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BackgroundUlcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) influenced by multiple factors. Berberine, an isoquinoline alkaloid derived from the root and bark of Coptis chinensis Franch., has shown promise in managing UC, but its underlying mechanisms remain unclear.MethodsTo elucidate the relationship between berberine, ulcerative colitis (UC), and the organism’s metabolome, we established a dextran sulfate sodium (DSS)-induced UC model in rats. Colonic tissue was collected for histopathological examination, while plasma samples were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with dynamic Multiple Reaction Monitoring (dMRM). This approach, characterized by its short analysis time of 20 min per sample, excellent reproducibility, and straightforward data processing, allowed for the comprehensive detection of a wide array of metabolites, including amino acids, lipids, and organic acids, many of which are implicated in the pathophysiology of UC.ResultsOur results showed that berberine modulated the metabolic disturbances of 33 compounds in the plasma of UC rats, primarily including amino acids, pyrimidines, organic phosphoric acids, fatty acyls, and organonitrogen compounds. These altered metabolites were associated with various pathways, such as amino acid metabolism, glutathione metabolism, nicotinate and nicotinamide metabolism, taurine and hypotaurine metabolism, pyrimidine metabolism, glyoxylate and dicarboxylate metabolism, and the citrate cycle (TCA cycle). Notably, 3-hydroxyproline, homocysteic acid, L-threonine, L-lysine, carbamoyl phosphate, O-phosphoethanolamine, taurine, leucine, and phosphorylcholine exhibited significant differences between the Treatment and Model groups, with levels reverting to those of the Control group (p < 0.001). These findings suggested that these compounds may serve as potential plasma biomarkers for UC.ConclusionThis study provided valuable insights into the mechanism by which berberine exerted its therapeutic effects on UC through metabolomics. Our results highlighted berberine’s potential to modulate key metabolic pathways and restore the levels of several metabolites, suggesting its utility as a therapeutic agent for UC. These findings underscored the importance of metabolomics in understanding the pathophysiology and treatment of UC.

Understanding salt and water transport mechanisms in reverse osmosis (RO) under high pressures and salinities is critical to advancing RO-based brine management technologies. In this study, we investigate the dependence of salt permeance and partitioning on feed salinity and applied pressure. Salt partitioning coefficients were determined using a novel high-pressure quartz crystal microbalance (QCM), and salt permeances were collected using a lab-scale high-pressure dead-end cell. Our results show that salt permeance decreases with respect to feed concentration, in contrast to conventional theories for charged RO membranes. We further show salt partitioning coefficients do not change with applied hydrostatic pressure but are dependent on feed salt concentration. We use non-equilibrium molecular dynamics simulations to show that these trends are explained by salinity and pressure-induced changes to the structure of the polyamide layer, namely osmotic deswelling and compaction. Changes in the polyamide layer thickness and pore size alter the frictional interactions of ions, affecting membrane performance at larger salinities and pressures. These results provide new insights on how structure-performance relationships affect salt transport at higher pressures.

Abstract Zero‐dimensional (0D) hybrid manganese halides have gained wide attention for the various crystal structures, excellent optical performance and scintillation properties compared with 3D lead halide perovskite nanocrystals. In this work, a new family of 0D hybrid manganese halides of A2MnBr4 (A = BzTPP, Br‐BzTPP, and F‐BzTPP) based on discrete [MnBr4]2− tetrahedral units is reported as highly efficient lead‐free scintillators. Excited by UV or blue light, these hybrids emit bright green light originating from the d–d transition of Mn2+ with near‐unity PLQY (99.5%). Significantly, high PLQY and low self‐absorption render extraordinary radioluminescence properties with the highest light yield of 80,100 photons MeV−1, which reached the climax of present hybrid manganese halides and surpassed most commercial scintillators. The radioluminescence intensity features a linear response to X‐ray doses with a detection limit of 30 nGyair s−1, far lower than the requirement of medical diagnostic (5.5 µGyair s−1). X‐ray imaging demonstrates ultrahigh spatial resolution of 14.06 lp mm−1 and short afterglow of 0.3 ms showcasing promising application prospects in radiography. Overall, we demonstrated new hybrid manganese halides as promising scintillators for advanced applications in X‐ray imaging with multiple superiorities of nontoxicity, facile‐assembly process, high irradiation light yield, excellent resolution, and stability.

