Yarn-electrospun PVDF-HFP/CNC smart textiles for self-powered sensor in wearable electronics by Jiawei Chen, Subhamoy Mahajan, Manisha Gupta, Cagri Ayranci, Tian Tang

“…The success of developing such sensor-integrated touchscreen gloves paves new avenues for human-technology interactions, highlights the dual functionality of these yarns as power sources and sensors, and represents a milestone in broadening the applications of wearable technologies.…”
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Characteristics of soil microbial phospholipid fatty acids in artificial, black-soil mountain degraded, and natural grasslands in the source region of the Three Rivers by Lele Xie, Yushou Ma, Yanlong Wang, Yuan Ma, Xiaoli Wang, Ying Liu

“…Background The source region of the Three Rivers is a concentrated distribution area of alpine grassland. …”
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Selective hydrogenation of 5-hydroxymethylfurfural triggered bya high Lewis acidic Ni-based transition metal carbide catalyst by Rulu Huang, Jianchun Jiang, Jie Liang, Shanyong Wang, Yuwei Chen, Xianhai Zeng, Kui Wang

“…Corresponding to the adsorption configuration of Ni/WC and substrate determined by in-situ FTIR characterization, this study provides a novel insight into the selective conversion of HMF process for functional biofuel and bio-chemicals.…”
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Optimal selection of tree species for ecological protection forests on the riverbank side of the lower Yellow River flood control dikes by Xiaolei Zhang, Kexin Li, Zhiheng Xu, Chaoqun Li, Zhengxiong Bai, Chen Yang

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Repeat-induced point mutations driving Parastagonospora nodorum genomic diversity are balanced by selection against non-synonymous mutations by Darcy A. B. Jones, Kasia Rybak, Mohitul Hossain, Stefania Bertazzoni, Angela Williams, Kar-Chun Tan, Huyen T. T. Phan, James K. Hane

“…Effector predictions identified 186 candidate secreted predicted effector proteins (CSEPs), 69 of which had functional annotations and included confirmed effectors. …”
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Redox-active sp2-c connected metal covalent organic frameworks for selective detection and reductive separation of uranium by Jin-Lan Liu, Zhi-Hai Peng, Jia-Xin Qi, Cheng-Rong Zhang, Zhen-Wen Zhang, Li Zhang, Ru-Ping Liang, Jian-Ding Qiu

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Serum albumin nanoparticles: Ligand functionalization for enhanced targeted therapeutics in precision medicine by Sakshi Shahapurmath, Bhuvaneshwari R. Sharannavar, Rahul Koli

“…This review provides a comprehensive analysis of recent advancements in the functionalization of albumin nanoparticles with diverse ligands to enhance targeting specificity and therapeutic efficacy. …”
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Whole-Genome Sequencing Identifies Functional Genes for Environmental Adaptability in Chinese Geese by Xufang Ren, Jincheng Yu, Xiurong Zhao, Xinye Zhang, Gang Wang, Xiaoyu Jiang, Xianyao Li, Changqing Qu, Lujiang Qu

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Crop domestication and improvement reshape root traits and the structure and function of their associated microbiome by Xiaoming HE, Frank HOCHHOLDINGER, Xingping CHEN, Peng YU

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DETECTIVE STORY: TO THE PROBLEM OF VARIABILITY OF THE MAIN EVENT AND CHARACTERS (BY THE CASE OF A. SARAKHOV’S STORIES) by I. A. KAZHAROVA

“…The functionality of stereotypes of perception and «memory of the genre» is briefly presented, which manifests itself in the history of understanding a domestic detective story as a constant appeal to the foreign sources of the genre. …”
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Study on Point Spread Function of Perovskite Fast Neutron Scintillation Imaging Screen by FENG Zhelin1, LIU Linyue2, , SONG Yan2, DUAN Baojun2, BAO Zizhen3, OUYANG Xiaoping2

“…Additionally, the limited availability of experimental machines for fast neutron imaging and the high cost of imaging systems hinders the efficient detection of large number of materials by using common fast neutron sources. …”

