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Background: Lymphadenectomy plays a crucial role in the surgical management of early- stage esophageal cancer. However, few studies have examined lymphadenectomy outcomes in advanced stages, particularly in patients who initially underwent concurrent chemoradiation therapy. This retrospective study investigates the effect of lymphadenectomy in patients diagnosed with AJCC 8th-edition clinical stage III esophageal squamous cell carcinoma who received concurrent preoperative chemoradiation. Methods: Data from 1994 to 2023 were retrieved from our retrospective database. All patients underwent a uniform evaluation and treatment protocol, including preoperative concurrent chemoradiation therapy comprising cisplatin and 5-fluorouracil, followed by esophagectomy. The analysis encompassed clinical T and N stages, tumor location, tumor grade, pathological T and N stages, pathological stage, and the extent of lymph node dissection. Overall survival, “Free-To-Recurrence”, and disease-free survival were assessed via Kaplan–Meier survival curves and the Cox regression model for multivariate analysis. Results: The dataset was stratified into two groups according to extent of lymph node dissection, with one group having Conclusions: The quantity of lymphadenectomy is a crucial determinant for patients with AJCC 8th-edition clinical stage III esophageal squamous cell carcinoma receiving preoperative concurrent chemoradiation.

Analyzing data from a survey of U.S. adults (N = 509), our study examines the role of partisan media in (a) shaping people’s perceptions of the Black Lives Matter (BLM) movement, (b) producing an echo-chamber effect, influencing users to believe that other people have an opinion similar to their own, and (c) creating a polarized public opinion environment, where people hold more extreme perspectives on BLM. Left-wing media consumption was positively correlated with favorable perceptions of BLM, while right-wing media consumption had a negative correlation, suggesting that partisan media could influence users’ own perceptions of BLM. Also, left-wing media consumption was positively correlated with the belief that others held favorable views of BLM, while right-wing media consumption was negatively correlated with such a belief, suggesting that partisan media could affect one’s beliefs about others’ perceptions. Supporting its role in producing an echo-chamber effect, frequent right-wing media usage was associated with a smaller difference between one’s own views and their assessment of others’ views regarding BLM. Finally, there was a positive correlation between left-wing media consumption and having extreme perceptions of BLM, suggesting that the use of left-wing media could play a role in creating a polarized public opinion climate.

Ethyl acetate is a critical medical indicator for detecting certain types of cancer. However, at present, available sensitive materials often exhibit drawbacks, such as high operating temperatures and poor responses to low concentrations of ethyl acetate. In this study, a ZnO nanorod sensing material was prepared using high-temperature annealing and a hydrothermally synthesized metal-organic framework (MOF) as a template. Au nanodots (AuNDs) were subsequently modified on the ZnO nanorods using an in situ ion reduction, which provided a better dispersion of Au nanodots compared with that obtained using the common reductant method. A variety of characterization methods indicate that the highly dispersed AuNDs, which possess a high catalytic activity, were loaded onto the surface as active centers, leading to a significant augmentation in the adsorption of oxygen on the surface compared with the original ZnO material. Consequently, the AuND@ZnO material exhibited heightened responsiveness to ethyl acetate at a lower operating temperature. The Au@ZnO-based sensor has a response rate (Ra/Rg) of 41.8 to 20 ppm ethyl acetate gas at 140 °C, marking a 17.4-fold increase compared with that of the original material. Due to its low power consumption and high responsiveness, AuND@ZnO is a promising candidate for the detection of ethyl acetate gas in medical applications.

Aiming to address the problem of underactuated unmanned surface vehicles (USVs) performing fixed-point operations at sea without dynamic positioning control systems, this paper introduces an original approach to positioning control: the virtual anchor control method. This method is applicable in environments with currents that change slowly and does not require prior knowledge of current information or vessel motion model parameters, thus offering convenient usability. This method comprises four steps. First, a concise linear motion model with unknown disturbances is proposed. Then, a motion planning law is designed by imitating underlying principles of ship anchoring. Next, an adaptive disturbance observer is proposed to estimate uncertainties in the motion model. In the last step, based on the observer, a sliding-mode method is used to design a heading control law, and a thrust control law is also designed by applying the Lyapunov method. Numerical simulation experiments with significant disturbances and tidal current variations are conducted, which demonstrate that the proposed method has a good control effect and is robust.

The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face challenges in multi-scene and multi-temporal water body detection, including the diverse variations in water body shapes and sizes that complicate detection; the complexity of land cover types, which easily leads to false positives and missed detections; the high cost of acquiring high-resolution images, limiting long-term applications; and the lack of effective handling of multi-temporal data, making it difficult to capture the dynamic changes in water bodies. To address these challenges, this study proposes a novel network for multi-scene and multi-temporal water body detection based on spatiotemporal feature extraction, named TSAE-UNet. TSAE-UNet integrates convolutional neural networks (CNN), depthwise separable convolutions, ConvLSTM, and attention mechanisms, significantly improving the accuracy and robustness of water body detection by capturing multi-scale features and establishing long-term dependencies. The Otsu method was employed to quickly process Sentinel-1A and Sentinel-2 images, generating a high-quality training dataset. In the first experiment, five rectangular areas of approximately 37.5 km2 each were selected to validate the water body detection performance of the TSAE-UNet model across different scenes. The second experiment focused on Jining City, Shandong Province, China, analyzing the monthly water body changes from 2020 to 2022 and the quarterly changes in 2022. The experimental results demonstrate that TSAE-UNet excels in multi-scene and long-term water body detection, achieving a precision of 0.989, a recall of 0.983, an F1 score of 0.986, and an IoU of 0.974, significantly outperforming FCN, PSPNet, DeepLabV3+, ADCNN, and MECNet.

The collapse of the World Trade Center (WTC) buildings in New York City generated a large plume of dust and smoke. WTC dust contained human carcinogens including metals, asbestos, polycyclic aromatic hydrocarbons (PAHs), persistent organic pollutants (POPs, including polychlorinated biphenyls (PCBs) and dioxins), and benzene. Excess levels of many of these carcinogens have been detected in biological samples of WTC-exposed persons, for whom cancer risk is elevated. As confirmed in this structured literature review (n studies = 80), all carcinogens present in the settled WTC dust (metals, asbestos, benzene, PAHs, POPs) have previously been shown to be associated with DNA methylation dysregulation of key cancer-related genes and pathways. DNA methylation is, therefore, a likely molecular mechanism through which WTC exposures may influence the process of carcinogenesis.

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field.

Insulator and defect detection is a critical technology for the automated inspection of transmission and distribution lines within smart grids. However, the development of a lightweight, real-time detection platform suitable for deployment on drones faces significant challenges. These include the high complexity of existing algorithms, limited availability of UAV images, and persistent issues with false positives and missed detections. To address this issue, this paper proposed a lightweight drone-based insulator defect detection method (LDIDD) that integrates data augmentation and attention mechanisms based on YOLOv8. Firstly, to address the limitations of the existing insulator dataset, data augmentation techniques are developed to enhance the diversity and quantity of samples in the dataset. Secondly, to address the issue of the network model’s complexity hindering its application on UAV equipment, depthwise separable convolution is incorporated for lightweight enhancement within the YOLOv8 algorithm framework. Thirdly, a convolutional block attention mechanism is integrated into the feature extraction module to enhance the detection of small insulator targets in aerial images. The experimental results show that the improved network reduces the computational volume by 46.6% and the mAP stably maintains at 98.3% compared to YOLOv8, which enables the implementation of a lightweight insulator defect network suitable for the UAV equipment side without affecting the detection performance.

