An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Research Progress on Detection Techniques of Fungicide Residues in Chinese Chives

Chinese chive is a kind of medicinal and edible plant,with many diseases,and chemical fungicides are usually used for control.In order to find out the risk of pesticide residues in Chinese chives,this paper summarized relevant literatures published in recent years,and sorted out and analyzed the types of pesticides used and detection techniques of common diseases in Chinese chives.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Gamma Approximation Based Multi-Antenna Covert Communication Detection

Covert communication technology makes wireless communication more secure,but it also provides more opportunities for illegal users to transmit harmful information.In order to detect the illegal covert communication of the lawbreakers in real time for subsequent processing,this paper proposes a Gamma approximation-based detection method for multi-antenna covert communication systems.Specifically,the Gamma approximation property is used to calculate the miss detection rate and false alarm rate of the monitor firstly.Then the optimization problem to minimize the sum of the missed detection rate and the false alarm rate is proposed.The optimal detection threshold and the minimum error detection probability are solved according to the properties of the Lambert W function.Finally,simulation results are given to demonstrate the effectiveness of the proposed method.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Major allergens in 8 types of allergenic foods and allergen detection techniques

In recent years,the prevalence of allergens in food warning notices,both domestically and internationally,has become the second leading concern after microbial contamination.Among the various factors that threaten human health reported by the World Health Organization,food allergy ranks fourth,and food allergy has become a global security problem.As of now,no definitive treatment for food allergies exists,making the avoidance of allergen-containing foods the most effective prevention method.Consequently,labeling foods with allergen information serves as a crucial warning for allergic populations.Moreover,to enhance comprehension of food allergies,this article provides a brief overview of their definition and sensitization mechanisms.The main focus lies in highlighting the structure of primary allergens found in eight commonly allergenic foods and the resulting clinical symptoms they cause.Additionally,a summary of commonly employed allergen detection techniques is presented,with an analysis of their principles,advantages,and limitations.Looking ahead,the integration of diverse technological approaches to establish an efficient,accurate,and affordable allergen detection method remains a significant trend.This article has certain reference value for understanding the direction of food allergies.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Social Robot Detection Method with Improved Graph Neural Networks

Social robot accounts controlled by artificial intelligence or humans are active in social networks,bringing negative impacts to network security and social life.Existing social robot detection methods based on graph neural networks suffer from the problem of many social network nodes and complex relationships,which makes it difficult to accurately describe the difference between the topological relations of nodes,resulting in low detection accuracy of social robots.This paper proposes a social robot detection method with the use of an improved neural network.First,social relationship subgraphs are constructed by leveraging the user’s social network to disentangle intricate social relationships effectively.Then,a linear modulated graph attention residual network model is devised to extract the node and network topology features of the social relation subgraph,thereby generating comprehensive social relation subgraph features,and the feature-wise linear modulation module of the model can better learn the differences between the nodes.Next,user text content and behavioral gene sequences are extracted to construct social behavioral features combined with the social relationship subgraph features.Finally,social robots can be more accurately identified by combining user behavioral and relationship features.By carrying out experimental studies based on the publicly available datasets TwiBot-20 and Cresci-15,the suggested method’s detection accuracies can achieve 86.73%and 97.86%,respectively.Compared with the existing mainstream approaches,the accuracy of the proposed method is 2.2%and 1.35%higher on the two datasets.The results show that the method proposed in this paper can effectively detect social robots and maintain a healthy ecological environment of social networks.

Prediction of Lung Cancer Stage Using Tumor Gene Expression Data

Lung cancer remains a significant global health challenge and identifying lung cancer at an early stage is essential for enhancing patient outcomes. The study focuses on developing and optimizing gene expression-based models for classifying cancer types using machine learning techniques. By applying Log2 normalization to gene expression data and conducting Wilcoxon rank sum tests, the researchers employed various classifiers and Incremental Feature Selection (IFS) strategies. The study culminated in two optimized models using the XGBoost classifier, comprising 10 and 74 genes respectively. The 10-gene model, due to its simplicity, is proposed for easier clinical implementation, whereas the 74-gene model exhibited superior performance in terms of Specificity, AUC (Area Under the Curve), and Precision. These models were evaluated based on their sensitivity, AUC, and specificity, aiming to achieve high sensitivity and AUC while maintaining reasonable specificity.

