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Although thermosonication (TS) treatment has been widely used in food sterilization, the viable but non‐culturable (VBNC) of bacteria with TS treatment has still concerned potential food safety and public health. The molecular mechanism of VBNC status of bacteria with TS treatment is not clearly known. Therefore, in this study, we used Shewanella putrefaciens, which was a common putrefactive bacteria in aquatic products, to study the VBNC state of bacteria with TS treatment. Firstly, our results revealed that S. putrefaciens still could enter the VBNC state after TS treatments: 50 kHz, 300 W, 30 min ultrasonic treatment and 70 °C heating; Subsequently, we found the VBNC state of S. putrefaciens can resist the damage of TS treatment, such as cell wall break, DNA degradation, etc; Finally, four-dimensional data-independent acquisition-based proteomics showed that under VBNC state, S. putrefaciens upregulated functional proteins to resist TS treatment, such as: ribosomal proteins to accelerate the synthesis of stress proteins to counteract TS treatments, ornithine decarboxylase SpeF and MraY to repair TS treatment-induced damage, etc. Meanwhile, S. putrefaciens downregulates metabolic and transport functional proteins such as dehydrogenase to reduce the metabolism. Importantly, among those proteins, the ribosomal transcriptional regulatory protein family, such as rpsB, etc, may be the key proteins for S. putrefaciens entering VBNC state. This finding can provide some new strategies for preventing VBNC status of bacteria with TS treatment, such as: inhibition of key proteins, etc.

The process of using powdered activated carbon (PAC) in conjunction with ultrafiltration (UF) has been widely adopted for the treatment of various types of water and wastewater. However, during the application of this integrated PAC-UF process, PAC tends to adhere significantly to the surface of the UF membrane, which exacerbates membrane fouling. To tackle this issue, this study proposed an innovative water treatment approach that simultaneously integrated granular activated carbon (GAC) and PAC/biochar with UF. In this setup, PAC/biochar was intended to enhance water quality, while the fluidized GAC particles were aimed at reducing membrane fouling and the deposition of PAC/biochar on the membrane surface. We systematically analyzed the operational performance of the integrated systems concerning fouling formation, PAC/biochar attachment, effluent quality, and foulant components. The results indicate that both PAC and biochar effectively improved effluent quality in terms of chemical oxygen demand (COD) and hardness, although they significantly deposited on the membrane surface during operation. Notably, PAC was more prone to attach to the membrane than biochar, and the fouling in biochar-UF systems was primarily attributed to the attachment of organic foulants rather than biochar itself. By combining with GAC, up to 46.01% of membrane fouling and 96.11% of PAC/biochar attachment were mitigated due to the strong mechanical action of the fluidized GAC particles. Importantly, the inclusion of fluidized GAC did not significantly affect effluent quality. Consequently, the GAC-PAC/biochar systems proposed in this study demonstrated dual benefits of improving effluent quality and ensuring stable operation, thereby providing a viable solution for efficient and sustainable water treatment.

Biological network similarity search plays a crucial role in the analysis of biological networks for human disease research and drug discovery. A biological network similarity search aims to efficiently identify novel networks biologically homologous to the query networks. Great progress has been achieved in biological network similarity searches. However, it remains a challenge to mine the biological network information fully to improve the accuracy of query results without increasing time overheads. In this study, we propose a biological network similarity search method based on graph neural networks named GBNSS, which combines topological and biological information (GO annotations) of biological networks into graph neural networks to find topologically and biologically similar biological networks in the database. Additionally, GBNSS is a topology-free biological network similarity search method with an arbitrary network structure. The experimental results on four benchmark datasets show that GBNSS outperforms the existing methods in terms of computational efficiency and search accuracy. Case studies further demonstrate that GBNSS is capable of searching similar networks in real-world biological networks.

The morphological characteristics of a product serve as essential carriers for conveying design intentions. These characteristics directly affect users’ comprehension of the product’s functions and proper usage, which are critical to the safety of product utilization and the overall comfort of the user experience. Incorporating prior experience to predict users’ cognitive intentions regarding product form characteristics can provide valuable evaluation and decision-making references for design. This approach effectively reduces product development risks and contributes to enhancing user acceptance and experience. The study established intention discrimination indicators for form characteristics, covering six dimensions: functional orientation, behavioral intention, recognizability, cognitive load, attention distribution, and experiential feeling. Combining multidimensional scaling (MDS) and systematic clustering, samples were screened, and the morphological decomposition method was used to categorize and extract form characteristic categories and feature factors. The entropy weight method was applied to assign weights to the feature categories, and a feedforward neural network (FNN) was employed to construct a prediction model for cognitive intentions regarding product form characteristics. The model was tested using leave-one-out cross-validation, yielding a mean squared error (MSE) of 0.0089 and an R correlation coefficient of 0.9998, indicating high reliability. Finally, the feasibility and effectiveness of this method were validated through a case study on earthquake science experience facilities.

In the traditional decision-making process for product form design, designers and experts often prioritize schemes based on their own knowledge and experience. This approach can lead to an oversight of user preferences, ultimately affecting decision outcomes. In contrast, crowd-intelligence-driven, multi-attribute decision-making for product form design in the cloud environment builds upon traditional approaches by leveraging the vast and diverse expertise of individuals on cloud platforms, engaging participants from various fields and roles in the decision-making process to enhance comprehensiveness and accuracy. To address the issue of a single decision-maker and limited user participation in the decision-making process for product form design schemes in the cloud environment, a multi-attribute decision-making method integrating expert knowledge and user preferences is proposed. This method aims to select a product form design scheme that optimally balances expert and user satisfaction. Initially, the Pythagorean Hesitant Fuzzy Set (PHFS) is used to quantify qualitative product attributes and to establish a comprehensive multi-attribute evaluation system. In the aspect of expert decision-making, a gray correlation coefficient decision matrix based on expert knowledge is established and the overall score of the base alternative is calculated by the ViseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method and the Improved Osculating Value method. In terms of user decision-making, weights are determined by calculating the similarity between user evaluation matrices, and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to calculate scores for product form designs based on user preferences. Ultimately, optimal selection is achieved by aggregating the aforementioned expert evaluation values and user preference values. The method’s effectiveness and feasibility are confirmed through a case study of coffee machine product form design schemes.

