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The cost of hyperspectral image (HSI) classification primarily stems from the annotation of image pixels. In real-world classification scenarios, the measurement and annotation process is both time-consuming and labor-intensive. Therefore, reducing the number of labeled pixels while maintaining classification accuracy is a key research focus in HSI classification. This paper introduces a multi-strategy triple network classifier (MSTNC) to address the issue of limited labeled data in HSI classification by improving learning strategies. First, we use the contrast learning strategy to design a lightweight triple network classifier (TNC) with low sample dependence. Due to the construction of triple sample pairs, the number of labeled samples can be increased, which is beneficial for extracting intra-class and inter-class features of pixels. Second, an active learning strategy is used to label the most valuable pixels, improving the quality of the labeled data. To address the difficulty of sampling effectively under extremely limited labeling budgets, we propose a new feature-mixed active learning (FMAL) method to query valuable samples. Fine-tuning is then used to help the MSTNC learn a more comprehensive feature distribution, reducing the model’s dependence on accuracy when querying samples. Therefore, the sample quality is improved. Finally, we propose an innovative dual-threshold pseudo-active learning (DSPAL) strategy, filtering out pseudo-label samples with both high confidence and uncertainty. Extending the training set without increasing the labeling cost further improves the classification accuracy of the model. Extensive experiments are conducted on three benchmark HSI datasets. Across various labeling ratios, the MSTNC outperforms several state-of-the-art methods. In particular, under extreme small-sample conditions (five samples per class), the overall accuracy reaches 82.97% (IP), 87.94% (PU), and 86.57% (WHU).

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, and ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, and soil quality. Despite the extensive application of deep learning models in this field, few studies have analyzed the effect of the large-scale geospatial characteristics of neighboring regions on crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. The datasets used in this study included multiple types of remote sensing, meteorological, soil, crop yield, and planting area data for Anhui, China, from 2005 to 2020. The results showed that multi-source data led to higher prediction performance than single-source data, and enabled accurate prediction of winter wheat yields three months prior to harvest. Furthermore, the ASTGNN model provided better prediction performance than two traditional crop yield prediction models (R2 = 0.70, RMSE = 0.21 t/ha, MAE = 0.17 t/ha). Therefore, ASTGNN enhances the accuracy of crop yield prediction by incorporating geospatial characteristics. This research has implications for improving agricultural production management, promoting the development of digital agriculture, and addressing climate change in agriculture.

Phenylalanine ammonia lyase (PAL) is a pivotal enzyme in the phenylalanine metabolic pathway in plants and has a crucial role in the plant’s response to environmental stress. Although the PAL family has been widely studied in many plant species, limited is known about its particular role in cucumbers under stress. We investigated the physicochemical properties, gene structure, gene duplication events, conserved motifs, cis-acting elements, protein interaction networks, stress-related transcriptome data, and quantitatively validated key stress-related genes. The main results indicated that 15 PAL genes were grouped into four clades: I, II, and III when arranged in a phylogenetic tree of PAL genes in angiosperms. The analysis of the promoter sequence revealed the presence of multiple cis-acting elements related to hormones and stress responses in the cucumber PAL genes (CsPALs). The analysis of protein interaction networks suggested that CsPAL1 interacts with eight other members of the PAL family through CsELI5 and CsHISNA, and directly interacts with multiple proteins in the 4CL family. Further investigation into the expression patterns of CsPAL genes in different tissues and under various stress treatments (NaCl, Cu2+, Zn2+, PEG6000, aphids) demonstrated significant differential expression of CsPALs across cucumber tissues. In summary, our characterization of the CsPAL family offers valuable insights and provides important clues regarding the molecular mechanisms of CsPALs in managing abiotic and biotic stress interactions in cucumbers.

This study focuses on the comparative analysis and research of the raindrop size distribution (DSD) in the outer rainband and inner rainband of Typhoon “Noru” in 2022, using the OTT-Parsivel raindrop spectrometer deployed on Yongxing Island, Sansha City. The results indicate that precipitation intensity is lower when composed mainly of small and medium raindrops and increases with the presence of larger raindrops. Stronger precipitation is associated with a higher number of large raindrops. Due to the interaction of cold and warm air masses, the raindrop concentration is higher, and the raindrop diameters are larger compared to Typhoons “LEKIMA” and “RUMBIA”. The entire process predominantly consists of numerous small- to medium-sized raindrops, characteristic of a tropical typhoon. The precipitation in the inner and outer rainbands exhibits consistent types, characterized by a unimodal raindrop size distribution with a narrow spectral width, typical of stratiform-mixed cloud precipitation, where stratiform precipitation constitutes a significant portion. Strong echo reflectivity factors are often associated with higher raindrop number concentrations and larger particle sizes. The Z-R relationship of the precipitation shows a smaller coefficient but a consistent exponent compared to the standard. The calculated shape parameter slope relationship is Λ=0.02μ2+0.696μ+1.539, providing a reference for localizing the Z-R relationship in the South China Sea.

Considering the potential warming potential and long lifetime of nitrous oxide (N2O) as a greenhouse gas, exploring its emission characteristics is of great significance for its control and the achievement of sustainable development goals. As vehicles are a significant source of N2O emissions, in this study we conducted a detailed investigation of N2O in the exhaust of light-duty vehicles using a chassis dynamometer. We selected one conventional gasoline vehicle and two hybrid electric vehicles. We found that the N2O emissions from all the tested vehicles complied with the China 6 emission regulation, with emission factors of 7.7 mg/km, 6.8 mg/km, and 17.1 mg/km, respectively, for the three vehicles. Driving conditions played a crucial role in N2O emissions, with emissions generated primarily during extra-high-speed conditions, possibly due to the higher driving speed and greater number of acceleration/deceleration events. Furthermore, while hybrid electric vehicles emitted less NOx compared to conventional gasoline vehicles, their N2O emissions were closely tied to their engine operating conditions. Surprisingly, we discovered that hybrid electric vehicles emitted more N2O during frequent engine start–stop cycles, which could be related to the mechanisms of N2O generation. These findings contribute to a better understanding of the N2O emission characteristics of vehicles and will inform the development of emission control strategies to better promote global sustainable development.

Mechanized harvesting is a crucial step in the agricultural production of foxtail millet (Setaria italica), as its peduncles are susceptible to bending and breaking during the harvesting process, leading to yield losses and deterioration in grain quality. To evaluate the suitability of foxtail millet for mechanical harvesting, this study comprehensively analyzed the biomechanical properties of the peduncles and related biological morphological characteristics of 116 foxtail millet accessions, establishing a system for indicator screening and comprehensive evaluation. Using partial correlation analysis and R-type cluster analysis, four biomechanical and seven related morphological indices of the peduncle were screened from 22 candidate indicators, with their coefficient of variation ranging from 6% to 80%. The entropy method was used to assign weights to the selected indices, with biomechanical factors contributing 47.4%, peduncle morphology 20.2%, spike morphology 27.6%, and plant height 4.8%. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Rank-Sum Ratio (RSR) methods were applied to rank and grade the classification of the 116 foxtail millet varieties into four performance groups: Excellent (8 varieties), Good (50 varieties), Moderate (51 varieties), and Poor (7 varieties). This study provides a scientific basis for the selection and evaluation of foxtail millet varieties.

