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This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT's performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4. [ABSTRACT FROM AUTHOR]

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction. [ABSTRACT FROM AUTHOR]

As humans' additional arms or legs, supernumerary robotic limbs (SRLs) have gained great application prospects in many fields. However, current SRLs lack both rigidity/flexibility adaptability and arm/leg function conversion. Inspired by the muscular hydrostat characteristics of octopus tentacles, fiber-reinforced actuators (FRAs) were employed to develop SRLs simultaneously realizing flexible operation and stable support. In this paper, an SRL with FRAs was designed and implemented. The analytic model of the FRA was established to formulate the movement trajectory and stiffness profile of the SRL. A hierarchical hidden Markov model (HHMM) was proposed to recognize the wearer's motion intention and control the SRL to complete the specific working mode and motion type. Experiments were conducted to exhibit the feasibility and superiority of the proposed robot. [ABSTRACT FROM AUTHOR]

In the process of driving, the steering, braking, and driving conditions and different road conditions affect the vibration characteristics of the vehicle in the vertical, roll, and pitch directions. These factors greatly impact the riding comfort of the vehicle. Among them, the uneven distribution of vertical load between the left and right or the front and rear suspension is one of the important factors affecting the performance indicators of the vehicle's roll angle acceleration and pitch angle acceleration. In order to improve the ride comfort of the vehicle in vertical, roll, and pitch motion, the inerter is introduced in this paper to form a new type of suspension structure with the "spring-damping" base element, inertial suspension. It breaks away from the traditional "spring-damping" base element of the inherent suspension structure. In this paper, the mechatronic inerter is taken as the actual controlled object, and the inertial suspension structure is considered as the controlled model based on the dynamic surface control theory and the pseudo-inverse matrix principle. Thus, the coordinated control of the inertial suspension can be achieved. Under random road input, compared with passive suspension, the ride comfort performance indicators of the vehicle with inertial suspension based on dynamic surface control are significantly improved. Finally, a Hardware-in-the-Loop (HiL) test of the controller based on dynamic surface control is carried out to verify that the performance of the vehicle inertial suspension using the dynamic surface control algorithm had improved in terms of vehicle ride comfort. The error between the experimental results and the simulation results is about 8%, which verifies the real-time performance and effectiveness of the dynamic surface controller in the real controller. [ABSTRACT FROM AUTHOR]

The Multidrug and Toxic Compound Extrusion (MATE) proteins serve as pivotal transporters responsible for the extrusion of metabolites, thereby playing a significant role in both plant development and the detoxification of toxins. The MATE gene family within the Brachypodium distachyon, which is an important model organism of the Poaceae family, remains largely unexplored. Here, a comprehensive identification and analysis of MATE genes that complement B. distachyon were conducted. The BdMATE genes were systematically categorized into five distinct groups, predicated on an assessment of their phylogenetic affinities and protein structure. Furthermore, our investigation revealed that dispersed duplication has significantly contributed to the expansion of the BdMATE genes, with tandem and segmental duplications showing important roles, suggesting that the MATE genes in Poaceae species have embarked on divergent evolutionary trajectories. Examination of ω values demonstrated that BdMATE genes underwent purifying selection throughout the evolutionary process. Furthermore, collinearity analysis has confirmed a high conservation of MATE genes between B. distachyon and rice. The cis-regulatory elements analysis within BdMATEs promoters, coupled with expression patterns, suggests that BdMATEs play important roles during plant development and in response to phytohormones. Collectively, the findings presented establish a foundational basis for the subsequent detailed characterization of the MATE gene family members in B. distachyon. [ABSTRACT FROM AUTHOR]

This paper introduces a semiconductor optical amplifier (SOA) with high power and narrow linewidth broadening achieved through active region mode control. By integrating mode control with broad-spectrum epitaxial material design, the device achieves high gain, high power, and wide band output. At a wavelength of 1550 nm and an ambient temperature of 20 °C, the output power reaches 757 mW when the input power is 25 mW, and the gain is 21.92 dB when the input power is 4 mW. The 3 dB gain bandwidth is 88 nm, and the linewidth expansion of the input laser after amplification through the SOA is only 1.031 times. The device strikes a balance between high gain and high power, offering a new amplifier option for long-range light detection and ranging (LiDAR). [ABSTRACT FROM AUTHOR]

Objective. To establish a risk prediction model for intradialytic hypotension (IDH) in maintenance hemodialysis (MHD) patients and to analyze the explainability of the risk prediction model. Methods. A total of 2,228,650 hemodialysis records of 1075 MHD patients were selected as the research objects. Thirteen important clinical features including demographic features and clinical features were screened, the blood pressure measured before hemodialysis was collected, then an IDH risk prediction model during hemodialysis was established based on a machine learning algorithm. The contribution of each feature to the risk prediction of IDH was measured based on the Gini evaluation index. The TreeSHAP method was used to provide global and individual explanations for the IDH risk prediction model. Results. Hemodialysis duration, pre-dialysis mean arterial pressure, and pre-dialysis systolic blood pressure were the most important predictive variables for the occurrence of IDH during hemodialysis in MHD patients. The best IDH risk prediction model based on machine learning had an accuracy of 0.92 (95% CI 0.90–0.94) and an AUC of 0.95 (95% CI 0.94–0.96), indicating that machine learning has a good effect on the prediction of IDH during hemodialysis treatment. Our research innovatively achieved IDH risk prediction during the entire hemodialysis period based on blood pressure before the start of hemodialysis and other clinical features, thus enabling the medical team to quickly adjust hemodialysis prescriptions or initiate treatment for timely management and prevention of IDH. Global and individual explanations of the IDH risk prediction model can help hemodialysis medical staff understand the overall prediction mechanism of the model, discover prediction outliers, and identify potential biases or errors in the model. Conclusions. The IDH risk prediction model has definite clinical value in actual hemodialysis treatment. [ABSTRACT FROM AUTHOR]

This study investigates the utilization of 3D laser scanning, GPS, BIM and GIS technologies for safeguarding historical wooden structures. As a case study, it focuses on two historic Chinese wooden buildings, the Guanyin Pavilion and Tangwang Palace, renowned for their exceptional historical significance and intricate timber architecture. The Guanyin Pavilion was subjected to high-precision modeling and tilt monitoring, which found that certain columns had a maximum tilt angle of 0.7892°. Additionally, a general tilt tendency was seen across various structural elements. The measurements from the point cloud data exhibited an error range of ±10 mm. However, the relative errors in smaller components approached 2.5%, which necessitated regular verification through human measurements. Using GIS and BIM technology in Tangwang Palace facilitated the meticulous documentation and dynamic supervision of architectural components, encompassing historical development and present state. The maintenance records revealed persistent problems with the roof tiles and wooden beams, exposing structural vulnerabilities. During various maintenance intervals, it was observed that the roof tiles were frequently loose and separated, indicating potential flaws in the design or materials used. The application of these modern technologies enhances the effectiveness and precision of conservation efforts and provides a robust scientific foundation for the continuous monitoring and study of cultural heritage. [ABSTRACT FROM AUTHOR]