Abstract The development of tumor drug microcarriers has attracted considerable interest due to their distinctive therapeutic performances. Current attempts tend to elaborate on the micro/nano‐structure design of the microcarriers to achieve multiple drug delivery and spatiotemporal responsive features. Here, the desired hydrogel microspheres are presented with spatiotemporal responsiveness for the treatment of gastric cancer. The microspheres are generated based on inverse opals, their skeleton is fabricated by biofriendly hyaluronic acid methacrylate (HAMA) and gelatin methacrylate (GelMA), and is then filled with a phase‐changing hydrogel composed of fish gelatin and agarose. Besides, the incorporated black phosphorus quantum dots (BPQDs) within the filling hydrogel endow the microspheres with outstanding photothermal responsiveness. Two antitumor drugs, sorafenib (SOR) and doxorubicin (DOX), are loaded in the skeleton and filling hydrogel, respectively. It is found that the drugs show different release profiles upon near‐infrared (NIR) irradiation, which exerts distinct performances in a controlled manner. Through both in vitro and in vivo experiments, it is demonstrated that such microspheres can significantly reduce tumor cell viability and enhance the efficiency in treating gastric cancer, indicating a promising stratagem in the field of drug delivery and tumor therapy.

Abstract Adsorbents with high adsorption efficiency and excellent biosafety for biomedical applications are highly required. MXene is a promising candidate owning these advantages, yet pristine MXene faces dilemmas including insufficient utility of surface site as well as limited processibility. Here, we develop MXene‐encapsulated porous microcapsules via microfluidics. The microcapsules have a biomass hydrogel shell that provides robust support for MXene in the core, by which the microcapsules are endowed with high MXene dosage and remarkable biosafety. Additionally, the MXene nanoflakes assemble into a three‐dimensional network via metal ion‐induced gelation, thereby avoiding restacking and significantly improving surface utilization. Moreover, a freeze‐pretreatment of the microcapsules during preparation results in the formation of a macroporous structure in the shell, which can facilitate the diffusion of the target molecules. These features, combined with additional magneto‐responsiveness rendered by the incorporation of magnetic nanoparticles, contribute to prominent performances of the microcapsules in cleaning uremia toxins including creatinine, urea, and uric acid. Thus, it is anticipated that the MXene‐encapsulated microcapsules will be promising adsorbents in dialysis‐related applications, and the combination of microfluidic encapsulation with metal ion gelation will provide a novel approach for construction of hybrid MXene materials with desired functions.

Abstract Pest disasters which occurs on crops is a serious problem that not only cause crop yield loss or even crop failure but can also spread a number of plant diseases.Sulfonate derivatives have been widely used in insecticide and fungicide research in recent years. On this basis, a series of sulfonate derivatives bearing an amide unit are synthesized and the biological activities are evaluated. The bioassay results showed that compounds A 8 , A 13 , A 16 , B 1 , B 3 , B 4 , B 5 , B 10 , B 12 − 20 , C 3 , C 5 , C 9 , C 10 , C 14 , C 15 , C 17 and C 19 showed 100% activity at a concentration of 500 µg/mL against the Plutella xylostella (P. xylostella). Among them, B 15 which contains a thiadiazole sulfonate structure still shows 100% activity at 50 µg/mL concentration against P. xylostella and had the lowest median lethal concentration (LC50) (7.61 µg/mL) among the target compounds. Further mechanism studies are conducted on compounds with better insecticidal activity. Molecular docking results shows that B 15 formed hydrophobic interactions π-π and hydrogen bonds with the indole ring of Trp532 and the carboxyl group of Asp384, respectively, with similar interaction distances or bond lengths as those of diflubenzuron. Moreover, chitinase inhibition assays are performed to further demonstrate its mode of action. In addition, the anti-bacterial activity of the series of compounds is also tested and the results showed that the series of compounds has moderate biological activity against Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc), with inhibition rates of 91%, 92% and 92%, 88% at the concentration of 100 µg/mL, respectively. Our study indicates that B 15 can be used as a novel insecticide for crop protection. Graphical Abstract

Abstract The AI-based small molecule drug discovery has become a significant trend at the intersection of computer science and life sciences. In the pursuit of novel compounds, fragment-based drug discovery has emerged as a novel approach. The Generative Pre-trained Transformers (GPT) model has showcased remarkable prowess across various domains, rooted in its pre-training and representation learning of fundamental linguistic units. Analogous to natural language, molecular encoding, as a form of chemical language, necessitates fragmentation aligned with specific chemical logic for accurate molecular encoding. This review provides a comprehensive overview of the current state of the art in molecular fragmentation. We systematically summarize the approaches and applications of various molecular fragmentation techniques, with special emphasis on the characteristics and scope of applicability of each technique, and discuss their applications. We also provide an outlook on the current development trends of molecular fragmentation techniques, including some potential research directions and challenges.

Rainfall infiltration affects permafrost-related slope stability by changing the pore water pressure in soil. In this study, the infiltration responses under rainfall conditions were elucidated. The instantaneous profile method and filter paper method were used to obtain the soil–water characteristic curve (SWCC) and hydraulic conductivity function (HCF). During the rainfall infiltration test, the vertical patters of volumetric moisture contents, total hydraulic head or suction and wetting front were recorded. Advancing displacement and rate of the wetting front, the cumulative infiltration, the instantaneous infiltration rate, and the average infiltration rate were determined to comprehensively assess the rainfall infiltration process, along with SWCC and HCF. Additionally, the effects of dry density and runoff on the one-dimensional vertical infiltration process of soil columns were evaluated. The results showed that the variation curve of wetting front displacement versus time obeys a power function relationship. In addition, the infiltration rate–time relationship curve and the unsaturated permeability curve could be roughly divided into three stages, and the SWCC and HCF calculated by volumetric moisture content are more sensitive to changes in dry density than to changes in runoff or hydraulic head height.

