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Objective To explore the efficacy and safety of intracranial-intracranial bypass for the treatment of complex intracranial aneurysms. Methods and Results A total of 32 patients with complex intracranial aneurysms who were hospitalized for treatment at Huashan Hospital, Fudan University from January 2015 to December 2023 were included. Surgical methods included in-situ bypass, reimplantation, reanastomosis and bypass with interposition graft. For middle cerebral artery (MCA) aneurysms (n = 16), there were 5 cases of in-situ bypass, one case of reimplantation, 3 cases of reanastomosis, and 7 cases of bypass with interposition graft. For anterior cerebral artery (ACA) aneurysms (n = 8), there were 5 cases of in-situ bypass and 3 cases of reimplantation. For anterior communicating artery (ACoA) aneurysms (n = 2), both cases were in-situ bypass. For posterior circulation aneurysms (n = 6), there were 3 cases of in-situ bypass, 2 cases of reanastomosis, and one case of bypass with interposition graft. The intraoperative graft vessels patency rate was 100%. Among the 23 patients who participated in follow-up imaging for more than 6 months postoperatively, no obvious recurrence was indicated by imaging. Among the 28 patients who participated in modified Rankin Scale (mRS) follow-up, there was one case of 5, 5 cases of 3, 2 cases of 2, one case of 1, and 19 cases of 0. Postoperative hemorrhagic complications occurred in 3 cases (9.37%), and ischemic complications occurred in 10 cases (31.25%), with no patients requiring unplanned secondary surgery for complication treatment. Conclusions Intracranial-intracranial bypass has good efficacy and safety for the treatment of complex intracranial aneurysms and has unique advantages in the microsurgical treatment of complex intracranial aneurysms.

Abstract Background Breast cancer is the most prevalent malignant tumor among women, with hormone receptor-positive cases constituting 70%. Fulvestrant, an antagonist for these receptors, is utilized for advanced metastatic hormone receptor-positive breast cancer. Yet, its inhibitory effect on tumor cells is not strong, and it lacks direct cytotoxicity. Consequently, there's a significant challenge in preventing recurrence and metastasis once cancer cells develop resistance to fulvestrant. Method To address these challenges, we engineered tumor-targeting nanoparticles termed 131I-fulvestrant-ALA-PFP-FA-NPs. This involved labeling fulvestrant with 131I to create 131I-fulvestrant. Subsequently, we incorporated the 131I-fulvestrant and 5-aminolevulinic acid (ALA) into fluorocarbon nanoparticles with folate as the targeting agent. This design facilitates a tri-modal therapeutic approach—endocrine therapy, radiotherapy, and PDT for estrogen receptor-positive breast cancer. Results Our in vivo and in vitro tests showed that the drug-laden nanoparticles effectively zeroed in on tumors. This targeting efficiency was corroborated using SPECT-CT imaging, confocal microscopy, and small animal fluorescence imaging. The 131I-fulvestrant-ALA-PFP-FA-NPs maintained stability and showcased potent antitumor capabilities due to the synergism of endocrine therapy, radiotherapy, and CR-PDT. Throughout the treatment duration, we detected no notable irregularities in hematological, biochemical, or histological evaluations. Conclusion We've pioneered a nanoparticle system loaded with radioactive isotope 131I, endocrine therapeutic agents, and a photosensitizer precursor. This system offers a combined modality of radiotherapy, endocrine treatment, and PDT for breast cancer. Graphical Abstract

Objective: The primary objective of this study is to investigate the chemical composition, volatile components, and biological activity of pickled bamboo shoots in the Guangxi and Guangdong regions, as well as the regional characteristics and flavor formation mechanisms of these pickled bamboo shoots. Methods: Physical and chemical experiments were used to analyze the major components in fermented bamboo shoots and their extracts. The volatile flavor components in fermented bamboo shoots were identified and analyzed using headspace solid-phase microextraction gas chromatography-mass spectrometry （HS-SPME-GC-MS）. The antioxidant activity of fermented bamboo shoot extracts was evaluated using in vitro antioxidant activity methods （ABTS free radical scavenging method, DPPH free radical scavenging method, and FRAP method）. Results: There were no significant differences in moisture, pH, total soluble sugars, fats, amino acid nitrogen, and nitrite content between the pickled bamboo shoots from Guangxi Liuzhou Tianyi Agricultural Technology Co Ltd （hereinafter referred to as GXLZTY） and those from the Shaoguan Nanxiong Agricultural Trade Market in Northern Guangdong （hereinafter referred to as GDYBNX）. However, there were significant differences between the two in terms of chloride, total acids, proteins, dietary fibers, total flavonoids, total polyphenols, and mineral element content. Analysis of volatile components indicated that the main constituents in GXLZTY fermented bamboo shoots were P-cresol, hexanoic acid, octanoic acid, 2-ethylhexanol, and sulfur dioxide, while the primary components in fermented bamboo shoots from GDYBNX were p-cresol, acetic acid, and serinol. Antioxidant activity test results showed that the GXLZTY sour bamboo shoot extracts were superior in terms of clearing ABTS free radicals, DPPH free radicals, and FRAP activity compared to the extracts from GDYBNX. Conclusion: GXLZTY fermented bamboo shoots have higher nutritional value and antioxidant activity. These research results provide a theoretical basis for the fermentation process and industrial production of fermented bamboo shoots.

