Search Results (975)

Background: Chinese immigrant families of autistic children in the United States face intersecting barriers related to language, culture, immigration, and fragmented service systems. Yet little is known about how Chinese immigrant parents engage in advocacy or how such efforts relate to disability and human rights. Methods: This qualitative study draws on in-depth interviews with fourteen Chinese immigrant parents of autistic children across multiple U.S. regions. Data were triangulated with analyses of publicly recorded advocacy events and parent-produced textual materials. Reflexive thematic analysis was used to examine motivations for advocacy, advocacy practices, and structural, linguistic, and cultural constraints. Results: Advocacy rarely emerged as an intentional or identity-driven pursuit. Instead, parents were compelled into advocacy through institutional exclusion, service denial, and unmet care needs. Parents engaged in diverse forms of advocacy, including migration, negotiation within institutions, information translation, community-building, and grassroots organizational leadership. Cultural norms shaped advocacy strategies, producing quiet, relational, and collective forms of action often overlooked in dominant rights-based models. Conclusions: Interpreted through a disability justice lens, parental advocacy functions as burdened and unequally distributed labor compensating for systemic failures. Findings underscore the need for institutional reforms that reduce reliance on families’ capacity to fight for access, dignity, and care. Full article

The cleaning unit is a key functional component of grain combine harvesters, yet its operating parameters are still predominantly adjusted according to operator experience, resulting in limited adaptability to fluctuating working conditions. To enhance the intelligence and stability of the cleaning process, this study develops a fuzzy control approach supported by data-driven performance modeling. Based on multi-condition bench experiments, feeding rate, fan speed, cleaning sieve vibration frequency, and sieve opening were selected as input variables. Gaussian Process Regression (GPR) models were established to describe the nonlinear relationships between operating parameters and cleaning loss rate and impurity rate, and impurity rate was inferred online to compensate for the absence of a reliable sensor. Taking feeding rate variation as the primary disturbance, a dual-input fuzzy control strategy was designed using loss rate monitoring and model-predicted impurity rate as feedback signals. Simulation and bench test results show that, under small and moderate load disturbances (±20% and ±35%), the proposed method reduces either impurity rate or cleaning loss rate through coordinated parameter adjustment. Under large disturbances (±50%), performance deterioration cannot be fully eliminated, but its extent is alleviated compared with open-loop conditions. Full article

State-of-charge (SOC) and state-of-health (SOH) of lithium-ion batteries are critical parameters in battery management systems, directly impacting the driving range, performance stability, and safety of electric vehicles. To improve the accuracy and stability of SOC and SOH estimation simultaneously, this paper proposes a joint estimation method with constant-current bias compensation. First, based on a second-order RC equivalent circuit model, a constant-current bias compensation term is introduced into the Kalman filter framework. The estimation accuracy and robustness of SOC are validated under multiple operating conditions and noise levels. Then, a model integrating Transformer and gated recurrent unit is constructed. The fata morgana algorithm (FATA) is adopted for hyperparameter optimization. Ablation studies and multi-model comparative experiments are conducted to verify the model’s accuracy. Finally, capacity correction is performed using SOH results. By combining current bias compensation and precise temporal features extracted from aging data, joint estimation of SOC and SOH is achieved. Results show that after introducing current bias compensation and aging-based capacity correction, the accumulated SOC estimation error is reduced by more than 10%, while SOH estimation achieves a MAPE below 0.90% and an RMSPE below 1.10%. The proposed joint method is thus verified to be accurate and practical. Full article