Clarifying the trade-offs and synergies of ecosystem services in Liaoning’s ecological restoration zones is crucial for strengthening the positioning of ecological restoration zones and optimizing ecosystem services. This study is based on “Liaoning Provincial Land Spatial Planning (2021–2035)” and divides the area into ecological restoration zones. We utilized the InVEST model, ArcGIS Pro, and Geoda in this study to quantify five ecosystem services (Soil Conservation, Carbon Storage, Habitat Quality, Water Yield, and Food Production) and constructed an evaluation framework to assess the trade-offs and synergies of ecosystem services at both global and local levels. The conclusions are as follows: (1) The global relationships among ecosystem services in different ecological restoration zones are ranked as: strong trade-offs (35.51%) > weak trade-offs (33.17%) > low synergies (29.09%) > high synergies (2.24%); (2) The area exhibiting synergistic relationships between pairs of local ecosystem services in ecological restoration zones is larger than the area exhibiting trade-offs; (3) The strongest synergy is observed between water yield and soil conservation, while the most significant trade-off occurs between food production and soil conservation. These relationships exhibit similar spatial characteristics in the WSFR, SWCR, and WCR zones; (4) The proportion of areas showing trade-offs and synergies differs between global and local scales.

With the rapid development of intelligent transportation systems and information technology, the security of road traffic signal systems has increasingly attracted the attention of managers and researchers. This paper proposes a new method for detecting attacks on traffic signal systems based on game theory and Generative Adversarial Networks (GAN). First, a game theory model was used to analyze the strategic game between the attacker and the defender, revealing the diversity and complexity of potential attacks. A Bayesian game model was employed to calculate and analyze the attacker’s choice of position. Then, leveraging the advantages of GAN, an adversarial training framework was designed. This framework can effectively generate attack samples and enhance the robustness of the detection model. Using empirical research, we simulated the mapping of real traffic data, road network data, and network attack data into a simulation environment to validate the effectiveness of this method. In a comparative experiment, we contrasted the method proposed in this paper with the traditional Support Vector Machine (SVM) algorithm, demonstrating that the model presented here can achieve efficient detection and recognition across various attack scenarios, with significantly better recall and F1 scores compared to traditional methods. Finally, this paper also discusses the application prospects of this method and its potential value in future intelligent transportation systems.

In the context of rapid industrialization, efficiently detecting metal corrosion areas has become a critical task in preventing material damage. Unlike conventional semantic segmentation targets, metal corrosion characteristics vary significantly in color, texture, and size. Traditional image segmentation methods need improvement in scenarios involving occlusions, shadows, and defects. This paper proposes a convolution and sequence encoding combined network, MCD-Net, for metal corrosion area segmentation. First, a visual Transformer sequence encoder is introduced into the convolutional encoder–decoder network to enhance global information processing capabilities and establish long-range feature dependencies. A feature fusion method based on an attention module is proposed to enhance the model’s ability to recognize corrosion boundaries, thereby enhancing segmentation accuracy and model robustness. Finally, in the model’s decoding stage, a score-based multi-scale feature enhancement method is employed to emphasize significant features in the corrosion areas. Experimental results indicate that this method attained an F1 score of 84.53% on a public corrosion dataset, demonstrating the model’s deeper understanding and reasoning capabilities for shadow and defect features, as well as excellent noise resistance performance.

Abstract The management of infected wounds is always of great significance and urgency in clinical and biomedical fields. Recent efforts in this area are focusing on the development of functional wound patches with effective antibacterial, drug delivery, and sensor properties. Here, we present novel hyaluronic acid (HA) microneedle patches with these features by encapsulating aminobenzeneboronic acid‐modified gold nanoclusters (A‐GNCs) for infected wound management. The A‐GNCs loaded microneedle patches were derived from negative‐mold replication and showed high mechanical strength to penetrate the skin. The release of the A‐GNCs was realized by the degradation of HA, and the self‐monitor of the released actives was based on the dynamic bright orange fluorescence emitted from A‐GNCs under ultraviolet radiation. As the A‐GNCs could destroy bacteria membranes, the microneedle patches were with excellent in vitro antibiosis ability. Based on these features, we have demonstrated the bacteria inhibition, residual drug self‐monitoring, and wound healing promotion abilities of the microneedle patches in Escherichia coli‐ or Staphylococcus aureus‐infected wound management. These results indicated the great potential of such A‐GNCs loaded microneedle patches for clinical applications.