The application of fish gelatin (FG) is limited due to its poor mechanical properties and thermal stability, both of which could be significantly improved by gellan gum (GG) found in previous research. However, the FG/GG composite hydrogel was brittle and easily damaged by external forces. It was found that the composite hydrogel with Fe2(SO4)3 showed good flexibility and self-healing properties in the pre-experiment. Thus, the synergistic effect of FG, GG and Fe2(SO4)3 on the mechanical properties of the composite hydrogel was investigated in this study. According to one-way experiments, response surface tests and Texture Profile Analysis, it was found that under the optimum condition of FG concentration 186.443 g/L, GG concentration 8.666 g/L and Fe2(SO4)3 concentration 56.503 g/L, the springiness of the composite cylindrical hydrogel with the height of 25 mm formed in 25 mL beakers (bottom diameter 30 mm) was 7.602 mm. Determination of the rheological properties, compression performance, adhesive performance and self-healing properties showed that the composite hydrogel had good thermal stability, flexibility and self-healing properties with good adhesion, skin compliance and compressive strength, and it was easy to remove. The composite hydrogel showed strong antimicrobial activity against A. salmonicida and V. parahaemolyticus. All hydrogels showed a uniform and porous structure. The 3D structure of the composite hydrogel was much looser and more porous than the pure FG hydrogel. The flexible and self-healing composite hydrogel with some antimicrobial activity is suitable for the development of medical dressings, which broadens the applications of the composite hydrogel.

Soil moisture content is a crucial indicator for understanding the water requirements of crops. The effective monitoring of soil moisture content can provide support for irrigation decision-making and agricultural water management. Traditional ground-based measurement methods are time-consuming and labor-intensive, and point-scale monitoring cannot effectively represent the heterogeneity of soil moisture in the field. Unmanned aerial vehicle (UAV) remote sensing technology offers an efficient and convenient way to monitor soil moisture content in large fields, but airborne multispectral data are prone to spectral saturation effects, which can further affect the accuracy of monitoring soil moisture content. Therefore, we aim to construct effective drought indices for the accurate characterization of soil moisture content in winter wheat fields by utilizing unmanned aerial vehicles (UAVs) equipped with LiDAR, thermal infrared, and multispectral sensors. Initially, we estimated wheat plant height using airborne LiDAR sensors and improved traditional spectral indices in a structured manner based on crop height. Subsequently, we constructed the normalized land surface temperature–structured normalized difference vegetation index (NLST-SNDVI) space by combining the SNDVI with land surface temperature and calculated the improved Temperature–Vegetation Drought Index (iTVDI). The results are summarized as follows: (1) the structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges; (2) the iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content; (3) the global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability. In conclusion, this study proved the effectiveness of the drought index based on a structured vegetation index, and the results can provide support for crop moisture monitoring and irrigation decision-making in the field.

CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people’s understanding of CO2 spatiotemporal variations, supporting carbon-related research.

The powdery mildew caused by Eeysiphe heraclei is a serious concern in Heracleum moellendorffii Hance. Therefore, exploring the mechanisms underlying sugar efflux from host cells to the fungus during the plant–fungus interaction showed great significance. The study successfully cloned HmSWEET8 and HmSTP1 genes based on RNA-seq technology. The complementation assays in yeast EBY.VW4000 found HmSWEET8 and HmSTP1 transporting hexose. Over-expressing or silencing HmSWEET8 in H. moellendorffii leaves increased or decreased powdery mildew susceptibility by changing glucose concentration in infective sites. Meanwhile, over-expressing HmSTP1 in H. moellendorffii leaves also increased powdery mildew susceptibility by elevating the glucose content of infective areas. Additionally, HmSTP1 expression was up-regulated obviously in HmSWEET8 over-expressed plants and inhibited significantly in HmSWEET8 silenced plants. Co-expressing HmSWEET8 and HmSTP1 genes significantly increased powdery mildew susceptibility compared with over-expressed HmSWEET8 or HmSTP1 plants alone. The results demonstrated that HmSTP1 may assist with HmSWEET8 to promote E. heraclei infection. Consequently, the infection caused by E. heraclei resulted in the activation of HmSWEET8, leading to an increased transfer of glucose to the apoplasmic spaces at the sites of infection, then, HmSTP1 facilitated the transport of glucose into host cells, promoting powdery mildew infection.

The introduction of the pilot flame can stabilize the lean premixed flame and promote its industrial application. However, the interaction mechanism between the pilot and main flames is complicated. To reveal the effect of the pilot flame on the main flame, a laminar lean premixed flame adjacent to a rich premixed pilot flame on one side and a similar lean premixed flame on the other side was considered. A two-dimensional numerical model was adopted with detailed chemistry and species transport, also with no artificial flame anchoring boundary conditions. The results show that the pilot flame could promote the main flame stabilized in different locations with various shapes, by adjusting the stretch, heat transfer, and preferential diffusion in a complicated manner. The pilot flame improves the local equivalence ratio and transfer more heat to the main flame. The growth of the pilot flame equivalence ratio and inlet velocity enhances the combustion on the rich side of the main flame and helps it anchor closer to the flame wall. Both the curvature and strain rate show a significant effect on the flame root, which contributes to the main flame bending towards the pilot flame.

In response to the issues of low outdoor thermal comfort and poor ventilation environment in Beijing Hutong, this paper proposes a rapid intelligent optimization method combining Pix2Pix (Image-to-Image Translation with Conditional Adversarial Networks) with a genetic algorithm. Firstly, the architectural types of the research objects are highly refined and summarized into four traditional building types. Then, they are placed in the site with open spaces in a certain proportion, and a multi-objective optimization model for the UTCI (Universal Thermal Climate Index) and building area is constructed using a genetic algorithm, generating and iteratively optimizing the spatial layout of the building population. Finally, Pix2Pix is used to learn and train a large number of Hutong combination samples, rapidly generating the UTCI and ventilation results, which serve as the optimization objectives to obtain the optimal solution set for Hutong spatial forms. Compared with traditional empirical design methods, this method allows for a rapid and efficient traversal of vast solution spaces, intelligently generating Hutong renovation schemes that balance cultural heritage and healthy comfort. The research results demonstrate that this method can quickly find (26.4 times faster than traditional performance simulation methods) that the reasonable proportions of Siheyuan, Sanheyuan, Erheyuan, new buildings, and empty spaces in the Da Yuan Hutong in Beijing should be controlled at 11.8%, 16.9%, 23.8%, 33.8%, and 13.7%, respectively. Meanwhile, the building density should be maintained between 0.5 and 0.58, and the floor area ratio should be kept between 0.96 and 1.14. This significantly improves outdoor comfort, enhances the living environment of the Hutong, and promotes sustainable urban development.