The energy sector, serving as a pivotal propellant within the macroeconomic framework, finds its production, distribution, and consumption aspects considerably influenced by climatic variations. In this study, a two-tier Stackelberg model for the green energy supply chain is developed, which is employed to analyze the profit disparities between suppliers and retailers both in the absence and presence of meteorological disasters. Furthermore, the research delves into the optimal supply chain strategies under three distinct scenarios in the context of meteorological disasters. A comparative analysis is conducted to examine the decision-making variances and the shifts in the interests of each supply chain participant under these scenarios. The findings reveal two critical insights: First, meteorological disasters detrimentally affect the environmental friendliness of energy products, as well as the profits of both retailers and suppliers. Second, the selection of the optimal strategy varies depending on the evaluation criterion used. When product greenness is the metric, subsidies aimed at promoting sales costs emerge as the superior strategy. Conversely, for retailers and suppliers, subsidies that target the environmental friendliness of energy products prove to be the most effective. Based on the conclusions, the paper also makes recommendations for governments and companies. [ABSTRACT FROM AUTHOR]

(1) Background: Discarded young fig fruits (DYFFs) result in a waste of resources, such as sparse fruits and residual fruits, and there has been no research on the relationship between phenolic compounds and biological activity in DYFFs (2) Methods: Different deep eutectic solvents (DESs) and 80% ethanol were used to prepare DYFF extracts, and polyphenol extraction efficiency and bioactivities in the DYFFs extracts were compared. (3) Results: More than 1700 phytochemicals were identified in DYFFs, and thirteen of these typical phenolic compounds were analyzed quantitatively; chlorogenic acid, rutin, luteolin 8-C-glucoside, and epicatechin are the main polyphenols in DYFFs, especially chlorogenic acid with 2720–7980 mg/kg. Ferulic acid, caffeic acid, epicatechin, (+)-catechin, luteolin 8-C-glucoside, rutin, hesperetin, and chlorogenic acid showed different degrees of correlation with in vitro antioxidant activity. Moreover, the highest total phenol content found in the extracts of ChCl-Ethylene glycol (Choline chloride:Ethylene glycol = 1:2) was 8.88 mg GAE/g DW, and all quantitatively analyzed phenolic compounds had high levels in various DESs and 80% ethanol. The 80% ethanol and Choline chloride (ChCl) solvent system showed the greatest antioxidant properties, and the Choline chloride-Urea (Choline chloride: Urea = 1:2) extract of DYFFs exhibited the highest inhibitory activity. (4) Conclusions: DESs have demonstrated potential as promising green solvents, especially the ChCl solvent system, which facilitates the extraction of polyphenols. [ABSTRACT FROM AUTHOR]

The accurate prediction of scallion prices can not only optimize supply chain management and help related practitioners and consumers to make more reasonable purchasing decisions, but also provide guidance for farmers' planting choices, thus enhancing market efficiency and promoting the sustainable development of the whole industry. This study adopts the idea of decomposition–denoising–aggregation, using three decomposition and denoising techniques combined with three single prediction models to form a base model. Various base models are divided into different combinations based on whether the computational structure is the same or not, and the optimal weights of the combinations are determined by using the improved particle swarm optimization–genetic algorithm (PSO-GA) optimization algorithm in different combinations. The experimental results show that the scallion price in Shandong Province from 2014 to 2023 shows an overall upward trend, and there is a cyclical and seasonal fluctuation pattern of "high in winter and low in summer"; the semi-heterogeneous-PSO-GA model reduces the MAPE by 49.03% and improves the directional accuracy by 41.52%, compared to the optimal single prediction model, ARIMA. In summary, the combined model has the most accurate prediction and strong robustness, which can provide ideas and references for the difficult problem of determining the optimal weights of the combined model in the field of predicting the prices of agricultural products. [ABSTRACT FROM AUTHOR]

The use of glass fiber-reinforced polymer (GFRP) composites in fluid transport systems can effectively reduce corrosion damage caused by corrosive media. However, collisions between solid particles and the surfaces of ball valve flow passages can cause erosion damage and lead to safety issues. The two-phase flow and erosion characteristics of ball valves manufactured from resin-based fiber-reinforced composite materials were studied under different openings and particle sizes using the CFD-DPM method. The results indicate that both smaller and larger relative openings are prone to erosion damage at the thin edges of the valve ball. As the relative opening increases, the average erosion amount in the flow passage first increases and then decreases. The maximum average erosion amount is 0.0051 kg/m2·s when the relative opening is Cv = 40. At Cv = 40, erosion damage in the flow channel mainly occurs at the bottom of the inlet flow channel and the valve seat position. With increasing particle size, both the average and maximum erosion amounts in the flow channel increase. Larger particle sizes in the inlet flow channel significantly raise the erosion rate nearby, while at other locations, larger particle sizes mainly increase the erosion rate in the same area. During the use of GFRP valves, it is important to avoid introducing large-sized particles into the medium. Keeping the valve's relative opening greater than 40 and using more erosion-resistant materials for the valve seat can effectively reduce the erosion of the composite ball valve and extend its service life. [ABSTRACT FROM AUTHOR]

Using a quantitative method, this paper examined the role of physical activity well-being before physical activity participation and gentrification architecture pursuit in 481 respondents (including 377 with childhood left-behind experiences) in Xi'an City, China, and tested the effect of the metacognition level of the group with childhood left-behind experiences on this pathway. The results showed that (1) physical activity participation of the group with childhood left-behind experiences had a significant positive predictive effect on gentrification architecture pursuit; (2) the level of physical activity participation of the group with childhood left-behind experiences could exert an indirect effect on gentrification architecture pursuit through physical activity well-being; (3) metacognition played a negative role in the pathway of physical activity participation predicting gentrification architecture pursuit of the group with childhood left-behind experiences; (4) the level of metacognition played a negative role in the pathway of physical activity participation and gentrification architecture pursuit of the group with childhood left-behind experiences; (5) metacognition also played a negative role in the path of physical activity well-being in predicting gentrification architecture pursuit in groups with childhood left-behind experiences. This paper is important in analyzing the complex effects of childhood left-behind experiences on individual consumption perceptions and develop a deeper understanding. [ABSTRACT FROM AUTHOR]

Recently, the low-rank representation (LRR) model has been widely used in the field of remote sensing image denoising due to its excellent noise suppression capability. However, those low-rank-based methods always discard important edge details as residuals, leading to a common issue of blurred edges in denoised results. To address this problem, we take a new look at low-rank residuals and try to extract edge information from them. Therefore, a hierarchical denoising framework was combined with a low-rank model to extract edge information from low-rank residuals within the edge subspace. A prior knowledge matrix was designed to enable the model to learn necessary structural information rather than noise. Also, such traditional model-driven approaches require multiple iterations, and the solutions may be very complex and computationally intensive. To further enhance the noise suppression performance and computing efficiency, a hierarchical low-rank denoising model based on deep unrolling (HLR-DUR) was proposed, integrating deep neural networks into the hierarchical low-rank denoising framework to expand the information capture and representation capabilities of the proposed shallow model. Sufficient experiments on optical images, hyperspectral images (HSI), and synthetic aperture radar (SAR) images showed that HLR-DUR achieved state-of-the-art (SOTA) denoising results. [ABSTRACT FROM AUTHOR]