The water environment of plain river networks can be self-cleaning to a certain extent, but if the wastewater load exceeds a certain threshold, it can disturb the natural balance and cause water pollution. This underlines the importance of water pollution control measures. However, the development of water pollution control measures requires a large number of hydrological and hydrodynamic parameters and the establishment of corresponding relationships through modelling. Therefore, this study mainly used the Infoworks ICM model to construct a detailed hydrological–hydrodynamic water environment analysis model for the Yundong area of Baoying County, Yangzhou City, China, screened the main pollution source areas and pollution time periods of the typical rivers in the study area, and proposed effective improvement measures according to the actual situation of the study area. The results show that after the synergistic effect of multiple measures, the water quality can reach the Class III standard (GB3838-2002). This study can provide a reference for the water environment management and improvement of the plain river network and has good application prospects.

The application of organic manure is an effective way to develop sustainable agriculture. However, the application of organic manure may be associated with a potential risk of heavy metal pollution for soil and crops. In this study, the effects of organic cow manure (T1) (as base fertilizer), organic pig manure (T2) (as base fertilizer) and chemical fertilizer (T3) on winter wheat grain yields, grain quality, heavy metal concentrations and heavy metal bioconcentration factors (BCFs) in a soil–wheat system were studied from November 2021 to June 2023. The results showed that the winter wheat grain yields in the T1 and T2 treatments were lower than those in the T3 treatment by 2.57–38.0% and 10.5–25%, respectively. There were no significant differences in quality indexes of winter wheat grain among different fertilizer treatments. The concentrations of heavy metals in topsoil and winter wheat were 0.12–76.11 μg/g and 0.01–43.25 μg/g, respectively. The BCFs of heavy mental in the soil–wheat grain system was 0–2.92. In general, there were no significant differences in heavy metals’ concentrations in topsoil and wheat grain among different fertilizer treatments. In summary, compared with chemical fertilizer, the short-term application of organic manures had no significant effect on heavy metals concentrations in topsoil and wheat.

The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher’s exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10−4 ± 3.06 × 10−5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10−1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data.

Prosthetic heart valves (PHVs) are employed to replace the diseased native valve as a treatment of severe aortic valve disease. This study aimed to evaluate the effect of curvature of the belly curve on valve performance, so as to support a better comprehension of the relationship between valve design and its performance. Five PHV models with different curvatures of the belly curve were established. Iterative implicit fluid–structure interaction simulations were carried out, analyzing in detail the effect of belly curvature on the geometric orifice area (GOA), coaptation area (CA), regurgitant fraction (RF), leaflet kinematics and stress distribution on the leaflets. Overall, GOA and CA were negatively and positively related to the curvature of the belly curve, respectively. Nevertheless, an excessive increase in curvature can lead to incomplete sealing of free edges of the valve during its closure, which resulted in a decrease in CA and an increase in regurgitation. The moderate curvature of the belly curve contributed to reducing RF and fluttering frequency. Valves with small curvature experienced a significantly higher frequency of fluttering. Furthermore, all stress concentrations intensified with the increase in the curvature of the belly curve. The valve with moderate curvature of the belly curve strikes the best compromise between valve performance parameters, leaflet kinematics and mechanical stress. Considering the different effects of the curvature of belly curve on valve performance parameters, the PHV design with variable curvature of belly curve may be a direction towards valve performance optimization.

In recent years, an increasing number of studies have begun to use conversational data in spontaneous speech to estimate cognitive function in older people. The targets of spontaneous speech with older people used to be physicians and licensed psychologists, but it is now possible to have conversations with fully automatic AI agents. However, it has not yet been clarified what difference there is in conversational communication with older people when the examiner is a human or an AI agent. This study explored the psychological burden experienced by elderly participants during cognitive function assessments, comparing interactions with human and AI conversational partners. Thirty-four participants, averaging 78.71 years of age, were evaluated using the Mini-Mental State Examination (MMSE), the Visual Analogue Scale (VAS), and the State-Trait Anxiety Inventory (STAI). The objective was to assess the psychological impact of different conversational formats on the participants. The results indicated that the mental strain, as measured by VAS and STAI scores, was significantly higher during the MMSE sessions compared to other conversational interactions (p < 0.01). Notably, there was no significant difference in the mental burden between conversations with humans and AI agents, suggesting that AI-based systems could be as effective as human interaction in cognitive assessments.

Newcastle disease virus (NDV) is a highly pathogenic avian infectious disease agent and also a promising oncolytic virus with broad application prospects. The Endosomal Sorting Complex Required for Transport (ESCRT) machinery has been increasingly recognized for its crucial role in the life cycles of enveloped viruses, influencing processes such as viral entry, replication, and budding. In this study, we employed an RNA interference screening approach to identify key ESCRT components that regulate NDV replication in tumor cells. qPCR, immunofluorescence, and Western blot assays demonstrated that knockdown of HRS, CHMP4A, CHMP4B, and CHMP4C significantly impaired NDV replication in HeLa cells, with HRS exhibiting the most pronounced inhibitory effect. Additionally, HRS knockout significantly inhibited viral budding and suppressed NDV-induced cell death in HeLa cells. Notably, NDV infection was shown to significantly upregulate HRS gene and protein expression in a time-dependent manner. In conclusion, this study systematically identifies critical ESCRT components involved in NDV replication within tumor cells, with a particular focus on the role of HRS in promoting NDV’s replication by promoting viral budding, offering new insights for the development of NDV-based oncolytic therapies.

With the prohibition of antibiotics in animal feed, the livestock industry faces significant challenges, including increased morbidity and mortality rates and reduced farming efficiency. Developing green, natural, and safe antibiotic alternatives has become a research hotspot. This study evaluated the effects of octapeptin as a feed additive on growth performance, diarrhea incidence, serum biochemistry, serum immune factors, and gut microbiota of weaned piglets. Seventy-two weaned piglets were randomly assigned to three groups based on body weight and sex, with each group receiving different dietary treatments: a negative control group (CON, basal diet), a positive control group (MC, basal diet + 5 mg/kg Microcin C7), and an octapeptin supplement group (OP, basal diet + 40 mg/kg octapeptin). After 28 days of feeding experimental diets, the results demonstrated that supplementing the diet of weaned piglets with octapeptin significantly improved the feed conversion ratio compared to the control group (p < 0.05) over the entire experimental period. Furthermore, a reduction in diarrhea incidence was observed during the late nursery period (14–28 d), resulting in an overall improvement in diarrhea compared to the other two groups (p < 0.01). Serum biochemical analysis results revealed a trend towards decreased alanine aminotransferase level in the octapeptin group, with no significant differences in other indicators, suggesting potential improvements in liver function without causing liver damage. In addition, compared to the control group, octapeptin enhanced mucosal immunity by decreasing TNF-α level (p < 0.05). Fecal microbiota analysis results showed a significant increase in beneficial bacteria such as Collinsella and Olsenella in the octapeptin group compared to the other two groups (p < 0.05), indicating a positive impact on gut health. These findings supported the potential of octapeptin as an alternative to antibiotic growth promoters in weaned piglets’ diets.

