To enhance the safety of power grid operations, this study proposes a high-precision short-term photovoltaic power prediction method that integrates information from surrounding pho-tovoltaic stations and the Conv-LSTM-ATT model. In the deep learning prediction model, not only is numerical weather prediction (NWP) data from the target photovoltaic station used as input features, but also highly correlated features from nearby photovoltaic stations are incor-porated. The research begins by analyzing the correlation between irradiance and power se-quences, along with distance factors, to calculate a composite similarity index between the target and other regional photovoltaic stations. Stations with high similarity indices are then selected as data sources. Subsequently, Bayesian optimization techniques are employed to find the optimal data fusion ratios. Ultimately, using the selected data, power prediction mod-eling is conducted via the Conv-LSTM-ATT deep neural network. Experimental results con-firm the superiority of the proposed model, which demonstrates higher predictive accuracy compared to three other classical models. The data fusion strategy determined by Bayesian optimization significantly enhances prediction accuracy, reducing the root mean square error (RMSE) of the test set by 20.04%, 28.24%, and 30.94% for three weather types, respectively.

The purpose of this study is to investigate the failure mechanism and preventive strengthening methods of prefabricated full length small box girder (PSBG) under car explosion. After damage analysis during blast loads using the commercial software Autodyn, three strengthening methods are presented to improve the blast mitigation performance. The results show that the failure modes and bearing capacity are significantly different with various strengthening measures. Adhering U-shaped steel plate can effectively enhance the shear performance of PSBG under blast loads. Strengthening the bottom of the upper flange of box girder with steel plate can effectively mitigate the local bending failure, and enhance the overall flexural capacity during car explosion.

This review provides a systematic analysis of the literature data on the electrodeposition of composite coatings using plating baths based on a new generation of room-temperature ionic liquids, known as deep eutectic solvents (DESs). Such systems offer several advantages over traditionally used aqueous electrolytes and organic solvent-based electrolytes. The colloidal-chemical properties of suspension and colloidal electrolytes for composite deposition are thoroughly examined. New theories describing the kinetics of co-deposition of composite layers are characterized. The kinetics and mechanism of electrochemical deposition processes of composite coatings with metallic matrices are discussed. Case studies regarding the electrodeposition of composite coatings based on electrodeposited copper, silver, zinc, tin, nickel, cobalt, and chromium from DES-assisted electroplating baths are described and systematized. The main prospective directions for further research in the discussed scientific area are highlighted.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

Skin cancer is a prevalent type of malignancy on a global scale, and the early and accurate diag-nosis of this condition is of utmost importance for the survival of patients. The clinical assessment of cutaneous lesions is a crucial aspect of medical practice, although it encounters several obsta-cles, such as prolonged waiting time and misinterpretation. The intricate nature of skin lesions, coupled with variations in appearance and texture, presents substantial barriers to accurate clas-sification. As such, skilled clinicians often struggle to differentiate benign moles from early ma-lignant tumors in skin images. Although deep learning-based approaches such as convolution neural networks have made significant improvements, their stability and generalization continue to confront difficulties, and their performance in accurately delineating lesion borders, capturing refined spatial connections among features, and using contextual information for classification is suboptimal. To address these limitations, we propose a novel approach for skin lesion classification which combines snake models of Active Contour (AC) Segmentation, ResNet50 for feature ex-traction and a Capsule Network with a fusion of lightweight attention mechanisms, to attain the different feature channels and spatial regions within feature maps, enhance the feature discrimi-nation, and improve accuracy. We employed the Stochastic Gradient Descent (SGD) optimization algorithm to optimize the model's parameters. The proposed model is implemented on publicly available datasets, namely HAM10000 and ISIC 2020. The experimental results showed that the proposed model achieved an accuracy of 98% and AUC-ROC of 97.3%, showcasing substantial potential in terms of effective model generalization compared to existing state-of-the-art (SOTA) approaches. These results highlight the potential for our approach to reshape automated derma-tological diagnosis and provide a helpful tool for medical practitioners.

Obesity is a global health concern. Recent research has suggested that the development of anti-obesity ingredients and functional foods should focus on natural products without side effects. We investigated the anti-obesity efficacy and mechanisms of action of Brassica juncea extract (BJE) using in vitro and in vivo obesity models. In in vitro experiments, BJE treatment downregulated adipogenic-related proteins CCAAT/enhancer-binding protein-α (C/EBP-α), peroxisome proliferator-activated receptor-γ (PPAR-γ), adipocyte protein 2 (aP2), and lipid synthesis-related protein acetyl-CoA carboxylase (ACC). It also upregulated the heat generation protein peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) and fatty acid oxidation protein carnitine palmitoyltransferase-1 (CPT-1). Oral administration of BJE decreased body weight, alleviated liver damage, and inhibited lipid accumulation in high-fat diet (HFD)-induced obese mice. Inhibition of lipid accumulation by BJE in vivo was associated with decreased expression of adipogenic and lipid synthesis proteins and increased expression of heat generation and fatty acid oxidation proteins. BJE administration improved obesity by decreasing adipogenesis and activating heat generation and fatty acid oxidation in 3T3-L1 cells and in HFD-induced obese C57BL/6J mice. These findings indicate that BJE is a promising natural means of preventing obesity-related metabolic diseases.

Street view images can help us better understand the city environment and potential characteristics. With the development of computer vision and deep learning, the technology of semantic segmentation algorithms has become more mature. However, DeeplabV3+, which is commonly used in semantic segmentation, has shortcomings such as a large number of parameters, high requirements for computing resources, and easy loss of detailed information. Therefore, this paper proposes LM-DeeplabV3+, which aims to greatly reduce parameters and computations of the model while ensuring the segmentation accuracy. Firstly, the lightweight network MobileNetV2 is selected as the backbone network, and the ECA attention mechanism is introduced after MobileNetV2 extracts shallow features to improve the ability of feature representation; secondly, the ASPP module is improved, and on its basis, the EPSA attention mechanism is introduced to achieve cross-dimensional channel attention and important feature interaction; thirdly, a loss function named CL loss is designed to balance the training offset of multiple categories and better indicate the segmentation quality. This paper conducted experimental verification on the Cityspaces dataset, and the results showed that the mIoU reached 74.9%, which was an improvement of 3.56% compared to DeeplabV3+; the mPA reached 83.01%, which was an improvement of 2.53% compared to DeeplabV3+.

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method Let-Net(Large kernel and attention mechanism Network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to broaden the receptive field and incorporates depthwise convolution with residual connections to reduce parameter count. Secondly, Let-Net integrates an attention mechanism to enhance the information flows in channels and spaces for more comprehensive and distinctive FV feature extraction. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

Shut-in after fracturing is generally adopted for wells in shale oil reservoirs, and imbibition occurring in matrix nanopores has been proven as an effective way to improve recovery. In this research, the molecular dynamics (MD) simulation was used to investigate the effects of wettability and pressure on nanopores imbibition during shut-in for a typical shale reservoir Jimsar. The results indicate that the microscopic advancement mechanism of the imbibition front is the competitive adsorption between “interfacial water molecules” at the imbibition front and “adsorbed oil molecules” on the pore wall. The essence of spontaneous imbibition involves the adsorption and aggregation of water molecules onto the hydroxyl groups on the pore wall. The flow characteristics of shale oil suggest that the overall push of the injected water to the oil phase is the main reason for the displacement of adsorbed oil molecules. Thus, shale oil especially the heavy hydrocarbon component in the adsorbed layer tends to slip on the walls. However, the weak slip ability of heavy components on the wall surface is an important reason that restricts the displacement efficiency of shale oil during spontaneous imbibition. The effectiveness of spontaneous imbibition is strongly dependent on the hydrophilicity of the matrix pore's wall. The better hydrophilicity of the matrix pore's wall facilitates higher levels of adsorption and accumulation of water molecules on the pore wall and requires less time for “interfacial water molecules” to compete with adsorbed oil molecules. During the forced imbibition process, the pressure difference acts on both the bulk oil and the boundary adsorption oil, but mainly on the bulk oil, which leads to the occurrence of wetting hysteresis. Meanwhile, shale oil still existing in the pore always maintains a good stratified adsorption structure. Because of the wetting hysteresis phenomenon, as the pressure difference increases, the imbibition effect gradually increases, but the actual capillary pressure gradually decreases and there is a loss in the imbibition velocity relative to the theoretical value. Simultaneously, the decline in hydrophilicity further weakens the synergistic effect of pressure difference because of the more pronounced wetting hysteresis. Thus, selecting an appropriate well pressure enables cost savings and maximizes the utilization of the formation's natural power for EOR.

Peanut leaf spot is a worldwide disease whose prevalence poses a major threat to peanut yield and quality, and accurate prediction models are urgently needed for timely disease management. In this study, we proposed a novel peanut leaf spot prediction method based on an improved long short-term memory (LSTM) model and multi-year meteorological data combined with disease survey records. Our method employed a combination of convolutional neural network (CNN) and LSTM to capture spatial-temporal patterns from the data and improve the model ability to recognize dynamic features of the disease. In addition, we introduced an attention mechanism module to enhance model performance by focusing on key features. Through several hyper-parameter optimization adjustments, we identified a peanut leaf spot disease condition index prediction model with a learning rate of 0.001, a number of cycles (Epochs) of 800, and an optimizer of Adma. The results showed that the integrated model demonstrated excellent prediction ability, obtaining an RMSE of 0.063 and an R2 of 0.951, which reduced the RMSE by 0.253 and 0.204, and raised the R2 by 0.155 and 0.122, respectively, compared to the single CNN and LSTM. Predicting the incidence and severity of peanut leaf spot disease based on the meteorological conditions and neural networks is feasible and valuable to help growers made accurate management decisions and reduced disease impacts through optimal fungicide application timing.

Due to the particular structure of the beveloid gear, it can’t be hobbed directly by an ordinary gear hobbing machine. The existing processing method is complex and high-cost. Therefore, the mass production and industrialization of beveloid gears are limited. To improve the machining efficiency and accuracy of processing beveloid gears, we proposed a hobbing method via the modification of ordinary hobbing machines. At first, we completed the derivation and calculation of the relevant processing parameters of the beveloid gear based on the study of the structural characteristics of the beveloid gear and the principle of hobbing machining. Then, we proposed and designed a beveloid gear hobbing method, and the modification of the ordinary hobbing machine was completed by using a hanging wheel mechanism in synchronous belt type. Finally, we completed the actual hobbing of the beveloid gear, and the feasibility of the proposed method was verified. After that, we analyzed the machining error of the trial-produced beveloid gear, the results showed that the accuracy of the trial-produced beveloid gear met the 6-level standard, which also verified the accuracy of the proposed method.