Diamond is known as the ultimate semiconductor material for electric devices with excellent properties such as an ultra-wide bandgap (5.47 eV), high carrier mobility (electron mobility 4000 cm2/V·s, hole mobility 3800 cm2/V·s), high critical breakdown electric field (20 MV/cm), and high thermal conductivity (22 W/cm·K), showing good prospects in high-power applications. The lack of n-type diamonds limits the development of bipolar devices; most of the research focuses on p-type Schottky barrier diodes (SBDs) and unipolar field-effect transistors (FETs) based on terminal technology. In recent years, breakthroughs have been made through the introduction of new structures, dielectric materials, heterogeneous epitaxy, etc. Currently, diamond devices have shown promising applications in high-power applications, with a BV of 10 kV, a BFOM of 874.6 MW/cm2, and a current density of 60 kA/cm2 already realized. This review summarizes the research progress of diamond materials, devices, and specific applications, with a particular focus on the development of SBDs and FETs and their use in high-power applications, aiming to provide researchers with the relevant intuitive parametric comparisons. Finally, the paper provides an outlook on the parameters and development directions of diamond power devices. [ABSTRACT FROM AUTHOR]

To enhance the selenium (Se) intake of the general public, the present study implemented biofortification techniques in alfalfa sprouts. Alfalfa sprouts possess unique nutritional value and provide an optimal Se-enriched supplemental Se source. The impact of sodium selenite (Na2SeO3) on alfalfa shoot germination, shoot length, and biomass was assessed experimentally, and changes in the antioxidant capacity of sprouts treated with optimal Se concentrations were investigated. In addition, the transcriptome of alfalfa sprouts treated with the optimal Na2SeO3 concentration was sequenced. Gene co-expression networks, constructed through differential gene analysis and weighted gene co-expression network analysis, were used to identify the core genes responsible for Se enrichment in alfalfa sprouts. The findings of the present study offer novel insights into the effects of Se treatment on the nutrient composition of alfalfa sprouts, in addition to introducing novel methods and references that could facilitate production of Se-enriched alfalfa sprouts and associated products. [ABSTRACT FROM AUTHOR]

Active vision systems (AVSs) have been widely used to obtain high-resolution images of objects of interest. However, tracking small objects in high-magnification scenes is challenging due to shallow depth of field (DoF) and narrow field of view (FoV). To address this, we introduce a novel high-speed AVS with a continuous autofocus (C-AF) approach based on dynamic-range focal sweep and a high-frame-rate (HFR) frame-by-frame tracking pipeline. Our AVS leverages an ultra-fast pan-tilt mechanism based on a Galvano mirror, enabling high-frequency view direction adjustment. Specifically, the proposed C-AF approach uses a 500 fps high-speed camera and a focus-tunable liquid lens operating at a sine wave, providing a 50 Hz focal sweep around the object's optimal focus. During each focal sweep, 10 images with varying focuses are captured, and the one with the highest focus value is selected, resulting in a stable output of well-focused images at 50 fps. Simultaneously, the object's depth is measured using the depth-from-focus (DFF) technique, allowing dynamic adjustment of the focal sweep range. Importantly, because the remaining images are only slightly less focused, all 500 fps images can be utilized for object tracking. The proposed tracking pipeline combines deep-learning-based object detection, K-means color clustering, and HFR tracking based on color filtering, achieving 500 fps frame-by-frame tracking. Experimental results demonstrate the effectiveness of the proposed C-AF approach and the advanced capabilities of the high-speed AVS for magnified object tracking. [ABSTRACT FROM AUTHOR]

With the improvement of social production efficiency and the enhancement of the supply chain system, the traditional linear supply chain model is gradually evolving into a more complex and dynamic industrial chain network. This article uses complex network theory combined with the basic attributes of the industrial chain and supply chain to conduct a comprehensive and in-depth analysis of the integrated circuit industry chain. Firstly, a cooperative network model of the integrated circuit industry chain in Shaanxi Province is established based on the supply chain relationships of enterprises. Secondly, the study analyzes the basic characteristics of the collaborative network model. Thirdly, this study explores the efficiency, resilience, and innovation capacity of industrial chains using a novel set of indexes: the industry chain efficiency index (ICEI), the industry chain resilience index (ICRI), and the industry chain innovation capability index (ICICI). By employing principal component analysis (PCA), the study provides a comprehensive evaluation of industrial chain performance. The findings reveal that the ICEI highlights the critical importance of average path length and network density, showing that shorter paths and higher density are associated with greater efficiency. The ICRI emphasizes the roles of average degree and standard deviation, indicating that higher connectivity and lower variability contribute to resilience. The ICICI identifies the clustering coefficient and network density as key factors, suggesting that tight-knit networks foster innovation. These results offer significant insights into the dynamics of industrial chain collaboration and provide practical recommendations for enhancing supply chain management. Finally, the effectiveness of the proposed method is demonstrated through a case study. The results of the case study indicate the following: (1) Key Enterprises' Identification: The analysis identified key enterprises like Samsung Semiconductor and HT-tech with the highest betweenness centrality, highlighting their crucial intermediary roles within the network; (2) Efficiency and Innovation Assessment: Compared with foreign-owned and other immigrant businesses, local businesses generally perform below average in terms of efficiency and resilience, indicating that there is room for improvement in technology adoption and innovation capabilities. [ABSTRACT FROM AUTHOR]

Polarization-insensitive semiconductor optical amplifiers (SOAs) in all-optical networks can improve the signal-light quality and transmission rate. Herein, to reduce the gain sensitivity to polarization, a multi-quantum-well SOA in the 1550 nm band is designed, simulated, and developed. The active region mainly comprises the quaternary compound InGaAlAs, as differences in the potential barriers and wells of the components cause lattice mismatch. Consequently, a strained quantum well is generated, providing the SOA with gain insensitivity to the polarization state of light. In simulations, the SOA with ridge widths of 4 µm, 5 µm, and 6 µm is investigated. A 3 dB gain bandwidth of >140 nm is achieved with a 4 µm ridge width, whereas a 6 µm ridge width provides more output power and gain. The saturated output power is 150 mW (21.76 dB gain) at an input power of 0 dBm but increases to 233 mW (13.67 dB gain) at an input power of 10 dBm. The polarization sensitivity is <3 dBm at −20 dBm. This design, which achieves low polarization sensitivity, a wide gain bandwidth, and high gain, will be applicable in a wide range of fields following further optimization. [ABSTRACT FROM AUTHOR]

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms. [ABSTRACT FROM AUTHOR]

This article presents a third-order, feedforward, single-bit Delta-Sigma analog-to-digital modulator (DSM) based on an output swing-enhanced gain-boost inverter for low-voltage low-power applications such as wearable devices, mobile health, and the Internet of Things (IoTs). The proposed output swing-enhanced structure addresses the output-swing reduction in the conventional structure while achieving high DC gain and large output swing simultaneously. Implemented in a 180 nm CMOS process, the entire chip is comprised of a delta-sigma modulator, an oscillator, and a current reference. It achieves 86.1 dB peak SNR and 92 dB dynamic range (DR) with 1.95 kHz signal bandwidth. The whole chip dissipates 54.5 μW, leading to a 167.6 dB Schreier Figure of Merit (FoMs). [ABSTRACT FROM AUTHOR]

