Search Results (1,169)

The driven damped pendulum is a foundational model in nonlinear dynamics, with applications ranging from Josephson junctions to MEMS oscillators. Conventional dimensionless treatments obscure the common physical origin of damping and driving in the inertia coefficient. Here we restore this dependence and establish inertia as a master control parameter governing stability, resonance, and bifurcations. Through linear analysis and perturbation theory, we derive universal scaling laws revealing a fundamental dichotomy: quantities at resonance—peak amplitude and nonlinear frequency shift—are independent of inertia due to exact algebraic cancellation between the inertia dependence of the effective driving amplitude and effective damping coefficient. Off resonance, however, amplitude scales inversely with inertia, bandwidth narrows proportionally, and the bistability threshold exhibits an even steeper dependence. A critical inertia separates underdamped from overdamped regimes, yielding non-monotonic relaxation times that maximize attractor memory at extreme inertia values. These scaling laws provide design guidelines: low inertia promotes broadband response for energy harvesting; high inertia suppresses off-resonant vibrations for precision timing and quantum applications. By establishing inertia as a physically realizable path through parameter space, this work unifies disparate phenomena and provides a framework for understanding stability in inertial-driven systems. Full article

Floating offshore wind turbines (FOWTs) exhibit complex motions under marine environmental loads and frequently undergo coupled oscillations across multiple degrees of freedom (DOFs). Accurate short-term motion prediction of these responses is crucial for operational safety and maintenance. To overcome the limitations of traditional “black-box” models under complex aero-hydrodynamic loads, this study proposes STL–TimesNet–NLinear, a novel physics-informed deep learning framework. The framework utilizes STL decomposition to explicitly decouple motion signals: NLinear captures non-stationary low-frequency slow drifts, while TimesNet extracts multi-periodic wave-frequency responses. The model was evaluated across different platform typologies—a 5 MW semi-submersible and a larger-scale 15 MW Spar-type platform—under various typical operational and extreme environmental conditions. Model performance was evaluated using comparative and ablation experiments. At a prediction-ahead time (PAT) of 5 s, the proposed model achieves coefficients of determination (R²) exceeding 0.95. Even at longer PATs, the R² remains above 0.90, consistently outperforming multiple benchmark models. Compared to traditional recurrent neural networks (e.g., LSTM), it decreases the Mean Absolute Error (MAE) for pitch motion under extreme sea states by 54.7% and increases the R² to 0.9573. Furthermore, the inference latency is only 2.4 ms per step. These findings confirm that the proposed STL–TimesNet–NLinear model provides fast and accurate solutions for the short-term motion response prediction of FOWTs, demonstrating valuable potential applications for enhancing the safety planning of offshore wind turbine operation and maintenance. Full article

The increasing integration of inverter-based distributed energy resources (DERs) significantly reduces power system inertia, posing critical challenges to transient stability. Traditional fault ride-through strategies, relying on passive and localized rules, often fail to provide effective coordinated support in low-inertia grids. To address these limitations, this paper proposes a Transient Dynamic Features Adaptation Distributed Cooperative Control (TDA-DCC) framework. This approach integrates a dynamic context-aware policy network based on multi-head attention mechanisms to extract temporal features from local observations, allowing agents to anticipate transient dynamics rather than merely reacting to instantaneous states. A multi-agent deep deterministic policy gradient algorithm is employed to optimize a global multi-dimensional objective function encompassing frequency, voltage, and rotor angle stability. Furthermore, to ensure engineering reliability, a hybrid execution architecture is introduced, which embeds a deterministic safety monitor to switch between the intelligent policy and a robust backup controller during extreme anomalies. Case studies on a modified IEEE 39-bus system demonstrate that the proposed method significantly enhances transient stability margins and robustness against sensor failures compared to conventional baselines. Full article

