False Alarm Rate Research Articles

A protocol for a single center, randomized, controlled trial assessing the effects of spectacles or orthokeratology on dry eye parameters in children and adolescents

BackgroundThe prevalence of myopia among adolescents is increasing precipitously in China, and the popularity of orthokeratology (OK) lenses as an effective treatment for controlling myopia progression is rising. This protocol assessed and compared the clinical dry eye parameters in children and adolescents with myopia treated with spectacles or OK lenses. Methods and analysisThis single-masked randomized control trial will include 300 participants (aged 8–17 years) with myopia treated with OK lens (study group) or spectacles (control group). We will record the ocular surface disease index, visual analog scale score, noninvasive tear breakup time, tear meniscus height, meibomian gland score, ocular redness score, visual acuity, tear Matrix Metalloproteinase-9 concentration, tear Lymphotoxin alpha levels at baseline, and after 1-, 3-, 6-, and 12-month. DiscussionThis study will be a standardized, scientific, clinical trial designed to evaluate the dry eye parameters in children and adolescents with myopia treated with OK lenses for myopia control. Ethics and disseminationThis study has been approved by the Ethics Committee of He Eye Specialist Hospital [ethics approval number: IRB(2023)K024.01]. Before participating in the trial, written informed consent will be obtained from all patient's parents or guardians. The findings of this study will be showcased at both local and international conferences and will also be submitted for publication in reputable peer-reviewed journals. Trial registration numberClinicaltrials.gov: NCT06023108 {2a, 2b}

Pengaruh Minat dan Bakat Terhadap Kesuksesan Berwirausaha Mahasiswa Darullughah Waddawah (DALWA) Bangil Pasuruan

This study aims to determine the influence of interest and talent on the entrepreneurial success of DarullughahWadawah (DALWA) Pasuruan students class of 2018, with a total of 79 respondents .This research is a quantitative descriptive research, namely research by obtaining data in the form of numbers or quantitative data that is raised. The data was obtained from the 2018 Darullughah Wadawah Pasuruan student response questionnaire with quantitative analysis techniques using SPSS 25.The results of the study show that it can be known that the Fcal value is 6,508 with a significant value of 0.000, meaning that the significant value is smaller than the alpha level of 0.05. Thus it can be concluded that the research hypothesis is accepted. This means that there is a positive and significant influence between factors (interests and talents) on entrepreneurial success.

A robust multi-stage intrusion detection system for in-vehicle network security using hierarchical federated learning

As connected and autonomous vehicles proliferate, the Controller Area Network (CAN) bus has become the predominant communication standard for in-vehicle networks due to its speed and efficiency. However, the CAN bus lacks basic security measures such as authentication and encryption, making it highly vulnerable to cyberattacks. To ensure in-vehicle security, intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks while remaining lightweight for practical deployment. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. These approaches overlook other exploitable features, making it challenging to adapt to new unseen attack variants and compromising security. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations. The proposed IDS employs a multi-stage approach: an artificial neural network (ANN) in the first stage to detect seen attacks, and a Long Short-Term Memory (LSTM) autoencoder in the second stage to detect new, unseen attacks. To understand and analyze diverse driving behaviors, update the model with the latest attack patterns, and preserve data privacy, we propose a theoretical framework to deploy our IDS in a hierarchical federated learning (H-FL) environment. Experimental results demonstrate that our IDS achieves an F1-score exceeding 0.99 for seen attacks and exceeding 0.95 for novel attacks, with a detection rate of 99.99%. Additionally, the false alarm rate (FAR) is exceptionally low at 0.016%, minimizing false alarms. Despite using DL algorithms known for their effectiveness in identifying sophisticated and zero-day attacks, the IDS remains lightweight, ensuring its feasibility for real-world deployment. This makes our model robust against seen and unseen attacks.

