Low Detection Rate Research Articles

Contamination rate of rare bacterial species detected by MALDI-TOFMS: a retrospective cohort study

When rare bacterial species are identified in blood cultures, determining the clinical significance is sometimes difficult. This study aimed to analyze the clinical significance of rare bacterial species detected in blood cultures using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOFMS) by comparing their contamination rates with those of common species. We retrospectively analyzed medical records of adult patients with positive blood cultures at Kyoto City Hospital from 2014 to 2022. Rare species were defined by low detection rates and few PubMed reports. Of 4880 microorganisms identified from 3441 individuals, 1150 (23.6%) were classified as contamination. Meanwhile, 24 rare microorganisms were identified, of which 14 (58.3%) were classified as contamination, which was significantly higher than common species (odds ratio 4.56, 95% confidence Interval 1.88–11.50, P < 0.001). These findings may help in determining the clinical significance of rare bacterial species in blood cultures with few reported cases.

An ALMA Survey of M-dwarfs in the Beta Pictoris Moving Group with two new debris disc detections

ABSTRACT Previous surveys in the far-infrared have found very few, if any, M-dwarf debris discs among their samples. It has been questioned whether M-dwarf discs are simply less common than earlier types, or whether the low detection rate derives from the wavelengths and sensitivities available to those studies. The highly sensitive, long-wavelength Atacama Large Millimetre/submillimetre Array (ALMA) can shed light on the problem. This paper presents a survey of M-dwarf stars in the young and nearby Beta Pictoris Moving Group with ALMA at Band 7 (880 μm). From the observational sample, we detect two new sub-mm excesses that likely constitute unresolved debris discs around GJ 2006 A and AT Mic A and model distributions of the disc fractional luminosities and temperatures. From the science sample of 36 M-dwarfs including AU Mic, we find a disc detection rate of 4/36 or 11.1$^{+7.4}_{-3.3}$ per cent that rises to 23.1$^{+8.3}_{-5.5}$ per cent when adjusted for completeness. We conclude that this detection rate is consistent with the detection rate of discs around G- and K-type stars and that the disc properties are also likely consistent with earlier type stars. We additionally conclude that M-dwarf stars are not less likely to host debris discs, but instead their detection requires longer wavelength and higher sensitivity observations than have previously been employed.

The application value of metagenomic next-generation sequencing in community-acquired purulent meningitis after antibiotic intervention

BackgroundBacteria account for nearly one third of the causes of community-acquired central nervous system infections, and traditional diagnostic methods are based on culture results, which are time-consuming and have a low detection rate leading to delayed diagnosis and treatment. Since metagenomic next-generation sequencing (mNGS) has the advantages of high timeliness and only detecting microbial trace gene fragments, it has been used more widely in recent years. Based on this, we explored whether the application of cerebrospinal fluid (CSF) mNGS is advantageous in patients with community-acquired purulent meningitis, especially in people who have already used antibiotics.MethodsThis was a retrospective study of 63 patients with community-acquired purulent meningitis admitted to the Department of Neurology of Shanxi Bethune Hospital from March 2018 to November 2022. Data were systematically collected and classified into CSF culture group, blood culture group and CSF mNGS group according to different detection methods, and the total detection rate of each method was calculated. Each group of patients was divided into two subgroups according to whether antibiotics were used before sampling. The detection rates of the three groups were compared within and between groups to explore whether mNGS has advantages over traditional methods and the influence of antibiotic use on detection rates of the three methods.ResultsAmong the 63 patients, the cases of CSF culture, blood culture and CSF mNGS were 56, 46, 44, respectively. The total detection rates of the three methods were 17.86%, 36.96%, 81.82%, with statistical differences (p < 0.05),suggesting that the detection rate of mNGS was higher than CSF culture (p < 0.05) and blood culture (p < 0.05),and the detection rate of blood culture higher than CSF culture (p < 0.05). Further grouping found that without antibiotics, the detection rates of CSF culture, blood culture and CSF mNGS were 28.57%, 56.25% and 88.89%, with statistical differences (p < 0.05), and the detection rate of CSF mNGS was higher than that of CSF culture (p < 0.05), but there was no statistical difference between CSF and blood culture (p > 0.05), nor between blood culture and CSF mNGS (p > 0.05). The detection rates of the three groups with antibiotics were 14.29%, 26.67% and 80.00%, with statistical differences (p < 0.05), and the detection rate of CSF mNGS was still higher than CSF culture (p < 0.05) and blood culture (p < 0.05). However, the detection rate of CSF mNGS also decreased after antibiotics were used for more than 3 days.ConclusionsThe detection rate of CSF mNGS in patients with purulent meningitis is higher than traditional methods, especially in patients who have been given antibiotics, but the detection rate will decrease with the extension of antibiotic use.