As an excellent single-stage object detector based on neural networks, YOLOv5 has found extensive applications in the industrial domain; however, it still exhibits certain design limitations. To address these issues, this paper proposes Efficient Scale Fusion YOLO (ESF-YOLO). Firstly, the Multi-Sampling Conv Module (MSCM) is designed, which enhances the backbone network’s learning capability for low-level features through multi-scale receptive fields and cross-scale feature fusion. Secondly, to tackle occlusion issues, a new Block-wise Channel Attention Module (BCAM) is designed, assigning greater weights to channels corresponding to critical information. Next, a lightweight Decoupled Head (LD-Head) is devised. Additionally, the loss function is redesigned to address asynchrony between labels and confidences, alleviating the imbalance between positive and negative samples during the neural network training. Finally, an adaptive scale factor for Intersection over Union (IoU) calculation is innovatively proposed, adjusting bounding box sizes adaptively to accommodate targets of different sizes in the dataset. Experimental results on the SODA10M and CBIA8K datasets demonstrate that ESF-YOLO increases Average Precision at 0.50 IoU (AP50) by 3.93 and 2.24%, Average Precision at 0.75 IoU (AP75) by 4.77 and 4.85%, and mean Average Precision (mAP) by 4 and 5.39%, respectively, validating the model’s broad applicability.

Given the rapid advancements in kinetic pursuit technology, this paper introduces an innovative maneuvering strategy, denoted as LSRC-TD3, which integrates line-of-sight (LOS) angle rate correction with deep reinforcement learning (DRL) for high-speed unmanned aerial vehicle (UAV) pursuit–evasion (PE) game scenarios, with the aim of effectively evading high-speed and high-dynamic pursuers. In the challenging situations of the game, where both speed and maximum available overload are at a disadvantage, the playing field of UAVs is severely compressed, and the difficulty of evasion is significantly increased, placing higher demands on the strategy and timing of maneuvering to change orbit. While considering evasion, trajectory constraint, and energy consumption, we formulated the reward function by combining “terminal” and “process” rewards, as well as “strong” and “weak” incentive guidance to reduce pre-exploration difficulty and accelerate convergence of the game network. Additionally, this paper presents a correction factor for LOS angle rate into the double-delay deterministic gradient strategy (TD3), thereby enhancing the sensitivity of high-speed UAVs to changes in LOS rate, as well as the accuracy of evasion timing, which improves the effectiveness and adaptive capability of the intelligent maneuvering strategy. The Monte Carlo simulation results demonstrate that the proposed method achieves a high level of evasion performance—integrating energy optimization with the requisite miss distance for high-speed UAVs—and accomplishes efficient evasion under highly challenging PE game scenarios.

With the development and strengthening of interception measures, the traditional penetration methods of high-speed unmanned aerial vehicles (UAVs) are no longer able to meet the penetration requirements in diversified and complex combat scenarios. Due to the advancement of Artificial Intelligence technology in recent years, intelligent penetration methods have gradually become promising solutions. In this paper, a penetration strategy for high-speed UAVs based on improved Deep Reinforcement Learning (DRL) is proposed, in which Long Short-Term Memory (LSTM) networks are incorporated into a classical Soft Actor–Critic (SAC) algorithm. A three-dimensional (3D) planar engagement scenario of a high-speed UAV facing two interceptors with strong maneuverability is constructed. According to the proposed LSTM-SAC approach, the reward function is designed based on the criteria for successful penetration, taking into account energy and flight range constraints. Then, an intelligent penetration strategy is obtained by extensive training, which utilizes the motion states of both sides to make decisions and generate the penetration overload commands for the high-speed UAV. The simulation results show that compared with the classical SAC algorithm, the proposed algorithm has a training efficiency improvement of 75.56% training episode reduction. Meanwhile, the LSTM-SAC approach achieves a successful penetration rate of more than 90% in hypothetical complex scenarios, with a 40% average increase compared with the conventional programmed penetration methods.

As the missions and environments of unmanned aerial vehicles (UAVs) become increasingly complex in both space and time, it is essential to investigate the dynamic task assignment problem of heterogeneous multi-UAVs aiming at ground targets in an uncertain environment. Considering that most of these existing tasking methods are limited to static allocation in a deterministic environment, this paper firstly constructs the fuzzy multiconstraint programming model for heterogeneous multi-UAV dynamic task assignment based on binary interval theory, taking into account the effects of uncertain factors like target location information, mission execution time, and the survival probability of UAVs. Then, the dynamic task allocation strategy is designed, consisting of two components: dynamic time slice setting and the four-dimensional information grey wolf optimization (4DI-GWO) algorithm. The dynamic time slices create the dynamic adjustment of solving frequency and effect, and the 4DI-GWO algorithm is improved by designing the four-dimensional information strategy that expands population diversity and enhances global search capability and other strategies. The numerical analysis shows that the proposed strategy can effectively solve the dynamic task assignment problem of heterogeneous multi-UAVs under an uncertain environment, and the optimization of fitness values demonstrates improvements of 5~30% in comparison with other optimization algorithms.

The standardized training of residents in the Department of Ultrasound Medicine plays a key role in the continuing education of medical students and specialists after graduation. In recent years, as graduate students, the students who have just graduated and also social people are included in the standardized training of residents in the Department of Ultrasound Medicine, they have encountered some problems, such as uneven quality of trainees, lack of individualization of training Objectives, insufficient methodology for implementation and management, etc. This paper discusses the specific contents of the construction of standardized training system in the Department of Ultrasound Medicine, including cultural construction, curriculum construction, process assessment, and faculty development and so on. We can create our own characteristics in the standardized training of residents in the Department of Ultrasound Medicine through measures such as integrating curriculum and other resources, optimizing the application, perfecting and strictly implementing the training and assessment system, paying more attention to the capacity building of teachers, good working environment in the department and humanistic care, so as to lay the foundation and provide experience for the construction of a standardized training management system for ultrasound doctors in the future.

