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Cyclocarya paliurus tea, also known as "sweet tea", an herbal tea with Cyclocarya paliurus leaves as raw material, is famous for its unique nutritional benefits and flavor. However, due to the unique "bittersweet" of Cyclocarya paliurus tea, it is still unable to fully satisfy consumers' high-quality taste experience and satisfaction. Therefore, this study aimed to explore metabolites in Cyclocarya paliurus leaves during their growth period, particularly composition and variation of sweet and bitter taste compounds, by combining multi-platform metabolomics analysis with an electronic tongue system and molecular docking simulation technology. The results indicated that there were significant differences in the contents of total phenols, flavonoids, polysaccharides, and saponins in C. paliurus leaves in different growing months. A total of 575 secondary metabolites were identified as potential active metabolites related to sweet/bitter taste using nontargeted metabolomics based on UHPLC-MS/MS analysis. Moreover, molecular docking technology was utilized to study interactions between the candidate metabolites and the sweet receptors T1R2/T1R3 and the bitter receptors T2R4/T2R14. Six key compounds with high sweetness and low bitterness were successfully identified by using computational simulation analysis, including cis-anethole, gluconic acid, beta-D-Sedoheptulose, asparagine, proline, and citrulline, which may serve as candidates for taste modification in Cyclocarya paliurus leaves. These findings provide a new perspective for understanding the sweet and bitter taste characteristics that contribute to the distinctive sensory quality of Cyclocarya paliurus leaves. [ABSTRACT FROM AUTHOR]

Deep learning technology has recently become increasingly prevalent in the field of education due to the rapid growth of artificial intelligence. Teachers' teaching behavior is a crucial component of classroom teaching activities, and identifying and examining teachers' classroom teaching behavior is an important way to assess teaching. However, the traditional teaching evaluation method involves evaluating by either listening to the class on-site or playing back the teaching video afterward, which is a time-consuming and inefficient manual method. Therefore, this paper obtained teaching behavior data from a real smart classroom scenario and observed and analyzed the teacher behavior characteristics in this scenario. Aiming at the problems of complex classroom environments and the high similarity between teaching behavior classes, a method to improve YOLOv7 for large target classroom behavior recognition in smart classroom scenarios is proposed. First, we constructed the Teacher Classroom Behavior Data Set (TCBDS), which contains 6660 images covering six types of teaching behaviors: facing the board (to_blackboard, tb), facing the students (to_student, ts), writing on the board (writing, w), teaching while facing the board (black_teach, bt), teaching while facing the students (student_teach, st), and interactive (interact, i). This research adds a large target detection layer to the backbone network so that teachers' instructional behaviors can be efficiently identified in complex classroom circumstances. Second, the original model's backbone was extended with an effective multiscale attention module (EMA) to construct cross-scale feature dependencies under various branches. Finally, the bounding box loss function of the original model was replaced with MPDIoU, and a bounding box scaling factor was introduced to propose the Inner_MPDIoU loss function. Experiments were conducted using the TCBDS dataset. The method proposed in this study achieved mAP@.50, mAP@.50:.95, and recall values of 96.2%, 82.5%, and 92.9%, respectively—improvements of 1.1%, 2.0%, and 2.3% over the original model. This method outperformed other mainstream models compared to the current state of the art. The experimental results demonstrate the method's excellent performance, its ability to identify various classroom behaviors of teachers in realistic scenarios, and its potential to facilitate the analysis and visualization of teacher classroom behaviors. [ABSTRACT FROM AUTHOR]

Forests play a vital role in atmospheric CO2 sequestration among terrestrial ecosystems, mitigating the greenhouse effect induced by human activity in a changing climate. The LUE (light use efficiency) model is a popular algorithm for calculating terrestrial GPP (gross primary production) based on physiological mechanisms and is easy to implement. Different versions have been applied for many years to simulate the GPP of different ecosystem types at regional or global scales. For estimating forest GPP using different approaches, we implemented five LUE models (EC-LUE, VPM, GOL-PEM, CASA, and C-Fix) in forests of type DBF, EBF, ENF, and MF, using the FLUXNET2015 dataset, remote sensing observations, and Köppen–Geiger climate zones. We then fused these models to additionally improve the ability of the GPP estimation using an RF (random forest) and an SVM (support vector machine). Our results indicated that under a unified parameterization scheme, EC-LUE and VPM yielded the best performance in simulating GPP variations, followed by GLO-PEM, CASA, and C-fix, while MODIS also demonstrated reliable GPP estimation ability. The results of the model fusion across different forest types and flux net sites indicated that the RF could capture more GPP variation magnitudes with higher R2 and lower RMSE than the SVM. Both RF and SVM were validated using cross-validation for all forest types and flux net sites, showing that the accuracy of the GPP simulation could be improved by the RF and SVM by 28% and 27%. [ABSTRACT FROM AUTHOR]

Dendritic cells (DCs) are crucial in initiating and shaping both innate and adaptive immune responses. Clinical studies and experimental models have highlighted their significant involvement in various autoimmune diseases, positioning them as promising therapeutic targets. Nicotinamide (NAM), a form of vitamin B3, with its anti-inflammatory properties, has been suggested, while the involvement of NAM in DCs regulation remains elusive. Here, through analyzing publicly available databases, we observe substantial alterations in NAM levels and NAM metabolic pathways during DCs activation. Furthermore, we discover that NAM, but not Nicotinamide Mononucleotide (NMN), significantly inhibits DCs over-activation in vitro and in vivo. The suppression of DCs hyperactivation effectively alleviates symptoms of psoriasis. Mechanistically, NAM impairs DCs activation through a Poly (ADP-ribose) polymerases (PARPs)-NF-κB dependent manner. Notably, phosphoribosyl transferase (NAMPT) and PARPs are significantly upregulated in lipopolysaccharide (LPS)-stimulated DCs and psoriasis patients; elevated NAMPT and PARPs expression in psoriasis patients correlates with higher psoriasis area and severity index (PASI) scores. In summary, our findings underscore the pivotal role of NAM in modulating DCs functions and autoimmune disorders. Targeting the NAMPT-PARP axis emerges as a promising therapeutic approach for DC-related diseases. [ABSTRACT FROM AUTHOR]

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest age, accurate high-resolution forest age data are still lacking, which causes uncertainty in carbon sink potential prediction. In this study, we obtained a 1 km resolution forest map based on the fusion of multiscale age information, i.e., the ninth (2014–2018) forest inventory statistics of China, with high accuracy at the province scale, and a field-observed dataset covering 6779 sites, with high accuracy at the site scale. Specifically, we first constructed a random forest (RF) model based on field-observed data. Utilizing this model, we then generated a spatially explicit forest age map with a 1 km resolution (random forest age map, RF map) using remotely sensed data such as tree height, elevation, meteorology, and forest distribution. This was then used as the basis for downscaling the provincial-scale forest inventory statistics of the forest ages and retrieving constrained maps of forest age (forest inventory constrained age maps, FIC map), which exhibit high statistical accuracy at both the province scale and site scale. The main results included the following: (1) RF can be used to estimate the site-scale forest age accurately (R2 = 0.89) and has the potential to predict the spatial pattern of forest age. However, (2) owing to the impacts of sampling error (e.g., field-observed sites are usually located in areas exhibiting relatively favorable environmental conditions) and the spatial mismatch among different datasets, the regional-scale forest age predicted by the RF model could be overestimated by 71.6%. (3) The results of the downscaling of the inventory statistics indicate that the average age of forests in China is 35.1 years (standard deviation of 21.9 years), with high spatial heterogeneity. Specifically, forests are older in mountainous and hilly areas, such as northeast, southwest, and northwest China, than in southern China. The spatially explicit dataset of the forest age retrieved in this study encompasses synthesized multiscale forest age information and is valuable for the research community in assessing the carbon sink potential and modeling carbon dynamics. [ABSTRACT FROM AUTHOR]