Cystic fibrosis (CF) is an autosomal recessive disorder caused by pathogenic variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene and primarily affects the respiratory, digestive, and reproductive systems. Globally, CF is most prevalent among European ancestry, with an incidence rate of approximately 1/2500 to 1/3500. In China, the incidence is about 1/128,000. However, CF is not extremely rare in the Chinese population; rather, its prevalence is significantly underestimated. The CFTR mutation spectrum in China is highly unique, characterized by an extremely low frequency of p.Phe508del. Instead, region-specific mutations such as p.Gly970Asp, p.Ile1023Arg, and p.Arg553Ter predominate, alongside a high proportion of splicing variants and complex rearrangements. A significant proportion of Chinese CF patients primarily present with CF-like phenotypes within the CF-related disease spectrum (such as congenital bilateral absence of the vas deferens and pseudo-Bartter syndrome), exhibiting overlapping features with classic CF but lacking typical respiratory-dominant symptoms. This review examines how these atypical symptoms deviate from the diagnostic pathways established in Western countries. Establishing localised data and functional platforms is a prerequisite for achieving precision medicine. Achieving a transition from symptom-focused care to defect-correcting therapy will require coordinated multicenter collaboration and sustained infrastructure development. Full article

Rapid and accurate recognition of radar active jamming is a prerequisite for cognitive electronic countermeasures. However, under complex electromagnetic environments with scarce training samples, existing deep learning models are prone to capturing spurious correlations induced by environmental confounders, resulting in notable performance degradation. To address this causal confounding issue, we propose a physics-guided variational causal intervention network (PG-VCIN). First, we reconstruct a structured causal model of jamming signal generation, decoupling observations into robust physical statistical features and sensitive time–frequency image representations. Physical priors are then leveraged to perform dynamic precision-weighted modulation of visual feature extraction, enforcing physical consistency at the representation learning stage. Second, we formulate deconfounding within an active inference framework and introduce a variational information bottleneck to optimize mutual information, thereby filtering out high-complexity redundant information attributable to confounders while preserving the essential causal semantics. Finally, we numerically approximate the causal effect by imposing dual intervention constraints in the latent space, including intra-class invariance and confounder invariance. Experiments on a semi-physical simulation dataset demonstrate that the proposed method achieves substantially higher recognition accuracy than several representative few-shot baselines in extremely low-sample regimes, validating the effectiveness of integrating physical mechanisms with causal inference. Full article

Modern agriculture has to alleviate extremes in water demand and/or water waste. In this regard, this work showcases how soil moisture instruments can be combined with low-end microcontrollers, energy-efficient communication protocols, single-board computers, flow and pressure sensors, and purpose-built actuators to form a synergistic platform able to generate and study realistic irrigation scenarios. These scenarios, potentially emulating anomalies such as clogged emitters or pipe leaks with a satisfactory time granularity of a few minutes, provide valuable data that pave the way for the creation of intelligent models intercepting water misuse events and/or irrigation failures. The proposed system utilizes widely available, well-documented, low-cost components to form a functioning whole which is optimized for outdoor, low-power, low-maintenance and long-term operation and is accessible remotely via typical end-user devices. Two drip irrigation points were set up, each having a TEROS 12 and a TEROS 10 instrument placed at different depths, while a prototype water flow/pressure control and report system was developed. All modules sent data in real time, via LoRa, to a central node implemented using a Raspberry Pi for further processing and to make them widely available via common network infrastructures, also provisioning for remote scenario invocation. The system does not claim to achieve specific irrigation water savings, but it contributes to maintaining/increasing the benefits of modern irrigation practices (such as drip irrigation). This goal is served by emulating a wide variety of irrigation events and by gathering and studying the corresponding data. These multimodal data are collected at a frequency of a few minutes, reflecting key irrigation-specific parameters with an accuracy better than or equal to 3%. The exact steps for specific hardware and software component interoperation are clearly explained, allowing other teams of researchers and/or university educators worldwide to be inspired and benefit from platform replication. Full article

In 2024 and 2025, field populations of Aphis gossypii were collected from eight regions in Xinjiang to monitor their resistance levels to five commonly used insecticides: sulfoxaflor, acetamiprid, imidacloprid, abamectin, and chlorpyrifos. The mutation frequencies of five sites in the acetylcholinesterase (AChE) gene (S431F, V332A, A302S, G221A, F139L) and three sites in the β1 subunit of the nicotinic acetylcholine receptor (nAChR) (R81T, V62I, K264E) were also analyzed. The results showed that from 2024 to 2025, the eight A. gossypii field populations exhibited the highest resistance to imidacloprid (primarily moderate to high resistance), followed by acetamiprid (all moderate resistance). Resistance to abamectin and sulfoxaflor was relatively low, but sulfoxaflor resistance increased rapidly (from low resistance in 2024 to moderate resistance in 2025). All populations remained consistently susceptible to chlorpyrifos. Gene analysis revealed that the mutation rate of S431F in the AChE gene was nearly 100%, while that of V332A remained stable at approximately 30%. The mutation rates of A302S and G221A showed a slight increase, whereas the F139L mutation rate was extremely low (<1.00%). In the β1 subunit of nAChR, the mutation rates of R81T and V62I remained stable at around 50%, and the K264E mutation rate was extremely low (<1.00%). This study clarifies the resistance evolution patterns of A. gossypii to different insecticides and the variation characteristics of key resistance genes in Xinjiang, providing a scientific basis for the integrated resistance management of A. gossypii and the rational selection of effective insecticides. Full article

