COMPOSITES PART B-ENGINEERING
·
2025
·
IF 14.2
Xu Yaojie,Zhang Hong,Huang Guangyan
Ultra-high molecular weight polyethylene (UHMWPE) fibers are extensively utilized in personal protective equipment owing to their lightweight and superior properties. Recent research has demonstrated that the protective efficacy of UHMWPE laminates decreases when subjected to high-temperature projectile impacts. This study proposes a novel composite structure comprising modified aramid fabric and UHMWPE laminate. Ballistic testing results reveal that the new compliant composite exhibits a 25.6% higher ballistic limit velocity ( v bl ) compared to pure UHMWPE laminate, while also demonstrating significantly enhanced flexibility. The modified aramid fabric effectively shields the UHMWPE laminate from high-temperature erosion. Leveraging dimensionless algorithms and support vector machine methodologies, the module distribution of the composite structure has been further optimized. The v bl of optimized composite fabric is 40.4% higher than that of UHMWPE laminate. Parameters such as transverse wave velocity, yarn friction coefficient, and tensile strength influence the arrangement order within the composite structure. The simulation error for the composite fabric is approximately 10%. The failure characteristics observed in numerical simulations closely align with experimental results, indicating that the optimized composite structure further enhances its impact resistance performance through a combination of factors, including tensile strength improvement, transverse wave velocity optimization, and yarn friction coefficient improvement.
研究领域:
材料科学
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机械工程
·
弹道学
·
复合结构
·
机器学习
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产品关联
DOI
高影响因子
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
·
2023
·
IF 3.4
Xiao-Jie Ouyang,Jia-Qi Li,Yong-Qi Zhong,Min Tang,Jiang Meng,Yue-Wei Ge,Sheng-Wang Liang,Shu-Mei Wang,Fei Sun
Typhae Pollen (TP) and its carbonized product (carbonized Typhae Pollen, CTP), as cut-and-dried herbal drugs , have been widely used in the form of slices in clinical settings. However, the two drugs exhibit a great difference in terms of their clinical efficacy, for TP boasts an effect of removing blood stasis and promoting blood circulation, while CTP typically presents a hemostatic function. Since the active ingredients of CTP, so far, still remain unclear, this study aimed at identifying the active ingredients of CTP by spectrum-effect relationship approach coupled with multi-block partial least squares (MBPLS), partial least squares (PLS), and support vector machine (SVM) algorithms. In this study, the chemical profiles of a series of CTP samples which were stir-fried for different duration (denoted as CTP0∼CTP9) were firstly characterized by UHPLC-QE-Orbitrap MS. Then the hemostatic effect of the CTP samples was evaluated from the perspective of multiple parameters—APTT, PT , TT, FIB , TXB 2 , 6-keto-PGF1α, PAI-1 and t-PA—using established rat models with functional uterine bleeding . Subsequently, MBPLS, PLS and SVM were combined to perform spectrum-effect relationship analysis to identify the active ingredients of CTP, followed by an in vitro hemostatic bioactivity test for verification. As a result, a total of 77 chemical ingredients were preliminarily identified from the CTP samples, and the variations occurred in these ingredients were also analyzed during the carbonizing process. The study revealed that all the CTP samples, to a varying degree, showed a hemostatic effect, among which CTP6 and CTP7 were superior to the others in terms of the hemostatic effect. The block importance in the projection (BIP) indexes of MBPLS model indicated that flavonoids and organic acids made more contributions to the hemostatic effect of CTP in comparison to other ingredients. Consequently, 9 bioactive ingredients, including quercetin-3- O -glucoside, kaempferol-3- O -rutinoside, quercetin , kaempferol , isorhamnetin , 2-methylenebutanedioic acid, pentanedioic acid , benzoic acid and 3-hydroxybenzoic acid, were further identified as the potential active ingredients based on PLS and SVM models as well as the in vitro verification. This study successfully revealed the bioactive ingredients of CTP associated with its hemostatic effect, and also provided a scientific basis for further understanding the mechanism of TP processing. In addition, it proposed a novel path to identify the active ingredients for Chinese herbal medicines.
