Procalcitonin and presepsin for detecting bacterial infection and spontaneous bacterial peritonitis in cirrhosis:A systematic review and meta-analysis

BACKGROUND Diagnosing bacterial infections(BI)in patients with cirrhosis can be challenging because of unclear symptoms,low diagnostic accuracy,and lengthy culture testing times.Various biomarkers have been studied,including serum procal-citonin(PCT)and presepsin.However,the diagnostic performance of these markers remains unclear,requiring further informative studies to ascertain their diagnostic value.AIM To evaluate the pooled diagnostic performance of PCT and presepsin in detecting BI among patients with cirrhosis.INTRODUCTION Bacterial infections(BI)commonly occur in patients with cirrhosis,resulting in poor outcomes,including the development of cirrhotic complications,septic shock,acute-on-chronic liver failure(ACLF),multiple organ failures,and mortality[1,2].BI is observed in 20%-30%of hospitalized patients,with and without ACLF[3].Patients with cirrhosis are susceptible to BI because of internal and external factors.The major internal factors are changes in gut microbial composition and function,bacterial translocation,and cirrhosis-associated immune dysfunction syndrome[4,5].External factors include alcohol use,proton-pump inhibitor use,frailty,readmission,and invasive procedures.Spontaneous bacterial peritonitis(SBP),urinary tract infection,pneumonia,and primary bacteremia are the common BIs in hospit-alized patients with cirrhosis[6].Early diagnosis and adequate empirical antibiotic therapy are two critical factors that improve the prognosis of BI in patients with cirrhosis.However,early detection of BI in cirrhosis is challenging due to subtle clinical signs and symptoms,low sensitivity and specificity of systemic inflammatory response syndrome criteria,and low sensitivity of bacterial cultures.Thus,effective biomarkers need to be identified for the early detection of BI.Several biomarkers have been evaluated,but their efficacy in detecting BI is unclear.Procalcitonin(PCT)is a precursor of the hormone calcitonin,which is secreted by parafollicular cells of the thyroid gland[7].In the presence of BI,PCT gene expression increases in extrathyroidal tissues,causing a subsequent increase in serum PCT level[8].Changes in serum PCT are detectable as early as 4 hours after infection onset and peaks between 8 and 24 hours,making it a valuable diagnostic biomarker for BI.Several studies have demonstrated the favorable diagnostic accuracy of PCT in the diagnosis of BI in individuals with cirrhosis[9-13]and without cirrhosis[14-16].Since 2014,two meta-analyses have been published on the diagnostic value of PCT for SBP and BI in patients with cirrhosis[17,18].Other related studies have been conducted since then[10-12,19-33].Serum presepsin has recently emerged as a promising biomarker for diagnosing BI.This biomarker is the N-terminal fraction protein of the soluble CD14 g-negative bacterial lipopolysaccharide–lipopolysaccharide binding protein(sCD14-LPS-LBP)complex,which is cleaved by inflammatory serum protease in response to BI[34].Presepsin levels increase within 2 hours and peaks in 3 hours[35].This is useful for detecting BI since presepsin levels increase earlier than serum Our systematic review and meta-analysis was performed with adherence to PRISMA guidelines[37].

Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

GAS DETECTING AND FORECASTING VIA TIME SERIES METHOD

The importance and urgency of gas detecting and forecasting in underground coal mining are self-evident. Unfortunately, this problem has not yet been solved thoroughly.In this paper, the author suggests that the time series analysis method be adopted for processing the gas stochastic data. The time series method is superior to the conventional Fourier analysis in some aspects, especially, the time series method possesses forecasting (or prediction) function which is highly valuable for gas monitoring.An example of a set of gas data sampled from a certain foul coal mine is investigated and an AR (3) model is established. The fitting result and the forecasting error are accepted satisfactorily.At the end of this paper several remarks are presented for further discussion.

Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus attacks.Further,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT systems.The research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis dataset.Moreover,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and recall.The study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT environments.Furthermore,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested methodology.This research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT devices.However,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world datasets.Utilizing WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT ecosystems.To summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning methods.The research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance analysis.Future research could focus on enhancing the recommended approach by adding more datasets and leveraging real-time monitoring capabilities to strengthen IoT devices’defenses against new cybersecurity threats.

Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware operations.This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models.For this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is analyzed.In the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are developed.Later,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested methodology.The proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%F1-score.The empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual MLPmodels.In expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

Detecting XSS with Random Forest and Multi-Channel Feature Extraction

In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by hackers.Currently,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low accuracy.To address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature enhancement.The model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information content.Further feature extraction is conducted through three parallel channels.Channel I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global features.Finally,effective classification and prediction of XSS are performed by fusing the features of the three channels.To test the effectiveness of the model,we conduct experiments on six datasets.We achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.

Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

YOLO-VSI: An Improved YOLOv8 Model for Detecting Railway Turnouts Defects in Complex Environments

Railway turnouts often develop defects such as chipping,cracks,and wear during use.If not detected and addressed promptly,these defects can pose significant risks to train operation safety and passenger security.Despite advances in defect detection technologies,research specifically targeting railway turnout defects remains limited.To address this gap,we collected images from railway inspectors and constructed a dataset of railway turnout defects in complex environments.To enhance detection accuracy,we propose an improved YOLOv8 model named YOLO-VSS-SOUP-Inner-CIoU(YOLO-VSI).The model employs a state-space model(SSM)to enhance the C2f module in the YOLOv8 backbone,proposed the C2f-VSS module to better capture long-range dependencies and contextual features,thus improving feature extraction in complex environments.In the network’s neck layer,we integrate SPDConv and Omni-Kernel Network(OKM)modules to improve the original PAFPN(Path Aggregation Feature Pyramid Network)structure,and proposed the Small Object Upgrade Pyramid(SOUP)structure to enhance small object detection capabilities.Additionally,the Inner-CIoU loss function with a scale factor is applied to further enhance the model’s detection capabilities.Compared to the baseline model,YOLO-VSI demonstrates a 3.5%improvement in average precision on our railway turnout dataset,showcasing increased accuracy and robustness.Experiments on the public NEU-DET dataset reveal a 2.3%increase in average precision over the baseline,indicating that YOLO-VSI has good generalization capabilities.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

A Novel YOLOv5s-Based Lightweight Model for Detecting Fish’s Unhealthy States in Aquaculture

Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.

Detecting Malicious Uniform Resource Locators Using an Applied Intelligence Framework

The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed framework.We apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from text.Further,we apply more state-of-the-art methods to create vectorized features,such as GloVe.Additionally,feature engineering that is specific to URL structure is deployed to detect scams and other threats.For framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error II.For the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more time.By applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in performance.Additionally,eXtreme Gradient Boost(XGB)outperforms other classifiers.With this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.

Standard-definition White-light,High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas:A Multicenter Randomized Controlled Trial

Objective This study aimed to compare the performance of standard-definition white-light endoscopy(SD-WL),high-definition white-light endoscopy(HD-WL),and high-definition narrow-band imaging(HD-NBI)in detecting colorectal lesions in the Chinese population.Methods This was a multicenter,single-blind,randomized,controlled trial with a non-inferiority design.Patients undergoing endoscopy for physical examination,screening,and surveillance were enrolled from July 2017 to December 2020.The primary outcome measure was the adenoma detection rate(ADR),defined as the proportion of patients with at least one adenoma detected.The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression.Results Out of 653 eligible patients enrolled,data from 596 patients were analyzed.The ADRs were 34.5%in the SD-WL group,33.5%in the HD-WL group,and 37.5%in the HD-NBI group(P=0.72).The advanced neoplasm detection rates(ANDRs)in the three arms were 17.1%,15.5%,and 10.4%(P=0.17).No significant differences were found between the SD group and HD group regarding ADR or ANDR(ADR:34.5%vs.35.6%,P=0.79;ANDR:17.1%vs.13.0%,P=0.16,respectively).Similar results were observed between the HD-WL group and HD-NBI group(ADR:33.5%vs.37.7%,P=0.45;ANDR:15.5%vs.10.4%,P=0.18,respectively).In the univariate and multivariate logistic regression analyses,neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL(HD-WL:OR 0.91,P=0.69;HD-NBI:OR 1.15,P=0.80).Conclusion HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients.It can be concluded that HD-NBI or HD-WL is not superior to SD-WL,but more effective instruction may be needed to guide the selection of different endoscopic methods in the future.Our study’s conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources,especially advanced imaging technologies.