Urban flood resilience is an important indicator for measuring a city’s capacity to respond to and recover from flood disasters. However, existing assessments often lack a long-term hydrological baseline. This study establishes the historical water system of Yuan Dadu (1267–1368 CE) as a control scenario to benchmark the flood resilience of modern Beijing. By integrating a historical geographic reconstruction with a hydrological–hydrodynamic simulation and the fuzzy analytic hierarchy process (FAHP), the research quantifies structural differences in resilience profiles between the nature-adapted historical system and the modern engineering-dominated system. The results indicate that Yuan Dadu’s urban flood resilience index (UFRI) is 3.44 and modern Beijing’s is 3.28. Despite modern Beijing’s significant advantage in drainage facility density (0.61 km/km²) and emergency management, the system exhibits a functional substitution failure, where gray infrastructure has failed to fully compensate for a 26% reduction in the unit area storage capacity (from 6.4 to 4.7 × 10⁴ m³/km²) and a 48.4% decline in the water system structural complexity. The findings indicate that, in rapidly urbanized cities on alluvial plains with high impervious coverage, expanding drainage networks alone may be insufficient to offset losses in a natural hydraulic buffering capacity. Accordingly, planning strategies are proposed that integrate distributed micro-storage and restore topological connectivity to recreate system-level hydraulic buffering functions. Full article

An electrical heating fluidized-bed thermal energy storage (EH-FB-TES) system is proposed for integration with a coal-fired power plant (CFPP) for deep peak shaving (DPS) due to its high energy storage density and extensive heat exchange performance. The primary objective of this study is to evaluate the thermodynamic performance and economic feasibility of the integrated EH-FB-TES system, specifically focusing on identifying the optimal coupling and heat recovery strategies for enhanced deep peak shaving performance. Since EH-FB-TES uses air flow for fluidization in the heating storage process, its coupling with the CFPP differs from other TES technologies, and the associated thermodynamic performance and cost are thereby analyzed. The results show that, in EH-FB-TES, the heat release efficiency is predominantly constrained by thermal losses. To increase the energy utilization efficiency, a two-stage heat recovery strategy is proposed to release the stored energy in the integration. The first stage is to heat up the feedwater extracted from the deaerator and the second one is to heat up the condensate water. The analyses also show that the selection of reinjection positions for the heated medium from EH-FB-TES greatly influences the system performance. Returning the stored thermal energy to heat up feedwater can effectively increase the output of the unit, while directly generating steam can be beneficial for coal saving. The integrated system achieves a maximum equivalent round-trip efficiency of 32.9% under 20 MW/800 °C conditions. An economic analysis reveals that, compared with other energy storage methods, EH-FB-TES can realize a relatively high energy storage density with a rather low cost. Under the present DPS compensation policy, for a 315 MW subcritical CFPP integrated with a 50 MW EH-FB-TES system, when heat storage is 8 h, heat release is 4 h per day, and the plant operates 100 days per year, the estimated static and dynamic payback periods are 3.06 years and 3.67 years, respectively. The integration of CFPP with EH-FB-TES could be promising for meeting DSP requirements. Full article

As urban renewal shifts toward inventory optimization, studentification-driven socio-spatial conflicts in university-adjacent communities have intensified. This study examines Changsha Hexi University Town using structural equation modeling (SEM) to analyze residential satisfaction and spatial injustice. Findings reveal that university–community interaction and indoor space perception are primary determinants of satisfaction, highlighting the demand for residential dignity under “spatial squeeze”. Conversely, public resources and social capital exhibit a “decoupling effect” caused by infrastructure “functional alienation” and social fragmentation. A profound “perceptual rift” exists between indigenous owners, facing “spatial deprivation” in resource competition, and student tenants, lacking “spatial dignity” in subdivided units. These tensions are exacerbated by “institutional gating”—where physical openness coexists with administrative restrictions. Consequently, renewal strategies must transcend aesthetics to implement systemic “spatial compensation”. We recommend opening institutional assets, regulating informal rental standards, and establishing collaborative platforms. This research facilitates a paradigm shift from “spatial squeeze” toward “university–community symbiosis”, providing a framework for socio-spatial justice in high-density academic enclaves. Full article

