High Efficiency Research Articles

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

In Operando X-ray Spectroscopic and DFT Studies Revealing Improved H2 Evolution by the Synergistic Ni-Co Electron Effect in the Alkaline Condition.

The different electrolyte conditions, e.g., pH value, for driving efficient HER and OER are one of the major issues hindering the aim for electrocatalytic water splitting in a high efficiency. In this regard, seeking durable and active HER electrocatalysts to align the alkaline conditions of the OER is a promising solution. However, the success in this strategy will depend on a fundamental understanding about the HER mechanism at the atomic scale. In this work, we have provided thorough understanding for the electrochemical HER mechanisms in KOH over Ni- and Co-based hollow pyrite microspheres by in operando X-ray spectroscopies and DFT calculations, including NiS2, CoS2, and Ni0.5Co0.5S2. We discovered that the Ni sites in hollow NiS2 microspheres were very stable and inert, while the Co sites in hollow CoS2 microspheres underwent reduction and generated Co metallic crystal domains under HER. The generation of Co metallic sites would further deactivate H2 evolution due to the large hydrogen desorption free energy (-1.73 eV). In contrast, the neighboring Ni and Co sites in hollow Ni0.5Co0.5S2 microspheres exhibited the electronic interaction to elevate the reactivity of Ni and facilitate the stability of Co without structure or surface degradation. The energy barrier in H2O adsorption/dissociation was only 0.73 eV, followed by 0.06 eV for hydrogen desorption over the Ni0.5Co0.5S2 surface, revealing Ni0.5Co0.5S2 as a HER electrocatalyst with higher durability and activity than NiS2 and CoS2 in the alkaline medium due to the synergy of neighboring Ni and Co sites. We believe that the findings in our work offer a guidance toward future catalyst design.

Study of The Microstructure and Mechanical Property Relationships of Gas Metal Arc Welded Dissimilar Protection 600T, DP450 and S275JR Steel Joints

The need for combining dissimilar materials is steadily increasing in the manufacturing industry, and the resulting products are expected to always have high performance. While there are various methods available for joining such material pairs, one of the commonly preferred techniques is fusion welding. In this study, three different steel materials (Protection 600T, DP450, and S275JR) were joined using gas metal arc welding (GMAW) in different combinations (similar/dissimilar). The microstructure and mechanical properties of the joints were evaluated. Tensile test, Vickers microhardness (HV 0.1), bending, Charpy V-notch impact testing, and microstructure examinations were conducted to analyze the weld and heat-affected zone. The tensile strengths of the base metal materials Protection 600T, DP450, and S275JR were found to be 1524.73 ± 18.7, 500.8 ± 10.4, and 508.5 ± 9.5 MPa, respectively. In welded samples of similar materials, the highest efficiency was found to be 103.05% for DP450/DP450, while in dissimilar welded joints, it was 105.5% for the DP450/S275JR pair. Hardness values for the base materials Protection 600T, DP450, and S275JR were measured as 526.5 ± 10.5, 153.8 ± 1.8, and 162.5 ± 5.2, respectively. In all welded samples, there was an increase in hardness in the weld zone (due to the welding wire) and the heat-affected zone (due to grain size refinement). While the impact energy values of similar material pairs were close to the base material impact energy values, the impact energy values of dissimilar material pairs varied according to the base materials. In addition, in joints made with similar materials, the bending force was close to the base materials, while a decrease in bending force was observed in joints formed with dissimilar materials. As a result, the welding of DP450 and S275JR materials was carried out efficiently. Protection 600T was welded with other materials, but its welding strength was limited to the strength of the material with low mechanical properties.