Boron-doped diamond thin films exhibit extensive applications in chemical sensing, in which the performance could be further enhanced by nano-structuring of the surfaces. In order to discover the relationship between diamond nanostructures and properties, this paper is dedicated to deep learning target detection methods. However, great challenges, such as noise, unclear target boundaries, and mutual occlusion between targets, are inevitable during the target detection of nanostructures. To tackle these challenges, DWS-YOLOv8 (DCN + WIoU + SA + YOLOv8n) is introduced to optimize the YOLOv8n model for the detection of diamond nanostructures. A deformable convolutional C2f (DCN_C2f) module is integrated into the backbone network, as is a shuffling attention (SA) mechanism, for adaptively tuning the perceptual field of the network and reducing the effect of noise. Finally, Wise-IoU (WIoU)v3 is utilized as a bounding box regression loss to enhance the model’s ability to localize diamond nanostructures. Compared to YOLOv8n, a 9.4% higher detection accuracy is achieved for the present model with reduced computational complexity. Additionally, the enhancement of precision (P), recall (R), mAP@0.5, and mAP@0.5:0.95 is demonstrated, which validates the effectiveness of the present DWS-YOLOv8 method. These methods provide effective support for the subsequent understanding and customization of the properties of surface nanostructures.

To determine the patterns and influencing factors of the mechanical properties of millet stems from different varieties during the maturity period, this study employed a complete block experiment method and conducted shearing and bending tests on millet stems using the INSTRON5544 electronic universal material testing machine. The research investigated the variation in the shear strength, specific shear energy, bending strength, elastic modulus, and bending stiffness at different internode positions of the stems of Changza 466, Zhangza 16, and Jingu 21 during their maturity period. The results indicated that the variety had a significant impact on the mechanical properties of millet stems: from largest to smallest, the order of shear and bending forces was Jingu 21, Zhangza 16, and Changza 466. The shear strength and bending strength of Jingu 21 were the greatest among the three stem samples. The internode position significantly affected the shear mechanical properties of the millet stems, showing a general trend of decreasing shear strength with ascending internode position. The effect of the internode position on the bending stiffness was highly significant, whereas its impact on the bending strength and elastic modulus was not significant. The shear strength of the millet stems ranged from 3.866 ± 1.086 to 6.953 ± 2.208 MPa, significantly lower than the bending strength, which ranged from 18.934 ± 4.374 to 34.286 ± 6.875 MPa. The lowest shear strength and specific shearing energy, recorded at the fifth internode, were 4.028 ± 1.918 MPa and 15.097 ± 12.633 MJ/mm2, respectively. Jingu 21 and Changza 466 exhibit better lodging resistance than Zhangza 16. It is recommended to use a cutting-type platform for harvesting millet stems, with the cutting height set at the fifth internode position. This study provides a theoretical basis for the design of millet harvesting machinery and the selection of harvest parameters.

Orange peel biochar (C)-supported cadmium sulfide composites (CdS-C) were prepared by the combination of hydrothermal and calcination methods. The structure and morphology were characterized in detail by X-ray diffraction (XRD) and scanning electron microscopy (SEM), respectively. The CdS-C composite with 60% CdS exhibited the highest photocatalytic hydrogen production rate of 7.8 mmol·g−1·h−1, approximately 3.69 times higher than that of synthesized CdS without biochar. These results indicate that biochar derived from orange peel could be a low-cost, renewable, environmentally friendly, and metal-free co-catalyst for CdS, enhancing its photostability.

Excessive salt intake, primarily from sodium chloride prevalent in modern food processing, poses a significant public health risk associated with hypertension, cardiovascular diseases and stroke. Researchers worldwide are exploring approaches to reduce salt consumption without compromising food flavor. One promising method is to enhance salty taste perception using multisensory synergies, leveraging gustatory, olfactory, auditory, visual, tactile and trigeminal senses to decrease salt intake while preserving food taste. This review provides a comprehensive overview of salt usage in foods, mechanisms of salty taste perception and evaluation methods for saltiness. Various strategies for reducing salt consumption while maintaining food flavor are examined, with existing salt reduction methods’ advantages and limitations being critically analyzed. A particular emphasis is placed on exploring the mechanisms and potential of multisensory synergy in salt reduction. Taste interactions, olfactory cues, auditory stimulation, visual appearance and tactile sensations in enhancing saltiness perception are discussed, offering insights into developing nutritious, appealing low-sodium foods. Furthermore, challenges in current research are highlighted, and future directions for effective salt reduction strategies to promote public health are proposed. This review aims to establish a scientific foundation for creating healthier, flavorful low-sodium food options that meet consumer preferences and wellness needs.

In this study, crumpled graphene oxide balls (CGBs) were prepared via capillary compression using a rapidly evaporating aerosol droplet method. The CGBs were observed using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and Raman spectroscopy. The size distributions of crumpled particles were obtained using a laser nanometer particle size analyzer (DLS). The dispersibility of the water and the ionic liquid (IL) was tested by ultrasonic dispersion. The tribological properties of water or ionic liquids containing crumpled graphene oxide ball additives (W/IL-CGB) were tested by a reciprocating friction tester and compared with water/ionic liquids with graphene oxide. The morphology of the wear scar was observed by a three-dimensional optical microscope and its lubrication mechanism was analyzed. The results show that the CGBs were successfully prepared by rapid evaporation of aerosol droplets, and the obtained CGBs were crumpled paper spheres. The CGBs had good water dispersion and ionic liquid dispersion, and IL-CGB has excellent anti-friction and anti-wear effects on steel–steel friction pairs. During the friction process, the CGB was adsorbed at the interface of the steel–steel friction pair to form a protective layer, which avoids the direct contact of the friction pair, thereby reducing friction and wear.

In recent years, dibenzyl disulfide (DBDS) in transformer oils has caused many transformer failures around the world, and its removal has attracted more attention. In this work, nine imidazolium-based ionic liquids (ILs) were applied as effective, green desulfurization extractants for DBDS-containing transformer oil for the first time. The results show that the desulfurization ability of the ILs for DBDS followed the order of [BMIM]FeCl4 > [BMIM]N(CN)2 > [BMIM]SCN > [BMIM](C4H9O)2PO2 > [BMIM]MeSO4 > [BMIM]NTf2 > [BMIM]OTf > [BMIM]PF6 > [BMIM]BF4. Especially, [BMIM]FeCl4 ionic liquid had excellent removal efficiency for DBDS, with its S partition coefficient KN (S) being up to 2642, which was much higher than the other eight imidazolium-based ILs. Moreover, the extractive performance of [BMIM]FeCl4 increased with an increasing molar ratio of FeCl3 to [BMIM]Cl, which was attributed to its Lewis acidity and fluidity. [BMIM]FeCl4 ionic liquid could also avail in the desulfurization of diphenyl sulfide (DPS) from model oils. The experimental results demonstrate that π−π action, π-complexation, and Lewis acid−base interaction played important roles in the desulfurization process. Finally, the ([BMIM]FeCl4) ionic liquid could be recycled five times without a significant decrease in extractive ability.