Addressing the insufficient sensitivity of typical Archimedean spiral antennas for detecting partial discharge (PD) in electrical equipment, this paper proposes a high-sensitivity regulation technique for PD Archimedean antennas based on rotating unit-cell reflective metasurfaces. First, a finite element model of the ultra-high-frequency Archimedean antenna was constructed. Then, employing metasurface electromagnetic wave reflection technology and phase compensation principles, a rotating-unit reflective metasurface was designed to optimize its full-bandwidth gain. A multi-parameter joint optimization method was used to obtain the optimal data for the antenna and metasurface parameters. Finally, simulations and experimental analyses of the super-surface-controlled Archimedean antenna revealed the following: The gain of the Archimedean antenna controlled by the rotating-unit super-surface increases by up to 15.61 dB in the 0.3–1.5 GHz band, with an average full-band gain enhancement of 3.42 dB. During electrostatic discharge (ESD), the amplitude of UHF signals detected by the Archimedean antenna increases by approximately 88.9%, and the amplitude detection of UHF signals during GIS discharges increases by approximately 138.6–150%. These results demonstrate that the metasurface significantly enhances the antenna’s gain performance, providing a reference for highly sensitive control technologies in detecting discharges in electrical equipment. Full article

To identify adaptive cultivation strategies for strong-gluten winter wheat under conditions of increasing autumn temperatures and changing precipitation patterns in the Huang–Huai–Hai region, a field experiment was conducted with cultivars Jimai 44 and Zhongmai 578. Field experiments were conducted during the 2023–2024 and 2024–2025 growing seasons, using three sowing dates (T2–T4, 20 October to 3 November) in the first year and four sowing dates (T1–T4, 13 October to 3 November) in the second year, each combined with three seeding rates (M1–M3) that were increased by 52.5 kg ha⁻¹ for every 7-day delay in sowing. This design evaluated how sowing date and seeding rate regulate photosynthesis, dry matter dynamics, and yield. The results showed that post-anthesis dry-matter accumulation, harvest index, grain number per unit area, and grain yield responded quadratically to delayed sowing and increased seeding rate. Delayed sowing increased flag-leaf SPAD but reduced dry matter at anthesis and maturity, pre-anthesis translocation, spike number, and thousand-kernel weight. Higher seeding rate decreased SPAD, net photosynthetic rate, grains per spike, and kernel weight. The T2M2 treatment optimized canopy structure, enhanced photosynthesis, maintained efficient dry matter production and partitioning, and balanced yield components, achieving the highest grain yield. Although severe delays in sowing reduced yield, increasing the seeding rate under late sowing compensated for the reduced spike number and mitigated yield losses. The T2M2 combination and the late-sowing with the incremental-seeding technique offer practical strategies for climate-resilient, high-yield wheat production in the region. Full article

The inherent randomness, high volatility, and non-stationarity of photovoltaic (PV) power generation pose substantial threats to the stability of modern power grids. Developing high-precision forecasting models is essential for grid operation, yet conventional architectures often encounter a performance bottleneck: they struggle to simultaneously achieve high computational efficiency for long-range dependency modeling and robust perception for local, abrupt fluctuations. To address these limitations, this paper proposes HDCF-Mamba, a novel forecasting framework that resolves the feature distribution gap between long-range trends and short-term volatility. The core innovation lies in the Heterogeneous Dual-branch Cross-Fusion (HDCF) mechanism, which enables the synergetic integration of a Mamba-based global branch and a Multi-Kernel Filter Unit-based multi-scale local branch. Specifically, we integrate the Mamba Selective State Space Mechanism into the global branch to efficiently capture long-term dependencies with $O\left(L\right)$ linear complexity, fundamentally overcoming the quadratic computational bottleneck of Transformers. Meanwhile, the Multi-Scale Feature Extraction Module (MSFEM) acts as a local compensator to capture high-frequency power fluctuations caused by transient weather changes. Unlike simple hybrid models that rely on linear addition, our HDCF design utilizes a temporal concatenation mechanism to ensure non-linear alignment of these heterogeneous features. Extensive experiments on four real-world PV operational datasets (including publicly available benchmark datasets and actual photovoltaic power station monitoring data: ECD-PV, LSP-PV, APS-PV, and PSB-PV) demonstrate that HDCF-Mamba consistently outperforms state-of-the-art models, achieving a reduction in Mean Absolute Error (MAE) of up to 11.4% compared to iTransformer and 8% compared to SCINet, while maintaining superior computational efficiency. Full article

