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IntroductionThis paper proposes a novel all-dielectric design of lens antenna and its performance is optimized using genetic algorithm (GA). The optimization objective are 1-dB and steady gain that are directly optimized. The GA also optimizes the topological design of the lens. MethodsThe method consists of two main components: the design of the objective function and the initial population selection. The first lens structure fed into the algorithm and the initial population match. The lens has a diameter of 150 mm and a thickness of 30 mm at its thickest point with working frequency of 6–18 GHz. The 3D printing technology is used for the antenna fabrication that reduces the implantation cost. ResultsThe experimental results show that the gain and peak aperture efficiency of the proposed antenna are 23.8 dBi and 51.9%, respectively, better than those of the existing designs. DiscussionIt advantages are low-cost, easy to fabricate, simple design, high gain, narrow beams, low side lobes. It can be used in future ultra-wideband (UWB) applications.

Low visibility is a major cause of flight delays and airport cancellations. Hence, providing accurate prediction of airport visibility is vital to prevent significant losses for airlines, accordingly to avoid catastrophic aviation accidents. Although many unimodal prediction methods have been developed, there is still room for improvement in airport visibility classification. Therefore, this study proposed an airport visibility classification model that employs a multimodal fusion of image and tabular data to improve classification performance. First, an enhanced image processing method on airport visibility was designed to extract more detailed features related to airport visibility. Next, EfficientNetB1 and Feature Tokenizer Transformer (FT-Transformer) were utilized to extract features from the images and tabular data, respectively. These features were then combined to classify airport visibility using a Multimodal Fusion Multilayer Perceptron with a focal loss function, validated through 5-fold stratified cross-validation. Experimental results on the 1864 pairs of image-tabular data from Nanjing Lukou International Airport showed that our model achieved an accuracy of 93.83%, and a Ma-F1 of 91.64%. Comparisons with various image extraction methods indicated that EfficientNetB1 with integrated images provides the best performance and shorter running time. The comparison of multimodal fusion and unimodal image classifications revealed that the accuracy and Ma-F1 of unimodal image classifications are lower than those for multimodal classification by 8.56% and 12.85%. In addition, ablation experiments also proved the effectiveness of the enhanced image processing and multimodal fusion modules.

Three-dimensional (3D) microstructure reconstruction is a key approach to exploring the relationship between pore characteristics and physical properties. Viewing the training image as a prior model, multiple-point statistics (MPS) focus on reproducing spatial patterns in the simulation grid. However, it is challenging to efficiently generate 3D nonstationary models with varying microstructures. In this work, we propose column-oriented simulation (ColSIM) to achieve the stochastic reconstruction of 3D porous media. A heterogeneous system is understood as a spatially evolving process that consists of frequent transitions of small magnitude and abrupt changes of large magnitude. First, a training image selection step is suggested to find representative microstructures. Our program applies modified Hausdorff distance, t-distributed stochastic neighboring embedding, and spectral clustering to organize two-dimensional (2D) candidate images. The medoid of each group is applied to guide the following programs. Second, we introduce column-oriented searching into MPS. To save simulation time, a subset of conditioning points is checked to find desired instances. Our program suggests an early stopping strategy to address complex microstructures. Third, a contrastive loss term is designed to create 3D models from 2D slice. To automatically calibrate the volume fraction and simplify parameter specification, the computer consistently monitors the difference between the present model and the target. The performance of ColSIM is examined by 3D multiphase material modeling and 3D heterogeneous shale simulation. To achieve quantitative evaluation, we compute various statistical functions and physical descriptors on simulated realizations. The proposed ColSIM exhibits competitive performance in terms of calculation efficiency, microstructure reproduction, and spatial uncertainty.

Abstract Purpose The choice of continuous renal replacement therapy (CRRT) anticoagulation program for patients at high risk of bleeding has always been a complex problem in clinical practice. Clinical regimens include regional citrate anticoagulation (RCA) and nafamostat mesylate (NM). This study aimed to evaluate the efficacy and safety of these two anticoagulants for CRRT in patients at high risk of bleeding to guide their clinical use better. Patients and methods Between January 2021 and December 2022, 307 patients were screened for this study. Forty-six patients were finally enrolled: 22 in the regional citrate anticoagulation group and 24 in the nafamostat mesylate group. We collected patients’ baseline characteristics, laboratory indicators before CRRT, and CRRT-related data. We then performed a statistical analysis of the data from both groups of patients. Results In our study, the baseline characteristics did not differ significantly between the two groups; the baseline laboratory indicators before CRRT of patients in the two groups were not significantly different. The duration of CRRT was 600 min in the regional citrate anticoagulation (RCA) group, 615 min in the nafamostat mesylate (NM) group; the success rate was 90.7% in the RCA group, and 85.6% in the NM group, the anticoagulant efficacy between the two groups was comparable. There was no significant difference in the safety of anticoagulation between the two groups. We used Generalized Estimating Equations (GEE) to test whether different anticoagulation methods significantly affected the success rate of CRRT and found no statistical difference between RCA and NM. Conclusion Our study suggests that nafamostat mesylate's anticoagulant efficacy and safety are not inferior to regional citrate anticoagulation for continuous renal replacement therapy in patients at high risk of bleeding.

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost.

With the rapid development of the Internet and the rapid popularity of the network. Increasing network traffic also leads to frequent abnormal attacks, which seriously affects user information security. Then intrusion detection is studied as an active defense technology. In order to increase the ability of network abnormal traffic detection, a network abnormal traffic detection algorithm based on variational autoencoder is proposed in this paper. In addition to normal data, the algorithm subdivides the intrusion attack types into 4 major categories, which contain 38 specific attacks, each specific attack category as an intrusion label, which will be used as the input of the decoder together with the hidden vector to obtain the classification results more accurately and efficiently and to recover the data features in the incomplete data set. Finally, the trained Deep Belief Nets (DBN) is used for further feature extraction and classification of the data. It is verified that the proposed variational autoencoder-based supervised classification scheme is simple and effective, which is able to identify new attacks well and outperforms other classification schemes in terms of data classification.