Product quality is a key factor affecting consumers' willingness to buy, providing greater advantages to an enterprise than product price. This paper investigates the impact of two factors, price and quality, on the operational decisions of hybrid competing supply chains. Supply chain I, which consists of a manufacturer and a retailer, is a decentralized structure. Supply chain II, where the manufacturer and retailer are integrated, is a centralized structure. Quality investment and vertical shareholding are introduced into the decentralized supply chain. Models are constructed for three different scenarios, examining whether the manufacturer makes a quality investment and whether the retailer holds shares in the quality investment. By comparing the equilibrium results, solved by the Stackelberg game method, the following conclusions are drawn: (1) Quality investment and shareholding can enhance product quality and price. (2) The retail price in a centralized supply chain is consistently lower than that in a decentralized one, leading to generally higher total profits for centralized supply chain. (3) The total profit of the decentralized supply chain only exceeds that of the centralized ones when the degree of substitution between products is lower than 0.6285 and the quality effort cost factor is within a specific range. While centralized supply chain is generally more advantageous, decentralized supply chain can outperform him under specific conditions. [ABSTRACT FROM AUTHOR]

The frequency support capacity of power loads is essential for maintaining active power symmetry and balance between the generation and demand sides of power systems. As the proportion of renewable energy sources and power electronic equipment increases, the inertia on the power generation side decreases, highlighting the growing importance of frequency support on the load side. As it is generally believed that the active power balance of power systems determines the frequency stability, few studies have considered the effect of voltage variation on the frequency response dynamics. It is important to note that the node voltage keeps fluctuating throughout the frequency dynamic process, which affects the active power of loads and should not be neglected. Based on the aforementioned rationales, this paper endeavors to investigate the modeling of power load frequency response and analyze its support capability considering the voltage variation effect. This paper initially establishes the small-signal model of dynamic load under frequency dynamics, derives the transfer function relating active power to system frequency deviation, and subsequently develops its frequency response model. Subsequently, commencing with the ZIP model of static load, the power fluctuation of load nodes is derived from the influence of preceding nodes, and the frequency response model of the static load is formulated and its frequency support capacity is scrutinized. Based on this foundation, a comprehensive aggregation model of the complex load is constructed, and its frequency support capability is assessed using actual data. Finally, the proposed model and analysis results are validated through simulation, confirming their correctness and effectiveness. [ABSTRACT FROM AUTHOR]

Retained placenta (RP) affects lactation and fertility in dairy cows and causes economic losses to the dairy industry. Therefore, screening for early warning of this disease is important. This study used multi omics techniques to reveal the metabolic differences of dairy cows before RP onset and to find potential warning markers. Fecal samples and serum samples of 90 healthy Holstein cows were collected 7 days pre-calving; 10 healthy and 10 RP cows were enrolled according to normal expulsion of fetal membranes after calving. Fecal samples were subjected to 16S rRNA sequencing and untargeted metabolomics analysis, while plasma was analyzed using targeted metabolomics. Pathogenic bacteria levels increased in the intestines of cows with RP compared to those in healthy cows. Lipid metabolites constituted the largest proportion of differential metabolites between feces and plasma. Six potential warning markers for RP in cows were identified, including two fecal microbiomics markers (Oscillospiraceae UCG-005 and Escherichia-Shigella), one fecal untargeted metabolomics marker (N-acetylmuramic acid), and three plasma targeted metabolomics markers (glycylcholic acid-3 sulfate, 7-ketolithocholic acid, and 12-ketolithocholic acid). These biomarkers can predict RP occurrence in the early perinatal period. These results lay a theoretical foundation for early nutritional intervention and pathogenesis research in dairy cows. [ABSTRACT FROM AUTHOR]

The safety condition of concrete gravity dams is influenced by multiple factors, and assessing their safety solely based on a single factor is difficult to comprehensively evaluate. Therefore, this paper proposes a comprehensive modeling and analysis approach to assess dam safety by considering long-term temperature, stress, and strain monitoring data of actual concrete gravity dams. Firstly, the K-means clustering algorithm is utilized to classify the data. Then, the study area of the dam is meshed and three indicator evaluation values for all the elements are calculated. The other elements' evaluation values can be obtained by the Inverse Distance Weighting (IDW) method. Finally, the analytic hierarchy process extended by the D numbers preference relation (D-AHP) method is applied to compute the weights of temperature, stress, and strain and evaluate the dam's safety comprehensively. The effectiveness of this method is validated through application to specific engineering cases. The results demonstrate that compared to assessing methods considering only single factors, the comprehensive evaluation method proposed in this paper can more comprehensively and accurately reflect the actual safety condition of concrete gravity dams, providing important references for engineering decision-making. [ABSTRACT FROM AUTHOR]

Compared to traditional biochar (BC), nano-biochar (NBC) boasts superior physicochemical properties, promising extensive applications in agriculture, ecological environments, and beyond. Due to its strong adsorption and migration properties, NBC may carry nutrients or pollutants to deeper soil layers or even groundwater, causing serious environmental risks. Nevertheless, the migration rules and mechanisms of NBC in soil are still unclear. Therefore, this study employed soil column migration experiments to systematically explore the migration rules and mechanisms of NBC under various flow rates, initial soil water contents, soil depths, and soil textures. The results showed that regulated by smaller particle size differences and greater surface charges, NBC exhibited a stronger migration ability compared with traditional BC. As the soil texture transitioned from fine to coarse, the migration capability of NBC significantly improved, driven by both pore structure and interaction forces as described by the DLVO theory. The migration ability of NBC was also greatly boosted as the soil transitioned from saturated to unsaturated conditions, primarily because of preferential flow. When the flow rate increased from 70% KS to 100% KS and 130% KS, the migration ability of NBC also increased accordingly, as changes in injection flow rates altered the velocity distribution of pore water. NBC in 25 cm soil columns was more prone to shallow retention compared with 10 cm soil columns, resulting in weaker overall migration ability. In addition, through fitting of the two-site kinetic model and related parameters, the penetration curves of NBC under various variable conditions were effectively characterized. These findings could offer valuable insights for NBC's future efficient, rational, and sustainable utilization, facilitating the evaluation and mitigation of its potential environmental risks. [ABSTRACT FROM AUTHOR]

Doping inorganic acid ions represents a promising pathway to improving the photocatalytic activity of TiO2, and oxygen vacancy has been regarded as the determinant factor for photocatalytic activity. A series of samples doped with Cl−, NO3−, and SO42− was prepared via a simple sol–gel method. Two different oxygen vacancies in the crystal layer of NO3−/TiO2 and Cl−/TiO2 were found, and those are [Ti3+]-V0-[Ti3+] and [Ti3+]-Cl, respectively. The photocurrent of NO3−/TiO2 with [Ti3+]-V0-[Ti3+] is significantly greater than that of Cl−/TiO2 with [Ti3+]-Cl. The least oxygen vacancy is in the gel layer of SO42−/TiO2, and the negligible photocurrent is due to difficulty in forming a stable sol. Furthermore, the process conditions for the application of TiO2 were investigated in this work. The optimal process parameters are to adjust the solution to pH = 3 during sol–gel preparation, to adopt 550 °C as the calcination temperature, and to use an alkaline electrolyte, while the rest of the preparation conditions remain unchanged. This work reveals a new avenue for designing efficient photocatalysts for air pollutant degradation. [ABSTRACT FROM AUTHOR]

