False Alarm Rate Research Articles

A two-stage fault diagnosis strategy for air handling units via a backpropagation multidimensional Taylor network fitter and a novel statistical process control

Faults in Heating, Ventilation, and Air Conditioning Air Handling Units can lead to increased energy consumption and failure to meet human comfort standards. Accurate and prompt fault diagnosis of Air Handling Units is therefore critical. Although widely-used deep learning methods have shown high precision in fault diagnosis, they present two main drawbacks: 1) deep learning approaches are often “black box” models lacking interpretability and rely on labeled data; 2) their complex structures entail substantial computational resources, which may not be conducive to timely fault diagnosis. To overcome these challenges, this paper introduces a method for diagnosing faults in air handling units that combines a novel statistical process control strategy, a backpropagation multidimensional Taylor network fitter, and a D-matrix approach. The backpropagation multidimensional Taylor network, using a polynomial network with only one hidden layer, approximates nonlinear functions. Compared to deep learning models, it maintains high accuracy with a simpler structure, enabling rapid fault diagnosis. Additionally, the novel statistical process control strategy employs the Markov property for fault detection, characterized by low false alarm rates, interpretability, robustness, minimal labeled data requirements, and suitability for real-time monitoring. Experimental results validate the effectiveness of our method in diagnosing faults in air handling units, achieving an average precision of 99.9%, surpassing several commonly used techniques.

Improving The Levels of Reading Skills Of Grade 1 Learners Through Story-telling Via Phone Calling

The study aimed to assess the effectiveness of storytelling via phone calling strategy to improve the levels of reading skills of Grade 1 pupils at San Miguel Elementary School, Schools Division of Zambales during the School Year 2022-2023. Further delimitation included the following: (1) levels of reading skills based on pre-test and post-test results of reading inventory; and (2) significant difference in the reading levels of the learners in pre-test and post-test result of reading inventory. This research undertaking used the One-Group Pre-Test and Post-Test Experimental Design where a single group was measured or observed before and after the intervention program. After careful evaluation of parental consent and the willingness of the students to undergo intervention program, the researcher purposively selected twenty-eight students under “frustration reading level”. The study used a two-tailed Wilcoxon Signed Ranks Test with an alpha level of 0.05. It means that there is a significant difference between learners’ reading levels in pre-reading and post-reading inventory indicating a positive effect of the intervention used to the learners in the “frustration reading level”. The marked increase of the performance of the Grade 1 pupils in the pre-test and post-test can be concluded that the intervention used came to play as factor. With that, storytelling via phone calling is effective and responsive as one of the strategies to use in reading. One of the recommendations is Storytelling via phone calling reading strategy could be used by other teachers also to improve the levels of reading skills of their learners as well, especially amidst pandemic where there is no face-to-face interaction between students and teachers.

Malware traffic detection based on type II fuzzy recognition

In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit. The primary objective of malware is to disrupt the normal functioning of computers and networks, all the while surreptitiously gathering users’ private and sensitive information. The formidable concealment and latency capabilities of malware pose significant challenges to its detection. In light of the operational characteristics of malware, this paper conducts an initial analysis of prevailing malware detection schemes. Subsequently, it extracts fuzzy features based on the distinct characteristics of malware traffic. The approach then integrates traffic detection techniques with Type II fuzzy recognition theory to effectively monitor malware-related traffic. Finally, the paper classifies the identified malware instances according to fuzzy association rules. Experimental results showcase that the proposed method achieves a detection accuracy exceeding 90%, with a remarkably low false alarm rate of approximately 5%. This method adeptly addresses the challenges associated with malware detection, thereby making a meaningful contribution to enhancing our country’s cybersecurity.

Adaptive radar pulse detection design based on difference of box filter

AbstractPrecise radar pulse detection is the premise of electronic support measures. A constant false alarm rate (CFAR) detection algorithm based on difference of box (DOB) filter is proposed to realise low‐complexity and environment‐adaptive radar pulse detection, and the principle behind the proposed algorithm is revealed. Specifically, the DOB filter is adopted to extract the rising edges and falling edges of radar pulses, and the signal variation law of probability density functions (PDFs) during the process of DOB filter are first theoretically analysed. Based on the derived PDFs, dynamic detection threshold and fixed threshold factor for the proposed CFAR algorithm based on DOB filter are deduced to detect the presence of radar pulses. Furthermore, the closed‐form expression of the detection probability is derived. Simulations and on‐board field programmable gate arrays implementation verify the superiority of the proposed CFAR algorithm based on DOB filter. Simulation results show that the proposed algorithm performs well in detecting various types of pulse signals, the detection probability ≥99% on condition that the signal‐to‐noise ratio ≥3 dB and false alarm probability = 10−8. Moreover, the proposed CFAR algorithm based on DOB filter can deal with both singular pulses and pulse on pulse signals.