Optical remote sensing ship detection faces significant challenges in realistic maritime scenes due to strong background clutter (e.g., docks, shorelines, wake streaks), extreme scale variation, and the elongated geometry of ships with diverse orientations. These factors frequently lead to geometric misalignment, unstable localization, and false alarms, particularly in congested ports and complex sea states. To enhance robustness under clutter while retaining the set prediction efficiency of DETR, we propose the Directional Efficient Network (DENet), a structure-aware enhancement built upon RT-DETR. DENet introduces two complementary components. First, Directional Strip Convolution (DSConv) replaces the standard $3\times 3$ convolution for spatial mixing. By predicting offsets conditioned on input features, DSConv performs strip aggregation that aligns with slender hull structures, thereby suppressing interference from line-shaped background patterns. Second, Efficient Scale Fusion (ESF) augments the Hybrid Encoder as an additive residual correction. It combines multiple receptive field branches with lightweight differential compensation to balance low-frequency context and high-frequency structural transitions, ensuring stable multi-scale fusion in cluttered scenes. Extensive experiments demonstrate the effectiveness of DENet. On ShipRSImageNet, ${\mathrm{AP}}^{\mathrm{val}}$ improves from 58.8% to 63.2% and ${\mathrm{AP}}_{50}^{\mathrm{val}}$ increases from 68.5% to 73.6%. Consistent gains are also observed on NWPU VHR-10, where ${\mathrm{AP}}^{\mathrm{val}}$ reaches 63.0% and ${\mathrm{AP}}_{50}^{\mathrm{val}}$ reaches 94.6%, alongside improvements on the Infrared Ship Database and VisDrone2019-DET, validating the method’s generalization capabilities. Full article

Thermoelectric generators (TEGs) enable compact waste-heat energy harvesting but require high-gain DC–DC conversion due to their low-output voltage for DC microgrid interfacing. This work proposes a novel TEG-supplied two-stage architecture consisting of a perturb-and-observe (P&O)-based MPPT boost converter followed by a modified Z-source converter regulated through an advanced model predictive control (MPC) framework. The modified Z-source topology enables high-voltage gain without extreme duty ratios and mitigates switching losses by eliminating diode reverse-recovery effects via synchronous operation. To enhance dynamic performance, the advanced MPC strategy incorporating an adaptive ripple-based weighting mechanism is applied to the modified Z-source converter and benchmarked against MPC and sliding mode control (SMC). Simulation results under multiple disturbance scenarios, including hot-side and cold-side temperature variations, multi-condition disturbances, coupling-factor variation, and measurement noise, demonstrate that the proposed system maintains stable 400 V regulation at a 100 W output level. In contrast, MPC exhibits switching frequency deviations that increase switching losses during transient operation, while SMC suffers from significant voltage deviations under source variations. The proposed strategy maintains tight voltage regulation with nearly fixed-frequency operation around 50 kHz, providing a new perspective for TEG researchers while supporting sustainable waste-heat energy utilization. Full article