Land use/cover change (LUCC) is a vital aspect representing global change and humans' impact on Earth's surface. This study utilized the ESRI Land Cover 2020 and China Land Cover Dataset (CLCD), along with historical imagery from Google Earth, to develop a method for the assessment of land use data quality. Based on the assessment, the CLCD was updated to generate an improved Re-CLCD for the Pan-Pearl River Basin (PPRB) from 1985 to 2020, and to analyze LUCC in the PPRB over the past 35 years. The results indicate the following: (1) Among the seven land uses, built-up land experienced the most dramatic change, followed by cropland, forestland, grassland, shrubland, waterbody, and bare land, with notable increases in built-up land and forestland, and rapid decreases in cropland, grassland, and shrubland. (2) The magnitude of land use changed very widely, with the highest change in the Pearl River Delta, followed by small coastal river basins in southern Guangdong and western Guangxi, the Dongjiang River Basin, the Hanjiang River Basin, the Xijiang River Basin, the Beijiang River Basin, and lastly, Hainan Island. (3) The largest increase happened in built-up land, with a total increase of 12,184 km2, mainly due to the occupation of cropland and forestland, corresponding to the highest decrease in cropland, with a net loss of 10,435 km2, which was primarily converted to forestland and built-up land. The study results are valuable in providing a scientific basis for policy overhaul regarding land resources and management to safeguard ecological balance and promote sustainable development in the Pan-Pearl River Basin. [ABSTRACT FROM AUTHOR]

Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L−1) and reaching lows in winter (43.5 μg·L−1); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA. [ABSTRACT FROM AUTHOR]

Before 2008, China lacked high-coverage regional surface observation data, making it difficult for the China Meteorological Administration Land Data Assimilation System (CLDAS) to directly backtrack high-resolution, high-quality land assimilation products. To address this issue, this paper proposes a deep learning model named UNET_DCA, based on the UNET architecture, which incorporates a Dual Cross-Attention module (DCA) for multiscale feature fusion by introducing Channel Cross-Attention (CCA) and Spatial Cross-Attention (SCA) mechanisms. This model focuses on the near-surface 10-m wind field and achieves spatial downscaling from 6.25 km to 1 km. We conducted training and validation using data from 2020–2021, tested with data from 2019, and performed ablation experiments to validate the effectiveness of each module. We compared the results with traditional bilinear interpolation methods and the SNCA-CLDASSD model. The experimental results show that the UNET-based model outperforms SNCA-CLDASSD, indicating that the UNET-based model captures richer information in wind field downscaling compared to SNCA-CLDASSD, which relies on sequentially stacked CNN convolution modules. UNET_CCA and UNET_SCA, incorporating cross-attention mechanisms, outperform UNET without attention mechanisms. Furthermore, UNET_DCA, incorporating both Channel Cross-Attention and Spatial Cross-Attention mechanisms, outperforms UNET_CCA and UNET_SCA, which only incorporate one attention mechanism. UNET_DCA performs best on the RMSE, MAE, and COR metrics (0.40 m/s, 0.28 m/s, 0.93), while UNET_DCA_ars, incorporating more auxiliary information, performs best on the PSNR and SSIM metrics (29.006, 0.880). Evaluation across different methods indicates that the optimal model performs best in valleys, followed by mountains, and worst in plains; it performs worse during the day and better at night; and as wind speed levels increase, accuracy decreases. Overall, among various downscaling methods, UNET_DCA and UNET_DCA_ars effectively reconstruct the spatial details of wind fields, providing a deeper exploration for the inversion of high-resolution historical meteorological grid data. [ABSTRACT FROM AUTHOR]

Selective laser melting (SLM) technology is a promising additive manufacturing technology. However, due to the numerous influencing factors in this complex process, a reliable real-time method is needed to monitor the forming process of SLM. The molten pool is the smallest forming unit in the SLM process, the consistency of which can effectively reflect the quality of the printing process. By using a coaxial optical path structure and a compound amplifier circuit, high-speed acquisition of molten pool radiation can be realized. Next, single factor analysis and orthogonal experimentation were used to investigate the influence levels of key process parameters on the radiation of molten pool. In addition, numerical simulation was carried out with the same parameter setting schemes, the results of which are consistent with those in radiation detection experiments. It is shown that the laser power has the greatest effect on the radiation of the molten pool, while the scanning speed and the hatch spacing have little effect on the radiation. Finally, the positioning experiment involving the small hole structure was carried out, and the experimental results showed that the device could accurately locate the position coordinates of the given hole structure. [ABSTRACT FROM AUTHOR]

The Pearl River is one of China's large rivers, the second-largest river and the fourth-longest river in China. Its unique geography, landform, and climate conditions create unique fluvial geomorphological processes. Affected by human activities and climate change, the fluvial geomorphological processes in the Pearl River Basin have undergone significant changes in recent decades, seriously affecting the river's sustainable development. This paper critically reviews changes in fluvial geomorphological processes and analyzes influencing factors in the Pearl River Basin with a focus on possibilities for policy overhaul and strategic adjustments. [ABSTRACT FROM AUTHOR]

Tensile-compression fatigue deformation tests were conducted on AZ31 magnesium alloy at room temperature. Electron backscatter diffraction (EBSD) scanning electron microscopy was used to scan the microstructure near the fatigue fracture surface. It was found that lamellar {10-11}-{10-12} secondary twins (STs) appeared inside primary {10-11} contraction twins (CTs), with a morphology similar to the previously discovered {10-12}-{10-12} STs. However, through detailed misorientation calibration, it was determined that this type of secondary twin is {10-11}-{10-12} ST. Through calculation and analysis, it was found that the matrix was under compressive stress in the normal direction (ND) during fatigue deformation, which was beneficial for the activation of primary {10-11} CTs. The local strain accommodation was evaluated based on the geometric compatibility parameter (m') combined with the Schmid factor (SF) of the slip system, leading us to propose and discuss the possible formation mechanism of this secondary twin. The analysis results indicate that when the local strain caused by basal slip at the twin boundaries cannot be well transmitted, {10-11}-{10-12} STs are activated to coordinate the strain, and different loading directions lead to different formation mechanisms. Moreover, from the microstructure characterization near the entire fracture surface, we surmise that the presence of such secondary twins is not common. [ABSTRACT FROM AUTHOR]

Particle size reduction is a commonly used pretreatment technique to promote methane production from anaerobic digestion (AD) of food waste (FW). However, limited research has focused on the effect of micron-sized particles on AD of FW. This research presented an ultrafine wet milling (UFWM) pretreatment method to reduce the particle size of FW particles. After four hours of milling, D90 was reduced to 73 μm and cumulative methane production boosted from 307.98 mL/g vs. to 406.75 mL/g vs. without ammonia inhibition. We evaluated the performance of the AD systems and explored their facilitation mechanisms. Kinetic analysis showed that the modified Gompertz model predicted experimental values most accurately. UFWM pretreatment increased the maximum methane production rate by 44.4% and reduced the lag time by 0.65 days. The mechanical stress and collisions of milling resulted in a scaly surface of the particles, which greatly increased the voids and surface area. A rise in the XPS peak area of the C–N and C=O bonds proved the promotion of the liberation of carbohydrates and fats. Further microbial community analysis revealed that the relative abundance of Bacteroidota and Methanosarcina were enriched by UFWM. Meanwhile, methane metabolism pathway analysis confirmed that module M00567, module M00357, and related enzymes were stimulated. This study provided a theoretical basis for UFWM pretreatment applications and improvements in AD of FW. [ABSTRACT FROM AUTHOR]