In response to the complex working conditions and poor driving stability of high-clearance self-propelled sprayers, a nonlinear model of the chassis power system was established based on the independently controllable torque of each wheel of the developed electric sprayer. A layered-architecture chassis drive control strategy was formulated, and a stability control framework comprising an instability judgment module, an upper controller, and a lower controller was constructed based on the analysis of the impact of the centroid slip angle, the yaw rate, and the wheel slip rate on driving stability. An ideal reference model was established based on the seven-degree-of-freedom model of the sprayer, and the current state of the sprayer body was determined using the instability judgment module. A drive anti-slip controller and a yaw moment controller based on fuzzy PID theory and sliding mode control theory were designed. Additionally, an optimal torque distribution algorithm was developed based on tire characteristics to rationally allocate drive torque to each wheel, ensuring the stability of the sprayer during operation. Simulation tests were conducted using MATLAB/Simulink to evaluate the sprayer under four different driving conditions during transport and field operations. The test results showed that the “SMC + optimal distribution” control method in the chassis stability control strategy reduced the maximum deviations of the yaw rate and centroid slip angle by an average of 89.5% and 13.6%, respectively, compared to no control. The wheel slip rate during straight driving was well maintained at around 15%, enhancing the driving stability of the sprayer.

Providing older people with quality long-term care (LTC) contributes to the Sustainable Development Goal (SDG) 3—Good Health and Well-being. Person-Centered Care (PCC) is the optimal approach that enhances the quality of life for older adults residing in LTC facilities. This study develops the Long-Term Care Unit Environment Assessment Tool (LTCU-EAT) to assess how LTC environments support PCC goals. The study was conducted in five steps. (1) Reviewing and revising assessment items based on existing literature; (2) Preliminary assessment and protocolling with expert opinions; (3) On-site assessments conducted by two raters among 21 LTC units across 13 facilities; (4) Reliability test of assessment items; (5) Scoring and reliability test of LTC unit samples. The LTCU-EAT, comprising 89 items distributed across 12 subscales within four themes, was developed based on a summary of 14 PCC goals. A total of 83 items (93.26%) demonstrated strong inter-rater reliability. The assessments of all LTC unit samples displayed good criterion-related validity. The LTCU-EAT is a valuable tool for conducting post-occupancy evaluation (POE) of LTC facilities, systematically evaluating the level of environment support for PCC, and providing empirical evidence for future research, policy and practice.

Concrete is widely used in large-scale hydraulic structures, which often need to undergo multiple pouring operations due to construction demands, temperature-induced shrinkage phenomena, and structural reinforcement and repair, which in turn creates the bonding surface of old and new concrete. Therefore, it is of great significance to study the strength of the bond between old and new concrete. We designed and completed a split tension test to investigate the evolution of the tensile strength of concrete joint surfaces with age at early ages. The test groups included three sets of matured concrete aged 3 days, 5 days, and 10 days, respectively. Within each group, multiple test specimens were prepared with different ages of the interface, ranging from 1 day to 15 days. The test utilized ready-mixed concrete materials from a commercial batching plant. To ensure uniform and standard roughness of the interface between new and matured concrete, a method employing non-destructive surface roughening tapes was employed. During the test, each specimen was subjected to tensile failure at its corresponding age. The maximum load applied by the testing machine at the point of tensile failure was recorded for each age group. Based on the fundamental principles of material mechanics and relevant formulas, the tensile strength of the interface at different ages was determined for each test group. The obtained data were then used to fit a curve representing the relationship between the early-age tensile strength of concrete and its age, using MATLAB R2020b software. The results show that there is a small increase in the tensile strength of the bonding surface as the age of the old test blocks is increased. This experiment revealed the changing pattern of early-age tensile strength of concrete at the interface with age, providing a basis for accurately simulating the mechanical properties of the interface during numerical simulations. Then, based on the existing temperature-controlled simulation program, a simplified simulation and calculation method of concrete cracking is proposed to make it possible to determine the tensile cracking (vertical cracking) when the stress exceeds the standard. The validity is verified by simulation calculations of a simplified model, using the tensile strength curves obtained from the tests.

Facial keypoint detection technology faces significant challenges under conditions such as occlusion, extreme angles, and other demanding environments. Previous research has largely relied on deep learning regression methods using the face’s overall global template. However, these methods lack robustness in difficult conditions, leading to instability in detecting facial keypoints. To address this challenge, we propose a joint optimization approach that combines regression with heatmaps, emphasizing the importance of local apparent features. Furthermore, to mitigate the reduced learning capacity resulting from model pruning, we integrate external supervision signals through knowledge distillation into our method. This strategy fosters the development of efficient, effective, and lightweight facial keypoint detection technology. Experimental results on the CelebA, 300W, and AFLW datasets demonstrate that our proposed method significantly improves the robustness of facial keypoint detection.

Background: Congenital pneumonia is a common respiratory disease in newborns, often influenced by obstetric factors. Clinical diagnosis can be delayed, prompting interest in using systemic inflammatory indicators to predict various diseases. Objective: Our aim was to evaluate the predictive value of maternal systemic inflammatory indicators before delivery for congenital pneumonia in newborns. Methods: This retrospective study included full-term, singleton infants born at the Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University between January 2017 and December 2022. Infants admitted to the neonatal department within 48 h of birth were divided into two groups: 46 with clinical congenital pneumonia (the observation group) and 65 without congenital pneumonia (the control group). Maternal peripheral blood, complete blood cell count, and general condition within 72 h before delivery, as well as neonatal admission data were recorded. Systemic inflammatory indicators were assessed. Receiver operating characteristic curves were used to evaluate the predictive value of the maternal systemic inflammatory indicators before delivery for congenital pneumonia. A prediction model for neonatal pneumonia was constructed by combining systemic inflammatory indicators before delivery with logistic regression. The association between this prediction model and the prognosis of neonatal congenital pneumonia was examined. Results: Maternal systemic inflammatory indicators before delivery as predictive markers for congenital pneumonia and the regression model jointly constructed by NLR, MLR, SII, SIRI, and PIV before delivery predicted the occurrence of congenital pneumonia better. Maternal systemic inflammatory indicators correlated with the severity of congenital pneumonia in neonates. Conclusions: Maternal systemic inflammatory indicators before delivery have predictive value for congenital pneumonia in neonates, aiding early identification and treatment.