Imbalanced data classification using improved synthetic minority over-sampling technique

In data mining, deep learning and machine learning models face class imbalance problems, which result in a lower detection rate for minority class samples. An improved Synthetic Minority Over-sampling Technique (SMOTE) is introduced for effective imbalanced data classification. After collecting the raw data from PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases, the pre-processing is performed using min-max normalization, cleaning, integration, and data transformation techniques to achieve data with better uniqueness, consistency, completeness and validity. An improved SMOTE algorithm is applied to the pre-processed data for proper data distribution, and then the properly distributed data is fed to the machine learning classifiers: Support Vector Machine (SVM), Random Forest, and Decision Tree for data classification. Experimental examination confirmed that the improved SMOTE algorithm with random forest attained significant classification results with Area under Curve (AUC) of 94.30%, 91%, 96.40%, and 99.40% on the PIMA, Yeast, E.coli, and Breast cancer Wisconsin databases.

Gastric-type adenocarcinoma of the endocervix: Potentially overcoming resistant behavior with surgery

ObjectiveGastric-type adenocarcinoma of the endocervix (GAS) is a rare form of human papillomavirus-independent cervical cancer commonly described as an insidious disease bearing a poor prognosis. Based on scarce data, uncertainty persists pertaining to its oncologic management. MethodFourteen cases of well-differentiated GAS treated at our institution from 2010 to 2021 were reviewed. Clinical characteristics, diagnostic tests results and oncologic outcomes were recorded and analyzed. Kaplan-Meier curves and log rank test were performed to compare survival curves between patients with tumors confined to the cervix (group 1: up to stage IB3) versus locally advanced or metastatic (group 2: stages II to IV). ResultsCervical cytologies and biopsies yielded low detection rates (38 and 42% respectively) leading to 87% of patients with locally advanced or metastatic disease at diagnosis. Median overall survival (OS) was 40.0 ± 15.9 months with a clear dichotomy in survival when comparing patients with disease confined to the cervix to those with higher stages (respectively 59.0 vs 12.0 months, p = 0.047). None of the 5 patients initially managed with concurrent chemoradiotherapy (CCRT) responded to treatment but fortunately 3 of the latter achieved remission after surgery. ConclusionWell-differentiated GAS did not show favorable response to chemotherapy and radiation. Surgical resection seems to be a cornerstone in the management of this disease, as all patients who achieved remission were treated with surgery, either upfront or after suboptimal response to CCRT. We suggest considering aggressive upfront surgery when feasible. If CCRT is selected to avoid upfront exenterative procedures, rapid evaluation of tumor response is recommended.

Linear epitope mapping in the E and NS1 proteins of dengue and Zika viruses: Prospection of peptides for vaccines and diagnostics.