Abstract Since sequential recommendation can effectively capture the user’s dynamic interaction pattern according to the user’s historical interaction, it plays one of the important roles in the field of recommendation systems. Nowadays, the sequential recommendation models based on deep learning approaches have achieved good results in reality due to their excellent performance in information retrieval and filtering. However, there are still some challenges that still should be overcome of them. A major disadvantage of them is that they are only considering users’ short-time interests while ignoring their long-term preferences. Moreover, they are incapable of considering the influence of time information in the original interaction sequence, which could be helpful to fully extract various patterns via the different temporal embedding forms. Therefore, this paper proposes a novel model named multi-temporal sequential recommendation model based on the fused learning preference named MTSR-FLP in short. In the proposed framework, this paper adopts an MLP-based state generation module to consider the user’s long-term preferences and the short-time interests simultaneously. In particular, the proposed MTSR-FLP model designs a global representation learning approach to obtain the user’s global preference, and a local representation learning approach to capture the users’ local preference via its historical information. Moreover, the proposed model develops a multiple temporal embedding scheme to encode the positions of user–item interactions of a sequence, in which multiple kernels are utilized for the absolute or relative timestamps to establish unique embedding matrices. Finally, compared with other advanced sequence recommendation models on five public real-world datasets, the experimental results show that the proposed MTSR-FLP model has improved the performance of HR@10 from the 6.68% through 31.10% and NDCG@10 from the 8.60% through 42.54%.

With the rapid growth of network bandwidth, traffic identification is currently an important challenge for network management and security. In recent years, packet sampling has been widely used in most network management systems. In this paper, in order to improve the accuracy of network traffic identification, sampled NetFlow data is applied to traffic identification, and the impact of packet sampling on the accuracy of the identification method is studied. This study includes feature selection, a metric correlation analysis for the application behavior, and a traffic identification algorithm. Theoretical analysis and experimental results show that the significance of behavior characteristics becomes lower in the packet sampling environment. Meanwhile, in this paper, the correlation analysis results in different trends according to different features. However, as long as the flow number meets the statistical requirement, the feature selection and the correlation degree will be independent of the sampling ratio. While in a high sampling ratio, where the effective information would be less, the identification accuracy is much lower than the unsampled packets. Finally, in order to improve the accuracy of the identification, we propose a Deep Belief Networks Application Identification (DBNAI) method, which can achieve better classification performance than other state-of-the-art methods.

Abstract Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.

Using α-cyano-4-hydroxy-cinnamic acid, a small molecule inhibitor of monocarboxylic acid transporters(MCTs), as the lead compound, a novel 18F-labeled α-cyano-4-hydroxy-cinnamic acid probe was designed and synthesized. The preliminary evaluation of stability and tumor PET/CT imaging were performed in this study. Taking α-cyano-4-(2-p-toluenesulfonyl-ethoxy)-methyl cinnamate as the precursor compound, the intermediate compound α-cyano-4-(2-［18F］-fluoroethoxy)-methyl cinnamate(［18F］4) was obtained by nucleophilic reaction with 18F-, which was separated and purified by high performance liquid chromatography(HPLC). α-cyano-4-(2-［18F］-fluoroethoxy)-cinnamic acid(［18F］FEtO-CHC, ［18F］5) can be obtained from the hydrolysis of ［18F］4 with 2 mol/L NaOH solution and the neutralization with 1 mol/L HCl solution. The results of cell proliferation inhibition assay show that compound ［19F］5 has better inhibitory activity than that of CHC. ［18F］FEtO-CHC shows good stability and biosafety from the in vitro and in vivo studies, and micro-PET/CT imaging in DU145 prostate cancer tumor-bearing mice shows that ［18F］5 has obvious radioactive accumulation at the tumor site. ［18F］FEtO-CHC has the advantages of simple synthesis process, good stability, high biological safety, and ability for tumor PET imaging. It is a potential PET agent for specific MCTs imaging.

Abstract The diverse bacterial populations within the symbiotic microbiota play a pivotal role in both health and disease. Microbiota modulates critical aspects of tumor biology including cell proliferation, invasion, and metastasis. This regulation occurs through mechanisms like enhancing genomic damage, hindering gene repair, activating aberrant cell signaling pathways, influencing tumor cell metabolism, promoting revascularization, and remodeling the tumor immune microenvironment. These microbiota‐mediated effects significantly impact overall survival and the recurrence of tumors after surgery by affecting the efficacy of chemoradiotherapy. Moreover, leveraging the microbiota for the development of biovectors, probiotics, prebiotics, and synbiotics, in addition to utilizing antibiotics, dietary adjustments, defensins, oncolytic virotherapy, and fecal microbiota transplantation, offers promising alternatives for cancer treatment. Nonetheless, due to the extensive and diverse nature of the microbiota, along with tumor heterogeneity, the molecular mechanisms underlying the role of microbiota in cancer remain a subject of intense debate. In this context, we refocus on various cancers, delving into the molecular signaling pathways associated with the microbiota and its derivatives, the reshaping of the tumor microenvironmental matrix, and the impact on tolerance to tumor treatments such as chemotherapy and radiotherapy. This exploration aims to shed light on novel perspectives and potential applications in the field.

As a component of the innate immune system, there is emerging evidence to suggest that neutrophils may play a critical role in the initiation and progression of hepatocellular carcinoma (HCC). Neutrophil extracellular traps (NETs) are web-like chromatin structures that protrude from the membranes during neutrophil activation. Recent research has shown that NETs, which are at the forefront of the renewed interest in neutrophil studies, are increasingly intertwined with HCC. By exploring the mechanisms of NETs in HCC, we aim to improve our understanding of the role of NETs and gain deeper insights into neutrophil biology. Therefore, this article provides a summary of key findings and discusses the emerging field of NETs in HCC.

Abstract Background In this study, we propose the deep learning model-based framework to automatically delineate nasopharynx gross tumor volume (GTVnx) in MRI images. Methods MRI images from 200 patients were collected for training-validation and testing set. Three popular deep learning models (FCN, U-Net, Deeplabv3) are proposed to automatically delineate GTVnx. FCN was the first and simplest fully convolutional model. U-Net was proposed specifically for medical image segmentation. In Deeplabv3, the proposed Atrous Spatial Pyramid Pooling (ASPP) block, and fully connected Conditional Random Field(CRF) may improve the detection of the small scattered distributed tumor parts due to its different scale of spatial pyramid layers. The three models are compared under same fair criteria, except the learning rate set for the U-Net. Two widely applied evaluation standards, mIoU and mPA, are employed for the detection result evaluation. Results The extensive experiments show that the results of FCN and Deeplabv3 are promising as the benchmark of automatic nasopharyngeal cancer detection. Deeplabv3 performs best with the detection of mIoU 0.8529 ± 0.0017 and mPA 0.9103 ± 0.0039. FCN performs slightly worse in term of detection accuracy. However, both consume similar GPU memory and training time. U-Net performs obviously worst in both detection accuracy and memory consumption. Thus U-Net is not suggested for automatic GTVnx delineation. Conclusions The proposed framework for automatic target delineation of GTVnx in nasopharynx bring us the desirable and promising results, which could not only be labor-saving, but also make the contour evaluation more objective. This preliminary results provide us with clear directions for further study.