With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper. [ABSTRACT FROM AUTHOR]

Seed storage underpins global agriculture and the seed trade and revealing the mechanisms of seed aging is essential for enhancing seed longevity management. Safflower is a multipurpose oil crop, rich in unsaturated fatty acids that are at high risk of peroxidation as a contributory factor to seed aging. However, the molecular mechanisms responsible for safflower seed viability loss are not yet elucidated. We used controlled deterioration (CDT) conditions of 60% relative humidity and 50 °C to reduce germination in freshly harvested safflower seeds and analyzed aged seeds using biochemical and molecular techniques. While seed malondialdehyde (MDA) and fatty acid content increased significantly during CDT, catalase activity and soluble sugar content decreased. KEGG analysis of gene function and qPCR validation indicated that aging severely impaired several key functional and biosynthetic pathways including glycolysis, fatty acid metabolism, antioxidant activity, and DNA replication and repair. Furthermore, exogenous sucrose and diethyl aminoethyl hexanoate (DA-6) treatment partially promoted germination in aged seeds, further demonstrating the vital role of impaired sugar and fatty acid metabolism during the aging and recovery processes. We concluded that energy metabolism and genetic integrity are impaired during aging, which contributes to the loss of seed vigor. Such energy metabolic pathways as glycolysis, fatty acid degradation, and the tricarboxylic acid cycle (TCA) are impaired, especially fatty acids produced by the hydrolysis of triacylglycerols during aging, as they are not efficiently converted to sucrose via the glyoxylate cycle to provide energy supply for safflower seed germination and seedling growth. At the same time, the reduced capacity for nucleotide synthesis capacity and the deterioration of DNA repair ability further aggravate the damage to DNA, reducing seed vitality. [ABSTRACT FROM AUTHOR]

Understanding the niche dynamic among distinct populations and delineating the dispersal corridors that exist between them under current climates is critical for elucidating the contemporary forces driving genetic divergence, facilitating population connectivity, and informing targeted conservation efforts, particularly for species exhibiting pronounced intraspecific lineages. In this study, we focus on evaluating the range and niche dynamics of the intraspecific lineages of Emmenopterys henryi and exploring potential patterns of population connectivity both within and across these lineages. Our findings unveiled a significant niche divergence between the two intraspecific lineages, characterized by limited overlap in climatic conditions and suitable ranges. Furthermore, our analysis of population connectivity revealed the presence of dispersal routes with varying degrees of connectivity within each lineage, while low connectivity was observed between the two lineages. Our results highlight the critical role of fine-scale ecological niche models (ENMs) and genetic connectivity analyses in elucidating the complexities of niche evolution and genetic connectivity, particularly for species with discrete intraspecific lineages. In addition, given the fact that rapid genetic erosion of species inhabiting the regions we focus on in this study is often associated with habitat loss and fragmentation, our findings will also offer valuable insights for designing targeted conservation strategies aimed at restoring connectivity and increasing local population sizes for this endangered species. [ABSTRACT FROM AUTHOR]

The recycling of waste tires avoids the environmental hazards of landfills and incineration, and its application in asphalt modification achieves resource sustainability. Currently, desulfurized rubber powder (DRP) is widely used as an asphalt modifier, mainly mixed with SBS, and fewer studies have been conducted on high-dose asphalt modification with ethylene vinyl acetate (EVA). In this paper, DR/EVA-composite-modified asphalt (DR/EVACMA) was prepared using 20% DRP and 4% EVA by adding four additives: furfural extract oil (FEO), a crosslinking agent (DCP), a vulcanizing agent (sulfur), and a silane coupling agent (KH-550). The aim was to study the effects of different additives on the physical properties, storage stability, and rheological properties of asphalt. First, conventional physical property measurements were carried out, and the data were analyzed using a polar analysis to determine the degree of influence of the four additives and the optimal ratios. Then, the rheological properties and fatigue resistance of DR/EVACMA were investigated through temperature scanning experiments, linear amplitude scanning (LAS) experiments, and multi-stress creep (MSCR) experiments. Finally, the reaction mechanism and microscopic properties were analyzed through infrared spectroscopy experiments (FTIR) and fluorescence microscopy (FM). The results showed that FEO had the greatest effect on asphalt characteristics. Compared to matrix asphalt and additive-free asphalt, DR/EVACMA has higher physical properties, fatigue resistance, and high temperature rheological properties due to its internal crosslinking structure. Its storage stability is also very good, with a difference of only 0.7 °C in the softening point. [ABSTRACT FROM AUTHOR]

The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a simplistic and direct approach in the feature fusion stage, potentially leading to spurious target detection and the amplification of internal noise. To address these concerns, we present a multi-scale attention network (MSANet) tailored for building extraction from high-resolution remote sensing images. In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features. Concurrently, we introduced a gating mechanism to facilitate the effective fusion of multi-scale features. The efficacy of the proposed MSANet was evaluated using the WHU aerial image dataset and the WHU satellite image dataset. The experimental results demonstrate compelling performance metrics, with the F1 scores registering at 93.76% and 77.64% on the WHU aerial imagery dataset and WHU satellite dataset II, respectively. Furthermore, the intersection over union (IoU) values stood at 88.25% and 63.46%, surpassing benchmarks set by DeepLabV3 and GSMC. [ABSTRACT FROM AUTHOR]

The mechanical strength of rice culm, an essential factor for lodging resistance and yield maintenance, is influenced by the composition and structure of the cell wall. In this study, we characterized a rice brittle culm mutant 22 (bc22), derived from LR005 through ethyl methanesulfonate (EMS) mutagenesis. The bc22 culm exhibited increased fragility and reduced mechanical strength compared to LR005. The mutant displayed pleiotropic effects, including a shorter plant height and panicle length, a smaller grain size, and the absence of the glume hairs. Scanning electron microscopy revealed a decrease in cell density and a looser structure in the bc22 culms. Biochemical analysis demonstrated a significant increase in hemicellulose content and a marked reduction in lignin content in the culm of bc22. Genetic analysis indicated that the brittle culm trait was governed by a single recessive gene. After employing bulked segregant analysis (BSA), whole-genome resequencing, and MutMap methods, LOC_Os02g25230 was identified as the candidate gene responsible for bc22. In bc22, a point mutation from proline (Pro) to leucine (Leu) in its coding region led to the pleiotropic phenotype. A complementation test further confirmed that the missense mutation causing the proline to leucine amino acid substitution in LOC_Os02g25230 was causative of the observed bc22 phenotype. Additionally, gene expression analysis showed that BC22 had higher expression levels in the culms, leaves, and spikelets compared to the roots. Taken together, our findings indicate that BC22 is a pleiotropic gene, and the influence of BC22 on brittleness may be associated with cell wall biosynthesis in rice culm. [ABSTRACT FROM AUTHOR]