Variable air volume (VAV) air-conditioning (AC) systems are widely employed to achieve a comfortable room thermal and humid environment depending on its better regulation performance and energy efficiency. In the single coil VAV AC system, conventional proportional-integral (PI) control algorithm is usually adopted to track the set-points of the room temperature and humidity by regulating the supply air flow rate and the chilled water flow rate, respectively. However, the control performance is usually not good due to the high coupling of the heat and mass transfer in the air-handling unit (AHU). A model-based control method is developed to realize the decoupling control of the room temperature and humidity according to the bilinear characteristics of the temperature and humidity variation. In this control method, a bilinear room temperature controller is used to track the room temperature set-point based on the real-time cooling load, while a room humidity controller is used to track the room humidity set-point depending on the real-time humidity load. The control performance was validated in a simulated VAV AC system. The test results show that comparing with the conventional PI control, the room temperature and humidity are controlled much more robustly and accurately by using the proposed model-based control method.

Aiming at the rapid development of anti-hypersonic collaborative interception technology, this paper designs an intelligent maneuver strategy of hypersonic vehicles (HV) based on deep reinforcement learning (DRL) to evade the collaborative interception by two interceptors. Under the meticulously designed collaborative interception strategy, the uncertainty and difficulty of evasion are significantly increased and the opportunity for maneuvers is further compressed. This paper, accordingly, selects the twin delayed deep deterministic gradient (TD3) strategy acting on the continuous action space and makes targeted improvements combining deep neural networks to grasp the maneuver strategy and achieve successful evasion. Focusing on the time-coordinated interception strategy of two interceptors, the three-player pursuit and evasion (PE) problem is modeled as the Markov decision process, and the double training strategy is proposed to juggle both interceptors. In reward functions of the training process, the energy saving factor is set to achieve the trade-off between miss distance and energy consumption. In addition, the regression neural network is introduced into the deep neural network of TD3 to enhance intelligent maneuver strategies’ generalization. Finally, numerical simulations are conducted to verify that the improved TD3 algorithm can effectively evade the collaborative interception of two interceptors under tough situations, and the improvements of the algorithm in terms of convergence speed, generalization, and energy-saving effect are verified.

BackgroundAs the impact of the COVID-19 pandemic winds down, both individuals and society are gradually returning to life and activities before the pandemic. This study aims to explore how people's emotions have changed from the pre-pandemic period during the pandemic to this post-emergency period and whether the sentiment level nowadays has returned to the pre-pandemic level.MethodWe collected Reddit social media data in 2019 (pre-pandemic), 2020 (peak period of the pandemic), 2021, and 2022 (late stages of the pandemic, transitioning period to the post-emergency period) from the subreddits communities in 128 universities/colleges in the U.S., and a set of school-level baseline characteristics such as location, enrollment, graduation rate, selectivity, etc. We predicted two sets of sentiments from a pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) and a graph attention network (GAT) that leverages both the rich semantic information and the relational information among posted messages and then applied model stacking to obtain the final sentiment classification. After obtaining the sentiment label for each message, we employed a generalized linear mixed-effects model to estimate the temporal trend in sentiment from 2019 to 2022 and how the school-level factors may affect the sentiment.ResultsCompared to the year 2019, the odds of negative sentiment in years 2020, 2021, and 2022 are 25%. 7.3%, and 6.3% higher, respectively, which are all statistically significant at the 5% significance level based on the multiplicity-adjusted p-values.ConclusionsOur study findings suggest a partial recovery in the sentiment composition (negative vs. non-negative) in the post-pandemic-emergency era. The results align with common expectations and provide a detailed quantification of how sentiments have evolved from 2019 to 2022 in the sub-population represented by the sample examined in this study.

Abstract Background A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. Methods We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. Results The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ( $$\epsilon \approx 1$$ ϵ ≈ 1 ). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When $$\epsilon

Accurate evaluation of cervical metastatic lymph nodes is of great significance to the risk staging, treatment and prognosis of patients with differentiated thyroid cancer(DTC). Ultrasonography is the main examination method to evaluate cervical lymph nodes. In recent years, with the emergence and extensive application of new technologies such as elastic imaging, contrast-enhanced ultrasonography, fine needle aspiration cytology and artificial intelligence, the diagnostic effectiveness of cervical metastatic lymph nodes of DTC has been further improved.