The arrival of the Zika virus (ZIKV) in dengue virus (DENV)-endemic areas has posed challenges for both differential diagnosis and vaccine development. Peptides have shown promise in addressing these issues. The aim of this study was to identify the linear epitope profile recognized by serum samples from dengue and Zika patients in the E and NS1 proteins of DENV and ZIKV. This cross-sectional study included individuals of all ages with laboratory-confirmed DENV and ZIKV infections, who were selected through convenience sampling. The serum samples from dengue and Zika patients detected epitopes evenly distributed across the viral proteins in a peptide microarray platform. However, several epitopes were located within "epitope hotspots", characterized by clusters of peptides recognized in more than 30% of the sub-arrays analyzed using individual or pooled serum samples. The serum samples from dengue and Zika patients showed a high level of cross-reactivity with peptides in the DENV and ZIKV proteins. Analysis using an additional peptide microarray platform, which contained peptides selected based on the results of the initial screening, revealed that two DENV and one ZIKV peptide, highly specific to their related viruses, were located within the epitope hotspots; however, they presented low detection rates (32.5, 35.0, and 28.6%, respectively). In addition, two DENV peptides detected at similarly high rates by both dengue and Zika patients were also found within the epitope hotspots. These hotspots contain several immunodominant epitopes that are recognized by a larger number of individuals when compared to 15-amino acid (aa) sequence peptides. Thus, epitope hotspots may have greater potential to serve as antigens in diagnostic tests and vaccine development than peptides composed of only 15 amino acids.

A Multicenter Epidemiological and Pathogenic Characteristics Study of Community-Acquired Bacterial Meningitis Children in China: Results from the Chinese Pediatric Bacterial Meningitis Surveillance (CPBMS) 2019-2020.

To explore the epidemiological and pathogenic characteristics of children with community-acquired bacterial meningitis. A multicenter, retrospective study was conducted among CABM patients under 15 years old from 33 hospitals in China from 2019 to 2020. The medical record, laboratory, and microbiological data were collected and analyzed. A total of 1610 children with CABM were identified and presented at a median onset age of 45 days of whom 955 (59.3%) were males. CABM occurred mostly in infants <1 year of age (84.0%, 1352/1610). In etiology-confirmed cases, the pathogens were isolated from CSF culture in 515 (32.0%), 400 (24.8%) in blood culture, and 186 (11.6%) both in CSF and blood culture. In total, 126 pathogens were identified through CSF mNGS in 330 CABM cases; 21 S. pneumoniae isolates were detected in 83 CABM cases by antigen detection method. Major pathogens were E. coli (195, 24.7%), GBS (170, 21.5%), and S. pneumoniae (157, 19.9%). GBS (29.3%, 22/75) was the first pathogen of CABM in neonates aged 0-6 days old, while E. coli (44.7%, 76/170) in 7 to 28 days of age; S. pneumoniae (96.2%, 151/157) was the most common pathogen in >3 months old cases. About 9.7% (19/195) strains of E. coli produced ultra‑broad‑spectrum β‑lactamases. The common intracranial imaging complications were subdural effusion and (or) empyema in 349 (21.7%), hydrocephalus in 233 (14.5%), and cerebral abscess in 178 (11.1%). A total of 389 (24.2%) cases were completely cured and 1088 (67.6%) cases improved. Among 166 patients (10.3%) with adverse outcomes, 32 cases (2.0%) died, and 37 cases (2.3%) relapsed. The onset age of CABM in children is usually within 1 year of age, especially <3 months. The primary pathogens in infants less than 3 months old are E. coli and GBS, and the dominant pathogen in children older than 3 months old is S. pneumoniae. Subdural effusion and (or) empyema and hydrocephalus are common complications. CABM should not be excluded even if CSF leukocyte counts are within normal range. Due to the low detection rate of pathogens in children with CABM, standardized CSF bacteriological examination should be paid more attention to increase the pathogen detection rate. Non‑culture CSF detection methods may facilitate pathogenic diagnosis.

