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HighlightsThe bioinspired self-sensing actuated gradient hydrogel was developed by a wettability-based method via precipitation of MoO2 nanosheets.Self-sensing actuated gradient hydrogel combined ultrafast thermo-responsive actuation (21° s–1), exceptional photothermal efficiency (3.7 °C s–1) and high sensing properties (GF = 3.94).The first self-sensing remote interaction system based on gradient hydrogel actuators and robotic hands was constructed.The development of bioinspired gradient hydrogels with self-sensing actuated capabilities for remote interaction with soft-hard robots remains a challenging endeavor. Here, we propose a novel multifunctional self-sensing actuated gradient hydrogel that combines ultrafast actuation and high sensitivity for remote interaction with robotic hand. The gradient network structure, achieved through a wettability difference method involving the rapid precipitation of MoO2 nanosheets, introduces hydrophilic disparities between two sides within hydrogel. This distinctive approach bestows the hydrogel with ultrafast thermo-responsive actuation (21° s−1) and enhanced photothermal efficiency (increase by 3.7 °C s−1 under 808 nm near-infrared). Moreover, the local cross-linking of sodium alginate with Ca2+ endows the hydrogel with programmable deformability and information display capabilities. Additionally, the hydrogel exhibits high sensitivity (gauge factor 3.94 within a wide strain range of 600%), fast response times (140 ms) and good cycling stability. Leveraging these exceptional properties, we incorporate the hydrogel into various soft actuators, including soft gripper, artificial iris, and bioinspired jellyfish, as well as wearable electronics capable of precise human motion and physiological signal detection. Furthermore, through the synergistic combination of remarkable actuation and sensitivity, we realize a self-sensing touch bioinspired tongue. Notably, by employing quantitative analysis of actuation-sensing, we realize remote interaction between soft-hard robot via the Internet of Things. The multifunctional self-sensing actuated gradient hydrogel presented in this study provides a new insight for advanced somatosensory materials, self-feedback intelligent soft robots and human–machine interactions.

Extreme weather conditions, specifically typhoons and strong gusts, create a highly transient environment for wind power data collection, leading to performance degradation that significantly impacts the safety and stability of the wind power system. To accurately predict wind power trends under these conditions, this paper proposes a prediction model integrating Singular Spectrum Analysis (SSA), Temporal Convolutional Network (TCN), Convolutional Neural Network (CNN), and a global average pooling layer, combined with inverse adaptive transfer learning. First, SSA is applied to reduce noise in the collected wind power operation data and extract key information. Subsequently, a prediction model is constructed based on TCN, CNN, and global average pooling. The model employs dilated causal convolutions to capture long-term dependencies and uses two-dimensional convolution kernels to extract local mutation features. Furthermore, a domain-adaptive transfer learning module is designed to adjust the model’s parameter weights via backward optimization based on the Maximum Mean Discrepancy (MMD) between the source and target domains. Experimental validation is conducted using real-world wind power operation data from a wind farm in Guangxi, containing 3000 samples sampled at 10 min intervals specifically during severe typhoon periods. Experimental results demonstrate that even with only 60% of the target data, the proposed method outperforms the traditional TCN neural network, reducing the Root Mean Square Error (RMSE) by 58.1% and improving the Coefficient of Determination (R2) by 32.7%, thereby verifying its effectiveness in data-scarce extreme scenarios.

With the large-scale development of the power industry, new requirements are put forward for the stable operation of the power system. Power system load forecasting refers to the use of historical load data to predict the future load value, which is an important part of energy management system. The short-term load forecasting process is often combined with the basic mechanism of power grid dispatching to achieve the balance of power grid supply and demand, reflecting the highly nonlinear computing ability. Power load forecasting is the premise of power grid real-time control, operation planning and development planning. At present, the grey model model used for power system load forecasting generally has the problems of large calculation amount, no mature theoretical basis for selecting structures and parameters, etc. This paper discusses the application of grey model in short-term power load forecasting, and puts forward a principal component analysis method suitable for ordinary daily power load forecasting data, which improves the accuracy of short-term power load forecasting.

The development and utilization of wind energy is of great significance to the sustainable development of China’s economy and the realization of the “dual carbon” goal. Under typhoon conditions, the randomness and volatility of wind speed significantly impact the energy efficiency and design of wind turbines. This paper analyzed the changes in wind speed and direction using the BFAST method and Hurst index based on data collected at 10 m, 30 m, 50 m, and 70 m heights from a wind power tower in Yancheng, Jiangsu Province. Furthermore, the paper examined the causes of wind speed and direction changes using wind speed near the typhoon center, distance from the typhoon center to the wind tower, topographic data, and mesoscale system wind direction data. The conclusions drawn are as follows: (i) Using the BEAST method, change points were identified at 10 m, 30 m, 50 m, and 70 m heights, with 5, 5, 6, and 6 change points respectively. The change points at 10 m, 30 m, and 50 m occurred around node 325, while the change time at 70 m was inconsistent with other heights. Hurst index results indicated stronger inconsistency at 70 m altitude compared to other altitudes. (ii) By analyzing the wind direction sequence at 10 m, 30 m, 50 m, and 70 m, it was found that the wind direction changes follow the sequence Southeast (SE)—East (E)—Southeast (SE)—Southwest (SW)—West (W)—Northwest (NW). Notably, the trend of wind direction at 70 m significantly differed from other altitudes during the wind speed strengthening and weakening stages. (iii) Wind speed at 10 m and 70 m altitudes responded differently to the distance from the typhoon center and the wind near the typhoon center. The correlation between wind speed and the distance to the typhoon center was stronger at 10 m than at 70 m. The surface type and the mesoscale system’s wind direction also influenced the wind speed and direction. This study provides methods and theoretical support for analyzing short-term wind speed changes during typhoons, offering reliable support for selecting wind power forecast indicators and designing wind turbines under extreme gale weather conditions.