Located at an average elevation of approximately 2000 m, the Minjiang River Basin (MJB), a key tributary of the Upper Yangtze River, straddles the Western Sichuan Plateau and the Sichuan Basin. Vegetation here is crucial for human life, providing oxygen and energy. However, the influence of climatic variables, human activities, and rugged terrain on vegetation vitality is still debated. This study mainly leverages data from the Normalized Difference Vegetation Index (NDVI), meteorological stations data, and land use data. Analytical techniques include trend analysis, partial correlation coefficient analysis (PCC), principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM). Results indicate a stable upward trend in vegetation growth with minimal fluctuations, with a growth rate of 0.95 × 10−3/a (p < 0.01). PCC analysis shows a positive correlation between NDVI and key climatic elements in over 60% of the area. The areas with significant vegetation growth had the highest average PCC. PCA and PLS-SEM identify temperature and precipitation as primary growth drivers, while elevation and land use intensity hinder growth. The MJB landscape reveals thresholds and tipping points, with specific temperature and precipitation benchmarks varying by elevation, delineating the boundary between flourishing vegetation and growth inhibition.

This study proposes a high-resolution rotation-measuring system for miniaturized MEMS ultrasonic motors using tunneling magnetoresistance (TMR) sensors for the first time. Initially, the architecture and principle of the rotation-measuring system are described in detail. Then, the finite element simulation is implemented to determine the miniaturized permanent magnet’s residual magnetization, dimensions, and TMR sensor position. Finally, the experiments are implemented to evaluate the performance. Using calibration based on a high-precision servo motor, it is found that the relationship between the output and rotational angle is highly linear and immune to the rotor’s out-of-plane movement. Meanwhile, the angle-detecting resolution is higher than 0.1°. After the calibration, the continuous rotation of the MEMS ultrasonic motor is tested. It is found that the angle testing result varies with a period close to 360°, which indicates that the rotation-measuring system has successfully detected the motor’s rotation.

Fiber length (FL) and strength (FS) are the core indicators for evaluating cotton fiber quality. The corresponding stages of fiber elongation and secondary wall thickening are of great significance in determining FL and FS formation, respectively. QTL mapping and high-throughput sequencing technology have been applied to dissect the molecular mechanism of fiber development. In this study, 15 cotton chromosome segment substitution lines (CSSLs) with significant differences in FL and FS, together with their recurrent parental Gossypium hirsutum line CCRI45 and donor parent G. barbadense line Hai1, were chosen to conduct RNA-seq on developing fiber samples at 10 days post anthesis (DPA) and 20 DPA. Differentially expressed genes (DEGs) were obtained via pairwise comparisons among all 24 samples (each one with three biological repeats). A total of 969 DEGs related to FL-high, 1285 DEGs to FS-high, and 997 DEGs to FQ-high were identified. The functional enrichment analyses of them indicated that the GO terms of cell wall structure and ROS, carbohydrate, and phenylpropanoid metabolism were significantly enriched, while the GO terms of glucose and polysaccharide biosynthesis, and brassinosteroid and glycosylphosphatidylinositol metabolism could make great contributions to FL and FS formation, respectively. Weighted gene co-expressed network analyses (WGCNA) were separately conducted for analyzing FL and FS traits, and their corresponding hub DEGs were screened in significantly correlated expression modules, such as EXPA8, XTH, and HMA in the fiber elongation and WRKY, TDT, and RAC-like 2 during secondary wall thickening. An integrated analysis of these hub DEGs with previous QTL identification results successfully identified a total of 33 candidate introgressive DEGs with non-synonymous mutations between the Gh and Gb species. A common DEG encoding receptor-like protein kinase 1 was reported to likely participate in fiber secondary cell thickening regulation by brassionsteroid signaling. Such valuable information was conducive to enlightening the developing mechanism of cotton fiber and also provided an abundant gene pool for further molecular breeding.

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar co-ordinate transformation method was employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) was utilized to classify these feature maps. The model was validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieved excellent classification performance. Experimental results on three public HSI datasets demonstrated that the proposed method accurately recognized different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.

Sarcopenia is an age-related syndrome characterized by the loss of skeletal muscle mass and function. Community screening, commonly used in early diagnosis, usually lacks features such as real-time monitoring, low cost, and convenience. This study introduces a promising approach to sarcopenia screening by dynamic plantar pressure monitoring. We propose a wearable flexible-printed piezoelectric sensing array incorporating barium titanate thin films. Utilizing a flexible printer, we fabricate the array with enhanced compressive strength and measurement range. Signal conversion circuits convert charge signals of the sensors into voltage signals, which are transmitted to a mobile phone via Bluetooth after processing. Through cyclic loading, we obtain the average voltage sensitivity (4.844 mV/kPa) of the sensing array. During a 6 m walk, the dynamic plantar pressure features of 51 recruited participants are extracted, including peak pressures for both sarcopenic and control participants before and after weight calibration. Statistical analysis discerns feature significance between groups, and five machine learning models are employed to screen for sarcopenia with the collected features. The results show that the features of dynamic plantar pressure have great potential in early screening of sarcopenia, and the Support Vector Machine model after feature selection achieves a high accuracy of 93.65%. By combining wearable sensors with machine learning techniques, this study aims to provide more convenient and effective sarcopenia screening methods for the elderly.

With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we still face a series of challenges, including repetitive fragments, inverted repeats, low sequencing coverage, and the limitations of sequencing technologies. These challenges lead to unknown gaps in small genomes, hindering complete genome assembly. Although there are many existing assembly software options, they do not fully utilize the potential of artificial intelligence technologies, resulting in limited improvement in gap filling. Here, we propose a novel method, DLGapCloser, based on deep learning, aimed at assisting traditional tools in further filling gaps in small genomes. Firstly, we created four datasets based on the original genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla. To further extract effective information from the gene sequences, we also added homologous genomes to enrich the datasets. Secondly, we proposed the DGCNet model, which effectively extracts features and learns context from sequences flanking gaps. Addressing issues with early pruning and high memory usage in the Beam Search algorithm, we developed a new prediction algorithm, Wave-Beam Search. This algorithm alternates between expansion and contraction phases, enhancing efficiency and accuracy. Experimental results showed that the Wave-Beam Search algorithm improved the gap-filling performance of assembly tools by 7.35%, 28.57%, 42.85%, and 8.33% on the original results. Finally, we established new gap-filling standards and created and implemented a novel evaluation method. Validation on the genomes of Saccharomyces cerevisiae, Schizosaccharomyces pombe, Neurospora crassa, and Micromonas pusilla showed that DLGapCloser increased the number of filled gaps by 8.05%, 15.3%, 1.4%, and 7% compared to traditional assembly tools.

In the present work, inexpensive and commercially available diethyltriamine pentaacetic acid (DTPA) was used as an initiator to catalyze the N-formylation reaction of CO2 with amines via the construction of C-N bonds in the presence of xanthone as the photosensitizer and PhSiH3 as the reducing agent. After a systematic study of various factors, the optimal conditions for the photocatalytic reaction were obtained: 2.5 mmol of amine, 2.5 mmol of PhSiH3, 10 mol% of DTPA, 20 mol% of xanthone, 1 mL of dimethylsulfoxide (DMSO), atmospheric pressure, and 35 W UV lamp irradiation for 48 h. Under the optimal conditions, the catalyst system afforded high performance for the N-formylation of amines (primary and secondary amines) and CO2, and the yields of the N-formylated products of dialkylamines were above 70%. Further studies exhibit that the catalytic system has a wide scope of substrate applications. For various alicyclic secondary amines, heterocyclic secondary amines, aliphatic primary amines, and aromatic primary amines, the corresponding N-formylation products can be obtained efficiently. In addition, the catalyst can be recycled by simple precipitation and filtration. After five cycles of recycling, there was no significant change in the catalytic and structural properties of DTPA. Finally, a possible reaction mechanism is proposed.

