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Introduction: Cabozantinib is one of the preferred treatment options in the latest metastatic renal cell carcinoma (mRCC) guidelines. Cabozantinib is also associated with high drug expenses irrespective of the used dose, because a flat-prizing model has been implemented. In addition, concomitant intake with a high-fat meal increases its bioavailability on average by 57%. Combined with the long terminal half-life of cabozantinib (99 h), this creates possibilities to extend the dosing interval to reduce drug expenses whilst maintaining equivalent exposure. Objectives: The primary objective was to evaluate the population pharmacokinetic (POPPK) model of cabozantinib developed for its registration using real-world patients' therapeutic drug monitoring (TDM) data. The secondary objective was to design, simulate, and evaluate alternative dose regimens with the aim to reduce drug expenses whilst maintaining comparable exposure. Methods: Retrospective TDM data from mRCC patients treated with cabozantinib were obtained. The data were evaluated using the published Food and Drug Administration (FDA) cabozantinib POPPK model, a two-compartment disposition model with a dual (fast and slow) lagged first-order absorption process derived from FDA registration documents, as a basis. Subsequently, simulations of alternative drug expenses saving regimens were evaluated. Results: Twenty-seven mRCC patients with 75 pharmacokinetic observations were included. Patients were treated for a median of 75 days with a median dose of 40 mg. Model evaluation results showed that the cabozantinib TDM concentrations were adequately predicted by the published FDA cabozantinib POPPK model, except for a slightly higher clearance (CL) of 3.11 L/h compared to the reported value (2.23 L/h). The simulation study indicated that an alternative dose regimen that consists of taking 60 mg of cabozantinib for 2 days and then skipping 1 day results in comparable average exposure when compared with a 40 mg daily dose, both without food interaction, while saving 33.3% of the total drug expenses per month. The food effect of a high-fat meal was also taken into account when simulating other alternative dose regimens; 40 mg every 72 h combined with a high-fat meal resulted in comparable exposure when compared with a 20 mg daily dose fasted, while saving 66.7% in drug expenses. Conclusions: In this study, the optimized cabozantinib POPPK model resulted in adequate prediction of real-world cabozantinib pharmacokinetic data. Alternative dosing regimens with and without using known food interactions were proposed that resulted in potential strategies to significantly reduce cabozantinib drug expenses. [ABSTRACT FROM AUTHOR]

Research in financial domain has shown that sentiment aspects of stock news have a profound impact on volume trades, volatility, stock prices and firm earnings. In-depth analysis of stock news is now sourced from financial reviews by various social networking and marketing sites to help improve decision making. Nonetheless, such reviews are in the form of unstructured text, which requires natural language processing (NLP) in order to extract the sentiments. Accordingly, in this study we investigate the use of NLP tasks in effort to improve the performance of sentiment classification in evaluating the information content of financial news as an instrument in investment decision support system. At present, feature extraction approach is mainly based on the occurrence frequency of words. Therefore low-frequency linguistic features that could be critical in sentiment classification are typically ignored. In this research, we attempt to improve current sentiment analysis approaches for financial news classification by focusing on low-frequency but informative linguistic expressions. Our proposed combination of low and high-frequency linguistic expressions contributes a novel set of features for sentiment classification. The experimental results show that an optimal Ngram feature selection (combination of optimal unigram and bigram features) enhances sentiment classification accuracy as compared to other types of feature sets. [ABSTRACT FROM AUTHOR]

A reliable, rapid, and effective bioanalytical method is essential for the determination of the pharmacokinetic, pharmacodynamic, and toxicokinetic parameters that inform the safety and efficacy profile of investigational drugs. The overall goal of bioanalytical method development is to elucidate the procedure and operating conditions under which a method can sufficiently extract, qualify, and/or quantify the analyte(s) of interest and/or their metabolites for the intended purpose. Given the difference in the physicochemical properties of small and large molecule drugs, different strategies need to be adopted for the development of an effective and efficient bioanalytical method. Herein, we provide an overview of different sample preparation strategies, analytical platforms, as well as procedures for achieving high throughput for bioanalysis of small and large molecule drugs. [ABSTRACT FROM AUTHOR]

Silicon is widely studied as a high-capacity lithium-ion battery anode. However, the pulverization of silicon caused by a large volume expansion during lithiation impedes it from being used as a next generation anode for lithium-ion batteries. To overcome this drawback, we synthesized ultrathin silicon nanowires. These nanowires are 1D silicon nanostructures fabricated by a new bi-metal-assisted chemical etching process. We compared the lithium-ion battery properties of silicon nanowires with different average diameters of 100 nm, 30 nm and 10 nm and found that the 30 nm ultrathin silicon nanowire anode has the most stable properties for use in lithium-ion batteries. The above anode demonstrates a discharge capacity of 1066.0 mAh/g at a current density of 300 mA/g when based on the mass of active materials; furthermore, the ultrathin silicon nanowire with average diameter of 30 nm anode retains 87.5% of its capacity after the 50th cycle, which is the best among the three silicon nanowire anodes. The 30 nm ultrathin silicon nanowire anode has a more proper average diameter and more efficient content of SiOx. The above prevents the 30 nm ultrathin silicon nanowires from pulverization and broken during cycling, and helps the 30 nm ultrathin silicon nanowires anode to have a stable SEI layer, which contributes to its high stability. Silicon nanowires (SiNWs) with average diameters of 10 nm, 30 nm and 100 nm were produced through bi-metal-assisted chemical etching method and metal-assisted chemical etching method. The 30 nm SiNWs anode showed a more stable LIB property than the 10 nm and 100 nm SiNWs anodes. [ABSTRACT FROM AUTHOR]