Maize/peanut intercropping is practiced widely to increase land productivity and considered a sustainable way for using and saving resources through peanut’s complementary N source via biological N2 fixation. Our study aims to understand how maize/peanut intercropping affects the nodulation of peanuts under water-limiting conditions and different nitrogen inputs. A two-year micro-plot experiment in 2015–2016 and a two-year field experiment in 2017–2018 were conducted to quantify nodulation in maize/peanut intercropping and sole peanut cropping under four N fertilization rates (N-free, low, medium, and high N) in rain-fed water-limited conditions. In the micro-plot experiment, intercropped peanuts increased nodule biomass compared to sole peanuts. The nodule number of intercropped peanuts was 51.6% (p = 0.001) higher than that of sole cropped peanuts, while nodule weights did not differ at high N fertilization rates and were lower in the no-N fertilization control. However, the results were different in the field experiment. Both the nodule number and single weight of the sole cropped peanut were 48.7% (p = 0.020) and 58.9% (p = 0.014) higher than that of the intercropped peanut. The ratio of the nodule weight to aboveground dry matter at the beginning peg in the dry year of 2017 was lower in intercropping than sole cropping, especially at low N fertilization rates. The potential increase in nodulation found in a well-controlled micro-plot environment might be limited by strong water and light competitions in field conditions. The results could contribute to the understanding of interspecific interactions in cereal/legume intercropping.

The Three Gorges Hydropower Station, the largest in the world, plays a pivotal role in hydroelectric power generation, flood control, navigation, and ecological conservation. The water level of the Three Gorges Reservoir has a direct impact on these aspects. Accurate prediction of the reservoir’s water level, especially in the dam area, is of utmost importance for downstream regions’ safety and economic development. This study investigates the application and performance of four distinct deep-learning models in predicting water levels. The models evaluated include the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), and Convolutional Neural Network–Attention–Long Short-Term Memory (CNN–Attention–LSTM). The performance of these models was assessed using several metrics, namely the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The findings indicate that the CNN–Attention–LSTM model outperforms the others in all metrics, achieving an R2 value of 0.9940, MAE of 0.5296, RMSE of 0.6748, and MAPE of 0.0032. Moreover, the CNN–LSTM model exhibited exceptional predictive accuracy for lower water levels. These results underscore the potential of deep-learning models in water-level forecasting, particularly highlighting the efficacy of attention mechanisms in enhancing predictive accuracy. Precise water-level predictions are instrumental in optimizing hydropower generation and providing a scientific basis for effective flood control and water resource management.

The utilization of remote sensing soil moisture products in agricultural and hydrological studies is on the rise. Conducting a regional applicability analysis of these soil moisture products is essential as a preliminary step for their effective utilization. The triple collocation (TC) method enables the estimation of the standard deviation of errors in products when true soil moisture values are unavailable. It assesses data uncertainty and mitigates the influence of product errors on fusion, thereby enhancing product accuracy significantly. In this study, the TC uncertainty error analysis was employed to integrate Soil Moisture Active Passive (SMAP), the Advanced Microwave Scanning Radiometer 2 (AMSR-2), and the European Space Agency Climate Change Initiative (ESA CCI) active (ESA CCI A) and passive (ESA CCI P) products, with ground-based measurements serving as a reference. Traditional evaluation metrics, such as the correlation coefficient (R), bias, root mean square error (RMSE), and unin situed root mean square error (ubRMSE), were employed to evaluate the accuracy of the product. The findings indicate that SMAP and ESA CCI P products demonstrate strong spatiotemporal continuity within the research area and exhibit low uncertainty across various land types. The products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) exhibit a high level of temporal and spatial continuity; however, there is a requirement for enhancing their accuracy. The products of ESA CCI A exhibit notable spatiotemporal disjunction, contributing significantly to their elevated level of uncertainty. After fusion with TC analysis, the correlation coefficient (R = 0.7) of the TC-2 product derived from the fusion of SMAP, AMSR-2, and ESA CCI P products is significantly higher than the correlation coefficient of the TC-1 product (R = 0.65) obtained from the fusion of SMAP, AMSR-2, and ESA CCI A products at a 95% confidence level. The integration of data can efficiently mitigate the challenges associated with spatiotemporal gaps and inaccuracies in products, offering a dependable foundation for the subsequent utilization of remote sensing products.

Urban areas, characterized by dense anthropogenic activities, are among the primary sources of nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, renowned for their continuity and global coverage, have emerged as an effective means to quantify pollutant emissions. Previous bottom-up emission inventories exhibit considerable discrepancies and lack a comprehensive and reliable database. To develop a high-precision emission inventory for individual cities, this study utilizes high-resolution single-pass observations from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite to quantify the emission rates of NOx. The Exponentially Modified Gaussian (EMG) model is validated for estimating NOx emission strength using real plumes observed in satellite single-pass observations, demonstrating good consistency with existing inventories. Further analysis based on the results reveals the existence of a weekend effect and seasonal variations in NOx emissions for the majority of the studied cities.

SPL (SQUAMOSA promoter binding protein-like), as one family of plant transcription factors, plays an important function in plant growth and development and in response to environmental stresses. Despite SPL gene families having been identified in various plant species, the understanding of this gene family in peanuts remains insufficient. In this study, thirty-eight genes (AhSPL1-AhSPL38) were identified and classified into seven groups based on a phylogenetic analysis. In addition, a thorough analysis indicated that the AhSPL genes experienced segmental duplications. The analysis of the gene structure and protein motif patterns revealed similarities in the structure of exons and introns, as well as the organization of the motifs within the same group, thereby providing additional support to the conclusions drawn from the phylogenetic analysis. The analysis of the regulatory elements and RNA-seq data suggested that the AhSPL genes might be widely involved in peanut growth and development, as well as in response to environmental stresses. Furthermore, the expression of some AhSPL genes, including AhSPL5, AhSPL16, AhSPL25, and AhSPL36, were induced by drought and salt stresses. Notably, the expression of the AhSPL genes might potentially be regulated by regulatory factors with distinct functionalities, such as transcription factors ERF, WRKY, MYB, and Dof, and microRNAs, like ahy-miR156. Notably, the overexpression of AhSPL5 can enhance salt tolerance in transgenic Arabidopsis by enhancing its ROS-scavenging capability and positively regulating the expression of stress-responsive genes. These results provide insight into the evolutionary origin of plant SPL genes and how they enhance plant tolerance to salt stress.

Metamorphosis is a crucial process in the life cycle of Rana (Aquarana) catesbeiana. R. catesbeiana tadpoles, in their short larval period, possess a high survival rate and also a highly competitive ability in the amphibious stage. In actual seed production, the economic traits of larval period and metamorphosis rate are used as quantifiable indicators of quality for individuals and populations, respectively. However, studies of these economic traits in larval cultivation and production are still lacking. In this study, we constructed 40 full-sib families of R. catesbeiana and measured the weight and metamorphosis rate of tadpoles at different developmental stages. Subsequently, we calculated the phenotypic and genetic association between weight and metamorphosis rate in tadpoles and assessed the heritability of these two traits. The heritabilities of weight at three developmental stages were all higher than 0.40 and decreased with advancement of the developmental stage; the heritability of the metamorphosis rate was 0.18 ± 0.20, a moderate level. Correlation analysis of weight and metamorphosis rate at each developmental stage in each tadpole family showed that weight at stages 25-I, 25-II, and 25-III was significantly correlated at the phenotypic level but non-significantly at the genetic level. The metamorphosis rate was only moderately associated with stage 25-III weight (0.38, p < 0.05). The results of this study confirm the importance and transportability of tadpole weight in actual seed production and provide basic data and a potentially optimized direction for the selective breeding of high-metamorphosis-rate bullfrogs.