Raman scattering has been employed to study in detail the concentration dependence of the vibrational modes for the hexamethylenetetramine (HMTA) aqueous solutions. The formation of protonated and/or aggregated species has been clarified by comparing the experimental with the theoretically predicted vibrational spectra by means of quantum mechanical calculations. The analysis has shown that the vibrational modes of the solutions arise from a contribution of the vibrational modes of the HMTA self-aggregates and hetero-aggregates of HMTA with water molecules that are formed in the low- and intermediate-concentration region, respectively. The protonation of HMTA is ruled out due to the large differences between the experimental and the theoretically calculated spectra of the protonated molecules of HTMA in the fingerprint region. In the low-concentration solutions, the hetero-aggregation reaction of HMTA with water is the dominant mechanism, while at higher concentrations a self-aggregation mechanism occurs. Ultrasonic absorption and velocity measurements were carried out for hexamethylenetetramine aqueous solutions. The acoustic spectra reveal the presence of only one single Debye-type relaxation process that is assigned to the aggregation mechanism of HMTA. The sound absorption data follow two different dependencies on HMTA mole fraction. The crossover 0.018 mole fraction signifies two separate regions with distinct structural characteristics. The relaxation mechanism observed in dilute solutions is attributed to hetero-association of HMTA with water molecules, while at higher concentrations the observed relaxation process is assigned to the self-association reaction of HMTA molecules. This structural transformation is also reflected in several physicochemical properties of the system, including the kinematic viscosity, the mass density, the sound speed, and the adiabatic compressibility of the HMTA aqueous solutions. The combination of vibrational and acoustic spectroscopies with molecular orbital calculations allowed us to disentangle the underlying processes and to elucidate the observed relaxation mechanism in the HMTA aqueous solutions.

. Manufacturers are increasingly adopting mixed-flow manufacturing models to meet the growing customized, diverse, and dynamic customer demand. In such an environment, material distribution scheduling optimization is vital for smooth operations and is integral to production management. However, manufacturers frequently encounter problems like disordered and delayed material distribution. Traditional scheduling methods suffer from problems like inadequate transparency, delayed decision directives, and suboptimal results, impacting performance. To this end, this study proposes a dynamic material distribution scheduling optimization model and strategy based on digital twin (DT) to address these problems. Firstly, we introduce workstation satisfaction and establish a material distribution path optimization model minimizing total distribution cost while maximizing workstation satisfaction. Subsequently, we present a cloud-edge computing-based decision framework and explain the DT-based material distribution system's components and operation. Furthermore, a dynamic material distribution scheduling optimization mechanism based on DT is designed. By incorporating a savings method and incentive, penalty strategies, improvements are made to the path node selection probabilities and the information pheromone update rules of the traditional ant colony optimization (ACO) algorithm. Finally, a numerical case study, using real data from collaborating enterprises, validates the proposed algorithm and strategy. This research offers valuable insights into logistics management and algorithm design in smart manufacturing environments.

To address the deficiency of existing relation extraction models in effectively extracting relational triplets pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieves an F1-score of 93.3\%, surpassing the baseline model CasRel by 0.8\%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1\% and 92.6\% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5\% and 0.8\%, which highlights the good generalization ability of the proposed model.

Understanding the thermal cracks of rock caused by high temperature is of great help to the development of underground engineering, such as geothermal mining, underground coal gasification. Although there has been many study on thermal cracking, it is not systematic and in-depth enough. In order to deeply study the thermal crack mechanism of rock, this paper conducted thermal cracking experiments on granite at different temperatures and heating/cooling paths, and qualitative and quantitative analysis was conducted on the crack initiation characteristics, propagation paths, and crack network morphology of rock thermal crack under different test conditions. The thermal crack mechanism was also analyzed from the perspectives of mineral petrology, fracture mechanics, thermodynamics, and other aspects. The research results show that there are two obvious mutation points in the study temperature range for samples with fast cooling paths (SF path: slow heating and fast cooling; FF path: fast heating and fast cooling), around 200-300 ℃ and 600 ℃, respectively, while for samples with slow cooling paths (SS path: slow heating and slow cooling; FS path: fast heating and slow cooling), there is only one mutation point around 600 ℃. The initiation positions of thermal cracking under all temperature paths are relatively similar, with intergranular crack located between particles at the edge of the sample or intercrystalline crack in the middle of feldspar or quartz aggregates. The initiation temperature of SF and FF path specimens is relatively low compared to SS and FS path specimens, and the number and size of cracks is small. The crack network structure formed by the SF path is the most complex, with the largest crack ratio and cumulative crack length; The crack network structure formed by the FF path is relatively complex, with a larger main crack size but relatively fewer secondary cracks; The FS and SS paths do not form a complex network with good connectivity. The process of thermal crack development can be divided into three stages: the development of small cracks, the joint development of main cracks and small cracks, and the connection of cracks into a network structure. The mechanism of thermal crack propagation is that the expansion and thermal conductivity between different crystals are different. The thermal stress caused by temperature gradient and the tension or shear stress caused by the inconsistent deformation of crystals form stress concentration in weak areas such as particles boundary, cleavage, and original cracks. Firstly, it causes some crystals with smaller strength or less rounded shape to crack, and when the combined stress is large, the cracks will gradually expand along the existing fine cracks. This also explains that the most main cracks of the SF and FF path specimens mainly surround some large mineral aggregates or between particles.

With the rapid development in Beijing, there is a critical need to explore the circumstance and reveal the mechanisms of precious urban natural resources. In this context, urban blue space has attracted more and more attention by driving microcirculation, cooling heat islands, and relaxing residential. We extracted the UBS at Beijing using remote sensing, explored the spatial and temporal development in the last two decades via USDA methods, uncovered the full spectrum of landscape patterns from an ecological perspective, and simulated the mechanisms of the UBS area and the landscape quantitatively. We found that: (1) The UBS area in Beijing increased with fluctuation from 2000 to 2020. (2) The spatial clustering has distributed stable with some subtle changes. (3)The ecological circumstance has improved in the last 21 years in Beijing, with the increasing habitat diversity and richness, while the inferior landscape fragmentation has indicated some severe challenges. (4) Natural factors impact urban blue space areas more than social ones, while both similarly influence the UBS landscape. (5) Vegetation circumstances and precipitation are the most important natural factors on both area and landscape of UBS, and population and artificial surface are the most important social factors.

In recent times, the soft robotics field is gaining numerous research focus owing to its high level of manipulation capabilities unlike traditional rigid robots, which gives room for an increasing use in other areas. However, compared to traditional rigid gripper robots, being capable of controlling/obtaining overall body stiffness when required is yet to be further explored since soft gripper robots have inherently less rigid properties. unlike previous designs with very complex variable stiffness systems, this paper demonstrates a soft gripper design with minimum system complexity while being capable of varying the stiffness of a continuum soft robotic actuator and proves to have potential applications in gripping objects of various shapes, weights & sizes. The soft gripper actuator comprises two separate mechanisms; the pneumatic mechanism for bending control and the mechanical structure for stiffness variation by pulling tendons using stepper motors which compresses the actuator, thereby changing the overall stiffness. The pneumatic mechanism was first fabricated and then embedded into another silicon layer during which it was as well merged with the mechanical structure for stiffness control. By first pneumatically actuating the actuator which causes bending and then pulling the tendons, we found out that the actuator stiffness value can be increased up to 145 % its initial value, and the gripper can grasp & lift a weight of up to 2.075 kg.

In view of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm all nodes participate in the consensus, the three-stage process has problems such as large communication overhead, random selection of master nodes and lack of reward and punishment mechanism. A Speculative Practical Byzantine Fault Tolerance (SP-PBFT) is proposed. Firstly, the consensus protocol was optimized, and a timeout mechanism was proposed to divide the consensus process into optimistic mode and pessimistic mode, which reduced the communication overhead under the premise of resisting Byzantine node attacks .Secondly, a node evaluation mechanism was proposed to calculate the node reputation value according to the node's historical behavior and give the corresponding reward or punishment, which reduced the possibility of malicious nodes participating in the consensus process. Finally, the Fibonacci grouping mechanism was used to reduce the number of nodes participating in the consensus process, fundamentally improving the consensus efficiency, avoiding the problem of centralization of the consensus process caused by the cumulative reputation value of nodes, and improving the enthusiasm of consensus nodes. Simulation experiments using Docker containers to simulate multiple nodes show that the SP-PBFT consensus algorithm proposed in this paper has better performance than PBFT consensus algorithm and other improved algorithms in terms of consensus delay, throughput, fault tolerance and communication complexity.

Remote sensing imagery is of great significance for policy decisions, especially for disaster assessment and disaster relief. To ensure the privacy and inviolability of personal buildings, the information containing these buildings must be anonymized during the remote sensing mapping process. Traditional processing methods for these targets in remote sensing mapping are mainly based on manual retrieval and image editing tools, which are inefficient. Deep learning provides a new direction for target hiding. Although the image inpainting method based on deep learning is faster than the manual method, the cost of training calculation is a disadvantage. And the element-wise product operation used in the model increases the risk of vanished or exploded gradients. We propose a Residual Attention Target Hiding (RATH) model for remote sensing target hiding based on deep learning. RATH uses residual attention modules to replace gated convolutions, reducing parameters and mitigating gradient issues. The residual attention module preserves gated convolution performance but provides an adjustable kernel size. RATH retains gated convolutions for dynamic feature selection and balances model depth and width. Furthermore, this paper modifies the contextual attention layer by adjusting the fusion process to enlarge the fusion patch size. Finally, we extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset prove RATH’s efficiency for image inpainting and target hiding. RATH achieves state-of-the-art results with lower complexity. And it has the highest similarity for edge-guided target hiding. RATH enables robust, efficient target hiding for privacy protection in remote sensing imagery while balancing performance and complexity. Experiments show RATH's superiority over existing methods in hiding arbitrary-shaped targets.

The ozonation reaction in a bubble column reactor (BCR) has been widely used in the removal of phenol from wastewater, but the phenol removal efficiency in this type of reactor is limited because of low ozone solubility and reactivity in the system. In the present study, the phenol degradation was enhanced using α-Al2O3 as a packing material and a ZnO nanocatalyst in the BCR. The reactor diameter and height were 8 cm and 180 cm, respectively. The gas distributor was designed to include 52 holes of 0.5-mm diameter. Also, the gas holdup, pressure drop, and bubble size were measured as a function of the superficial gas velocity (i.e., 0.5, 1, 1.5, 2, 2.5, and 3 cm/s). The evaluation of the hydrodynamic parameters provided a deep understanding of the ozonation process to select the optimal operating parameters in the reactor. It was found that the best superficial gas velocity was 2.5 cm/s. A complete (100%) phenol removal was achieved for phenol concentrations of 15, 20, and 25 ppm at reaction times of 80, 90, and 100 min, respectively, using α-Al2O3 packing material and a ZnO nanocatalyst in the BCR. Additionally, the results provided a clear reaction mechanism describing the ozonized BCR. Finally, the applied treatment method can be used efficiently to remove phenol from wastewater at a low cost, with little consumed energy, and a simple operation.