To address current product styling design issues, such as ignoring the joint effects of multiple styling elements when constructing perceptual imagery fitting models and thus failing to effectively identify the relationships between styling elements, a product styling design method based on fuzzy set qualitative comparative analysis (fsQCA) is proposed. This method first uses semantic differential and statistical methods to obtain users’ evaluative vocabulary for the product’s perceptual imagery. Then, morphological analysis and cluster analysis are employed to establish typical product samples and extract styling elements to create a styling feature library. Perceptual imagery ratings of these styling features are obtained through expert evaluation. fsQCA is then used to analyze the different grouping relationships between styling elements and their influence on product styling imagery, aiming to match user intentions through different element combination paths. The results show that this method achieves a consistency value of 0.9 for the most optimal styling configurations, demonstrating that fsQCA can effectively identify the multiple paths of product styling elements that meet users’ needs. The contributions of this study to the related fields are: (1) providing a new perspective on the relationship between user perceptual imagery and predicted product styling elements, and (2) advancing the theoretical basis for studying multiple paths of product styling elements. The research results demonstrate that using the fsQCA-based product styling design method can accurately portray the multiple paths of product styling elements that meet users’ needs, thereby effectively improving design efficiency. Finally, a teapot styling design study is used as an example to further verify the method’s feasibility.

To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph construction based on graph attention neural networks (GAT). Using Tang Dynasty gold and silver artifacts as samples, we establish a joint knowledge extraction model based on GAT. The model employs the BERT pretraining model to encode collected textual knowledge data, conducts sentence dependency analysis, and utilizes GAT to allocate weights among entities, thereby enhancing the identification of target entities and their relationships. Comparative experiments on public datasets demonstrate that this model significantly outperforms baseline models in extraction effectiveness. Finally, the proposed method is applied to the construction of a knowledge graph for Tang Dynasty gold and silver artifacts. Taking the Gilded Musician Pattern Silver Cup as an example, this method provides designers with a visualized and interconnected knowledge collection structure.

The empowerment of educational reform and innovation through AI technology has become a topic of increasing interest in the field of education. The advent of AI technology has made comprehensive and in-depth teaching evaluation possible, serving as a significant driving force for efficient and precise teaching. There were few empirical studies on the application of high-quality precision teaching models in the field of compulsory education, and the learning difficulty of technology and the teaching burden on teachers have become significant factors hindering the use of technology to support education. This study analyzed teaching models from the perspectives of teachers’ teaching burdens and students’ learning obstacles, and was committed to relying on intelligent technology to construct a new precision teaching model, an educational diagnosis–feedback–intervention path that covered the entire teaching process, from the dimensions of teacher behavior, student behavior, and parent behavior, aiming to assist teachers in efficient teaching and students in personalized learning. This study was conducted with nine science classes, including about 540 people in the second year of high school at a Middle School in China; six classes were the intervention groups while the last three classes were control groups, and a survey of 19 teachers from the intervention classes was carried out. The results showed that this model can significantly improve students’ academic performance in science subjects, especially in mathematics and chemistry. It has increased the proportion of high-achieving students, reduced the proportion of low-achieving students, stimulated students’ self-directed learning ability, cultivated a positive attitude towards science learning, and explained the key points of using a precision teaching model in different disciplines. It has achieved a deep integration of education and technology, helping to increase the efficiency and reduce the burden of teaching.

Extracting knowledge from cyber threat intelligence is essential for understanding cyber threats and implementing proactive defense measures. However, there is a lack of open datasets in the Chinese cybersecurity field that support both entity and relation extraction tasks. This paper addresses this gap by analyzing vulnerability description texts, which are standardized and knowledge-dense, to create a vulnerability knowledge ontology comprising 13 entities and 15 relations. We annotated 27,311 unique vulnerability description sentences from the China National Vulnerability Database, resulting in a dataset named BVTED for cybersecurity knowledge triple extraction tasks. BVTED contains 97,391 entities and 69,614 relations, with entities expressed in a mix of Chinese and English. To evaluate the dataset’s value, we trained five deep learning-based named entity recognition models, two relation extraction models, and two joint entity–relation extraction models on BVTED. Experimental results demonstrate that models trained on this dataset achieve excellent performance in vulnerability knowledge extraction tasks. This work enhances the extraction of cybersecurity knowledge triples from mixed Chinese and English threat intelligence corpora by providing a comprehensive ontology and a new dataset, significantly aiding in the mining, analysis and utilization of the knowledge embedded in cyber threat intelligence.