研究领域:
药理学
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分析化学
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中药学
·
生物信息学
·
止血
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产品关联
DOI
PubMed
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2020
·
IF 3.232
Zijun Xu,Zhaokun Wang,Mingyang Liu,Binwei Yan,Xueqin Ren,Zideng Gao
The detection and differentiation of tetracyclines (TCs) has received increasing attention due to the severe threat they pose to human health and the ecological balance. A dual-channel fluorescence sensor array based on two carbon quantum dots (CDs) was fabricated to distinguish between four TCs, including tetracycline (TC), oxytetracycline (OTC), doxycycline (DOX), and metacycline (MTC). A distinct fluorescence variation pattern (I/I 0 ) was produced when CDs interacted with the four TCs. This pattern was analyzed by LDA and SVM. This was the first time that SVM was used for data processing of fluorescence sensor arrays. LDA and SVM showed that the array has the capacity for parallel and accurate determination of TCs at concentrations between 1.0 μM and 150 μM. In addition, the interference experiment using metal ions and antibiotics as possible coexisting interference substances proves that the sensor array has excellent selectivity and anti-interference ability. The array was also used for the accurate detection and identification of TCs in binary mixtures, and furthermore, the four TCs were successfully identified in river water and milk samples. Besides, the sensor array successfully identified the four TCs in 72 unknown samples with a 100% accuracy. The results proved that SVM can achieve the same accurate classification and prediction as LDA, and considering its additional advantages, it can be used as an optional supplementary method for data processing, thereby expanding the data processing field.
研究领域:
分析化学
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环境科学
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生物医学工程
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DOI
高影响因子
Biosensors-Basel
·
2021
·Vol. 11
·期 6
·198
·
IF 5.519
Xiaoming Wang,Yineng Xiao,Fangming Deng,Yugen Chen,Hailiang Zhang
To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Considering the poor deformability and biocompatibility of rigid metal electrodes, we propose a flexible hydrogel biosensor made of conductive HPC/PVA (Hydroxypropyl cellulose/Polyvinyl alcohol) hydrogel and flexible PDMS (Polydimethylsiloxane) substrate. The proposed biosensor is affixed to the wheelchair user’s forehead to collect electrooculogram (EOG) and strain signals, which are the basis to recognize eye movements. The low Young’s modulus (286 KPa) and exceptional breathability (18 g m−2h−1of water vapor transmission rate) of the biosensor ensures a conformal and unobtrusive adhesion between it and the epidermis. To improve the recognition accuracy of eye movements (straight, upward, downward, left, and right), the WT-SVM algorithm is introduced to classify EOG and strain signals according to different features (amplitude, duration, interval). The average recognition accuracy reaches 96.3%, thus the wheelchair can be manipulated precisely.Keywords:flexible hydrogel biosensor;human-wheelchair interaction;eye movement detection;signal classification algorithm
研究领域:
生物医学工程
·
康复医学
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产品关联
DOI
PubMed
高影响因子
ACS Omega
·
2026
·
IF 5.2
Hong Zhang,Nan Li,Wuliji Hasi,Wenjuan Liu
This study addresses critical concerns in epilepsy management regarding generic drug substitution and the bioequivalence risks associated with switching antiepileptic drugs (AEDs). For patients undergoing long-term treatment, precise brand identification of levetiracetam (LEV) holds significant clinical value to ensure therapeutic consistency and safety. To meet this need, we developed a qualitative brand discrimination method based on surface-enhanced Raman spectroscopy (SERS) combined with machine learning. Silver sol was used as the SERS substrate, with potassium iodide as an aggregating agent and Rhodamine 6G (R6G) as an optimized probe. After validating LEV's characteristic Raman peaks through integrated theoretical and experimental approaches, spectra from three commercial LEV brands were analyzed using principal component analysis (PCA) and support vector machine (SVM) classification. The model achieved 100% accuracy, precision, and sensitivity in 10-fold stratified cross-validation, demonstrating robust brand-discrimination capability. Unlike conventional quantitative assays that verify API content but cannot distinguish between different manufacturers, this SERS-based approach detects formulation-specific differencessuch as variations in crystal form and preparation processthrough unique spectral fingerprints. It thus provides a rapid, label-independent tool for brand authentication, batch consistency evaluation, and on-site pharmaceutical screening, effectively complementing pharmacopeial methods. This work supports medication safety and offers a reliable strategy for drug quality control and market surveillance, particularly in preventing counterfeit drugs and ensuring formulation stability in long-term epilepsy therapy.