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

In order to effectively utilize red mud and reduce its occupation of land resources as well as its impact on the environment, experiments were conducted to develop stabilized soil materials using nano-SiO2 synergistically modified red mud and to investigate the curing mechanism of stabilized soil. The unconfined compressive strength, microscopic morphology and curing mechanism of the red mud-based stabilized soil materials with different amounts of modified materials were investigated. The test results show that after 7-days curing, the unconfined compressive strength of red mud-based stabilized soil meets the compressive strength requirement of road base material when nano-SiO2, gypsum and cement are synergistically modified. In such case, the soil structure has the lowest fracture rate and the best structural compactness when the amount of nano-SiO2 is 1%; It is found that the needle-like and columnar calcium alumina in the modified red mud-based stabilized soil increases, and the binding energy of hydration product ions in the modified material is improved; The chemical curing mechanism of modified red mud-based stabilized soil includes hydration reaction, volcanic ash reaction, promotion effect of nano-SiO2, and enhancement effect of gypsum. On this base, a model of early start hydration process of red mud-based stabilized soil promoted by nano-SiO2 is established.

Respiratory syncytial virus (RSV) RNA synthesis takes place in cytoplasmic viral factories also called inclusion bodies (IBs), which are membrane-less organelles concentrating the viral RNA polymerase complex. IBs assembly is driven by liquid-liquid phase separation promoted by interactions between the viral nucleoprotein N and the phosphoprotein P. We recently demonstrated that cyclopamine (CPM) inhibits RSV multiplication by disorganizing and hardening IBs. Although a single mutation in the viral transcription factor M2-1 induced resistance to CPM, the mechanism of action of CPM still remains to be characterized. Here, using FRAP experiments on reconstituted pseudo-IBs both in cellula and in vitro, we first demonstrated that CPM activity depends on the presence of M2-1 together with N and P. We show that CPM impairs the competition between P and RNA binding to M2-1. As mutations on both P and M2-1 induced resistance against CPM activity, we suggest that CPM may affect the dynamics of the M2-1−P interaction, thereby affecting the relative mobility of proteins contained in RSV IBs. Overall, our results reveal that stabilizing viral protein-protein interactions is an attractive new antiviral approach. They pave the way for the rational chemical optimization of new specific anti-RSV molecules.

The development of high-performance sensing materials for detecting or removing SF₆ decomposition gases is crucial for gas-insulated switchgear (GIS). In this work, the adsorption characteristics of the pristine porphyrin (Pr) and Fe-doped porphyrin (FePr) monolayers towards SF₆ decomposition gases were investigated using the first-principles calculations. Additionally, the electronic properties and sensing-mechanism were also examined. The findings indicate that both Pr and FePr monolayers exhibit thermodynamic stability. The intrinsic Pr monolayer only exhibits a moderate affinity towards the SO₂ molecule, however, its poor sensitivity of the Pr monolayer renders it unsuitable as the sensing-material. In contrast, the FePr monolayer exhibits favorable adsorption strength towards all five toxic gases (H₂S, SO₂, SOF₂, SO₂F₂, HF). The FePr monolayer exhibits chemisorption towards SO₂/SOF₂ gases, Furthermore, the microscopic mechanism of gases-substrate interaction is elucidated through the analysis of charge density difference, charge transfer, and density of states. Moreover, the FePr has excellent sensitivity for H₂S, SO₂, SO₂F₂, SOF₂ and HF molecules owing to the significant variations of the band gap, electrical conductivity and work function. In addition, the τ of H₂S, SO₂, and SOF₂ is 12.6 μs, 18.5 s and 15.4 s at 298 K, respectively. Our calculated results can offer theoretical guidance for the practical application of FePr-based sensing materials to detect SF6 decompositions gases.

With an increase in energy consumption globally, Fischer-Tropsch (FT) synthesis is a good alternative for producing fuels and chemicals from coal, natural gas or biomass. Among them, coal to liquids has been put into production in countries that have large coal reserves. In this process, Fe-based catalysts are commonly used due to their earth abundance, comparatively wide operation range and ready availability to handle low H2/CO ratio from coal. Despite their extensive applications, the kinetic and mechanistic understandings of Fe carburization and FT reaction on Fe-carbides are relatively limited due to the complexity of phase composition of the applied catalysts. This review summarizes the current state of knowledge of FT synthesis on Fe-carbide with an emphasis on underlying mechanism. Specifically, the employment of model catalyst, such as Raney-Fe, could provide a convenient way to furnish kinetic information regarding Fe carburization and subsequent FT reaction. A major challenge for further understanding catalytic reactions occurring at the Fe-carbide surface is correlating FT activity and selectivity to specific active site. To address this issue, the advancements of both DFT calculations and surface science techniques are highly demanded.

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is a minimal and substantial intra-class variance. The current methods address the issue by first locating selective regions with Region Proposal Networks (RPN), object localization, or part localization, followed by implementing a CNN Network or SVM classifier to those selective regions. This approach, however, makes the process simple by implementing a single-stage end-to-end feature encoding with a localization method, which leads to improved feature representations of individual tokens/regions by integrating the transformer encoder blocks into the Yolov5 backbone structure. These Transformer Encoder Blocks, with their self-attention mechanism, effectively captured the global dependencies and enabled the model to learn relationships between distant regions. This improved the model ability to understand context and captured long-range spatial relationships in the image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative features maps from transformer encoder blocks. We established the potential of the single stage detector for the fine-grained image recognition task, by achieving state of the art 93.4% accuracy, as well as outperforming the existing Yolov5 model. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.

Forest fires are one of the world's deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding attention mechanism in the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection effect of the model and the robustness of the model. In addition, random Mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model responsibility to improve the detection speed. Experimental results on homemade forest fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection.

Citation: To be added by editorial staff during production.

The successive steps of the oxidation mechanism of crocin, major compound of saffron, by the free OH• radical are investigated by the pulse radiolysis, steady-state (gamma) radiolysis methods and molecular simulations. The optical absorption properties of the transient species and their reaction rate constants are determined. The absorption spectrum of the oxidized radical of crocin resulting from the H-abstraction presents a maximum at 678 nm and a band at 441 nm, almost as intense as that of crocin. The spectrum of the covalent dimer of this radical contains an intense band at 441 nm and a weaker one at 330 nm. The final oxidized crocin, issued from radical disproportionation, absorbs weaker with a maximum at 330 nm. The molecular simulation results suggest that the OH• radical is electrostatically attracted by the terminal sugar and is scavenged predominantly by the neighbor methyl site of the polyene chain as in a sugar-driven mechanism. Based on detailed experimental and theoretical investigations, the antioxidant properties of crocin are highlighted.

Since the worldwide spread of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), treating taste and saliva secretory disorders associated with coronavirus disease 2019 (COVID-19) has become one of the most critical issues in the COVID-19 era. The aim of the present study was to update information on treatments being applicable to such oral symptoms and discuss their pathogenic mechanisms. Promising treatments include different types of methods using tetracycline, corticosteroid, zinc, stellate ganglion block, phytochemical curcumin, traditional herbal medicine, nutraceutical vitamin D, photobiomodulation, antiviral drug, malic acid sialagogue, chewing gum, acupuncture, and/or moxibustion. At present, however, fully validated treatments are still lacking for COVID-19-associated ageusia/dysgeusia/hypogeusia and xerostomia/dry mouth/hyposalivation. An appropriately selected treatment and oral healthcare should be provided to COVID-19 patients and survivors suffering from taste and saliva secretory disorders. Understanding of currently available treatment options is required for dental profes-sionals because they not only experience patients who were infected with SARS-CoV-2 or recov-ered from COVID-19 but first become aware of their abnormal taste and salivary secretion. By doing so, dentists and dental hygienists can play a crucial role in managing COVID-19-associated oral symptoms and contribute to improving the oral health-related quality of life of the relevant dental patients.

One of the most broadly used models for membrane fouling is the Hermia model, which separates this phenomenon into four blocking mechanisms, each with an associated parameter n. These mechanisms are complete blocking (n=2), intermediate blocking (n=1), standard blocking (n=3/2), and cake formation (n=3/2). The original model, which was obtained through experimental data, is given by an Ordinary Differential Equation (ODE) dependent on n. At the time, this ODE was only solved for these four values of n, which limits the effectiveness of the model when adjusted to experimental data. This paper aims to not only mathematically prove the original Hermia model but also to broaden the scope of this model for any real number n by using the original ODE, the equations of fluid mechanics, and the properties of single and multivariable calculus. The extended Hermia model (EHM) is given by a power law for any n≠2 and is given by an exponential function at n=2. To better test the model, we have performed the model fitting of the EHM and compared its performance to the original four pore-blocking mechanisms in 6 micro- and ultrafiltration examples. In all examples, the EHM performed consistently better than the four original pore-blocking mechanisms. Changes in the blocking mechanisms concerning transmembrane pressure (TMP), crossflow rate (CFR), crossflow velocity (CFV), membrane composition, and pretreatments are also discussed.

The multi-modal data fusion method based on IA-net and CHMM technical proposed is designed to solve the problem that the incompleteness of target behavior information in complex family environment leads to the low accuracy of human behavior recognition.The two improved neural networks(STA-ResNet50、STA-GoogleNet)are combined with LSTM to form two IA-Nets respectively to extract RGB and skeleton modal behavior features in video. The two modal feature sequences are input CHMM to construct the probability fusion model of multi-modal behavior recognition.The experimental results show that the human behavior recognition model proposed in this paper has higher accuracy than the previous fusion methods on HMDB51 and UCF101 datasets. New contributions: attention mechanism is introduced to improve the efficiency of video target feature extraction and utilization. A skeleton based feature extraction framework is proposed, which can be used for human behavior recognition in complex environment. In the field of human behavior recognition, probability theory and neural network are cleverly combined and applied, which provides a new method for multi-modal information fusion.

The nature of bone homeostasis is the coordination between the osteoblasts (OBs) and osteoclasts (OCs). However, abnormal activation of osteoclasts (OCs) could compromise the bone homeostasis. Thus, it is imperatively urgent to explore effective medical interventions for patients. NO/guanylate cyclase (GC)/cGMP signaling cascade has been widely reported in regulating bone metabolism, and GC plays a significantly critical role. Vericiguat, a novel oral soluble guanylate cyclase (sGC) stimulator, has been firstly reported in 2020 to treat patients with heart failure. However, the effect of Vericiguat on the function of OCs has not been explored. In this present study, we found that the concentration range of Vericiguat between 0-8uM was none- cytotoxic to BMMs. Vericiguat could enhance differentiation of OCs at concentration of 500nM, whereas it inhibited differentiation at 8 uM in terms of the number and size of OCs. In addition, Veirciguat also showed dural effect on RANKL‐induced OC fusion and bone resorption in a concentration‐dependent manner. Further, molecular assay suggested that the dually regulatory effect of Vericiguat on OCs was mediated by the bidirectional activation of IκB-α/NF-κB signaling pathway. Taken together, our present study demonstrated the dual effects of Vericiguat on the formation of functional OCs in a concentration-dependent manner. The regulatory effect of Vericiguat on OCs was mediated by the bidirectional activation of IκB-α/ NF-κB signaling pathway, and a potential balance between IκB-α/ NF-κB signaling pathway and sGC/cGMP/VASP may exist.