In the extreme space environment, spacecraft endure dramatic temperature variations that can impair their functionality. A VO2-based smart radiator device (SRD) offers an effective solution by adaptively adjusting its radiative properties. However, current research on VO2-based thermochromic films mainly focuses on optimizing the emissivity tunability (Δε) of single-cycle sandwich structures. Although multi-cycle structures have shown increased Δε compared to single-cycle sandwich structures, there have been few systematic studies to find the optimal cycle structure. This paper theoretically discusses the influence of material properties and cyclic structure on SRD performance using Finite-Difference Time-Domain (FDTD) software, which is a rigorous and powerful tool for modeling nano-scale optical devices. An optimal structural model with maximum emissivity tunability is proposed. The BaF2 obtained through optimization is used as the dielectric material to further optimize the cyclic resonator. The results indicate that the tunability of emissivity can reach as high as 0.7917 when the BaF2/VO2 structure is arranged in three periods. Furthermore, to ensure a longer lifespan for SRD under harsh space conditions, the effects of HfO2 and TiO2 protective layers on the optical performance of composite films are investigated. The results show that when TiO2 is used as the protective layer with a thickness of 0.1 µm, the maximum emissivity tunability reaches 0.7932. Finally, electric field analysis is conducted to prove that the physical mechanism of the smart radiator device is the combination of stacked Fabry–Perot resonance and multiple solar reflections. This work not only validates the effectiveness of the proposed structure in enhancing spacecraft thermal control performance but also provides theoretical guidance for the design and optimization of SRDs for space applications.

Robotic manipulators play a key role in modern industrial manufacturing processes. Monitoring their operational health is of paramount importance. In this paper, a novel anomaly detection framework named U-TFF is introduced for energy consumption auditing of robotic manipulators. It comprises a cascade of Time–Frequency Fusion (TFF) blocks to extract both time and frequency domain features from time series data. The block applies the Fast Fourier Transform to convert the input to the frequency domain, followed by two separate dense layers to process the resulting real and imaginary components, respectively. The frequency and time features are then combined to reconstruct the input. A U-shaped architecture is implemented to link corresponding TFF blocks of the encoder and decoder at the same level through skip connections. The semi-supervised model is trained using data exclusively from normal operations. Significant errors were generated during testing for anomalies with data distributions deviating from the training samples. Consequently, a threshold based on the magnitude of reconstruction errors was implemented to identify anomalies. Experimental validation was conducted using a custom dataset, including physical attacks as abnormal cases. The proposed framework achieved an accuracy and recall of approximately 0.93 and 0.83, respectively. A comparison with other benchmark models further verified its superior performance.

In recent years, smart windows have attracted widespread attention due to their ability to respond to external stimuli such as light, heat, and electricity, thereby intelligently adjusting the ultraviolet, visible, and near-infrared light in solar radiation. VO2(M) undergoes a reversible phase transition from an insulating phase (monoclinic, M) to a metallic phase (rutile, R) at a critical temperature of 68 °C, resulting in a significant difference in near-infrared transmittance, which is particularly suitable for use in energy-saving smart windows. However, due to the multiple valence states of vanadium ions and the multiphase characteristics of VO2, there are still challenges in preparing pure-phase VO2(M). Machine learning (ML) can learn and generate models capable of predicting unknown data from vast datasets, thereby avoiding the wastage of experimental resources and reducing time costs associated with material preparation optimization. Hence, in this paper, four ML algorithms, namely multi-layer perceptron (MLP), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB), were employed to explore the parameters for the successful preparation of VO2(M) films via magnetron sputtering. A comprehensive performance evaluation was conducted on these four models. The results indicated that XGB was the top-performing model, achieving a prediction accuracy of up to 88.52%. A feature importance analysis using the SHAP method revealed that substrate temperature had an essential impact on the preparation of VO2(M). Furthermore, characteristic parameters such as sputtering power, substrate temperature, and substrate type were optimized to obtain pure-phase VO2(M) films. Finally, it was experimentally verified that VO2(M) films can be successfully prepared using optimized parameters. These findings suggest that ML-assisted material preparation is highly feasible, substantially reducing resource wastage resulting from experimental trial and error, thereby promoting research on material preparation optimization.

Here, a proactively optimized fusion model (FM) for predicting the product yield of coal pyrolysis was developed. Eight coal characteristics (including pyrolysis temperature and proximate and ultimate analyses) were chosen as input parameters. Multiple linear regression (MLR), support vector machine (SVM), and random forest (RF) models were applied as the base models to form the FM. Sixty sets of experimental data from the literature were used for training and testing the base models. Different learning weights are assigned to the base models according to their predictive performance. The FM proactively improve the model outputs by means of the dynamical learning weight results. The coefficient of determination (R2) and the root-mean-squared error (RMSE) derived from the FM model were better than those of the base models. Moreover, the maximum relative error between the experimental data and model outputs was just 0.37%. These results suggest that FMs can be used to develop better predictive models for the yields of co-pyrolysis products. The FM proactively optimized the outputs base on learning weight algorithm and had better predicted performance than base models with less data.

Fluoride is a toxic and harmful ingredient in the spent cathode carbon blocks (SCCB). Gaseous fluoride pollution is a significant potential pollution source in the process of boiler collaborative disposal of SCCB. In this paper, meager coal and SCCB were used as fuel, the mixing ratio and temperature were variables, the law of thermal conversion of fluorine during the co-combustion process was studied, and the properties of ash residue were discussed. At the same time, CaO was added to the furnace to reveal the influence of temperature on the effect of fluorine fixation in the furnace. The results showed that combustion was the most suitable method for removing soluble fluorine from SCCB, and the thermal conversion rate of soluble fluorine was up to 99.68–99.98%. The higher the combustion temperature, the higher the conversion of gaseous fluoride, and the worse the effect of fluorine fixation in the furnace. It is not suitable for fluorine fixation in the furnace when the furnace temperature exceeded 1100 ℃. The higher the mixing ratio of SCCB, the higher the production of gaseous fluoride, and a proper mixing ratio is vital for co-combustion. Ash residue after mixing 10% SCCB combustion belongs to general industrial solid waste. This study has important theoretical significance for determining the applicable furnace type, the appropriate mixing ratio and effective fluoride treatment measures in the co-combustion process of coal and SCCB.