Resource Recovery from Abandoned Mine Drainage Galleries via Ion Exchange: A Case Study from Freiberg Mining Area, Germany by Janith Abeywickrama, Katayoun Karimi, Marlies Grimmer, Nils Hoth, Carsten Drebenstedt

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A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1 by Adedayo O. Adeyanju, Patrick J. Rich, Gebisa Ejeta

“…The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.…”
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Specific detection of tartaric acid chiral isomers based on centrosymmetric terahertz metamaterial sensors by Xujun Xu, Zhen Sun, Guocui Liu, Jianjun Liu, Yong Du

“…Traditional detection methods, such as fluorescence detection, have problems such as destructive and non-specific characters. …”
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Generation of Stochastic Daily Precipitation for China Based on Empirical Orthogonal Function Analysis by Yang Yifei, Fang Weihua, Zheng Jinli, Fu Jingxuan

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The Relevance of Osteoscintigraphy Technique in Early Detection of Bone Metastatic Lesions: a Systematic Review by E. A. Litvinenko, I. V. Burova

“…OSG is an effective and informative technique for early detection of bone metastases, allowing to assess the functional state of the tumor and its surrounding tissues, even before the appearance of structural disorders visible by other diagnostic methods. …”
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Network security traffic detection and legal supervision based on adaptive metric learning algorithm by Liu Zhen

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App-DDoS detection method using partial binary tree based SVM algorithm by Bin ZHANG, Zihao LIU, Shuqin DONG, Lixun LI

“…As it ignored the detection of ramp-up and pulsing type of application layer DDoS (App-DDoS) attacks in existing flow-based App-DDoS detection methods,an effective detection method for multi-type App-DDoS was proposed.Firstly,in order to fast count the number of HTTP GET for users and further support the calculation of feature parameters applied in detection method,the indexes of source IP address in multiple time windows were constructed by the approach of Hash function.Then the feature parameters by combining SVM classifiers with the structure of partial binary tree were trained hierarchically,and the App-DDoS detection method was proposed with the idea of traversing binary tree and feedback learning to distinguish non-burst normal flow,burst normal flow and multi-type App-DDoS flows.The experimental results show that compared with the conventional SVM-based and naïve-Bayes-based detection methods,the proposed method has more excellent detection performance and can distinguish specific App-DDoS types through subdividing attack types and training detection model layer by layer.…”
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Deep learning vulnerability detection method based on optimized inter-procedural semantics of programs by Yan LI, Weizhong QIANG, Zhen LI, Deqing ZOU, Hai JIN

“…In recent years, software vulnerabilities have been causing a multitude of security incidents, and the early discovery and patching of vulnerabilities can effectively reduce losses.Traditional rule-based vulnerability detection methods, relying upon rules defined by experts, suffer from a high false negative rate.Deep learning-based methods have the capability to automatically learn potential features of vulnerable programs.However, as software complexity increases, the precision of these methods decreases.On one hand, current methods mostly operate at the function level, thus unable to handle inter-procedural vulnerability samples.On the other hand, models such as BGRU and BLSTM exhibit performance degradation when confronted with long input sequences, and are not adept at capturing long-term dependencies in program statements.To address the aforementioned issues, the existing program slicing method has been optimized, enabling a comprehensive contextual analysis of vulnerabilities triggered across functions through the combination of intra-procedural and inter-procedural slicing.This facilitated the capture of the complete causal relationship of vulnerability triggers.Furthermore, a vulnerability detection task was conducted using a Transformer neural network architecture equipped with a multi-head attention mechanism.This architecture collectively focused on information from different representation subspaces, allowing for the extraction of deep features from nodes.Unlike recurrent neural networks, this approach resolved the issue of information decay and effectively learned the syntax and semantic information of the source program.Experimental results demonstrate that this method achieves an F1 score of 73.4% on a real software dataset.Compared to the comparative methods, it shows an improvement of 13.6% to 40.8%.Furthermore, it successfully detects several vulnerabilities in open-source software, confirming its effectiveness and applicability.…”
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Design and investigation of charge plasma-based TMD heterojunction TFET biosensor for ultrasensitive detection by Monika Kumari, Niraj Kumar Singh, Vinay Kantipudi, Manodipan Sahoo

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