A novel application of data-consistent inversion to overcome spurious inference in genome-wide association studies.

The genome-wide association studies (GWAS) typically use linear or logistic regression models to identify associations between phenotypes (traits) and genotypes (genetic variants) of interest. However, the use of regression with the additive assumption has potential limitations. First, the normality assumption of residuals is the one that is rarely seen in practice, and deviation from normality increases the Type-I error rate. Second, building a model based on such an assumption ignores genetic structures, like, dominant, recessive, and protective-risk cases. Ignoring genetic variants may result in spurious conclusions about the associations between a variant and a trait. We propose an assumption-free model built upon data-consistent inversion (DCI), which is a recently developed measure-theoretic framework utilized for uncertainty quantification. This proposed DCI-derived model builds a nonparametric distribution on model inputs that propagates to the distribution of observed data without the required normality assumption of residuals in the regression model. This characteristic enables the proposed DCI-derived model to cover all genetic variants without emphasizing on additivity of the classic-GWAS model. Simulations and a replication GWAS with data from the COPDGene demonstrate the ability of this model to control the Type-I error rate at least as well as the classic-GWAS (additive linear model) approach while having similar or greater power to discover variants in different genetic modes of transmission.

Effect of Meta-Cognitive Strategy-Based Activities on Executive Functioning, Social Networking, and Instrumental Activities of Daily Living Among Women.

Background Aging has an impact on women's quality of life and is closely correlated with deteriorating social support systems and cognitive abilities. This project intends to improve women's performance, independence, and quality of life by using meta-cognitive strategy-based activities to improve executive functioning and their capacity to maintain social networks. The ultimate objective is to keep older women's cognitive abilities from declining and to give them more independence in carrying out essential everyday tasks. By enhancing their executive functioning skills, women will be better equipped to navigate daily challenges and maintain their autonomy. Additionally, the project aims to provide a supportive environment for older women to engage in social activities and strengthen their social connections, ultimately leading to a more fulfilling and enriched quality of life. Aims and objectives The study aims to evaluate the impact of meta-cognitive strategy-based activities on executive functioning, social networking, and instrumental activities of daily living. This study is specifically for women. Materials and method This is aquasi-experimental designwith convenience sampling and selected samples(n=70), who were then splitinto two groups: an experimental group (n=35) and a control group (n=35) based on the inclusion and exclusion criteria. The experimental group received intervention based on meta-cognitive strategies through various activities, while the control group did not receive any specific intervention except health education. Pre- and post-tests were conducted to measure the changes. Executive functioning was assessed using the Brief Executive Function Assessment Tool (BEAT), social networking was assessed using the Lubben Social Networking Scale, and instrumental activities of daily living were assessed using the Lawton Brody Instrumental Activities of Daily Living Scale. The duration of the study was six months,three sessions per week, lasting 45 minutes to an hour each. The statistical analysis was done with significance at a 5% alpha level using IBM SPSS version 29.0 (Armonk, NY: IBM Corp). Results The pre-test and post-test data were analyzed using the Wilcoxon signed-rank test and the Mann-Whitney test.Since the p-value of the experimental group was 0.00 for the Lubben Social Networking Scale, the BEAT, and the Lawton Brody Instrumental Activities of Daily Living Scale, the alternate hypothesis was accepted. Thus, the results showed significant improvement in executive functioning, social networking, and instrumental daily living activities in the experimental group compared to the control group. Conclusion The clinically significant finding of this study was that the participants were enthusiastic and motivated to engage in meta-cognitive strategy-based activities; furthermore, there was a significant improvement in the experimental group when compared with the control group in executive functioning and social networking abilities. This will enhance functions like instrumental activities of daily living and occupational performance. Meta-cognitive strategy-based activities appeared beneficial for improving the cognitive functions and quality of life of women. Although the findings from the studies arepositive for therapeutic and health benefits, more clinical trials are needed in order to prove the effect of meta-cognitive strategy-based activities as a therapeutic approach.