Battery ageing is inevitable during operation, leading not only to performance degradation but to potential safety concerns. Consequently, accurate prediction of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safety and reliability. This study proposed a novel hybrid neural network architecture that integrates a transformer module, an empirical degradation (ED) model, and a gated recurrent unit (GRU). The transformer module enhances the global representation of the feature sequence, while the ED model comprehensively considers the impact of temperature on the rate of battery capacity degradation, compensating the un-interpretability of the transformer architecture in predicting SOH. In addition, pseudo-incremental capacity curves have been obtained using charging fragments from multi-stage constant current fast charging, which solves the issue of extracting mechanism features under fast charging conditions. Experimental results demonstrate that, across a wide temperature range, the model maintains a low average RMSE between 0.43% and 0.59% for prediction horizons of 4 to 128 cycles. Specifically, the average RMSE is 0.87% at −5 °C and 0.37% between 25 °C and 55 °C. Compared to standalone data-driven models, the proposed hybrid architecture reduces prediction error by approximately 50% at 25 °C, exhibiting superior predictive performance and robustness. Full article

The rapid integration of renewable energy sources has accelerated the adoption of DC microgrids as an effective platform for flexible and reliable power generation and management. However, conventional droop-based control suffers from inherent limitations, particularly voltage deviations at the DC bus, which compromise stability, power-sharing accuracy, and overall system performance. To address these challenges, this paper presents a distributed secondary control framework for a standalone PV battery-based DC microgrid that achieves bus voltage regulation, precise power distribution, and state-of-charge (SoC) balancing across multiple energy storage units (ESUs). At the primary level, an adaptive mechanism is introduced that dynamically adjusts droop coefficients in response to the real-time SoC of each ESU, promoting balanced utilization of storage resources. At the secondary level, the strategy leverages limited peer-to-peer communication to exchange only aggregate power information, thereby enabling accurate load sharing while preserving scalability and plug-and-play capability. The control architecture further incorporates voltage and current error compensation, with parameters tuned using a Whale Optimization Algorithm to enhance dynamic response. Validation is carried out through a real-time simulation environment developed in MATLAB/Simulink R2024b and executed on a Speedgoat^TM platform. The results demonstrate robust SoC equalization, improved bus voltage stability, and reliable cooperative coordination, positioning the scheme as a practical solution for next-generation DC microgrids. Full article

This paper presents a patented design of a gravity flow rack with an energy recovery system, intended for pallet storage in first-in–first-out (FIFO) and last-in–first-out (LIFO) modes. Compared with conventional flow racks, the proposed solution integrates control of load-unit motion dynamics with energy recovery, thereby reducing losses and stabilising pallet flow. A Rack Energy Performance Index (REPI) is proposed to enable quantitative assessment of the energy consumption of storage racks in intralogistics applications. The research methodology comprised: (i) development of the mechanical architecture and pallet guidance principles; (ii) numerical modelling in the MSC Adams environment at Technology Readiness Level 3 (TRL-3); and (iii) validation using a full-scale prototype installed in a logistics centre. Operational tests confirmed stable operation, the required throughput, and the capability for energy compensation and recovery during storage cycles. The results indicate that energy-recovering racks can support the design of energetically passive warehouses. Full article