As the most stable phase of gallium oxide, β-Ga2O3 can enable high-quality, large-size, low-cost, and controllably doped wafers by the melt method. It also features a bandgap of 4.7–4.9 eV, a critical electric field strength of 8 MV/cm, and a Baliga's figure of merit (BFOM) of up to 3444, which is 10 and 4 times higher than that of SiC and GaN, respectively, showing great potential for application in power devices. However, the lack of effective p-type Ga2O3 limits the development of bipolar devices. Most research has focused on unipolar devices, with breakthroughs in recent years. This review mainly summarizes the research progress fora different structures of β-Ga2O3 power diodes and gives a brief introduction to their thermal management and circuit applications. [ABSTRACT FROM AUTHOR]

Pavement defect detection technology stands as a pivotal component within intelligent driving systems, demanding heightened precision and rapid detection rates. Addressing the complexities arising from diverse defect types and intricate backgrounds in visual sensing, this study introduces an enhanced approach to augment the network structure and activation function within the foundational YOLOv5 algorithm. Initially, modifications to the YOLOv5′s architecture incorporate an adjustment to the Leaky ReLU activation function, thereby enhancing regression stability and accuracy. Subsequently, the integration of bi-level routing attention into the network's head layer optimizes the attention mechanism, notably improving overall efficiency. Additionally, the replacement of the YOLOv5 backbone layer's C3 module with the C3-TST module enhances initial convergence efficiency in target detection. Comparative analysis against the original YOLOv5s network reveals a 2% enhancement in map50 and a 1.8% improvement in F1, signifying an overall advancement in network performance. The initial convergence rate of the algorithm has been improved, and the accuracy and operational efficiency have also been greatly improved, especially on models with small-scale training sets. [ABSTRACT FROM AUTHOR]

Responding to the low-carbon demands of power users helps the power industry unlock carbon emission reduction potential, transmit carbon reduction responsibilities, and hold significant importance for energy conservation and carbon reduction. Currently, there is relatively limited empirical research on carbon emission demand responses in different industries. Therefore, this article, based on dynamic carbon accounting technology and combined with power planning and operation simulation data in Fangshan District, proposes a low-carbon demand response mechanism guided by user-side carbon reduction, using dynamic carbon emission factors as guiding signals. Furthermore, an assessment model for the carbon reduction potential of a low-carbon demand response technology is constructed. Finally, empirical research is conducted on the estimated carbon emission reduction of 108 enterprises in 6 different industries in Fangshan District based on the assessment model. The results indicate that carrying out low-carbon demand response can reduce enterprise carbon emissions, and changes in enterprise capacity and industry affiliation will have a significant impact on the carbon reduction effect. This study introduces a novel approach to the low-carbon development of the power system load side, offering a fresh perspective on the empirical analysis of low-carbon development for typical receiving-end power grids. [ABSTRACT FROM AUTHOR]

The stiffness of a long-span cable-stayed bridge under construction may be much lower than that observed in service, making it more susceptible to wind effects, especially for a bridge designed using high piers crossing a deep canyon. To study the buffeting characteristics of such cable-stayed bridges under construction, a long-span cable-stayed bridge (the main span is 575 m) is taken as the engineering background. In this study, the buffeting responses and vibration countermeasures at three different construction states were systematically studied using time-domain analysis. It was found that the buffeting response enlarges with an increase in the wind attack angle. The RMS values of the vertical buffeting of the bridge deck end are relatively greater at the maximum double cantilever construction state and maximum single cantilever state. At maximum double cantilever construction state, the traditional wind-resistant cable connecting the bridge deck end to the bridge pile cap significantly reduces the vertical buffeting response, while the suppression effect on lateral and torsional buffeting is limited. When the bridge deck nears completion, wind-resistant cables installed at both cantilever ending in the 'soft connection' method would effectively suppress the vertical, lateral, and torsional buffeting. The suppression effect of cross-arranged wind-resistant cables is superior to that of the parallel arrangement. It is recommended that a reasonable wind-resistant cable layout scheme according to different construction conditions is selected. [ABSTRACT FROM AUTHOR]

Pigs play important roles in agriculture and bio-medicine; however, porcine viral infections have caused huge losses to the pig industry and severely affected the animal welfare and social public safety. During viral infections, many non-coding RNAs are induced or repressed by viruses and regulate viral infection. Many viruses have, therefore, developed a number of mechanisms that use ncRNAs to evade the host immune system. Understanding how ncRNAs regulate host immunity during porcine viral infections is critical for the development of antiviral therapies. In this review, we provide a summary of the classification, production and function of ncRNAs involved in regulating porcine viral infections. Additionally, we outline pathways and modes of action by which ncRNAs regulate viral infections and highlight the therapeutic potential of artificial microRNA. Our hope is that this information will aid in the development of antiviral therapies based on ncRNAs for the pig industry. [ABSTRACT FROM AUTHOR]

We present a novel architecture for the design of single-photon detecting arrays that captures relative intensity or timing information from a scene, rather than absolute. The proposed method for capturing relative information between pixels or groups of pixels requires very little circuitry, and thus allows for a significantly higher pixel packing factor than is possible with per-pixel TDC approaches. The inherently compressive nature of the differential measurements also reduces data throughput and lends itself to physical implementations of compressed sensing, such as Haar wavelets. We demonstrate this technique for HDR imaging and LiDAR, and describe possible future applications. [ABSTRACT FROM AUTHOR]

Against the backdrop of the global energy transition, wind power generation has seen rapid development. However, the intermittent and fluctuating nature of wind power poses a challenge to the stability of grid operation. To solve this problem, a solution based on a hybrid energy storage system is proposed. The hybrid energy storage system is characterized by fast and precise control and bidirectional energy throughput, which can improve the impact of wind power fluctuations on grid stability. An ensemble empirical modal decomposition method was used to assign the raw wind power data to the grid-connected power and energy storage power commands with two reasonable corrections to meet the power allocation of the hybrid energy storage characteristics. In addition, a hybrid energy storage system model considering the whole life cycle cost was developed, and the optimal energy storage power cutoff was determined by exhaustively enumerating the high- and low-frequency power cutoffs. Finally, a comparison with a single storage capacity optimization model was carried out to verify the technical and economic advantages of hybrid energy storage in smoothing wind power fluctuations. To address the shortcomings of the traditional fuzzy c-means clustering algorithm, such as the need to specify the number of clusters in advance and sensitivity to the selection of the initial clustering centers, a combination of the cloud modeling theory and fuzzy c-means was used to make the process more automated and efficient. The improved clustering method algorithmic scheme had capacity error, power error, and cost error of around 3%, and the computational time was also significantly reduced and was computationally efficient compared to the full-year time series simulation. Through MATLAB (2020b) experimental simulation, it was found that the algorithm had a better balance of computational accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this study, an analytical approach is introduced for predicting the bending behavior of a timber–concrete composite (TCC) beam subjected to a mechanical load and a non-uniform temperature field, in which the orthotropy of timber as well as interfacial slip are taken into consideration. The analytical model addresses the non-uniform temperature field using Fourier series expansion based on the heat transport theory. The stresses and displacements of the TCC beam under the thermo-mechanical condition are governed by the thermo-elasticity theory, and the corresponding solution is derived analytically by solving a group of non-homogeneous partial differential equations. The proposed solution is in good agreement with the finite element solution and exhibits higher accuracy compared to the Euler–Bernoulli beam solution that relies on the assumption of transverse shear deformation and isotropy. An extensive investigation is carried out to analyze how the bending behavior of TCC beams is influenced by variations in interfacial shear stiffness and temperature field. [ABSTRACT FROM AUTHOR]