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DOI
高影响因子
Journal of Environmental Sciences
·
2025
·
IF 6.3
Jia-Wei Song,Ju-Xiang Chen,Wen-Hai Chu,Yu-Qiong Gao,Nai-Yun Gao
Conventional treatment methods struggle to effectively eliminate sulfamethoxazole (SMX) from wastewater due to its persistent aromatic structure and sulfonamide moiety, while existing advanced oxidation processes often suffer from high energy consumption and inadequate degradation pathway control. This study demonstrates an advanced approach for SMX degradation in aqueous systems through dielectric barrier discharge (DBD) plasma technology synergistically integrated with COMSOL multiphysics simulation and particle swarm optimization-support vector machine (PSO-SVM) modeling. Computational simulations revealed that the DBD plasma generates a diffuse arc (D-A) mode electric field with Penning ionization effects, where primary reactive species formation predominantly occurs within the sheath layer. Under optimized operational parameters (input power: 50 W, pH 7, conductivity: 3.16 μS/cm, initial concentration: 10 mg/L, the carrier gas: air), the system achieved an exceptional SMX removal efficiency of 99.82 %, corresponding to a constant reaction rate of 0.3087 min −1 . The machine learning-optimized process simultaneously enhanced energy yield by 18.11 mg/kW·h and improved energy utilization efficiency by 15.11%. Notably, the system maintained robust performance (>75% SMX removal) in the presence of competing inorganic anions (NO 3 − , HCO 3 − and Cl − ). Mechanistic investigations combining DFT, EPR and UPLC-MS/MS elucidated that •OH, O 2 •− , 1 O 2 , •NO 2 and ONOOH preferentially attack the electrophilic N(4), N(7), and N(17) sites, initiating sequential diazotization and nitration pathways. Practical validation using hospital wastewater demonstrated significant removal of COD (56.17 %) and TOC (40.16 %), while effectively suppressing antimicrobial resistance genes. This work establishes PSO-SVM optimized DBD plasma as a sustainable and intelligent solution for pharmaceutical wastewater remediation.
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产品关联
DOI
高影响因子
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
·
2022
·
IF 4.831
Shan Tu,Zhigang Wang,Wentao Zhang,Yuanpeng Li,Yulai She,Hao Du,Cancan Yi,Bo Qin,Zhiqiang Liu
This study investigated the feasibility of using terahertz (THz) technology for the rapid identification of isomers. The time-domain spectra of 2-hydroxybenzoic acid (2-HA), 3-hydroxybenzoic acid (3-HA), and 4-hydroxybenzoic acid (4-HA) were measured by a THz time-domain spectroscopy system (THz-TDS) in the range of 0.3–1.8 THz. Aiming at the isomer classification problem, a THz spectral data classification model based on a variational mode decomposition–particle swarm optimization–support vector machine (VMD-PSO-SVM) method was proposed. Empirical mode decomposition (EMD) and variational mode decomposition (VMD) were used to extract the first eight intrinsic mode functions (IMFs) of the time-domain signal. Principal component analysis (PCA) was used to extract the first 80 principal components of each modal component as the classification feature vector. The particle swarm optimization (PSO) and support vector machine (SVM) algorithms were used to construct 2-, 3-, and 4-HA classification models. We found that the prediction accuracy of the VMD-PSO-SVM model was significantly higher than that of EMD-PSO-SVM model regardless of the modal components. For both EMD and VMD, with the increase in the IMF number, the corresponding classification recognition accuracy tended to decrease. The results showed that the rapid identification model of hydroxybenzoic acid isomers based on THz spectroscopy and SVM was effective and feasible, providing an accurate and rapid method for the chemical synthesis and quality monitoring of biomedicine.