The reduction of CO2 is an important method to produce chemicals such as methanol, formic acid, formaldehyde, etc. In general, the reduction of CO2 is carried out at high temperatures and pressures with precious metals as catalysts, which is not favorable for industrial procedures. Thus, it will be very useful if researchers can find cost-effective catalysts for industrial application in CO2 reduction. In this work, commercially available ethylenediaminetetraacetic acid (EDTA) was tested as a cheap, non-toxic, and recyclable catalyst to initiate the N-carbonylation reaction of CO2 with amines. After screening various reaction parameters, including temperature, pressure, time, solvent, and reducing agent, the optimal reaction conditions were obtained: 80 °C, 2 MPa, 6 h, 50 mmol% catalyst dosage, 1 mL DMSO, and 1:1 molar ratio of amine to reducing agent. Notably, further studies confirmed that EDTA could also be effective for N-formylation even under ambient conditions (0.1 MPa and room temperature). The suitability of the catalyst for 26 kinds of substrates (including aliphatic amines, aromatic amines, and alicyclic amines) and its reusability were also investigated, with satisfactory results. Scale-up research has been performed effectively with a high conversion of amine (83%) to obtain the mono-formylated product selectively. Finally, the mechanism of the reaction between amine and CO2 has been proposed via control experiments and compared with results in the literature.

Increasing array gains is one of the keys to improving underwater targets’ detection capabilities. This paper presents a high-gain approach for a vector hydrophone vertical array (VHVA) that combines white noise gain constraint (WNGC) with vector joint processing to preserve strong robustness and provide noticeable gains. Firstly, this approach treats the VHVA as four independent sub-arrays and achieves sub-array supergains by decorrelating noise using WNGC. The beam outputs of the four sub-arrays are then equated to a single-vector hydrophone, the combination gain of which is obtained by leveraging the strong signal correlation and the weak noise correlation between the sound pressure and the particle velocity. Lastly, the sub-array supergain and combination gain are superposed to provide the spatial gain of the VHVA. It is also summarized that low-frequency signals, coherent noise, accurate elevation-angle estimation, and stable phase differences are required for the VHVA to achieve supergain. The simulation and sea trial confirm that this approach can effectively boost the array gain. The maximum spatial gain in the experiment was increased by 9 dB at a range twice the sea’s depth while operating at a low frequency. This method shows enormous potential for improving the performance of deep-sea target detection.

Cotton is highly sensitive to potassium, and Xinjiang, China’s leading cotton-producing region, faces a severe challenge due to reduced soil potassium availability. Biofertilizers, particularly potassium-solubilizing rhizobacteria (KSR), convert insoluble potassium into plant-usable forms, offering a sustainable solution for evergreen agriculture. This study isolated and characterized KSR from cotton, elucidated their potassium solubilization mechanisms, and evaluated the effects of inoculating KSR strains on cotton seedlings. Twenty-three KSR strains were isolated from cotton rhizosphere soil using modified Aleksandrov medium. Their solubilizing capacities were assessed in a liquid medium. Strain A10 exhibited the highest potassium solubilization capacity (21.8 ppm) by secreting organic acids such as lactic, citric, acetic, and succinic acid, lowering the pH and facilitating potassium release. A growth curve analysis and potassium solubilization tests of A10 under alkali stress showed its vigorous growth and maintained solubilization ability at pH 8–9, with significant inhibition at pH 10. Furthermore, 16S rRNA sequencing identified strain A10 as Pseudomonas aeruginosa. Greenhouse pot experiments showed that inoculating cotton plants with strain A10 significantly increased plant height and promoted root growth. This inoculation also enhanced dry biomass accumulation in both the aerial parts and root systems of the plants, while reducing the root–shoot ratio. These results suggest that Pseudomonas aeruginosa A10 has potential as a biofertilizer, offering a new strategy for sustainable agriculture.

The gut microbiota is closely associated with human health, and alterations in gut microbiota can influence various physiological and pathological activities in the human body. Therefore, microbiota regulation has become an important strategy in current disease treatment, albeit facing numerous challenges. Nanomaterials, owing to their excellent protective properties, drug release capabilities, targeting abilities, and good biocompatibility, have been widely developed and utilized in pharmaceuticals and dietary fields. In recent years, significant progress has been made in research on utilizing nanomaterials to assist in regulating gut microbiota for disease intervention. This review explores the latest advancements in the application of nanomaterials for microbiota regulation and offers insights into the future development of nanomaterials in modulating gut microbiota.

Preventing eutrophication requires a deep understanding of nutrient sources and migration processes. The Guanshan River in the Danjiangkou Reservoir was selected as a typical agricultural basin. River water, sediment, and soil samples were collected to determine nitrogen (N) and phosphorus (P) contents and forms. Multivariate statistical analysis, buffer analysis, and extreme gradient boosting regression (XGBoost) were used to investigate the nutrient sources, the sources’ scale effects, and migration. The results showed that the exogenous sources of river nutrients were fertilizer (provided phosphate (PO4-P) and ammonium (NH4-N)), domestic wastewater (provided nitrate (NO3-N) and organic nitrogen), and natural soil and rock leaching (provided PO4-P and organic phosphorus). Fertilization within 300–1200 m and domestic wastewater discharge within 150 m of the river affected the contents of the river nutrients significantly (with R ranging between 0.40 and 0.73, p ≤ 0.01). The sediment was the N source and P sink of the overlying water. With NH4-N/PO4-P compound fertilization, the NO3-N and calcium-bound phosphorus (Ca-P) exhibited co-enrichment. Appropriately increasing NH4-N application could stimulate P biological uptake, thus inhibiting P emigration from agricultural soil under N-limited states. In conclusion, this study effectively recognized river nutrient sources and their scale impacts and also explored more effective fertilization strategies, which are beneficial for the optimized management of agricultural activities.

This study evaluated the properties of soybean oil/SBR reclaimed asphalt (SSRA). The optimal preparation method for SSRA was determined. Additionally, the feasibility of the optimal SSRA scheme was verified through asphalt mixture performance tests. With the soybean oil dosage enhanced, the penetration and low-temperature rheological performance of SSRA were improved. The incorporation of soybean oil lowered the softening point, viscosity, and rutting index of aged asphalt. The softening points of SBR-4%+Oil-7.5% and SBR-6%+Oil-7.5% were 79.4 °C and 82.9 °C, respectively. The stiffness modulus of SBR-6%+oil-10% decreased by 35.37%. When the soybean oil dosage was 10% and the SBR dosage was 6% (SBR-6%+oil-10%), the properties of RTFOT+PAV aged asphalt were restored to those of its original state. The splitting tensile strength ratio of the SBR-6%+oil-10% mixture was 89%, with a decrease of 1.5% compared to the original asphalt mixture. The SBR-6%+oil-10% mixture exhibited improved high-temperature and low-temperature service properties. The total deformation of the SBR-6%+oil-10% mixture decreased by 8.43%, while its dynamic stability increased by 22.21%. This degree of improvement compared to the original asphalt mixture was not significant. The rejuvenation of the aged asphalt and mixture performance can mainly be attributed to the soybean oil supplementing the lost lightweight components of the aged asphalt, while SBR supplemented the degraded polymers. Utilizing soybean oil as a rejuvenating asphalt agent facilitates waste material recycling. Furthermore, this study provides a new idea for the recycling of polymer-modified asphalt and reclaimed asphalt pavement.