As a representative example of an environmental chemical carcinogen, MNNG exposure is closely associated with the onset of gastric cancer (GC) where N6-methyladenosine (m6A) RNA methylation tends to be the critical epigenetic event. However, the effect of m6A modification on long non-coding RNAs (lncRNAs) in MNNG-induced GC onset is still unclear. To address the above issue, based on the Methylated RNA immunoprecipitation sequencing (MeRIP-seq) data of MNNG-induced malignant cells (MCs) and GC cells, we comprehensively analyzed the MNNG exposure-associated vital lncRNAs. MeRIP-seq analysis identified 1432 lncRNA transcripts in the MC cell, and 3520 lncRNA transcripts were found to be m6A modified in the GC cell, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that MNNG exposure could spark cellular localization change, which might be the critical cellular note variation for malignant transformation. We demonstrated that METTL3 is responsible for N6 methylation of lncRNAs and identified SNHG7 as a downstream target of METTL3. More importantly, we observed that SNHG7 was progressively up-regulated during gastric carcinogenesis by MNNG exposure. Finally, we investigated SNHG7 expression in different stages of GC malignancies and found that elevated SNHG7 expression correlated with advanced clinical features and poor prognosis in GC. In conclusion, our study found for the first time that METTL3 regulates the m6A methylation level of lncRNA SNHG7 and its expression in MNNG exposure-induced GC, suggesting that SNHG7 as a predictive biomarker or therapeutic target for GC. [ABSTRACT FROM AUTHOR]

In recent years, the development and utilization of China's wind energy resources have been greatly developed, but the large-scale wind power grid connection has brought threats to the safe and stable operation of the power grid. In order to ensure the stability of the power grid, it is necessary to reduce wind power output fluctuation and improve the tracking accuracy of dispatch instructions. Therefore, based on the distributed model predictive control of wind farm active power distribution strategy, an ultra-short-term wind power hybrid deep learning predictive model is proposed. The prediction results of a wind farm in North China show that the hybrid neural network model can achieve high ultra-short-term wind power prediction accuracy and is suitable for active power control prediction models. A two-layer distributed model is proposed to predict the active power control architecture of wind farms by implementing the clustering process with the Crow Search Algorithm. The distributed model predictive control strategy is proposed in the upper layer, and the centralized model predictive control algorithm is adopted in the lower control structure and optimized. The results show that the dual-layer distributed model predictive control strategy can better track the active power distribution instructions, reduce output fluctuation and scheduling value changes, and enhance the robustness of active power regulation, which is suitable for active power online control in wind farms. [ABSTRACT FROM AUTHOR]

This study aimed to establish the effects of elevated CO2 concentration and temperature on the photosystem II (PSII) performance and photosynthetic characteristics of Populus simonii × P. nigra '1307' leaves. Different CO2 concentrations (400 and 800 µmol·mol−1) and temperatures (room temperature and room temperature +2 °C) were set in artificial climate change simulation and control chambers, and the rapid chlorophyll fluorescence induction kinetics curve (OJIP curve) of Populus simonii × P. nigra '1307' was determined. The generated OJIP curve was used to analyze the change characteristics in photosynthetic performance. The results revealed that under elevated temperature conditions, the concentrations of chlorophyll a, b in Populus simonii × P. nigra '1307' leaves were significantly increased. At the same time, there were no significant changes in the chlorophyll concentration under the superimposed effect of elevated CO2 concentration and temperature. The PSII comprehensive performance index (PIABS) of Populus simonii × P. nigra '1307' was significantly inhibited under elevated temperatures due to the increased closure degree (Vj) of the PSII reaction center and the damage of the receptor side. This reduced the electron transfer capacity per unit reaction center (ETo/RC) and unit cross-sectional area, which decreased the quantum yield of the electron transfer. Under the elevated CO2 concentration, ETo/RC was also inhibited. Still, PIABS was enhanced owing to the increased number of active PSII per unit area and the low reduction rate of the primary quinone receptor (QA). Under the superimposed effect of the two factors, the electron transfer performance of the donor and receptor sides of PSII was improved compared to the treatments only subjected to elevated temperature; thus, PIABS was not significantly reduced compared to the control. Therefore, the continuous increase in temperature by 2 °C significantly inhibits the electron transfer capacity of the photosynthetic system of Populus simonii × P. nigra '1307' leaves. On the other hand, an increase in CO2 concentration expands the PSII reaction center, while enhancing the electron transfer capacity of the donor and receptor sides, which alleviates the photosynthetic inhibition caused by the elevated temperature. [ABSTRACT FROM AUTHOR]

In the era of big data, making full use of remote sensing images to automatically extract surface water bodies (WBs) in complex environments is extremely challenging. Due to the weak capability of existing algorithms in extracting small WBs and WB edge information from remote sensing images, we proposed a new method—Multiscale Fusion SegFormer (MF-SegFormer)—for WB extraction in the Weihe River Basin of China using Landsat 8 OLI images. The MF-SegFormer method adopts a cascading approach to fuse features output by the SegFormer encoder at multiple scales. A feature fusion (FF) module is proposed to enhance the extraction of WB edge information, while an Atrous Spatial Pyramid Pooling (ASPP) module is employed to enhance the extraction of small WBs. Furthermore, we analyzed the impact of four kinds of band combinations on WB extraction by the MF-SegFormer model, including true color composite images, false color images, true color images, and false color images enhanced by Gaussian stretch. We also compared our proposed method with several different approaches. The results suggested that false color composite images enhanced by Gaussian stretching are beneficial for extracting WBs, and the MF-SegFormer model achieves the highest accuracy across the study area with a precision of 77.6%, recall of 84.4%, F1-score of 80.9%, and mean intersection over union (mIoU) of 83.9%. In addition, we used the determination coefficient (R2) and root-mean-square error (RMSE) to evaluate the performance of river width extraction. Our extraction results in an overall R2 of 0.946 and an RMSE of 28.21 m for the mainstream width in the "Xi'an-Xianyang" section of the Weihe River. The proposed MF-SegFormer method used in this study outperformed other methods and was found to be more robust for WB extraction. [ABSTRACT FROM AUTHOR]

With stereo cameras becoming widely used in invasive surgery systems, stereo endoscopic images provide important depth information for delicate surgical tasks. However, the small size of sensors and their limited lighting conditions lead to low-quality and low-resolution endoscopic images and videos. In this paper, we propose a stereo endoscopic video super-resolution method using transformer with a hybrid attention mechanism named HA-VSR. Stereo video SR aims to reconstruct high-resolution (HR) images from corresponding low-resolution (LR) videos. In our method, the stereo correspondence and temporal correspondence are incorporated into the HA-VSR model. Specifically, the Swin transformer architecture is utilized in proposed framework with hybrid attention mechanisms. The parallel attention mechanism is utilized by using the symmetry and consistency of left and right images, and the temporal attention mechanism is utilized by using the consistency of consecutive frames. Detailed quantitative evaluation and experiments on two datasets show the proposed model achieves advanced SR reconstruction performance, showing that the proposed stereo VSR framework outperforms alternative approaches. [ABSTRACT FROM AUTHOR]