This study aims to investigate the correlation between plasma fat-soluble vitamin levels and blood lipid in elderly patients with coronary heart disease (CHD). A total of 120 participants were enrolled, including 60 CHD patients and 60 controls without CHD. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to quantify plasma levels of vitamins A, D3, E, and K. Data analysis was conducted using the statistical analysis system module of MetaboAnalyst 5.0. The CHD group showed significantly higher levels of plasma total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) but not high-density lipoprotein cholesterol (HDL-C) compared to controls. The CHD group exhibited significantly higher plasma levels of VA and VE, positively correlating with TC, TG, and LDL-C. After adjusted by TG levels, the CHD group had significantly lower plasma levels of VA and VE, negatively correlating with TC, TG, and LDL-C. The CHD group also had significantly lower concentrations of VD3, independent of TG modification, compared to controls. VD3 negatively correlated with TC, TG, and LDL-C. Elderly individuals with CHD display abnormal blood lipid metabolism, and fat-soluble vitamins adjusted by TG levels can more accurately and timely response to implicit fat-soluble vitamins deficiency in CHD patients.

Flexible sensors can measure various stimuli owing to their exceptional flexibility, stretchability, and electrical properties. However, the integration of multiple stimuli into a single sensor for measurement is challenging. To address this issue, the sensor developed in this study utilizes the natural biopolymers sodium alginate and carboxymethyl cellulose to construct a dual interpenetrating network, This results in a flexible porous sponge that exhibits a dual-modal response to strain and magnetic stimulation. The dual-mode flexible sensor achieved a maximum tensile strength of 429 kPa and elongation at break of 24.7%. It also exhibited rapid response times and reliable stability under both strain and magnetic stimuli. The porous foam sensor is intended for use as a wearable electronic device for monitoring joint movements of the body. It provides a swift and stable sensing response to mechanical stimuli arising from joint activities, such as stretching, compression, and bending. Furthermore, the sensor generates opposing response signals to strain and magnetic stimulation, enabling real-time decoupling of different stimuli. This study employed a simple and environmentally friendly manufacturing method for the dual-modal flexible sensor. Because of its remarkable performance, it has significant potential for application in smart wearable electronics and artificial electroskins.

This research aims to address the limited consideration given to non-motorized transport facilities in current studies on shared bike travel demand forecasting. This study is the first to propose a method that applies complete citywide non-motorized facility data to predict bike-sharing demand. This study employs a multiscale geographically weighted regression (MGWR) model to examine the effects of non-motorized transport facility conditions, quantity of intersections, and land use per unit area on riding demand at various spatial scales. The results of comparison experiments reveal that riding demand is substantially affected by non-motorized transport facilities and the quantity of intersections.

In order to improve the precision of phase recognition and reduce the rate of misdetection, this paper applies the deep learning method to automatic phase recognition. In this paper, an automatic seismic phase recognition model based on the Bi-LSTM network is designed. To test the performance of this model, the STEAD dataset is used for training and testing, and this model is compared with the traditional STA/LTA and AIC methods. The experimental results show that, compared to STA/LTA and AIC methods, the Bi-LSTM network can reduce the misdetection rate by about 8–15%, and improve the RSEM; especially, the prediction error of S-wave is greatly reduced.

Nitrobenzene (NB) is one of the major organic pollutants that has seriously endangered human health and the environment even in trace amounts. Therefore, it is of great significance to detect trace NB efficiently and sensitively. Herein, a porphyrinic metal-organic framework (MOF) of Mn-PCN-222 (PCN, porous coordination network) was first synthesized by the coordination between Zr6 cluster and tetrakis (4-carboxyphenyl)-porphyrin-Mn (Ⅲ) (MnTCPPCl) ligand. To regulate its structure and the electrochemical properties, a phenyl group was inserted in each branched chain of TCPP to form the TCBPP organic ligand. Then, we used Zr6 clusters and manganese metalloporphyrin (MnTCBPPCl) to synthesize a new porphyrin-based MOF (Mn-CPM-99, CPM, crystalline porous material). Due to the extended chains of TCPP, the rod-shaped structure of Mn-PCN-222 was switched to concave quadrangular bipyramid of Mn-CPM-99. Mn-CPM-99 exhibited higher porosity, larger specific surface area, better electrochemical performances than those of Mn-PCN-222. By using modular assembly technique, Mn-CPM-99 film was sequentially assembled on the surface of indium-tin-oxide (ITO) to prepare an electrochemical sensor (Mn-CPM-99/ITO). The proposed sensor showed excellent electrochemical reduction of NB and displayed three linear response ranges in the wide concentration ranges. The obtained low limit of detection (LOD, 1.3 nM), high sensitivity and selectivity, and good reproducibility of the sensor for NB detection fully illustrate that Mn-CPM-99 is an excellent candidate for electrochemical sensor interface material. Moreover, the sensor was successfully applied to the detection of NB in lake water and vegetable samples showing satisfactory recovery of 98.9%–101.8%.