Weichang'an pill (WCAP) is a traditional Chinese patent medicine, which is clinically used for the treatment of bowel syndrome and functional dyspepsia such as diarrhea, abdominal distension, and enteritis. So far, quality control studies of WCAP have mainly focused on the determination of chemical composition content, which has little relevance to biological activity and clinical effects. With the aim of identifying the multi-index ingredients with NF-κB inhibitory activities related to WCAP clinical effect, this present work described the chemical profile of WCAP by UHPLC-QE-MS, established the correlated relationship between chromatographic fingerprints and the NF-κB inhibitory activities based on multivariate statistical analysis, including hierarchical clustering analysis (HCA), Pearson correlation analysis, and Partial least squares regression analysis (PLSR). The spectrum-effect relationship analysis indicated 10 compounds, which were ferulic acid, naringin, narirutin, hesperidin, neohesperidin, aloe emodin, emodin, honokiol, magnolol, and physcion, might be the potential NF-κB inhibitory constituents in the pill. The NF-κB inhibitory effects of the ten compounds were verified by in vitro dual luciferase reporting detection system. Considering that the detection index should be representative of more medicinal materials, a rapid and efficient UPLC-DAD method was eventually developed to determine the content of the 13 components. Our findings will provide data support for WCAP quality control and advance the understanding of the quality assessment of traditional Chinese patent medicines.

Fiber-reinforced polymer (FRP) laminates are popular in the strengthening of concrete structures, but the durability of the strengthened structures is of great concern. Due to the susceptibility of the epoxy resin used for bonding and the deterioration of materials, the bond performance of the FRP–concrete interface could be degraded due to environmental exposure. This paper aimed to establish a data-driven method for bond strength prediction using existing test results. Therefore, a method composed of a Back Prorogation Net (BPNN) and Meta-learning Net was proposed, which can be used to solve the implicit regression problems in few-shot learning and can obtain the deteriorated bond strength and the impact weight of each parameter. First, the pretraining database Meta1, a database of material strength degradation, was established from the existing results and used in the meta-learning network. Then, the database Meta2 was built and used in the meta-learning network for model fine-tuning. Finally, combining all prior knowledge, not only the degradation of the FRP–concrete bond’s strength was predicted, but the respective weights of the environment parameters were also obtained. This method can accurately predict the degradation of the bond performance of FRP–concrete interfaces in complex environments, thus facilitating the further assessment of the remaining service life of FRP-reinforced structures.

Currently, few deep models are applied to pepper-picking detection, and existing generalized neural networks face issues such as large model parameters, prolonged training times, and low accuracy. To address these challenges, this paper proposes the YOLO-chili target detection algorithm for chili pepper detection. Initially, the classical target detection algorithm YOLOv5 serves as the benchmark model. We introduce an adaptive spatial feature pyramid structure that combines the attention mechanism and the concept of multi-scale prediction to enhance the model’s detection capabilities for occluded and small target peppers. Subsequently, we incorporate a three-channel attention mechanism module to improve the algorithm’s long-distance recognition ability and reduce interference from redundant objects. Finally, we employ a quantized pruning method to reduce model parameters and achieve lightweight processing. Applying this method to our custom chili pepper dataset, we achieve an average precision (AP) value of 93.11% for chili pepper detection, with an accuracy rate of 93.51% and a recall rate of 92.55%. The experimental results demonstrate that YOLO-chili enables accurate and real-time pepper detection in complex orchard environments.

To determine the evolution of microbial community and microbial shift under anaerobic processes, this study investigates the use of denaturing gradient gel electrophoresis (DGGE). In the DGGE, short- and medium-sized DNA fragments are separated based on their melting characteristics, and this technique is used in this study to understand the dominant bacterial community in mesophilic and thermophilic anaerobic digestion processes. Dairy manure is known for emitting greenhouse gases (GHGs) such as methane, and GHG emissions from manure is a biological process that is largely dependent on the manure conditions, microbial community presence in manure, and their functions. Additional efforts are needed to understand the GHG emissions from manure and develop control strategies to minimize the biological GHG emissions from manure. To study the microbial shift during anaerobic processes responsible for GHG emission, we conducted a series of manure anaerobic digestion experiments, and these experiments were conducted in lab-scale reactors operated under various temperature conditions (28 °C, 36 °C, 44 °C, and 52 °C). We examined the third variable region (V3) of the 16S rRNA gene fingerprints of bacterial presence in anaerobic environment by PCR amplification and DGGE separation. Results showed that bacterial community was affected by the temperature conditions and anaerobic incubation time of manure. The microbial community structure of the original manure changed over time during anaerobic processes, and the community composition changed substantially with the temperature of the anaerobic process. At Day 0, the sequence similarity confirmed that most of the bacteria were similar (>95%) to Acinetobacter sp. (strain: ATCC 31012), a Gram-negative bacteria, regardless of temperature conditions. At day 7, the sequence similarity of DNA fragments of reactors (28 °C) was similar to Acinetobacter sp.; however, the DNA fragments of effluent of reactors at 44 °C and 52 °C were similar to Coprothermobacter proteolyticus (strain: DSM 5265) (similarity: 97%) and Tepidimicrobium ferriphilum (strain: DSM 16624) (similarity: 100%), respectively. At day 60, the analysis showed that DNA fragments of effluent of 28 °C reactor were similar to Galbibacter mesophilus (strain: NBRC 10162) (similarity: 87%), and DNA fragments of effluent of 36 °C reactors were similar to Syntrophomonas curvata (strain: GB8-1) (similarity: 91%). In reactors with a relatively higher temperature, the DNA fragments of effluent of 44 °C reactor were similar to Dielma fastidiosa (strain: JC13) (similarity: 86%), and the DNA fragments of effluent of 52 °C reactor were similar to Coprothermobacter proteolyticus (strain: DSM 5265) (similarity: 99%). To authors’ knowledge, this is one of the few studies where DGGE-based approach is utilized to study and compare microbial shifts under mesophilic and thermophilic anaerobic digestions of manure simultaneously. While there were challenges in identifying the bands during gradient gel electrophoresis, the joint use of DGGE and sequencing tool can be potentially useful for illustrating and comparing the change in microbial community structure under complex anaerobic processes and functionality of microbes for understanding the consequential GHG emissions from manure.