The autonomous shipping industry is increasingly focusing on enhancing the safety and reliability of software-based systems. Conducting a risk assessment is a requirement for demonstrating the safety equivalence of autonomous ships based on such systems to conventional vessels. Traditional risk assessment models, however, primarily focus on hardware failures, often overlooking potential software-related failures and functional inadequacies. This study proposes a framework integrating Software Failure Mode and Effects Analysis (FMEA), System–Theoretic Process Analysis (STPA), and Bayesian Network (BN) for risk identification of autonomous ship software systems. The results of a case study reveal that the framework sufficiently addresses the multifaceted nature of risks related to software in autonomous ships. Based on the findings of this study, we suggest the need for standardization of software architecture development in the autonomous ship industry and highlight the necessity for an enhanced understanding of AI-specific risks and the development of tailored risk assessment methodologies. [ABSTRACT FROM AUTHOR]

Zearalenone (ZEN) is a mycotoxin produced by Fusarium spp., which commonly and severely contaminate food/feed. ZEN severely affects food/feed safety and reduces economic losses owing to its carcinogenicity, genotoxicity, reproductive toxicity, endocrine effects, and immunotoxicity. To explore efficient methods to detoxify ZEN, we identified and characterized an efficient ZEN-detoxifying microbiota from the culturable microbiome of Pseudostellaria heterophylla rhizosphere soil, designated Bacillus amyloliquefaciens D-1. Its highest ZEN degradation rate reached 96.13% under the optimal condition. And, D-1 can almost completely remove ZEN (90 μg·g−1) from coix semen in 24 h. Then, the D-1 strain can detoxify ZEN to ZEM, which is a new structural metabolite, through hydrolyzation and decarboxylation at the ester group in the lactone ring and amino acid esterification at C2 and C4 hydroxy. Notably, ZEM has reduced the impact on viability, and the damage of cell membrane and nucleus DNA and can significantly decrease the cell apoptosis in the HepG2 cell and TM4 cell. In addition, it was found that the D-1 strain has no adverse effect on the HepG2 and TM4 cells. Our findings can provide an efficient microbial resource and a reliable reference strategy for the biological detoxification of ZEN. [ABSTRACT FROM AUTHOR]

As an emerging additive manufacturing technology with potential, selective laser melting (SLM) technology is one of the current mainstream additive manufacturing processes. Many complex conditions affect the quality of the SLM products, such as extremely short forming time, rapidly changing forming environment, and micron-level melting area. Numerical simulation and a printing experiment were used to study the influence of typical technological parameters on the forming quality of SLM products, where TA1 was used as the printing material. The typical technological parameters mainly considered laser power, laser scanning speed, hatch spacing, and laser energy density. Simulation analysis was based on the maximum temperature and the size of the molten pool, and the printing experiment was focused on the parameter design methods for typical processes and the forming quality of printed products. The results show the following: (1) An increase in laser power and laser energy density can significantly improve the forming quality. However, excessive input energy will lead to poor surface quality; (2) Scanning speed and hatch spacing need to be selected according to the size of the molten pool, which mainly influences the lap rate of the molten pool. The results of this study may provide some guidance for parameter setting and forming quality optimization in SLM manufacturing process. [ABSTRACT FROM AUTHOR]

The Western Pacific Coasts (WPC) are the outlets of many large Asian rivers. In recent years, the interplay of climate changes and human activities has persistently altered the suspended sediment concentrations (SSC) in the WPC, triggering substantial shifts in coastal ecosystems. However, the scarcity of coastal observation stations hampered comprehensive investigations at large scales. This study employed three representative SSC retrieval models and utilized Landsat images acquired from 1990 to 2020 to estimate the SSC in the WPC with a focused endeavor to dissect the intricate spatial and temporal variability of SSC in the region. The findings revealed the following insights: (1) The outcomes derived from the three distinct SSC models consistently manifested a discernible decreasing pattern in SSC changes over the past three decades across all six major estuaries (Liao River Estuary, Yellow River Estuary, Yangtze River Estuary, Hangzhou Bay, Pearl River Estuary, and Mekong River Estuary). (2) The seasonal attributes of the six major estuaries differed, primarily due to distinct dominant influencing factors like precipitation, upstream sediment load, wind, and tides. (3) Collectively, SSC tends to be relatively higher in the Yangtze River Estuary, Hangzhou Bay, and Yellow River Estuary, while the Pearl River and Mekong River Estuaries exhibit relatively lower levels. Notably, the SSC exhibited distinct spatial traits along the coastlines of different estuaries. (4) SSC in the non-estuarine regions along the WPC, a similar significant declining trend in SSC is observed as in the estuaries, albeit the rate of decline generally appeared to be less pronounced. Furthermore, regions with faster rates of SSC reduction are typically concentrated near major estuaries in the northern part of the Coasts. The decline in estuarine SSC plays an important role in the overall decrease in SSC across the WPC. These study outcomes held substantial significance for advancing the stability and sustainable evolution of the WPC. [ABSTRACT FROM AUTHOR]

Liver fibrosis refers to a complex inflammatory response caused by multiple factors, which is a known cause of liver cirrhosis and even liver cancer. As a valuable medicine food homology herb, saffron has been widely used in the world. Saffron is commonly used in liver-related diseases and has rich therapeutic and health benefits. The therapeutic effect is satisfactory, but its mechanism is still unclear. In order to clarify these problems, we planned to determine the pharmacological effects and mechanisms of saffron extract in preventing and treating liver fibrosis through network pharmacology analysis combined with in vivo validation experiments. Through UPLC-Q-Exactive-MS analysis, a total of fifty-six nutrients and active ingredients were identified, and nine of them were screened to predict their therapeutic targets for liver fibrosis. Then, network pharmacology analysis was applied to identify 321 targets for saffron extract to alleviate liver fibrosis. Functional and pathway enrichment analysis showed that the putative targets of saffron for the treatment of hepatic fibrosis are mainly involved in the calcium signaling pathway, the HIF-1 signaling pathway, endocrine resistance, the PI3K/Akt signaling pathway, lipid and atherosclerosis, and the cAMP signaling pathway. Based on the CCl4-induced liver fibrosis mice model, we experimentally confirmed that saffron extract can alleviate the severity and pathological changes during the progression of liver fibrosis. RT-PCR and Western blotting analysis confirmed that saffron treatment can prevent the CCl4-induced upregulation of HIF-1α, VEGFA, AKT, and PI3K, suggesting that saffron may regulate AKT/HIF-1α/VEGF and alleviate liver fibrosis. [ABSTRACT FROM AUTHOR]

The permutation flow shop scheduling problem (PFSP) stands as a classic conundrum within the realm of combinatorial optimization, serving as a prevalent organizational structure in authentic production settings. Given that conventional scheduling approaches fall short of effectively addressing the intricate and ever-shifting production landscape of PFSP, this study proposes an end-to-end deep reinforcement learning methodology with the objective of minimizing the maximum completion time. To tackle PFSP, we initially model it as a Markov decision process, delineating pertinent states, actions, and reward functions. A notably innovative facet of our approach involves leveraging disjunctive graphs to represent PFSP state information. To glean the intrinsic topological data embedded within the disjunctive graph's underpinning, we architect a policy network based on a graph isomorphism network, subsequently trained through proximal policy optimization. Our devised methodology is compared with six baseline methods on randomly generated instances and the Taillard benchmark, respectively. The experimental results unequivocally underscore the superiority of our proposed approach in terms of m a k e s p a n and computation time. Notably, the m a k e s p a n can save up to 183.2 h in randomly generated instances and 188.4 h in the Taillard benchmark. The calculation time can be reduced by up to 18.70 s for randomly generated instances and up to 18.16 s for the Taillard benchmark. [ABSTRACT FROM AUTHOR]