Abstract Epilepsy is a disorder of the brain and a seriously debilitating condition, which has been associated with injury, social stigmatisation and in some cases, sudden unexpected and premature death. A sense of profound isolation is felt by many individuals with epilepsy, and this community has expressed an urgent desire for an early warning system to allow them time to prepare for seizure onset. Surveys of dog owners with epilepsy have previously reported that some dogs can predict the onset of a seizure. The current study used an experimental design to investigate the alleged propensity of untrained pet dogs to react to human epileptic seizures. We hypothesised that seizures are associated with specific volatile organic compounds resulting in detectable odours which are the biomarker that triggers these reported behavioural changes in the dogs. Here we provide details of the first empirical test to demonstrate that pet dogs display several significant behavioural changes when they are exposed to seizure-related odours that apparently emanate from their owners. Using a repeated measures design experiment, recordings were made of the reactions of 19 untrained pet dogs to odours from sweat samples provided by three people with epilepsy and by two people without epilepsy (controls). The seizure-associated sweat samples were captured pre-seizure, during seizure and post-seizure. All samples were randomly delivered to individual dogs in a test area, using two bespoke pieces of apparatus called Remote Odour Delivery Mechanisms (RODM). One RODM delivered only experimental odours, the other delivered only control odours. Behavioural changes by the dogs on encountering the odour samples were recorded by video for later analysis. Consistent with our hypothesis, seizure-associated odours evoked significant behavioural changes in the dogs which were affiliative in nature and directed at their owners.

The research is designed for developing the pilot small-scale clean development mechanism bundled project activities in Vietnam electricity/ energy sector. Its overall purpose is to assess the potential of rice husk - fuelled bio-power development projects in Mekong delta. Based on estimating the electricity potential of a bundle of rice husk-fuelled bio-power development projects in Mekong delta with the capacity of 11 MW per project, assessing their CO₂ emission reductions (CERs) and CER credits, calculating and comparing their financial indices (NPV, B/C, IRR) in two cases: W/O CDM and W/CDM, the research expects to establish a rice husk energy balance flowchart for the whole Mekong delta in the year 2021 and recommend policies to use for bio-power generation the unused rice husk that is dumped and discharged from local paddy milling centers into rivers and canals, as well as, to put forward a safe and environmentally friendly solution to minimize thoroughly the current serious pollution of rivers and canals in Mekong delta with the increasing unused rice husk quantity in the context is where the sea level rise phenomenon is the strongest in the world .

The morbidity and mortality caused by invasive fungal infections is increasing across the globe due to developments in transplant surgery, the use of immunosuppressive agents, and the emergence of drug-resistant fungal strains, which has led to a challenge in terms of treatment due to the limitations of three classes of drugs. Hence, it is imperative to establish effective strategies to identify and design new antifungal drugs. Drug repurposing is an effective way of expanding the application of existing drugs. In the last years, various existing drugs have been shown to be useful in the prevention and treatment of the invasive fungi. In this review, we summarize the currently used antifungal agents. In addition, the most up to date information on the effectiveness of existing drugs with antifungal activity is discussed. Moreover, the antifungal mechanisms of existing drugs are highlighted. These data will provide valuable knowledge to stimulate further investigation and clinical application in this field.

The complement system is part of the innate immune response, where it provides immediate protection from infectious agents and plays a fundamental role in homeostasis. Complement dysregulation occurs in several diseases, where the tightly regulated proteolytic cascade turns offensive. Prominent examples are atypical hemolytic uremic syndrome, paroxysmal nocturnal hemoglobinuria and Alzheimer’s disease. Therapeutic intervention targeting complement activation may allow treatment of such debilitating diseases. In this review, we describe a panel of complement targeting nanobodies that allow modulation at different steps of the proteolytic cascade, from the activation of the C1 complex in the classical pathway to formation of the C5 convertase in the terminal pathway. Thorough structural and functional characterization has provided a deep mechanistic understanding of the mode of inhibition for each of the nanobodies. These complement specific nanobodies are novel powerful probes for basic research and offer new opportunities for in vivo complement modulation.

The pandemic of coronavirus disease (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARSCoV-2), is causing substantial morbidity and mortality. Older age and presence of diabetes mellitus, hypertension and obesity significantly increases the risk for hospitalization and death in COVID-19 patients. In this Perspective, informed by the studies on SARS-CoV-2, Middle East respiratory syndrome (MERS-CoV), and the current literature on SARS-CoV-2, we discuss potential mechanisms by which diabetes modulates the host-viral interactions and host-immune responses. We hope to highlight gaps in knowledge that require further studies pertinent to COVID-19 in patients with diabetes mellitus, hypertension and obesity.

The corrected mechanism model of battery voltammetric function is helpful to guide the development and application of battery. There are two scientific issues that need to be answered: First, how many mechanisms do batteries have; Second, how to establish the mechanism model separately under the overlapping of these mechanisms. Volt-ampere characteristics of both linear state and nonlinear state exist; the monotonic decreasing of volt-ampere characteristics indicates that the battery have only three kinds of mechanisms. Without changing the basic form of the function and under the principle of the mechanism function’s working region considered, we propose a mechanism function which satisfies the monotonic decreasing characteristic of the voltammetric curve of battery, via the derivative law of each mechanism function in the voltammetric function of battery. By using the voltammetric data, the obtained cell mechanism function can accurately predict the potential (current or voltage) when the independent variable of the cell is zero, and provide the theoretical basis for the internal working mechanism of the cell, which can guide the practice.

In this paper, a voice coil motor (VCM) actuated fast tool servo (FTS) system is developed for diamond turning. To guide motions of the VCM actuator, a crossed double parallelogram flexure mechanism is selected featuring totally symmetric structure with high lateral stiffness. To facilitate the determination of the multi-physical parameters, analytical models of both electromagnetic and mechanical systems are developed. The designed FTS with balanced stroke and natural frequency is then verified through the finite element analysis. Finally, the prototype of the VCM actuated FTS is fabricated and experimentally demonstrated to have a stroke of ±59.02 μm and a first natural frequency of 253 Hz. By constructing a closed-loop control using PID controller with the internal-model based resonant controller, the error for tracking a harmonic trajectory with ±10 μm amplitude and 120 Hz frequency is obtained to be ±0.2 μm, demonstrating the capability of the FTS for high accuracy trajectory tracking.

The development of a stationary phase material for high performance liquid chromatography based on a surface of silica hydride as opposed to silanols on ordinary silica is discussed including synthetic approaches, characterization and applications. There are several synthetic approaches available to create a silica hydride surface. Modification of the Si-H moiety on the silica surface can be accomplished through use of a hydrosilation reaction. Both the intermediate silica hydride and the material modified with an organic moiety can be characterized by a number of spectroscopic as well as a variety of other methods. Further insights into the retention mechanism are provide through chromatographic measurements. The ultimate utility of any chromatographic stationary phase material is determined by its success for solving challenging analytical problems. A broad range of applications are reviewed to illustrate the versatility and usefulness of silica hydride-based stationary phases.

Visual question answering (VQA) is receiving increasing attention from researchers in both the computer vision and natural language processing fields. There are two key components in the VQA task: feature extraction and multi-modal fusion. For feature extraction, we introduce a novel co-attention scheme by combining Sentence-guide Word Attention (SWA) and Question-guide Image Attention (QIA) in a unified framework. To be specific, the textual attention SWA relies on the semantics of the whole question sentence to calculate contributions of different question words for text representation. For the multi-modal fusion, we propose a “Cross-modal Multistep Fusion (CMF)” network to generate multistep features and achieve multiple interactions for two modalities, rather than focusing on modeling complex interactions between two modals like most current feature fusion methods. To avoid the linear increase of the computational cost, we share the parameters for each step in the CMF. Extensive experiments demonstrate that the proposed method can achieve competitive or better performance than the state-of-the-art.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed Adown down sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the complete intersection over union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieves an mAP50 of 94.3% and an mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3% respectively compared to the original model. The model size is reduced by 0.53 MB to just 5.45 MB, and the number of floating-point operations is reduced by 0.3 G to only 7.8 G. The improved model meets the deployment requirements of mobile embedded devices and can provide a theoretical reference for the automated spraying operations of mulberry leaves.

Despite polycarbonate (PC) being a widely used engineering plastic, its notch and crack sensitivity pose challenges in critical applications. To address this, PC was blended with elastomeric polymers to explore the improvement in toughness. This study systematically investigates the toughening mechanisms of PC blended with acrylonitrile-butadiene-styrene (ABS), copolyether ester elas-tomer (COPE), and ABS and styrene-ethylene-butylene-styrene (SEBS) copolymer grafted with maleic anhydride (MA). The morphology and mechanical behavior were evaluated under qua-si-static and high-speed tensile tests and Charpy impact tests using optical, electronic, and atomic force microscopy and Raman spectroscopy. The morphological analysis reveals cavitation and crazing phenomena for COPE and SEBS-g-MA systems, and mostly debonding for ABS, indicating an improvement in toughening. While the addition of ABS improves the PC plastic deformation, modifying ABS with maleic anhydride enhances the elastic modulus. Blending PC with SEBS-g-MA increases the strain at break, and the addition of COPE significantly improves the deformation behavior of PC (by around 115%). This comparative study provides valuable insights into the performance of different PC-elastomer blends under similar conditions, supporting the selection of appropriate materials for given applications.

Ti17 alloy is mainly used to manufacture aero-engine discs due to its excellent properties such as high strength, toughness and hardenability. It is often subjected to creep-fatigue cyclic loading in service environments. Shakedown theory describes the state in which the accumulated plastic strain of the material stabilizes after several cycles of cyclic loading, without affecting its initial function and leading to failure. This theory includes three behaviors: elastic shakedown, plastic shakedown, and ratcheting. In this paper, the creep-fatigue tests (CF) were conducted on Ti17 alloy at 300℃ to study its shakedown behavior under creep-fatigue cyclic loading. Based on the plasticity-creep superposition model, a theory model that accurately describes the shakedown behavior of Ti17 alloy was constructed, and ABAQUS finite element software was used to validate the accuracy of the model. TEM analysis was performed to observe the micro-mechanisms of shakedown in Ti17 alloy. The results reveal that the Ti17 alloy specimens exhibit plastic shakedown behavior after three cycles of creep-fatigue loading. The established finite element model can effectively predict the plastic shakedown process of Ti17 alloy, with a relative error between the experimental and simulation results within 4%. TEM results reveal that anelastic recovery controlled by dislocation bending and back stress hardening caused by inhomogeneous deformation are the main mechanisms for the plastic shakedown behavior of Ti17 alloy.