Development of biocompatible and biodegradable 3D scaffold for biomedical application has been challenging. Herein, the application of a novel nanocomposite 3D scaffold based on poly vinyl alcohol and sodium alginate embedded with Ca2+ doped titanium dioxide nanoparticle (PVA + SA/Ca@TiO2) as bioactive material was investigated. The 3D scaffold was prepared using a solvent casting method and characterized using FTIR, SEM, XRD and TGA to study their physiochemical properties. The spectroscopy techniques were used to confirm the formation of the required poly vinyl alcohol and sodium alginate with Ca doped TiO2 nanoparticles fillers interaction by the identification of bands. The TiOH bonds and the bonds related to the interconnected network between the inorganic and the organic components from hybrids. The PVA + SA/Ca@TiO2 nanocomposite 3D scaffold was demonstrated good bioactivity to promote nucleation and growth of hydroxyapatite (HAp) in the simulated body fluid. The PVA + SA/Ca@TiO2 nanocomposite 3D scaffold supported the growth and proliferation of 3 T3 mouse fibroblast cells with strong proliferation and cell attachment. The biocompatibility and nontoxic character of the 3D scaffold was probed using fibroblast cell with over 90 % of cell viability. It offers a potential for cell attachment and captured for biomedical application.

Extracellular vesicles (EVs) play crucial role in mediating intercellular communication. Small RNA is an important component in EVs. However, the proportion of small RNA sequencing (smRNA‐seq) reads in EVs mapped to the human genome is much lower than that of cells, suggesting the existence of many nonhuman sequences in EVs. However, there is no systematic study on EV fragments unmapped to the human genome. Herein, using EV smRNA‐seq data, the landscape of exogenous RNA cargoes in human EVs is portrayed. The results show the distribution of nonhuman sequence fragments in 1838 EV samples; an average of 21.82% of reads are unmapped to the human genome, and 12.33% are mapped to the collected exogenous reference sequences. Furthermore, the proportion of exogenous sequences in plasma EV samples is the lowest, while in the cell line EV samples, it is much higher, mainly from animals, bacteria, or contaminants. Exogenous sequences from plants are mainly from food, and the exogenous bacteria are mainly gut microbiota. Virus‐derived sequences reflect the high prevalence of viruses in the population, such as herpesvirus and hepatitis virus. This study provides the first landscape of exogenous fragments in human EVs and implies diverse RNA sources in the human body.

Wide weaving machines traditionally enhance the weaving width by increasing the shuttle’s initial velocity. However, this approach introduces challenges like pronounced equipment vibration, elevated noise levels, heightened energy consumption, and a reduced lifespan. Moreover, its efficacy in significantly widening fabric is constrained. Addressing these concerns, this paper proposes a wide-width warp insertion solution that involves driving the high-temperature superconducting shuttle to achieve high-speed horizontal flight through a traveling magnetic field. The inductive weft insertion system structure of wide weaving machines comprises an insertion guideway with an iron core and wound electromagnetic coils. The shuttle consists of a high-temperature superconducting block and a conductive plate, serving as the driving element. By establishing the equivalent circuit of the weft insertion guideway and the suspended shuttle, the calculation formula for the dynamic driving performance of the weft insertion guideway is obtained. Utilizing a transient 3D magnetic field simulation model, the impact of parameters like the current frequency, shuttle conductive plate thickness, and suspension gap on weft insertion performance is explored. Successful wide-width weft insertion motion is achieved by controlling coil input current parameters. Finally, an experimental platform is constructed to validate the correctness of the weft insertion system structure and simulation model through practical experiments.

Among the slope hazards caused by rainfall, not all of them occur directly during storm washout, and the wetting–drying cycles’ effect on the rainfall–evaporation process is an important cause of shallow slope instability. In this study, taking the slope of the open-pit coal mine dumps in Cam Pha, in the Quang Ninh region of Vietnam, as the research object, we carry out experiments on the physical properties of the rock body under different wetting–drying cycles, as well as numerical analyses. The results show that the wetting–drying cycles significantly affect the physical and mechanical parameters and permeability of the rock body. In the process of the wetting–drying cycle, a transient saturated zone occurs on the surface of the slope, and the range of the unsaturated zone inside the slope body decreases with the increase in the number of wetting–drying cycles. Moreover, the infiltration line keeps moving downward, but the rate of downward movement is slowed down by the decrease in the gradient of matrix suction affected by rainfall. Under the influence of the wetting–drying cycles, the slope displacement, plastic zone, and maximum shear strain increment range gradually approach the slope surface with the wetting–drying cycles, and the displacement peak gradually increases. A dump is a site for the centralized discharge of mining waste, formed by the crushing and stockpiling of the original rock formation. Bang Nau is the name of the dump considered in this study. After multiple rainfall events, the slope stability under five wetting–drying cycles decreases from 1.721 to 1.055, and the landslide mode changes from a whole landslide to a single-step shallow landslide, with a certain landslide risk. It is necessary to strengthen the slope stability as the landslide risk is very high, and it is necessary to strengthen the monitoring and inspection of the slope.