Image desmoking is a significant aspect of endoscopic image processing, effectively mitigating visual field obstructions without the need for additional surgical interventions. However, current smoke removal techniques tend to apply comprehensive video enhancement to all frames, encompassing both smoke-free and smoke-affected images, which not only escalates computational costs but also introduces potential noise during the enhancement of smoke-free images. In response to this challenge, this paper introduces an approach for classifying images that contain surgical smoke within endoscopic scenes. This classification method provides crucial target frame information for enhancing surgical smoke removal, improving the scientific robustness, and enhancing the real-time processing capabilities of image-based smoke removal method. The proposed endoscopic smoke image classification algorithm based on the improved Poolformer model, augments the model’s capacity for endoscopic image feature extraction. This enhancement is achieved by transforming the Token Mixer within the encoder into a multi-branch structure akin to ConvNeXt, a pure convolutional neural network. Moreover, the conversion to a single-path topology during the prediction phase elevates processing speed. Experiments use the endoscopic dataset sourced from the Hamlyn Centre Laparoscopic/Endoscopic Video Dataset, augmented by Blender software rendering. The dataset comprises 3,800 training images and 1,200 test images, distributed in a 4:1 ratio of smoke-free to smoke-containing images. The outcomes affirm the superior performance of this paper’s approach across multiple parameters. Comparative assessments against existing models, such as mobilenet_v3, efficientnet_b7, and ViT-B/16, substantiate that the proposed method excels in accuracy, sensitivity, and inference speed. Notably, when contrasted with the Poolformer_s12 network, the proposed method achieves a 2.3% enhancement in accuracy, an 8.2% boost in sensitivity, while incurring a mere 6.4 frames per second reduction in processing speed, maintaining 87 frames per second. The results authenticate the improved performance of the refined Poolformer model in endoscopic smoke image classification tasks. This advancement presents a lightweight yet effective solution for the automatic detection of smoke-containing images in endoscopy. This approach strikes a balance between the accuracy and real-time processing requirements of endoscopic image analysis, offering valuable insights for targeted desmoking process.

In view of the problem that path planning and trajectory tracking are rarely solved simultaneously in the current research, which hinders their practical implementation, this paper focuses on enhancing the autonomous flight planning capability of unmanned aerial vehicles (UAVs) by investigating integrated path planning and trajectory tracking technologies. The autonomous flight process is divided into two sub-problems: waypoint designing/optimizing and waypoint tracking. Firstly, an improved DB-RRT* algorithm is proposed for waypoint planning to make the algorithm have higher planning efficiency, better optimization results, and overcome the defects of accidental and low reliability of single RRT* planning results. Secondly, the scheme of “offline design + online flight” is adopted to lead the UAV to fly online according to the waypoints’ instructions by using the sliding mode guidance based on angle constraint with finite-time convergence so that it can fly to the destination autonomously. In order to check the performance of the proposed algorithm, a variety of simulations are conducted to verify the feasibility of the proposed algorithm.

In order to ensure the quality of the results of ocean investigation, observation and monitoring as well as to ensure their compliance of relative laws, branches of Ministry of Natural Resources regularly assessed the running status of the quality management systems of the ocean inspection bodies and laboratories, which was to confirm their ability of obtaining reliable data and results from the marine tasks. However, these assessments were usually based on qualitative methods; a systematic and quantitative method required further research. The paper focused on the particular requirements of marine tasks, recognized 3 relative aspects: deployment of resources, processes of implementation and effectiveness of results, and divided them into 6 indicators: employees, instruments, methods and their application, quality controlling, carriers of outputs and improvement. Meanwhile, 3 "one-vote veto" indicators were distinguished: legitimacy of the organization’s qualification, authenticity of the data and result of the quality acceptance test. On the basis of these, an evaluation model depending on weighted accumulation method was established, the weights were acquired through Analytic Hierarchy Process method, the standards and the rule of grading were set, and an index system to evaluate running effectiveness of quality management systems of ocean inspection bodies and laboratories was constructed. At the end of the paper, an advised procedure to conduct the evaluation method was illustrated, and a demonstration on assessing the marine ecology monitoring organizations was given, which showed the practicability of the evaluation method.

In order to make the detection capability of the instrument meet the performance requirements of the ocean observation business development for the tidal level measuring instrument, combined with the verification procedures of various tide gauges, advanced electrical control technology was applied for precise water level control and tidal simulation, and to realize instrument fixed-point value measurement and dynamic response test, realize the dynamic identification of water level and scale through image recognition software, and to improve the accuracy of standard value, integrating remote control function to improve instrument detection efficiency, so that the device could carry out perfect, reasonable and efficient detection of tide level measuring instruments, so as to conduct a comprehensive and systematic evaluation of instrument performance.

Harmful algae blooms (HABs) can have significant adverse effects on coastal ecosystems and aquaculture resiliency. We collected samples from March to August at eight different stations in Haizhou Bay (China), a region with a high frequency of HABs, and used Illumina Novaseq high-throughput sequencing and multivariate statistical analysis to characterize the bacterial communities and their relationships with different environmental factors. We identified 27 phyla, 49 classes, 158 orders, 294 families, and 522 genera. Gammaproteobacteria, Alphaproteobacteria, Bacteroidia, Acidimicrobiia, Bacilli, Actinobacteria, Cyanobacteria, Clostridia, and Acidobacteria were the most abundant classes, and Acidobacteria, Bacteroidetes, Firmicutes, Actinobacteria, Chloroflexi, Proteobacteria, and Cyanobacteria were the keystone phyla. Based on the Mantel test and redundancy analysis, temperature was the main environmental factor affecting the structure of the bacterial communities, followed by silicate, dissolved organic phosphorus (DOP), and dissolved oxygen (DO). Among the genera with high OTU abundance, Nautella was co-related positively with DO and negatively with salinity; Planktomarina was co-related positively with salinity and negatively with nitrate and nitrite. Certain families (Flavobacteriaceae, Rhodobacteraceae, and Clade_I (SAR11 clade)) and genera (Methylophaga, Alteromonas, Oleiphilus, Marinobacter, Bacillus, Nautella, and Vibrio) had associations with phytoplankton species that were responsible for HABs. This research provides new insights into the characteristics of the bacterial communities that occur in coastal areas that have HABs and provides detailed descriptions of the spatial and temporal changes in the structure of these communities.