Determination of 13 potential anti-obesity agents in hair by dispersive liquid-liquid microextraction-assisted UHPLC-MS/MS

As the adulteration of dietary supplements with synthetic drugs remains a prevalent issue, the inclusion of anti-obesity agents may pose health risks, potentially leading to central nervous system or cardiovascular diseases. However, surveillance studies on the use of anti-obesity agents by the Chinese population are limited. This study aims to establish an efficient and rapid hair pretreatment method using dispersive liquid-liquid microextraction (DLLME) combined with high-speed grinding and develop a sensitive and accurate analytical method employing ultra high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) for detecting 13 potential anti-obesity agents in hair samples. Herein, hair samples were washed sequentially with 0.1% sodium dodecyl sulfate (SDS), water and acetone, and then ground at high speed using 1 mL of an extraction solution (internal standard solution-n-butanol-1.2 mol/L Na2HPO4, pH10.0, 100:400:500, v/v/v for procaterol; internal standard solution-ethyl acetate-1.2 mol/L Na2HPO4, pH8.0, 100:300:600, v/v/v for other 12 anti-obesity agents) while simultaneously performing DLLME. The developed method successfully detected 13 anti-obesity agents within 11 min, including bambuterol, clenbuterol, ractopamine, clorprenaline, formoterol, salbutamol, terbutaline, procaterol, phentermine, bupropion, sibutramine, desmethyl sibutramine, and N,N-didesmethyl sibutramine, which improved the screening efficiency. The calibration curves exhibited good linearity of 0.025–5 ng/mg, achieving correlation coefficients of r ≥ 0.99. The lower limits of quantification (LLOQs) for the analytes were 0.025 ng/mg, demonstrating acceptable levels of accuracy and precision. Recovery rates ranged between 73.30% and 107.47% across the three concentrations of 0.075, 0.375, and 3.75 ng/mg. The validated method was successfully applied to 369 real cases and detected six analytes, including bambuterol, salbutamol, terbutaline, sibutramine, desmethyl sibutramine, and N,N-didesmethyl sibutramine. This method offers several advantages, including simple pretreatment, high extraction efficiency, rapid extraction, solvent economy, and pollution mitigation, making it highly suitable for large-scale surveillance of usage of added anti-obesity agents.

Enhancing therapeutic efficacy: sustained delivery of 5-fluorouracil (5-FU) via thiolated chitosan nanoparticles targeting CD44 in triple-negative breast cancer

Our current study reports the successful synthesis of thiolated chitosan-based nanoparticles for targeted drug delivery of 5-Fluorouracil. This process was achieved through the ionic gelation technique, aiming to improve the efficacy of the chemotherapeutic moiety by modifying the surface of the nanoparticles (NPs) with a ligand. We coated these NPs with hyaluronic acid (HA) to actively target the CD44 receptor, which is frequently overexpressed in various solid malignancies, including breast cancer. XRD, FTIR, SEM, and TEM were used for the physicochemical analysis of the NPs. These 5-Fluorouracil (5-FU) loaded NPs were evaluated on MDA-MB-231 (a triple-negative breast cell line) and MCF-10A (normal epithelial breast cells) to determine their in vitro efficacy. The developed 5-FU-loaded NPs exhibited a particle size within a favorable range (< 300 nm). The positive zeta potential of these nanoparticles facilitated their uptake by negatively charged cancer cells. Moreover, they demonstrated robust stability and achieved high encapsulation efficiency. These nanoparticles exhibited significant cytotoxicity compared to the crude drug (p < 0.05) and displayed a promising release pattern consistent with the basic diffusion model. These traits improve the pharmacokinetic profile, efficacy, and ability to precisely target these nanoparticles, offering a potentially successful anticancer treatment for breast cancer. However, additional in vivo assessments of these formulations are obligatory to confirm these findings.

Experimental Investigation of Solid Rocket Scramjet Based on Central Strut

Scramjet based on solid propellant has become a potential choice for the development of future hypersonic vehicles. In this paper, a boron-containing solid rocket scramjet based on the central strut injection was proposed, and the ground direct-connect experiment with the equivalence ratios of 0.43 to 2.4 under the flight condition of Mach 6, 25 km was carried out. The pressure and flow rate over time were measured in the experiment. The results show that the engine can realize stable supersonic mode or subsonic mode combustion by changing the gas flow rate. The engine can effectively increase the combustor pressure, reduce the unstable combustion time, and advance the strong combustion position by increasing the gas flow rate. The engine achieved high combustion efficiency when the equivalence ratio was about 1, with a maximum of 88.28%. A numerical simulation analysis was also carried out in this paper. Compared to the experimental results, the pressure error obtained by numerical simulation was less than 4%, and the typical position error was less than 3%, suggesting that the simulation model can be used to predict the behavior of scramjet.