Enhancing photocarrier separation is a key step of photocatalysis, and in situ constructed composition interface is an advanced method to achieve this aim. Therefore, we report a face-to-face Bi2WO6/BiOCl (BWOC) which was synthesized via the continuous in situ ion-exchange method. As UV light is harmful to the human body, BWOC exhibits excellent photocatalytic activity only in visible light, and this is an important feature because visible light is a human-friendly operating condition. Under 50 W visible LED lamp illumination, unexcited BiOCl (BOC) only extracts electrons of excited Bi2WO6 (BWO), and holes remain on BWO, resulting in excellent photocarrier spatial separation efficiency through the face-to-face interface. This is why BWOC can be safe to use for the removal of hazardous substances. Compared with BWO and BOC, BWOC possesses 2.6 and 5.6 times higher photodegradation activity than RhB. This work provides a novel insight of efficient visible light photocatalytic system for environmental remediation.

The power ultrasonic generator (PUG) is the core device of power ultrasonic technology (PUT), and its performance determines the application of this technology in biomedicine, semiconductor, aerospace, and other fields. With the high demand for sensitive and accurate dynamic response in power ultrasonic applications, the design of PUG has become a hot topic in academic and industry. However, the previous reviews cannot be used as a universal technical manual for industrial applications. There are many technical difficulties in establishing a mature production system, which hinder the large-scale application of PUG for piezoelectric transducers. To enhance the performance of the dynamic matching and power control of PUG, the studies in various PUT applications have been reviewed in this article. Initially, the demand design covering the piezoelectric transducer application and parameter requirements for ultrasonic and electrical signals is overall summarized, and these parameter requirements have been recommended as the technical indicators of developing the new PUG. Then the factors affecting the power conversion circuit design are analyzed systematically to realize the foundational performance improvement of PUG. Furthermore, advantages and limitations of key control technologies have been summarized to provide some different ideas on how to realize automatic resonance tracking and adaptive power adjustment, and to optimize the power control and dynamic matching control. Finally, several research directions of PUG in the future have been prospected.

A facile hydrothermal process was used to prepare peanut shells derivatives (PS)-supported MoS2 nanosheets (PS-MoS2), which showed much better hydrogen evolution reaction (HER) catalytic activity than MoS2. PS-MoS2 displayed excellent catalytic performance with lower onset overpotential (94.5 mV) and good cyclic stability. By analyzing the results of characterization of superficial properties, structure, morphological, and elemental valence state, 11.1%-PS-MoS2 exhibits exceptional catalytic activity due to an effective increase in the amount of exposure of active sites and in the content of lower valence Mo and S with higher HER activity owing to the addition of PS.

Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve’s local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.

Graphene oxide is a two-dimensional material that has been extensively studied in various fields due to its good mechanical properties, water dispersibility, and a large number of oxygen-containing functionalities on its surface. In this study, graphene oxide powder was prepared using graphite powder to take advantage of its large specific surface area and abundance of oxygen-containing functional groups. The graphene oxide powder was cross-linked with acrylic acid and acrylamide and polymerized to produce graphene oxide hydrogels, which were used to adsorb four metal ions including Cu(II), Pb(II), Zn(II), and Cd(II) from aqueous solutions. The adsorption performance of the graphene oxide hydrogels was investigated at different pHs, temperatures, initial metal ion concentrations, and competition principles, as well as their adsorption and desorption after three repeated adsorption–desorption experiments. It was found that the graphene oxide hydrogels exhibited good adsorption performance for all four metal ions under different conditions. The graphene oxide hydrogels for the adsorption of Cu(II), Pb(II), Zn(II), and Cd(II) ions were best fitted using the Langmuir monolayer adsorption model and the quasi-secondary reaction kinetic model. Good adsorption was achieved for all four metal ions under different competing adsorption principles. After three adsorption–desorption cycles, the adsorption capacity of the graphene oxide hydrogels for all four metal ions remained at 88% and above. These results indicate that graphene oxide hydrogels are a stable, efficient, low-cost, and reusable adsorbent material for the treatment of metal ions in solution.

This study aimed to explore the relationship between change of direction deficit (CODD), change of direction total time (CODTT), and linear sprint time and to compare the differences between CODD and CODTT in assessing an athlete’s change of direction (COD) ability. Forty-four highly trained male young athletes underwent Y-shaped pre-planned agility, Pro-agility, and 30 m linear sprint tests. The results showed a moderate to highly significant correlation between CODTT and linear speed time at 0–5 m, 0–10 m, and 0–30 m (r = 0.5–0.8), while there was no statistically significant relationship between CODD and linear speed time at 0–5 m and 0–10 m (r = 0.0–0.3). CODD and CODTT were moderate to highly correlated (r = 0.4–0.8), with CODD for 180° COD showing a higher predictive value for CODTT compared to 45° COD (14–35% vs. 49–63%). Additionally, 13–22% of the participants showed opposing results for COD ability when comparing the standardized Z-score of Pro-agility 0–10 m CODTT and CODD. Pro-agility 0–10 m CODD also resulted in a higher asymmetry ratio (2% vs. 7%) and COD ability imbalances (0% vs. 38%) than Pro-agility 0–10 m CODTT. In conclusion, CODD may provide a more accurate assessment of an athlete’s COD ability than CODTT.