The base layer constructed by cement-stabilized macadam (CSM) has been widely used in highway construction due to its low elasticity deformation and high carrying capacity. As a bearing layer, the CSM base is not exempt from fatigue cracking under cyclic loading in the service process. Cracks in the base will create irreversible structural and functional deficiencies, such as the potential for reflective cracking of subsequently placed asphalt concrete overlays. The fracture of the base will shorten the service life of the pavement. The quality of the CSM base is directly related to the bearing capacity and integrity of the whole pavement structure. It is of practical significance to further study the fatigue failure behavior of CSM material for the long-term performance of the pavement. The CSM material is a typical heterogeneous multiphase composite. On the mesoscale, CSM consists of aggregate, cement mortar, pores, and the interface transitional zone (ITZ). On the microscale, the hardened mortar contains a large number of capillary pores, unhydrated particles, hydrated crystals, etc., which makes the spatial distribution of its material properties stochastic. In addition, cement hydration, dry shrinkage, and temperature shrinkage can also produce micro-crack defects in cement mortar. These microcracks will have cross-scale evolution under load, resulting in structural fracture. Macroscopic complex deformation and mechanical response are the reflections of its microscopic and even mesoscale composition and structure. This study summarized the existing studies on the mesoscopic properties of CSM materials, respectively from the three aspects of mesostructure, structural characterization, and mesoscale fatigue damage analysis, to help the development of long-life pavement. The future research direction is to explore the mesoscale characteristics of CSM using multi-scale representation and analysis methods, to establish the connection between mesoscale characteristics and macroscopic mechanical properties.

Anthropogenic activities including slashing and burning (or swidden agriculture), illegal logging, and agricultural residue burning, are important drivers and driving mechanisms for the occurrence and development of global active fires especially in the tropics. In the past, limited by the access to the global datasets of gridded population density and consistent active fires, the research on the correlation between the occurrence and development of active fires and human activities was relatively insufficient. It is of great significance to clarify the relationship between human and fire for the sake of revealing the nature of fire regime, estimating carbon emissions and public health management. With Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) active fire products provided by the Fire Information for Resource Management System (FIRMS) and LandScan Global Population Database (e.g., population density) developed by the Department of Energy's Oak Ridge National Laboratory (ORNL) during 2003-2019, the dynamic characteristics of spatial and temporal correlations between occurrence intensity of agricultural and forest active fires and population distribution in Mainland Southeast Asia were quantitatively revealed through GIS-based fishnet (1km) statistical analysis and bivariate spatial correlation. The results show that: (1) Nearly 80% of the "fire-affected grids" in Mainland Southeast Asia were positively correlated with population distribution during the study period. Grids with medium positive correlation and above (r>0.4) accounted for approximately 75%, mainly distributed in areas with low population density (

In recent years, with the rise of digital currency, its underlying technology, blockchain, has become increasingly well-known. This technology has several key characteristics, including decentralization, time-stamped data, consensus mechanism, traceability, programmability, security, and credibility, and block data is essentially tamper-proof. Due to these characteristics, blockchain can address the shortcomings of traditional financial institutions. As a result, this emerging technology has garnered significant attention from financial intermediaries, technology-based companies, and government agencies. This article offers an overview of the fundamentals of blockchain technology and its various applications. The introduction defines blockchain and explains its fundamental working principles, emphasizing features such as decentralization, immutability, and transparency. The article then traces the evolution of blockchain, from its inception in cryptocurrency to its development as a versatile tool with diverse potential applications. The main body of the article explores fundamentals of block chain systems, its limitations, various applications, applicability etc. Finally, the study concludes by discussing the present state of blockchain technology and its future potential, as well as the challenges that must be surmounted to unlock its full potential.

In the environmental safety area, the widespread use of the herbicide Paraquat (PQ) poses a great threat to hydrobionts and mammals. Due to the lack of specific antidote, it may lead to irreversible pulmonary fibrosis with a mortality rate of 60%. Therefore, it is necessary to develop an effective and specific PQ antidote. The g-C3N4 (HPCN) with excellent surface physicochemical properties was prepared by a two-step calcination method using urea and dicyandiamide as raw materials, showing a significant photocatalyst against environmental PQ pollution. The SEM results showed that HPCN possesses a porous layered structure. X-ray diffraction and infrared spectroscopy indicated that the conjugated aromatic rings were orderly stacked, forming a 2D layered structure of g-C3N4. The HPCN had a larger specific surface area (56.84 m2 g−1) and pore volume (0.2718 cm3 g−1), which enhanced its adsorption capacity and photocatalytic activity. HPCN exhibited an effective adsorption rate of 38.25% for PQ in water under light. Compared with the PQ group (54.8%), the cell viability of the HPCN group (91.4%) significantly increased by 36.6%, and the SEM observation revealed the restoration of normal cell morphology. The HPCN effectively reduced PQ content in zebrafish and mice in vivo, resulting in an approximately 70% increase in survival rate. The UV-Vis results indicated that the adsorption rate of HPCN for PQ in zebrafish was 43.5%. The enhanced catalytic performance of HPCN provides a promising solution for the detoxification of PQ and of other environmental pollutants.