Abstract With the rapid development of the social economy, monumental changes have taken place in the urban and rural environments. Urban and rural areas play a vital role in the interactions between humans and society. Traditional machine learning methods are used to perceive the massive changes in the urban and rural areas, though it is easy to overlook the detailed information about the changes made to the intentional target. As a result, the perception accuracy needs to be improved. Therefore, based on a deep neural network, this paper proposes a method to perceive the spatiotemporal changes in urban and rural intentional connotations through the perspective of remote sensing. The framework first uses multibranch DenseNet to model the multiscale spatiotemporal information of the intentional target and realizes the interaction of high-level semantics and low-level details in the physical appearance. Second, a multibranch and cross-channel attention module is designed to refine and converge multilevel and multiscale temporal and spatial semantics to perceive the subtle changes in the urban and rural intentional targets through the semantics and physical appearance. Finally, the experimental results show that the multibranch perception framework proposed in this paper has the best performance on the two baseline datasets A and B, and its F-Score values are 88.04% and 53.72%, respectively.

The urgent requirement for improving the efficiency of agricultural plant protection operations has spurred considerable interest in multiple plant protection UAV systems. In this study, a performance-guaranteed distributed control scheme is developed in order to address the control of multiple plant protection UAV systems with collision avoidance and a directed topology. First, a novel concept called predetermined time performance function (PTPF) is proposed, such that the tracking error can converge to an arbitrary small preassigned region in finite time. Second, combined with the two-order filter for each UAV, the information estimation from the leader is generated. The distributed protocol avoids the use of an asymmetric Laplace matrix of a directed graph and solves the difficulty of control design. Furthermore, by introducing with a collision prediction mechanism, a repulsive force field is constructed between the dynamic obstacle and the UAV, in order to avoid the collision. Finally, it is rigorously proved that the consensus of the multiple plant protection UAV system can be achieved while guaranteeing the predetermined time performance. A numerical simulation is carried out to verify the effectiveness of the presented method, such that the multiple UAVs system can fulfill time-constrained plant protection tasks.

Accurate classification and identification of Internet traffic are crucial for maintaining network security. However, unknown network traffic in the real world can affect the accuracy of current machine learning models, reducing the efficiency of traffic classification. Existing unknown traffic classification algorithms are unable to optimize traffic features and require the entire system to be retrained each time new traffic data are collected. This results in low recognition efficiency, making the algoritms unsuitable for real-time application detection. To solve the above issues, we suggest a multi-feature fusion-based incremental technique for detecting unknown traffic in this paper. The approach employs a multiple-channel parallel architecture to extract temporal and spatial traffic features. It then uses the mRMR algorithm to rank and fuse the features extracted from each channel to overcome the issue of redundant encrypted traffic features. In addition, we combine the density-ratio-based clustering algorithm to identify the unknown traffic features and update the model via incremental learning. The cassifier enables real-time classification of known and unknown traffic by learning newly acquired class knowledge. Our model can identify encrypted unknown Internet traffic with at least 86% accuracy in various scenarios, using the public ISCX-VPN-Tor datasets. Furthermore, it achieves 90% accuracy on the intrusion detection dataset NSL-KDD. In our self-collected dataset from a real-world environment, the accuracy of our model exceeds 96%. This work offers a novel method for identifying unknown network traffic, contributing to the security preservation of network environments.

In recent years, there has been a growing interest in changes in dynamic mechanical properties of mixed rubber during dynamic shear, yet the influence of vulcanized characteristics on the dynamic shear behavior of vulcanized rubber, particularly the effect of cross-linking density, has received little attention. This study focuses on styrene–butadiene rubber (SBR) and aims to investigate the impact of different cross-linking densities (Dc) on dynamic shear behavior using molecular dynamics (MD) simulations. The results reveal a remarkable Payne effect, where the storage modulus experiences a significant drop when the strain amplitude (γ0) exceeds 0.1, which can be attributed to the fracture of the polymer bond and the decrease in the molecular chain’s flexibility. The influence of various Dc values mainly resides at the level of molecular aggregation in the system, where higher Dc values impede molecular chain motion and lead to an increase in the storage modulus of SBR. The MD simulation results are verified through comparisons with existing literature.

This paper investigates a novel robust adaptive dynamic surface control scheme based on the barrier Lyapunov function (BLF), online composite learning, disturbance observer, and improved saturation function. It is mainly designed for a class of skid-to-turn (STT) interceptor integrated guidance and control (IGC) design problems under multi-source uncertainties, state constraints, and input saturation. The serial-parallel estimation model used in this study estimates the system states and provides “critic” information for the neural network and disturbance observer; then, these three are combined to realize online composite learning of the multiple uncertainties of the system and improve the interception accuracy. In addition, the state and input constraints are resolved by adopting the BLF and the improved saturation function, while the design of the auxiliary system ensures stability. Finally, a series of simulation results show that the proposed IGC scheme with a direct-hit intercept strategy achieves a satisfactory effect, demonstrating the validity and robustness of the scheme.