Ensuring the harmonization between urbanization and water environment systems is imperative for fostering sustainable regional development in the future. With urban agglomerations and metropolitan areas increasingly dominating urbanization trends in China, it is crucial to explore the interdependent relationship between urbanization and the water environment. Such exploration holds significant implications for water resource management and the formulation of urbanization policies. This study utilizes a comprehensive index system encompassing urbanization and the water environment. It examines the coupled and coordinated spatial and temporal dynamics of these systems within the Chengdu-Chongqing Urban Agglomeration from 2011 to 2019. This analysis employs the Coupled Coordination Degree model alongside the spatial autocorrelation model. The results show that there is still much room for improving the urbanization development level and the water environment quality. During the study period, a nonlinear and nearly U-shaped evolutionary trajectory was observed between the two systems. The results suggest that there is a progression from basic to more advanced coordination between urbanization and water environment at the city cluster scale. Urbanization appears to generally lag behind the water environment in terms of coordination. At the municipal scale, there is a gradient in which some cities show better coordination compared to others. Spatially, the coupling and coordination of this region exhibited dual-core development characteristics centered around Chengdu and Chongqing. The region is in the transition stage towards a core-type networked and decentralized development mode, which has not yet formed an integrated pattern. This offers a theoretical and technical framework for harmonizing water environments and urbanization in similar regions globally.

The effect of moisture on the fracture resistance of asphalt concrete is a significant concern in pavement engineering. To investigate the effect of the water vapor concentration on the fracture properties of asphalt concrete, this study first designed a humidity conditioning program at the relative humidity (RH) levels of 2%, 50%, 80%, and 100% for the three types of asphalt concrete mixtures (AC-13C, AC-20C, and AC-25C).The finite element model was developed to simulate the water vapor diffusion and determine the duration of the conditioning period. The semi-circular bending (SCB) test was then performed at varying temperatures of 5 °C, 15 °C, and 25 °C to evaluate the fracture energy and tensile strength of the humidity-conditioned specimens. The test results showed that the increasing temperature and the RH levels resulted in a lower peak load but greater displacement of the mixtures. Both the fracture energy and tensile strength tended to diminish with the rising temperature. It was also found that moisture had a significant effect on the tensile strength and fracture energy of asphalt concrete. Specifically, as the RH level increased from 2% to 100% (i.e., the water vapor concentration rose from 0.35 g/m3 to 17.27 g/m3), the tensile strength of the three types of mixtures was reduced by 34.84% on average, which revealed that the water vapor led to the loss of adhesion and cohesion within the mixture. The genetic expression programming (GEP) model was developed to quantify the effect of water vapor concentrations and temperature on the fracture indices.

Pecan forests (Carya illinoinensis) are significant contributors to both food and oil production, and thrive in diverse soil environments, including coastal regions. However, the interplay between soil microbes and pecan forest health in coastal environments remains understudied. Therefore, we investigated soil bacterial and fungal diversity in coastal (Dafeng, DF) and inland (Guomei, GM) pecan plantations using high-throughput sequencing. The results revealed a higher microbial diversity in the DF plantation than in the GM plantation, significantly influenced by pH and edaphic factors. The dominant bacterial phyla were Proteobacteria, Acidobacteriota and Bacteroidota in the DF plantation, and Acidobacteriota, Proteobacteria, and Verrucomicrobiota in the GM plantation. Bacillus, Nitrospira and UTCFX1 were significantly more abundant bacterial genera in DF soil, whereas Candidatus Udaeobacter, HSB_OF53-F07 and ADurbBin063-1 were more prevalent in GM soil. Basidiomycota dominated fungal sequences in the GM plantation, with a higher relative abundance of Ascomycota in the DF plantation. Significant differences in fungal genus composition were observed between plantations, with Scleroderma, Hebeloma, and Naucoria being more abundant in DF soil, and Clavulina, Russula, and Inocybe in GM soil. A functional analysis revealed greater carbohydrate metabolism potential in GM plantation bacteria and a higher ectomycorrhizal fungi abundance in DF soil. Significantly positive correlations were detected between certain bacterial and fungal genera and pH and total soluble salt content, suggesting their role in pecan adaptation to coastal environments and saline–alkali stress mitigation. These findings enhance our understanding of soil microbiomes in coastal pecan plantations, and are anticipated to foster ecologically sustainable agroforestry practices and contribute to coastal marshland ecosystem management.

Negative self-evaluative emotions arise when an individual engages in behavior that is perceived as inadequate or inconsistent with personal or societal norms and values, leading to feelings of inadequacy, shame, and dissatisfaction with oneself. These emotions are a central motivating force for changing unhealthy behaviors. However, negative evaluative emotions may also direct individuals towards defensive reactions such as reactance and avoidance. This can cause negative self-evaluative emotions to be less effective in reducing unhealthy behavior. More importantly, empirical evidence is needed to explore strategies for enhancing the effectiveness of interventions. In this study, we used an online experiment with 100 student participants to examine if increasing self-affirmation can increase the effectiveness of negative self-evaluative emotions in reducing unhealthy food consumption. We found that negative self-evaluative emotions can significantly increase healthy food consumption. However, our analysis did not reveal a significant moderating impact of self-affirmation on the effectiveness of negative self-evaluative emotions in reducing unhealthy consumption. This is the first study to explore the moderating impact of self-affirmation on the effectiveness of negative self-evaluative emotions on health behavioral change, which opens new avenues for studying how to apply the combination of stimulating negative self-evaluative emotions and increasing self-affirmation to induce behavioral change regarding healthy diets and even a broader range of fields.

This paper proposes a novel censored autoregressive conditional Fréchet (CAcF) model with a flexible evolution scheme for the time-varying parameters, which allows deciphering tail risk dynamics constrained by price limits from the viewpoints of different risk preferences. The proposed model can well accommodate many important empirical characteristics of financial data, such as heavy-tailedness, volatility clustering, extreme event clustering, and price limits. We then investigate tail risk dynamics via the CAcF model in the price-limited stock markets, taking entropic value at risk (EVaR) as a risk measurement. Our findings suggest that tail risk will be seriously underestimated in price-limited stock markets when the censored property of limit prices is ignored. Additionally, the evidence from the Chinese Taiwan stock market shows that widening price limits would lead to a decrease in the incidence of extreme events (hitting limit-down) but a significant increase in tail risk. Moreover, we find that investors with different risk preferences may make opposing decisions about an extreme event. In summary, the empirical results reveal the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited stock markets, providing a new tool for financial risk management.

