Search Results (1,523)

To enhance power dispatching and mitigate grid connection fluctuations, this paper proposes a wind power prediction model based on Long Short-Term Memory-Back Propagation Neural Network (LSTM-BP) optimized by an adaptive Particle Swarm Optimization algorithm (aPSO). Initially, anomalies and missing values in raw wind farm data are addressed using the quartile method and filled via cubic spline interpolation. The data is then denoised using the Autoregressive Integrated Moving Average (ARIMA) model. Statistical and combined features are extracted, and Bayesian optimization is applied for optimal feature selection. To overcome the limitations of single models, a hybrid approach is adopted where a BP neural network is used in conjunction with LSTM. The optimal features are first input into the BP neural network to learn the current relationship between features and wind power. Then, historical data of both the features and wind power are fed into the LSTM to generate preliminary predictions. These LSTM outputs are subsequently passed into the trained BP neural network, and the final wind power prediction result is obtained through network integration. This combined model leverages the temporal learning capabilities of LSTM and the fitting strengths of BP, while aPSO ensures optimal parameter tuning, ultimately enhancing prediction accuracy and robustness in wind power forecasting. The experimental results show that the proposed model achieves a MAE of 0.54 MW and a MAPE of 10.5% in one-step prediction, reducing the error by over 35% compared to benchmark models such as ARIMA-LSTM and LSTM-BP. Multi-step prediction validation on 2000 sets of real wind farm data demonstrates the robustness and generalization capabilities of the proposed model. Full article

Background/Objectives: GATA transcription factors play pivotal roles in regulating plant growth and development, physiological metabolism, and responses to environmental stress. However, research on GATA genes in sweetpotato remains limited. Methods: In this study, we identified 25 IbGATA genes in sweetpotato (Ipomoea batatas [Lam.] L.) through a genome-wide analysis. These genes were analyzed for their physicochemical properties, chromosomal localization, synteny, phylogenetic relationships, gene structure, promoter cis-elements, protein interaction networks, and expression profiles across various tissues and under drought stress. To elucidate the function of drought-resistant candidate genes, an in situ one-step transformation method was employed. Results: Sweetpotato GATA genes have a complex evolutionary history, including replication events, different selection pressures, and functional diversification. They may be involved in multiple plant stress signaling pathways. Furthermore, functional analysis revealed that IbGATA17 enhances drought tolerance in sweetpotato by promoting proline biosynthesis and reinforcing ROS scavenging capacity. Our findings provide novel insights into the roles of IbGATAs, particularly IbGATA17, in mediating drought-stress responses in sweetpotato. Conclusions: This study provides foundational insights into the GATA gene family in sweetpotato and reveals the pivotal role of IbGATA17 in simulated drought-stress response, providing a potential candidate gene for the development of drought-resistant varieties. Full article

Building energy conservation through the development of transparent thermal insulation materials that selectively block near-infrared radiation while maintaining visible light transmittance has emerged as a key strategy for global carbon neutrality. WO₃ is a semiconductor oxide with near-infrared absorption capabilities. However, the limited absorption efficiency and narrow spectral coverage of pure WO₃ significantly diminish its overall transparent thermal insulation performance, thereby restricting its practical application in energy-saving glass. Therefore, this study successfully prepared Sn-doped WO₃ materials using a one-step hydrothermal method, controlling the Sn:W molar ratio from 0.1:1 to 2.0:1. Through evaluation of transparent thermal insulation performance of a series of Sn-doped WO₃ samples, we found that Sn:W = 0.9:1 exhibited the most excellent performance, with NIR shielding efficiency reaching 93.9%, which was 1.84 times higher than pure WO₃. Moreover, this sample demonstrated a transparent thermal insulation index (THI) of 4.38, representing increases of 184% and 317%, respectively, compared to pure WO₃. These enhancements highlight the strong NIR absorption capability achieved by Sn-doped WO₃ through structural regulation. When Sn doping reaches a certain concentration, it triggers a structural transformation of WO₃ from monoclinic to tetragonal phase. After reaching the critical solubility threshold, phase separation occurs, forming a multiphase structure composed of a Sn-doped WO₃ matrix and secondary SnO₂ and WSn_0.33O₃ phases, which synergistically enhance oxygen vacancy formation and W⁶⁺ to W⁵⁺ reduction, achieving excellent NIR absorption through small polaron hopping and localized surface plasmon resonance effects. This study provides important insights for developing high-performance transparent thermal insulation materials for energy-efficient buildings. Full article