What is known and objective: Low‐dose methotrexate (LDMTX) has been widely used for many decades in clinical settings, with good safety profiles compared with those of high‐dose methotrexate. LDMTX is also used as one of the off‐label conservative therapies in treating placenta accreta (PA). Until now, only a few mild adverse drug reactions (ADRs) have been published after short‐term use of LDMTX, and no severe cases have been reported. Case summary: We present a case of a 30‐year‐old female who developed acute severe oral ulcerative mucositis with degree IV myelosuppression and degree III hepatic injury, after three doses of LDMTX to treat placenta accrete. The symptoms gradually improved after leucovorin rescue and supportive treatments. What is new and conclusion: The present case provides the first severe ADR report for the short‐term use of LDMTX for treating PA, indicating that potentially life‐threatening complications can also occur when using LDMTX. Early recognition and immediate leucovorin rescue could result in a favourable outcome. [ABSTRACT FROM AUTHOR]

Summary Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME. Availability and implementation The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296. [ABSTRACT FROM AUTHOR]

The prosperity of social IoT data brings revolutionary changes to our daily lives and greatly increases the existing data volume. The edge server trains the local generator and discriminator using the perturbed data, which provides privacy guarantees for the gradient attack on the FedGAN without compromising accuracy. The authors empirically compare the proposed approach with several state-of-the-art anomaly detection methods on real-world datasets, anomaly benchmarks and synthetic datasets. [Extracted from the article]

Detection of Denial-of-Service (DoS) attacks has attracted researchers since 1990s. A variety of detection systems has been proposed to achieve this task. Unlike the existing approaches based on machine learning and statistical analysis, the proposed system treats traffic records as images and detection of DoS attacks as a computer vision problem. A multivariate correlation analysis approach is introduced to accurately depict network traffic records and to convert the records into their respective images. The images of network traffic records are used as the observed objects of our proposed DoS attack detection system, which is developed based on a widely used dissimilarity measure, namely Earth Mover’s Distance (EMD). EMD takes cross-bin matching into account and provides a more accurate evaluation on the dissimilarity between distributions than some other well-known dissimilarity measures, such as Minkowski-form distance Lp and X^2 statistics. These unique merits facilitate our proposed system with effective detection capabilities. To evaluate the proposed EMD-based detection system, ten-fold cross-validations are conducted using KDD Cup 99 dataset and ISCX 2012 IDS Evaluation dataset. The results presented in the system evaluation section illustrate that our detection system can detect unknown DoS attacks and achieves 99.95 percent detection accuracy on KDD Cup 99 dataset and 90.12 percent detection accuracy on ISCX 2012 IDS evaluation dataset with processing capability of approximately 59,000 traffic records per second. [ABSTRACT FROM PUBLISHER]

Interconnected systems, such as Web servers, database servers, cloud computing servers and so on, are now under threads from network attackers. As one of most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy. [ABSTRACT FROM AUTHOR]

Summary Terminal restriction fragment length polymorphism (T-RFLP) analysis of PCR-amplified nitrogenase gene (nifH ) fragments is a rapid technique for profiling of diazotrophic microbial communities without the necessity of cultures for study. Here, we examined the impact of N-fertilization, plant genotype and environmental conditions on diazotrophic microbial populations in association with roots of rice (Oryza species) by T-RFLP community profiling and found marked effects on the composition of the microbial community. We found a rapid change of the diazotrophic population structure within 15 days after application of nitrogen fertilizer and a strong effect of environmental conditions and plant genotype. Control experiments revealed that phylogenetically distantly related nifH genes were proportionately amplified, and that signal strength reflected the relative abundance of nifH genes in the sample within a 10-fold range of template concentrations. These results clearly demonstrated that our T-RFLP method was suitable to reflect compositional differences in the diazotrophic community in a semiquantitative manner and that the diazotrophic rhizosphere communities of rice are not static but presumably rather highly dynamic. [ABSTRACT FROM AUTHOR]

Segmentation-based methods are widely used for scene text detection due to their superiority in describing arbitrary-shaped text instances. However, two major problems still exist: (1) current label generation techniques are mostly empirical and lack theoretical support, discouraging elaborate label design; and (2) as a result, most methods rely heavily on text kernel segmentation which is unstable and requires deliberate tuning. To address these challenges, we propose a human cognition-inspired framework, termed, Conceptual Text Region Network (CTRNet). The framework utilizes Conceptual Text Regions (CTRs), which is a class of cognition-based tools inheriting good mathematical properties, allowing for sophisticated label design. Another component of CTRNet is an inference pipeline that, with the help of CTRs, completely omits the need for text kernel segmentation. Compared with previous segmentation-based methods, our approach is not only more interpretable but also more accurate. Experiment results show that CTRNet achieves state-of-the-art performance on benchmark CTW1500, Total-Text, MSRA-TD500, and ICDAR 2015 datasets, yielding performance gains of up to 2.0%. Notably, to the best of our knowledge, CTRNet is among the first detection models to achieve F-measures higher than 85.0% on all four of the benchmarks, demonstrating remarkable consistency and stability. [ABSTRACT FROM AUTHOR]

Utilising the mobilisation of a sink node in a wireless sensor network to combat the energy hole, or hotspot issue, is well referenced. However, another issue, that of energy spikes may remain. With the mobile sink node potentially communicating with some nodes more than others. In this study we propose the Mobility Aware Duty Cycling and Dynamic Preambling Algorithm (MADCaDPAL). This algorithm utilises an existing solution where a communication threshold is built between a mobile sink node using predictable mobility and static nodes on its path. MADCaDPAL bases decisions relating to node sleep function, moving to clear channel assessment and the subsequent sending of preambles on the relation between the threshold built by the static node and the position of the mobile sink node. MADCaDPAL achieves a reduction in average energy consumption of up to 80%, this when used in conjunction with a lightweight carrier-sense multiple access based MAC implementation. Maximum energy consumption amongst individual nodes is also brought closer to the average, reducing energy spikes and subsequently improving network lifetime. Additionally, frame delivery to the sink is improved overall. [ABSTRACT FROM AUTHOR]