Hyperuricemia is mainly caused by the overproduction of uric acid (UA) catalyzed from xanthine by xanthine oxidase (XO) in abnormal purine metabolism in human body. There is an increasing demand for developing novel XO inhibitors of plant origins. The inhibition kinetics and type as well as actions of hesperetin against XO were investigated in vitro using multi-spectroscopic methods including UV-Vis absorption, Fourier transform infrared spectroscopy (FT-IR), fluorescence spectroscopy and molecular docking simulation. Results indicated that hesperetin reversibly inhibited XO in a competitive manner with an inhibition constant (Ki) value of (2.15 ± 0.05) × 10-6 mol/L. Hesperetin had a higher scavenging rate against O2- radical generated by the XO reaction system. The binding of hesperetin to the active site of XO altered the protein’s secondary structure as demonstrated by FT-IR and fluorescence spectra. Molecular docking study revealed that hesperetin had highly affinity to XO and the bonding of hesperetin to the molybdopterin (Mo-pt) active center inhibited not only substrate entry, but also prevented oxygen binding to flavin adenine dinucleotide (another active center in XO). Additionally, hydrophobic interactions and hydrogen bonds contributed to the stabilization of the hesperetin-XO complex. The results suggested that hesperetin be a promising natural XO inhibitor to alleviate hyperuricemia and gout.

The Azores Archipelago is a collection of volcanic islands near northwestern Portugal in the Atlantic Ocean. Because of the importance of accurate environmental monitoring for the advancement of research, transportation and tourism, a LoRaWan Sensor Network was developed to monitor levels of air pollution across these islands. This study presents an approach to the implementation of a blockchain network that is both lightweight and scalable, with the goal of preventing man-in-the-middle attacks on the design of sensor networks.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Among the various types of gas sensors, the chemoresistive gas sensors based on metal oxides are considered the most attractive ones due to their simple operation, low costs and miniaturization. Thin films of V2O5 undoped and doped with TiO2 were studied as nitrogen dioxide, methane and hydrogen sensors. The experimental kinetics of the electrical conductivity changes due to introduction and removal of gas on metal oxide gas sensors were successfully explained in terms of the Langmuir adsorption theory.

Topological defects such as magnetic monopoles that may have formed during cosmological phase transitions in the early universe. The Kibble mechanisms govern the formation of this defects and their abundance is influenced by the correlation length and the temperature-dependent Higgs mass. The Magnetic monopoles are particularly interested but their excessive production poses the monopole problem which is challenge to the standard model. Many solutions including symmetry breaking patterns and thermal pair production have been proposed to mitigate the monopole problem. Detecting monopoles would offer valuable insights into high-energy physics and the early universe. Detection of (even) a single monopole could revolutionize our current knowledge of the universe.

TAU is the main disease driver in Alzheimer’s and many other sporadic and genetic tauopathies. TAU is differently spliced, the role of individual splice-isoforms unclear. Murine Mapt-KO mice are healthy, and protected from Alzheimer’s, but no such case has been reported in humans. Using gnomad database, we here demonstrate that the only isoform expressed during fetal human brain development is intolerant to mutation, while other brain and peripheral nervous system TAU isoforms are dispensable. With TAU targeted therapies for Alzheimer’s and other tauopathies on the rise, we caution that TAU is essential human brain development.

Since China’s economic reform launched in the late 1970s, rural industrial parks (RIPs) have played a crucial role in the rise of the manufacturing industry in the Pearl River Delta (PRD), transforming the former agricultural land into a location of world-famous factories. However, as China has put forward higher requirements for economic development and stricter control measures for environmental pollution, RIPs with low-end industries and low efficiency present an urgent need for renovation and upgrade. By analyzing the spatial and socioeconomic cross-sectional dataset of rural industrial parks in Nanhai District of Foshan City, this paper attempts to classify the existing industrial parks into groups and propose corresponding redevelopment strategies. The results of the spatial analysis show that the spatial distribution of rural industrial parks is closely related to their location and transportation conditions. Failure of the market has resulted in large tracts of advantageous land being taken up by inefficient industrial parks. Cluster analysis and overlay analysis are used evaluate the difficulty of redevelopment and divide the industrial parks into three clusters: retained RIPs, medium-term-removed RIPs, and near-term-removed RIPs. It is shown that some of the industrial parks are still in good form while the majority need substantial reconstruction. Taking economic and institutional factors into consideration, the authors put forward that different strategies should be adopted for the future renovation of medium-term-removed and near-term-removed RIPs.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

The open hole tensile and flexural test of composites holds immense importance in aerospace and structural engineering. These tests provide critical insights into how composites perform under real-world conditions, allowing for the development of materials that meet rigorous standards, thereby ensuring the safety and durability of aircraft and other applications. This study investigates the influence of hole size on the open hole tensile (OHT) and flexural properties (OHF) of plane woven aramid/epoxy (AFRP), basalt/epoxy (BFRP), and aramid-basalt/epoxy (ABFRP) sandwiched laminates. Specimens with three different hole sizes viz., 4 mm, 6 mm, 8 mm were prepared and tested according to ASTM D5766 and ASTM D7264 for OHT and OHF performance respectively. Results indicated that the hybridization of aramid-basalt layers in a sandwich lay up significantly improved the mechanical performance when compared to only aramid laminate. However, with the increase in hole size the mechanical properties declined irrespective of the material. Basalt fibres showed higher hole sensitivity under tensile loading due to its brittle fracture mechanism while aramid fibres exhibited higher hole sensitivity under flexural loading which can be attributed to its the poor compressive strength. The effect of hole size on the damage mechanism has also been highlighted through scanning electron microscope (SEM). Major failure modes observed are delamination around the hole edges, ductile and brittle fibre fractures, and delamination.

The carbonation of industrial calcium-rich byproducts such as steel slag demonstrates significant potential for CO2 sequestration. This technique aids in reducing carbon emissions while also promoting waste recycling. Despite its advantages, gaps remain in understanding how steel slag characteristics and operational parameters influence the carbonation process, as well as the underlying mechanism of direct aqueous carbonation. We evaluated the carbonation performance of three types of steel slag at temperatures below 100°C using a gas–liquid–solid reaction system. The slag with the highest CO2 sequestration capacity was chosen for a systematic evaluation of the effects of operating conditions on carbonation efficiency. Thermodynamic analysis indicated that the reactivity of CaO and Ca(OH)2 with CO2 exceeded that of CaO•SiO2 and 2CaO•SiO2. Under conditions of 85°C, a particle size less than 75 μm, an initial CO2 pressure of 0.5 MPa, a liquid-to-solid ratio of 5 mL/g, and a stirring speed of 200 rpm, the steel slag achieved a sequestration capacity (K) of 283.5 gCO2/kg and a carbonation efficiency (ζCa) of 51.61%. Characterization of the slag before and after carbonation using X-ray diffraction, SEM‒EDS, thermogravimetric analysis, and Fourier transform infrared spectrometry confirmed the formation of new carbonates. Mechanistic analysis revealed that the rate-limiting step initially involved the mass transfer of CO2, transitioning to Ca2+ mass transfer as time progressed. Our research provides a viable technique for CO2 capture and a beneficial approach for reutilizing waste steel slag. Furthermore, solid residues after capturing CO2 have the potential for conversion into carbon-negative building materials, offering a sustainable strategy for steel companies and other enterprises with high carbon emissions.

The green peach aphid, Myzus persicae (Sulzer) (Hemiptera: Aphididae), is an economically important pest of crops worldwide. A M. persicae (SEF-R) population from a cabbage field in southeastern China was tested for susceptibilities to 13 insecticides. Compared with the susceptible clone (FFJ-S), extremely high and high-resistance to beta-cypermethrin (324-fold) and imidacloprid (106.9-fold) were detected in SEF-R. More importantly, this is the first report of resistance in field M. persicae population to sulfoxaflor, flupyradifurone, pymetrozine, spirotetramat, flonicamid, and broflanilide in China. The resistance factor decayed to a low level to sulfoxaflor and pymetrozine after 15 generations without any selection pressure. The resistance-related mutations (R81T and kdr) were detected in SEF, conferring target-site resistance to neonicotinoids and pyrethroids, respectively. Biochemistry bioassays confirmed the involvement of monooxygenase enzyme, carboxylesterase, superoxide dismutase and peroxidase in multi-insecticide resistance mechanism. The overexpression of P450s, esterases, and a UDP-glycosyltransferase, might be responsible for the multi-insecticide resistance in SEF-R. The knockdown of CYP6CY3 in SEF-R increased its susceptibility to imidacloprid, thiacloprid, and thiamethoxam, which verified that P450s play vital roles in neonicotinoid metabolism. This study will be helpful for chemical control, as well as for resistance monitoring and management of GPA, in China.

The growing interest in Na-ion batteries as a “beyond lithium” technologies for energy storage drives the research for high-performance and environment-friendly materials. Na2Ti3O7 (NTO) as an eco-friendly, low-cost anode material shows a very low working potential of 0.3 V vs Na+/Na but suffers from poor cycling stability, which properties can be significantly influenced by materials synthesis and treatment. Thus, in this work, the influence of the calcination time on the electrochemical performance and the reaction mechanism during cycling were investigated. NTO heat-treated for 48 h at 800 °C (NTO-48h) demonstrated enhanced cycling performance in comparison to NTO heat-treated for only 8 h (NTO-8h). The pristine material was thoroughully characterized by X-ray diffraction, laser granulometry, X-ray photoelectron spectroscopy, and specific surface area measurements. The reaction mechanisms induced by sodiation/desodiation and cycling were investigated by opernado XRD. Electrochemical impedance spectroscopy was used to evidence the evolution of the solid electrolyte interface layer (SEI) and modification of charge transfer resistances as well as the influence of cycling on capacity decay. The evolution of the crystallographic structure of NTO-48h revealed a more ordered structure and lower surface contamination compared to NTO-8h. Moreover, the residual Na4Ti3O7 phase detected after the sodium extraction step in NTO-8h seems correlated to the lower electrochemical performance of NTO-8h compared to NTO-48h.