Cinnamomum migao H. W. Li is an evergreen woody plant that is only distributed in southwest China. The volatile oils from the fruits of C. migao have long been used as a special authentic medicinal herb by local ethnic minorities. Due to its low seed germination rate, destructive lumbering and low rates of artificial planting, C. migao is facing the danger of extinction. Therefore, it is urgent to exploit and protect this species using molecular biological technology, especially to target the genes involved in the biosynthesis of terpenoids in the volatile oil. However, the genomic data for this plant are not available. In this study, the transcriptome sequencing of C. migao was performed to obtain the key genes involved in terpenoid biosynthesis via a combination of full-length transcriptome and next-generation sequencing (NGS). More than 39.9 Gb of raw data was obtained and 515,929 circular consensus sequences (CCSs) were extracted. After clustering similar 472,858 full-length non-chimeric (FLNC) reads and correction with NGS data, 139,871 consensus isoforms were obtained. Meanwhile, 73,575 non-redundant transcripts were generated by removing redundant transcripts. Then, 70,427 isoforms were successfully annotated using public databases. Moreover, differentially expressed transcripts (DETs) in four different developmental stages of the C. migao fruit were analyzed and 5764 transcripts showed period-specific expression. Finally, 15 transcripts, 6 transcripts, and only 1 transcript were identified as being involved in the biosynthesis of sesquiterpenoids, diterpenoids, and monoterpenoids, respectively. This study provides a basis for future research in the gene mining, genetic breeding, and metabolic engineering of C. migao. [ABSTRACT FROM AUTHOR]

Estimating object counts within a single image or video frame represents a challenging yet pivotal task in the field of computer vision. Its increasing significance arises from its versatile applications across various domains, including public safety and urban planning. Among the various object counting tasks, crowd counting is particularly notable for its critical role in social security and urban planning. However, intricate backgrounds in images often lead to misidentifications, wherein the complex background is mistaken as the foreground, thereby inflating forecasting errors. Additionally, the uneven distribution of crowd density within the foreground further exacerbates predictive errors of the network. This paper introduces a novel architecture with a three-branch structure aimed at synergistically incorporating hierarchical foreground information and global scale information into density map estimation, thereby achieving more precise counting results. Hierarchical foreground information guides the network to perform distinct operations on regions with varying densities, while global scale information evaluates the overall density level of the image and adjusts the model's global predictions accordingly. We also systematically investigate and compare three potential locations for integrating hierarchical foreground information into the density estimation network, ultimately determining the most effective placement.Through extensive comparative experiments across three datasets, we demonstrate the superior performance of our proposed method. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) is a novel modulation scheme that enables reliable communication in high-mobility environments. In this paper, we propose a Transformer-based channel estimation method for OTFS systems. Initially, the threshold method is utilized to obtain preliminary channel estimation results. To further enhance the channel estimation, we leverage the inherent temporal correlation between channels, and a new method of channel response prediction is performed. To enhance the accuracy of the preliminary results, we utilize a specialized Transformer neural network designed for processing time series data for refinement. The simulation results demonstrate that our proposed scheme outperforms the threshold method and other deep learning (DL) methods in terms of normalized mean squared error and bit error rate. Additionally, the temporal complexity and spatial complexity of different DL models are compared. The results indicate that our proposed algorithm achieves superior accuracy while maintaining an acceptable computational complexity. [ABSTRACT FROM AUTHOR]

The article titled "Effect of Final Rolling Temperature on Microstructures and Mechanical Properties of AZ31 Alloy Sheets Prepared by Equal Channel Angular Rolling and Continuous Bending" has been retracted by the journal. The authors raised concerns about redundancy and the improper reuse of figures from one of their previous articles. An investigation confirmed that several figures in the retracted article were duplicated without proper citation or permission. The authors and the journal have decided to retract the article in accordance with the journal's retraction policy. [Extracted from the article]

The reliable circulation of automotive supply chain data is crucial for automotive manufacturers and related enterprises as it promotes efficient supply chain operations and enhances their competitiveness and sustainability. However, with the increasing prominence of privacy protection and information security issues, traditional data sharing solutions are no longer able to meet the requirements for highly reliable secure storage and flexible access control. In response to this demand, we propose a secure data storage and access control scheme for the supply chain ecosystem based on the enterprise-level blockchain platform Hyperledger Fabric. The design incorporates a dual-layer attribute-based auditable access control model for access control, with four smart contracts aimed at coordinating and implementing access policies. The experimental results demonstrate that the proposed approach exhibits significant advantages under large-scale data and multi-attribute conditions. It enables fine-grained, dynamic access control under ciphertext and maintains high throughput and security in simulated real-world operational scenarios. [ABSTRACT FROM AUTHOR]

Plant viruses, as obligate intracellular parasites, induce significant changes in the cellular physiology of host cells to facilitate their multiplication. These alterations often lead to the development of symptoms that interfere with normal growth and development, causing USD 60 billion worth of losses per year, worldwide, in both agricultural and horticultural crops. However, existing literature often lacks a clear and concise presentation of the key information regarding the mechanisms underlying plant virus-induced symptoms. To address this, we conducted a comprehensive review to highlight the crucial interactions between plant viruses and host factors, discussing key genes that increase viral virulence and their roles in influencing cellular processes such as dysfunction of chloroplast proteins, hormone manipulation, reactive oxidative species accumulation, and cell cycle control, which are critical for symptom development. Moreover, we explore the alterations in host metabolism and gene expression that are associated with virus-induced symptoms. In addition, the influence of environmental factors on virus-induced symptom development is discussed. By integrating these various aspects, this review provides valuable insights into the complex mechanisms underlying virus-induced symptoms in plants, and emphasizes the urgency of addressing viral diseases to ensure sustainable agriculture and food production. [ABSTRACT FROM AUTHOR]

Soil erosion is the primary factor inducing soil deterioration in large river basins. Management and conservation of the soil erosion of the red bed desert, a unique gully landform shaped by intensive weathering and hydraulic erosion under the humid climate in south-eastern China, is crucial for its long-term sustainable development. This study, focusing on the Nanxiong Basin, a representative area with red beds, applied Gaofen(Gf)-2 satellite images to the RUSLE Model to analyze soil erosion modulus before and after implementing treatment for soil erosion control. We qualified the volume of soil erosion and mapped the spatial erosion variability in the basin. The results exhibited a decrease in the average erosion modulus from 3943.56 t·km−2·yr−1 to 2023.1 t·km−2·yr−1, which caused a reduction in total soil loss from 4.64 million tons to 2.38 million tons after treatment, with a reduction of about 48.7%. The mitigation reduced soil erosion from a moderate level to a light level. The areas that integrated soil and water conservation practices (SWCP) with management measures showed evident improvement in soil erosion, with a dramatic reduction in areas with annual erosion of a strong level (5000~8000 t·km−2·yr−1), extremely strong level (8000~15,000 t·km−2·yr−1), and severe level (>15,000 t·km−2·yr−1). Despite spatial heterogeneity in soil erosion intensity in the basin, this study demonstrates the great performance of SWCP in mitigating red bed degradation. The study provides a solid scientific basis for soil erosion control using ecological engineering in the Nanxiong Basin. It can also serve as an illustrative case study for further applications on soil erosion assessment in similar small basins. [ABSTRACT FROM AUTHOR]