Existing transmission lines and pipelines are frequently crossed and erected in parallel, meaning that if lightning strikes a wire and causes insulator flashovers, the resulting lightning current will spread through the grounding of the tower where the flashover insulator is located. This dispersion of current can lead to overvoltage effects on nearby pipelines. This study performs simulation calculations to analyze the overvoltage experienced by pipelines due to the dispersion of grounding current from the tower. Furthermore, this paper proposes a method for protecting the pipeline from such an overvoltage. Firstly, the lightning transient calculation model of a transmission line tower is constructed using the electromagnetic transient software ATP-EMTP 5.5. The model calculates the effects of lightning peak currents and soil resistivity on the distribution characteristics of lightning current in the tower, specifically in the area where the flashover insulator is located. Subsequently, a calculation model of the tower grounding grid–natural gas pipeline is developed, taking into account the distribution characteristics of lightning current in the tower. This model analyzes the impact of lightning peak currents, soil resistivity, and pipeline spacing on pipeline overvoltage. Finally, the effectiveness of the solid-state decoupler in mitigating lightning overvoltage in the pipeline is verified. The results demonstrate a positive correlation between the lightning current entering the tower grounding grid through the flashover insulator and the lightning current distribution characteristics. The solid-state decoupling device proves to be effective in reducing the voltage of the pipeline insulation layer, and the simulation results provide the optimal laying length of the bare copper wire.

Tight sandstone gas has become one of the important unconventional resources. It is of great significance to study the pore throat structure of tight sandstone for the development of tight sandstone gas reservoirs. On the basis of previous research results, this paper studies the tight reservoir characteristics of Yanchang Formation by combining geological data with geophysical logging. The reservoir characteristics are described and studied in detail by ordinary thin section, casting thin section, graphic granularity, and scanning electron microscope experiment. The experimental results show that the reservoir sandstone types of long 6 segments are mainly lithic feldspathic sandstone and feldspathic lithic sandstone, with high component maturity and structural maturity. The components of cement mainly include authigenic clay minerals illite, chlorite, and carbonate minerals. The reservoir space is mainly composed of feldspar-dissolved pores and residual intergranular pores. Compaction and cementation are the decisive factors of pore reduction. At present, the average porosity of the reservoir is 8.33% and the average permeability is 0.12 × 10−3 μm2, which belong to an ultralow porosity and ultralow permeability reservoir. On the basis of studying the origin, superimposition, and distribution of sand bodies and taking into account the sedimentary characteristics, origin, and trigger mechanism of gravity flow, a multisource sedimentary model is established in the study area, which is dominated by sandy debris flow, accompanied by turbidity current and local development of sandy slump. The thick massive sandstone with the most exploration significance in the study area is formed by the superimposition of multistage sandy clastic flow sediments along the gully in the direction of delta to lake. Summarizing the characteristics of reservoir development and evaluating and predicting it can provide geological basis for the next exploration and development of deep-water tight reservoirs in the study area.

Photothermal therapy has the advantages of non-invasiveness, low toxicity, simple operation, a broad spectrum of antibacterial ability, and non-proneness to developing drug resistance, which provide it with irreplaceable superiority in fighting against microbial infection. The effect of photothermal therapy is closely related to the choice of photothermal agent. Conjugated nanomaterials are potential candidates for photothermal agents because of their easy modification, excellent photothermal conversion efficiency, good photostability, and biodegradability. In this paper, the application of photothermal agents based on conjugated nanomaterials in photothermal antimicrobial treatment is reviewed, including conjugated small molecules, conjugated oligomers, conjugated polymers, and pseudo-conjugated polymers. At the same time, the application of conjugated nanomaterials in the combination of photothermal therapy (PTT) and photodynamic therapy (PDT) is briefly introduced. Finally, the research status, limitations, and prospects of photothermal therapy using conjugated nanomaterials as photothermal agents are discussed.

Heparanase, a member of the carbohydrate-active enzyme (CAZy) GH79 family, is an endo-β-glucuronidase capable of degrading the carbohydrate moiety of heparan sulphate proteoglycans, thus modulating and facilitating remodeling of the extracellular matrix. Heparanase activity is strongly associated with major human pathological complications, including but not limited to tumour progress, angiogenesis and inflammation, which make heparanase a valuable therapeutic target. Long-due crystallographic structures of human and bacterial heparanases have been recently determined. Though the overall architecture of human heparanase is generally comparable to that of bacterial glucuronidases, remarkable differences exist in their substrate recognition mode. Better understanding of regulatory mechanisms of heparanase in substrate recognition would provide novel insight into the anti-heparanase inhibitor development as well as potential clinical applications.

Ortho functionalisation of phenols can be achieved using N-phenoxy amide directing groups. Here a method for chemodivergent C-H alkenylation, alkylation, carboetherification, or [3 + 2] annulation is presented, with product selectivity determined by the choice of solvent.

Wafer bin maps (WBMs) are essential test data in semiconductor manufacturing. WBM defect classification can provide critical information for the improvement of manufacturing processes and yield. Although deep-learning-based automatic defect classification models have demonstrated promising results in recent years, they require a substantial amount of labeled data for training, and manual labeling is time-consuming. Such limitations impede the practical application of existing algorithms. This study introduces a low-data defect classification algorithm based on contrastive learning. By employing momentum contrastive learning, the network extracts effective representations from large-scale unlabeled WBMs. Subsequently, a prototypical network is utilized for fine-tuning with only a minimal amount of labeled data to achieve low-data classification. Experimental results reveal that the momentum contrastive learning method improves the defect classification performance by learning feature representation from large-scale unlabeled data. The proposed method attains satisfactory classification accuracy using a limited amount of labeled data and surpasses other comparative methods in performance. This approach allows for the exploitation of information derived from large-scale unlabeled data, significantly reducing the reliance on labeled data.

The design of Pt-based electrocatalysts with high efficiency towards acid oxygen reduction reactions is the priority to promote the development and application of proton exchange membrane fuel cells. Considering that the Pt atoms on the surfaces of the electrocatalysts face the problems of interference of non-active species (such as OHad, OOHad, CO, etc.), high resistance of mass transfer at the liquid–solid interfaces, and easy corrosion when working in harsh acid. Researchers have modified the surfaces’ local environment of the electrocatalysts by introducing surface modifiers such as silicon or carbon layers, amine molecules, and ionic liquids on the surfaces of electrocatalysts, which show significant performance improvement. In this review, we summarized the research progress of surface modified Pt-based electrocatalysts, focusing on the surface modification strategies and their mechanisms. In addition, the development prospects of surface modification strategies of Pt-based electrocatalysts and the limitations of current research are pointed out.