Nano-cubosomes of the phyto-active principle in Withania somnifera: LC-MS-NMR, anti-microbial, and insights of the anti-neuropathic and anti-inflammatory mechanism

Withania somnifera (W. somnifera) has a long history of safety in the amelioration of neuro-active ailments. The current study aims to explore Withania somnifera phyto-active principle anti-microbial, ant-neuropathic, and anti-inflammatory activities, and to modify these activities utilizing nano-cubosomes exploiting their mechanisms of action. Bio-guided fractionation technique was utilized, to identify the most phyto-active compound, using LC-MS-NMR online technique and biological models of diabetes, neuropathy, and inflammation. In-vitro antibacterial activity was also monitored. The HbA1c, in-vivo antioxidant (serum-catalase, TBARS, and GSH), serum insulin, and pro-inflammatory serum cytokines (TNF alpha, IL-six, and IL-ten) levels have been assessed to establish the anti-neuropathic and anti-inflammatory mechanisms. The nano-cubosomal formulations (CUB 1–3) were utilized to improve the W. somnifera most active compound efficacy. W. somnifera has shown ten major peaks; coagulin Q (10.2 %), dihydrowithanolide A (2.4 %), dihydrowithaferin D (1.8 %), physagulin D (7.6 %), withanoside V (2.3 %), withanolide A (WDA, 10.3 %), withafrin A (4.9 %), withaferin D (7.7 %), withanone 9 (9.9 %), withanolide D (4.8 %). The bio-guided fractionation technique utilizing LC-MS-NMR technique has proved that withanolide A (WDA) is the most phyto-active compound in W. somnifera. The latter has shown better results than WDA, which might be due to other effective compounds in Ws. However, CUB 3 (WDA nano-cubosomes dispersion) has shown more prominent anti-diabetic, anti-neuropathic, anti-inflammatory, and anti-bacterial potentials than Ws and WDA. Thus, CUB 3 modified WDA activity, and improved its efficacy. The normalization of HbA1c levels, increased insulin secretagogue potential, and the amelioration of the oxidative-stress may be the underlying Ws, WDA, and CUB 3 antidiabetic neuropathy mechanism. Moreover, the Ws, WDA, and CUB 1–3 anti-inflammatory mechanism might be due to the amelioration of the pro-inflammatory serum cytokines (decreasing TNF alpha and IL-six levels and increasing IL-ten). Thus, CUB 3 might be a powerful tool in augmenting Withania somnifera activity as an oral drug-delivery system and improving its efficacy against neuropathy and inflammation.

Cross media knowledge information retrieval model based on D-S evidence theory

Cross media knowledge information retrieval provides strong support for information processing and utilization in the information society, but there are problems such as heterogeneity in cross media knowledge information. Therefore, a cross media knowledge information retrieval model using D-S evidence theory is proposed, which involves using approximate calculation methods to improve this theory for information fusion, reducing computational complexity, and using deep networks for fine-grained information retrieval to improve retrieval accuracy. The results showed that the improved theory enhanced computational efficiency by about 27.23 %. The memory usage was <60 %, and the average accuracy of information fusion reached 93.14 %. It also exhibited high recall and low false alarm rates. The cross media knowledge information retrieval model proposed in the study achieved accuracy of 92.64 %, 96.49 %, and 97.46 % on the three datasets used in the experiment, respectively. The study provides an effective, computationally efficient, and highly accurate model for cross media knowledge information retrieval, which is expected to promote research and application in this field. The combination of improved D-S evidence theory and deep networks provides a powerful approach to solving the problem of cross media heterogeneous information retrieval, which has a positive promoting effect on the processing and utilization of information in the information society.

Jabuticaba (Myrciaria trunciflora) extract improves metabolic and behavioral markers of obese rats fed on a hypercaloric diet.

Obesity is a metabolic disease that affects many individuals around the world, related to imbalance between energy consumption and expenditure, which can lead to comorbidities. A healthy diet can significantly contribute to the prevention or treatment of this condition. Jabuticaba is an emerging fruit presenting a wide range of bioactive compounds and is being extensively studied due to its effects on lipid metabolism. The aim of this study was to evaluate the jabuticaba extract in the anxious-like behavior and in the lipid and oxidative metabolism in the context of obesity. Forty male Wistar rats divided into five groups were used. The animals received a standard diet and/or a hypercaloric diet and after 60 days of induction, interventions were carried out with jabuticaba extract (5% and 10%) via gavage for 30 days. It can be observed that the jabuticaba extract was able to reverse the anxious behavior observed in obese animals and modulate parameters of lipid and oxidative metabolism. We observed a reduction in cholesterol and triglyceride levels compared to obese animals. Furthermore, we observed an improvement in oxidative parameters, with a reduction in protein carbonylation in the liver and modulation of antioxidant enzymes such as superoxide dismutase and catalase. Contrary to expectations, we did not observe changes in leptin, adiponectin and tumor necrosis factor alpha (TNF-α) levels. Our work demonstrates that jabuticaba extract can improve metabolic, oxidative and behavioral changes in animals with obesity. © 2024 Society of Chemical Industry.