Cronobacter spp. are the most concerning foodborne pathogen in infant formula milk powder. Currently, there are many reports on the prevalence of Cronobacter spp. in infant formula milk and its processing environment, but there are few studies on the prevalence of Cronobacter spp. on dairy farms. We have, therefore, undertaken this study to investigate and track genomic epidemiology of Cronobacter spp. isolates from Chinese dairy farms in the provinces of Jiangsu and Shandong. In this study, forty Cronobacter spp. strains, consisting of thirty Cronobacter sakazakii, eight Cronobacter malonaticus, and two Cronobacter dublinensis, were obtained from 1115 dairy farm samples (raw milk, silage, bedding, and feces), with a prevalence rate of 3.57%. These isolates were classified into 10 Cronobacter serotypes and 31 sequence types (STs), including three novel STs which were isolated for the first time. Notably, pathogenic Cronobacter STs 7, 8, 17, 60, and 64, which are associated with clinical infections, were observed. Antimicrobial susceptibility testing showed that all the Cronobacter spp. were highly resistant to cephalothin and fosfomycin, which was consistent with the antimicrobial genotype. All isolates carried core virulence genes related to adherence, invasion, endotoxin, immune evasion, secretion system, and regulation. Approximately half the isolates were also able to produce a strong biofilm. Twenty-one prophages and eight plasmids were detected, with the most common prophage being Cronobacter_ENT47670 and the most common plasmid being IncFIB (pCTU1). In addition, two isolates harbored the transmissible locus of stress tolerance (tLST) which confers high environmental persistence. Phylogenetic analysis showed strong clustering by species level and sequence types. Isolates from different sources or regions with a similar genomic background suggests the cross-contamination of Cronobacter spp. The presence of diverse genotypes of Cronobacter spp. in dairy farms in Jiangsu and Shandong provinces indicates that surveillance of Cronobacter spp. on dairy farms should be strengthened, to prevent and control transmission and ensure the quality and safety of raw dairy products.

Significant public opinion events often trigger pronounced fluctuations in online discourse. While existing models have been extensively employed to analyze the propagation of public opinion, they frequently overlook the intricacies of information dissemination among heterogeneous users. To comprehensively address the implications of public opinion outbreaks, it is crucial to accurately predict the evolutionary trajectories of such events, considering the dynamic interplay of multiple information streams. In this study, we propose a SEInR model based on cellular automata to simulate the propagation dynamics of multi-information. By delineating information dissemination rules that govern the diverse modes of information propagation within the network, we achieve precise forecasts of public opinion trends. Through the concurrent simulation and prediction of multi-information game and evolution processes, employing Weibo users as nodes to construct a public opinion cellular automaton, our experimental analysis reveals a significant similarity exceeding 98% between the proposed model and the actual user participation curve observed on the Weibo platform.

Long-term plant residue retention can effectively replenish soil quality and fertility. In this study, we collected rhizosphere soil from the residual roots of annual Cenchrus fungigraminus in the Ulan Buh Desert over the past 10 years. The area, depth, and length of these roots decreased over time. The cellulose content of the residual roots was significantly higher in the later 5 years (2018–2022) than the former 5 years (2013–2017), reaching its highest value in 2021. The lignin content of the residual roots did not differ across samples except in 2015 and reached its highest level in 2021. The total sugar of the residual roots in 2022 was 227.88 ± 30.69 mg·g−1, which was significantly higher than that in other years. Compared to the original sandy soil, the soil organic matter and soil microbial biomass carbon (SMBC) contents were 2.17–2.41 times and 31.52–35.58% higher in the later 3 years (2020–2022) and reached the highest values in 2020. The residual roots also significantly enhanced the soil carbon stocks from 2018–2022. Soil dehydrogenase, nitrogenase, and N-acetyl-β-D-glucosidase (S-NAG) were significantly affected from 2019–2022. The rhizosphere soil community richness and diversity of the bacterial and fungal communities significantly decreased with the duration of the residual roots in the sandy soil, and there was a significant difference for 10 years. Streptomyces, Bacillus, and Sphigomonas were the representative bacteria in the residual root rhizosphere soil, while Agaricales and Panaeolus were the enriched fungal genera. The distance-based redundancy analysis and partial least square path model results showed that the duration of residual roots in the sandy soil, S-NAG, and SMBC were the primary environmental characteristics that shaped the microbial community. These insights provide new ideas on how to foster the exploration of the use of annual herbaceous plants for sandy soil improvement in the future.

Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential.

The inherent weakness in the deformation resistance of plastic centrifugal pumps makes them prone to vibrations. This study explores the impact of flow components on the vibrations of such pumps. Utilizing the given parameters, the primary structural parameters of the plastic centrifugal pump were calculated. The study focused on the torsional vibrations within the pump shaft transmission system, employing a silicone oil damper for vibration mitigation. A dual-mass forced torsion vibration model was developed for the pump shaft transmission system with the added damper. The optimal damping coefficient for this damper was determined, and its structural design was completed, including the creation of three-dimensional models and two-dimensional part drawings. Utilizing a ZT-3 type rotor test bench, a mathematical model correlating centrifugal pump speed with deflection was developed, and the effectiveness of various polynomials was assessed. The analysis showed that the optimal damping value for the silicone oil damper is 0.22. Compared to the system without the damper, the vibration frequency reduced by 10.45%, and the vibration amplitude decreased by 49.29%, demonstrating excellent vibration reduction effectiveness.

Alfalfa (M. sativa), a perennial legume forage, is known for its high yield and good quality. As a long-day plant, it is sensitive to changes in the day length, which affects the flowering time and plant growth, and limits alfalfa yield. Photoperiod-mediated delayed flowering in alfalfa helps to extend the vegetative growth period and increase the yield. We isolated a blue-light phytohormone gene from the alfalfa genome that is an ortholog of soybean FKF1 and named it MsFKF1. Gene expression analyses showed that MsFKF1 responds to blue light and the circadian clock in alfalfa. We found that MsFKF1 regulates the flowering time through the plant circadian clock pathway by inhibiting the transcription of E1 and COL, thus suppressing FLOWERING LOCUS T a1 (FTa1) transcription. In addition, transgenic lines exhibited higher plant height and accumulated more biomass in comparison to wild-type plants. However, the increased fiber (NDF and ADF) and lignin content also led to a reduction in the digestibility of the forage. The key genes related to GA biosynthesis, GA20OX1, increased in the transgenic lines, while GA2OX1 decreased for the inactive GA transformation. These findings offer novel insights on the function of MsFKF1 in the regulation of the flowering time and plant height in cultivated M. sativa. These insights into MsFKF1’s roles in alfalfa offer potential strategies for molecular breeding aimed at optimizing flowering time and biomass yield.

Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.