Hierarchical Multifeature Fusion via Audio-Response-Level Modeling for Depression Detection

The clinical diagnosis of major depressive disorder (MDD) relying heavily on the subjective judgment assisted by questionnaires, could result in a low detection rate of MDD. Automatic depression detection (ADD) technology based on physiological and psychological information provides an objective and quantitative way for MDD detection. As a useful data modality, audio signals have attracted increasing interest in mental disorder detection. However, most recent audio-based depression detection methods underestimate the importance of subtly organizing audio data. They either simply use equally split audio segments, or directly build the model upon the entire data sequence, which pose challenges to learning task-specific features. To address this issue, we propose to reorganize the audio data at response level. Based on that, we construct a novel end-to-end model that hierarchically learns discriminative features for accurate depression detection. The stages of intraresponse fusion and interresponse fusion facilitate the extraction and aggregation of ADD-specific information from multiple kinds of acoustic features. Experimental results show that our proposed method significantly outperforms other state-of-the-art audio-based methods. In addition, the flexibility and the robustness of our model are also validated.

Added value of electrical impedance spectroscopy in adjunction of colposcopy: a prospective cohort study

ObjectiveTo assess whether electrical impedance spectroscopy (EIS) as an adjunctive technology enhances the performance of colposcopy.DesignProspective cohort study.SettingUniversity Hospital colposcopy clinic.ParticipantsColposcopy with EIS for 647 women and conventional colposcopy for...

Dual-modality image feature fusion network for gastric precancerous lesions classification

Gastric precancerous conditions are closely linked to the development of gastric cancer. However, the detection of gastric precancerous lesions (GPL) is limited by the indistinct symptoms and the low detection rate of microscope images. This paper proposes an RGB and Hyperspectral Dual-modality imaging Feature Fusion Network (DuFF-Net) to improve the classification accuracy of GPL. To fully exploit information of different modality images, we customize a dual-stream ResNet-based model for feature sharing and fusion. Skip-Connections are added between inter-path of networks to achieve information interaction. In the decision step, we adopt the SE-based attention module and Pearson Correlation to highlight and select effective features. Experimental results show that the DuFF-Net increases the screening accuracy to 96.15 % for two types of gastric precancerous tissues with high morphological similarity. Furthermore, our approach reduces the labeling workload for classification tasks by approximately 50 %. These findings provide valuable guidance for the screening and subsequent lesion segmentation of GPLs.

A study on teaching English in higher education based on an improved deep belief network

Abstract This paper combines machine learning with acoustic features to design an automatic pronunciation error correction system. The article first adopts Meier’s inverse spectral coefficients and random forest algorithm to classify and detect learners’ pronunciation errors and clarify learners’ pronunciation problems, from which the MFCC-RF model is proposed. Then, using the feature self-learning capability of deep belief networks and the OneClass idea of SVM, we proposed a DBN-SVM model to overcome the shortcomings of the MFCC-RF model in pronunciation classification and error detection due to unbalanced samples and missing data, which resulted in low error detection rate and poor coverage of error types. By comparing the model’s performance for pronunciation error detection, the DBN-SVM model was more accurate than the other two algorithms in detecting the three error types with a stable accuracy of around 80%. Finally, when the experimental class was taught with the automatic pronunciation error correction system, the experimental class improved by 19.5 points after one semester of study, while the control class only improved by 6.8 points. Hence, the DBN-SVM model-based pronunciation mistake correction system has significantly impacted the speed of change and advancement in English teaching techniques while substantially enhancing the quality of oral pronunciation and learning efficiency of English learners.

Comparative detection of syndecan-2 methylation in preoperative and postoperative stool DNA in patients with colorectal cancer.