Advancements in RP-HPLC Method Development and Validation: A Comprehensive Review

Reverse-phase high-performance liquid chromatography (RP-HPLC) has emerged as a cornerstone technique in analytical chemistry, offering high sensitivity, selectivity, and efficiency in separating and quantifying a wide range of compounds. This comprehensive review explores recent advancements in RP-HPLC method development and validation, focusing on key strategies, challenges, and emerging trends. The review encompasses a detailed examination of critical parameters such as column selection, mobile phase optimization, detection techniques, and validation protocols. Furthermore, it discusses innovative approaches and technologies that enhance the robustness, speed, and accuracy of RP-HPLC analyses. Through a thorough analysis of current literature and case studies, this review provides valuable insights for researchers and practitioners engaged in method development and validation in the field of chromatographyies.

Transport and separation behaviors of crosslinked GO/PVA sponge with high porosity

Separation materials (technology) with high efficiency and high precision is a goal that researchers are relentlessly pursuing. Here, crosslinked graphene oxide and polyvinyl alcohol (GO/PVA) sponges with high porosity are reported, which were constructed by crosslinking a dilute solution of GO/PVA. The structure and transport characteristics of the sponges were investigated. It was found that it took 150 days for 30 mg/L methylene blue (MB) to diffuse from the side of crosslinked GO/PVA sponge (10 mm thickness) to the other side (breakthrough) even though the porosity of sponge was up to 92.9 %, while it needed only 2 days in PVA sponge and 13 days for methyl orange (MO) in the sponge. Hence, the transport and separation behaviors of more substances were explored and it is found that not only a precise separation of more than 99 % was achieved for MB/MO (similar molecular weight) mixed solution due to the extremely wide difference of transport rate, but also a water permeability of (4.3 × 105 L·m−2·h−1·Mpa-1) was reached under 10 cm height of water column pressure (0.01 bar), which is three orders of magnitude higher than that of nanofiltration membrane. The extremely high selectivity and efficiency to MB are attributed to the structural features of the sponge, i.e., the fully dispersed GO nanosheets and their crosslinking structure with PVA. Thus, the adsorption amount, adsorption rate, and adsorption strength to MB were greatly improved due to a synergistic effect of GO and PVA, and the highly efficient, precise, and rapid separation of MB was realized. In addition, the structural characteristics of the sponges favor the slow transport of many substances, which will extend their applications to the controlled release field.

Linker aggregation engineering of TADF materials to tune carrier balance for highly efficient organic LEDs with long operational lifetime

AbstractThermally activated delayed fluorescence (TADF) molecules are regarded as promising materials for realizing high‐performance organic light‐emitting diodes (OLEDs). The connecting groups between donor (D) and acceptor (A) units in D–A type TADF molecules could affect the charge transfer and luminescence performance of TADF materials in aggregated states. In this work, we design and synthesize four TADF molecules using planar and twisted linkers to connect the aza‐azulene donor (D) and triazine acceptor (A). Compared with planar linkers, the twisted ones (Az‐NP‐T and Az‐NN‐T) can enhance A–A aggregation interaction between adjacent molecules to balance hole and electron density. As a result, highly efficient and stable deep‐red top‐emission OLEDs with a high electroluminescence efficiency of 57.3% and an impressive long operational lifetime (LT95 ∼ 30,000 h, initial luminance of 1000 cd m−2) are obtained. This study provides a new strategy for designing more efficient and stable electroluminescent devices through linker aggregation engineering in donor–acceptor molecules.