BackgroundThe Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) is a widely used developmental assessment tool for children aged 0–6 years in China. The communication warning behavior subscale of CNBS-R2016 is used to assess the symptoms of autism spectrum disorder (ASD), and its value of >30 points indicates ASD based on CNBS-R2016. However, we observed that children with relatively lower values were also diagnosed with ASD later on in clinical practice. Thus, this study aimed to identify the suitable cutoff value for ASD screening recommended by the communication warning behavior of CNBS-R2016.Materials and methodsA total of 90 typically developing (TD) children and 316 children with developmental disorders such as ASD, developmental language disorder (DLD), and global developmental delay (GDD; 130 in the ASD group, 100 in the DLD group, and 86 in the GDD group) were enrolled in this study. All subjects were evaluated based on the CNBS-R2016. The newly recommended cutoff value of communication warning behavior for screening ASD was analyzed with receiver operating curves.ResultsChildren in the ASD group presented with lower developmental levels than TD, DLD, and GDD groups in overall developmental quotient assessed by CNBS-R2016. We compared the consistency between the scores of communication warning behavior subscale and Autism Behavior Checklist (ABC), Childhood Autism Rating Scale (CARS), Autism Diagnostic Observation Schedule, second edition (ADOS-2), and clinical diagnosis for the classification of ASD at a value of 30 based on the previously and newly recommended cutoff value of 12 by the CNBS-R2016. The Kappa values between the communication warning behavior and ABC, CARS, ADOS-2, and clinical diagnosis were 0.494, 0.476, 0.137, and 0.529, respectively, with an agreement rate of 76.90%, 76.26%, 52.03%, and 82.27%, respectively, when the cutoff point was 30. The corresponding Kappa values were 0.891, 0.816, 0.613, and 0.844, respectively, and the corresponding agreement rate was 94.62%, 90.82%, 90.54%, and 93.10%, respectively, when the cutoff point was 12.ConclusionThe communication warning behavior subscale of CNBS-R2016 is important for screening ASD. When the communication warning behavior score is 12 points or greater, considerable attention and further comprehensive diagnostic evaluation for ASD are required to achieve the early detection and diagnosis of ASD in children.

BackgroundCellular prion protein (PRPC) exerts brain-protective effects. We determined the relationship between plasma PRPC levels and disease severity plus clinical outcome after acute intracerebral hemorrhage (ICH).MethodsA total of 138 ICH patients and 138 healthy controls were included in this prospective, observational study. Hematoma volume and Glasgow coma scale (GCS) score were used to assess disease severity. Glasgow outcome scale (GOS) scores of 1–3 and 4–5 at 90 days after stroke were defined as a poor outcome and good outcome, respectively. Using multivariate analysis, we discerned the relation of plasma PRPC levels to disease severity and poor outcome. The receiver operating characteristic (ROC) curve was built to evaluate the prognostic predictive capability.ResultsPlasma PRPC levels in ICH patients were significantly higher than those in healthy controls (median, 4.20 vs. 2.02 ng/ml; P < 0.001), and were independently correlated with GCS score (r = −0.645, P < 0.001) and hematoma volume (r = 0.627, P < 0.001). Plasma PRPC levels were highly correlated with GOS score (r = −0.762, P < 0.001), and were substantially higher in patients with poor outcomes than in those with the good outcomes. Using maximum Youden index, plasma PRPC levels >3.893 ng/ml distinguished the risk of poor outcome at 90 days, with a sensitivity of 86.4% and a specificity of 65.8% (area under the curve, 0.809; 95% confidence interval (CI), 0.737–0.881, P < 0.001). Plasma PRPC levels >3.893 ng/ml were independently associated with a poor 90-day outcome with an odds ratio of 12.278 (95% CI, 5.101–29.554).ConclusionElevated plasma PRPC levels are significantly associated with disease severity and poor 90-day outcome in ICH patients, indicating that plasma PRPC may be used as a potential prognostic biomarker after ICH.

ObjectiveThe complement cascade is activated early following intracerebral hemorrhage (ICH) and causes acute brain injury. We intended to explore the effects of plasma complement component 1q (C1q) levels on hemorrhagic severity and functional outcome in ICH patients.MethodsIn this prospective cohort study, we measured the plasma C1q levels of 101 ICH patients and 101 healthy controls. The Glasgow Coma Scale (GCS) score and hematoma volume were used to assess the ICH severity. Poor prognosis was referred to as a Glasgow Outcome Scale (GOS) score of 1-3 at three months following a stroke. A multivariate logistic regression model was configured to determine the independent relation of plasma C1q levels to severity and poor prognosis. Under receiver operating characteristic (ROC) curve, prognostic capability of plasma C1q levels was evaluated.ResultsThere was a significant elevation of plasma C1q levels in patients, as compared to controls [median (percentiles 25th-75th), 225.04 mg/l (156.10-280.15 mg/l) versus 88.18 mg/l (70.12-117.69 mg/l); P 270.11 mg/l distinguished patients at risk of poor prognosis at 3 months with 56.52% sensitivity and 94.55% specificity. Meanwhile, the prognostic predictive ability of plasma C1q levels was equivalent to those of GCS score and hematoma volume (both P>0.05). Moreover, plasma C1q levels > 270.11 mg/l independently predicted a poor prognosis at 3 months (odds ratio, 4.821; 95% confidence interval, 1.211-19.200; P=0.026).ConclusionPlasma C1q levels are closely related to the illness severity and poor prognosis of ICH at 3 months. Hence, complement C1q may play an important role in acute brain injury after ICH and plasma C1q may represent a promising prognostic predictor of ICH.

OBJECTIVES Due to the advantages of simple sample preparation, non-destructive determination, and rapid detection, the X-ray fluorescence spectrometry technique has become a robust method for determination of manganese in soil or sediment. Manganese in soil and sediment standard material has the maximum content of 2490mg/kg, thus the traditional X-ray fluorescence spectrometry (XRF) can easily suffer from the drawbacks of standard curve measurement range due to the limited manganese content in standard soil or sediment samples. OBJECTIVES To develop a good method for the determination of manganese in polluted soil or sediment. METHODS To address this major concern, manganese solutions were added to the commercially available soil standards in a quantitative manner to give a series of new soil standards with higher manganese content. The content of manganese can be determined by X-ray fluorescence spectrometry. RESULTS The establishment of a novel standard curve by taking advantage of these new soil standards could significantly promote the upper limit of manganese measurement from 2490mg/kg to 3780mg/kg. The analytical results of manganese were consistent with the reference value and a recovery of 97.8%-108.3%. The results of high-manganese samples were consistent with the values acquired by inductively coupled plasma-optical emission spectrometry, with the relative deviation less than 5.7% and the relative standard deviation lower than 0.4% (n=7). Conclusion Results show good accuracy and precision in the determination of high-manganese soils and sediments.