Multi-sensor fault detection and correction for automated IAQ monitoring in smart buildings through attention-aware autoencoders with spatial prediction module

The integration of interconnected monitoring and control devices has significantly enhanced the management of indoor building environments, notably in underground subway stations. In these sophisticated settings, the accuracy of monitoring systems is critical, as data-driven control systems rely extensively on sensor feedback. However, sensor faults arising from hardware damage, wear, and cyberattacks compromise the reliability of these complex systems. Despite extensive efforts, existing methods still face challenges in accurately detecting and reconstructing faults, particularly when dealing with multiple sensor failures. This study introduces a hybrid sensor validation framework that combines a multi-head attention-based denoising autoencoder (AE) with a deep learning prediction module. The performance assessment of the proposed gated recurrent unit (GRU) integrated attention-based fault detection, and reconstruction network (GADRN) shows a significant 33 % and 21 % improvement in critical success index compared to Independent Component Analysis and GRU-AE frameworks. GADRN consistently identified faulty sensors across various fault periods, achieving the lowest false alarm rate of 11 % against several conventional and machine learning-based models. A ventilation control analysis highlights the significance of incorporating a sensor validation framework in modern buildings. The outputs by the GADRN led to a 22.5 % decrease in energy consumption by preventing unnecessary resource use attributed to sensor faults.

Wind turbine anomaly detection and identification based on graph neural networks with decision interpretability

Abstract Condition monitoring of wind turbines with supervisory control and data acquisition (SCADA) data has attracted increasing interest due to its great benefits in improving operation reliability and reducing unplanned downtimes of wind turbines. However, existing SCADA data-based studies focus primarily on anomaly detection, and few studies have attempted to identify the underlying causes of the anomaly. To this end, this paper proposes a new system-level wind turbine anomaly detection and identification method based on an emerging graph neural network with decision interpretability (DIGNN). A correlation-based GNN is first used to capture complex inter-sensor correlations in SCADA data. More importantly, a decision interpretability module is designed to further analyze anomaly causes and effects through a twp-step global and local anomaly decision process. It can greatly improve the interpretability of the model and provide more accurate and meaningful decision results for maintenance purposes. The effectiveness and robustness of the proposed model were verified by four fault cases with SCADA datasets from a real wind farm. The experimental results demonstrated that the proposed model can provide earlier warning of anomalies with lower false alarm rates and accurate anomaly identification with good interpretability, providing valuable help for field maintenance.maintenance.

Decreased Systemic Monocyte Colony Protein-1 (MCP-1) Levels and Reduced sCD14 Levels in Curcumin-Treated Patients with Moderate Anxiety: A Pilot Study.

Psychosocial stress may alter cortisol and/or affect the normal functioning of the immune system. Curcuminoids can promote beneficial effects in neuropsychiatric diseases. We evaluated whether curcumin supplementation for 15 consecutive days (1800 mg/day) would decrease systemic MCP-1, sCD14, and TNF alpha levels in patients with moderate anxiety (n = 81). A total number of 81 subjects were enrolled in this study, divided into the following groups according to their Hamilton scores: a control group including patients without anxiety who were not taking curcumin (Cont, n = 22) and an anxiety group including patients with moderate anxiety (Anx, n = 22). The curcumin-treated patients experienced moderate anxiety, and they take curcumin for 15 consecutive days (Anx-Cur (after), n = 15, 1800 mg/day). An evaluation of 128 patients was conducted, which allowed for their assignment to the study groups according to their scores on Hamilton scale II. The cortisol levels were quantified in salivary samples through ELISA (ng/mL), and malonaldehyde (MDA) levels were measured in plasma via the TBARS assay as an index of lipoperoxidation. Several systemic proinflammatory cytokines (pg/mL: MCP-1, TNF alpha, IL-1 beta) and mediators were quantified through ELISA (pg/mL), including systemic sCD14 levels as a marker of monocyte activation. A two-way bifactorial ANOVA was conducted to evaluate the contributions of the anxiety factor (Anx) and/or curcumin factor (Cur) in all the tested markers, including interactions between both factors. High systemic MCP-1 and elevated sCD14 levels were observed in patients with moderate anxiety, which were reduced with curcumin supplementation. In addition, curcumin prevented cortisol overexpression and decreased MDA levels as an antioxidant response in these patients. Collectively, curcumin presented anti-chemotactic effects by reducing systemic MCP-1 levels in anxiety. Curcumin decreased systemic MCP-1 as well as sCD14 levels in patients with moderate anxiety.