Early detection of colorectal cancer (CRC) is essential to reduce cancer-related morbidity and mortality. Stool DNA (sDNA) testing is an emerging method for early CRC detection. Syndecan-2 (SDC2) methylation is a potential biomarker for the sDNA testing. Aberrant DNA methylation is an early epigenetic event during tumorigenesis and can occur in the normal colonic mucosa during aging, which can compromise the sDNA test results. To determine whether methylated SDC2 in sDNA normalizes after surgical resection of CRC. In this prospective study, we enrolled 151 patients with CRC who underwent curative surgical resection between September 2016 and May 2020. Preoperative stool samples were collected from 123 patients and postoperative samples were collected from 122 patients. A total of 104 samples were collected from both preoperative and postoperative patients. Aberrant promoter methylation of SDC2 in sDNA was assessed using linear target enrichment quantitative methylation-specific real-time polymerase chain reaction. Clinicopathological parameters were analyzed using the results of SDC2 methylation. Detection rates of SDC2 methylation in the preoperative and postoperative stool samples were 88.6% and 19.7%, respectively. Large tumor size (3 cm, P = 0.019) and advanced T stage (T3-T4, P = 0.033) were positively associated with the detection rate of SDC2 methylation before surgery. Female sex was associated with false positives after surgery (P = 0.030). Cycle threshold (CT) values were significantly decreased postoperatively compared with preoperative values (P < 0.001). The postoperative negative conversion rate for preoperatively methylated SDC2 was 79.3% (73/92). Our results suggested that the SDC2 methylation test for sDNA has acceptable sensitivity and specificity. However, small size and early T stage tumors are associated with a low detection rate of SDC2 methylation. As the cycle threshold values significantly decreased after surgery, SDC2 methylation test for sDNA might have a diagnostic value for CRC.

Clinical characteristics of patients with KRAS mutation detected by liquid biopsy.

KRAS mutation positive lung cancer is known to be clinically characterized by older age, males, and smokers. It is reported to be more common in mucinous adenocarcinoma, but all reports are based on analysis of tissue samples. Recently, blood samples have become available for analysis, suggesting a low detection rate of circulating tumor DNA in histological types, especially mucinous adenocarcinoma. In this study, we investigated the clinical characteristics of KRAS mutation-positive cases in the analysis of blood specimens, as these remain unclear. The clinical background of patients with KRAS mutation among those who underwent next-generation sequencing (NGS) analysis using blood samples was evaluated. NGS analysis was performed on 214 blood samples. KRAS mutations were detected in blood samples in 33 cases (15.4%), of which 31 cases (14.5%) had a histological pathology diagnosis. Mucinous adenocarcinoma accounted for 28.6% of cases with positive blood and tissue specimens, 10.0% of cases with positive blood specimens only, and 57.1% of cases with positive tissue specimens only. Mucinous adenocarcinoma tended to be less common in cases with positive blood specimens. In KRAS-positive patients with lung metastasis only, only one nonmucinous adenocarcinoma had a positive blood sample, and the others all had mucinous adenocarcinomas with positive tissue samples only. The results showed that the detection rate of KRAS-positive lung cancers detected by blood and tissue samples differs, and that the detection rate of blood samples may be poor, especially in the case of mucinous adenocarcinoma with lung metastases only.

Salmonella in reptiles: a review of occurrence, interactions, shedding and risk factors for human infections.

Salmonella are considered a part of the normal reptile gut microbiota, but have also been associated with disease in reptiles. Reptile-associated salmonellosis (RAS) can pose a serious health threat to humans, especially children, and an estimated 6% of human sporadic salmonellosis cases have been attributed to direct or indirect contact with reptiles, although the exact number is not known. Two literature searches were conducted for this review. The first evaluated reports of the prevalence of Salmonella in the intestinal tracts of healthy reptiles. Salmonella were most commonly detected in snakes (56.0% overall), followed by lizards (36.9%) and tortoises (34.2%), with lower detection rates reported for turtles (18.6%) and crocodilians (9%). Reptiles in captivity were significantly more likely to shed Salmonella than those sampled in the wild. The majority of Salmonella strains described in reptiles belonged to subspecies I (70.3%), followed by subspecies IIIb (29.7%) and subspecies II (19.6%). The second literature search focused on reports of RAS, revealing that the highest number of cases was associated with contact with turtles (35.3%), followed by lizards (27.1%) and snakes (20.0%). Reptiles associated with RAS therefore did not directly reflect prevalence of Salmonella reported in healthy representatives of a given reptile group. Clinical symptoms associated with RAS predominantly involved the gastrointestinal tract, but also included fever, central nervous symptoms, problems with circulation, respiratory symptoms and others. Disease caused by Salmonella in reptiles appears to be dependent on additional factors, including stress, inadequate husbandry and hygiene, and other infectious agents. While it has been suggested that reptile serovars may cause more severe disease than human-derived strains, and some data is available on invasiveness of individual strains in cell culture, limited information is available on potential mechanisms influencing invasiveness and immune evasion in reptiles and in RAS. Strategies to mitigate the spread of Salmonella through reptiles and to reduce RAS focus mostly on education and hygiene, and have often been met with some success, but additional efforts are needed. Many aspects regarding Salmonella in reptiles remain poorly understood, including the mechanisms by which Salmonella persist in reptile hosts without causing disease.