研究领域:
分析化学
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生物医学工程
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产品关联
DOI
PubMed
高影响因子
HUMAN & EXPERIMENTAL TOXICOLOGY
·
2017
·
IF 1.802
J Yu,H Wu,Z Lin,K Su,J Zhang,F Sun,X Wang,C Wen,H Cao,L Hu
Suberoylanilide hydroxamic acid (SAHA) exerts marked anticancer effects via promotion of apoptosis, cell cycle arrest, and prevention of oncogene expression. In this study, serum metabolomics and artificial intelligence recognition were used to investigate SAHA toxicity. Forty rats (220 ± 20 g) were randomly divided into control and three SAHA groups (low, medium, and high); the experimental groups were treated with 12.3, 24.5, or 49.0 mg kg−1 SAHA once a day via intragastric administration. After 7 days, blood samples from the four groups were collected and analyzed by gas chromatography–mass spectrometry, and pathological changes in the liver were examined using microscopy. The results showed that increased levels of urea, oleic acid, and glutaconic acid were the most significant indicators of toxicity. Octadecanoic acid, pentadecanoic acid, glycerol, propanoic acid, and uric acid levels were lower in the high SAHA group. Microscopic observation revealed no obvious damage to the liver. Based on these data, a support vector machine (SVM) discrimination model was established that recognized the metabolic changes in the three SAHA groups and the control group with 100% accuracy. In conclusion, the main toxicity caused by SAHA was due to excessive metabolism of saturated fatty acids, which could be recognized by an SVM model.
研究领域:
药理学
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代谢组学
·
人工智能
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产品关联
DOI
PubMed
高影响因子
JOURNAL OF RAMAN SPECTROSCOPY
·
2025
·
IF 1.9
Hao Jiang,Han-Quan Qiu,Heng-Su Liu,Shi-Ying Fu,Jia-Sheng Lin,Ze-Hui Chen,Yue-Jiao Zhang,Jian-Feng Li,Zhong-Qun Tian
Vibrio parahaemolyticus is a kind of Gram-negative bacteria widely distributed in estuarine and coastal areas. It was discovered in dried mackerel in Osaka and has now become one of the main pathogens causing foodborne diseases. The seafood contamination caused by it poses a serious threat to human health and safety. Although most V. parahaemolyticus strains are non-pathogenic, they are often ignored in the routine detection process. The thermostable direct hemolysin (TDH) and TDH-related hemolysin (TRH) encoded by the tdh and trh genes, respectively, are considered its main virulence factors. Different genotypes of strains are closely related to antibiotic resistance phenotypes. Therefore, the related tdh and trh genes are considered the main virulence markers in the epidemiology of V. parahaemolyticus . Here, based on label-free surface-enhanced Raman spectroscopy (SERS), V. parahaemolyticus with different tdh and trh expression were directly detected. By combining support vector machine (SVM), the test samples were divided into four categories according to different genotypes, and the inference of different genotypes from phenotypic spectra was achieved within 7 min, and the classification accuracy of the model was 98.46%. The results of this work demonstrate that the combination of label-free SERS spectroscopy and SVM algorithms has great potential in the field of bacterial classification.
研究领域:
微生物学
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食品安全
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分析化学
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生物信息学
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病原体检测
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产品关联
DOI
高影响因子
BIOMEDICAL CHROMATOGRAPHY
·
2025
·Vol. 39
·期 5
·e70060
·
IF 1.7
Weijie Pan,Qianru Chen,Menghua Wu,Yue Sun,Ming Li,Shumei Wang,Xingyang Xue,Jiang Meng
Moutan Cortex (MC) is a renowned Chinese medicine used for promoting blood circulation and removing blood stasis. However, the active ingredients are unclear. This study aimed to identify and validate the active ingredients of MC. UPLC fingerprints of 23 batches of MC from various origins were analyzed. The activating blood efficacy of MC was assessed by evaluating the inhibitory effects on thrombin and factor Xa (FXa) using the chromogenic substrate method. Active ingredients were identified through spectrum-effect relationship analysis using gray relation analysis (GRA), partial least squares (PLS), and support vector machine (SVM) algorithms. Consequently, five components were identified as potential active ingredients: mudanpioside H, oxypaeoniflorin, 1,2,3,4,6-penta-O-galloyl-β-d-glucose (PGG), benzoyloxypaeoniflorin, and suffruticoside A/B/C/D. The pharmacological activities of these five active ingredients were further confirmed by measuring their thrombin inhibition ability and antithrombotic effects in zebrafish, and their interactions with thrombin and FXa were examined using molecular docking technology. Oxypaeoniflorin, benzoyloxypaeoniflorin, and PGG demonstrated significant efficacy in promoting blood circulation and resolving blood stasis, as well as strong binding affinities. This study provides a biochemical foundation for the anticoagulant effects of MC and offers valuable insights for quality control and the development of novel anticoagulant ingredients drugs.