The development of subterranean non-motorized traffic infrastructure, commonly referred to as the underground pedestrian system (UPS), has become increasingly necessary in densely populated megacities worldwide as a means of advancing the Sustainable Development Goal 11, which aims to promote sustainable cities and communities. To improve the overall spatial performance, it is imperative to decipher the fundamental formation mechanism of sustainable underground pedestrian systems (SUPS) that is simultaneously influenced by spatial morphology and pedestrian behaviors. Thereby, two representative case studies, namely Wujiaochang UPS and Loushanguanlu UPS located in Shanghai, were selected for an in-depth investigation. This study employed correlation and regression analysis to examine the impact of spatial configuration variables and spatial attribute factors on pedestrian flow distributions in distinct SUPSs. The findings indicate that the variables of Betweenness, as measured by both Euclidean and Angular metrics, along with the presence of metro station locations and commercial space connected by UPS, are the three most significant factors influencing pedestrian behaviors in both scenarios. The disclosure has been made that Wujiaochang UPS is seamlessly integrated into a comprehensive three-dimensional pedestrian network both above and below ground. On the other hand, it appears that Loushanguanlu UPS exhibits a greater degree of self-sufficiency as an underground system. This study aims to elucidate the mechanism underlying the development of SUPSs, thus offering effective guidance for the implementation of three-dimensional walking systems in cities that prioritize sustainability.

To realize the energy recovery of wheat straw, the pyrolysis behavior of wheat straw was studied at three heating rates (10, 20, and 30 K/min) based on thermogravimetric analysis (TG–DTG). Kinetics and thermodynamics were analyzed using Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) model-free methods, and reaction mechanism was determined using Coats–Redfern (CR) model-fitting method. The results show that there are three weightlessness stages in the pyrolysis process, of which the second stage was the main weightlessness stage and two distinct peaks of weightlessness were observed in this stage. With increasing heating rate, the main pyrolytic weightlessness peaks of DTG curve shifts to higher temperature. The pyrolysis activation energy calculated by FWO and KAS methods are 165.17–440.02 kJ/mol and 163.72–452.07 kJ/mol, and the pre-exponential factor vary in the range of 2.58×1012–7.45×1036 s-1 and 1.91×1012–8.66×1037 s-1, respectively. The thermodynamic parameters indicates that wheat straw has the favorable conditions for product formation and containes potential energy to be utilized for bioenergy production, its pyrolysis reaction was non-spontaneous and the energy output is stable. CR method analysis shows that the A1/3 random nucleation model is the most suitable mechanism to characterize the pyrolysis process, and random nucleation may be in charge of the main pyrolysis stage. This study can provide a theoretical basis for the thermochemical conversion and utilization of wheat straw.

Under the increasingly severe global heat threat, continuous high temperature has far-reaching effects on plant growth and development and become a major constraint to crop production. The development of heat-resistant varieties has become research hotspot in many fields, and it is also necessary to establish effective identification methods. In this study, twenty Brassica rapa varieties were selected to to investigate the physiological and biochemical characteristics under heat stress, explore the relationship between physiological response and the heat resistance mechanism, select some typical heat-resistant and heat-sensitive varieties. The effects of photosynthetic electron transfer and antioxidant pathway on heat resistance of Brassica rapa were identified. These findings will providing an important reference for the physiological regulation and identification method of high-temperature stress in plants.

Staphylococcus aureus (S. aureus), particularly Methicillin-resistant S. aureus (MRSA), is a life-threatening pathogen that causes a variety of infections in hospital and community settings. It poses a significant risk and challenge for global health that hinders our ability to control and treat bacterial infections. Recently, the emergence and spread of variant strains of bacteria, misuse of the limited available options of effective antibiotics, spread of fake drugs, and climate change that increased the contact between humans and animals’ populations carrying different bacterial has significantly increased the incidence of Multi-Drug Resistance strains (MDR). This in turn, has created a severe problem in the infection control treatment of S. aureus infected patients. Treatment options for MRSA infection are increasingly limited and complicated. Unfortunately, different strains of MRSA are showing tolerance and resistance toward vancomycin, which is the standard of care for complicated MRSA. Therefore, it is crucial now to invest developing new effective anti-biotics and synergies, develop and implement national action plans for the management of an-tibiotic resistance, as well as improving our understanding of the antimicrobial resistance mechanisms. Here, in this review we explain the main mechanisms of antibiotic resistance in MRSA and describes different approaches to manipulate it in MRSA, providing a basis for de-signing effective drugs, and shedding some light on the evolution of S. aureus.

Carbazole is one of the typical heterocyclic aromatic compounds (NSO-HETs) observed in pol-luted urban atmosphere, which has become a serious environmental concern. The most im-portant atmospheric loss process of carbazole is the reaction with OH radical. The present work investigated the mechanism of OH-initiated atmospheric oxidation degradation of carbazole by using density functional theory (DFT) calculations at the M06-2X/6-311++G(3df,2p)//M06-2X/6-311+G(d,p) level. The rate constants were determined by the Rice-Ramsperger-Kassel-Marcus (RRKM) theory. The lifetime of carbazole determined by OH was compared with other typical NSO-HETs. The theoretical results show that the degradation of carbazole initiated by OH radical includes four types of reactions: OH additions to “bend” C atoms, OH additions to “benzene ring” C atoms, H abstractions from C-H bonds and the H ab-straction from N-H bond. The OH addition to C1 atom and the H abstraction from N-H bond are energetically favorable. The main oxidation products are hydroxycarbazole, dialdehyde, carba-zolequinone, carbazole-ol, hydroxy-carbazole-one and hydroperoxyl-carbazole-one. The calcu-lated overall rate constant of carbazole oxidation by OH radical is 6.52 × 10−12 cm3 molecule−1 s−1 and the atmospheric lifetime is 43.92 h under the condition of 298 K and 1 atm. The lifetime of carbazole determined by OH radical is similar with that of dibenzothiophene oxidation but longer than those of pyrrole, indole, dibenzofuran and fluorene. This work provides a better understanding of the reactivity of carbazole in the atmospheric environment, the formation of secondary organic aerosols, and the chemical degradation and removal of carbazole in the at-mospheric environment.

The high entropy alloy (HEA) of equiatomic composition CrNiFeCoMn and with FCC crystal structure was additively manufactured with a selective laser melting (SLM) process starting from mechanically alloyed powders. The as produced alloy shows finenitride and  phase pre-cipitates, which are Cr-rich and stable up to about 900 K. The precipitates increase in number and dimensions after long-period annealing at 900-1300 K, with a change in the HEA mechanical properties. Higher aging temperatures in furnace, above 1300 K, turn the alloy in a single FCC structure, with disappearance of the nitrate and  phase precipitates inside the grains and at the grain boundaries, but with still a presence of a finer Cr-rich nitride precipitation phase. These re-sults suggest that the as-produced HEA is a supersatured solid solution at low and intermediate temperature with nitrides and  nanostructures.

In-depth research on the rehabilitation mechanism and motion characteristics of a rehabilitation training robot based on 4-UPUS parallel mechanism. The overall design and the working principle of each key component are explained, and the robot rehabilitation scheme is described. The static analysis and modal analysis of the robot bearing structure are carried out to verify whether the static mechanical characteristics of the mechanism meet the application requirements under working conditions.A simplified mathematical model of the six-bar linkage mechanism is established, and the kinematics of the mechanism is solved by the geometric analysis method. The motion characteristic diagram of each key point is obtained through simulation calculation. Establish the motion model of the parallel mechanism, and discuss the working space and motion performance analysis of the mechanism. The forward solution analysis of the mechanism position is carried out by using the numerical analysis method, and the three-dimensional graphics of the attitude angle and linear displacement of the reachable working space are obtained.Taking the UPUS single branch chain as the analysis object, the single open chain analysis method is used to solve the kinematics image of the corresponding surface in the working space of the mechanism, which verifies the correctness of the kinematics theoretical solution and the feasibility of simulation.The research results show that the 4-UPUS parallel mechanism rehabilitation training robot can cooperate with the ankle joint for rehabilitation training. It makes up for the single movement of the current lower limb rehabilitation robot and the unsatisfactory rehabilitation effect, and provides a reference for the practical application of the subsequent ankle joint rehabilitation robot system.

Traditional refractory materials such as nickel-based superalloys have been gradually unable to meet the performance requirements of advanced materials. The Mo-Si-based alloy, as a new type of high temperature structural material, has entered the vision of researchers due to its charming high temperature performance characteristics. However, its easy oxidation and even "pesting oxidation" at medium temperatures limit its further applications. In order to solve this problem, researchers have conducted large numbers of experiments and made breakthrough achievements. Based on these research results, the effects of rare earth elements like La, Hf, Ce and Y on the microstructure and oxidation behavior of Mo-Si-based alloys were systematically reviewed in the current work. Meanwhile, this paper also provided an analysis about the strengthening mechanism of rare earth elements on the oxidation behavior for Mo-Si-based alloys after discussing the oxidation process. Furthermore, the research focus about the oxidation protection of Mo-Si-based alloys in the future was prospected to expand the application field.

Poly-ether-ether ketone (PEEK) is a great potential thermoplastic in industry and medical treatment and health. In this work, the PEEK/GO and PEEK/MoS2 composites were prepared by a novel hot isostatic pressing method. The addition of GO alters the tribological behaviours mechanism, fatigue wear mechanism is predominant to PEEK/GO composites. However, the combination of abrasive and adhesive wear mechanisms is observed for PEEK/MoS2 composites and PEEK. The reason is the hardness and tensile strength of composites are increased with the appropriate addition of GO. The response time to stable friction state of PEEK/GO and PEEK/MoS2 composites reduces in compared with PEEK, which conduce to shorten running-in time, reduce energy consumption and improve the tribological performances of composites. The addition of GO and MoS2 can effectively decrease the friction coefficient and wear rate, and the optimal content of GO and MoS2 was 0.7 wt.% and 15 wt.%, respectively. The results indicate that PEEK/GO and PEEK/MoS2 are impressive composites that possess super tribological properties.

Ghana has had a long-standing problem of illegal gold mining that has led to the destruction of the environment. The government of Ghana is taking steps to not only curb illegal mining but also to restore destroyed lands that resulted from illegal mining. The government intends to spend financially in the area of ecological restoration to returned disturbed lands to their natural states possible, but the question remains whether restoring those disturbed lands will be beneficial to the country. The study was undertaken in Bekwai Municipal Area in the Ashanti region of Ghana where most locals are farmers. The research studies whether the benefits of ecological restoration outweigh the cost of ecological restoration? The research deployed a quantitative data collection. The data collected was analyzed using benefit-Cost ratio. The result shows that the benefit of ecological restoration outweighs the cost incurred as dependent on the land use as a carbon sequestration project. In conclusion, investment in ecological restoration is a step in the right direction for a country endowed with gold resources. This will spur growth and at the same time improve and protect the country’s natural resources and environment.