Change detection in high resolution (HR) remote sensing images faces more challenges than in low resolution images because of the variations of land features, which prompts this research on faster and more accurate change detection methods. We propose a pixel-level semantic change detection method to solve the fine-grained semantic change detection for HR remote sensing image pairs, which takes one lightweight semantic segmentation network (LightNet), using the parameter-sharing SiameseNet, as the architecture to carry out pixel-level semantic segmentations for the dual-temporal image pairs and achieve pixel-level change detection based directly on semantic comparison. LightNet consists of four long–short branches, each including lightweight dilated residual blocks and an information enhancement module. The feature information is transmitted, fused, and enhanced among the four branches, where two large-scale feature maps are fused and then enhanced via the channel information enhancement module. The two small-scale feature maps are fused and then enhanced via a spatial information enhancement module, and the four upsampling feature maps are finally concatenated to form the input of the Softmax. We used high resolution remote sensing images of Lake Erhai in Yunnan Province in China, collected by GF-2, to make one dataset with a fine-grained semantic label and a dual-temporal image-pair label to train our model, and the experiments demonstrate the superiority of our method and the accuracy of LightNet; the pixel-level semantic change detection methods are up to 89% and 86%, respectively.

Boron-doped diamond (BDD) thin film electrodes have great application potential in water treatment. However, the high electrode energy consumption due to high resistance directly limits the application range of existing BDD electrodes. In this paper, the BDD/graphene/BDD (DGD) sandwich structure electrode was prepared, which effectively improved the conductivity of the electrode. Meanwhile, the sandwich electrode can effectively avoid the degradation of electrode performance caused by the large amount of non-diamond carbon introduced by heavy doping, such as the reduction of the electrochemical window and the decrease of physical and chemical stability. The microstructure and composition of the film were characterized by scanning electron microscope (SEM), atomic force microscopy (AFM), Raman spectroscopy, and transmission electron microscopy (TEM). Then, the degradation performance of citric acid (CA), catechol, and tetracycline hydrochloride (TCH) by DGD electrodes was systematically studied by total organic carbon (TOC) and Energy consumption per unit TOC removal (ECTOC). Compared with the single BDD electrode, the new DGD electrode improves the mobility of the electrode and reduces the mass transfer resistance by 1/3, showing better water treatment performance. In the process of dealing with Citric acid, the step current of the DGD electrode was 1.35 times that of the BDD electrode, and the energy utilization ratio of the DGD electrode was 2.4 times that of the BDD electrode. The energy consumption per unit TOC removal (ECTOC) of the DGD electrode was lower than that of BDD, especially Catechol, which was reduced to 66.9% of BDD. The DGD sandwich electrode, as a new electrode material, has good electrochemical degradation performance and can be used for high-efficiency electrocatalytic degradation of organic pollutants.

Ce-MOF was synthesized by a solvothermal synthesis method and was used to simultaneously remove phosphate, fluoride and arsenic (V) from water by adsorption. Ce-MOF was characterized by a nitrogen adsorption–desorption isotherm, scanning electron microscopy, and infrared spectroscopy. The effects of initial concentration, adsorption time, adsorption temperature, pH value and adsorbent on the adsorption properties were investigated. A Langmuir isotherm model was used to fit the adsorption data, and the adsorption capacity of phosphate, fluoride, and arsenic (V) was calculated to be 41.2 mg·g−1, 101.8 mg·g−1 and 33.3 mg·g−1, respectively. Compared with the existing commercially available CeO2 and other MOFs, Ce-MOF has a much higher adsorption capacity. Furthermore, after two reuses, the performance of the adsorbent was almost unchanged, indicating it is a stable adsorbent and has good application potential in the field of wastewater treatment.

The purpose of this study was to investigate the effects of replacing a portion of whole-plant corn silage with straw on the rumen microbial community structure and carbohydrate-active enzyme activity. The experiment employed a single-factor randomized trial design, with eight late-lactation Chinese Holstein dairy cows being randomly divided into two groups of four replicates each. The control group (CS group) was fed a diet consisting of alfalfa silage and a mixture of alfalfa and whole-plant corn silage, while the experimental group (RS group) received a diet in which one-third of the corn silage was replaced with straw while keeping the other components unchanged. The experiment lasted for a total of 21 days, with a pre-feeding period of 14 days and a formal period of 7 days. The rumen fluid collected on day 21 was used for the rumen fermentation parameters and metagenomic analysis. The concentrations of acetic acid, propionic acid, butyric acid, and total volatile fatty acids (TVFA) in the rumen of RS group cows were significantly lower than those in the CS group (p < 0.01). The ratio of acetic acid to propionic acid was significantly higher in the RS group compared to the CS group (p < 0.01). Metagenomic sequencing revealed that at the genus level, compared to the CS group, the abundance of unclassified bacteria, Bacteroides, Alistipes, Butyrivibrio, Chlamydia, Fibrobacter, unclassified Ruminococcaceae, and unclassified Bacteroidetes in the rumen of RS group cows increased, while the abundance of Eubacterium decreased ([LDA > 3.6], p < 0.05). Compared to the CS group, the enzymatic activities of α-L-arabinofuranosidase (EC3.2.1.55), β-xylosidase (EC3.2.1.37), β-glucosidase (EC3.2.1.21), β-glucosylceramidase (EC3.2.1.45), xylanase (EC3.2.1.8), and arabinanase (EC3.2.1.99) in the rumen of RS group cows increased (p < 0.05). According to the correlation analysis, Alistipes, Bacteroides, and Butyrivibrio showed a significant negative correlation with propionic acid (p < 0.05) and a significant positive correlation with the acetic acid-to-propionic acid ratio (p < 0.05). They also showed a significant positive correlation with GH2, GH3, GH20, GH29, GH43, GH78, GH92, CE1, GT4, β-glucosidase (EC3.2.1.21), α-L-arabinofuranosidase (EC 3.2.1.55), β-xylosidase (EC 3.2.1.37), β-glucosylceramidase (EC 3.2.1.45), xylanase (EC 3.2.1.8), and arabinanase (EC 3.2.1.99) (p < 0.05). In summary, straw can not only alter the composition and structure of the rumen microbiota in cows but also affect the relative abundance of CAZymes at different levels within the rumen. Cows may, thus, potentially improve the degradation efficiency of straw diets by increasing the abundance of certain rumen microbiota and enzymes.