In this article, we introduce an innovative hybrid quantum search algorithm, the Robust Non-oracle Quantum Search (RNQS), which is specifically designed to efficiently identify the minimum value within a large set of random numbers. Distinct from the Grover's algorithm, the proposed RNQS algorithm circumvents the need for an oracle function that describes the true solution state, a feature often impractical for data science applications. Building on existing non-oracular quantum search algorithms, RNQS enhances robustness while substantially reducing running time. The superior properties of RNQS have been demonstrated through careful analysis and extensive empirical experiments. Our findings underscore the potential of the RNQS algorithm as an effective and efficient solution to combinatorial optimization problems in the realm of quantum computing. [ABSTRACT FROM AUTHOR]

In the past few decades, global industrial development and population growth have led to a scarcity of water resources, making sustainable management of groundwater a global challenge. The Water Quality Index (WQI) serves as a comprehensive method for assessing water quality and can provide valuable recommendations at the water quality level, optimizing policies for groundwater management. However, the subjectivity and uncertainty of the traditional WQI have negative impacts on evaluation outcomes, particularly in determining indicator weights and selecting aggregation functions. The proposed water quality index for groundwater based on the random forest (RFWQI) model in this study addresses these issues. It selects water quality indicators based on the actual pollution situation in the study area, employs an advanced random forest model to rank water quality indicators, determines indicator weights using the rank centroid method, scores the indicators using a sub-index function designed for groundwater development, and compares the results of two commonly used aggregation functions to identify the optimal one. Based on the aggregated scores, the water quality at 137 monitoring sites is classified into five levels: "Excellent", "Good", "Medium", "Poor", or "Unacceptable". Among the 11 water quality indicators (sodium, sulfate, chloride, bicarbonate, total dissolved solids, fluoride, boron, nitrate, pH, CODMn, and hardness), chloride was given the highest weight (0.236), followed by total dissolved solids (0.156), and sodium was given the lowest weight (0.008). The random forest model exhibits a good prediction capability before hyperparameter tuning (86% accuracy, RMSE of 0.378), and after grid search and five-fold cross-validation, the optimal hyperparameter combination is determined, further improving the performance of the random forest model (94% accuracy, F1-Score of 0.967, AUC of 0.91, RMSE of 0.232). For the newly developed groundwater sub-index function, interpolation is used to score each indicator, and after comparing two aggregation functions, the NSF aggregation function is selected as the most suitable for groundwater assessment. Overall, most of the groundwater in the study area was of poor quality (52.5% of low quality) and not suitable for drinking. [ABSTRACT FROM AUTHOR]

Through analyzing the triggering factors and activity characteristics of avalanches in Aerxiangou in the Western Tianshan Mountains, the formation and disaster-causing process of avalanches were studied to provide theoretical support and a scientific basis for avalanche disaster prevention. In this paper, based on remote sensing interpretation and field investigation, a spatial distribution map of avalanches was established, and the induced and triggering factors in disaster-prone environments were analyzed using the certainty factor model. The degree of influence (E) of the disaster-causing factors on avalanche triggering was quantified, and the main control conditions conducive to avalanche occurrence in different periods were obtained. The RAMMS-avalanche model was used to analyze the activity characteristics at points where multiple avalanches occurred. Research results: (1) The E values of the average temperature, average snowfall, and surface roughness in February were significantly higher than those of other hazard-causing factors, reaching 1.83 and 1.71, respectively, indicating strong control. The E values of the surface cutting degree, average temperature, and average snow depth in March were all higher than 1.8, indicating that these control factors were more prominent than the other factors. In contrast, there were four hazard-causing factors with E values higher than 1.5 in April: the mean temperature, slope, surface roughness, and mean wind speed, with clear control. (2) Under the influence of the different hazard-causing factors, the types of avalanches from February–April mainly included new full-layer avalanches, surface avalanches, and full-layer wet avalanches. (3) In the RAMMS-avalanche simulation test, considering the deposition effect, compared to the previous avalanche movement path, the secondary avalanche flow accumulation area impact range changes were slight, while the movement area within the avalanche path changes was large, as were the different categories of avalanches and their different movement characteristic values. Overall, wet snow avalanches are more hazardous, and the impact force is larger. The new snow avalanches start in a short period, the sliding rate is fast, and the avalanche sliding surface (full-snow surface and face-snow) of the difference is mainly manifested in the differences in the value of the flow height. [ABSTRACT FROM AUTHOR]

Cellular-connected unmanned aerial vehicles (UAVs) present a viable solution to address communication and navigation limitations by leveraging base stations for air–ground communication. However, in complex urban scenarios with stringent communication requirements, achieving asymmetrical control becomes crucial to strike a balance between communication reliability and flight safety. Moreover, existing cellular-connected UAV trajectory planning algorithms often struggle to handle real scenes with sudden and intricate obstacles. To address the aforementioned challenges, this paper presents the multi-tier trajectory planning method (MTTP), which takes into account air–ground communication service assurance and collision avoidance in intricate urban environments. The proposed approach establishes a flight risk model that accounts for both the outage probability of UAV-ground base station (GBS) communication and the complexity of flight environments, and transforms the inherently complex three-dimensional (3D) trajectory optimization problem into a risk distance minimization model. To optimize the flight trajectory, a hierarchical progressive solution approach is proposed, which combines the strengths of the heuristic search algorithm (HSA) and deep reinforcement learning (DRL) algorithm. This innovative fusion of techniques empowers MTTP to efficiently navigate complex scenarios with sudden obstacles and communication challenges. Simulations show that the proposed MTTP method achieves a more superior performance of trajectory planning than the conventional communication-based solution, which yields a substantial reduction in flight distance of at least 8.49% and an impressive 10% increase in the mission success rate. Furthermore, a real-world scenario is chosen from the Yuhang District, Hangzhou (a southern Chinese city), to validate the practical applicability of the MTTP method in highly complex operating scenarios. [ABSTRACT FROM AUTHOR]

We explore the origins of the marked improvement in enantioselectivity in the inner-sphere (PHOX)Pd-catalyzed allylic alkylation of N-benzoyl lactam nucleophiles over their carbocyclic counterparts. We employ density functional theory calculations to aid in the interpretation of experimental results. Ultimately, we propose that the enhancement in enantioselectivity arises primarily from noncovalent interactions between the substrate and ligand rather than secondary substrate chelation, as previously hypothesized. [ABSTRACT FROM AUTHOR]