Viral maintenance and excretion dynamics of coronaviruses within an Egyptian rousette fruit bat maternal colony: considerations for spillover

Novel coronavirus species of public health and veterinary importance have emerged in the first two decades of the twenty-first century, with bats identified as natural hosts for progenitors of many coronaviruses. Targeted wildlife surveillance is needed to identify the factors involved in viral perpetuation within natural host populations, and drivers of interspecies transmission. We monitored a natural colony of Egyptian rousette bats at monthly intervals across two years to identify circulating coronaviruses, and to investigate shedding dynamics and viral maintenance within the colony. Three distinct lineages were detected, with different seasonal temporal excretion dynamics. For two lineages, the highest periods of coronavirus shedding were at the start of the year, when large numbers of bats were found in the colony. Highest peaks for a third lineage were observed towards the middle of the year. Among individual bat-level factors (age, sex, reproductive status, and forearm mass index), only reproductive status showed significant effects on excretion probability, with reproductive adults having lower rates of detection, though factors were highly interdependent. Analysis of recaptured bats suggests that viral clearance may occur within one month. These findings may be implemented in the development of risk reduction strategies for potential zoonotic coronavirus transmission.

Multilayered safety framework for living diagnostics in the colon

Introduction: Colorectal cancer is the second most deadly cancer worldwide. Current screening methods have low detection rates and frequently provide false positive results, leading to missed diagnoses or unnecessary colonoscopies. To tackle this issue, the Wageningen UR iGEM team from 2022 developed “Colourectal”, a living diagnostic tool for colorectal cancer. Following a synthetic biology approach, the project used an engineered Escherichia coli Nissle 1917 strain capable of binding to tumour cells that detects two distinct cancer biomarkers, and secretes a coloured protein observable in stool. Due to the utilization of genetically modified bacteria in vivo, precautionary biosafety measures were included within a three level safe-by-design strategy.Results: The first genetic safeguard ensured confinement of the living diagnostic to the colon environment by implementing auxotrophy to mucin that is abundant in the colon lining. For this, a synthetic chimeric receptor was generated to ensure expression of essential genes in the presence of mucin. The second strategy limited the viability of the engineered bacteria to the human body, preventing proliferation in open environments. The use of a temperature sensitive kill switch induced bacterial cell death at temperatures below 37°C. The third biocontainment strategy was installed as an emergency kill switch to stop the Colourectal test at any point. By inducing a highly genotoxic response through CRISPR-Cas-mediated DNA degradation, cell death of E. coli Nissle is triggered.Discussion: While the use of engineered microorganisms in human applications is not yet a reality, the safety considerations of our multi-layered strategy provide a framework for the development of future living diagnostic tools.

Review of Fabric Defect Detection using Machine Learning Algorithms

This research article focuses on the critical task of fabric defect detection in textile corporations. Fabric defects adversely affect quality and profitability, making their identification crucial. Traditional human visual inspection is inefficient and impractical for mass production due to low detection rates and labor-intensive processes. To overcome these challenges, we propose an alternative approach to enhance efficiency and accuracy in fabric defect detection, ensuring the financial success of textile manufacturers in the face of increasing production complexity.