痰涂片镜检、基因芯片技术及GeneXpert MTB/RIF对疑似肺结核患者的检测效能分析

目的分析痰涂片镜检、基因芯片及多色巢式荧光定量聚合酶链反应(GeneXpert MTB/RIF)对疑似肺结核患者的检测效能。方法选取2017年1月至2022年12月该院收治的疑似肺结核患者97例作为研究对象,均实施痰涂片镜检、基因芯片、GeneXpert MTB/RIF及痰液罗氏培养检查。以痰液罗氏培养结果为金标准,探究不同检测方式及联合检测对疑似肺结核的诊断效能,采用Kappa值检验与金标准诊断结果的一致性。结果痰液罗氏培养检测结果显示,97例疑似肺结核患者阳性55例,阴性42例,阳性检出率为56.70%(55/97)。痰液罗氏培养检出非结核分枝杆菌18株,包括鸟分枝杆菌4株,胞内分枝杆菌9株,偶发分枝杆菌1株,堪萨斯分枝杆菌3株,海分枝杆菌1株。痰涂片镜检检出阳性34例,真阳性29例,阳性检出率为35.05%(34/97),基因芯片检出阳性41例,真阳性37例,阳性检出率为42.27%(41/97);GeneXpert MTB/RIF检出阳性44例,真阳性37例,阳性检出率为45.36%(44/97)。基因芯片、GeneXpert MTB/RIF灵敏度、准确率高于痰涂片镜检,基因芯片与GeneXpert MTB/RIF敏感度一致,但基因芯片特异度高于GeneXpert MTB/RIF。非结核分枝杆菌中,痰涂片镜检检出鸟分枝杆菌1株、胞内分枝杆菌2株,基因芯片检出胞内分枝杆菌1株,GeneXpert MTB/RIF检出鸟分枝杆菌1株。痰涂片镜检、基因芯片、GeneXpert MTB/RIF三项联合检出阳性55例,真阳性53例,三项联合检测灵敏度为96.36%(53/55)、特异度为95.24%(40/42)、准确率为95.88%(93/97),均高于单一方法检测的灵敏度与准确率,差异均有统计学意义(P<0.05)。痰涂片镜检与痰液罗氏培养一致性为68.04%(Kappa=0.59);基因芯片与痰液罗氏培养一致性为77.32%(Kappa=0.70);GeneXpert MTB/RIF与痰液罗氏培养一致性为74.23%(Kappa=0.66);三项联合与痰液罗氏培养一致性为95.88%(Kappa=0.89)。结论较GeneXpert MTB/RIF、痰涂片镜检技术,基因芯片诊断效能及一致性更高,且3种技术联合诊断效能更高,临床可根据需求选择适宜诊断技术。

