states of a PMSG under varying severities is generated. To improve the power of computer vision, 1D time-series signals were transformed into 2D images. Specifically, Gramian Angular Fields (GAFs) and Recurrence Plots (RPs) were applied to magnetic flux density signals, while Markov Transition Fields (MTFs) were applied to back-EMF signals. These representations were then fused into multi-channel Red-Green-Blue (RGB) images and processed via a ResNet-18 Deep Transfer Learning model using a strictly non-overlapping, leakage-free dataset partitioning strategy. The proposed framework achieved a classification accuracy of 99.45% on noise-free data. Furthermore, robustness testing under varying levels of Additive White Gaussian Noise (AWGN) (30 dB, 40 dB, and 50 dB Signal-to-Noise Ratio (SNR)) demonstrated sustained high performance, maintaining over 90% accuracy even under severe 30 dB noise conditions. Comparative analysis proved that this multi-channel fusion significantly outperforms single-channel encoding methods, which collapse under heavy noise, validating the scalability of the framework and applicability for next-generation condition monitoring in harsh offshore environments. Full article

Surfactant flooding is a promising enhanced oil recovery (EOR) method for mobilizing residual oil after primary recovery and conventional waterflooding. Its performance is highly sensitive to reservoir chemistry, particularly in associated gas reservoirs where CO₂, H₂S, and CH₄ may alter aqueous-phase behavior, surfactant stability, adsorption, and chemical transport. This study evaluates salinity-controlled surfactant flooding performance in a synthetic three-dimensional associated gas–oil reservoir using a simulation-guided diagnostic workflow. The model examines surfactant transport, adsorption, oil rate response, and block-level oil saturation across ultralow-, low-, and moderate-to-high-salinity ranges. Performance was evaluated using field oil production rate (FOPR), cumulative field oil production (FOPT), block oil saturation (BOSAT), block total surfactant concentration (BTCNFSUR), and block total adsorbed surfactant (BTADSUR). Because the simulation does not independently vary gas composition, the results should be interpreted as salinity effects under an associated gas reservoir setting rather than as isolated gas composition effects. The strongest sustained production response occurred in the ultralow- to low-salinity cases, especially 400 ppm and 1000 ppm, where surfactant propagation was more stable and late-time FOPR recovery was stronger. The 15,000 ppm case was the best performer only within the moderate-salinity group and should not be interpreted as the global optimum across all salinity cases. Above 25,000 ppm, FOPR declined to approximately 50–60 Sm³/day, while BOSAT remained high in poorly swept layers, indicating channelized flow, localized chemical contact, and greater retention risk. The results show that salinity compatibility is a dominant control on surfactant flood efficiency and that salinity screening is necessary before applying surfactant flooding in gas-rich reservoirs. Full article

The operational health state of motor bearings is critical to the operational safety of harbor portal slewing cranes. However, in harsh industrial environments with strong noise and time-varying rotational speeds, existing bearing fault diagnosis methods still suffer from the problems of incomplete fault feature extraction from single-sensor signals and the excessively large size of multi-source fusion models, which makes them unable to adapt to edge deployment. To address these issues, this paper proposes a Multi-source Feature Fusion Lightweight Network (MTFL-Net) integrated with targeted structured channel pruning. First, vibration and current signals are preprocessed via differentiated time-frequency transformation and converted into 2D time-frequency images, to fully preserve transient impact and spectral fault features. Second, a multi-branch feature extraction architecture embedded with residual connections, multi-scale convolution and channel attention gating is designed, to alleviate feature degradation and adaptively enhance fault-sensitive features. Third, targeted structured channel pruning is performed on the feature extraction branches, to remove redundant channels while retaining the multi-source fusion logic and core feature extraction structure. Experiments on two public bearing datasets show that the original model achieves 99% diagnostic accuracy, and the pruned model still maintains an accuracy of 95%. The results demonstrate that MTFL-Net can significantly reduce model size and computational cost while retaining high diagnostic precision. Full article

The mechanosensitive PIEZO family channels, PIEZO1 and PIEZO2, are essential for mechanotransduction and play roles in many cellular processes, including cell volume regulation, tissue development, touch sensation, and proprioception. Emerging evidence suggests roles for PIEZO channels in cancer biology; however, direct mechanistic evidence in breast cancer remains limited. They have been shown to promote proliferation, epithelial-to-mesenchymal transition (EMT), and migration; however, these roles are varied and context-dependent. In breast cancer specifically, the two PIEZO channels may play opposing and complex roles in tumor progression, the tumor microenvironment (TME), and the tumor immune microenvironment (TIME), potentially impacting therapeutic response and prognosis. Where breast cancer-specific mechanistic data are lacking, we integrate findings from other tumor types to generate testable hypotheses relevant to breast cancer. In this review, we will explore the importance of PIEZO channels in breast cancer development, progression, and therapeutic response, and explore therapeutics and potential strategies to improve patient outcomes. Full article