The current neural networks for tomato leaf disease recognition have problems such as large model parameters, long training time, and low model accuracy. To solve these problems, a lightweight convolutional neural network (LBFNet) is proposed in this paper. First, LBFNet is established as the base model. Secondly, a three-channel attention mechanism module is introduced to learn the disease features in tomato leaf disease images and reduce the interference of redundant features. Finally, a cascade module is introduced to increase the depth of the model, solve the gradient descent problem, and reduce the loss caused by increasing the depth of the model. The quantized pruning technique is also used to further compress the model parameters and optimize the model performance. The results show that the LBFNet model achieves 99.06% accuracy on the LBFtomato dataset, with a training time of 996 s and a single classification accuracy of over 94%. Further training using the saved weight file after quantized pruning enables the model accuracy to reach 97.66%. Compared with the base model, the model accuracy was improved by 28%, and the model parameters were reduced by 96.7% compared with the traditional Resnet50. It was found that LBFNet can quickly and accurately identify tomato leaf diseases in complex environments, providing effective assistance to agricultural producers.

In this study, computer vision applicable to traditional agriculture was used to achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and used it to identify four different rice leaf diseases. The rice leaf disease test set with a complex background was named the CBG-Dataset, and a new single background rice leaf disease test set was constructed, the SBG-Dataset, based on the original dataset. The Res-Attention module used 3 × 3 convolutional kernels and denser connections compared with other attention mechanisms to reduce information loss. The experimental results showed that RiceDRA-Net achieved a recognition accuracy of 99.71% for the SBG-Dataset test set and possessed a recognition accuracy of 97.86% on the CBG-Dataset test set. In comparison with other classical models used in the experiments, the test accuracy of RiceDRA-Net on the CBG-Dataset decreased by only 1.85% compared with that on the SBG-Dataset. This fully illustrated that RiceDRA-Net is able to accurately recognize rice leaf diseases with complex backgrounds. RiceDRA-Net was very effective in some categories and was even capable of reaching 100% precision, indicating that the proposed model is accurate and efficient in identifying rice field diseases. The evaluation results also showed that RiceDRA-Net had a good recall ability, F1 score, and confusion matrix in both cases, demonstrating its strong robustness and stability.

Aiming at the problems of the traditional planetary gear fault diagnosis method of wind turbines, such as the poor timeliness of data transmission, weak visualization effect of state monitoring, and untimely feedback of fault information, this paper proposes a planetary gear fault diagnosis method for wind turbines based on a digital twin. The method was used to build the digital twin model of wind turbines and analyze the wind turbines’ operating state utilizing virtual and real data. Empirical mode decomposition (EMD) was used, and an atom search optimization–support vector machine (ASO-SVM) model was established for planetary gear fault diagnosis. The digital twin model diagnoses faults and constantly revises the model based on the diagnostic results. The digital twin fault diagnosis system was implemented in the Unity3D platform. The experimental results demonstrate the feasibility of the proposed early-warning system for the real-time diagnosis of planetary gear faults in wind turbines.

The transdermal administration of collagen is an important method used for wound healing and skin regeneration. However, due to the limitations of previous approaches, the process and degree of collagen transdermal absorption could only be quantitatively and qualitatively evaluated in vitro. In the present study, we introduced a novel approach that combines second-harmonic generation with two-photon excited fluorescence to visualize the dynamics of collagen transdermal absorption in vivo. High-resolution images showed that exogenous recombinant human collagen permeated the epidermis through hair follicles and sebaceous glands reached the dermis, and formed reticular structures in real time. We also validated these findings through traditional in vitro skin scanning and histological examination. Thus, our approach provides a reliable measurement for real-time evaluation of collagen absorption and treatment effects in vivo.

Adsorption performance and stability of the carboxyl groups modified straws and their extracts of cellulose, lignin, and hemicellulose for Pb2+ were investigated, and the optimum pH range for Pb2+ adsorption was determined by considering both the stability and capacity of the modified biosorbents for the first time. Results showed that adsorption capacity and stability of the straws and extracts were both improved significantly after modification. Adsorption capacities of the modified straws and extracts followed the order: modified hemicellulose > modified lignin, modified straw > modified cellulose, while stability of them followed the reverse order. In the optimum pH range from 4.0 to 5.0, modified rape and cotton straw showed better stability than the modified maize straw, and total organic carbon (TOC) values determined from the two modified straws and extracts were lower than 5.0 mg L−1 even after adsorption for 30 days, which reached the drinking water standard in China.

Tomatoes are a crop of significant economic importance, and disease during growth poses a substantial threat to yield and quality. In this paper, we propose IBSA_Net, a tomato leaf disease recognition network that employs transfer learning and small sample data, while introducing the Shuffle Attention mechanism to enhance feature representation. The model is optimized by employing the IBMax module to increase the receptive field and adding the HardSwish function to the ConvBN layer to improve stability and speed. To address the challenge of poor generalization of models trained on public datasets to real environment datasets, we developed an improved PlantDoc++ dataset and utilized transfer learning to pre-train the model on PDDA and PlantVillage datasets. The results indicate that after pre-training on the PDDA dataset, IBSA_Net achieved a test accuracy of 0.946 on a real environment dataset, with an average precision, recall, and F1-score of 0.942, 0.944, and 0.943, respectively. Additionally, the effectiveness of IBSA_Net in other crops is verified. This study provides a dependable and effective method for recognizing tomato leaf diseases in real agricultural production environments, with the potential for application in other crops.