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

Spatially separated HOMO-LUMO sites in highly intra- and inter-plane crystalline g-C3N4 photocatalyst for exceptional H2 generation

Spatially separated HOMO-LUMO sites in the highly crystalline g-C3N4 can vastly facilitate the rapid transfer and separation of photogenerated charges to greatly improve its photocatalytic performance, which remains a great challenge to be achieved. In this study, the highly intra- and inter-plane crystalline g-C3N4 (c-CN) with the spatially separated HOMO-LUMO sites is explicitly produced through a moderate Na-modulated strategy to efficiently promote the directional transfer and separation of photoinduced charges. As a result, the optimized c-CN1.0 photocatalyst exhibits exceptional H2-evolution performance (313.5 μmol h−1, AQE = 13.38 %), which is ca. 14.3 times of the bulk g-C3N4. The excellent hydrogen-generation efficiency of c-CN photocatalyst is mainly ascribed to the synergism of high intra- and inter-layer crystallization of g-C3N4 and spatially separated HOMO-LUMO sites, which can vastly boost the directional transfer and effective separation of photoexcited electrons and holes. This research may open novel avenues to construct other photocatalysts with high efficiency.

CuO nanoflowers: Multifaceted implications of various precipitating agents on rectification behaviour

In order to achieve the best possible use of traditional energy resources for the preservation of the environment, semiconductor-based optoelectronic devices are one of the most intriguing methods for converting light energy into electrical energy. CuO has been extensively explored as a material for energy conversion and the development of photo-detectors due to its notable characteristics among other metals, especially its non-toxicity, abundance, high efficiency, electrochemical stability and low cost/promising photodiode. However, some drawbacks limit its practical application in white light and photo-detector, including the rapid recombination rate of photo-generated electron-hole pairs and destitute response to visible light. As a result, several strategies have been developed from an optoelectronics perspective to eradicate these deficiencies and make an effort to enhance CuO photodiode activities. In this research work, the electrical characteristics and rectification behaviour of CuO with various precipitating agents have been examined. The CuO nanoparticles can be synthesized by co-precipitation technique prepared with different precipitants. Through this research, several parameters, such as the structural, functional, morphological, optical and electrical characteristics, were investigated by changing the precipitants. The functional group formation of pure CuO phase was confirmed by FT-IR studies. UV–Vis absorption measurements indicate that the band gap of the CuO nanoparticles was ranging from 2.40 to 2.85 eV. As a diode, CuO/p-Si exhibits a non-ideal behaviour and under light conditions with Na2CO3 precipitant, the better ideality factor is about 2.35. The barrier height is determined by the thermionic emission theory to be 0.77 and 0.79 eV in the dark and light conditions respectively.

Artificial Floating Islands for the Removal of Nutrients and Improvement of the Quality of Urban Wastewater

A high amount of nutrients can be found in urban wastewater (UW), which makes it difficult to treat. The purpose of this research was to evaluate the potential of the aquatic macrophytes Eichhornia crassipes, Pistia stratiotes, and Salvinia molesta in the treatment of UW. To evaluate the potential of each macrophyte, phytoremediation bioassays were established; the hydraulic retention time for each bioassay was 15 days. The physicochemical analysis of the water samples considered pH, turbidity, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), chemical oxygen demand (COD), total carbon (TC), phosphates (PO43-P), nitrate (NO3-N), and total nitrogen (TN). To evaluate the phytoremediation potential of each plant, the bioconcentration factors (BCFs) and translocation factors (TFs) for NO3-N and PO43-P were evaluated. Likewise, the relative growth rates (RGRs) and total chlorophyll production of the macrophytes were measured. The results showed that the highest efficiency was achieved with the bioassays with E. crassipes, with removal values of 69.7%, 68.8%, 58.7%, 69.4%, 56.3%, and 40.9% for turbidity, COD, TOC, PO43-P, NO3-N, and TN, respectively. The phytoremediation potential results showed that, for BCF, the highest value was 4.88 mg/g of PO43-P with E. crassipes, and for TF, it was 6.17 mg/g of PO43-P with S. molesta. The measurement of RGR and total chlorophyll for E. crassipes showed an increase of 0.00024 gg−1d−1 and an increase of 4.5%, respectively. On the other hand, the other macrophytes suffered decreases in chlorophyll content and RGR. Thus, E. crassipes is defined as the macrophyte with the greatest potential for the UW phytoremediation process.