The Parallel Coordinates Plot (PCP) is a popular technique for the exploration of high-dimensional data. In many cases, researchers apply it as an effective method to analyze and mine data. However, when today's data volume is getting larger, visual clutter and data clarity become two of the main challenges in parallel coordinates plot. Although Arc Coordinates Plot (ACP) is a popular approach to address these challenges, few optimization and improvement have been made on it. In this paper, we do three main contributions on the state-of-the-art PCP methods. One approach is the improvement of visual method itself. The other two approaches are mainly on the improvement of perceptual scalability when the scale or the dimensions of the data turn to be large in some mobile and wireless practical applications. 1) We present an improved visualization method based on ACP, termed as double arc coordinates plot (DACP). It not only reduces the visual clutter in ACP, but use a dimension-based bundling method with further optimization to deals with the issues of the conventional parallel coordinates plot (PCP). 2)To reduce the clutter caused by the order of the axes and reveal patterns that hidden in the data sets, we propose our first dimensional reordering method, a contribution-based method in DACP, which is based on the singular value decomposition (SVD) algorithm. The approach computes the importance score of attributes (dimensions) of the data using SVD and visualize the dimensions from left to right in DACP according the score in SVD. 3) Moreover, a similarity-based method, which is based on the combination of nonlinear correlation coefficient and SVD algorithm, is proposed as well in the paper. To measure the correlation between two dimensions and explains how the two dimensions interact with each other, we propose a reordering method based on non-linear correlation information measurements. We mainly use mutual information to calculate the partial similarity of dimensions in high-dimensional data visualization, and SVD is used to measure global data. Lastly, we use five case scenarios to evaluate the effectiveness of DACP, and the results show that our approaches not only do well in visualizing multivariate dataset, but also effectively alleviate the visual clutter in the conventional PCP, which bring users a better visual experience. [ABSTRACT FROM AUTHOR]

In this paper, we propose a pre-configured error pattern ordered statistics decoding (PEPOSD) algorithm and discuss its application to short cyclic redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the most reliable independent symbols, we regard the decoding process as testing the error patterns, like guessing random additive noise decoding (GRAND). Also, the pre-configurator referred from ordered reliability bits (ORB) GRAND can better control the range and testing order of EPs. An offline–online structure can accelerate the decoding process. Additionally, we also introduce two orders to optimize the search order for testing EPs. Compared with CRC-aided OSD and list decoding, PEPOSD can achieve a better trade-off between accuracy and complexity. [ABSTRACT FROM AUTHOR]

The Dirichlet multinomial mixtures mode was used to analyse illumina sequencing data to reveal both temporal and spatial variations of the fungi community present in the aerobic composting. Results showed that 670 operational taxonomic units (OTUs) were detected, and the dominant phylum was Ascomycota . There were four types of samples fungi communities during the composting process. Samples from the early composting stage were mainly grouped into type I and Saccharomycetales sp . was dominant. Fungi community in the medium composting stage were fallen into type II and III, Sordariales sp. and Acremonium alcalophilum , Saccharomycetales sp. and Scedosporium minutisporum were the dominant OTUs respectively. Samples from the late composting stage were mainly grouped into type IV and Scedosporium minutisporum was the dominant OTU; Scedosporium minutisporum was significantly affected by depth ( P < 0.05). Results indicate that time and depth both are factors that influence fungi distribution and variation in c waste during static aerobic composting. [ABSTRACT FROM AUTHOR]

Malicious traffic detection is one of the most important parts of cyber security. The approaches of using the flow as the detection object are recognized as effective. Benefiting from the development of deep learning techniques, raw traffic can be directly used as a feature to detect malicious traffic. Most existing work usually converts raw traffic into images or long sequences to express a flow and then uses deep learning technology to extract features and classify them, but the generated features contain much redundant or even useless information, especially for encrypted traffic. The packet header field contains most of the packet characteristics except the payload content, and it is also an important element of the flow. In this paper, we only use the fields of the packet header in the raw traffic to construct the characteristic representation of the traffic and propose a novel flow-vector generation approach for malicious traffic detection. The preprocessed header fields are embedded as field vectors, and then a two-layer attention network is used to progressively generate the packet vectors and the flow vector containing context information. The flow vector is regarded as the abstraction of the raw traffic and is used to classify. The experiment results illustrate that the accuracy rate can reach up to 99.48% in the binary classification task and the average of AUC-ROC can reach 0.9988 in the multi-classification task. • We proposed an approach to gradually construct flow vectors from the field vector. • Extract information irrelevant to the payload from the raw traffic as input. • Unique field value representation makes the embedded vector more effective. • The adjustable number of packets in-flow makes the model more flexible. [ABSTRACT FROM AUTHOR]