Introduction: We here describe a new method for distinguishing authentic Bletilla striata from similar decoctions (namely, Gastrodia elata, Polygonatum odoratum, and Bletilla ochracea schltr).Methods: Preliminary identification and analysis of four types of decoction pieces were conducted following the Chinese Pharmacopoeia and local standards. Intelligent sensory data were then collected using an electronic nose, an electronic tongue, and an electronic eye, and chromatography data were obtained via high-performance liquid chromatography (HPLC). Partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), and back propagation neural network (BP-NN) models were built using each set of single-source data for authenticity identification (binary classification of B. striata vs. other samples) and for species determination (multi-class sample identification). Features were extracted from all datasets using an unsupervised approach [principal component analysis (PCA)] and a supervised approach (PLS-DA). Mid-level data fusion was then used to combine features from the four datasets and the effects of feature extraction methods on model performance were compared.Results and Discussion: Gas chromatography–ion mobility spectrometry (GC-IMS) showed significant differences in the types and abundances of volatile organic compounds between the four sample types. In authenticity determination, the PLS-DA and SVM models based on fused latent variables (LVs) performed the best, with 100% accuracy in both the calibration and validation sets. In species identification, the PLS-DA model built with fused principal components (PCs) or fused LVs had the best performance, with 100% accuracy in the calibration set and just one misclassification in the validation set. In the PLS-DA and SVM authenticity identification models, fused LVs performed better than fused PCs. Model analysis was used to identify PCs that strongly contributed to accurate sample classification, and a PC factor loading matrix was used to assess the correlation between PCs and the original variables. This study serves as a reference for future efforts to accurately evaluate the quality of Chinese medicine decoction pieces, promoting medicinal formulation safety.

This paper studies the containment control problem of heterogeneous multi-agent systems (MASs) with multiple leaders. The follower agent dynamics are assumed to be unknown and nonlinear. First, each follower is transformed into an incremental data description based on the dynamic linearization technique. Then, a distributed model-free adaptive containment control law is proposed such that all followers will be driven into the convex hull of the leaders. Furthermore, the algorithm is extended to the time-switching and dynamic leaders case. As a data-driven approach, the proposed controller design uses only the received input and output (I/O) data of these agents rather than agent mathematical models. Finally, to test the potential in real applications, three representative examples considering various environment factors, including external disturbances, are simulated to show the effectiveness and resilience of this method.

In the context of the digital evolution of the logistics industry, the interconnection of logistics information systems and associated data have become an obstacle of business collaboration among various stakeholders. A critical challenge in this domain is ensuring controllable access to logistics business data, given the industry’s current state characterized by independence, autonomy, disconnection, and heterogeneity, alongside the non-negotiable requirement for data privacy. We propose a novel model of trusted data access control based on of logistics business collaboration semantics. This approach incorporates semantic inference technologies into attribute-based access control mechanisms, thereby enabling the streamlined formulation of access control policies and facilitating unified authorization and control. Moreover, the method addresses the issue of access control policy management and maintenance at the semantic level. The proposed solution can pave the way for enhanced business collaboration between business entities, and further enable the building of a data exchange service platform within the logistics industry.

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education.

Sentence Boundary Disambiguation (SBD) is crucial for building datasets for tasks such as machine translation, syntactic analysis, and semantic analysis. Currently, most automatic sentence segmentation in Tibetan adopts the methods of rule-based and statistical learning, as well as the combination of the two, which have high requirements on the corpus and the linguistic foundation of the researchers and are more costly to annotate manually. In this study, we explore Tibetan SBD using deep learning technology. Initially, we analyze Tibetan characteristics and various subword techniques, selecting Byte Pair Encoding (BPE) and Sentencepiece (SP) for text segmentation and training the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model. Secondly, we studied the Tibetan SBD based on different BERT pre-trained language models, which mainly learns the ambiguity of the shad (“།”) in different positions in modern Tibetan texts and determines through the model whether the shad (“།”) in the texts has the function of segmenting sentences. Meanwhile, this study introduces four models, BERT-CNN, BERT-RNN, BERT-RCNN, and BERT-DPCNN, based on the BERT model for performance comparison. Finally, to verify the performance of the pre-trained language models on the SBD task, this study conducts SBD experiments on both the publicly available Tibetan pre-trained language model TiBERT and the multilingual pre-trained language model (Multi-BERT). The experimental results show that the F1 score of the BERT (BPE) model trained in this study reaches 95.32% on 465,669 Tibetan sentences, nearly five percentage points higher than BERT (SP) and Multi-BERT. The SBD method based on pre-trained language models in this study lays the foundation for establishing datasets for the later tasks of Tibetan pre-training, summary extraction, and machine translation.