Mitigating Missing Rate and Early Cyberattack Discrimination Using Optimal Statistical Approach with Machine Learning Techniques in a Smart Grid

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches.

Gross alpha/beta activity concentrations in imported bottled drinking water in Saudi Arabia

This research assessed the gross alpha and beta activity levels in bottled drinking water imported to Saudi Arabia, applying the evaluation process recommended by the World Health Organization (WHO). The WHO's approach for examining alpha and beta activity in drinking water consists of an initial phase of screening for gross alpha and beta levels to determine the need for further analysis. This is followed by a detailed assessment of specific radionuclide concentrations if initial screening levels are surpassed. The study utilized the evaporation method as a standard procedure to quantify the gross alpha and beta activity levels. Each water sample underwent evaporation, after which the activity levels of the residual matter were measured. Gross alpha activity was analyzed using a ZnS (Ag) scintillator detector system, while gross beta activity levels were gauged using a proportional counting system. The methodologies for determining gross alpha and beta activities align with the International Organization for Standardization (ISO) standards ISO 9696 and ISO 9697, respectively. The research identified the maximum alpha and beta activity levels to be 0.18 Bq/L and 0.34 Bq/L, respectively. These values were evaluated against the WHO guidance threshold values of 0.5 Bq/L for gross alpha and 1.0 Bq/L for gross beta activities, confirming that the exposure remains below the individual dose criterion of 0.1mSv/year. The estimated committed effective doses fell under the WHO's advised cap of 0.1 mSv annually, indicating the safety of the water samples for consumption. The study recommends the analysis of a larger set of samples over extended periods for increased assurance.

An effective ensemble classification framework for intrusion detection system using LSTM autoencoder

In intrusion detection, the curse of dimensionality and the trade-off between maintaining a low false alarm rate and achieving a high detection rate are significant challenges. This research suggests a unique strategy based on dimensionality reduction methods to improve the performance of network intrusion detection systems (NIDS). Compressing high-dimensional network traffic data using a Long Short-Term Memory Autoencoder (LSTMAE) allows the reduced characteristics to be submitted to a classifier to identify anomalies that may indicate an attack. Using standard datasets, including Network Security Laboratory - Knowledge Discovery in Datasets (NSL-KDD), UNSW-NB15, and Canadian Institute for Cyber Security - Intrusion Detection Systems (CICIDS2017), the proposed model is tested with classifiers like Random Forest (RF) and LightGBM (Light Gradient Boosting Machines). It is hoped that by adopting this method, NIDS response times may be improved while costs associated with storing and processing data are minimized. Precision, recall, F-score, accuracy, detection rate (DR), and false alarm rate (FAR) are only a few of the performance measures used to assess the quality of the suggested models. The experimental findings show that the proposed LSTMAE model reduces prediction errors more effectively than classic machine learning techniques such as Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVM), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Autoencoder (AE), and Long Short-Term Memory (LSTM). The results also show that the proposed solution outperforms the state-of-the-art methods of detection accuracy and computing complexity using accuracy, precision, recall, F1_Score, detection rate, and FAR.

Change detection on multi-sensor imagery using mixed interleaved group convolutional network

The difference of the spatio-spectral features of multi-sensor image causes big difficulty in change detection because of the difficulties of the feature extraction. Unlike the traditional approaches that mainly relying on manually feature design, the advances of deep learning-based methods in deep feature extraction provide new alternatives for multi-sensor imagery change detection. Specifically, the incorporation of multi-scale information from remote sensing images holds paramount importance in change detection, consistently applied in the design of various deep learning models. This study investigated a change detection approach utilizing a mixed interleaved group convolutional network (MIGCNet) on multi-sensor remote sensing imagery, with a specific focus on fine-grained kernel space and multi-scale feature analysis within convolution operations. The proposed MIGCNet, with parallel branches as the fundamental architecture, can distinguish the change information effectively by the proposed mixed interleaved group convolution (MIGC) module, which combined mixed convolution with interleaved group convolution. Meanwhile, multi-loss supervision is utilized to promote the performance of the proposed MIGCNet. Experimental results demonstrate the outperformance of the MIGCNet to handle change detection with multi-sensor images on urban area. Considering different datasets, the Overall Accuracy and Kappa Coefficient are reaching 0.97 and 80.67%, respectively, and the miss detection rate and the false alarm rate are as low as 0.17 and 0.18, respectively.