A robust and cost-effective fiber-optic ultrasound sensor based on a slope-symmetric Fabry–Perot interferometer (FPI) is presented, employing dual-channel quadrature-biased heterodyne interrogation with an acousto-optic modulator (AOM). By introducing a 200 MHz frequency shift that yields an effective π/2 phase offset between the direct (unshifted) and frequency-shifted optical paths, the system ensures complementary sensitivity: when one channel operates at zero slope on the FPI transfer function (minimum sensitivity), the other resides at maximum slope, providing inherent immunity to laser wavelength drift and environmental perturbations. Experimental validation demonstrates reliable ultrasound detection across varying operating points. At quadrature extremes, one channel achieves peak amplitudes of ±2 V while the other is quiescent, whereas intermediate points enable simultaneous detection with amplitudes of ±1.5 V (AOM channel) and ±0.05–0.1 V (direct channel), accompanied by corresponding DC levels ranging from ~0.4 V to 1.6 V. The AOM channel utilizes simple envelope detection after 9.5–11.5 MHz bandpass filtering, maintaining low cost, though coherent mixing is suggested for enhanced weak-signal performance. The angle-symmetric FPI design, combined with gold-disk reflector adaptations and potential femtosecond laser micromachining, further reduces fabrication costs without sacrificing finesse or sensitivity. This quadrature-biased approach offers superior stability compared to single-channel systems, making it highly suitable for practical applications in photoacoustic imaging, nondestructive testing, and structural health monitoring. Full article

Under the dual pressures of global warming and high-density urbanization, extreme heatwaves have emerged as a critical ecological risk constraining the sustainable development of Chinese urban agglomerations. Based on multi-source remote sensing, meteorological, and economic data for 19 major urban agglomerations from 2014 to 2023, this study develops an emerging industrial agglomeration–energy activity–thermal environment response framework. Using XGBoost-SHAP interpretable machine learning and GeoSHAPLEY spatial decomposition, the nonlinear and spatially heterogeneous impacts of industrial agglomeration on heatwave characteristics are systematically quantified. Results indicate that the heatwave index increased from 0.619 to 0.637, with the model explaining 80.7 percent and 74.7 percent of variance in duration and frequency, respectively. Moreover, emerging industrial agglomeration ranks among the top contributors to both duration and frequency, explaining over 20 percent of duration variability and surpassing traditional industrial and socioeconomic factors. Heatwave duration and frequency exhibit nonlinear relationships. During early agglomeration, energy efficiency improvements generated marginal cooling of five to eight percent, whereas intensified agglomeration amplifies duration by over ten percent through energy-intensive activities and infrastructure heat islands. Meanwhile, green innovation at high agglomeration levels mitigates six to nine percent of the warming effect. In addition, spatial differentiation of industrial agglomeration, reflected by a Gini increase from 0.685 to 0.728 and inter-regional contribution around 62 percent, underpins heat risk heterogeneity. Furthermore, natural endowments, socioeconomic development, infrastructure, environmental regulation, and technological innovation significantly moderate these effects, with high-tech innovation attenuating heatwave amplification. Consequently, the thermal effects of industrial agglomeration follow a three-stage spatial evolution of warming, stabilization, and counter-regulation. These findings highlight that coordinated optimization of industrial spatial layout and green technological innovation is crucial for enhancing climate resilience and promoting low-carbon transformation in urban agglomerations. Full article

This study aims to address gaps in understanding healthcare accessibility inequality in rural China, where traditional distance-based assessments and urban-centric biases are insufficient. By integrating real-time travel data from Amap and the two-step floating catchment area (2SFCA) method, we conducted a high-resolution (1 km grid) analysis across transportation modes, administrative scales, and time-sensitive populations. Results reveal that driving enables more stable, equitable access (characterized by higher supply–demand ratios and lower variability) than public transport, which distorts ratios due to limited coverage. Accessibility disparities are most pronounced at the county scale, with eastern rural counties (e.g., Yangtze River Delta) showing far higher accessibility (log₁₀(A-value) > 5.0) than remote western counties (log₁₀(A-value) < 1.5). High time-sensitive populations (urgent care) face extreme accessibility gaps, with only 15% of counties providing optimal access. In contrast, low time-sensitive groups benefit from extended travel time thresholds, achieving 62% coverage of optimal access. Targeted interventions—investing in rural high-tier hospitals, enhancing transit frequency, and county-specific policies—are needed to advance health equity. The findings of this study provide the first nationwide high-resolution healthcare accessibility map for rural China, improve assessment accuracy via real-time data, and identify county-level gaps—offering data-driven insights for targeted policies to advance health equity and support rural revitalization. Full article