36 years after the publication of the important article by Busa and Nuccitelli on the variability of intracellular pH (pH_i) and the interdependence of pH_i and intracellular Ca²⁺ concentration ([Ca²⁺]_i), little research has been carried out on pH_i and calcium signaling. Moreover, the results appear to be contradictory. Some authors claim that the increase in [Ca²⁺]_i is due to a reduction in pH_i, others that it is caused by an increase in pH_i. The reasons for these conflicting results have not yet been discussed and clarified in an exhaustive manner. Variations in pH_i have a significant impact on the increase in [Ca²⁺]_i and hence on some of the basic biochemical mechanisms of calcium signaling. This paper focuses on the possible triggering role of protons, highlighting the mechanisms potentially involved and the open issues that could be clarified by research.

Malignant gliomas are heterogeneous neoplasms. Glioma stem-like cells (GSCs) are undifferentiated and self-renewing cells that develop and maintain these tumors. These cells are the main population that resist current therapies. Genomic and epigenomic analyses has identified various molecular subtypes. Bone morphogenetic protein 4 (BMP4) reduces the number of GSCs through differentiation and induction of apoptosis, thus increasing therapeutic sensitivity. However, the short half-life of BMP4 impedes its clinical application. We have previously reviewed BMP4 signaling in central nervous system development and glioma tumorigenesis and its’ potential as a treatment target in human gliomas. Recent advances in understanding both adult and pediatric malignant gliomas highlight critical roles of BMP4 signaling pathways in the regulation of tumor biology, and indicate its’ potential as a therapeutic molecule. Furthermore, significant progress has been made on synthesizing BMP4 biocompatible delivery materials, which can bind to and markedly extend BMP4 half-life. Here, we review current research associated with BMP4 in brain tumors, especially in pediatric malignant gliomas. We also summarize BMP4 delivery strategies, with a focus on biocompatible BMP4 binding peptide amphiphile nanostructures as promising novel delivery platforms for treatment of these devastating tumors.

The paper presents an innovative hexapod walking robot built with 3-UPU parallel mechanism. In the robot, the parallel mechanism is used as both an actuator to generate walking and also a connecting body to connect two groups of three legs, thus enables the robot to walk with simple gait by very few motors. In the paper, the forward and inverse kinematics solutions are obtained. The workspace of the parallel mechanism is analyzed using limit boundary search method. The walking stability of the robot is analyzed, which yields the robot’s maximum step length. The gait planning of the hexapod walking robot is studied for walking on both flat and uneven terrains. The new robot, combining the advantages of parallel robot and walking robot, has a large carrying capacity, strong passing ability, flexible turning ability, and simple gait control for its deployment for uneven terrain.

Metal oxide p-n heterojunction nanofibers (NFs) are among the most promising approaches to enhancing the efficiency of gas sensors. In this paper, we report the preparation of a series of p-NiO-loaded n-ZnO NFs, namely (1 − x) ZnO-xNiO (x = 0.03, 0.05, and 0.1 wt%), for hydrogen gas sensing experiments. Samples were prepared through the electrospinning technique followed by a calcination process. The sensing experiments showed that the sample with 0.05 wt% NiO loading resulted in the highest sensing performance at an optimal sensing temperature of 200 °C. The sensing mechanism is discussed in detail and contributions of the p-n heterojunctions, metallization of ZnO and catalytic effect of NiO on the sensing enhancements of an optimized gas sensor are analyzed. This study demonstrates the possibility of fabricating high-performance H₂ sensors through the optimization of p-type metal oxide loading on the surfaces of n-type metal oxides.

The increased expression of β4-galactosyltransferase (β4GalT) 4 was closely associated with poor prognosis of colon cancer. Recently, we showed that the expression of the β4GalT4 gene is regulated by the 0.17 kb core promoter region containing one binding site for Specificity protein 1 (Sp1). To develop a novel screening method for anti-colon cancer drugs, two sensor cell lines having the luciferase gene under the control of two β4GalT4 gene promoters that differed in length were established from SW480 human colon cancer cells. The hGT4-0.17-sensor cells possessed the luciferase reporter driven by the 0.17 kb promoter, while the hGT4-0.3-sensor cells possessed the luciferase reporter driven by the 0.3 kb promoter containing one binding site each for colon cancer-related transcription factors including activator protein 2, E2F, caudal-related homeobox transcription factors, and Runt-related transcription factors besides Sp1. Upon treatment with mitogen-activated protein kinase inhibitor U0126, the promoter activities of the hGT4-0.3-sensor cells decreased significantly, while those of the hGT4-0.17-sensor cells unchanged. These results suggest that the responsiveness to U0126 differs between two sensor cell lines due to the different regulation of the luciferase reporters. This study provides the novel screening method for anti-colon cancer drugs by the combination of two sensor cell lines.

In this study, a graphene-doped porous silicon (G-doped/p-Si) substrate for low ppm H₂ gas detection by an inexpensive synthesis route was proposed as a potential noble graphene-based gas sensor material and to understand the sensing mechanism. The G-doped/p-Si gas sensor was synthesized by a simple capillary force-assisted solution dropping method on p-Si substrates, whose porosity was generated through an electrochemical etching process. G-doped/p-Si was fabricated with various graphene concentrations and exploited as a H₂ sensor operated at room temperature. The sensing mechanism of the sensor with/without graphene decoration on p-Si was proposed to elucidate the synergetic gas sensing effect generated from the interface between the graphene and p-type silicon.

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image segmentation [15]-[19]. In ultrasound (US) breast images, compared with background areas, tumor is more salient because of its higher contrast. In this paper, we develop a novel automatic localization method based on HVM for automatic segmentation of ultrasound (US) breast tumors. First, the input image is smoothed by convolution with a linearly separable Gaussian filter and then subsampled into a 9-layer Gaussian pyramid. Then intensity, blackness ratio, and superpixel contrast features are combined to compute saliency map, in which Winner Take All algorithm is used to localize the most salient region, presenting with a circle on the localized target. Finally the circle is taken as the initial contour of CV level set to finish the extraction of breast tumor. The localization method has been tested on 400 US beast images, among which 378 images have higher saliency than background areas and succeed in localization, with high accuracy 92.00%. The HVM localization method can be used to localize the tumors, combined with this method, CV level set can achieve the fully automatic segmentation of US breast tumors. By combing intensity, blackness ratio and superpixel contrast features, the proposed localization method can successfully avoid the interference caused by background areas with low echo and high intensity. Moreover, multi-object localization of US breast images can be considered in future employment.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

As a new plant slope protection technology, living stumps can effectively improve slope stability. The current study attemptes to investigate the mechanical characteristics of the root system of living stumps and their reinforcement mechanism on slopes by carrying out a three-dimensional living stump slope modeling test. Using ABS materials, a 3D model of a living elm stump was created by 3D printing and a slope model test was carried out. The reinforcement mechanism of the living stump was studied through a combination of model testing and numerical simulation. The results indicate that, due to the presence of living stumps in the lower and middle parts of the slope, the maximum shear stress area of the soil moved to a deeper direction. The stress state of the soil around the living stump was effectively improved due to the existence of the lateral root system.Living stumps in the lower part of the slope cross the potential sliding surface and transferred the soil shear stress gradually to the root system through root-soil interaction. Moreover, the tap roots and lateral roots of living stumps form a robust spatial structure that bears the shear stress of soil together, thus increasing the stability of the slope.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

This study utilises NaOH to treat composite cementitious materials containing aeolian sand and zeolite powders, which were used to replace 50% of the cement in aeolian-sand concrete (ASC). The mechanical properties of treated ASC considerably improved, especially when the NaOH dosage was 4% by mass. After curing this sample (denoted as AAZC-4) for 28 d, its compressive strength improved by 17.2% and its split tensile was increased by 16.3%. Potassium feldspar and montmorillonite in zeolite powder and SiO2 in the sand were decomposed by OH− and combined with other elements to generate various silicate gels and A-type potassium zeolite crystals inside the concrete. Microscopic examination showed that the gels and crystals intertwined to fill the pores, decreasing (increasing) the percentage of large (small) pores, thus optimising the pore structure. This substantially improved the mechanical properties of ASC. Freeze–thaw salt-intrusion tests showed that the extent of mass loss, degree of damage and loss of compressive strength of AAZC-4 were similar to those of ordinary concrete but were reduced by 36.8%, 19% and 52.1%, respectively, compared with those of ASC. Therefore, AAZC-4 has sustainable working performance in the chloride ion permeable environment in cold and arid areas.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

Upon the implementation of the European Union Carbon Border Adjustment Mechanism (CBAM), substantial challenges are anticipated to impact the international trade of Chinese steel products. To safeguard the competitiveness of Chinese steel products on the global stage, this paper establishes a tripartite evolutionary game model, involving large steel enterprises, small and medium-sized steel enterprises, and the government. The model integrates collaborative emission reduction and free-riding benefits among enterprises, along with the government's dynamic subsidies and penalties. The paper analyzes the evolutionary trends and stable states of the three parties during different stages of CBAM, including a sensitivity analysis of key parameters. The findings indicate that large enterprises demonstrate a heightened sensitivity to CBAM, while small and medium-sized enterprises are more significantly influenced by free-riding benefits. Government intervention should be strategically timed and incline towards passive management during the strengthening phase of CBAM. Additionally, the refinement of the Chinese carbon market emerges as an effective means to counter CBAM. This paper provides theoretical support for how steel enterprises and the government can respond to CBAM, aiding stakeholders in selecting optimal strategies during different implementation stages and mitigating the impacts of CBAM to the maximum extent possible.

The demand for precise indoor localization services is steadily increasing. Among various methods, fingerprint-based indoor localization has become a popular choice due to its exceptional accuracy, cost-effectiveness, and ease of implementation. However, its performance degrades significantly as a result of multipath signal attenuation and environmental changes. In this paper, we propose an indoor localization method based on fingerprints using self-attention and long short-term memory (LSTM). By integrating a self-attention mechanism and LSTM network, the proposed method exhibits outstanding positioning accuracy and robustness in diverse experimental environments. The performance of the proposed method is evaluated under two different experimental scenarios, which involve 2D and 3D moving trajectories, respectively. The experimental results demonstrate that our approach achieves an average localization error of 1.76 m and 2.83 m in the respective scenarios, outperforming the existing state-of-the-art methods by 42.67% and 31.64%.

By planting LEDs on the surfaces of orifice baffles, a novel batch oscillatory baffled photoreactor (OBPR) together with polymer-supported Rose Bengal (Ps-RB) beads are used to investigate reaction kinetics of a photo-oxidation reaction between α-terpinene and singlet oxygen (1O2). In the widely used NMR data analysis for this reaction, α-terpinene and ascaridole are treated as a reaction pair, assuming kinetically singlet oxygen is in excess or constant. We have, for the first time, examined the validity of the method, discovered that increasing α-terpinene initially leads to an increase of ascaridole, indicating that the supply of singlet oxygen is in excess. Applying kinetic analysis, a pseudo first order reaction kinetics is confirmed, supporting this assumption. We have subsequently initiated a methodology of estimating the 1O2 concentrations based on the proportionality of ascaridole concentrations with respect to its maximum under these conditions. With the help of the estimated singlet oxygen data, the efficiency of 1O2 utilization and the photo efficiency of converting molecular oxygen to 1O2 are further proposed and evaluated. We have also identified conditions where further increase of α-terpinene has caused decreases in ascaridole, implying kinetically that 1O2 has now become a limiting reagent, the method of treating α-terpinene and ascaridole as a reaction pair in the data analysis would no longer be valid under those conditions.