The proliferation of malware has exhibited a substantial surge in both quantity and diversity, posing significant threats to the Internet and indispensable network applications. The accurate and effective classification makes a pivotal role in defending against malware. Numerous approaches employ supervised learning techniques, specifically Convolutional Neural Networks (CNNs), to train feature extractors. However, acquiring a substantial quantity of labeled samples incurs significant expenses, and relying solely on CNNs as feature extractors may result in restricted local receptive fields, consequently compromising the preservation of crucial features. In order to address these constraints, we propose an effective malware classification approach, denoted as MalSort, which leverages the masked self-supervised framework with Swin Transformer. Initially, each instance of malware is transformed into a color image. Furthermore, the Swin Transformer self-supervised framework is utilized to extract multi-scale key feature vectors from a randomly masked partial color image, while the prediction module is employed to predict the masked image. Ultimately, the pre-trained encoder is fine-tuned using the malware dataset to effectively carry out a malware classification task. Our MalSort exhibits a reduced reliance on labeled data samples during the training phase, thereby obviating the necessity for extensive amounts of labeled data. Consequently, the MalSort conserves hardware resources and improve its training efficiency. The experimental results indicate that the MalSort outperforms existing models by achieving a classification accuracy of 97.85%, a recall of 97.63%, a precision of 97.85%, and an F1-score of 97.85% on the BIG2015 dataset. Similarly, on the Malimg dataset, the model achieves percentages of 98.28%, 98.18%, 98.19%, and 98.28% for classification accuracy, recall, precision, and F1-score, respectively. • An asymmetric structure extracts features to handle labeled samples and feature loss. • The fine-tune incorporates class re-balancing to handle imbalance and overfitting. • A designed lightweight module improves convergence and decreases resource dependence. [ABSTRACT FROM AUTHOR]

The increased use of social media among digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, this anonymity feature which gives users freedom of speech and allows them to conduct activities without being judged by others can also encourage cyberbullying and hate speech. Predators can hide their identity and reach a wide range of audience anytime and anywhere. According to the detrimental effect of cyberbullying, there is a growing need for cyberbullying detection approaches. In this survey paper, a comparative analysis of the automated cyberbullying techniques from different perspectives is discussed including data annotation, data preprocessing, and feature engineering. In addition, the importance of emojis in expressing emotions as well as their influence on sentiment classification and text comprehension leads us to discuss the role of incorporating emojis in the process of cyberbullying detection and their influence on the detection performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored. [ABSTRACT FROM AUTHOR]

Therefore, in this paper the authors [[14]] study the role of big data component analysis for handling and protecting sensitive information from disclosure along with preserving the data utility. An implementation of the algorithm is tested on standard datasets from the UCI repository and performed comparably, if not better than, other algorithms for dealing with uncertain and fuzzy data. Big data analytics has received numerous attentions in many areas [[1]-[5]]. Jiang et al. [[6]] consider the structural information of the posts and the semantic information of users' interests and use UICD (User Interest Community Detection) model to analyze text streams from microblogging sites for detecting users' interest communities. [Extracted from the article]

The petrography, major, and trace elements concentration of the sandstones from the Zhanjin Formation in Qiwu area, south Qiangtang Basin, have been studied to determine their provenance, intensity of paleo-weathering and depositional tectonic setting. Petrography indicates that the composition and texture maturity of the sandstones are moderate. The detrital composition of Zhanjin Formation samples are dominated by quartz (70.1–80.3, average 74.1%) and feldspar (18.3–26.5, average 21.9%), but is low in lithic fragment (1.4–8.6, average 3.9%). The sandstone can be classified as arkose and litharenite arkose. The detrital model composition reflects that these sandstones are probably derived from a passive continental margin. The index of chemical variability (ICV), and Th/Sc vs. Zr/Sc discrimination diagram suggests that the compositional maturity and recycling were moderate to low. The index of alteration (CIA) and the A–CN–K diagram indicated that the intensity of weathering in the source area was low. The Al2O3/TiO2 vs. SiO2, Th/Sc, Co/Th, La/Sc, Cr/Th ratio values of the Zhanjin sandstones indicated that the sandstones were mainly derived from felsic source rocks. The Gondwana passive continental margin belt is probably the primary provenance area as evidenced by petrography and geochemistry features of the Zhanjin Formation. All kinds of tectonic discrimination diagrams based on major elements showed a rift setting for Zhanjin sandstones, which is consistent with the general views of study areas. [ABSTRACT FROM AUTHOR]

A complete 28-GHz/50-kW gyrotron system was developed by the Institute of Applied Electronics, China Academy of Engineering Physics, Mianyang, China, as a plasma heating source for the EXL50 fusion device, a compact spherical tokamak constructed by the ENN Group in 2019. The gyrotron employs a triode magnetron injection gun, a TE02-mode cylindrical cavity, a built-in quasi-optical mode converter, a single-stage depressed collector, and a single-disk boron nitride window. The gyrotron system features a match optical unit, a superconducting magnet, a calorimetric dummy load, a high-voltage power supply system, and other auxiliary components, was delivered to the EXL50 site. In the acceptance test, five successive shots of each power over 50 kW were generated for 30 s at 28 GHz. An average power of 51 kW, a maximum power of 55 kW, and 46% overall efficiency were obtained. The gyrotron system was assembled in the EXL50 and applied to a plasma discharge experiment. The gyrotron is the first practical domestic gyrotron produced for fusion applications in China. The design and test results of this instrument are presented in this article. [ABSTRACT FROM AUTHOR]

• CNN_Text_Word2vec is a microblog (Chinese Twitter) emotional classification model. • The word vectors are used to learn microblog text features. • Different semantic units impact the accuracy of CNN_Text_Word2vec. Traditional text emotion analysis methods are primarily devoted to studying extended texts, such as news reports and full-length documents. Microblogs are considered short texts that are often characterized by large noises, new words, and abbreviations. Previous emotion classification methods usually fail to extract significant features and achieve poor classification effect when applied to processing of short texts or micro-texts. This study proposes a microblog emotion classification model, namely, CNN_Text_Word2vec, on the basis of convolutional neural network (CNN) to solve the above-mentioned problems. CNN_Text_Word2vec introduces a word2vec neural network model to train distributed word embeddings on every single word. The trained word vectors are used as input features for the model to learn microblog text features through parallel convolution layers with multiple convolution kernels of different sizes. Experiment results show that the overall accuracy rate of CNN_Text_Word2vec is 7.0% higher than that achieved by current mainstream methods, such as SVM, LSTM and RNN. Moreover, this study explores the impact of different semantic units on the accuracy of CNN_Text_Word2vec, specifically in processing of Chinese texts. The experimental results show that comparing to using feature vectors obtained from training words, feature vector obtained from training Chinese characters yields a better performance. [ABSTRACT FROM AUTHOR]