The unique structure of G4.0 PAMAM dendrimers allows a drug to be enclosed in internal spaces or immobilized on the surface. In the conducted research, the conditions for the formation of the active G4.0 PAMAM complex with doxorubicin hydrochloride (DOX) were optimized. The physicochemical properties of the system were monitored using dynamic light scattering (DLS), circular dichroism (CD), and fluorescence spectroscopy. The Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) method was chosen to determine the preferential conditions for the complex formation. The highest binding efficiency of the drug to the cationic dendrimer was observed under basic conditions when the DOX molecule was deprotonated. The decrease in the zeta potential of the complex confirms that DOX immobilizes through electrostatic interaction with the carrier’s surface amine groups. The binding constants were determined from the fluorescence quenching of the DOX molecule in the presence of G4.0 PAMAM. The two-fold way of binding doxorubicin in the structure of dendrimers was visible in the Isothermal calorimetry (ITC) isotherm. Fluorescence spectra and release curves identified the reversible binding of DOX to the nanocarrier. Among the selected cancer cells, the most promising anticancer activity of the G4.0-DOX complex was observed in A375 malignant melanoma cells. Moreover, the preferred intracellular location of the complexes concerning the free drug was found, which is essential from a therapeutic point of view.

Fatigue cracking is one of the primary distresses of asphalt pavements, which significantly affects the asphalt pavement performance. The fatigue behavior of the asphalt mixture observed in the laboratory test can vary depending on the type of fatigue test and the dimension and shape of the test specimen. The variations can make it difficult to accurately evaluate the fatigue properties of the field asphalt concrete. Accordingly, this study proposed a reliable method to evaluate the fatigue behavior of the asphalt field cores based on discrete element modeling (DEM). The mesoscopic geometric model was built using discrete element software PFC (Particle Flow Code) and CT scan images of the asphalt field cores. The virtual fatigue test was simulated in accordance with the semi-circular bending (SCB) test. The mesoscopic parameters of the contacting model in the virtual test were determined through the uniaxial compression dynamic modulus test and SCB test. Based on the virtual SCB test, the displacement, contact forces, and crack growth were analyzed. The test results show that the fatigue life simulated in the virtual test was consistent with that of the SCB fatigue test. The fatigue cracks in the asphalt mixture were observed in three stages, i.e., crack initiation, crack propagation, and failure. It was found that the crack propagation stage consumes a significant portion of the fatigue life since the tensile contact forces mainly increase in this stage.

This study delved into the larval development and the morphological and anatomical transformations that occur in the galls of chestnut trees (Castanea mollissima BL.) and are induced by the chestnut gall wasp Dryocosmus kuriphilus Yasumatsu (GWDK) across various stages: initial, growth, differentiation, maturity, and lignification. Chestnut galls in the five development stages were collected. Gall structural characteristics were observed with an anatomical stereomicroscope, and anatomical changes in galls were analyzed with staining and scanning electron microscope techniques. The chestnut gall wasp laid its eggs on young leaves and buds. Chestnut gall wasp parasitism caused plant tissues to form a gall chamber, with parenchyma, protective, and epidermal layers. The development of the gall structure caused by the infestation of the GWDK gall led to the weakening of the reactive oxygen species (ROS) elimination ability of the host. The accumulation of ROS led to cell wall peroxidation, resulting in structural damage and diminished host resistance, and the parenchyma layer exhibited significant nutrient supply and thickening. The thickness of the protective and epidermal layers varied notably across different growth stages. The oviposition of the chestnut gall wasp induced modifications in the original plant tissues, with gall formation being most favorable in young tissues, correlating with the maturity level of the host plant tissues. Variances in the internal structures of the galls primarily stemmed from nutrient supplementation, while those in the external structure were attributed to defensive characteristics. This research contributes a foundational understanding of gall development induced by the chestnut gall wasp in Chinese chestnut, offering valuable insights into the intricate interplay between insect infestation and plant physiology.

Remote sensing technology has been modernized by artificial intelligence, which has made it possible for deep learning algorithms to extract useful information from images. However, overfitting and lack of uncertainty quantification, high-resolution images, information loss in traditional feature extraction, and background information retrieval for detected objects limit the use of deep learning models in various remote sensing applications. This paper proposes a Bayes by backpropagation (BBB)-based system for scene-driven identification and information retrieval in order to overcome the above-mentioned problems. We present the Bayes R-CNN, a two-stage object detection technique to reduce overfitting while also quantifying uncertainty for each object recognized within a given image. To extract features more successfully, we replace the traditional feature extraction model with our novel Multi-Resolution Extraction Network (MRENet) model. We propose the multi-level feature fusion module (MLFFM) in the inner lateral connection and a Bayesian Distributed Lightweight Attention Module (BDLAM) to reduce information loss in the feature pyramid network (FPN). In addition, our system incorporates a Bayesian image super-resolution model which enhances the quality of the image to improve the prediction accuracy of the Bayes R-CNN. Notably, MRENet is used to classify the background of the detected objects to provide detailed interpretation of the object. Our proposed system is comprehensively trained and assessed utilizing the state-of-the-art DIOR and HRSC2016 datasets. The results demonstrate our system’s ability to detect and retrieve information from remote sensing scene images.

In order to address the issues of poor stability in vehicle speed and deteriorated spraying quality caused by changes in road slope and the decrease in overall mass due to liquid spraying, this study focuses on analyzing the structure and longitudinal dynamic characteristics of a 4WID high ground clearance self-propelled electric sprayer. By utilizing MATLAB/Simulink software, three subsystems, namely, the inverse longitudinal dynamics model, torque distribution model, and motor model, are established. The model takes into account the effects of longitudinal driving resistance, slope, and vehicle roll angle on the distribution of loads among the four wheels during slope driving. A seven-degrees-of-freedom dynamic model is developed. A hierarchical control structure is designed, incorporating an upper-level PID controller and a lower-level fuzzy PID controller, to control the overall system. The control algorithms are tailored to the specific characteristics of the sprayer’s operation, and simulation experiments are conducted under the corresponding operating conditions. Building upon this, a sensor-equipped experimental platform is set up in the self-propelled sprayer manufactured by the team in the preliminary stage. Real vehicle tests are conducted in two scenarios: transition transportation and field operations, with the evaluation of the overall vehicle speed serving as the performance metric to validate the correctness of the model and the control theory.

Capillary water, serving as a crucial intermediary between groundwater and crop root layer moisture, is important for both soil retention and crop utilization. To investigate the effect of mulched drip irrigation (MDI) on upward capillary water in cotton fields with different application years (0, 10, 14, 18, 20, and 24 years) in the saline–sodic region of Northwest China, an indoor soil column test (one-dimensional capillary water rise experiment) was conducted. The results showed that the wetting front transport law, capillary water recharge, and wetting front transport rate over time exhibited an increasing trend in the early stages of MDI application (10 and 14 years), peaking at 18 years of application, followed by a decreasing trend. The relationship between the capillary water recharge and rising height was fitted based on the Green–Ampt model, and their slopes reveal that 14 and 18 years of MDI application required the largest amount of water per unit distance, indicating an excellent water-holding capacity beneficial for plant growth. Conversely, 0 years required the smallest amount of water per unit distance. Based on the movement characteristics of upper capillary water, we confirmed that the MDI application years (0–18 years) improves soil infiltration capacity, while the long-term application years (18–24 years) reduces groundwater replenishment to the soil. Furthermore, the HYDRUS-1D model was employed to simulate the capillary water rise process and soil moisture distribution under different MDl application years. The results showed an excellent consistency with the soil column experiments, confirming the accuracy of HYDRUS-1D in simulating the capillary water dynamics in saline–sodic areas. The results would provide suggestions to achieve the sustainable development of long-term drip-irrigated cotton fields.