A corrosion-resistant coating was fabricated on the surface of LZ91 Mg–Li alloy via a one-step hydrothermal method under varying reaction temperatures (70, 90, 110, and 130 °C). This involved immersing bare Mg–Li alloy substrates in a 10 wt.% Na₂CO₃ aqueous solution for 3 h. The microstructure, elemental distribution, and phase composition of the as-prepared coatings were systematically characterized using scanning electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray diffraction. The corrosion resistance was evaluated by electrochemical impedance spectroscopy and potentiodynamic polarization measurements. The results revealed that the hydrothermal treatment led to the formation of a dense nanostructured coating composed of fine nanosheets, with their morphology and population density being highly dependent on the reaction temperature. Phase analysis confirmed that the coating primarily consisted of Mg(OH)₂, MgCO₃, and Li₂CO₃. The electrochemical tests demonstrated that the coatings substantially enhanced the corrosion resistance of the alloy. Additionally, the corrosion resistance decreased in the following order: 130 °C > 110 °C > 90 °C > 70 °C > bare LZ91 substrate. Full article

(1) Background: Water buffaloes (Bubalus bubalis) are important for dairy and meat production. Up to now, genomic analysis has focused on female subjects, leaving the Y chromosome essentially unknown. Advances in third-generation sequencing (TGS) made it possible to improve the study of complex genome sequences, such as buffalo and other mammalian species including humans. (2) Methods: In this study, we applied TGS-based long-read sequencing to generate, in one step, high-quality whole-genome sequences, which can take full advantage of a rapid bioinformatic pipeline, such as that described in the companion paper. (3) Results: Five male buffalo genomes have been fully sequenced at relatively high depth (20–40×) which, combined with the read length typical of TGS, provide the basis for important insights into male-specific genetic traits, including those linked to meat and milk production. (4) Conclusions: With the use of TGS technologies, we offer a complete strategy for fast, one-step genome sequencing which can also be applied to other farm animals with a comparably large genome. This approach can help in revealing genetic features characteristic of an animal individual beyond the simple assessment of a number of SNPs or other known sequence variations, thus supporting improved genetic selection for dairy productivity and future research on genetic variability in buffalo breeds. Full article

The Electrical Capacitance Tomography (ECT) imaging pipeline relies on accurate estimation of electric field distributions to compute electrode capacitances and reconstruct permittivity maps. Traditional ECT forward model methods based on the Finite Element Method (FEM) offer high accuracy but are computationally intensive, limiting their use in real-time applications. In this proof-of-concept study, we investigate the use of Graph Convolutional Networks (GCNs) for direct, one-step prediction of electric field distributions associated with a circular ECT sensor numerical model. The network is trained on FEM-simulated data and outputs of full 2D electric field maps for all excitation patterns. To evaluate physical fidelity, we compute capacitance matrices using both GCN-predicted and FEM-based fields. Our results show strong agreement in both direct field prediction and derived quantities, demonstrating the feasibility of replacing traditional solvers with fast, learned approximators. This approach has significant implications for further real-time ECT imaging and control applications. Full article

6-Nitrodopamine is an endogenous catecholamine responsible for numerous biological activities. Here, an improved method for the synthesis of both 6-nitrohomovanillic acid (6-NHVA) and its regioisomer 5-nitrohomovanillic acid (5-NHVA) is reported. The developed one-step synthetic procedures ensured the efficient preparation of the target compounds in good yields. Comprehensive structural characterization was achieved through one- and two-dimensional NMR studies and by high-resolution mass spectrometry (HR-MS/MS). The presence of both substances was identified in human amniotic fluid by LC-MS/MS. Full article