Organizations’ own personnel now have a greater ability than ever before to misuse their access to critical organizational assets. Insider threat detection is a key component in identifying rare anomalies in context, which is a growing concern for many organizations. Existing perimeter security mechanisms are proving to be ineffective against insider threats. As a prospective filter for the human analysts, a new deep learning based insider threat detection method that uses the Dempster-Shafer theory is proposed to handle both accidental as well as intentional insider threats via organization’s channels of communication in real time. The long short-term memory (LSTM) architecture together with multi-head attention mechanism is applied in this work to detect anomalous network behavior patterns. Furthermore, belief is updated with Dempster’s conditional rule and utilized to fuse evidence to achieve enhanced prediction. The CERT Insider Threat Dataset v6.2 is used to train the behavior model. Through performance evaluation, our proposed method is proven to be effective as an insider threat detection technique. [ABSTRACT FROM AUTHOR]

Summary: Heterogeneous wireless sensor networks (WSNs) consist of resource‐starving nodes that face a challenging task of handling various issues such as data redundancy, data fusion, congestion control, and energy efficiency. In these networks, data fusion algorithms process the raw data generated by a sensor node in an energy‐efficient manner to reduce redundancy, improve accuracy, and enhance the network lifetime. In literature, these issues are addressed individually, and most of the proposed solutions are either application‐specific or too complex that make their implementation unrealistic, specifically, in a resource‐constrained environment. In this paper, we propose a novel node‐level data fusion algorithm for heterogeneous WSNs to detect noisy data and replace them with highly refined data. To minimize the amount of transmitted data, a hybrid data aggregation algorithm is proposed that performs in‐network processing while preserving the reliability of gathered data. This combination of data fusion and data aggregation algorithms effectively handle the aforementioned issues by ensuring an efficient utilization of the available resources. Apart from fusion and aggregation, a biased traffic distribution algorithm is introduced that considerably increases the overall lifetime of heterogeneous WSNs. The proposed algorithm performs the tedious task of traffic distribution according to the network's statistics, ie, the residual energy of neighboring nodes and their importance from a network's connectivity perspective. All our proposed algorithms were tested on a real‐time dataset obtained through our deployed heterogeneous WSN in an orange orchard and also on publicly available benchmark datasets. Experimental results verify that our proposed algorithms outperform the existing approaches in terms of various performance metrics such as throughput, lifetime, data accuracy, computational time, and delay. [ABSTRACT FROM AUTHOR]

The hotspot issue in wireless sensor networks, with nodes nearest the sink node losing energy fastest and degrading network lifetime, is a well-referenced problem. Mobile sink nodes have been proposed as a solution to this. They do not completely remove the hotspot problem though, with nodes the sink passes most closely still expending more energy than others. This study proposes a lightweight algorithm, located in the media access control (MAC) layer of static nodes and utilising knowledge of predictable sink node mobility. This is in order to create a dynamic communication threshold between static nodes and the sink, within which static nodes awaken, lessening competition for sink communication between nodes. In utilising predictable mobility and factors already known to the static node, such as location and interference range, there is no need for energy-consuming messaging. Analysis and simulation results, tested on a lightweight implementation of a carrier-sense multiple-access-based MAC protocol, show a significant improvement in energy consumption in both controlled and random environments, with frame delivery improved to the point where sink speed is negated. This is when compared to the existing duty cycling approach. [ABSTRACT FROM AUTHOR]

The North Qilian Orogenic Belt is on the northeastern margin of the Tibetan Plateau. It is connected with the Alxa Block in the north and the Tethyan orogenic assemblage in the south. The Lower Silurian Angzanggou Formation, deposited in the northern area of the North Qilian Orogenic Belt, is the most important for revealing the architecture and orogenic processes of the North Qilian Belt. Provenance analysis of the Angzanggou Formation can reveal not only the tectonic evolution of the central orogenic belt of China but also Palaeozoic Asia plate reconstructions. Petrographic analysis indicated that the compositional and textural maturity of the sandstones was low. The detrital composition of the Angzanggou Formation samples consists of quartz (8–14 %), feldspar (6–29 %) and lithic fragments (56–86 %). The sandstones could be classified as litharenites or feldspathic litharenites. The detrital modal composition suggests that these sandstones were probably deposited in a fore-arc basin. The element ratios and some discrimination diagrams based on geochemistry indicate that felsic and intermediate rocks were the main source rocks. The SiO2/Al2O3 ratio, the index of chemical variability and the Th/Sc versus Zr/Sc discrimination diagram suggest that the compositional maturity and degree of recycling were moderate to low. The index of alteration (CIA) and the A–CN–K diagram indicate the intensity of weathering was moderate. The discrimination diagrams based on major and trace elements and petrographic discrimination diagrams imply that the Angzanggou Formation rocks were derived from a continental island arc, and a sedimentary cover probably overlaid the volcanic arc. Therefore, we infer that during Early Silurian time the North Qilian Belt sediments accumulated in a fore-arc basin. [ABSTRACT FROM AUTHOR]