Quinolone antibiotics (QNs) are widely used in animal production and may pose potential health risks through dietary exposure. A total of 1612 animal-derived food samples covering 10 food categories were collected in Guangzhou, China, from 2016 to 2023. Residues of six QNs were determined using ultra-performance liquid chromatography coupled with tandem mass spectrometry. Dietary exposure among different age groups was assessed using a probabilistic approach based on local food consumption data, and non-carcinogenic health risks were characterized using hazard quotient (HQ) and hazard index (HI) methods. QN residues were detected in 7.75% of samples, with an exceedance rate of 2.23%. Aquatic products, particularly fish and crustaceans, exhibited the highest detection frequencies and contributed most to overall dietary exposure. Enrofloxacin (ENR) was the most frequently detected compound, while sporadic samples showed extremely high residue concentrations (1003 unit/g in eggs). Children aged 3–6 years had the highest HI (mean is 1.94 × 10⁻²). All HQ and HI values were below 1, indicating low non-carcinogenic health risks under current exposure scenarios. Although dietary exposure to QNs among Guangzhou residents is unlikely to pose appreciable non-carcinogenic health risks, elevated exposure in children and sporadic high-residue events highlight the need for continued risk-based monitoring and targeted food safety management. Full article

To address the issue that current ripple in traditional switching power amplifiers (SPA) for active magnetic bearing (AMB) systems is constrained by the switching frequency, this paper proposes a novel LCL filter-based switching power amplifier (LCL-SPA) along with its parameter design and high-performance control strategy. Without altering the original full-bridge topology or the switching frequency, the proposed scheme achieves superior ripple suppression. To tackle the inherent resonance problem of the LCL filter, a sensorless capacitor current feedback active damping (CCFAD) strategy is proposed. This approach effectively suppresses resonance without additional hardware sensors and ensures system stability under digital control delays. Furthermore, to overcome the limitations of traditional PI controllers in terms of the dynamic performance and parameter tuning of the LCL-SPA, a high-performance LESO-based control algorithm within the Linear Active Disturbance Rejection Control (LADRC) framework is designed. By utilizing a Linear Extended State Observer (LESO) to estimate and compensate for total lumped disturbances in real-time, the algorithm simplifies the parameter tuning process and achieves rapid current tracking with nearly zero overshoot. Experimental results demonstrate that the proposed LCL-SPA achieves extremely low current ripple across various reference currents, with the ripple minimized to 20 mA at a 3 A load. Frequency response tests confirm that the system possesses a closed-loop bandwidth of up to 2 kHz, satisfying the high dynamic requirements of magnetic bearings. Full article

Extreme hydro-meteorological events are intensifying under climate change, disproportionately disrupting last-mile healthcare in flood-prone geographies. In this study, flood-adaptive primary care clinics (FAPCCs) integrated with islandable smart microgrids and a rapid-deploy medical technology stack (MedTech) are developed and evaluated to ensure continuity of essential services (triage, maternal and child health, vaccination cold-chain, minor procedures, diagnostics, and telemedicine) during fluvial, pluvial, and coastal flooding. Evidence on resilient health facilities, microgrid architectures, distributed energy resources, and modular clinical systems is presented in a multi-layer systems design: (1) a modular, amphibious, and elevatable clinic chassis; (2) a photovoltaic–battery–diesel hybrid system with demand-aware energy management; (3) redundant connectivity long-term evolution/fifth-generation, satellite, and very high frequency; (4) a rapid-deploy MedTech kit including point-of-care diagnostics, low-temperature cold-chain, negative-pressure isolation, and sterilization modules; and (5) flood-aware logistics using unmanned aerial vehicle/unmanned surface vehicle. A mixed-integer linear programming sizing is formulated and dispatched with a continuity-of-care reliability metric that couples energy availability to clinical throughput. Simulation across three archetypal sites (peri-urban delta, inland riverine, coastal estuary) shows that FAPCCs achieve the service availability of higher than 99.5% across 7-day grid outage scenarios while reducing fuel use by 62–81% relative to diesel-only baselines, maintaining vaccine temperatures within 2–8 °C with <0.1% thermal excursion time, and sustaining telemedicine quality of service with <150 ms median uplink latency in hybrid networks. A life-cycle cost analysis indicates a 7.1–9.8 year discounted payback from fuel displacement and avoided service loss. Deployment playbooks and policy guidance are also proposed for Ministries of Health and Disaster Agencies in monsoon-impacted regions. Full article