Ischemic stroke is a common cerebrovascular disease with high mortality, high morbidity, and high disability. Cerebral ischemia/reperfusion injury seriously affects the quality of life of patients. Luteolin-7-O-β-d-glucuronide (LGU) is a major active flavonoid compound extracted from Ixeris sonchifolia (Bge.) Hance, a Chinese medicinal herb mainly used for the treatment of coronary heart disease, angina pectoris, cerebral infarction, etc. In the present study, the protective effect of LGU on cerebral ischemia/reperfusion injury was investigated in an oxygen–glucose deprivation/reoxygenation (OGD/R) neuronal model and a transient middle cerebral artery occlusion (tMCAO) rat model. In in vitro experiments, LGU was found to improve the OGD/R-induced decrease in neuronal viability effectively by the MTT assay. In in vivo experiments, neurological deficit scores, infarction volume rates, and brain water content rates were improved after a single intravenous administration of LGU. These findings suggest that LGU has significant protective effects on cerebral ischemia/reperfusion injury in vitro and in vivo. To further explore the potential mechanism of LGU on cerebral ischemia/reperfusion injury, we performed a series of tests. The results showed that a single administration of LGU decreased the content of EB and S100B and ameliorated the abnormal expression of tight junction proteins ZO-1 and occludin and metalloproteinase MMP-9 in the ischemic cerebral cortex of the tMCAO 24-h injury model. In addition, LGU also improved the tight junction structure between endothelial cells and the degree of basement membrane degradation and reduced the content of TNF-α and IL-1β in the brain tissue. Thereby, LGU attenuated cerebral ischemia/reperfusion injury by improving the permeability of the blood–brain barrier. The present study provides new insights into the therapeutic potential of LGU in cerebral ischemia.

Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These “neural fingerprints” elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.

The proliferation of unmanned aerial vehicle (UAV) networks is increasing, driven by their capacity to deliver automated services tailored to the varied demands of numerous smart city applications. Trust, security, and privacy remain paramount in the public domain. Traditional centralized network designs fall short of ensuring device authentication, data integrity, and privacy within the highly dynamic and adaptable environments of UAV networks. Decentralized reputation systems have emerged as a promising solution for enhancing the reliability and trustworthiness of data and communications within these networks while safeguarding UAV security. This paper presents an exhaustive survey of trust and reputation systems, exploring existing frameworks and proposed innovations alongside their inherent challenges. The crucial role of reputation systems is to strengthen trust, security, and privacy throughout these networks, and various strategies can be incorporated to mitigate existing vulnerabilities. As a useful resource for researchers and practitioners seeking to advance the state of the art in UAV network security, we hope this survey will spark further community discussion and stimulate innovative ideas in this burgeoning field.

Low-frequency communication constitutes a vital component of essential communication systems, serving a pivotal role in remote radio communication, navigation, timing, and seismic analysis. To enhance the predictive precision of low-frequency skywave propagation and address the demands of engineering applications, we propose a high-precision prediction method based on the ITU-R P.684 wave-hop theory and real-time environmental parameter forecasts. This method features several distinctive attributes. Firstly, it employs real-time ionospheric prediction data instead of relying on long-term ionospheric model predictions. Secondly, it utilizes a detailed map of land–sea surface electrical characteristics, surpassing the simplistic land–sea dichotomy previously employed. Compared with measured data, the findings demonstrate that we attained a reasonable propagation pattern and achieved high-precision field strength predictions. Comparatively, the improved method exhibits an improvement in the time and spatial domains over the ITU-R P.684 standard. Finally, the improved method balances computational efficiency with enhanced prediction accuracy, supporting the advancement of low-frequency communication system design and performance evaluation.

Studying the ecological niches of species and their responses to climate change can provide better conservation strategies for these species. Myrothamnaceae is endemic to Africa, comprising only two species that belong to Myrothamnus (M. flabellifolius and M. moschatus). These closely related species exhibit allopatric distributions, positioning them as ideal materials for studying the species ecological adaptation. This study explores the ecological niche differentiation between M. flabellifolius and M. moschatus and their response capabilities to future climate change. The results indicate that M. flabellifolius and M. moschatus have undergone niche differentiation. The main drivers of niche differences are the minimum temperature of the coldest month (Bio6) for M. flabellifolius, precipitation of the driest month (Bio14), and precipitation of the coldest quarter (Bio19) for M. moschatus. M. flabellifolius demonstrated a stronger adaptation to environments characterized by lower precipitation, relatively lower temperatures, and greater annual temperature variations compared to M. moschatus. Under future climate scenarios (SSP5-8.5, 2081–2100 years), the results show that approximately 85% of the total suitable habitat for M. flabellifolius will be lost, with an 85% reduction in high-suitability areas and almost complete loss of the original mid-low suitability areas. Concurrently, about 29% of the total suitable habitat for M. moschatus will be lost, with a 34% reduction in high suitability areas and roughly 60% of the original mid-low suitability areas becoming unsuitable. This suggests that M. flabellifolius will face greater threats under future climate change. This study contributes novel insight into niche differentiation in Myrothamnaceae and provides useful information for the conservation of this distinctive African lineage.

Self-assembled DNA nanospheres, as versatile and ideal vehicles, have offered new opportunities to create intelligent delivery systems for precise bioimaging and cancer therapy, due to their good biostability and cell permeability, large loading capacity, and programmable self–assembly behaviors. DNA nanospheres can be synthesized by the self–assembly of Y–shaped DNA monomers, ultra–long single-stranded DNA (ssDNA), and even metal–DNA coordination. Interestingly, they are size–controllable by varying some parameters including concentration, reaction time, and mixing ratio. This review summarizes the design of DNA nanospheres and their extensive biomedical applications. First, the characteristics of DNA are briefly introduced, and different DNA nanostructures are mentioned. Then, the design of DNA nanospheres is emphasized and classified into three main categories, including Y–shaped DNA unit self-assembly by Watson–Crick base pairing, liquid crystallization and the dense packaging of ultra–long DNA strands generated via rolling circle amplification (RCA), and metal–DNA coordination–driven hybrids. Meanwhile, the advantages and disadvantages of different self–assembled DNA nanospheres are discussed, respectively. Next, the biomedical applications of DNA nanospheres are mainly focused on. Especially, DNA nanospheres serve as promising nanocarriers to deliver functional nucleic acids and drugs for biosensing, bioimaging, and therapeutics. Finally, the current challenges and perspectives for self-assembled DNA nanospheres in the future are provided.