西安地区256例Gene Xpert MTB/RIF阳性肺结核患者rpoB基因突变特征分析

目的分析西安地区256例利福平耐药实时荧光定量核酸扩增检测技术(Gene Xpert MTB/RIF)阳性肺结核患者rpoB基因突变特征。方法本研究为回顾性分析。256株结核分枝杆菌(MTB)菌株分离自2020年6月至2022年6月于陕西省结核病防治院就诊的Gene Xpert MTB/RIF阳性肺结核门诊及住院患者,菌株无重复收集,来自痰液标本174份、支气管肺泡灌洗液标本82份,患者年龄(45.67±8.36)岁。采用DNA直接测序法对256株利福平耐药MTB菌株rpoB基因的PCR产物进行分析。将256株利福平耐药MTB菌株根据利福平耐药程度分为低、中、高耐药MTB菌株,采用χ^(2)检验比较3种菌株突变位点。人工诱导3株利福平耐药MTB菌株,采用DNA直接测序法对其rpoB基因的PCR产物进行分析。结果测序报告显示,256株利福平耐药MTB菌株中有253株发生rpoB基因位点突变,突变率为98.83%(253/256)。突变类型包括C→T、T→G、C→G、A→T、C→A、A→G、G→A、G→T、T→C、A→C共计10种,涉及丝氨酸、亮氨酸、丙氨酸、组氨酸、酪氨酸、谷氨酸、赖氨酸、精氨酸、天冬氨酸、脯氨酸、蛋氨酸、缬氨酸、异亮氨酸、甘氨酸共14个氨基酸密码子,均为点突变。利福平耐药菌株突变主要集中在531位[53.75%(136/253)]、526位[23.32%(59/253)],其他位点包括513、516、533、515、513、532、522、511、519、518、533。高耐药MTB菌株531位氨基酸突变发生率与低、中耐药MTB菌株比较[66.91%(91/136)比37.88%(25/66)、37.04%(20/54)],差异有统计学意义(χ^(2)=22.154,P<0.001);低、中耐药MTB菌株531位氨基酸突变发生率比较,差异无统计学意义(P>0.05)。低、中、高耐药MTB菌株526位氨基酸突变发生率比较[22.73%(15/66)、25.93%(14/54)、22.06%(30/136)],差异无统计学意义(χ^(2)=0.331,P=0.847)。人工诱导的3株利福平耐药MTB菌株均发生rpoB基因位点突变,低、中耐药MTB菌株突变均位于526位点,高耐药MTB菌株突变位于531位点。结论西安地区Gene Xpert MTB/RIF阳性肺结核患者rpoB基因突变率较高,以点突变为主,主要集中在531位、526位,531位TCG→TTG突变在rpoB基因突变类型中突变频率最高,且与高耐药有关。

Genetic Variation Analysis of 3D Gene and Molecular Detection of Porcine Kobuvirus in 2013-2014

[Objective] This study aimed to investigate the prevalence and variation of porcine kobuvirus （PKV） in suckling piglets in China. [Method] In 2013-2014, 224 feces samples from suckling piglets with diarrhea in 27 pig farms of five provinces in China were collected to detect 3D genes of PKV with RT-PCR method; the sequences and genetic variation of 29 PKV 3D genes were analyzed. [Result] Total positive rate of PKV in feces samples from suckling piglets with diarrhea was 65.18% （146/224）; total positive rate of PKV in pig farms was 85,2% （23/27）; nucleotide sequences and the deduced amino acid sequences of 29 PKV 3D genes shared 87.0%-100% and 92.7%-100% homologies with six PKV-related 3D sequences, respectively. [Conclusion] PKV infection is prevalent in suckling piglets in China; PKV 3D genes exhibit high diversity.

APPLICATION OF GENETIC DEAFNESS GENE CHIP FOR DETECTION OF GENE MUTATION OF DEAFNESS IN PREGNANT WOMEN

Objective The study is to identify the carrier rate of common deafness mutation in Chinese pregnant women via detecting deafness gene mutations with gene chip. Methods The pregnant women in obstetric clinic without hearing impairment and hearing disorders family history were selected. The informed consent was signed. Peripheral blood was taken to extract genom- ic DNA. Application of genetic deafness gene chip for detecting 9 mutational hot spot of the most common 4 Chinese deafness genes, namely GJB2 （35delG, 176del16bp, 235delC, 299delAT）, GJB3 （C538T） ,SLC26A4 （ IVS72A〉G, A2168G） and mito- chondrial DNA 12S rRNA （A1555G, C1494T） . Further genetic testing were provided to the spouses and newborns of the screened carriers. Results Peripheral blood of 430 pregnant women were detected, detection of deafness gene mutation carri- ers in 24 cases（4.2%）, including 13 cases of the GJB2 heterozygous mutation, 3 cases of SLC26A4 heterozygous mutation, 1 cases of GJB3 heterozygous mutation, and 1 case of mitochondrial 12S rRNA mutation. 18 spouses and 17 newborns took further genetic tests, and 6 newborns inherited the mutation from their mother. Conclusion The common deafness genes muta- tion has a high carrier rate in pregnant women group, 235delC and IVS7-2A〉G heterozygous mutations are common.