Graph-based phishing detection: URLGBM model driven by machine learning

Phishing attacks are a form of social engineering that involves the transmission of deceptive communications imitating a trustworthy source to deceive users into revealing confidential information. Antiphishing systems have made significant strides in recent years, allowing internet users to secure confidential and private information against such attacks. In this study, we propose a URL graph representation based on a random walk algorithm, specifically PageRank, for weighting URL tokens. To create the graph, an imaginary walker visits the URL tokens one at a time and assigns a value to each token based on the probability of encountering the target URL during the walk. We studied different random walk (rw) variations and their effects on the URL string. The BM25 algorithm was employed to produce a sparse matrix for the classification task from the token scores obtained. Experiments conducted with logistic regression revealed that the proposed model achieved an accuracy of 98.98%, a false alarm rate of 1.72%, and a missing alarm rate of 0.302%. The model also attained a 97.17% accuracy on a benchmark dataset.

Dispatch Categories as Indicators of Out-of-Hospital Time Critical Interventions and Associated Emergency Department Outcomes

Objectives Emergency medical services (EMS) systems increasingly grapple with rising call volumes and workforce shortages, forcing systems to decide which responses may be delayed. Limited research has linked dispatch codes, on-scene findings, and emergency department (ED) outcomes. This study evaluated the association between dispatch categorizations and time-critical EMS responses defined by prehospital interventions and ED outcomes. Secondarily, we proposed a framework for identifying dispatch categorizations that are safe or unsafe to hold in queue. Methods This retrospective, multi-center analysis encompassed all 9-1-1 responses from 8 accredited EMS systems between 1/1/2021 and 06/30/2023, utilizing the Medical Priority Dispatch System (MPDS). Independent variables included MPDS Protocol numbers and Determinant levels. EMS treatments and ED diagnoses/dispositions were categorized as time-critical using a multi-round consensus survey. The primary outcome was the proportion of EMS responses categorized as time-critical. A non-parametric test for trend was used to assess the proportion of time-critical responses Determinant levels. Based on group consensus, Protocol/Determinant level combinations with at least 120 responses (∼1 per week) were further categorized as safe to hold in queue (<1% time-critical intervention by EMS and <5% time-critical ED outcome) or unsafe to hold in queue (>10% time-critical intervention by EMS or >10% time-critical ED outcome). Results Of 1,715,612 EMS incidents, 6% (109,250) involved a time-critical EMS intervention. Among EMS transports with linked outcome data (543,883), 12% had time-critical ED outcomes. The proportion of time-critical EMS interventions increased with Determinant level (OMEGA: 1%, ECHO: 38%, p-trend < 0.01) as did time-critical ED outcomes (OMEGA: 3%, ECHO: 31%, p-trend < 0.01). Of 162 unique Protocols/Determinants with at least 120 uses, 30 met criteria for safe to hold in queue, accounting for 8% (142,067) of incidents. Meanwhile, 72 Protocols/Determinants met criteria for unsafe to hold, accounting for 52% (883,683) of incidents. Seven of 32 ALPHA level Protocols and 3/17 OMEGA level Protocols met the proposed criteria for unsafe to hold in queue. Conclusions In general, Determinant levels aligned with time-critical responses; however, a notable minority of lower acuity Determinant level Protocols met criteria for unsafe to hold. This suggests a more nuanced approach to dispatch prioritization, considering both Protocol and Determinant level factors.

Smoke Detection with Dual Convolutional Networks From Infrared Frames

In many smoke detection fields, such as fire detection in confined space of aircraft cargo hold, the false alarm rate is still high. In the closed and dark environment of the confined space cabin, the traditional video smoke detection method is difficult to find the fire early because of the limitation of lighting conditions. The advantage of fire detection based on infrared video image is that it does not need lighting conditions and has better performance in dark environment. There is a rapid temperature rise process in the confined space at the beginning of the fire, which is more easily captured by infrared cameras. However, there is little research on infrared frame detection methods in confined space. Therefore, based on the limited space environment of aviation industry, this paper studies the smoke detection problem under the infrared framework, and proposes a high-precision fire and smoke image detection algorithm based on infrared double convolution neural network. By modeling the texture features of neural network and infrared smoke frames, and using video frames as an auxiliary means to increase the number of available training images, the problem of insufficient infrared video data sets is solved. The experimental results show that the detection effect of this method is better than other comparison algorithms in limited space, and the detection false alarm rate is effectively reduced.