The risk-based assessment is a new approach to the voltage stability assessment in power systems. Under several uncertainties, the security risk of static voltage stability with the consideration of wind power can be evaluated. In this paper, we first build a probabilistic forecast model for wind power generation based on real historical data. Furthermore, we propose a new probability voltage stability approach based on Conditional Value-at-Risk (CVaR) and Quasi-Monte Carlo (QMC) simulation. The QMC simulation is used to speed up Monte Carlo (MC) simulation by improving the sampling technique. Our CVaR-based model reveals critical characteristics of static voltage stability. The distribution of the local voltage stability margin, which considers the security risk at a forecast operating time interval, is estimated to evaluate the probability voltage stability. Tested on the modified IEEE New England 39-bus system and the IEEE 118-bus system, results from the proposal are compared against the result of the conventional proposal. The effectiveness and advantages of the proposed method are demonstrated by the test results.

We consider the problem of distributed optimal power flow (OPF) for multi-area electric power systems. A novel distributed algorithm is proposed, referred to as the rotated coordinate descent critical region exploration (RCDCRE). It allows each entity to independently update its boundary information and optimally solve its local OPF in an asynchronous fashion. RCDCRE method stitches coordinate descent and parametric programming using coordinate system rotation to reduce coordination, keep privacy and ensure convergence. The solution process does not require warm starts and can iterate from infeasible initial points using penalty-based formulations. The effectiveness of RCDCRE is verified based on IEEE 2-area 44-bus and 4-area 472-bus systems.

The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.

The study aims to explore the effect of PPARγ signaling on ferroptosis and preeclampsia (PE) development. Serum and placental tissue are collected from healthy subjects and PE patients. The PPARγ and Nrf2 decreases in the PE. Rosiglitazone intervention reverses hypoxia-induced trophoblast ferroptosis and decreases lipid synthesis by regulating Nfr2 and SREBP1. Compared to the Hypoxia group, the migratory and invasive abilities enhance after rosiglitazone and ferr1 treatment. Rosiglitazone reduces the effect of hypoxia and erastin. The si-Nrf2 treatment attenuats the effects of rosiglitazone on proliferation, migration, and invasion. The si-Nrf2 does not affect SREBP1 expression. PPARγ agonists alleviates ferroptosis in the placenta of the PE rats. The study confirms that PPARγ signaling and ferroptosis-related indicators were dysregulated in PE. PPARγ/Nrf2 signaling affects ferroptosis by regulating lipid oxidation rather than SREBP1-mediated lipid synthesis. In conclusion, our study find that PPARγ can alleviate PE development by regulating lipid oxidation and ferroptosis. Hypertension, and organ system dysfunctions associated with preeclampsia (PE) result in poor pregnancy outcomes. In the current study, the authors examined molecular pathogenesis of PE with a focus Peroxisome proliferator-activated receptor gamma (PPARγ), lipid metabolism, and ferroptosis in the placenta of PE patients.

Background The snail Biomphalaria straminea is one of the intermediate hosts of Schistosoma mansoni . Biomphalaria straminea is also an invasive species, known for its strong capability on peripheral expansion, long-distance dispersal and colonization. Using molluscicides to control snail populations is an important strategy to interrupt schistosomiasis transmission and to prevent the spread of the invasive species. In this study, a series of pyridylphenylurea derivatives were synthesized as potential molluscicides. Their impact on adult snails and egg masses was evaluated. Acute toxicity to fish of the derivatives was also examined to assess their effect on non-target organisms. The preliminary mechanisms of action of the derivatives were studied by enzyme activity assays. Results The representative compounds, 1-(4-chlorophenyl)-3-(pyridin-3-yl)urea (compound 8) and 1-(4-bromophenyl)-3-(pyridin-3-yl)urea (compound 9), exhibited strong molluscicidal activity against adult snails with LD 50 values of 0.50 and 0.51 mg/l and potent inhibitory effects on snail egg hatchability with IC 50 values of 0.05 and 0.09 mg/l. Notably, both compounds showed good target specificity with potent molluscicidal capability observed in snails, but very low toxicity to local fishes. Furthermore, the exposure of compounds 8 and 9 significantly elevated the enzyme activities of acid phosphatase and nitric oxide synthase of the snails, while no significant change was recorded in the activities of alkaline phosphatase, acetylcholine esterase and superoxide dismutase. Conclusion The results suggested that compounds 8 and 9 of pyridylphenylurea derivatives could be developed as promising molluscicide candidates for snail control.

Ecosystem services play a crucial role in maintaining ecological balance, providing essential functions. This study examines the trade-offs and synergies among five key ecosystem services in ecological forests across different regions of Hunan Province, China. Various machine learning models are compared to predict ecosystem service value (ESV) levels, with the most effective predictive model identified. The SHAP (SHapley Additive exPlanations) analysis is employed to identify key environmental and management factors influencing ecosystem services. Our findings reveal significant regional variations in ecosystem services, with the eastern and western regions showing superior soil conservation and forest nutrient retention. In contrast, the southern and western regions, particularly karst areas, display fewer trade-offs between ecosystem services, likely due to the effectiveness of ecological policies. SHAP analysis further reveals that factors such as precipitation during the warmest quarter, central government compensation funds, and timber harvesting volume strongly influence regional ESV. This study provides valuable insights for improving ecosystem service management and policy-making in rapidly developing regions, underscoring the importance of ecological protection strategies for sustainable development.