One of the key goals of Informetrics is to identify citation-based popular articles among so many other aspects, such as determining popular research topics, identifying influential scholars, and predicting hot trends in science. These can be achieved by applying network science approaches to scientific networks and formulating the problem as a popular (most-cited) node ranking task. To rank the papers based on their future citation gain. In this work a deep learning based framework is proposed. Which helps in automatic node level feature extraction and can make node level prediction in dynamic graphs such as citation networks. To achieve this we have learned global ranking preserve d dimensional node embedding. We have only considered temporal features, which makes it suitable for generalisation to other networks. Although our model can consider node level explicit features also. Further we have given novel cost function which can be easily solve ranking problem for dynamic graphs using probabilistic regression method. Which can be easily optimised. Another novelty of our work is that our model can be trained using different snapshots of the graph and different time. Further trained model can be used to make future prediction. The proposed model has been tested on an arXiv paper citation network using six standard information retrieval-based metrics. The results show that our proposed model outperforms, on average, other state-of-the-art static models as well as dynamic node ranking models. The outcome of this research study leads to informed data-driven decision-making in science, such as the allocation and distribution of research funds and investment in strategic research centers. When considering past time window size as 10 months and making prediction after 10 months our proposed model’s performance on various ranking based evaluation metrics are as follows: AUC-0.974, Kendal’s rank correlation tau-0.455, Precision- 0.643, Novelty-0.0456, Temporal novelty-0.375 and on NDCG-0.949. Our model is able to make long term trend prediction with just training on short time window.

One of the big challenges of our modern life is to find the right items or contents on the Internet and particularly in social media. One way of addressing the information overload problem in social media is to predict the future trends and popularity of online items. The popularity of an item can be measured by its attractiveness, i.e., the number of times it is being used. This popularity prediction can be translated to a link prediction and ranking problem, which aims to predict the link gain of the items in a user-item interaction network. User-item interactions in an online environment can be modelled as a bipartite network, where a link represents an event, reflecting a user buys or collects an item. Popularity prediction problem in temporal bipartite networks is of great interest to researchers. In this study, we propose a heuristic based model which only consider nodes collective link gain in a recent past time window of time as well as total link gain. To evaluate our model’s efficiency, we tested them on co-evolving social media items. We also evaluated the models’ performance on five information retrieval metrics (i.e., Area Under the Receiver Operating Characteristic, Kendall’s rank correlation tau, Precision, Novelty, and temporal novelty). The proposed model does not need hyper-parameter learning, which makes it the best choice for highly temporal and data streaming scenarios.

IntroductionTumor-associated macrophage 2 (TAM2) abundantly infiltrates pancreatic ductal adenocarcinoma (PAAD), and its interaction with malignant cells is involved in the regulation of tumor metabolism. In this study, we explored the metabolic heterogeneity involved in TAM2 by constructing TAM2-associated metabolic subtypes in PAAD.Materials and methodsPAAD samples were classified into molecular subtypes with different metabolic characteristics based on a multi-omics analysis strategy. 20 PAAD tissues and 10 normal pancreatic tissues were collected for proteomic and metabolomic analyses. RNA sequencing data from the TCGA-PAAD cohort were used for transcriptomic analyses. Immunohistochemistry was used to assess TAM2 infiltration in PAAD tissues.ResultsThe results of transcriptomics and immunohistochemistry showed that TAM2 infiltration levels were upregulated in PAAD and were associated with poor patient prognosis. The results of proteomics and metabolomics indicated that multiple metabolic processes were aberrantly regulated in PAAD and that this dysregulation was linked to the level of TAM2 infiltration. WGCNA confirmed pyruvate and glycolysis/gluconeogenesis as co-expressed metabolic pathways of TAM2 in PAAD. Based on transcriptomic data, we classified the PAAD samples into four TAM2-associated metabolic subtypes (quiescent, pyruvate, glycolysis/gluconeogenesis and mixed). Metabolic subtypes were each characterized in terms of clinical prognosis, tumor microenvironment, immune cell infiltration, chemotherapeutic drug sensitivity, and functional mechanisms.ConclusionOur study confirmed that the metabolic remodeling of pyruvate and glycolysis/gluconeogenesis in PAAD was closely related to TAM2. Molecular subtypes based on TAM2-associated metabolic pathways provided new insights into prognosis prediction and therapy for PAAD patients.

Pancreatic cancer is a strongly malignant gastrointestinal carcinoma characterized by late detection, high mortality rates, poor patient prognosis and lack of effective treatments. Consequently, there is an urgent need to identify novel therapeutic strategies for this disease. Pancreatic stellate cells, which constitute a significant component of the mesenchymal cellular layer within the pancreatic tumor microenvironment, play a pivotal role in modulating this environment through their interactions with pancreatic cancer cells. This paper reviews the mechanisms by which pancreatic stellate cells inhibit antitumor immune responses and promote cancer progression. We also discuss preclinical studies focusing on these cells, with the goal of providing some theoretical references for the development of new therapeutic approaches for pancreatic cancer.

To evaluate the thermal stability of composite polymer-modified asphalt, thermoplastic elastomer styrene-butadiene rubber (SBR)/polypropylene (PP) pellets were prepared using a hot-melt blending technique, with butyl rubber powder and waste polypropylene pellets as raw materials. The effects of different evaluation indexes on the thermal stability of SBR/PP-modified asphalt were investigated using a frequency scan test and a multi-stress creep recovery (MSCR) test, and the compatibility of SBR/PP particles with asphalt was studied using the Cole–Cole diagram and microstructure images. The tests show that, firstly, the performance grade (PG) classification of asphalt can be improved by adding an SBR/PP thermoplastic elastomer to enhance the adaptability of asphalt in high- and low-temperature environments, and the evaluation separation index can reflect the high-temperature storage stability of composite-modified asphalt more reasonably. Additionally, the larger the rubber-to-plastic ratio the worse the high-temperature thermal stability of composite-modified asphalt. Moreover, the addition of additives to the composite particles can promote the SBR/PP particles in the asphalt to be more uniformly dispersed, forming a more desirable microstructure and improving the thermal stability of composite-modified asphalt. Ultimately, the semicircular curve of the Cole–Cole diagram can reflect the compatibility characteristics of the two-phase structure of SBR/PP-modified asphalt, which can be used as an auxiliary index to evaluate the compatibility of polymer-modified asphalt.