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose.

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacrylate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10-12 m2, and a range of height of theoretical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt%) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.

An accurate object detection of wood defects using an improved Faster R-CNN model

ABSTRACT Wood defect classification and location are crucial for automatic repair and reuse in wood product manufacture, which requires high accuracy and efficiency in the engineering practice. In this paper, a novel modeling method is proposed for faster R-CNN to improve the object detection of wood defect. An improved ResNet-50 network, a focal loss function, and a soft non-maximum suppression (soft NMS) were adopted to replace the counterparts of the initial Faster R-CNN. Experiments were conducted on a wood defect dataset with seven types of defects. The experimental results indicate that the using of the improved ResNet-50 network brings a significant increase in mean average precision (mAP) by 4.4% and reduces the detection time by 3.6% than that of the traditional Faster R-CNN on wood defect detection. Besides, mAPs are 3.4% and 2.8% higher than that of the traditional Faster R-CNN as the employment of the focal loss function and soft NMS, respectively. Finally, the proposed model obtains better mAP than the classic target detection networks. Results demonstrate the effectiveness and feasibility of the proposed modeling method in engineering applications.

Effects of Plant Growth Regulators on Micropropagation of Black Pepper (Piper nigrum) in Commercial-Scales

Aim: Black pepper (Piper nigrum) is renowned as a popular spice used to flavor dishes around the world and is also an agricultural export commodity that brings high economic value to many countries. However, traditional propagation methods by seed, cuttings, layering, and grafting reveal many limitations such as the low multiplication coefficient, the low survival rate of seedlings, and especially when there is an outbreak of disease, the ability to control the disease source is exceedingly difficult, leading to the spread of disease. Therefore, an alternative method through apical meristem micropropagation has been applied to contribute to sustainable and disease free pepper cultivars. Propagation by apical shoot meristem gives high propagation efficiency, good and uniform seedling quality, and can be propagated in large numbers in a brief period. Methodology: This study is conducted to contribute to optimizing the process of micropropagation of pepper plants to meet the needs of producing excellent quality seedlings, serving the Vietnamese pepper industry. The two steps in the micropropagation sequence studied in this project are shoot formation and bud cluster multiplication. In this study, we investigate the effects of BA (6-Benzylaminopurine) and NAA (α-Naphthalene acetic acid) in shoot formation. Regenerated shoots were then cultured in MS media supplemented with different combinations of Kinetin (6-Furfurylaminopurine) and NAA in bud cluster multiplication experiment. Results: The results showed that the apical shoot meristem cultured on MS medium (Murashige and Skoog, 1962) supplemented with 0.5 mg L-1 NAA and 2.0 mg L-1 BA was the optimal medium for shoot formation rate (100%). Besides, MS culture medium supplemented with Kinetin 1.5 mg L-1 + NAA 0.5 mg L-1 produced superior results in bud cluster multiplication compared with other treatments (8, 20 buds) after 8 weeks (about 2 months). Conclusion: BA, Kinetin and NAA give important rules on micropropagation of black pepper in commercial-scale.

Study on the performance of photothermal catalyzed carbon dioxide hydrogenation to CH4 over doped sodium tantalate-based perovskite catalysts

NaTaO3 (ABO3)-based catalysts with B-site substituted metals M= (Co, Ni, Fe, Ga, Ti) for photothermal-catalyzed carbon dioxide reduction hydrogenation to C1 products were prepared via a simple one-step hydrothermal method. The B-site metal doping catalysts exhibited excellent performance for the aimed photothermal catalytic reaction. Among them, the NaTa0.9Co0.1O3 was the most eye-catching catalyst with a stable crystal cell, an excellent heterojunction structure, and a bandgap structure for light absorption, The fine structure properties results in a low electron-hole recombination rate and high photoelectron transfer efficiency under light conditions. The suitable alkaline adsorption sites and thermal reduction properties also be the effective assistance for the catalytic process. The CO yield and CH4 yield of the NaTa0.9Co0.1O3 catalyst were 6.6 times and 120 times higher than those of the pure-phase NaTaO3, respectively, and the CH4 selectivity was up to 98% under the illumination condition of 300 ℃. For the photothermal hydrogenation reaction, thermal catalysis dominates the reaction, and light illumination promotes the generation and transfer of photogenerated electrons and improves the catalyst stability. The above studies show that NaTaO3 based perovskite has a great potential in the field of photothermal catalyzed carbon dioxide reduction.