Alkaline mineral complex buffer can improve rumen fermentation and affect the rumen microbiota of dairy cows. Here, we studied the effects of alkaline mineral complex buffer on serum immunity indexes, rumen fermentation and the microbiota of newborn calves. We also investigated changes in the rumen epithelial transcriptome expression profile. Compared with the control group, at 15 d, the serum contents of TP and GLB in the treatment group increased significantly (p < 0.05). At 30 d, the serum contents of GLB in the treatment group increased significantly (p < 0.05). At 45 d, the serum contents of IgG in the treatment group increased significantly (p < 0.05). At 60 d, the serum contents of TP and IgG in the treatment group increased significantly (p < 0.05). Rumen pH in the treatment groups was significantly increased at different days of age (p < 0.05). The microbial community composition in the rumen was determined using bacterial and archaeal 16S ribosomal RNA (rRNA) gene amplicon-sequencing. Analysis of bacterial composition in the rumen showed that there was no significant difference in bacterial diversity (p > 0.05). At the phylum level, Firmicutes were significantly decreased and Bacteroidetes were significantly increased in the treatment group at 30 d (p < 0.05). At the genus level, Prevotella_1, Olsenella, Christensenellaceae_R-7_group were significantly increased, and Lachnospiraceae_NK3A20_group, Ruminococcaceae_UCG-014 and Ruminococcus_2 were significantly decreased in the treatment group at 30 d (p < 0.05). Christensenellaceae_R-7_group was significantly increased in the treatment group (p < 0.05) at 45 d. Prevotella_9 was significantly decreased, and Prevotellaceae_UCG_001, Christensenellaceae_R-7_group were significantly increased in the treatment group at 60 d (p < 0.05). RNA sequence analysis of the rumen epithelium showed that 232 differentially expressed genes were screened, of which 158 were upregulated and 74 were downregulated. The main enrichment pathway was related to immune regulation. In conclusion, alkaline mineral complex buffer can enhance the body’s immune response, regulate rumen fermentation by regulating the abundance of rumen microbiota and upregulate immune-related genes in rumen tissues to promote immune regulation. The results of this study provide a reference for the early nutritional regulation of newborn calves with an alkaline mineral complex buffer.

Strategically upcycling industrial wastes such as petroleum coke and dye wastewater into value-added materials through scalable and economic processes is an effective way to simultaneously tackle energy and environmental issues. Doping carbon electrodes with heteroatoms proves effective in significantly enhancing electrochemical performance through alterations in electrode wettability and electrical conductivity. This work reports the use of dye wastewater as the sole dopant source to synthesize N and S co-doped petroleum coke-based activated carbon (NS-AC) by the one-step pyrolysis method. More importantly, our wastewater and petroleum coke-derived activated carbon produced on a large scale (20 kg/batch) shows a specific surface area of 2582 m2 g−1 and an energy density of about 95 Wh kg−1 in a soft-packaged full cell with 1 M TEATFB/PC as the electrolyte. The scalable production method, together with the green and sustainable process, can be easily adopted and scaled by industry without the need for complex processes and/or units, which offers a convenient and green route to produce functionalized carbons from wastes at a low cost.

To develop teff-based food products with acceptable quality, the composition, structure, and properties of teff protein fractions should be better understood. In this study, teff proteins were extracted, and their protein composition, structure, and properties were calculated, analyzed, and compared with those of wheat gliadin and glutenin. Results showed that teff flour contained 9.07% protein, with prolamin as its main protein fraction. The isoelectric points of albumin, globulin, prolamin, and glutelin were at pH 3.6, 3.0, 4.4, and 3.4, respectively. Teff prolamin and glutelin showed a significant difference in amino acids and free energy of hydration compared to wheat gliadins and glutenins. The protein chain length of teff prolamins was smaller than that of wheat gliadins, and teff glutelins lacked high molecular weight glutelin subunits. Teff prolamin had the highest α-helices content (27.08%), whereas no random coils were determined, which is different from wheat gliadin. Teff glutelin had a lower content of β-turn than wheat glutenin, and no α-helices were determined in it. Teff prolamin and glutelin had lower disulfide bond content and surface hydrophobicity. Teff prolamin had significantly higher thermal stability than wheat gliadin, whereas the thermal stability of teff glutelin was significantly lower than that of wheat glutenin.

Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness.

The rapid development of solar energy technology has led to significant progress in recent years, but the daily maintenance of solar panels faces significant challenges. The diagnosis of solar panel failures by infrared detection devices can improve the efficiency of maintenance personnel. Currently, due to the scarcity of infrared solar panel failure samples and the problem of unclear image effective features, traditional deep neural network models can easily encounter overfitting and poor generalization performance under small sample conditions. To address these problems, this paper proposes a solar panel failure diagnosis method based on an improved Siamese network. Firstly, two types of solar panel samples of the same category are constructed. Secondly, the images of the samples are input into the feature model combining convolution, adaptive coordinate attention (ACA), and the feature fusion module (FFM) to extract features, learning the similarities between different types of solar panel samples. Finally, the trained model is used to determine the similarity of the input solar image, obtaining the failure diagnosis results. In this case, adaptive coordinate attention can effectively obtain interested effective feature information, and the feature fusion module can integrate the different effective information obtained, further enriching the feature information. The ACA-FFM Siamese network method can alleviate the problem of insufficient sample quantity and effectively improve the classification accuracy, achieving a classification accuracy rate of 83.9% on an open-accessed infrared failure dataset with high similarity.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural–functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Shaoxing rice wine is a notable exemplar of Chinese rice wine. Its superior quality is strongly correlated with the indigenous natural environment. The results indicated that Firmicutes (75%), Actinobacteria (15%), Proteobacteria (5%), and Bacteroidetes (3%) comprised the prevailing bacterial groups. Among the main bacterial genera, Lactobacillus was the most abundant, accounting for 49.4%, followed by Lactococcus (11.9%), Saccharopolyspora (13.1%), Leuconostoc (4.1%), and Thermoactinomyces (1.1%). The dominant fungal phyla were Ascomycota and Zygomycota. Among the dominant genera, Saccharomyces (59.3%) prevailed as the most abundant, followed by Saccharomycopsis (10.7%), Aspergillus (7.1%), Thermomyces (6.2%), Rhizopus (4.9%), Rhizomucor (2.2%), and Mucor (1.3%). The findings demonstrate that the structure of the bacterial and fungal communities remains stable in the environment, with their diversity strongly influenced by climatic conditions. The continuous fluctuations in environmental factors, such as temperature, air pressure, humidity, rainfall, and light, significantly impact the composition and diversity of microbial populations, particularly the dominant bacterial community.

Changes in behavior, lifestyle, and nutritional patterns have influenced many potential risk variables globally. In recent decades, food allergies (FAs) have been elevated to a severe public health issue both in developed countries and developing countries (third-world countries). This study aims to evaluate the effects caused by certain factors such as lifestyle and dietary habits on food allergies, review the association of lifestyle and dietary habit status with FAs, and outline why more people are allergic to food sources as a result of lifestyle changes and dietary habits. We searched electronic international databases including Scopus, PubMed, Google Scholar, and Web of Science using combinations of keywords. Utilizing Excel, the relevant studies were included and the irrelevant studies were excluded, and Mendeley was used for referencing and also to remove duplicates. The framework proposed by Arksey and O’Malley was used for this scoping review. The papers published in the databases from 2016 to 2020 were extracted. A total of eight studies were extracted, and this scoping review was carried out according to the risk factors. In our review, we found that some lifestyle choices (Caesarean section and antibiotics) and dietary habits (n-3 PUFA, fast food, duration of dietary intervention, and vitamin D), were important contributing factors for FA.