研究领域:
中药学
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生物信息学
·
药理学
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产品关联
DOI
PubMed
高影响因子
RSC Advances
·
2016
·Vol. 6
·期 33
·28038-28046
·
IF 3.289
Lina Chi,Jie Wang,Tianshu Chu,Yingjia Qian,Zhenjiang Yu,Deyi Wu,Zhenjia Zhang,Zheng Jiang,James O. Leckie
Mathematical models play an important role in performance prediction and optimization of ultrafiltration (UF) membranes fabricated via dry/wet phase inversion in an efficient and economical manner. In this study, a systematic approach, namely, a supervised, learning-based experimental data analytics framework, is developed to model and optimize the flux and rejection rate of poly(vinyl chloride) (PVC) and polyvinyl butyral (PVB) blend UF membranes. Four supervised learning (SL) approaches, namely, the multiple additive regression tree (MART), the neural network (NN), linear regression (LR), and the support vector machine (SVM), are employed in a rigorous fashion. The dependent variables representing membrane performance response with regard to independent variables representing fabrication conditions are systematically analyzed. By comparing the predicting indicators of the four SL methods, the NN model is found to be superior to the other SL models with training and testing R-squared values as high as 0.8897 and 0.6344, respectively, for the rejection rate, and 0.9175 and 0.8093, respectively, for the flux. The optimal combination of processing parameters and the most favorable flux and rejection rate for PVC/PVB ultrafiltration membranes are further predicted by the NN model and verified by experiments. We hope the approach is able to shed light on how to systematically analyze multi-objective optimization issues for fabrication conditions to obtain the desired ultrafiltration membrane performance based on complex experimental data characteristics.
研究领域:
材料科学
·
化学工程
·
机器学习
·
膜技术
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产品关联
DOI
高影响因子
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
·
2025
·
IF 4.6
Qian Liu,Xiaoying Yang,Longhui Luo,Wei Tian,Chao Kang,Tao Sun,Yuegang Chen,Juan Tao,Dongmei Chen
Benzimidazole fungicides are widely used in agriculture, and residues in food will threaten public health and safety. In this study, MXene@PEI@AgNPs composites were successfully constructed by electrostatic self-assembly method using polyethyleneimine (PEI) as a linker and used as a surface-enhanced Raman scattering (SERS) active substrate. The material combines the electromagnetic enhancement of silver nanoparticles and the chemical enhancement mechanism of MXene, showing excellent SERS performance. The experimental results show that the SERS substrate has excellent enhancement performance and successfully obtains the characteristic spectra of six target fungicides. After that, it was combined with chemometrics methods, through principal component analysis (PCA), it was found that the SERS spectra of different fungicides showed obvious clustering distribution. The constructed support vector machine (SVM) classification model shows excellent prediction performance, with an accuracy rate of 100 %. In terms of quantitative analysis, the method showed good sensitivity, and the detection recoveries of benzimidazole fungicides in actual samples such as kiwifruit, tomato and citrus were 81.62 % −113.87 %. Finally, in order to verify the reliability of the method, the high performance liquid chromatography (HPLC) method was used for comparative verification. The results showed that the two methods had good consistency.
研究领域:
分析化学
·
食品安全
·
纳米技术
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光谱学
·
农业科学
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产品关联
DOI
高影响因子
危险品化学品经营许可证（带存储）