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

The safe and efficient mining of coal seams with low porosity, low permeability and high inhomogeneity under complex geological conditions is a major challenge, among which the permeability of coal seams is crucial for coal mine gas extraction. The development of coal seam permeability enhancement technology can help coal mines to produce safely and efficiently, and the extracted coal bed methane can be utilized as a green energy. In order to study the effect of freezing and thawing on the evolution of mechanical and permeability properties of coal, triaxial permeability tests were carried out on low-permeability coal under two kinds of surrounding pressures. At the same time, dry, saturated and freeze-thaw coal were set up for comparison, and the effects of water and freeze-thaw were separated from each other. The triaxial mechanics and percolation laws of dry, saturated and freeze-thaw coal rocks were obtained, and the mechanism of the influence of freeze-thaw on the permeability evolution law of coal was revealed. The research results can provide theoretical guidance for the gas extraction technology of low permeability coal seams with increased permeability.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

In this paper, the quality change of scallop (Patinopecten yessoensis) in the process of anhydrous storage and transportation after cold-acclimation and induced dormancy was studied, and the regulation mechanism of quality degradation in the storage and transportation in the process of gradient chilling stress and drying exposure was further explored. The results show that while being transferred from anhydrous storage and transportation from hydrous storage and transportation, the breathing pattern of scallops were changed from aerobic to anaerobic. Their gill filaments were altered, and the apparent vitality was constantly declining, which was reflected by the edge shrinkage of pallium and the direct proportion between the edge reduction rate and the stimulus response period. After being in the anhydrous state for 4d, the AEC value dropped to 67.59%. At this time, if they were under hydration again, they would resume a nice growth state. By virtue of proteomics, it was revealed from the analysis that cold acclimation and dry exposure mainly led to changes in biological functions and pathways such as mitochondrial inner membrane and ATP hydrolysis activity. In addition, it can be seen from the functional annotation and enrichment analysis of the metabolite KEGG that cold acclimation promoted the purine metabolism of scallops, while dry exposure inhibited the metabolism of saturated fatty acids. Besides, in this study, the infrared sensing mode was used for the first time to record the heart rate changes of the scallops during circulation, which provides feasibility for the nondestructive vitality monitoring of Lamellibranchia.

This paper aims to introduce an overview of several aspects of the so-called Artificial Intelligence, their potential impacts on economic and social dimen- sions, and suggestions for possible approaches of investiment based upon effective and mature techniques. In this fashion, it is important to address from educational and academic issues to industrial densities and profiles, rel- atively to a given region, country or continent. Even etymological adequacy and psychological consequences of the denomination ”Artificial Intelligence” need some reflection, and suggestions for a lucid replacement are presented. In addition, suggestions about how can specific firms choose the right type of technique in order to improve profit and organizational efficiency. After all, what are the main transformations needed to amplify gains and structural improvements from the use of higher level technological mecha- nisms? Which connections can be established between the pillars of evolu- tionary economics and this field of knowledge? Which institutional contexts are able to benefit from AI tools, inducing constructive externalities to firms in terms of education, technical and scientific skills upgrading, so as to reach higher levels of employment in the long term and limit unemployment in the short one? Which branches of the so-called Artificial Intelligence are best suited to which types of activities? This work aims to contribute in the search for answers to these questions.

In the new period, the state has made every effort to promote the new urbanization of public sports service. Entropy method, coupled coordination model, cold hot spot analysis is used to explore the dynamic relationship between public sports services and the development of new urbanization in China from 2008 to 2017. The results show that: (1) The development of the internal subsystem of public sports service is not balanced, the sports financial investment system grows rapidly, but the development of professional sports talent team is slow. (2) The imbalance of regional development cannot be ignored, and the public sports service level shows a decreasing gradient from east to west. (3) The regional development features are different. The eastern region is dominated by the coordinated development of sports public service and urbanization, while the central region is mainly in the type of public sports service lag, while the western region has multiple complex characteristics of low-level coordination and public sports service lag. Therefore, it is proposed that the construction of modern public sports service system should be investigated in the context of the development of new urbanization. It should be promoted from the following aspects: promoting the extension of public sports service to rural areas, engendering the "citizen's right" of sports health to the migrant population, conforming to the trend of mass sports intelligence, developing green sports and revitalizing idle sports assets in cities.

A theoretical description of quantum jumps at the level of elementary particles is proposed, based on a micro-cosmological interpretation of their de Broglie phase. The third quantization formalism proposed in current literature for the description of baby universes in quantum cosmology is used here to describe the breakdown of unitarity in the transition from the pre-jump to the post-jump wave function. The corpuscular aspect manifested by the particle in the micro-interaction that originates the jump is represented by a pair of evanescent "micro-universes", respectively pre- and post-jump, connected by a wormhole. The latter represents the actual implementation of the interaction that leads to the projection on the outgoing state; this interaction is always local, even when the selected outgoing state is entangled. Therefore, the decoherence which leads to the emergence of classicality is originated by the same fundamental interactions of the Standard Model involved in the unitary evolution of the wave function. The objective nature of the reduction process admits implications on the possibility of using the formalism in the cosmological context, which are briefly discussed.

MEMS (Micro-electromechanical Systems) becomes important increasingly due to the smarter and smaller fuze used in OICW (Objective Individual Combat Weapon). MEMS Safety and Arming (S&A) device is employed in different platforms and regions for small caliber projectile. Therefore, it is necessary to make a parametric study of the MEMS S&A device in different apply environments and explore the main sensitive factors of the MEMS S&A device to provide reference for designs. In this paper, based on the MEMS S&A device designed by our term, theory and finite element models are established, and the centrifugal insurance mechanism of the MEMS S&A device is parametric studied under the different speeds, temperature and thickness of the model by nonlinear dynamic method. By comparing the experimental and predicted results, the established FEM model is verified, and the conclusion is that the temperature and the centrifugal force are the main sensitive factors in the centrifugal insurance mechanism. In summary, we can suggest that the application environment, which the MEMS S&A device is suitable for, is the temperature equal to or slightly greater than normal temperature and the rotating speed higher than35000r/min of small caliber projectile.

Predatory research field in science is when an emerging scientific topic destroys current topics and characterizes a main scientific change. Predatory research field can be a basic driver of scientific and technological change that generates a 'creative destruction' in science and society in contexts of knowledge-based competition and rapid changes. The prediction of proposed theory of predatory research fields is that it destroys with a fast growth other research fields. The theoretical approach is tested here in research fields of large language models (LLM) by analyzing the transformers (a deep learning architecture based on the multi-head attention mechanism) proposed in 2017 and from November 2022 started main applications in generative artificial intelligence with innovations of BERT, ChatGPT, Microsoft Copilot (launched on February 7, 2023) and other natural language processing tools driven by AI technology for engaging conversations, gain insights, automate tasks, etc., etc. Statistical evidence suggests that growth rate of transformer technologies is 80.58%, a high level compared to all other research fields in machine learning (having a growth rate of 13.83%). Moreover, predatory research field of transformers has a destructive power such that all other domains in LLM from 2021 to 2023 have a general reduction of scientific growth. The impact of transformers is much more drastic of previous radical technologies such as CNN having a temporal growth rate of 0.16%, lower than 0.38% by transformers, ceteris paribus. These analysis reveals that transformers have characteristics to generate a radical scientific and technological change in a not-too-distant future. Overall, then, the study suggests that predatory research field on emerging topics and technologies can generate path-breaking innovations and the examination here can clarify the essential elements of the science dynamics for a better theory of scientific and technological change, providing also main implications for knowledge policy to support promising research fields and technologies to guide economic and social change.

Plant activators have emerged as promising alternatives to conventional pesticides for crop disease manage-ment due to their unique mode of action. By priming the plant's innate immune system, these compounds can induce systemic acquired resistance against a broad spectrum of pathogens without directly inhibiting their proliferation. Key advantages of plant activators include prolonged defense activity, lower effective dosages, and negligible risk of developing pathogen resistance. Among the various defensive pathways tar-geted, the salicylic acid (SA) signaling cascade has been extensively explored, leading to the successful devel-opment of commercial activators like benzothiadiazole for widespread application in crop protection. While the action sites of many SA-targeting activators have been preliminarily mapped to different steps along the pathway, a comprehensive understanding of their precise mechanisms remains elusive. This review provides a historical perspective on plant activator development and outlines diverse screening strategies employed across multiple levels, from whole-plant bioassays to molecular and transgenic approaches. We expound on the intricate components, biological relevance, and regulatory circuits governing the SA pathway, while criti-cally examining the structural features, bioactivities, and proposed modes of action of classical activators such as benzothiadiazole derivatives, salicylic acid analogs, and other small molecules. Insights from field trials as-sessing their practical applicability are also discussed. Furthermore, we highlight the current status, challenges, and future prospects in the realm of SA-targeting activator development globally, with a focus on recent en-deavors in China. Collectively, this comprehensive review aims to synthesize existing knowledge and provide a roadmap for future research toward developing more potent and mechanistically understood plant activa-tors that fortify crop immunity against disease.

Since the beginning of the internet era, there has been an explosion of growth in structured data (such as numbers, symbols, and labels) as well as unstructured data (including images, videos, and text). Efficient and accurate mixed query of these two types of data is a key technology to achieve high-quality information retrieval, and it is also a major challenge that needs to be solved urgently in the industry. In this study, we employ an advanced Transformer model that combines strategies and fine-tuning techniques for multi-task learning. Specifically, the model is first pre-trained on a large-scale, general-purpose dataset to learn different types of data representations and basic language comprehension skills. After that, we fine-tuned the parameters of the model to better suit these specific data processing tasks for specific application scenarios, such as image annotation, video content analysis, and structured data query. At the heart of the model is the self-attention mechanism, which allows the model to automatically emphasize the important parts and ignore irrelevant information when processing the input data. In addition, we have introduced task-specific adaptation layers that are designed to add additional processing power to the original Transformer architecture, such as a semantic analysis layer for unstructured text data and a relational extraction layer for structured data. This combination of general pre-training and task-specific fine-tuning allows the model to flexibly process and integrate information from different data sources, improving processing efficiency and accuracy. Experimental results show that the model performs well in a variety of data processing tasks, significantly improves the accuracy and efficiency of information retrieval, and verifies the strong potential and adaptability of large language models in processing mixed data types.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the [email protected] metric, and 72.03%, 65.37%, and 38.91% in the more stringent [email protected]:0.95 metric, respectively.