Tailored ionically conductive graphene oxide-encased metal ions for ultrasensitive cadaverine sensor

Intelligent chemical sensors have been extensively used in food safety and environmental assessment, while limited sensitivity and homogeneity bring about huge obstacles to their practical application. Herein, novel ionically conductive sensitive materials were elaborately designed based on metal ion decorated graphene oxide (GO) via a facile and general in-situ spin-coating strategy, where the abundant functional groups (-OH and -COOH) of GO layer could provide natural binding sites for various bivalent metal cations (such as Cu2+, Ni2+, Zn2+, Co2+, and Mg2+) through coordination and electrostatic interaction. The intercalated metal cations on the layered GO nanosheets can be regarded as charge carriers and complexation with targeted gas (cadaverine, Cad), which is a typical metabolites production and food degradants. By contrast, the designed GO@Cu(II) sensor exhibited the optimal sensing performance toward Cad molecules at room temperature, including ultra-low detection limit (ca. 3 nL), excellent sensitivity, and rapid low concentration detection rate (only 16 s). Interestingly, the sensor exhibited an irreversible and specific response toward Cad, while it showed a transient and reversible response to other interfering gases, implying its outstanding selectivity. In addition, the GO@Cu(II) sensor enabled real-time monitoring of the decay progression of cheese, and it exhibited great potential for large-scale production via its excellent homogeneity. It provides an efficient approach to tailoring intelligent chemical sensors for real-time food safety monitoring and human health warning.

Machine Learning-Based Intrusion Detection for Rare-Class Network Attacks

Due to the severe imbalance in the quantities of normal samples and attack samples, as well as among different types of attack samples, intrusion detection systems suffer from low detection rates for rare-class attack data. In this paper, we propose a geometric synthetic minority oversampling technique based on the optimized kernel density estimation algorithm. This method can generate diverse rare-class attack data by learning the distribution of rare-class attack data while maintaining similarity with the original sample features. Meanwhile, the balanced data is input to a feature extraction module built upon multiple denoising autoencoders, reducing information redundancy in high-dimensional data and improving the detection performance for unknown attacks. Subsequently, a soft-voting ensemble learning technique is utilized for multi-class anomaly detection on the balanced and dimensionally reduced data. Finally, an intrusion detection system is constructed based on data preprocessing, imbalance handling, feature extraction, and anomaly detection modules. The performance of the system was evaluated using two datasets, NSL-KDD and N-BaIoT, achieving 86.39% and 99.94% multiclassification accuracy, respectively. Through ablation experiments and comparison with the baseline model, it is found that the inherent limitations of a single machine-learning model directly affect the accuracy of the intrusion detection system, while the superiority of the proposed multi-module model in detecting unknown attacks and rare classes of attack traffic is demonstrated.

Toward a sustainable transition in automated production: enabling the vision-based approach and synchronous control for textile surface inspection systems

The textile and apparel industry is one of the largest industries in the world, with significant environmental and social impacts, such as manual labor in poor working conditions and resource depletion and environmental degradation due to ineffective production. As a result, there is an increasing need for sustainable practices and a transition toward a more circular and responsible industry. In this paper, we introduce the proposed approach in the context of a sustainable transition for the industrial textiles. Instead of human labor, the automated solution to detect defects is proposed for the 4.0 revolution in general and the textile industry in particular. Specifically, the mechanical design of a three-dimensional model for small- and medium-sized fabric rolls is portrayed. Some computational mechanics are created to ensure a stable mechanism and proper actuators. For acquiring accurate data and measuring the machine state, several installations, that is, a digital camera, load cell, and positioning sensor, and the layout of the light source are improved to intensify the image quality. The control theme, such as the cross-coupling controller, offers superior performance in synchronization and vibration-less motion. Lastly, the excellent detection method is provided by the convolutional neural network scheme for offline training to distinguish defects on products. Instead of a manual check or low rate of defect detection on products, the contributions of this research are as follows: (a) analyzing the mechanical design of the textile machining system in terms of dynamic characteristics; (b) implementing a smooth scheme based on motion control synchronization; and (c) integrating various strategies, including vision-based computation and control topology, to demonstrate superior performance. These results highlight that our approach can be widely adapted to address sustainability challenges not only in the textile field but also in global manufacturing practices.