• 5-Hydroxymethylfurfural(HMF) oxidation was carried out in aqueous phase. • Metal porphyrin as biomimetic oxidase catalyzed HMF conversion. • Excellent HMF conversion of 100% and FDCA yield of 94.94% were achieved. 2,5-Furandicarboxylic acid (FDCA) has the potential to replace terephthalic acid (TPA) as a building block in the preparation of bio-based and degradable polyester materials. However, the lack of efficient and stable catalysts limits the commercial viability of FDCA production. In this work, metalloporphyrins were immobilized on mesoporous SiO 2 and used as a novel oxidase to convert 5-hydroxymethylfurfural (HMF) to FDCA. As the catalytic active center of P450 monooxygenases and horseradish peroxidase, supported palladium, manganese, ferri, and cobalt porphyrins via ionic-liquid-functionalized SiO 2 (PdPOP-SiO 2 , MgPOP-SiO 2 , FePOP-SiO 2 and CoPOP-SiO 2) were able to simulate oxidase alone under mild conditions. 100% HMF (0.02 M) conversion and 94.94% FDCA yield were achieved using PdPOP-SiO 2 as a biomimetic oxidase after a 12 h reaction at 373 K in water at atmospheric pressure. This type of mimetic oxidase is stable, inexpensive, and readily available, and the method developed in this work can potentially be used to oxidize HMF to FDCA in an industrial setting. [ABSTRACT FROM AUTHOR]

• The 1D/2D MnO 2 /MXene heterostructures generate built-in electric fields that greatly accelerating the ionic and electronic diffusion/transport kinetics. • The synergistic mechanism generated by 1D/2D heterostructures achieve the balance between high energy storage activity and high active substance proportion of MnO 2 /MXene flexible electrode. • The quasi-solid-state FZIBs with MnO 2 /MXene paper-like film provide high total specific capacity and stability in flexible applications. Paper-like flexible electrode plays a critical importance role in booming the development of flexible energy storage devices. One-dimensional manganese dioxide (1D MnO 2) shows immense advantage in flexible zinc-ion batteries (FZIBs) on account of renowned tunnel crystal structure and high operating voltage. However, inherent inferior electrical conductivity and sluggish reaction kinetics seriously hinder the practical application of FZIBs, resulting in unsatisfactory rate capability and cycle performance. Herein, 1D/2D MnO 2 /MXene-5 heterostructure materials were constructed by electrostatic self-assembly, which were then prepared into paper-like membrane with high energy storage active substance ratio (95.2 %) towards high capacity and ultrastable FZIBs. The built-in electric field induced by the heterostructure accelerates the electron transfer, reduces the charge transfer resistance and promotes ion diffusion, enabling the 1D/2D phase collaboration to improve the zinc ion storage of the paper-like electrode. The design achieves a high total specific capacity (based on the total cathode mass) of 314.7 mAh g−1 at 0.1 A g−1 and ultrastable of 8000 cycles at 5 A g−1 with a retention of 81.1 %. Remarkably, quasi-solid-state FZIBs with MnO 2 /MXene-5 membrane can deliver an outstanding reversible capacity of 273.5 mAh g−1 at 0.1 A g−1 and maintain 500 cycles at 1 A g−1 across various bending angles without decay, demonstrating excellent stability. This work provides a novel approach for the targeted design of self-standing flexible electrodes. [Display omitted] [ABSTRACT FROM AUTHOR]

The North Qilian orogenic belt is the key in the exploration of the formation and assembly of Asia. The Early–Middle Devonian Xueshan Formation, which is deposited in the north area of North Qilian Orogenic Belt, is the most important for revealing the evolution basin–mountain transition of the North Qilian Belt. In order to reveal the tectonic evolution of the central orogenic belt in China and Paleozoic global plate reconstruction, it is necessary to perform provenance analysis of the Xueshan Formation. Eighteen samples were collected from Yumen and Yongchang profiles in the west of North Qilian Belt (WN), and 16 samples were obtained from Gulang and Jingtai profiles in the east of North Qilian Belt (EN). The compositions of clastic conglomerates and the sandstones suggested that felsic volcanic was the main source of the WN Xueshan Formation deposits and that the sedimentary and metamorphic rocks were the main source of the EN sediments. According to major geochemistry indexes, such as Al2O3/SiO2 ratio ranging from 0.08 to 0.37 and the chemical index of alteration less than 80, the source rocks were moderately weathered and of moderately matured. The samples from the EN were relatively enriched in Cr, Ni, Sc, and V elements and depleted in Hf element. However, those from the WN were enriched in Hf without the enrichment of Cr, Ni, Sc, or V. The samples from WN showed a strong negative Eu anomaly, but the samples from EN showed a weak negative Eu anomaly or a positive Eu anomaly. All the above geochemical characteristics suggested the prominent input of felsic clasts with granitic rocks into the WN. Meanwhile, the discrimination diagrams of trace elements implied that WN rocks were derived from continental island arc and that the EN rocks were derived from orogenic belt and the passive continental margin. The North China Plate subducted southwards and formed subduction‐related arc magmatism along the southern margin of the North Qilian Terrane during the Early–Middle Devonian. In conclusion, the WN is a fore‐arc basin, but EN is a foreland basin. [ABSTRACT FROM AUTHOR]