Machine learning photodynamics simulations are revolutionary tools to resolve elusive photochemical reaction mechanisms with time-dependent high-fidelity structure information. Besides the recent advances in neural networks (NNs) potentials, it still lacks a general rule for designing training data for learning photochemical reaction mechanisms with Wigner sampling and geometry interpolation. We present an in-depth investigation of the relationship between the accuracy of the multiple layer NNs and the combinations of training data based on the Wigner sampling and geometry interpolation using model photochemical reactions of the [3]-ladderdiene systems. The NNs trained with Wigner sampling data show underfitting, where the NN errors increase with the structural complexity and diversity. The NNs trained with composite Wigner sampling and geometry interpolation data show one magnitude reduced errors, suggesting an essential role of geometry interpolation in facilitating NNs learning the potential energy surfaces. However, increasing the interpolation steps results in overfitting if the Wigner sampled configuration space is narrowed. Correlating the mean absolute errors (MAE) of the NN predicted energies for the sampled and out-of-sample structures shows an optimal combination ratio of 100:10 between the Wigner sampling structures and geometry interpolation steps for 1000 training data, where the MAE of the sampled structures achieve chemical accuracy while the MAE of the out-of-sample structures is minimized. The NNs trained with the optimally combined data can detect the out-of-sample structures in adaptive sampling with a positive correlation between the maximum standard deviation and MAE of the predicted energies. Collectively, our findings suggest a general rule for designing the training data for ML photodynamics.

Biotemplates are often used in the preparation of catalysts due to availability of raw materials and environment-friendly. Herein, three biomorphic CeO2 were prepared from waste tobacco materials and impregnated with 1 wt% Ru for the catalytic combustion of vinyl chloride. The results showed that the biotemplates greatly affected the properties of the catalysts. 1Ru/CeO2-YS exhibited the best catalytic activity and superior stability because of its abundant oxygen vacancies, sufficient acidic sites and the optimal redox properties. SEM images suggested that CeO2-YS and CeO2-YM maintained the basic structure of the biotemplates. Moreover, a possible reaction mechanism over Ru/CeO2 was proposed.

Plastics are ubiquitous and indispensable in our daily life because of their low cost, portability, durability, and processability. However, due to the overuse, short service life, and chemical inert character, the accumulated discarded plastics pose a great threat to the sustainable development of ecology and environment. Photocatalysis represents highly promising technology in transforming plastic wastes into value‐added products via green and mild method. In this perspective, the advantages of photocatalysis are discussed and compared with other catalysis technologies including thermal catalysis, electrocatalysis, and enzyme catalysis. Then the possible photocatalytic upcycling path of plastic wastes is clarified under different experimental conditions. The types of plastic wastes that can be upcycled by photocatalysis, the integrating modus between plastic wastes and the photocatalysts as well as the modulation of the product selectivity are also emphasized. Finally, the challenges and insights into the future development of photocatalytic plastic waste upcycling fields are presented. It is expected that this timely and critical review provides the instructive guidance for the design of photocatalysts with high efficiency and high selectivity toward plastic waste upcycling.

For video style transfer, naively applying still image techniques to process a video frame-by-frame independently often causes flickering artefacts. Some works adopt optical flow into the design of temporal constraint loss to secure temporal consistency. However, these works still suffer from incoherence (including ghosting artefacts) where large motions or occlusions occur, as optical flow fails to detect the boundaries of objects accurately. To address this problem, we propose a novel framework which consists of the following two stages: (1) creating new initialization images from proposed mask techniques, which are able to significantly reduce the flow errors; (2) process these initialized images iteratively with proposed losses to obtain stylized videos which are free of artefacts, which also increases the speed from over 3 min per frame to less than 2 s per frame for the gradient-based optimization methods. To be specific, we propose a multi-scale mask fusion scheme to reduce untraceable flow errors, and obtain an incremental mask to reduce ghosting artefacts. In addition, a multi-frame mask fusion scheme is designed to reduce traceable flow errors. In our proposed losses, the Sharpness Losses are used to deal with the potential image blurriness artefacts over long-range frames, and the Coherent Losses are performed to restrict the temporal consistency at both the multi-frame RGB level and Feature level. Overall, our approach produces stable video stylization outputs even in large motion or occlusion scenarios. The experiments demonstrate that the proposed method outperforms the state-of-the-art video style transfer methods qualitatively and quantitatively on the MPI Sintel dataset.

Object detection is a fundamental task of remote-sensing image processing. Most existing object detection detectors handle regression and classification tasks through learning from a fixed set of learnable anchors or queries. To simplify object candidates, we propose a denoising diffusion process for remote-sensing image object detection, which directly detects objects from a set of random boxes. During the training phase, the horizontal detection boxes are transformed into oriented detection boxes firstly. Then, the model learns to reverse this transformation process by diffusing from the ground truth-oriented box to a random distribution. During the inference phase, the model incrementally refines a set of randomly generated boxes to produce the final output result. Remarkable results have been achieved using our proposed method. For instance, on commonly used object detection datasets such as DOTA, our approach achieves a mean average precision (mAP) of 76.59%. Similarly, on the HRSC2016 dataset, our method achieves a 72.4% mAP.