Rapid Detection of rpoB Gene Mutations in Rif-resistant M.tuberculosis Isolates by Oligonucleotide Microarray

Objective To detect the specific mutations in rpoB gene of Mycobacterium tuberculosis by oligonucleotide microarray. Methods Four wild-type and 8 mutant probes were used to detect rifampin resistant strains. Target DNA of M. tuberculosis was amplified by PCR, hybridized and scanned. Direct sequencing was performed to verify the results of oligonucleotide microarray Results Of the 102 rifampin-resistant strains 98 （96.1%） had mutations in the rpoB genes. Conclusion Oligonucleotide microarray with mutation-specific probes is a reliable and useful tool for the rapid and accurate diagnosis of rifampin resistance in M. tuberculosis isolates.

Gene X-pert MTB/RIF检测对肺结核病的诊断价值研究

目的 研究分析肺结核病应用Gene X-pert MTB/RIF检测的诊断价值。方法 选取2022年3—10月在本院就诊的疑似肺结核病患者164例为研究对象,所有患者均给予结核分枝杆菌培养检查、痰涂片抗酸染色法诊断、Gene X-pert MTB/RIF检测,以结核分枝杆菌培养检查结果为金标准,比较痰涂片抗酸染色法诊断与Gene X-pert MTB/RIF检测的阳性率以及诊断准确性、灵敏度、特异度。结果 痰涂片抗酸染色法诊断阳性率为21.34%(35/164),Gene X-pert MTB/RIF检测阳性率为35.98%(59/164),Gene X-pert MTB/RIF检测明显高于痰涂片抗酸染色法诊断(P<0.05)。痰涂片抗酸染色法诊断的准确性为85.37%,灵敏度为59.65%,Gene X-pert MTB/RIF检测的准确性为95.12%,灵敏度为94.74%,Gene X-pert MTB/RIF检测明显高于痰涂片抗酸染色法诊断(P<0.05)。结论 在肺结核病诊断中,Gene X-pert MTB/RIF检测的阳性率更高,同时诊断准确率与灵敏度也更高,为肺结核病的诊断与治疗提供了可靠的指导依据。

Research Progress on Analysis and Detection Techniques of Veterinary Drug Residues in Animal Foods

As an important guarantee for the prevention and control of animal diseases,veterinary drugs have important functions in improving animal production performance and product quality and maintaining ecological balance.They are an important guarantee for the healthy development of animal husbandry,food safety and public health.However,the irrational use and abuse of veterinary drugs and feed pharmaceutical additives are widespread,causing harmful substances in animal foods and damage to human health,and threatening the sustainable development of the environment and animal husbandry as well.In order to ensure human health,it is urgent to develop a simple,rapid,high-sensitivity,high-throughput and low-cost veterinary drug residue detection technology.In this paper,the sample pretreatment methods and detection techniques for the analysis of veterinary drug residues in animal foods were reviewed.

New technologies and techniques to improve adenoma detection in colonoscopy

Adenoma detection rate(ADR) is a key component of colonoscopy quality assessment, with a direct link between itself and future mortality from colorectal cancer. There are a number of potential factors, both modifiable and non-modifiable that can impact upon ADR. As methods, understanding and technologies advance, so should our ability to improve ADRs, and thus, reduce colorectal cancer mortality. This article will review new technologies and techniques that improve ADR, both in terms of the endoscopes themselves and adjuncts to current systems. In particular it focuses on effective techniques and behaviours, developments in image enhancement, advancement in endoscope design and developments in accessories that may improve ADR. It also highlights the key role that continued medical education plays in improving the quality of colonoscopy and thus ADR. The review aims to present a balanced summary of the evidence currently available and does not propose to serve as a guideline.

Transfer and Detection of barstar Gene to Maize Inbred Line 18-599 (White) by Particle Bombardment

In China, the purity of maize hybrid strain is discomforting to the development of seed industrialization. Finding a new method for reproduction of maize hybrid strain is necessary. In this study, using particle bombardment, barstar gene was transferred into maize inbred line 18-599 （White）, which is an antiviral and high quality maize inbred line. By molecular detection of the anther of transgenic maize, two plants transferred with barstar gene were gained in this study, which are two restorer lines. The two plants showed normal male spike, and lively microspores. But the capacity of the two restorer lines should be studied in the future. The aim of this study is to find a new method of reproduction of maize hybrid strain using engineering restorer lines and engineering sterility lines by gene engineering technology.