Establishing a simple and effective method for the visual detection of formaldehyde plays an important role in environmental emergency monitoring. In this work, L-cysteine-stabilized copper clusters were synthesized via a green, mild, and facile one-step preparation method. Through the optimization of reaction conditions, including reactant concentration and pH, the clusters exhibited stable red fluorescence. Upon exposure to formaldehyde, the fluorescence intensity of copper clusters gradually quenched with increasing formaldehyde concentration, enabling the development of a visual detection method that was successfully applied to analyze formaldehyde samples in air. Furthermore, by immobilizing the copper clusters into hydrogels, the visual detection performance and portability of the material were significantly enhanced. This method offers the advantages of simple preparation and rapid and accurate determination, demonstrating potential for semi-quantitative field detection of formaldehyde in emergency scenarios. Full article

Accurately forecasting the interval-valued green electricity (GE) supply is challenging due to the unpredictable and instantaneous nature of its source; yet, reliable multi-step-ahead forecasting is essential for providing the lead time required in operations, resource allocation, and system management. This study proposes an augmented-feature multi-step interval-valued forecasting (AFMIF) scheme that aims to address the challenges in forecasting interval-valued GE supply data by extracting additional features hidden within an interval. Unlike conventional methods that rely solely on original interval bounds, AFMIF integrates augmented features that capture statistical and dynamic properties to reveal hidden patterns. These features include basic interval boundaries and statistical distributions from an interval. Three effective forecasting methods, based on gated recurrent units (GRUs), long short-term memory (LSTM), and a temporal convolutional network (TCN), are constructed under the proposed AFMIF scheme, while the mean ratio of exclusive-or (MRXOR) is used to evaluate the forecasting performance. Two different real datasets of wind-based GE supply data from Belgium and Germany are used as illustrative examples. Empirical results demonstrate that the proposed AFMIF scheme with GRUs can generate promising results, achieving a mean MRXOR of 0.7906 from the Belgium data and 0.9719 from the Germany data for one-step- to three-steps-ahead forecasting. Moreover, the TCN yields an average improvement of 13% across all time steps with the proposed scheme. The results highlight the potential of the AFMIF scheme as an effective alternative approach for accurate multi-step-ahead interval-valued GE supply forecasting that offers practical benefits supporting GE management. Full article

A series of guaiazulene derivatives were efficiently synthesized by one-step reaction using guaiazulene as the substrate. Their structures were fully characterized by comprehensive spectroscopic methods, and their antiviral activities against influenza A (H1N1) virus were evaluated. Compounds 2b, 2d, 2e, 2f, 3a, and 3b exhibited significant anti-influenza activity, with IC₅₀ values of 89.03 µM, 98.48 µM, 78.38 µM, 108.20 µM, 50.96 µM, and 56.09 µM, respectively. Ribavirin was used as a positive control (IC₅₀ = 130.22 µM). Full article

Hierarchical NiO-SnO₂ nanoflowers were prepared via a one-step hydrothermal method. The morphology, structure and components of the hierarchical NiO-SnO₂ nanoflowers were examined via scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray powder diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The ethanol gas-sensing performance was systematically analyzed between pure hierarchical SnO₂ nanoflowers and the hierarchical NiO-SnO₂ nanoflowers. The results indicated that the hierarchical NiO-SnO₂ nanoflowers showed better gas-sensing properties than the pure hierarchical SnO₂ nanoflowers at 164 °C. The enhanced gas-sensing performance was ascribed to the formation of p-n heterojunctions between p-type NiO and n-type SnO₂. Additionally, NiO has a catalytic role. Therefore, hierarchical NiO-SnO₂ nanoflowers could be a potential gas-sensing material for the fabrication of high-quality ethanol gas sensors. Full article

This study outlines a high-yield green method for synthesizing MBT using aniline, carbon disulfide and sulfur as raw materials via a one-step reaction combined with high–low-temperature extraction. The process is supported by experimental results and lab-scale tests, and the operating conditions of the amplification process are evaluated using Aspen Plus simulation software, supplemented with Gaussian09 calculations. The sensitivity analysis results indicate that the MBT yield reaches its maximum value when the feed mass ratio of S:CS₂:C₆H₇N:C₇H₈ is 6:17:20:90. Additionally, setting the reaction temperature to 240 °C and pressure to 10 MPa improves the MBT synthesis yield from 58% to 82.5%. Optimal condensation and extraction conditions are achieved at −30 °C and 1 atm, followed by a separation step at 40 °C. The simulation results provide valuable guidance for the industrial production of MBT. Full article