Indoleamine 2,3-dioxygenase 1 (IDO1) has attracted much attention in the field of cancer immunotherapy as an immunomodulatory enzyme. To identify potential IDO1 inhibitors, a novel series of compounds with N,N-diphenylurea and triazole structures were synthesized. The designed compounds underwent organic synthesis, and subsequent enzymatic activity experiments targeting IDO1 confirmed their activity at the molecular level. These experiments provided validation for the efficacy of the designed compounds in inhibiting IDO1, compound 3g exhibited an IC50 value of 1.73 ± 0.97 μM. Further molecular docking study further explained the binding mode and reaction potential of compound 3g with IDO1. Our research has resulted in a series of novel IDO1 inhibitors, which is beneficial to the development of drugs targeting IDO1 in numerous cancer diseases.

Introduction: Sanjin tablets (SJT) are a well-known Chinese patent drug that have been used to treat urinary tract infections (UTIs) for the last 40 years. The drug consists of five herbs, but only 32 compounds have been identified, which hinders the clarification of its effective substances and mechanism.Methods: The chemical constituents of SJT and their effective substances and functional mechanism involved in the treatment of UTIs were investigated by using high performance liquid chromatography-electrospray ionization-ion trap-time of flight-mass spectrometry (HPLC-ESI-IT-TOF-MSn), network pharmacology, and molecular docking.Results: A total of 196 compounds of SJT (SJT-MS) were identified, and 44 of them were unequivocally identified by comparison with the reference compounds. Among 196 compounds, 13 were potential new compounds and 183 were known compounds. Among the 183 known compounds, 169 were newly discovered constituents of SJT, and 93 compounds were not reported in the five constituent herbs. Through the network pharmacology method, 119 targets related to UTIs of 183 known compounds were predicted, and 20 core targets were screened out. Based on the “compound–target” relationship analysis, 94 compounds were found to act on the 20 core targets and were therefore regarded as potential effective compounds. According to the literature, 27 of the 183 known compounds were found to possess antimicrobial and anti-inflammatory activities and were verified as effective substances, of which 20 were first discovered in SJT. Twelve of the 27 effective substances overlapped with the 94 potential effective compounds and were determined as key effective substances of SJT. The molecular docking results showed that the 12 key effective substances and 10 selected targets of the core targets have good affinity for each other.Discussion: These results provide a solid foundation for understanding the effective substances and mechanism of SJT.

This paper focuses on determining the authenticity and identifying the species of Fritillariae cirrhosae using electronic nose, electronic tongue, and electronic eye sensors, near infrared and mid-level data fusion. 80 batches of Fritillariae cirrhosae and its counterfeits (including several batches of Fritillaria unibracteata Hsiao et K.C. Hsia, Fritillaria przewalskii Maxim, Fritillaria delavayi Franch and Fritillaria ussuriensis Maxim) were initially identified by Chinese medicine specialists and by criteria in the 2020 edition of Chinese Pharmacopoeia. After obtaining the information from several sensors we constructed single-source PLS-DA models for authenticity identification and single-source PCA-DA models for species identification. We selected variables of interest by VIP value and Wilk’s lambda value, and we subsequently constructed the three-source fusion model of intelligent senses and the four-source fusion model of intelligent senses and near-infrared spectroscopy. We then explained and analyzed the four-source fusion models based on the sensitive substances detected by key sensors. The accuracies of single-source authenticity PLS-DA identification models based on electronic nose, electronic eye, electronic tongue sensors and near-infrared were respectively 96.25%, 91.25%, 97.50% and 97.50%. The accuracies of single-source PCA-DA species identification models were respectively 85%, 71.25%, 97.50% and 97.50%. After three-source data fusion, the accuracy of the authenticity identification of the PLS-DA identification model was 97.50% and the accuracy of the species identification of the PCA-DA model was 95%. After four-source data fusion, the accuracy of the authenticity of the PLS-DA identification model was 98.75% and the accuracy of the species identification of the PCA-DA model was 97.50%. In terms of authenticity identification, four-source data fusion can improve the performance of the model, while for the identification of the species the four-source data fusion failed to optimize the performance of the model. We conclude that electronic nose, electronic tongue, electronic eye data and near-infrared spectroscopy combined with data fusion and chemometrics methods can identify the authenticity and determine the species of Fritillariae cirrhosae. Our model explanation and analysis can help other researchers identify key quality factors for sample identification. This study aims to provide a reference method for the quality evaluation of Chinese herbs.