A Novel Anomaly Intrusion Detection Method based on RNA Encoding and ResNet50 Model

Cybersecurity refers to the actions that are used by people and companies to protect themselves and their information from cyber threats. Different security methods have been proposed for detecting network abnormal behavior, but some effective attacks are still a major concern in the computer community. Many security gaps, like Denial of Service, spam, phishing, and other types of attacks, are reported daily, and the attack numbers are growing. Intrusion detection is a security protection method that is used to detect and report any abnormal traffic automatically that may affect network security, such as internal attacks, external attacks, and maloperations. This paper proposed an anomaly intrusion detection system method based on a new RNA encoding method and ResNet50 Model, where the encoding is done by splitting the training records into different groups. These groups are protocol, service, flag, and digit, and each group is represented by the number of RNA characters that can represent the group's values. The RNA encoding phase converts network traffic records into RNA sequences, allowing for a comprehensive representation of the dataset. The detection model, utilizing the ResNet architecture, effectively tackles training challenges and achieves high detection rates for different attack types. The KDD-Cup99 Dataset is used for both training and testing. The testing dataset includes new attacks that do not appear in the training dataset, which means the system can detect new attacks in the future. The efficiency of the suggested anomaly intrusion detection system is done by calculating the detection rate (DR), false alarm rate (FAR), and accuracy. The achieved DR, FAR, and accuracy are equal to 96.24%, 6.133%, and 95.99%. The experimental results exhibit that the RNA encoding method can improve intrusion detection.

Automatic Aircraft Identification with High Precision from SAR Images Considering Multiscale Problems and Channel Information Enhancement

The SAR system possesses the ability to carry out all-day and all-weather imaging, which is highly valuable in the application of aircraft identification. However, aircraft identification from SAR images still faces great challenges due to speckle noise interference, multiscale problems, and complex background interference. To solve these problems, an efficient bidirectional path multiscale fusion and attention network (EBMA-Net) is proposed in this paper. It employs bidirectional connectivity to fuse the features of aircraft with different scales to perform the accurate detection of aircraft even when the background is highly complex. In the presented EBMA-Net, a module called efficient multiscale channel attention fusion (EMCA) and three parallel squeeze efficient channel attention (SECA) modules are proposed. In the EMCA module, the bidirectional paths are created by stacking upper and lower fusion modules, which effectively integrate shallow detailed features and deep semantic information. So, the detection performance of aircraft at different scales is improved. In the SECA module, the dependency relationship between feature channels is explicitly modeled, which can automatically learn the importance of different channels, prioritize key features, so as to improve the precision and robustness of aircraft identification. In the experiment, the public dataset of aircraft identification (i.e., SAR-AIRcraft-1.0, which is generated from the GF-3 satellite) from high-resolution SAR systems is used, and several other excellent target-detection networks are used for performance comparison, namely, YOLOv5s, YOLOv7, MGCAN, and EBPA2N. According to the results, the average aircraft detection accuracy of EBMA-Net is 91.31%, which is 4.5% higher than YOLOv7; and the false alarm rate is decreased by 5%. Its accuracy in the identification of aircraft can reach 95.6%, which is about 3.7% higher than YOLOv7. Therefore, the EBMA-Net obviously outperforms the other networks for aircraft detection and identification. The proposed EBMA-Net, which can capture the detailed information and better restrain the background interference, could also be used to perform the detection and identification of dense targets with different scales and background from SAR images.