This study explored the antioxidant and anticancer potential of extracts obtained from the mangaba, cambuí, and red propolis blend. The extracts were obtained using ultrasound-assisted pressurized fluid extraction (UAPFE) at 50 bar, 60 °C, and a flow rate of 2 mL/min. Both sequential extraction with solvents of increasing polarity (propane followed by ethanol/water) and one-step extraction were employed for 30 min. Extracts were characterized by ultra-high-resolution mass spectrometry, total phenolic content, antioxidant activity (via DPPH and FRAP assays), and cytotoxicity using the sulforhodamine B colorimetric method. Among the tested conditions, the sequential extraction with ethanol/water (UAPFE-SE) yielded 16.2 ± 3.0% (overall extraction yield), with high phenolic content (24.1 ± 0.4 µg/mg). Mass spectrometry revealed the presence of antiproliferative phenolics. The UAPFE-SE extract demonstrated moderate antioxidant activity, with FRAP values of 394.0 ± 6.0 µg Fe²⁺/mg and DPPH scavenging capacity of 28.5 ± 0.3 µg Trolox equivalents/mg. Additionally, it exhibited cytotoxic inhibition of 82.3 ± 1.7% against lung carcinoma cells at a concentration of 100 μg/mL. The results suggest that the antioxidant properties and cytotoxic effect against lung cancer cells in vitro warrant further investigation to assess therapeutic potential. Full article

In recent years, paper-based humidity sensors have emerged as a highly promising technology for humidity detection. In this work, a polymerizable deep eutectic solvent (PDES) was prepared via a one-step blending method, which was applied to modify filter paper. The modification process did not alter the overall structure of the paper cellulose but rather targeted only its internal cellulose channels, thereby minimizing any impact on the paper’s original moisture-independent properties. The filter paper functioned both as the substrate and the humidity-sensing material in the fabricated sensor. The finger-like electrodes were designed using AutoCAD 2018 software and then printed onto the modified paper using screen-printing technology to fabricate the humidity sensor. Different saturated salt solutions were used to simulate corresponding humidity environments and evaluate the humidity performance of sensors. Compared with that of the blank paper-based humidity sensor, the sensitivity of the sensor modified by the PDES was significantly greater, and the recovery time was greatly shorter. Specifically, the sensitivity increased from 1.34 to 10.36 at 54% RH and from 166.24 to 519.2 at 98% RH. Additionally, the sensor response time was reduced from 728 s to 137 s. PDES modification significantly improved the moisture-sensitive characteristics and detection performance of the sensor. Full article

The potential of silicon carbide (SiC) as a promising high-capacity and stable anode material is hindered by poor electronic conductivity and slow lithium diffusion kinetics. Here, we report a one-step laser chemical vapor deposition (LCVD) process to directly synthesize porous graphene@SiC heterostructures on carbon fiber substrates. This in situ method yields an integral, binder-free electrode architecture that enhances mechanical robustness against pulverization. A critical feature of this heterostructure is the built-in electric field at the graphene–SiC interface, which is revealed by theoretical calculations to significantly accelerate charge transport and lithium-ion diffusion. The resulting anode delivers a high reversible capacity of 668 mAh·g⁻¹ after 100 cycles at 0.1 A·g⁻¹. More remarkably, a unique multi-stage activation mechanism is discovered, leading to an unprecedented capacity rebound to 735 mAh·g⁻¹ after cycling at rates up to 5 A·g⁻¹. This activation process is observed to accelerate with increasing current density in the 0.1–2 A·g⁻¹ range. Furthermore, post-cycling analysis via XRD, TEM, and XPS confirms both the structural durability of the electrode and a reversible lithium intercalation mechanism, providing a critical foundation for the future design of high-performance LIB anodes. Full article