Interfacial charge transfer and separation are both critical to the solar energy conversion efficiency, whereas it remains an enduring challenge. This work demonstrates the synergetic enhancement of interfacial charge transfer efficiency and bulk charge separation by depositing p-type Co3O4 cocatalyst onto n-type TiO2 nanotube arrays (TNTAs) that are uniformly coated with an ultrathin carbon layer, forming a p − n Co3O4/TNTAs heterojunction. Being highly dispersed on the surface of photoanodes, Co3O4 quantum dots retard forming charge recombination centers at the photoanode/cocatalyst interface, which facilitates hole transport. Simultaneously, ultrathin intermediate carbon layer boosts interfacial electron transfer kinetics, resulting in effective spatial charge separation and considerably improved photoelectrochemical (PEC) water splitting performances.

BackgroundThe COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media.MethodWe collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. We applied the pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn text embedding from the Reddit messages, and leveraged the relational information among posted messages to train a graph attention network (GAT) for sentiment classification. Finally, we applied model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment.ResultsThe results suggest that the odds of negative sentiments in 2020 (pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a p-value < 0.001; and the odds of negative sentiments associated in-person learning were 48.3% higher than with remote learning in 2020 with a p-value of 0.029.ConclusionsOur study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states. Our study contributes to the growing real-world evidence on the various negative impacts of the pandemic on our society; it also provides a good example of using both ML techniques and statistical modeling and inference to make better use of real-world data.

As defense technology develops, it is essential to study the pursuit–evasion (PE) game problem in hypersonic vehicles, especially in the situation where a head-on scenario is created. Under a head-on situation, the hypersonic vehicle’s speed advantage is offset. This paper, therefore, establishes the scenario and model for the two sides of attack and defense, using the twin delayed deep deterministic (TD3) gradient strategy, which has a faster convergence speed and reduces over-estimation. In view of the flight state–action value function, the decision framework for escape control based on the actor–critic method is constructed, and the solution method for a deep reinforcement learning model based on the TD3 gradient network is presented. Simulation results show that the proposed strategy enables the hypersonic vehicle to evade successfully, even under an adverse head-on scene. Moreover, the programmed maneuver strategy of the hypersonic vehicle is improved, transforming it into an intelligent maneuver strategy.

Given the limitations of escape maneuvers and decoy deployment of combat aircraft under missile attacks; active defense dramatically improves the survival chances by launching active defense missiles to intercept incoming missiles. Different from previous work, this paper implemented impact angle constraints on the defense missile to achieve a better defense effect. The low-cost active defense missile with limited maneuverability is considered to cooperate with the aircraft through three mechanisms, namely two-way cooperation without any predetermined strategy, one-way cooperation with the defense missile employing a linear guidance strategy, and one-way cooperation with independent evasion maneuver for the target. Three optimal cooperative guidance strategies with impact angle constraints were investigated. Finally, a nonlinear two-dimensional model for agents with first-order autopilot dynamics was simulated to verify the performance of the proposed strategies. The simulation results indicated that the cooperative mechanism directly affects the available range of impact angles, and the constraints of a big impact angle can be realized by introducing the nonlinear model parameters and considering the angle variation between the velocity vector and the initial line-of-sight. Furthermore, the two-way cooperative mechanism achieves the best performance and more flexible solutions to accommodate different vehicle maximum overload limits.

With the wide application of Internet of things (IoT) devices in enterprises, the traditional boundary defense mechanisms are difficult to satisfy the demands of the insider threats detection. IoT insider threat detection can be more challenging, since internal employees are born with the ability to escape the deployed information security mechanism, such as firewalls and endpoint protection. In order to detect internal attacks more accurately, we can analyze users’ web browsing behaviors to identify abnormal users. The existing web browsing behavior anomaly detection methods ignore the dynamic change of the web browsing behavior of the target user and the behavior consistency of the target user in its peer group, which results in a complex modeling process, low system efficiency and low detection accuracy. Therefore, the paper respectively proposes the individual user behavior model and the peer-group behavior model to characterize the abnormal dynamic change of user browsing behavior and compare the mutual behavioral inconsistency among one peer-group. Furthermore, the fusion model is presented for insider threat detection which simultaneously considers individual behavioral abnormal dynamic changes and mutual behavioral dynamic inconsistency from peers. The experimental results show that the proposed fusion model can accurately detect insider threat based on the abnormal user web browsing behaviors in the enterprise networks.

Aimed at the poor performance of guidance algorithms designed based on a linearized model in active defense under large leading angle deviation, both-way and one-way cooperative sliding mode guidance algorithms based on the smooth nonsingular terminal sliding mode for the defense missile are proposed. The relative kinematics and linearized models of the target, the active defense missile, and the attacking missile are established. In the design process, two smooth nonsingular terminal sliding mode surfaces are constructed based on zero-effort miss distance and zero-effort velocity, as well as their integral values. A tracking differentiator is introduced for excessive initial command deviation to meet the overload constraints of the active missile and the target. The sensitivity of guidance law to the estimated time-to-go error is reduced, and the target is allowed to perform an independent evasive maneuver. The effectiveness of the proposed guidance strategy is verified by numerical simulation and compared to the existing guidance strategies, the high accuracy, anti-chattering, and strong robustness of the proposed guidance algorithm are verified.