Abstract Background Alzheimer's Disease (AD) is a serious neurodegenerative disease and there is currently no effective treatment for AD progression. The use of TCM as a potential treatment strategy for AD is an evolving field of investigation. Asafoetida (ASF), an oleo-gum-resin isolated from Ferula assa-foetida root, has been proven to possess antioxidative potential and neuroprotective effects, which is closely associated with the neurological disorders. However, the efficacy and further mechanisms of ASF in AD experimental models are still unclear. Methods A cognitive impairment of mouse model induced by scopolamine was established to determine the neuroprotective effects of ASF in vivo, as shown by behavioral tests, biochemical assays, Nissl staining, TUNEL staining, Immunohistochemistry, western blot and qPCR. Furthermore, the PC12 cells stimulated by H2O2 were applied to explore the underlying mechanisms of ASF-mediated efficacy. Then, the UPLCM analysis and integrated network pharmacology approach was utilized to identified the main constitutes of ASF and the potential target of ASF against AD, respectively. And the main identified targets were validated in vitro by western blot, qPCR and immunofluorescence staining. Results In vivo, ASF treatment significantly ameliorated cognitive impairment induced by scopolamine, as evidenced by improving learning and memory abilities, and reducing neuronal injury, cholinergic system impairment, oxidative stress and apoptosis in the hippocampus of mice. In vitro, our results validated that ASF can dose-dependently attenuated H2O2-induced pathological oxidative stress in PC12 cells by inhibiting ROS and MDA production, as well as promoting the activities of SOD, CAT, GSH. We also found that ASF can significantly suppressed the apoptosis rate of PC12 cells increased by H2O2 exposure, which was confirmed by flow cytometry analysis. Moreover, treatment with ASF obviously attenuated H2O2-induced increase in caspase-3 and Bax expression levels, as well as decrease in Bcl-2 protein expression. KEGG enrichment analysis indicated that the PI3K/Akt/GSK3β/Nrf2 /HO-1pathway may be involved in the regulation of cognitive impairment by ASF. The results of western blot, qPCR and immunofluorescence staining of vitro assay proved it. Conclusions Collectively, our work first uncovered the significant neuroprotective effect of ASF in treating AD in vivo. Then, we processed a series of vitro experiments to clarify the biological mechanism action. These data demonstrate that ASF can inhibit oxidative stress induced neuronal apoptosis to foster the prevention of AD both in vivo and in vitro, and it may exert the function of inhibiting AD through PI3K/Akt/GSK3β/Nrf2/HO-1pathway.

Objective To detect the pathological changes in the liver of mice with non-alcoholic fatty liver disease (NAFLD) and explore an experimental method to obtain a large number of primary hepatocytes with high activity and purity from the mice with NAFLD. Methods A total of 60 mice were randomly divided into control group (chow diet, CD) and high-fat group (high-fat diet, HFD) and had been fed separately for 20 weeks. The body weight of the mice was regularly measured and the pathological characteristics of the liver tissue were observed by hematoxylin-eosin (HE) staining and Sirius red staining. After 15 weeks of high-fat diet, 6-8 mL collagenase Ⅳ were added for digestion to obtain mixed hepatocytes, which were spread on 40% Percoll separation solution and subjected to density gradient centrifugation to obtain purified hepatocytes. The viability of the cells was determined by trypan blue staining, and the morphology was observed by microscope. Results With the prolongation of modeling time, the body weight of mice in the HFD group were gradually increased, which was significantly higher than that in the CD group. In the HFD group, the liver of mice gradually developed fatty degeneration, and liver fibrosis could be detected at week 20. At week 15, about 5×107-7×107 hepatocytes could be obtained from each NAFLD mouse with cell viability ≥85% and cell purity ≥95%. Cells were round or elliptical, with smooth edges and few clusters. The number and quality of cells obtained were significantly improved by optimizing conditions such as accurate temperature control, reduced the concentration of collagenase, and the use of low-speed centrifugation and low-speed density gradient centrifugation. Conclusions The NAFLD mouse model was successfully established and the pathological characteristics of liver in different disease stages were revealed. An efficient and stable method for obtaining primary hepatocytes of NAFLD mice was established. The number and activity of hepatocytes obtained met the requirements of most experiments.

Abstract Objective Pancreatic body tail carcinoma (PBTC) is a relatively few pancreatic cancer in clinical practice, and its specific clinicopathological features and prognosis have not been fully described. In this study, we aimed to create a nomogram to predict the overall survival (OS) of patients with advanced PBTC. Methods We extracted clinical and related prognostic data of advanced PBTC patients from 2000 to 2018 from the Surveillance, Epidemiology, and End Results database. Independent prognostic factors were selected using univariate and multivariate Cox analyses, and a nomogram was constructed using R software. The C-index, area under the curve (AUC) of receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA) were used to assess the clinical utility of the nomogram. Finally, OS was assessed using the Kaplan–Meier method. Results A total of 1256 patients with advanced PBTC were eventually included in this study. Age, grade, N stage, M stage, surgery, and chemotherapy were identified as independent risk factors using univariate and multivariate Cox regression analyses (p

Abstract Acute pancreatitis (AP) is a common clinical abdominal emergency, with a high and increasing incidence each year. Severe AP can easily cause systemic inflammatory response syndrome, multiple organ dysfunction and other complications, leading to higher hospitalization rates and mortality. Currently, there is no specific treatment for AP. Thus, we still need to understand the exact AP pathogenesis to effectively cure AP. With the rise of transcriptomics, RNA molecules, such as microRNAs (miRNAs) transcribed from nonprotein-coding regions of biological genomes, have been found to be of great significance in the regulation of gene expression and to be involved in the occurrence and development of many diseases. Increasing evidence has shown that miRNAs, as regulatory RNAs, can regulate pancreatic acinar necrosis and apoptosis and local and systemic inflammation and play an important role in the development and thus potentially the diagnosis and treatment of AP. Therefore, here, the current research on the relationship between miRNAs and AP is reviewed.

Early and accurate diagnosis and treatment of pancreatic cancer (PC) remain challenging endeavors globally. Late diagnosis lag, high invasiveness, chemical resistance, and poor prognosis are unresolved issues of PC. The concept of metabolic reprogramming is a hallmark of cancer cells. Increasing evidence shows that PC cells alter metabolic processes such as glucose, amino acids, and lipids metabolism and require continuous nutrition for survival, proliferation, and invasion. Glucose metabolism, in particular, regulates the tumour microenvironment (TME). Furthermore, the link between glucose metabolism and TME also plays an important role in the targeted therapy, chemoresistance, radiotherapy ineffectiveness, and immunosuppression of PC. Altered metabolism with the TME has emerged as a key mechanism regulating PC progression. This review shed light on the relationship between TME, glucose metabolism, and various aspects of PC. The findings of this study provide a new direction in the development of PC therapy targeting the metabolism of cancer cells.