The panel block is a quite important "intermediate product" in the shipbuilding process. However, the assembly efficiency of the panel block assembly line is not high. Therefore, rational scheduling optimization is of great significance for improving shipbuilding efficiency. Currently, the processing sequence of the panel blocks in the panel block assembly line is mainly determined using heuristic and metaheuristic algorithms. However, these algorithms have limitations, such as small problem-solving capacity and low computational efficiency. To address these issues, this study proposes an end-to-end approach based on deep reinforcement learning to solve the scheduling problem of the ship's panel block assembly line. First, a Markov decision model is established, and a disjunctive graph is creatively used to represent the current scheduling status of the panel block assembly line. Then, a policy function based on a graph isomorphism network is designed to extract information from the disjunctive graph's state and train it using Proximal Policy Optimization algorithms. To validate the effectiveness of our method, tests on both real shipbuilding data and publicly available benchmark datasets are conducted. We compared our proposed end-to-end deep reinforcement learning algorithm with heuristic algorithms, metaheuristic algorithms, and the unimproved reinforcement learning algorithm. The experimental results demonstrate that our algorithm outperforms other baseline methods in terms of model performance and computation time. Moreover, our model exhibits strong generalization capabilities for larger instances. [ABSTRACT FROM AUTHOR]

The piston pump is the significant source of motive force in a hydraulic transmission system. Owing to the changeable working conditions and complex structural characteristics, multiple friction pairs in the piston pump are prone to wear and failure. An accurate fault diagnosis method is a crucial guarantee for system reliability. Deep learning provides a great insight into the intelligent exploration of machinery fault diagnosis. Hyperparameters are very important to construct an effective deep model with good performance. This research fully mines the feature component from vibration signals, and converts the failure recognition into a classification issue via establishing a deep model. Furthermore, Bayesian algorithm is introduced for hyperparameter optimization as it considers prior information. An adaptive convolutional neural network is established for typical failure pattern recognition of an axial piston pump. The proposed method can automatically complete fault classification and represents a higher accuracy by experimental verification. Typical failures of an axial piston pump are intelligently diagnosed with reduced subjectivity and preprocessing knowledge. The proposed method achieves an identification accuracy of more than 98% for five typical conditions of an axial piston pump. [ABSTRACT FROM AUTHOR]

With the continuous and large-scale development of renewable energy, there is a prominent decrease in the level of inertia in new power systems. This decrease leads to the weakening of the system's capability to provide inertia support and frequency regulation during disturbance events. The wind turbines (WT), as the main representatives of renewable energy generation, should be more efficiently involved in the power system frequency regulation dynamics. However, optimal frequency regulation is difficult to achieve through the combined inertial control strategy of wind turbines because it greatly depends on control parameters and fluctuates in different scenarios. To cope with disturbance efficiently and quickly in different scenarios and obtain the optimal frequency regulation results, this paper presents an improved combined inertial intelligent control strategy of WT based on contractive autoencoder (CAE) and deep neural network (DNN). This method obtains the optimal parameters for combined inertial control using the particle swarm optimization (PSO) algorithm, then effectively extracts features from actual data using CAE followed by building a network model to predict the optimal combined inertial control parameters online. To verify and test the proposed method, it is applied in the IEEE 9-bus test system. The simulation results show that the method can obtain optimal control parameters with a faster computational time, good prediction accuracy, and generalization capability. [ABSTRACT FROM AUTHOR]

"Alto2" is a new biomass sorghum variety, which has the characteristics of fast growth, high growth, and strong cadmium (Cd) resistance, so it has the application prospect of soil remediation plants. In order to reveal the Cd resistance mechanism of this plant and pave the way for genetic breeding and cultivation of efficient remediation plants in the future, in this research, through the determination of Cd content in various tissues of sorghum under Cd stress and the physicochemical response combined with RNA-Seq analysis, the mechanism of Cd resistance of "Alto2" was initially revealed. The results show biomass sorghum "Alto2" was mainly connected with aboveground and underground parts through the MAPK signaling pathway and plant hormone signaling pathway, and transmit stress signal in response to Cd stress. Chelase and metal-binding proteins may be the functional genes mainly responsible for Cd enrichment and transport and regulated by stress signals. However, the expression of aboveground transporters was not significant. This may be because Cd in biomass sorghum is mainly concentrated in the underground part and is enriched by the chelation of secondary metabolites from plant roots by the cell wall leading to inhibition of aboveground transporter expression. The results of this study indicate that the biomass sorghum "Alto2" on Cd has high resistance, but the lack of the aboveground enrichment of transportability requires further research to improve the Cd transportability of this plant. [ABSTRACT FROM AUTHOR]

In this paper, a photonic-enabled image rejection mixer (IRM) that features an ultrawideband self-interference cancellation (SIC) function and a compact configuration is proposed. The parameter tuning of SIC is realized in an optical domain, which avoids the use of electrically tuned devices with limited bandwidth and precision, so that high-precision parameter matching can be realized in the optical domain to realize deep and ultrawideband SIC. The key point of image rejection (IR) is to construct a pair of orthogonal local oscillation (LO) signals through DC-bias-induced phase shift. This not only avoids a high-frequency electrical 90-degree hybrid coupler (HC) applied in the traditional Hartley structure, but also compensates the phase deviation in the electrical intermediate frequency (IF) 90-degree HC flexibly, ensuring wideband and deep IR operation. The simulation results show that the proposed IRM can achieve ultrawideband SIC and IR with the simultaneous high-efficiency recovery of useful signals. They also verify that the scheme has good resistance to strong interference, and can cope with the phase imbalance of the IF 90-degree electrical HC, ensuring the good performance of the system, which has a wide application prospect in various in-band full-duplex (IBFD) systems. [ABSTRACT FROM AUTHOR]

Describing travel patterns and identifying significant locations is a crucial area of research in transportation geography and social dynamics. Our study aims to contribute to this field by analyzing taxi trip data from Chengdu and New York City. Specifically, we investigate the probability density distribution of trip distance in each city, which enables us to construct long- and short-distance trip networks. To identify critical nodes within these networks, we employ the PageRank algorithm and categorize them using centrality and participation indices. Furthermore, we explore the factors that contribute to their influence and observe a clear hierarchical multi-centre structure in Chengdu's trip networks, while no such phenomenon is evident in New York City's. Our study provides insight into the impact of trip distance on important nodes within trip networks in both cities and serves as a reference for distinguishing between long and short taxi trips. Our findings also reveal substantial differences in network structures between the two cities, highlighting the nuanced relationship between network structure and socio-economic factors. Ultimately, our research sheds light on the underlying mechanisms shaping transportation networks in urban areas and offers valuable insights into urban planning and policy making. [ABSTRACT FROM AUTHOR]

In terms of pump structure optimization, Li et al. [[1]] developed an energy-recovery-type recessed multiple-acting pump that integrates a high-pressure pump, energy recovery devices, and a booster pump. The nozzle is the core component of a high-pressure water jet, and an excellent nozzle structure can improve the energy conversion efficiency of the water jet and achieve outstanding spraying performance. Fluid components and systems are major components of modern mechanical equipment and have been widely used in various fields such as engineering machinery, rotating machinery, and hydraulic machinery. [Extracted from the article]