Design of adaptive hybrid MPPT controllers with universal input voltage DC–DC converter for RES’s

At present, conventional energy production is absent because of the more hazardous gases released into the environment, the high effect on human health, more cost required for maintenance, plus less usefulness for highly populated areas. So, the Renewable Energy Sources are more focused for the present automotive industry application. In this work, the Proton Exchange Membrane Fuel Stack is considered for analyzing the proposed DC–DC converter circuit. The advantages of this fuel stack are high energy density, fast functioning nature, more robustness, and more usefulness for the various water membrane conditions of the fuel stack. However, the disadvantages of the fuel stack are excessive current generation, plus more current conduction losses. So, the wide voltage supply single switch power converter is introduced in this work for optimizing the current production of the fuel stack network. The merits of this converter circuit are high stability, good reliability, low voltage appearing across the switches, plus a uniform power supply. Here, the converter switching pulses are obtained by proposing the Modified Continuous Step Change Adaptive Fuzzy Logic with Grey Wolf Optimization hybrid controller. This controller provides high maximum power extraction efficiency from the fuel stack which is equal to 99.421%. Also, this controller's Maximum Power Point Tracking time is 0.0285 s.

Influence of distance section classes on aesthetic gymnastics on the physical condition of students of 18-19 years of higher education institution

The article deals with the possibility of using aesthetic gymnastics in the program of remote section classes among students of 18-19 years of higher education. The relevance and practical significance of this study is due to the need to improve the overall physical condition of the student youth, especially in recent years, which are characterized by the unjustified transition of many universities of Ukraine mainly to the sectional form of work with students, the consequences of the world pandemic at the Covid-19 and the military condition. In connection with the above, an evaluation of the use of aesthetic gymnastics tools was used in the program of sectional distance classes of students of higher education. It is proved that the use of the studio girls of the said program contributed to a significant improvement in their overall physical condition. The completion of the study was a significant increase in the levels of functional state of circulatory systems and external respiration (respectively by 36.37%and 26.33%), physical health (by 27.97%), physical work capacity (by 33.13%), weight loss (by 3.74%), values of deviation of body weight from proper values (by 7.63%), fat component (by 20.27%) and score for the type of body construction (by 15.57%). The results testified to the high efficiency of the use of aesthetic gymnastics in the program of section remote classes for students of higher education institution and the possibility of practical use in the boiling process of higher education in physical education.

Optimizing the functional state of the cardiorespiratory system of female students aged 17-19 with the help of a modified program of distance classes in physical education

The article deals with the peculiarities of the impact of a modified program of distance training on physical education on the functional state of circulatory systems and external breathing of students of 17-19 years of higher education. The relevance and practical importance of this study is due to the complex modern conditions of life of our country, the inability to conduct full -fledged classes among student youth and the need to maintain the optimal level of physical and functional state of student youth. The use among female students of the experimental group of the program of remote classes proposed by us contributed to significantly higher rates of improvement in heart rate, all types of blood pressure, Robinson's index, total peripheral resistance of blood vessels, coefficient of efficiency of blood circulation, vital capacity of the lungs (in 2-3 times), breath retention time on inhalation and exhalation (3 and 5 times, respectively), Skibinsky index 5 times, hypoxia index (20 times) and almost 5 times the levels of the functional state of the cardiovascular system and the external system breath. The results of changes in their intragroup distribution by the levels of levels of functional state of circulatory and external respiration, which were reflected in the increase of their representation in functional classes high and above average, were significantly more optimal in the experimental group. Similar changes in the girls of the control group were insignificant. The results testified to the high efficiency of the proposed program of distance classes with the use of fitness and the possibility of its practical use among students of higher education.