The selective hydrogenation of CO2 into high-value chemicals is an effective approach to address environmental issues. Cobalt-based catalysts have significant potential in CO2 hydrogenation reaction systems; however, there is a need to control their selectivity better. In this study, copper is introduced onto Co3O4 nanosheets using the ion exchange reverse loading method. The unique interaction of these materials significantly alters the selectivity of the cobalt-based catalyst. Results from scanning transmission electron microscopy and scanning electron microscopy indicate that this catalyst enables a more even dispersion of copper species in the Co3O4 nanosheets. Temperature-programmed reduction and X-ray photoelectron spectroscopy reveal that the catalyst facilitates the metal–metal interaction between Co and Cu. Temperature-programmed desorption experiments for CO2 and H2 demonstrate that the close interaction between Co and Cu modifies CO2 adsorption, leading to differences in catalytic activity. Moreover, the catalyst effectively suppresses CO2 methanation and promotes methanol formation by altering the alkalinity of the catalyst surface and weakening the hydrogen dissociation ability.

We investigate in-band pumping of a Tm,Ho,Lu:CaGdAlO4 laser using a Raman-shifted Er-fiber laser (1678 nm), in the continuous-wave (CW) and mode-locked (ML) regimes. A maximum output power of 524 mW is obtained in the CW regime with a 5% output coupler at an absorbed pump power of 2.04 W, corresponding to a slope efficiency of 27.9%. A maximum CW wavelength tuning range of 160 nm at the zero level, from 1984 to 2144 nm, is obtained with a 0.2% output coupler. In the ML regime, pumping with 5.5 W (unpolarized), the average output power (0.2% output coupler) reaches 148 mW at a repetition rate of ~96 MHz. The output spectrum is centered at 2071.5 nm with a FWHM of 21.5 nm (σ-polarization). The pulse duration amounts to 218 fs (time-bandwidth product equal to 0.327).

Multi-agent collaborative target search is one of the main challenges in the multi-agent field, and deep reinforcement learning (DRL) is a good way to learn such a task. However, DRL always faces the problem of sparse reward, which to some extent reduces its efficiency in task learning. Introducing intrinsic motivation has proved to be a useful way to make the sparse reward in DRL. So, based on the multi-agent deep deterministic policy gradient (MADDPG) structure, a new MADDPG algorithm with the emotional intrinsic motivation name MADDPG-E is proposed in this paper for the multi-agent collaborative target search. In MADDPG-E, a new emotional intrinsic motivation module with three emotions, joy, sadness, and fear, is designed. The three emotions are defined by corresponding psychological knowledge to the multi-agent embodied situations in an environment. An emotional steady-state variable function H is then designed to help judge the goodness of the emotions. Based on H, an emotion-based intrinsic reward function is finally proposed. With the designed emotional intrinsic motivation module, the multi-agent system always tries to make itself joy, which means it always learns to search the target. To show the effectiveness of the proposed MADDPG-E algorithm, two kinds of simulation experiments with a determined initial position and random initial position, respectively, are carried out, and comparisons are performed with MADDPG as well as MADDPG-ICM (MADDPG with an intrinsic curiosity module). The results show that with the designed emotional intrinsic motivation module, MADDPG-E has a higher learning speed and better learning stability, and the advantage is more obvious when facing complex situations.

Due to the imbalance between the supply and demand of oxygen, the oxygen systems of iron- and steel-making enterprises in China have problems with high oxygen emissions and high pressure in the pipelines, resulting in the energy consumption of oxygen production being high. To reduce the energy consumption of oxygen systems, this study took a large-scale iron- and steel-making enterprise as a case study and developed a two-stage forecasting and scheduling model. The novel aspect and progressiveness of this work are as follows: First, an oxygen demand forecasting model was developed based on the backpropagation neural network with genetic algorithm optimization (GABP) and is driven only by historical data. Compared with some complex models in the literature, although the accuracy of this model has been reduced, the model does not need to consider production plans for other process steps, making it more practical and feasible. Second, different from the existing literature, an oxygen production scheduling model was developed for load-variable ASUs with an internal compression process, and both the oxygen emissions and pipeline pressure are included in the objective function. The case study showed that based on the oxygen demand forecast and optimal scheduling, the oxygen emissions and pipeline pressure in the studied iron- and steel-making enterprise can be significantly reduced, thereby achieving considerable energy-saving effects and economic benefits. Specifically, the following conclusions were obtained: (1) For the oxygen demand forecast, the prediction accuracy of the GABP model was better than that of the ARIMA model. The average MAPE of the 12 sets of data of the ARIMA and GABP models was 23.8% and 20.2%, respectively. (2) By comparing the scheduling results and the field data, it was found that after scheduling, the amount of oxygen emissions decreased by 6.32%, the pipeline pressure decreased by 0.61%, and the energy consumption of oxygen compression decreased by 1.6%. Considering both the oxygen emission loss and the energy consumption of oxygen compression, the total power consumption of the studied oxygen system was reduced by 1.38%, resulting in electricity cost savings of approximately 9.03 million RMB per year.