The enormous developments in the field of wireless communication technologies have made the unlicensed spectrum bands crowded, resulting uncontrolled interference to the traditional wireless network applications. On the other hand, licensed spectrum bands are almost completely allocated to the licensed users also known as Primary users (PUs). This dilemma became a blackhole for the upcoming innovative wireless network applications. To mitigate this problem, the cognitive radio (CR) concept emerges as a promising solution for reducing the spectrum scarcity issue. The CR network is a low cost solution for efficient utilization of the spectrum by allowing secondary users (SUs) to exploit the unoccupied licensed spectrum. In this paper, we model the PU’s utilization activity by a two-state Discrete-Time-Markov Chain (DTMC) (i.e., Free and busy states), for identifying the temporarily unoccupied spectrum bands,. Furthermore, we propose a Cognitive Radio Sense-and-Wait assisted HARQ scheme, which enables the Cluster Head (CH) to perform sensing operation for the sake of determining the PU’s activity. Once the channel is found in free state, the CH advertise control signals to the member nodes for data transmission relying on Stop-and-Wait Hybrid- Automatic Repeat-Request (SW-HARQ). By contrast, when the channel is occupied by the PU, the CH waits and start sensing again. Additionally, the proposed CRSW assisted HARQ scheme is analytical modeled, based on which the closed-form expressions are derived both for average block delay and throughput. Finally, the correctness of the closed-form expressions are confirmed by the simulation results. It is also clear from the performance results that the level of PU utilization and the reliability of the PU channel have great influence on the delay and throughput of CRSW assisted HARQ model. [ABSTRACT FROM AUTHOR]

The North Qilian orogenic belt is the key to figure out the evolution and assembly of Asia. The Upper Silurian Hanxia Formation which is deposited in the north area of North Qilian Orogenic Belt is of utmost important to reveal the architecture and orogenic process of the North Qilian belt. So provenance analysis of the Hanxia Formation is of significance to reveal not only that the tectonic evolution of the central orogenic belt China, but also that Paleozoic Asia plate reconstruction. The ratios of elements and some discrimination diagrams based on geochemistry indicate that felsic rocks constitute their main source rocks. The chemical index of alteration is less than 80, indicating that the source rocks are relatively fresh and of low maturity. On the Th/Sc versus Zr/Sc scatter plot, samples from Hanxia Formation occur along the magmatic compositional variation trend of rocks, indicating that the rocks did not undergo obvious sedimentary sorting and recycling. The major and trace elements discrimination diagrams imply that Hanxia Formation rocks were derived from continental island arc. Recent studies show that the North China plate subducted southwards and produced subduction-related arc magmatism along the southern margin of the North Qilian Terrane during the Silurian. Therefore, we infer that in the late Silurian period the subduction-related arc became accreted to the Central Qilian terrane to the south, forming a composite continental arc, and the North Qilian belt accumulated in a fore-arc basin. [ABSTRACT FROM AUTHOR]

Room-temperature switchable dielectric materials are of interest for many applications, including solar energy storage, smart switches, automatic filters, and next-generation sensors. Here, a temperature-triggered dielectric switchable nanocomposite by dispersing octadecylamine-grafted multiwalled carbon nanotubes (ODA-MWCNTs, for short) into hexadecane is reported. The composite has low permittivity at molten state and high permittivity at frozen state, and the permittivity switch is triggered around 18 °C. The highest permittivity contrast ratio reaches 106.4 at 2.0% CNT volume fraction. The composite shows frequency-sensitive and temperature-ramping-rate-sensitive properties. Further investigation indicates that the permittivity switch is caused by the change of the ODA-MWCNT percolating networks during phase transition. [ABSTRACT FROM AUTHOR]

Key message: Using a combination of phenotypic screening, genetic and statistical analyses, and high-throughput genome-wide sequencing, we have finely mapped a dominant Phytophthora resistance gene in soybean cultivar Wayao. Abstract: Phytophthora root rot (PRR) caused by Phytophthora sojae is one of the most important soil-borne diseases in many soybean-production regions in the world. Identification of resistant gene(s) and incorporating them into elite varieties are an effective way for breeding to prevent soybean from being harmed by this disease. Two soybean populations of 191 F individuals and 196 F recombinant inbred lines (RILs) were developed to map Rps gene by crossing a susceptible cultivar Huachun 2 with the resistant cultivar Wayao. Genetic analysis of the F population indicated that PRR resistance in Wayao was controlled by a single dominant gene, temporarily named RpsWY, which was mapped on chromosome 3. A high-density genetic linkage bin map was constructed using 3469 recombination bins of the RILs to explore the candidate genes by the high-throughput genome-wide sequencing. The results of genotypic analysis showed that the RpsWY gene was located in bin 401 between 4466230 and 4502773 bp on chromosome 3 through line 71 and 100 of the RILs. Four predicted genes ( Glyma03g04350, Glyma03g04360, Glyma03g04370, and Glyma03g04380) were found at the narrowed region of 36.5 kb in bin 401. These results suggest that the high-throughput genome-wide resequencing is an effective method to fine map PRR candidate genes. [ABSTRACT FROM AUTHOR]

To achieve tumor-specific delivery of doxorubicin, TiO2@Fe3O4/PEI/delivery of doxorubicin conjugates were designed and synthesized. Fe3O4 could act as magnetically responsive carriers and enhance the visible light photodynamic activities of TiO2. Delivery of doxorubicin was conjugated via coordination bond. The drug release rate at pH 5.2 was much faster than that at pH 7.4, due to pH-sensitive coordination bond. Besides, TiO2@Fe3O4/PEI/delivery of doxorubicin showed high antitumor efficacy combining with phototherapy, good bio-safety, higher cellular uptake with an external magnetic field, and less toxicity in vitro and in vivo. These results suggested that TiO2@Fe3O4/PEI/delivery of doxorubicin may be promising for high tumor treatment efficacy with minimal side effects in future. [ABSTRACT FROM AUTHOR]