Abstract Pancreatic cancer is a highly malignant tumour of the digestive tract. Despite advances in treatment, its 5-year survival rate remains low, and its prognosis is the worst among all cancers; innovative therapeutic methods are needed. Ferroptosis is a form of regulatory cell death driven by iron accumulation and lipid peroxidation. Recent studies have found that ferroptosis plays an important role in the development and treatment response of tumours, particularly pancreatic cancer. This article reviews the current understanding of the mechanism of ferroptosis and ferroptosis-related treatment in pancreatic cancer.

A tunnel health monitoring (THM) system ensures safe operations and effective maintenance. However, how to effectively process and denoise several data collected by THM remains to be addressed, as well as safety early warning problems. Thus, an integrated method for Savitzky–Golay smoothing (SGS) and Wavelet Transform Denoising (WTD) was used to smooth data and filter noise, and the coefficient of the non-uniform variation method was proposed for early warning. The THM data, including four types of sensors, were attempted using the proposed method. Firstly, missing values, outliers, and detrend in the data were processed, and then the data were smoothed by SGS. Furthermore, data denoising was carried out by selecting wavelet basis functions, decomposition scales, and reconstruction. Finally, the coefficient of non-uniform variation was employed to calculate the yellow and red thresholds. In data smoothing, it was found that the Signal Noise Ratio (SNR) and Root Mean Square Error (RMSE) of SGS smoothing were superior to those of the moving average smoothing and five-point cubic smoothing by approximately 10% and 30%, respectively. An interesting phenomenon was discovered: the maximum and minimum values of the denoising effects with different wavelet basis functions after selection differed significantly, with the SNR differing by 14%, the RMSE by 8%, and the r by up to 80%. It was found that the wavelet basis functions vary, while the decomposition scales are consistently set at three layers. SGS and WTD can effectively reduce the complexity of the data while preserving its key characteristics, which has a good denoising effect. The yellow and red warning thresholds are categorized into conventional and critical controls, respectively. This early warning method dramatically improves the efficiency of tunnel safety control.

Tumor-associated macrophages M2 (TAM2), which are highly prevalent infiltrating immune cells in the stroma of pancreatic cancer (PC), have been found to induce an immunosuppressive tumor microenvironment, thus enhancing tumor initiation and progression. However, the immune therapy response and prognostic significance of regulatory genes associated with TAM2 in PC are currently unknown. Based on TCGA transcriptomic data and single-cell sequencing data from the GEO database, we identified TAM2-driven genes using the WGCNA algorithm. Molecular subtypes based on TAM2-driven genes were clustered using the ConsensusClusterPlus algorithm. The study constructed a prognostic model based on TAM2-driven genes through Lasso-COX regression analysis. A total of 178 samples obtained by accessing TCGA were accurately categorized into two molecular subtypes, including the high-TAM2 infiltration (HMI) cluster and the low-TAM2 infiltration (LMI) cluster. The HMI cluster exhibits a poor prognosis, a malignant tumor phenotype, immune-suppressive immune cell infiltration, resistance to immunotherapy, and a high number of genetic mutations, while the LMI cluster is the opposite. The prognostic model composed of six hub genes from TAM2-driven genes exhibits a high degree of accuracy in predicting the prognosis of patients with PC and serves as an independent risk factor. The induction of TAM2 was employed as a means of verifying these six gene expressions, revealing the significant up-regulation of BCAT1, BST2, and MERTK in TAM2 cells. In summary, the immunophenotype and prognostic model based on TAM2-driven genes offers a foundation for the clinical management of PC. The core TAM2-driven genes, including BCAT1, BST2, and MERTK, are involved in regulating tumor progression and TAM2 polarization, which are potential targets for PC therapy.

ObjectivesThe prognosis of severe acute pancreatitis (SAP) patients is closely related to early nutritional support. It is well-established that changes in glutamine (Gln), an important amino acid and nutritional supplement, can reflect disease severity. However, no consensus has been reached on the role of Gln nutrition therapy for SAP patients. We conducted this systematic review and meta-analysis to summarize and evaluate the advantages of Gln supplementation in SAP.MethodsPubMed, Web of Science, the Embase, Cochrane Library, and Chinese databases (CNKI, SinoMed, Wanfang, and VIP) were systematically searched for eligible studies that included glutamine supplementation in SAP patients from inception to October 31 2021, excluding non-SAP studies. Primary outcome measures included mortality, APACHE II score, complications, and length of hospital stay. The meta-analysis was registered with PROSPERO (CRD42021288371) and was conducted using Review Manager and Stata softwares.ResultsThis meta-analysis included 30 randomized controlled trials (RCTs) with a total of 1,201 patients. Six primary outcomes and six secondary outcomes were analyzed. For the primary outcomes, Gln supplementation was associated with lower mortality (OR = 0.38, 95% CI: 0.21–0.69, P = 0.001), total hospital stay (MD = −3.41, 95% CI: −4.93 to −1.88, P < 0.0001) and complications (OR = 0.45, 95% CI: 0.31–0.66, P < 0.0001) compared with conventional nutrition. Further subgroup analysis found that parenteral glutamine was more effective in reducing mortality. In terms of secondary outcomes, Gln supplementation helped restore liver, kidney and immune function, with significantly increased serum albumin (SMD = 1.02, 95% CI: 0.74–1.31, P < 0.00001) and IgG levels (MD = 1.24, 95% CI: 0.82–1.67, P < 0.00001), and decreased serum creatinine (Scr) (MD = −12.60, 95% CI: −21.97 to −3.24, P = 0.008), and inflammatory indicators such as C-reaction protein (CRP) (SMD = −1.67, 95% CI: −2.43 to −0.90, P < 0.0001).ConclusionAlthough Gln supplementation is not routinely recommended, it is beneficial for SAP patients. Indeed, glutamine nutrition has little effect on some indicator outcomes but contributes to improving the prognosis of this patient population.Systematic Review Registration: PROSPERO (york.ac.uk). Unique Identifier: CRD42021288371.

Abstract Background Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug–drug, drug–disease, and protein–protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. Results We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and $$LRW_5$$ L R W 5 are the top 3 best performers on all five datasets. Conclusions This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug–drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.