As wireless networks evolve from 5G toward 6G, the complexity of the electromagnetic environment increases sharply. Spectrum usage expands significantly into millimetre-wave (mmWave) and terahertz (THz) high-frequency bands. Network node density and mobility increase markedly. Moreover, communication-sensing-computation functions are deeply integrated. Accurate, real-time, full-band Electromagnetic Spectrum Maps (ESMs) have become a core infrastructure for 6G spectrum situational awareness, Dynamic Spectrum Access (DSA), interference coordination, and Integrated Sensing and Communication (ISAC). However, while a growing body of recent work extends radio mapping to multi-band and temporal domains, the predominant focus of existing Radio Map research remains the two-dimensional spatial power distribution at a single fixed frequency—essentially a degenerate special case of ESM after the frequency and time dimensions are collapsed—and no existing survey unifies 3D spatial construction, time-varying prediction, and full 6G system integration under a shared 4D formalism. This paper focuses on the three core research dimensions of ESMs, i.e., 3D spatial ESM construction, dynamic time-varying ESM modelling and prediction, and ESM integration with 6G systems. Under a unified four-dimensional ESM framework (space × frequency × time × power), we clarify the hierarchical relationships among ESM/SEM/REM/Radio Map/Channel Knowledge Maps (CKMs). Then, we systematically review 3D ESM construction, dynamic ESM modelling and prediction, and the integration of ESM with CKM/Digital Twin Networks (DTNs)/ISAC. Finally, we identify five, core open problems that constrain the development of the field to provide a systematic reference for 6G intelligent spectrum management research. Full article

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems have emerged as a promising architecture for beyond-5G wireless networks because they can provide user-centric coverage, improved spectral efficiency, and reduced cell-boundary limitations. However, dynamic downlink power allocation remains challenging due to user mobility, time-varying channel conditions, interference coupling, and the need to maintain Quality of Service (QoS) under practical transmit-power constraints. This paper proposes a Deep Hybrid Intelligent (DHI) framework for dynamic downlink power allocation in CF-mMIMO systems. The proposed framework integrates Soft Actor–Critic (SAC) reinforcement learning with three power-control strategies: DHI-Max-Min, DHI-Max-Product, and DHI-Max-Sum-Rate. The SAC agent learns adaptive power-allocation policies from the network state, while L-BFGS-B refinement is applied to the Max-Product and Max-Sum-Rate strategies to improve the power-allocation decisions under bounded transmit power. The framework is evaluated using a CF-mMIMO scenario with 64 access points and 32 pieces of user equipment distributed over a 1000 × 1000 m² area. The simulation results show that DHI-Max-Sum-Rate achieves the highest sum spectral efficiency, while DHI-Max-Min provides the strongest QoS-oriented performance with a QoS satisfaction rate of 93.75%. In addition, DHI-Max-Product and DHI-Max-Sum-Rate achieve mean computational times of 0.0690 s and 0.0696 s, respectively, compared with 0.63 s for the DDPG benchmark. These results demonstrate that the proposed DHI framework provides an adaptive and computationally efficient solution for QoS-aware downlink power allocation in dynamic CF-mMIMO networks. Full article

Efficient Distributed Computing is still a major challenge, especially in networks composed of very-low-resource embedded systems, e.g., tiny microcontrollers deployed in sensor networks. This work will, firstly, address the design and implementation of event-driven and real-time capable low-resource Virtual Machines (VMs) tightly coupled to communication-centric systems, and secondly, address messaging and routing in mesh-grid networks. The distributed VM network herein forms one big virtual computer executing typically the same program on each node, but processing different data with different control states. The VM provides an integrated program code compiler and an optimized Bytecode processor. The programming language of the VM supports channel-based communication, multi-tasking, and event-based (asynchronous) data processing following the CSP model. The VM fits in microcontrollers with only a few kB of RAM and ROM. A major part of this work is dedicated to network messaging (supported by the VM, too) and routing in two-dimensional mesh-grid networks with a varying degree k of communication ports per node (connectivity degree k), and especially considering the odd but technical relevant case, k = 3, which introduces challenges in message routing that are solved herein. This study demonstrates the performance and suitability of our VM approach for distributed sensor networks performing distributed Machine Learning and clustering by using local sensor data only. Full article

Shipboard helicopter landing in the near-deck region requires stable attitude regulation and high-precision deck-relative motion control under substantial model uncertainty and environmental disturbances, conditions under which conventional model-based controllers may lose performance or become overly conservative. This paper proposes a task-oriented, learning-enhanced control algorithm for ship-relative near-deck station keeping and landing by integrating a model-based baseline controller with residual reinforcement learning in a deck-relative closed-loop framework. The algorithmic contribution is the deck-relative baseline–residual control architecture: a split-channel incremental nonlinear dynamic inversion (INDI) outer loop and a reduced-order dynamic inversion (DI) inner loop provide the nominal baseline pathway, while a bounded residual Proximal Policy Optimization (PPO) policy supplies compensation in the same physical outer-loop command channels to suppress unmodeled nonlinearities and time-varying disturbances. Simulation results show that Residual PPO improves hover robustness and landing performance relative to the baseline controller and Pure PPO. With approximately 20–30% residual authority, it achieved 90.0% Desired landing rates in both tested descent-and-landing scenes. Full article