A proposal for detecting weak electromagnetic waves around 2.6μm wavelength with Sr optical clock

Infrared signal detection is widely used in many fields.Due to the detection principle,however,the accuracy and range of detection are limited.Thanks to the ultra stability of the^(87)Sr optical lattice clock,external infrared electromagnetic wave disturbances can be responded to.Utilizing the ac Stark shift of the clock transition,we propose a new method to detect infrared signals.According to our calculations,the theoretical detection accuracy in the vicinity of its resonance band of 2.6μm can reach the order of 10-14W,while the minimum detectable signal of common detectors is on the order of 10^(-10)W.

Improved Mechanism for Detecting Examinations Impersonations in Public Higher Learning Institutions: Case of the Mwalimu Nyerere Memorial Academy (MNMA)

Currently, most public higher learning institutions in Tanzania rely on traditional in-class examinations, requiring students to register and present identification documents for examinations eligibility verification. This system, however, is prone to impersonations due to security vulnerabilities in current students’ verification system. These vulnerabilities include weak authentication, lack of encryption, and inadequate anti-counterfeiting measures. Additionally, advanced printing technologies and online marketplaces which claim to produce convincing fake identification documents make it easy to create convincing fake identity documents. The Improved Mechanism for Detecting Impersonations (IMDIs) system detects impersonations in in-class exams by integrating QR codes and dynamic question generation based on student profiles. It consists of a mobile verification app, built with Flutter and communicating via RESTful APIs, and a web system, developed with Laravel using HTML, CSS, and JavaScript. The two components communicate through APIs, with MySQL managing the database. The mobile app and web server interact to ensure efficient verification and security during examinations. The implemented IMDIs system was validated by a mobile application which is integrated with a QR codes scanner for capturing codes embedded in student Identity Cards and linking them to a dynamic question generation model. The QG model uses natural language processing (NLP) algorithm and Question Generation (QG) techniques to create dynamic profile questions. Results show that the IMDIs system could generate four challenging profile-based questions within two seconds, allowing the verification of 200 students in 33 minutes by one operator. The IMDIs system also tracks exam-eligible students, aiding in exam attendance and integrates with a Short Message Service (SMS) to report impersonation incidents to a dedicated security officer in real-time. The IMDIs system was tested and found to be 98% secure, 100% convenient, with a 0% false rejection rate and a 2% false acceptance rate, demonstrating its security, reliability, and high performance.

高速列车关键部件涡流自动成像检测方法

涡流检测是一种非破坏性检测方法。以高速列车构架为检测对象,结合涡流与自动化设备检测技术,针对人工涡流检测效率低、结果一致性差等问题,提出涡流自动成像检测方法。通过建立构架焊缝的涡流探头仿真模型,分析激励电流方向对检测信号的影响,优化激励线圈相关参数并制作涡流探头;设计开发构架涡流自动成像检测系统,开展构架焊缝表面缺陷自动化检测试验。对比分析激励频率、激励电压等涡流检测工艺参数以及探头扫查速度、扫查轨迹间距等路径参数对缺陷涡流信号的影响,研究结果表明,利用涡流自动成像检测系统可以对构架整车进行自动探伤,能够将构架焊缝处缺陷以图像形式呈现,可以有效检出0.3、0.5 mm深的人工缺陷,对构架批量产线化检测具有参考价值。

基于改进YOLOv7-Tiny的车辆检测研究

为了提高计算机识别检测车辆的准确度与速度,提出了一种基于改进YOLOv7-Tiny的车辆检测算法。在众多目标检测模型中,YOLOv7有着非常快的检测速度和较高的检测精度,非常适用于实时检测任务。在原YOLOv7-Tiny模型的基础上进行改进,将最浅层ELAN-T模块纳入特征金字塔,通过跳跃连接的方式将浅层特征与深层特征跨层融合,使输出的特征信息更加丰富。同时引入SE注意力机制,将计算资源分配给对当前任务更为关键的信息。并且更换了非线性激活函数HardSwish,以提高模型的表达能力。在华为发布的2D自动驾驶数据集SODA10M上进行实验,结果表明,改进后的模型对所有四种目标的检测精度都有所提高,平均精度mAP@0.5达到了66.1%,比原YOLOv7-Tiny模型61.0%提升了5.1%。