Abstract This study extracted 16 climatic data variables including annual temperature, freeze thaw, precipitation, and snowfall conditions from the Long-term Pavement Performance (LTPP) program database to evaluate the climatic regionalization for pavement infrastructure. The effect and significance of climate change were firstly evaluated using time as the only predictor and t-test. It was found that both the temperature and humidity increased in most States. Around one third of the 800 weather stations record variation of freeze and precipitation classifications and a few of them show significant change of classifications over time based on the results of logistic regression analyses. Three unsupervised machine learning including Principle Component Analysis (PCA), factor analysis and cluster analysis were conducted to identify the main component and common factors for climatic variables, and then to classify datasets into different groups. Then, two supervised machine learning methods including Fisher’s discriminant analysis and Artificial Neural Networks (ANN) were adopted to predict the climatic regions based on climatic data. Results of PCA and factor analysis show that temperature and humidity are the first two principle components and common factors, accounting for 71.6% of the variance. The 4-means clusters include wet no freeze, dry no freeze, dry freeze and snow freeze. The best k-mean clustering suggested 9 clusters with more temperature clusters. Both the Fisher’s linear discriminant analysis and ANN can effectively predict climatic regions with multiple climatic variables. ANN performs better with higher R square and low misclassification rate, especially for those with more layers and nodes.

Abstract Collapsibility determination in loess area is expensive, and it also requires a large amount of experimentation. This paper aims to find the association rules between physical parameters and collapsibility of the loess in Xining through the method of data mining, so to help researchers predict the collapsibility of loess. Related physical parameters of loess collapsibility, collected from 1039 samples, involve 13 potential influence factors. According to Grey Relational Analysis, the key influence factors that lead to collapsing are identified from these potential influence factors. Subsequently, take the key influence factors, δs (coefficient of collapsibility) and δzs (coefficient of collapsibility under overburden pressure) as input items, and use the Apriori algorithm to find multiple association rules between them. Then, through analysing the results of association rules between these key influence factors and collapsibility, the evaluation criteria for collapsibility in this area is proposed, which can be used to simplify the workload of determining collapsibility. Finally, based on these research results, recommendations for projects construction were made to ensure the safety of construction in the area.

Pancreatic cancer is one of the most common malignant tumors in the digestive system with a poor prognosis. Accordingly, better understanding of the molecular mechanisms and innovative therapies are warranted to improve the prognosis of this patient population. In addition to playing a crucial role in coagulation, platelets reportedly contribute to the growth, invasion and metastasis of various tumors, including pancreatic cancer. This narrative review brings together currently available evidence on the impact of platelets on pancreatic cancer, including the platelet-related molecular mechanisms of cancer promotion, pancreatic cancer fibrosis, immune evasion, drug resistance mechanisms, thrombosis, targeted platelet therapy, combined radiotherapy and chemotherapy treatment, platelet combined with nanotechnology treatment and potential applications of pancreatic cancer organoids. A refined understanding of the role of platelets in pancreatic cancer provides the foothold for identifying new therapeutic targets.

The human body is thoroughly colonized by a wide variety of microorganisms, termed microbiota. Pancreatic cancer, one of the most aggressive forms of cancer, is no exception. The microbiota of pancreatic cancer largely influences and even dominates the occurrence, development and outcome of pancreatic cancer in many ways. Studies have shown that microbiota could change the malignant phenotype and prognosis of pancreatic cancer by stimulating persistent inflammation, regulating the antitumor immune system, changing the tumor microenvironment and affecting cellular metabolism. This is why the association of the microbiota with pancreatic cancer is an emerging area of research that warrants further exploration. Herein, we investigated the potential microbial markers of pancreatic cancer, related research models, the mechanism of action of microbiota in pancreatic cancer, and pancreatic cancer-microbiota-related treatment.

Aristolochia yachangensis B.G.Huang, Yan Liu & Y.S.Huang, a new species from limestone areas in Guangxi, China, is described and illustrated. It is morphologically most similar to A. fangchi Y.C.Wu ex L.D.Chow & S.M.Hwang, A. petelotii O.C. Schmidt and A. championii Merr. & Chun in shape of leaf blade, anther, gynostemium and inflorescence on old woody stems. However, it can be easily distinguished from the latter by shape of inflorescence, length of upper and lower portions of perianth tube, colour of the limb and throat. A table and a key to distinguish the new species from other morphologically similar Aristolochia species are also provided.

The Distributed Denial of Service (DDoS) attack has seriously impaired network availability for decades and still there is no effective defense mechanism against it. However, the emerging Software Defined Networking (SDN) provides a new way to reconsider the defense against DDoS attacks. In this paper, we propose two methods to detect the DDoS attack in SDN. One method adopts the degree of DDoS attack to identify the DDoS attack. The other method uses the improved K-Nearest Neighbors (KNN) algorithm based on Machine Learning (ML) to discover the DDoS attack. The results of the theoretical analysis and the experimental results on datasets show that our proposed methods can better detect the DDoS attack compared with other methods.

Background: Alzheimer’s disease (AD) as an age-related, irreversible neurodegenerative disease, characterized by cognitive dysfunction, has become progressively serious with a global rise in life expectancy. As the failure of drug elaboration, considerable research effort has been devoted to developing therapeutic strategies for treating AD. TCM is gaining attention as a potential treatment for AD. Gastrodia elata Blume, Polygala tenuifolia Willd., Cistanche deserticola Ma, Rehmannia lutinosa (Gaertn.)DC., Acorus gramineus Aiton, and Curcuma longa L. (GPCRAC) are all well-known Chinese herbs with neuroprotective benefits and are widely used in traditional Chinese decoction for AD therapy. However, the efficacy and further mechanisms of GPCRAC extracts in AD experimental models are still unclear. The purpose of this study was to investigate the synergistic protective efficacy of GPCRAC extracts (composed of extracts from these six Chinese medicines), and the protein targets mediated by GPCRAC extracts in treating AD.Methods: Scopolamine-induced cognitive impairment mouse model was established to determine the neuroprotective effects of GPCRAC extracts in vivo, as shown by behavioral tests and cerebral cholinergic function assays. To identify the potential molecular mechanism of GPCRAC extracts against AD, label-free quantitative proteomics coupled with tandem mass spectrometry (LC-MS/MS) were performed. The integrated bioinformatics analysis was applied to screen the core differentially expressed proteins in vital canonical pathways. Critical altered proteins were validated by qPCR and Western blotting.Results: Administration of GPCRAC extracts significantly recovered scopolamine-induced cognitive impairment, as evidenced by the improved learning and memory ability, increased Ach content and ChAT activity, as well as decreased AchE activity in the hippocampus of mice. In total, 390 proteins with fold-change>1.2 or

Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives. Predicting future links among the nodes in these dynamic networks has many practical implications. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given’s angle method. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. These models are applied to eight dynamic protein–protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics.