Hemophilia B (HB) is an X-linked recessive disease caused by F9 gene mutation and functional coagulation factor IX (FIX) deficiency. Patients suffer from chronic arthritis and death threats owing to excessive bleeding. Compared with traditional treatments, gene therapy for HB has obvious advantages, especially when the hyperactive FIX mutant (FIX-Padua) is used. However, the mechanism by which FIX-Padua works remains ambiguous due to a lack of research models. Here, in situ introduction of F9-Padua mutation was performed in human induced pluripotent stem cells (hiPSCs) via CRISPR/Cas9 and single-stranded oligodeoxynucleotides (ssODNs). The hyperactivity of FIX-Padua was confirmed to be 364% of the normal level in edited hiPSCs-derived hepatocytes, providing a reliable model for exploring the mechanism of the hyperactivity of FIX-Padua. Moreover, the F9 cDNA containing F9-Padua was integrated before the F9 initiation codon by CRISPR/Cas9 in iPSCs from an HB patient (HB-hiPSCs). Integrated HB-hiPSCs after off-target screening were differentiated into hepatocytes. The FIX activity in the supernatant of integrated hepatocytes showed a 4.2-fold increase and reached 63.64% of the normal level, suggesting a universal treatment for HB patients with various mutations in F9 exons. Overall, our study provides new approaches for the exploration and development of cell-based gene therapy for HB. [ABSTRACT FROM AUTHOR]

Flavor, which mainly depends on volatile compounds, is an important index for evaluating the quality of clam sauce. This study investigated the volatile compounds in clam sauce prepared using four different methods and the influence of aroma characteristics. Fermenting a mixture of soybean koji and clam meat improved the flavor of the final product. Solid-phase microextraction (SPME) combined with gas chromatography-mass spectrometry (GC-MS) identified 64 volatile compounds. Nine key flavor compounds, namely, 3-methylthio-1-propanol, 2-methoxy-4-vinylphenol, phenylethyl alcohol, 1-octen-3-ol, α-methylene phenylacetaldehyde, phenyl-oxirane, 3-phenylfuran, phenylacetaldehyde, and 3-octenone, were selected using variable importance in projection (VIP). The results of the electronic nose and tongue detection of the aroma characteristics of the samples prepared by four different fermentation methods were consistent with those of GC-MS analysis. The clam sauce prepared by mixing soybean koji with fresh clam meat possessed better flavor and quality than that prepared via other methods. [ABSTRACT FROM AUTHOR]

As the market for polyethylene consumption continues to expand, the amount of waste polyethylene is also increasing. Modifying asphalt with waste polyethylene (PE) is economical and environmentally friendly. The low-temperature performance and storage stability of PE-modified asphalt has long been an insurmountable problem. The high vinyl acetate (VA) content of ethylene–vinyl acetate (EVA) and PE blended into asphalt can improve the compatibility of PE and asphalt. It compensates for the high VA content of EVA brought about by the lack of high-temperature resistance to permanent deformation but is still not conducive to the stable storage of PE at high temperatures. The effect of furfural extraction oil, a crosslinking (DCP) agent, a silicone coupling agent (KH-570), and calcium carbonate (CaCO3) on the rheological properties and compatibility of PE/EVA-modified asphalt was investigated in this study. The conventional physical properties of PE/EVA-modified asphalt were tested after introducing furfural extraction oil, DCP, KH570, and CaCO3 to determine the correlations of these materials. In addition, frequency sweep, multiple stress creep and recovery (MSCR), and linear amplitude sweep (LAS) were utilized to characterize the rheological properties and fatigue behavior. The results reveal that the addition of suitable ratios of furfural extract oil, DCP, KH-570, and CaCO3 to PE/EVA-modified asphalt produces a remarkable improvement in the viscoelastic characteristics and viscosity compared with PE/EVA-modified asphalt. Furthermore, fluorescence microscopy (FM) was utilized to evaluate the modification mechanism, which shows that PE/EVA undergoes significant crosslinking in asphalt, forming a three-dimensional network structure that dissolves in the asphalt. The storage stability of the PE-modified bitumen was fully determined, and its high-temperature rheology was substantially improved. [ABSTRACT FROM AUTHOR]

With the construction of the Power Internet of Things (PIoT) in full swing as well as the development of wireless communication technology, the deployment of wireless sensor networks is the key to the intelligent transformation of power systems. This paper proposes an asymmetric double-layer wireless sensor network coverage scheme for the intelligent substation. We conducted field measurements in the substation to record the received power at different locations, which were compared with the prediction results of the simulation model established by Winprop to verify the effectiveness of the simulation method. Based on the simulation platform, two different simulation models of substation scenarios are built, including a 220 kV outdoor substation and a 110 kV GIS room. The coverage of received power and line-of-sight (LOS) transmission are analyzed for various nodes of different layers. Several node distribution planning methods are proposed and proven to be feasible according to the simulation results. The wireless coverage scheme can provide useful references for the implementation of PIoT in substations. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) strongly contributes to the operation of the global carbon cycling, and topographical factors largely influence its spatial distribution. However, SOC distribution and its leading topographical impact factors in subtropical forest ecosystems (e.g., the Zhejiang Province in China) have received relatively limited attention from researchers. In this study, 255 forest soil samples were collected from the Zhejiang Province to quantify the spatial variation in SOC and impact factors in subtropical forests. The SOC contents over soil profiles were 35.95 ± 22.58 g/kg, 20.98 ± 15.26 g/kg, and 13.77 ± 11.28 g/kg at depths of 0–10 cm, 10–30 cm, and 30–60 cm, respectively. The coefficient variations at different depths were 62.81% (0–10 cm), 72.74% (10–30 cm), and 81.92% (30–60 cm), respectively. SOC content shows a moderate intensity variation in the Zhejiang Province. The nugget coefficients of the SOC content for the three depths were 0.809 (0–10 cm), 0.846 (10–30 cm), and 0.977 (30–60 cm), respectively. Structural factors mainly influence SOC content. SOC content is positively correlated with elevation and slope, and negatively correlated with slope position (p < 0.05). However, the SOC content was negatively correlated with slope in mixed coniferous and broad-leaved forest. The distribution of the SOC content was relatively balanced between different slope positions. However, the differences became obvious when forest types were distinguished. Topographical factors affected the SOC content differently: elevation > slope > slope position. Slope becomes the main influencing factor in 30–60 cm soil. Forest type significantly influenced the SOC content but with a low statistical explanation compared to topographical factors. Topography has different effects on SOC of different forest types in subtropical forests. This reminds us that in future research, we should consider the combination of topography and forest types. [ABSTRACT FROM AUTHOR]

Subtropical forests play an important role in the global carbon cycle and climate change mitigation. In order to understand the effects of climate factors on soil carbon in subtropical forest ecosystems, it is necessary to make full use of carbon sequestration potential. Soil organic carbon (SOC) and soil alkali-hydrolyzed nitrogen (SAN) were tested in 255 plots of subtropical forests in Zhejiang Province, and their forest reserves from 2020 in Zhejiang Province were compared with those from 2010. The results showed that SOC content significantly increased, but SAN content decreased over those ten years. Combined with random forest (RF) and correlation analysis, the contribution of different climate factors (temperature, precipitation, etc.) to soil carbon storage was analyzed, and the main driving factors were evaluated. The RF model explained that winter (December to February) and spring (March to May) were the most dominant drivers to the 0–10 cm and 10–30 cm increases in SOC. There was a significant positive correlation between precipitation and SOC accumulation (0–30 cm) during winter and spring. The minimum temperatures in summer (June to August) and autumn (September to November) were negatively correlated with SOC accumulation (0–30 cm). Increasing the precipitation or irrigation (cloud seeding) in winter could improve the carbon sequestration capacity of subtropical forest soils. This study provides a new perspective on the sensitivity and potential response of the carbon cycle to climate change in subtropical forest ecosystems. [ABSTRACT FROM AUTHOR]