基于KNN-SVM的混合气体检测方法研究

当今混合气体检测的研究中,针对多组分气体数据进行分类预测的数学算法百花齐放,如何快速且准确的检测出气体的成分和浓度逐渐成为当今研究的热门。然而在一些研究中,气体数据特征难以捕捉和判断,泛化能力不足,对气体数据进行分类预测的精度和效率表现较差。为此,针对一些数据偏差和泛化误差无界的问题,提出了一种K最近邻-支持向量机(KNN-SVM)算法,对一些难以作出分类的模糊气体数据进行二次分类,采用KNN和SVM两种算法共同抉择,更加全面的捕捉数据特征,根据实验确定各自算法的权重比从而提高判别气体类别的准确率,两种算法的集成也能提高算法的效率,对于不同种类的气体也能有良好的适应性的稳定性。该实验气体组分由12 mg·L^(-1)的C_(2)H_(2)、NO_(2)、SF_(6),10 mg·L^(-1)的NO_(2)、SF_(6)和5 mg·L^(-1)的C_(2)H_(2)(背景气体皆为N_(2))以及两瓶纯N_(2)的气瓶组成;通过互相混合和与N_(2)配比制备出实验设定的气体浓度。实验过程通过单一气体的检测可分别对三种气体获得60组训练集,并通过这60组数据可进行线性拟合得到每种气体的拟合线,得到气体浓度与气体吸收峰值的关系,通过实验检测得到的三种气体拟合线,其中C_(2)H_(2)拟合线的调整后R^(2)为0.991,NO_(2)拟合线的调整后R^(2)为0.981,SF_(6)拟合线的调整后R^(2)为0.987,可得气体检测的准确性。再通过互相混合进行检测可分别获得40组训练集,采用KNN-SVM算法对混合气体进行分类和预测,后通过拟合线即可反演出混合气体中每种气体的浓度。将该算法与传统SVM算法进行各种分类指标对比均可显示出该算法的有效性和优越性。实验结果表明,KNN-SVM算法在气体分类预测方面表现出卓越的性能,准确率高达99.167%,AUC(area under curve)值达99.375%。这一算法不仅提高了气体检测的准确性,还增强了泛化能力可适应多样化的气体组分,为实时气体检测系统提供了有力支持。

凉垫、凉枕降温性能检测方法研究

通过模拟实际使用环境,对降温凉垫、凉枕的降温性能进行了研究。结果表明:大多数凉垫、凉枕使用后最低温度在20~25℃之间。当降温时间大于2 h、平均温差>5℃时,可认为该凉垫、凉枕具有持续降温性能,凉垫、凉枕在炎热的夏季可以起到凉爽降温的作用。该检测方法可为相关生产企业控制产品质量提供参考。

表面增强拉曼光谱法灵敏检测鸡肉中拉沙洛西残留

为提高鸡肉中抗球虫类兽药拉沙洛西(LAS)残留的监控检测效率,本研究利用表面增强拉曼光谱(SERS)技术建立了LAS残留的灵敏检测方法。本研究首先制备不同粒径的金纳米粒子(AuNPs),根据SERS增强效果选择最优粒径AuNPs作为基底,并利用4-氨基苯硫酚(PATP)作为探针分子评估AuNPs基底重现性;然后通过添加HNO_(3)改变AuNPs聚集程度并增大LAS溶解度,进一步增强SERS信号;最后通过建立标准曲线并进行鸡肉样品加标回收试验对方法进行评价。结果表明,粒径40 nm的AuNPs增强效果较好,1118 cm^(-1)处为LAS的SERS特征峰,基底具有良好的重现性,HNO_(3)最优添加浓度为0.5 mol/L。本方法的线性范围为0.55×10^(-6)~0.55mg/mL,检测限为0.23ng/mL,鸡肉样品的加标回收率为91.9%~107.3%,相对标准偏差为3.6%~5.7%,与国标法(GB31613.5-2022)的检测结果一致。本研究为LAS残留的灵敏检测提供了新方法,对于其他兽药残留的SERS检测方法研究具有参考价值。