The addition of fertilizers is indispensable in agricultural production, and currently, there is a wide variety of new types of fertilizers available. For example, commonly used are stabilized fertilizers with inhibitors and coated slow-release fertilizers, among others. However, the long-term effects of these fertilizers, when applied continuously are still uncertain. This study will provide scientific and theoretical support for the development and promotion of these fertilizers. A 16-year paddy field with brown soil treated with different urease and nitrification inhibitors, sulfur-coated urea (SCU), and resin-coated urea (PCU) was studied. The study showed that long-term use of conventional urea nitrogen fertilizer resulted in a significant reduction in soil total phosphorus (TP). Long-term application of NBPT and conventional urea significantly increased soil organic matter (SOM). Moreover, except for HQ and NBPT+DMPP, the prolonged application of new urea fertilizers also significantly enhanced soil total potassium (TK). Application of SCU fertilizer in brown soil type paddy fields resulted in a significant decrease in soil pH over time. However, changes in pH had no effect on the abundance of ammonia-oxidizing bacteria (AOB), as AOB was mainly affected by soil-available N. DMPP, HQ+DCD, NBPT+DMPP, SCU, and PCU significantly reduced the 16S rRNA gene copy number of soil bacteria, with the greatest effect of coated urea fertilizer (SCU and PCU). Long-term application of stable urea fertilizer with HQ significantly reduced the bacterial community in paddy soil. Conversely, HQ+DCD-stabilizede urea fertilizer significantly increased the population structure and abundance of Basidiomycota fungi while decreasing the population structure and abundance of Rozellomycota fungi. DMPP-stabilized urea fertilizer notably increased the population structure and abundance of Ascomycota fungi while decreasing the population structure and abundance of Rozellomycota and Chytridiomycota fungi. Furthermore, HQ-stabilized urea fertilizer significantly reduced the population structure and abundance of Chytridiomycota fungi.

The concentration of particulate matter (PM2.5) can be estimated using satellite data collected during the daytime. However, there are currently no long-term evening PM2.5 datasets, and the application of low-light satellite data to analyze nighttime PM2.5 concentrations is limited. The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), meteorology, Digital Elevation Model, moon phase angle, and Normalized Digital Vegetation Index were used in this study to develop a Deep Neural Network model (DNN) for estimating the nighttime concentrations of PM2.5 in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2021. To evaluate the model’s performance from 2015 to 2021, a ten-fold cross-validation coefficient of determination was utilized (CV − R2 = 0.51 − 0.68). Using a high spatial resolution of 500 m, we successfully generated a PM2.5 concentration map for the BTH region. This finer resolution enabled a detailed representation of the PM2.5 distribution over the area. Interannual and seasonal trends in nighttime PM2.5 concentrations were analyzed. Winter had the highest seasonal spatial PM2.5, followed by spring and autumn, whereas summer had the lowest. The annual concentration of PM2.5 at night steadily decreased. Finally, the estimation of nighttime PM2.5 was applied in scenarios such as continuous day–night changes, rapid short-term changes, and single-point monitoring. A deeper understanding of PM2.5, enabled by nightly PM2.5, will serve as an invaluable resource for future research.

Natural gas is one of the most common forms of energy in our daily life, and it is composed of multicomponent hydrocarbon gas mixtures (mainly of methane, ethane and propane). It is of great significant to reveal the condensation mechanism of multicomponent mixtures for the development and utilization of natural gas. A numerical model was adopted to analyze the heat and mass transfer characteristics of propane condensation in binary and ternary gas mixtures on a vertical cold plate. Multicomponent diffusion equations and the volume of fluid method (VOF) are used to describe the in-phase and inter-phase transportation. The conditions of different wall sub-cooled temperatures (temperature difference between the wall and saturated gas mixture) and the inlet molar fraction of methane/ethane are discussed. The numerical results show that ethane gas is more likely to accumulate near the wall compared with the lighter methane gas. The thermal resistance in the gas boundary layer is one hundred times higher than that of the liquid film, revealing the importance of diffusion resistance. The heat transfer coefficients increased about 11% (at ΔT = 10 K) and 7% (at ΔT = 40 K), as the molar fraction of ethane increased from 0 to 40%. Meanwhile, the condensation heat transfer coefficient decreased by 53~56% as the wall sub-cooled temperature increased from 10 K to 40 K.

Rail-transit hub classification in TOD refers to the categorization of transit stations based on their level of connectivity and ridership and the potential for development around them as part of a Transit-Oriented Development (TOD) strategy. TOD, as an essential concept in developing smart cities and public transportation accessibility, has attracted the focus of many policymakers. To this end, many research projects have been dedicated to classifying the rail-transit stations, although the necessity of integrated models for rail-transit hubs could have been mentioned in previous papers. Therefore, this parametric case study is directed to apply the Node–Place–Ridership–Time (NPRT) model to provide a logical classification model for Chengdu rail-transit hubs at the junctions of high-speed railway and subway stations. Multiple Linear Regression (MLR) provided a series of equations, including the effective parameters of the NPRT model. These equations were then verified by the Artificial Neural Network (ANN) to provide the effect of each node and place values on the integrated ridership of rail-transit hubs in different time periods. The results proved the consistent contribution of the integrated ANN-NPRT-HUB algorithm to the TOD concept for smart cities.

Due to cost-push inflation, the trade-off between safety costs and risk prevention (safety) has become difficult worldwide. Most companies experience the difficulty of safety cost overruns and allocate safety resource inefficiently. In this paper, a forward model maximizing safety input is formulated. Because there is a wide range of variation of safety resource cost coefficient parameters, it is hard to determine safety resource cost coefficients in the forward model, to make the decisions on which types of safety resources are allocated to which potentially risky locations with what prices, and to ensure total input is as close to the benchmark as possible. Taking allocation, themes, resources, and cost coefficient parameters as new decision variables, the inverse optimization model is formulated based on a bi-level model. With consideration of quaternion decision, bi-level programming, and NP-hard problem, based on the comparison of exact penalty algorithm and an improved PSO algorithm, in which the inertia weight is adaptively changing with the number of iterations, the PSO is suitable for solving the specific inverse model. Numerical experiments demonstrated the effectiveness of the PSO algorithm, proving that it can allocate the right amount and types of safety resources with the right prices at the right places.

The fibroblast growth factor 21 (FGF21) gene plays an important role in the mechanism of glucose and lipid metabolism and is a promising therapeutic target for metabolic disease. Camels display a unique regulation characteristic of glucose and lipid metabolism, endowing them with the ability to adapt to survive drought and chronic hunger. However, the knowledge about the camel FGF21 gene regulation and its differences between humans and mice is still limited. In this study, camel FGF21 gene promoter was obtained for ~2000 bp upstream of the transcriptional start site (TSS). Bioinformatics analysis showed that the proximal promoter region sequences near the TSS between humans and camels have high similarity. Two potential core active regions are located in the −445–612 bp region. In addition, camel FGF21 promoter contains three CpG islands (CGIs), located in the −435~−1168 bp regions, significantly more and longer than in humans and mice. The transcription factor binding prediction showed that most transcription factors, including major functional transcription factors, are the same in different species although the binding site positions in the promoter are different. These results indicated that the signaling pathways involved in FGF21 gene transcription regulation are conservative in mammals. Truncated fragments recombinant vectors and luciferase reporter assay determined that camel FGF21 core promoter is located within the 800 bp region upstream of the TSS and an enhancer may exist between the −1000 and −2000 bp region. Combining molecular docking and in silico ADMET druggability prediction, two compounds were screened as the most promising candidate drugs specifically targeting FGF21. This study expanded the functions of these small molecules and provided a foundation for drug development targeting FGF21.