Abstract ZnO@NiO core–shell heterostructures with high photocatalytic efficiency and reusability were prepared via electrochemical deposition on carbon fiber cloth substrates. Their photocatalytic properties were investigated by measuring the degradation of rhodamine B and methyl orange (MO) under ultraviolet light irradiation. The photodegradation efficiency of the ZnO@NiO heterostructures toward both dyes was better than those of the pure ZnO nanorods and NiO nanosheets. The higher performance could be attributed to the formation of p–n heterojunction between ZnO and NiO. Especially, the ZnO@NiO heterostructure formed upon deposition of NiO for 10 min degraded 95% of MO under ultraviolet light irradiation for 180 min. The high photodegradation efficiency of the ZnO@NiO heterostructures was also attributed to the high separation efficiency of photogenerated carriers, as confirmed by the higher photocurrent of the ZnO@NiO heterostructures (eightfold) when compared with that of the pure ZnO nanorods. Moreover, the high photodegradation efficiency of the ZnO@NiO heterostructures was maintained over three successive degradation experiments and decreased to 90% after the third cycle.

Aiming at the shortcomings of traditional PID control method, such as parameter perturbation and poor anti-jamming ability of double-rotor permanent magnet synchronous motor, a single neuron fuzzy PID control method is proposed. Firstly, a mathematical model of double-rotor permanent magnet synchronous motor is established, and a single neuron fuzzy PID controller is designed. And then the system is designed and simulated by MATLAB. Finally, the simulation results of traditional PID and single neuron fuzzy PID control are compared and analyzed.The simulation results show that the single neuron fuzzy PID control can improve the robustness of the system effectively, so that the double rotor permanent magnet synchronous motor control system has better dynamic and static performance and anti-interference ability.

Golden tides dominated by Sargassum spp. are occurring at an accelerated rate worldwide. In China, Sargassum has started to bloom in the Yellow Sea and led to tremendous economic losses, but the underlying biological causes and mechanisms are still unclear. Although algae-associated bacteria were suggested to play crucial roles in algal blooms, the profiles of bacteria associated with drifting Sargassum remain unexplored. In this study, the community structures and functions of Sargassum-associated bacteria were analyzed using the high-throughput sequencing data of the V5–V7 hypervariable region of the 16S rRNA gene. Molecular identification revealed that the golden tide analyzed in the Yellow Sea was dominated by a single species, Sargassum horneri. They were a healthy brown color nearshore but were yellow offshore with significantly decreased chlorophyll contents (P < 0.01), which indicates that yellow S. horneri was under physiological stress. The structural and functional analyses of bacterial communities indicated that the drifting S. horneri had an obvious selectivity on their associated bacteria against surrounding seawater. Although the bacterial communities phylogenetically differed between brown and yellow S. horneri (P < 0.01), their dominant functions were all nitrogen and iron transporters, which strongly indicates microbial contribution to blooming of the algal host. For the first time, potential epiphytic and endophytic bacteria associated with Sargassum were independently analyzed by a modified co-vortex method with silica sand. We showed that the composition of dominant endophytes, mainly Bacillus and Propionibacterium, was relatively consistent regardless of host status, whereas the epiphytic operational taxonomic units (OTUs) greatly varied in response to weakness of host status; however, dominant functions were consistent at elevated intensities, which might protect the host from stress related to nitrogen or iron deficiency. Thus, we propose that host physiological status at different intensities of functional demands, which were related to variable environmental conditions, may be a critical factor that influences the assembly of epiphytic bacterial communities. This study provided new insight into the structure and potential functions of associated bacteria with golden tide blooms.

Streptomyces species 1-14 isolated from cassava rhizosphere soil were evaluated for their antibacterial efficacy against Fusarium oxysporum f.sp. cubense race 4 (FOC4). Of the 63 strains tested, thirteen exhibited potent antibacterial properties and were further screened against eight fungal pathogens. The strain that showed maximum inhibition against all of the test pathogens was identified by 16S rDNA sequencing as Streptomyces sp. 1-14, was selected for further studies. Through the propagation of Streptomyces sp. 1-14 in soil under simulated conditions, we found that FOC4 did not significantly influence the multiplication and survival of Streptomyces sp. 1-14, while indigenous microorganisms in the soil did significantly influence Streptomyces sp. 1-14 populations. To achieve maximum metabolite production, the growth of Streptomyces 1-14 was optimized through response surface methodology employing Plackett-Burman design, path of steepest ascent determinations and Box-Behnken design. The final optimized fermentation conditions (g/L) included: glucose, 38.877; CaCl2•2H2O, 0.161; temperature, 29.97°C; and inoculation amount, 8.93%. This optimization resulted in an antibacterial activity of 56.13% against FOC4, which was 12.33% higher than that before optimization (43.80%). The results obtained using response surface methodology to optimize the fermentation medium had a significant effect on the production of bioactive metabolites by Streptomyces sp. 1-14. Moreover, during fermentation and storage, pH, light, storage temperature, etc., must be closely monitored to reduce the formation of fermentation products with reduced antibacterial activity. This method is useful for further investigations of the production of anti-FOC4 substances, and could be used to develop bio-control agents to suppress or control banana fusarium wilt.