Vessel trajectory prediction is pivotal for maritime traffic safety and autonomous collision avoidance. However, existing studies predominantly rely on massive public AIS (Automatic Identification System) datasets, often overlooking the challenges of data sparsity and long-tailed distributions inherent in practical engineering scenarios, where high-dynamic maneuvering samples are scarce. Furthermore, as a low-frequency kinematic observation system, AIS suffers from inherent kinematic lag relative to the vessel’s true dynamic state, particularly failing to timely reflect turning intentions during the maneuver initiation phase. To address these challenges, this paper proposes a Physics-Aware Multimodal Fusion Framework. By incorporating high-frequency acceleration and angular velocity from an Inertial Measurement Unit (IMU), the framework applies physical compensation to AIS kinematic observations, thereby enhancing the model’s perception of maneuvering intent. Validation based on real-vessel experimental data demonstrates that the proposed method effectively mitigates the prediction lag observed in pure-AIS models at the 60 s horizon, significantly improving accuracy in turning scenarios. Moreover, in 180 s long-term predictions, the multimodal fusion mechanism effectively suppresses integration drift, ensuring superior trajectory shape consistency and engineering stability. The study indicates that incorporating IMU inertial information is critical for enhancing the robustness of vessel trajectory prediction under practical engineering conditions characterized by sparse samples and complex maneuvers. Full article

Pursuing the so-defined biorefinery approach, residual biomass, such as agro-industrial wastes, should first be exploited for the extraction and production of high-value-added products and then processed for energy valorisation through anaerobic digestion (AD). However, the treatments applied to achieve the first goal could impact biogas yield. This problem can be solved by co-digesting the treated biomass with others. In this study, Brewery’ Spent Grain (by itself, a good biogas producer) was treated with an ionic liquid (IL) composed of triethylamine and sulfuric acid [TEA][HSO₄] for lignin removal. The residual biomass (pulp, BSGp) was then used for biogas production. The tests revealed a marked reduction in the total quantity of biomethane (per unit of volatile solid—VS). In detail, 6.82 × 10⁻⁴ Nm³CH₄/gVS of biomethane was produced with BSGp, against 1.31 × 10⁻³ Nm³CH₄/gVS with BSG. The lack of organic nitrogen after the IL-based treatment prevented biogas production, resulting in a shorter production period. To compensate for the nitrogen deficiency and restore the optimal C/N ratio, BSGp was mixed with Lemna minor (LM), an aquatic weed with a high nitrogen content. By itself, LM cannot be considered a good biogas producer as proven in this study. However, the co-digestion of LM with BSGp extended the production period and kept the daily production close to that registered in test made with the sole BSGp, thus achieving a total biomethane production equal to 1.83 × 10⁻³ Nm³CH₄/gVS, even higher than the one registered with untreated BSG. Full article

With the rapid expansion of renewable energy and the acceleration of electricity market reforms, coal-fired units are facing increasing difficulty in recovering fixed costs due to marginal cost-based bidding competition and depressed clearing prices caused by low-cost renewable integration, circumstances in which reasonable returns and investment incentives for coal-fired power plants are not guaranteed. To address this issue, this paper proposes a capacity cost compensation mechanism for coal-fired power in the electricity market environment. First, a joint clearing model for the electricity spot market considering both energy and reserve services is established, and annual market operation simulations are conducted to obtain unit output schedules, clearing prices, and annual revenues. Second, based on the long-term simulation results, the marginal clearing probability and fixed cost recovery deficit of each coal-fired unit are calculated, and a capacity compensation pricing method based on marginal clearing probability weighting is proposed to determine the system unit capacity compensation price. Subsequently, the compensated capacity is determined using the availability factor method, comprehensively reflecting each unit’s actual contribution to system capacity adequacy. Finally, case studies conducted on a modified IEEE 30-bus system validate the effectiveness of the proposed mechanism. The results demonstrate that following the implementation of the proposed mechanism, the investment payback periods of all coal-fired units are reduced to within the planned 20-year horizon, thereby ensuring the sustainable operation of coal-fired units and maintaining adequate reliability margins in the power system. Full article