Group-wise monitoring of multivariate data with missing values

In this article, we aim to detect the changes in the process that generates multivariate data by monitoring their mean shift. To do this, we utilize a graphical tool known as a multivariate control chart. However, monitoring the mean of multivariate data poses two challenges: the grouped structure of individual observations and the presence of missing values. In this research, we introduce a novel method called HTC for monitoring group-wise multivariate data that includes missing values. HTC offers several advantages over existing methods. First, it is applicable to various types of dependence among individual observations within a group. Second, it provides a unique upper control limit (UCL) regardless of the missing data pattern. Lastly, HTC is computationally more efficient compared to resampling-based techniques. We conduct comprehensive numerical studies to evaluate the performance of the HTC method and compare it with the existing group-wise monitoring method, referred to as HTM. Compared to HTM, HTC achieves a higher true positive rate (TPR) while effectively controlling the in-control false alarm rate ( FAR 0 ) at a pre-determined level across various settings considered in our study. To illustrate its effectiveness, we applied HTC to monitoring multivariate environmental data collected from the manufacturing process of a semiconductor company in Korea.

An integrated approach combining randomized kernel PCA, Gaussian mixture modeling and ICA for fault detection in non-linear processes

Principal component analysis (PCA) and independent component analysis (ICA), as well as their kernel extensions, have been widely applied in the past for industrial fault detection with Gaussian or non-Gaussian process data with linear or non-linear characteristics. Kernel-based techniques lead to computational complexity due to the high dimensionality of the dataset in the feature space. In this work, a randomization approach is used to obtain a low-rank approximation of the high-dimensional kernel matrix. A hybrid machine learning technique is proposed that integrates randomized kernel PCA (RKPCA) with ICA and Gaussian mixture modeling (GMM). The proposed approach, ICA-RKPCA-GMM, addresses the Gaussian and non-Gaussian characteristics of non-linear process data. Another hybrid algorithm combining three basic techniques of ICA, PCA and GMM is also developed (ICA-PCA-GMM). The fault detection performances of the proposed techniques (ICA-RKPCA-GMM and ICA-PCA-GMM) are compared with PCA, ICA, KPCA and combined ICA-PCA techniques by applying the techniques to two benchmark systems. Monitoring performances were evaluated by determining the false alarm rate and fault detection rate for different types of process and sensor faults. The simulation results show that the proposed ICA-RKPCA-GMM approach yields better results than individual ICA, PCA and KPCA techniques, the combined ICA-PCA and the proposed ICA-PCA-GMM technique.

Self-healing hybrid intrusion detection system: an ensemble machine learning approach

The increasing complexity and adversity of cyber-attacks have prompted discussions in the cyber scenario for a prognosticate approach, rather than a reactionary one. In this paper, a signature-based intrusion detection system has been built based on C5 classifiers, to classify packets into normal and attack categories. Next, an anomaly-based intrusion detection was built based on the LSTM (Long-Short Term Memory) algorithm to detect anomalies. These anomalies are then fed into the signature generator to extract attributes. These attributes get uploaded into the C5 training set, aiding the ensemble model in continual learning with expanding signatures of unknown attacks. By generating signatures of unknown attacks, the self-healing attribute of the ensemble model contributes to the early detection of attacks. For the C5 classifier, the proposed model is evaluated on the UNSW-NB15 dataset, while for the LSTM model, it is evaluated on the ADFA-LD dataset. Compared to conventional models, the experimental results show better detection rates for both known and unknown attacks. The C5 classifier achieved a True Positive Rate of 97% while maintaining a false positive rate of 8%. Also, the LSTM model achieved a detection rate of 90% while retaining a 17% False Alarm Rate. As the proposed model learns, its performance in real network traffic also improves and it also eliminates human intervention when updating training data.