The Membrane Attack Complex and Perforin (MACPF) proteins play a crucial role in plant development and adaptation to environmental stresses. Heretofore, few MACPF genes have been functionally identified, leaving gaps in our understanding of MACPF genes in other plants, particularly in the Solanaceae family, which includes economically and culturally significant species, such as tomato, potato, and pepper. In this study, we have identified 26 MACPF genes in three Solanaceae species and in the water lily, which serves as the base group for angiosperms. Phylogenetic analysis indicates that angiosperm MACPF genes could be categorized into three distinct groups, with another moss and spikemoss lineage-specific group, which is further supported by the examination of gene structures and domain or motif organizations. Through inter-genome collinearity analysis, it is determined that there are 12 orthologous SolMACPF gene pairs. The expansion of SolMACPF genes is primarily attributed to dispersed duplications, with purifying selection identified as the principal driving force in their evolutionary process, as indicated by the ω values. Furthermore, the analysis of expression patterns revealed that Solanaceae genes are preferentially expressed in reproductive tissues and regulated by various environmental stimuli, particularly induced by submergence. Taken together, these findings offer valuable insights into and a fresh perspective on the evolution and function of SolMACPF genes, thereby establishing a foundation for further investigations into their phenotypic and functional characteristics. [ABSTRACT FROM AUTHOR]

mRNA-based therapeutics have emerged as a promising strategy for cancer treatment. However, the effective delivery of mRNA into hard-to-transfect cancer cells remains a significant challenge. This study introduces a novel approach that utilizes iron oxide nanoparticles (NPs) synthesized through a layer-by-layer (LbL) method for safe and efficient mRNA delivery. The developed NPs consist of an iron oxide core modified with a thin charge-bearing layer, an mRNA middle layer, and an outer layer composed of perfluorinated polyethyleneimine with heparin (PPH), which facilitates efficient mRNA delivery. Through a comparative analysis of four nanoparticle delivery formulations, we investigated the effects of the iron oxide core's surface chemistry and surface charge on mRNA complexation, cellular uptake, and mRNA release. We identified an optimal and effective mRNA delivery platform, namely, (IOCCP)-mRNA-PPH, capable of transporting mRNA into various hard-to-transfect cancer cell lines in vitro. The (IOCCP)-mRNA-PPH formulation demonstrated significant enhancements in cellular internalization of mRNA, facilitated endosomal escape, enabled easy mRNA release, and exhibited minimal cytotoxicity. These findings suggest that (IOCCP)-mRNA-PPH holds great promise as a solution for mRNA therapy against hard-to-transfect cancers. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive controller (MPC) design based on the optimal tip-speed ratio method for maximum power point tracking (MPPT) of a direct-driven permanent magnet synchronous generator (D-PMSG)-based wind energy conversion system (WECS). To eliminate system nonlinearity and time-varying characteristics, a control variable was added at the wind turbine and the system model was feedback-linearized to create a linear time-invariant system, reducing the computational burden of the MPC and improving system performance. MATLAB/Simulink simulations were performed and the results show that the linearized system has high fidelity. Compared to traditional MPC that use an operating point to linearize the system, it has better adaptability to turbulent wind speeds, improving the stability and rapidity of the system. [ABSTRACT FROM AUTHOR]

SDN is a modern internet architecture that has transformed the traditional internet structure in recent years. By segregating the control and data planes of the network, SDN facilitates centralized management, scalability, dynamism, and programmability. However, this very feature makes SDN controllers vulnerable to cyber attacks, which can cause network-wide crashes, unlike conventional networks. One of the most stealthy attacks that SDN controllers face is the relay link forgery attack in topology deception attacks. Such an attack can result in erroneous overall views for SDN controllers, leading to network functionality breakdowns and even crashes. In this article, we introduce the Relay Link Forgery Attack detection model based on the Transformer deep learning model for the first time. The model (RLFAT) detects relay link forgery attacks by extracting features from network flows received by SDN controllers. A dataset of network flows received by SDN controllers from a large number of SDN networks with different topologies was collected. Finally, the Relay-based Link Forgery Attack detection model was trained on this dataset, and its performance was evaluated using accuracy, recall, F1 score, and AUC metrics. For better validation, comparative experiments were conducted with some common deep learning models. The experimental results show that our proposed model (RLFAT) has good performance in detecting RLFA and outperforms other models. [ABSTRACT FROM AUTHOR]

Simple Summary: EBV (Epstein-Barr virus) miRNA is a signaling molecule between infected and non-infected cells, which plays a biological role through exosome delivery. However, the mechanism of its effect on proliferation, apoptosis and immune response of EBVaGC is poorly understood. The aim of this study was to clarify the miRNA expression profiles of EBV-positive and -negative GC cells and their exosomes, and the mechanism of the effect of exosome-based miR-BART1-3p on the biological behavior of GC cells. We found that miR-BART1-3p can affect the growth of tumor cells through the exosome pathway. Co-culture with exosomes with miR-BART1-3p expression silence can improve the proliferation, healing, migration and invasion of GC cells. miR-BART1-3p may regulate the proliferation of GC cells through potential target genes USP37 and MACC1. Aim: EBV encodes at least 44 miRNAs involved in immune regulation and disease progression. Exosomes can be used as carriers of EBV-miRNA-BART intercellular transmission and affect the biological behavior of cells. We characterized exosomes and established a co-culture experiment of exosomes to explore the mechanism of miR-BART1-3p transmission through the exosome pathway and its influence on tumor cell proliferation and invasion. Materials and methods: Exosomes of EBV-positive and EBV-negative gastric cancer cells were characterized by transmission electron microscopy. NanoSight and Western blotting, and miRNA expression profiles in exosomes were sequenced with high throughput. Exosomes with high or low expression of miR-BART1-3p were co-cultured with AGS cells to study the effects on proliferation, invasion, and migration of gastric cancer cells. The target genes of EBV-miR-BART1-3p were screened and predicted by PITA, miRanda, RNAhybrid, virBase, and DIANA-TarBase v.8 databases, and the expression of the target genes after co-culture was detected by qPCR. Results: The exosomes secreted by EBV-positive and negative gastric cancer cells range in diameter from 30 nm to 150 nm and express the exosomal signature proteins CD9 and CD63. Small RNA sequencing showed that exosomes expressed some human miRNAs, among which hsa-miR-23b-3p, hsa-miR-320a-3p, and hsa-miR-4521 were highly expressed in AGS-exo; hsa-miR-21-5p, hsa-miR-148a-3p, and hsa-miR-7-5p were highly expressed in SNU-719-exo. All EBV miRNAs were expressed in SNU-719 cells and their exosomes, among which EBV-miR-BART1-5p, EBV-miR-BART22, and EBV-miR-BART16 were the highest in SNU-719 cells; EBV-miR-BART1-5p, EBV-miR-BART10-3p, and EBV-miR-BART16 were the highest in SNU-719-exo. After miR-BART1-3p silencing in gastric cancer cells, the proliferation, healing, migration, and invasion of tumor cells were significantly improved. Laser confocal microscopy showed that exosomes could carry miRNA into recipient cells. After co-culture with miR-BART1-3p silenced exosomes, the proliferation, healing, migration, and invasion of gastric cancer cells were significantly improved. The target gene of miR-BART1-3p was FAM168A, MACC1, CPEB3, ANKRD28, and USP37 after screening by a targeted database. CPEB3 was not expressed in all exosome co-cultured cells, while ANKRD28, USP37, MACC1, and FAM168A were all expressed to varying degrees. USP37 and MACC1 were down-regulated after up-regulation of miR-BART1-3p, which may be the key target genes for miR-BART1-3p to regulate the proliferation of gastric cancer cells through exosomes. Conclusions: miR-BART1-3p can affect the growth of tumor cells through the exosome pathway. The proliferation, healing, migration, and invasion of gastric cancer cells were significantly improved after co-culture with exosomes of miR-BART1-3p silenced expression. USP37 and MACC1 may be potential target genes of miR-BART1-3p in regulating cell proliferation. [ABSTRACT FROM AUTHOR]