Abstract Compared with surveillance video, user‐created videos contain more frequent shot changes, which lead to diversified backgrounds and a wide variety of content. The high redundancy among keyframes is a critical issue for the existing summarising methods in dealing with user‐created videos. To address the critical issue, we designed a salient‐ area‐size‐based spatial attention model (SAM) on the observation that humans tend to focus on sizable and moving objects in videos. Moreover, the SAM is taken as guidance to refine frame‐wise soft selected probability for the bi‐directional long short‐term memory model. The reinforcement learning framework, trained by the deep deterministic policy gradient algorithm, is adopted to do unsupervised training. Extensive experiments on the SumMe and TVSum datasets demonstrate that our method outperforms the state‐of‐the‐art in terms of F‐score.

Betulinic acid (BA) is a pentacyclic triterpene compound that can be obtained by separation, chemical synthesis and biotransformation from birch. BA has antitumour activity, and its mechanisms of action mainly include the induction of mitochondrial oxidative stress; the regulation of specificity protein transcription factors, and the inhibition of signal transducer and activator of transcription 3 and nuclear factor-κB signalling pathways. In addition, BA can increase the sensitivity of cancer cells to other chemotherapy drugs. Recent studies have shown that BA plays an anticancer role in several kinds of tumour diseases. In this article, the anticancer mechanism of BA and its application in the treatment of tumour diseases are reviewed.

Accurate prediction of air temperature is of great significance to outdoor activities and daily life. However, it is important and more challenging to predict air temperature in complex terrain areas because of prevailing mountain and valley winds and variable wind directions. The main innovation of this paper is to propose a regional temperature prediction method based on deep spatiotemporal networks, designing a spatiotemporal information processing module to align temperature data with regional grid points and further transforming temperature time series data into image sequences. Long Short-Term Memory network is constructed on the images to extract the depth features of the data to train the model. The experiments demonstrate that the deep learning prediction model containing the spatiotemporal information processing module and the deep learning prediction module is fully feasible in short-term regional temperature prediction. The comparison experiments show that the model proposed in this paper has better prediction results for classical models, such as convolutional neural networks and LSTM networks. The experimental conclusion shows that the method proposed in this paper can predict the distribution and change trend of temperature in the next 3 h and the next 6 h on a regional scale. The experimental result RMSE reached 0.63, showing high stability and accuracy. The model provides a new method for local regional temperature prediction, which can support the planning of production and life in advance and tend to save energy and reduce consumption.

The industrial transformation led by digitization-related technologies has attracted research attention in recent decades, enhancing its application in different sectors. The transport industry is a crucial driving force for economic growth and social development. It is still necessary to make transportation infrastructure and services safer, cleaner, and more affordable to cope with increasing urbanization and mobility. This paper systematically examines the science mapping of building information modeling and digital twins technologies in the digitalization of transportation. Through the bibliometric and content analysis approaches, 493 related documents were screened and analyzed from the Web of Science and Scopus databases. The software programs VOSviewer and Bibliometrix were used to determine research trends and current gaps, which will be beneficial to future research in this vital field. The results showed that over 80% of the relevant documents have been published since 2018. China is the most productive country, followed by the United States and Italy, and Germany is the most cited and influential country. Moreover, research also revealed the leading authors, top journals, and highly cited papers. The findings may be used as a guide for: (1) improving the efficiency of intelligent transportation system element management; (2) the development and application of digital technologies; (3) the flow and goals of entire-life-cycle management; and (4) the optimization of related algorithms and models.

The deflection measurements made using Falling Weight Deflectometers (FWDs) are widely used in the back-calculation of pavement layer moduli. Pavement structural characteristics, changes in temperature, and other related factors exert a significant effect on the deflection measurements. Therefore, three machine learning methods—Classification and Regression Tree (CART), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT)—were used to evaluate the importance of influencing factors including FWD test conditions, pavement structural parameters, climatic factors, traffic level, rehabilitation level, and service age, on the FWD measurements of deflection basin in this study. The results indicated that structural number was an important feature for all FWD measurements but its importance on lg(D0–D20) and lg(D0–D30) was smaller than other FWD measurements. The relative feature importance of the asphalt layer, base, and subbase on lg(D0–D20) and lg(D0–D30) was asphalt layer > subbase > base; their relative importance on lg(D20–D60), lg(D30–D60), and lg(D30–D90) was asphalt layer > base > subbase; and their relative importance on lg(D90−D120) and lg(D60–D120) was base > subbase > asphalt layer. Among the FWD test condition variables, drop load was the most significant factor influencing deflection measurements. The second-layer temperature was also important for lg(D0–D20), lg(D0–D30), and lg(D0–D45). The importance of precipitation was greater than the freeze index. The prediction results shown that the accuracy of GBDT was as high as 99%. Besides, GBDT outperformed RF, and RF outperformed CART. The analyses between FWD deflection parameters and influencing factors, especially the structural characteristics of the pavement, provide theoretical evidence for the evaluation of pavement layer strength on the basis of FWD data.

The four-direction performance of a TOSA and power tested with different jumpers are affected by the optical field distribution of the TOSA. In order to improve the optical field distribution of a TOSA, this paper analyzes the factors affecting optical field distribution in the core and cladding of fiber and establishes a 3D defocusing coupling model to improve the uniform distribution of the optical field. The verification between the 2D coupling model and 3D defocusing coupling model shows that four-direction performance with 3D defocusing coupling is less than 1.0 dB, and the difference of power tested with different jumpers is not more than 0.5 dB. The results are better than that of a TOSA with a conventional coupling model, and the reliability and yield of the TOSA are higher. The 3D defocusing coupling model has practical guiding significance and economic value in TOSA production.