Intelligent jamming detection‐based robust principal components analysis in communication system for security and defense

AbstractThreats to physical layer security from jamming attacks make wireless cognitive communication systems vulnerable. Global Positioning System signal is vulnerable to these attacks. Over the last decade, several types of jamming detection techniques have been proposed, antijamming‐based classical and machine learning (ML) techniques. Most of these techniques are inefficient in detecting jammers. Thus, there is a great need for efficient and quickest jamming detection technique‐based classifier using receiver operating characteristic (ROC) curve for different threshold values with high accuracy. In this work, we compare the efficiency of the proposed orthogonal distance (OD) and score distance (SD) method‐based robust principal component analysis (PCA) in ML classification in detecting jamming signals. Two hypotheses are proposed to distinguish between the presence and absence attack problem. Using this compressed data matrix obtained from modulated wideband converter (MWC) structure via centralized cooperation directly as input of the proposed classifier combined‐based ROC curve for real‐time detection scenarios. The performance of this proposed algorithm‐based robust PCA was evaluated and compared using the detection anomaly rate (DAR%), and false alarm rate (FAR%), area under curve (AUC), and accuracy. The performance of obtained results is good.

Circular RNA COL1A1 promotes Warburg effect and tumor growth in nasopharyngeal carcinoma

ObjectiveCircular RNAs (circRNAs), pivotal in the pathogenesis and progression of nasopharyngeal carcinoma (NPC), remain a significant point of investigation for potential therapeutic interventions. Our research was driven by the objective to decipher the roles and underlying mechanisms of hsa_circ_0044569 (circCOL1A1) in governing the malignant phenotypes and the Warburg effect in NPC.MethodsWe systematically collected samples from NPC tissues and normal nasopharyngeal epithelial counterparts. The expression levels of circCOL1A1, microRNA-370-5p (miR-370-5p), and prothymosin alpha (PTMA) were quantitatively determined using quantitative polymerase chain reaction (qPCR) and Western blotting. Transfections in NPC cell lines were conducted using small interfering RNAs (siRNAs) or vectors carrying the pcDNA 3.1 construct for overexpression studies. We interrogated the circCOL1A1/miR-370-5p/PTMA axis's role in cellular functions through a series of assays: 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide for cell viability, colony formation for growth, Transwell assays for migration and invasion, and Western blotting for protein expression profiling. To elucidate the molecular interactions, we employed luciferase reporter assays and RNA immunoprecipitation techniques.ResultsOur investigations revealed that circCOL1A1 was a stable circRNA, highly expressed in both NPC tissues and derived cell lines. A correlation analysis with clinical pathological features demonstrated a significant association between circCOL1A1 expression, lymph node metastasis, and the tumor node metastasis staging system of NPC. Functionally, silencing circCOL1A1 led to substantial suppression of cell proliferation, migration, invasion, and metabolic alterations characteristic of the Warburg effect in NPC cells. At the molecular level, circCOL1A1 appeared to modulate PTMA expression by acting as a competitive endogenous RNA or 'sponge' for miR-370-5p, which in turn promoted the malignant characteristics of NPC cells.ConclusionTo conclude, our findings delineate that circCOL1A1 exerts its oncogenic influence in NPC through the modulation of the miR-370-5p/PTMA signaling axis.

Study on advanced partial least squares for quality-related fault detection

The issue of quality-related fault detection in the industrial process has attracted much attention in recent years. The partial least squares (PLS) is considered an efficient tool for predicting and monitoring. The modified partial least squares (MPLS) is an extended algorithm for solving the oblique decomposition of PLS, however, the study indicated that the loss of quality variable information may affect the prediction of quality information in the decomposition process of the MPLS algorithm. Furthermore, the detection rate of traditional statistics and static control limit is low, and the existing dynamic control limit has certain limitations. Therefore, a new PLS space-decomposition algorithm called advanced partial least squares (APLS) is proposed. APLS avoids the loss of quality information by orthogonal decomposition of process variables according to their relationship with quality. APLS has a more accurate prediction of quality when process variables contain more noise; the fault false alarm rates (FAR) of quality-related faults are reduced by using the new statistics and thresholds combined with local information increment technology in the process variable principal component subspace. Finally, the effectiveness of the proposed approach is verified by a numerical example and an industrial benchmark problem.

Anomaly Detection for Landslide Displacement Monitoring Data Based on TCN-Transformer

In view of the high false alarm rate of landslide early warning, this paper proposes a classification method of landslide displacement monitoring data based on TCN and attention mechanism to identify normal data and abnormal data. First, the original monitoring data is processed and denoised to improve the data quality. Then, TCN is used to capture the long-term dependence of time series data and highlight key features in combination with attention mechanism. Finally, the landslide displacement monitoring data can be accurately classified by building a classification model to find out the abnormal data and reduce the false alarm rate of early warning. Experiments show that this method can recognize abnormal displacement monitoring data well while ensuring a high recall rate, and reduce the false alarm rate of early warning.