Aimed at the difficulty of path planning resulting from the variable configuration of the wheel-legged robot for future deep space explorations, this paper proposes a path planning algorithm based on the Theta* algorithm and Timed Elastic Band (TEB) algorithm. Firstly, the structure of the wheel-legged robot is briefly introduced, and the workspace of a single leg is analyzed. Secondly, a method to judge complete obstacles and incomplete obstacles according to the height of the obstacles is proposed alongside a method to search for virtual obstacles, to generate a grid map of the wheel and a grid map of the body, respectively. By dividing obstacles into complete obstacles and incomplete obstacles, the path planning of the wheel-legged robot is split into the planning of the body path and the planning of the wheel path. The body can be still simplified as a point by searching for the virtual obstacle, which avoids the difficulty of a planning path of a variable shape. Then, we proposed hierarchical planning and multiple optimization algorithms for the body path and wheel path based on the Theta* algorithm and TEB algorithm. The path can be optimized and smoothed effectively to obtain a shorter length and higher safety. On that basis, the proposed algorithm is simulated by Matlab. The results of simulations show that the algorithm proposed in this paper can effectively plan the path of the wheel-legged robot by using variable configurations for different types of obstacles. The path-planning algorithm of the wheel-legged robot proposed in this paper has a broad prospect for deep space exploration. [ABSTRACT FROM AUTHOR]

Severe phenomena of across range-Doppler unit (ARDU) and decoherence occur when radar detects high-speed and high-maneuvering targets, causing degradation in detection performance of traditional FFT radar detection methods. The improvement in radar resolution causes a multi-dimensional spread phenomenon, where different scattering centers of the target are distributed on different range units, along with motion parameters such as velocity and acceleration. Unfortunately, current radar detection methods focus solely on range spread targets and cannot handle multi-dimensional spread, leading to a significant decline in detection performance. To overcome this problem, this paper proposes several methods to achieve high detection performance for multi-dimensional spread target detection with ARDU phenomenon. Firstly, the generalized likelihood ratio test (GLRT) is derived, and the energy integration generalized Rayleigh Fourier transform (EI-GRFT) is introduced to improve the detection performance of range spread cross-unit targets. Additionally, the double-threshold based hybrid GRFT (DT-HGRFT) is presented as an enhancement of EI-GRFT, enabling long-time integration along slow time and integration among multiple scatters by using HGRFT and multi-dimensional sliding double-threshold detection, respectively. Furthermore, a method for joint detections of multiple DT-HGRFTs is provided to handle the case where the number of scattering centers of multi-dimensional spread targets is unknown. Finally, a detailed theoretical analysis of the performance of the proposed method is presented, along with extensive simulations and practical experiments to demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The problem of antibiotic contamination in the environment has attracted much attention in recent years. However, studies on antibiotic contamination in groundwater have only emerged in the last 15 years. In this study, we systematically reviewed the detection methods, distribution characteristics, risk, fate, and sources of antibiotics in groundwater in China, listed the concentrations of the main antibiotic types, and obtained the maximum concentrations by comparing the literature published in the last 10 years. The results show that 65 antibiotics were detected in groundwater in China, with sulfonamides and quinolones receiving the most attention. Antibiotic concentrations are influenced by hydrogeological conditions and seasonal variations, and the ecological risk in most areas is low to medium risk, which is relatively manageable. The highest concentrations found in most of the literatures were in the range of 10–1000 ng/L, but the maximum concentration can reach 47,444.5 ng/L, which requires extra attention. In addition, this study makes recommendations for improving groundwater monitoring surveys and protection measures to prevent the antibiotic contamination of groundwater more effectively. [ABSTRACT FROM AUTHOR]

Given their occupation's inherent income instability, the promotion of endowment insurance among construction workers is essential. This research reports the barriers and facilitators involved in promoting endowment insurance to construction workers, by contextualizing it in China. It adopts a qualitative research approach combining semi-structured interviews and thematic analysis. It is discovered that the barriers include (1) construction workers' low willingness to participate in insurance, caused by the low level of awareness and understanding of the benefits of endowment insurance, low income, unstable employment situation, and lack of trust in the insurance system, (2) companies' irresponsibility, caused by cost pressure, lack of policy support, uncertain legal responsibilities, and (3) governments' incomplete management, caused by insufficient funding, complex administrative procedures, weak regulation, and regional differences. A series of facilitating measures are also proposed, including (1) increasing awareness and understanding of the benefits of endowment insurance, (2) adjusting the design of the endowment insurance scheme, and (3) strengthening legislation and enforcement. This study presents an analysis of the promotion practices of endowment insurance in China's construction industry, which can provide policymakers with valuable references for optimizing the endowment insurance system. The findings can have significant implications for China and other developing countries that may face similar challenges in promoting social security programs in aging societies. [ABSTRACT FROM AUTHOR]

The role of ecological niches in lineage diversification has been the subject of long-standing interest of ecologists and evolutionary biologists. Specific responses to climate change can arise below the species level, resulting in differentiated adaptation or movement patterns within a given species. Thus, the urgent need to model potential responses to ongoing climate changes among genetically distinct populations within a species is increasingly recognized. In this study, we utilize the information of intraspecific variation within Quercus aquifolioides as a priori, and then focus on the potential distribution ranges and niche dynamics of its three intraspecific lineages (WSP, HDM, and Tibet) under current environmental conditions via ecological niche models (ENMs) and PCA-env ordination method, respectively. Our results indicated that the three lineages have occupied differentiated climatic niches. Although the three lineages have distinctly adaptive strategies for homogeneous environmental conditions, some lineages had sympatric projecting areas. The PCA-env demonstrated that the lineage pair WSP vs. HDM had the largest niche overlap while Tibet vs. HDM showed the smallest one. Moreover, the hypothesis of the niche was indistinguishable within the three lineages rejected, indicating the presence of niche divergence rather than niche conservatism below the species level. Our findings highlight the potential of modeling intraspecific responses to climate change and provide insights into lineage diversification within Q. aquifolioides, permitting the exploration of the information determined by niche evaluations and comparisons to understand plant diversification processes below the species level in biodiversity hotspots. [ABSTRACT FROM AUTHOR]

Hydrological connectivity is the main non-biological driving factor of wetland ecological processes and is key to maintaining the stability and biodiversity of the whole ecosystem. Socio-economic activities have had a significant impact on the hydrological connectivity of wetlands, resulting in the loss of biodiversity and the degradation of the ecological functions of wetlands. Wetland biodiversity hotspots in Sanjiang Plain that were identified in the previous literature using the Systematic Conservation Planning (SCP) method were chosen as the research objects. The SCP method was combined with the structural hydrological connectivity index (Integral Index of Connectivity (IIC) and Probability of Connectivity (PC)) and the functional hydrological connectivity index (Morphological Spatial Pattern Analysis) to analyze the spatio-temporal changes in the hydrological connectivity of the wetland biodiversity hotspots in Sanjiang Plain. The results showed that the hydrological connectivity within the eight identified wetland biodiversity hotspots in Sanjiang Plain experienced varying degrees of decline in the period between 1995 and 2015. Structurally, the IIC values of wetlands in all of the biodiversity hotspots were more than 0.5, and the PC values were more than 0.9, but most of the hotspots showed declining trends of varying degrees from 2010 to 2015. Functionally, the average proportion of core wetlands in the hotspots has decreased by 4.82%, and the average proportion of edge wetlands has increased by 2.71% over the last 20 years. The findings on the hydrological connectivity evolution patterns can aid in the conservation and restoration of wetlands and biodiversity hotspots. [ABSTRACT FROM AUTHOR]