Redundant and irrelevant features in data have caused a long-term problem in network traffic classification. These features not only slow down the process of classification but also prevent a classifier from making accurate decisions, especially when coping with big data. In this paper, we propose a mutual information based algorithm that analytically selects the optimal feature for classification. This mutual information based feature selection algorithm can handle linearly and nonlinearly dependent data features. Its effectiveness is evaluated in the cases of network intrusion detection. An Intrusion Detection System (IDS), named Least Square Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected by our proposed feature selection algorithm. The performance of LSSVM-IDS is evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99, NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature selection algorithm contributes more critical features for LSSVM-IDS to achieve better accuracy and lower computational cost compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC, to understand and use. To alleviate these limitations, this paper devises a succinct and efficient optimization algorithm called Nomad Algorithm (NA). It is inspired by the migratory behavior of nomadic tribes on the prairie. Extensive experiments are implemented with respects to accuracy, rate, stability, and cost of optimization. Mathematical proof is given to guarantee the global convergence, and the nonparametric tests are conducted to confirm the significance of experiment results. The statistical results of optimization accuracy denote NA outperforms its rivals for most cases (23/28) by orders of magnitude significantly. It is considered as a promising optimizer with excellent efficiency and adaptability. [ABSTRACT FROM AUTHOR]

A novel phage vB_VpP_DE17, which infects Vibrio parahaemolyticus , was isolated from the sewer of the Huangsha aquatic market in Guangzhou. Transmission electron microscopy indicated that DE17 had an icosahedral head (47 ± 2 nm diameter) and a short, non-contractile tail (17 ± 2 nm). The genome of DE17 was a double-stranded linear DNA with a length of 43,397 bp and GC content of 49.23%. In total, 49 putative open reading frames (ORFs) were predicted and could be divided into six modules: DNA metabolism, lysis, packaging, structure, additional function, and hypothetical proteins. Taxonomic analysis revealed that the phage belonging to the genus of Maculvirus, Autographivirinae subfamily , Podoviridae family. DE17 had a short latent period of 5 min with burst size of 80 pfu/cell. Its optimum temperature and pH ranges were 4 °C–50 °C and 5–10, respectively; it was completely inactivated after 20 min of ultraviolet irradiation. No transfer RNA (tRNA), virulence associated, or antibiotic resistance genes were identified. Bacterial challenge test revealed that DE17 had a certain inhibitory effect on V. parahaemolyticus within 6 h. Characterization, genomic analysis and in vitro antibacterial assays of DE17 will further enhance our understanding of phage biology and diversity. [ABSTRACT FROM AUTHOR]

Thirty-three endophytic diazotrophs were isolated from surface-sterilized leaves, stem, and roots of wild rice Oryza alta. The SDS-PAGE profile of total protein and insertion sequence-based polymerase chain reaction (IS-PCR) fingerprinting grouped the isolates into four clusters (I-IV). The 16S rRNA gene sequence homology of the representative strains B21, B31, B1, and B23 of clusters I, II, III, and IV were assigned to Pseudomonas oleovorans (99.2% similarity), Burkholderia fungorum (99.4% similarity), Enterobacter cloacae (98.9% similarity), and Acinetobacter johnsonii (98.4% similarity), respectively. The results showed wide genetic diversity of the putative diazotrophic strains of the wild rice, O. alta, and the strains of cluster IV are the first report of nitrogen-fixing Acinetobacter species. The cell size, phenotypic characters, total protein profile, genomic DNA fingerprinting, DNA-DNA hybridization, and antibiotic resistance differentiated strain B23 from its closest relatives A. johnsonii LMG999 and Acinetobacter haemolyticus LMG996. The DNA-DNA hybridization also distinguished the strain B23 from the closely related Acinetobacter species. Based on these data, a novel species, Acinetobacter oryzae sp. nov., and strain B23 (=LMG25575 = CGMCC1.10689) as the type strain were proposed. [ABSTRACT FROM AUTHOR]

There can be performance and vulnerability concerns with block ciphers, thus stream ciphers can used as an alternative. Although many symmetric key stream ciphers are fairly resistant to side-channel attacks, cryptographic artefacts may exist in memory. This paper identifies a significant vulnerability within OpenSSH and OpenSSL and which involves the discovery of cryptographic artefacts used within the ChaCha20 cipher. This can allow for the cracking of tunneled data using a single targeted memory extraction. With this, law enforcement agencies and/or malicious agents could use the vulnerability to take copies of the encryption keys used for each tunnelled connection. The user of a virtual machine would not be alerted to the capturing of the encryption key, as the method runs from an extraction of the running memory. Methods of mitigation include making cryptographic artefacts difficult to discover and limiting memory access. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology that aims to enable the interconnection of a large number of smart devices and heterogeneous networks. Ad hoc networks play an important role in the designing of IoT-enabled platforms due to their efficient, flexible, low-cost and dynamic infrastructures. These networks utilize the available resources efficiently to maintain the Quality of Service (QoS) in a multi-hop communication. However, in a multi-hop communication, the relay nodes can be malicious, thus requiring a secured and reliable data transmission. In this paper, we propose a QoS-aware secured communication scheme for IoT-based networks (QoS-IoT). In QoS-IoT, a Sybil attack detection mechanism is used for the identification of Sybil nodes and their forged identities in multi-hop communication. After Sybil nodes detection, an optimal contention window (CW) is selected for QoS provisioning, that is, to achieve per-flow fairness and efficient utilization of the available bandwidth. In a multi-hop communication, the medium access control (MAC) layer protocols do not perform well in terms of fairness and throughput, especially when the nodes generate a large amount of data. It is because the MAC layer has no capability of providing QoS to prioritized or forwarding flows. We evaluate the performance of QoS-IoT in terms of Sybil attack detection, fairness, throughput and buffer utilization. The simulation results show that the proposed scheme outperforms the existing schemes and significantly enhances the performance of the network with a large volume of data. Moreover, the proposed scheme is resilient against Sybil attack. [ABSTRACT FROM AUTHOR]