Shallow water source depth discrimination based on a vertical linear array using deep learning

This study aims to explore source depth discrimination in shallow water waveguides using a vertical line array. Due to the similarity in physical characteristics of features and environmental parameters, the feature distributions of shallow and deep sources overlap in feature space. This overlap is further exacerbated by strong background noise, reducing detection reliability. Therefore, a deep learning-based source depth discrimination (DL-SDD) scheme is proposed. Specifically, this scheme is based on a residual structure, embedding channel attention mechanisms into the deep structure to eliminate noise-related information gradually. Furthermore, a specially designed loss function considers inter-class and intra-class distances to achieve compact and mutually distant distributions of source features. When this loss function is applied, the overlap of source feature distributions is suppressed in end-to-end feature learning, leading to a high detection probability. The numerical simulations demonstrate that the proposed DL-SDD outperforms traditional method, achieving a 7 % increase in detection probability and a 15 % decrease in false alarm rate, even near the discrimination depth. Furthermore, the discrimination depth is reduced by nearly half. Experimental results from the South China Sea validate the effectiveness of the proposed DL-SDD.

Hereditary C1q Deficiency is Associated with Type 1 Interferon-Pathway Activation and a High Risk of Central Nervous System Inflammation

Hereditary C1q deficiency (C1QDef) is a rare monogenic disorder leading to defective complement pathway activation and systemic lupus erythematosus (SLE)-like manifestations. The link between impairment of the complement cascade and autoimmunity remains incompletely understood. Here, we assessed type 1 interferon pathway activation in patients with C1QDef. Twelve patients with genetically confirmed C1QDef were recruited through an international collaboration. Clinical, biological and radiological data were collected retrospectively. The expression of a standardized panel of interferon stimulated genes (ISGs) in peripheral blood was measured, and the level of interferon alpha (IFNα) protein in cerebrospinal fluid (CSF) determined using SIMOA technology. Central nervous system (encompassing basal ganglia calcification, encephalitis, vasculitis, chronic pachymeningitis), mucocutaneous and renal involvement were present, respectively, in 10, 11 and 2 of 12 patients, and severe infections recorded in 2/12 patients. Elevated ISG expression was observed in all patients tested (n = 10/10), and serum and CSF IFNα elevated in 2/2 patients. Three patients were treated with Janus-kinase inhibitors (JAKi), with variable outcome; one displaying an apparently favourable response in respect of cutaneous and neurological features, and two others experiencing persistent disease despite JAKi therapy. To our knowledge, we report the largest original series of genetically confirmed C1QDef yet described. Additionally, we present a review of all previously described genetically confirmed cases of C1QDef. Overall, individuals with C1QDef demonstrate many characteristics of recognized monogenic interferonopathies: particularly, cutaneous involvement (malar rash, acral vasculitic/papular rash, chilblains), SLE-like disease, basal ganglia calcification, increased expression of ISGs in peripheral blood, and elevated levels of CSF IFNα.

Difficulty with mobility among the aged in Ghana: Evidence from Wave 2 of the World Health Organization's Study on Global Ageing and Adult Health.

Globally, the population is rapidly ageing, stemming from a recent decline in mortality, and an increase in life expectancy. About 727 million people globally were aged ≥65 in 2020, and 1 in 6 people will be ≥65 years by 2050. About 7% of Ghana's population was over 60 years in 2010, and projected to be 12% by 2050. However, the aged are confronted with degenerative conditions that translate into difficulty with mobility. The study was conducted to investigate the difficulty with mobility among the aged in Ghana. The study utilised a cross-sectional dataset of the 2014/2015 (wave 2) Study on Global Ageing and Adult Health and included 1,856 participants aged ≥50 years. The survey command was applied to adjust for sampling biases and the design of the study. At 5% alpha level, a chi-square test of independence was conducted to determine the association between dependent and independent variables. At 95% confidence interval and 5% alpha level, three-level multilevel logistic regression models were performed. The fixed-effects were presented in odds ratio and the random effects were presented using the Intra-Class Correlation. All analysis were performed using STATA statistical software version 16.0. Out of the 1,856 participants, 40.3% had difficulty with mobility. Additionally, age (80 and above) [AOR = 3.05, 95%CI = 1.78-5.22], self-reported poor/bad health status [AOR = 2.39, 95%CI = 1.35-4.23], having severe/extreme difficulty performing household activities [AOR = 25.12, 95%CI = 11.49-44.91], experienced severe/extreme bodily pains [AOR = 4.56, 95%CI = 2.16-9.64], severe/extreme sleep problems [AOR = 4.15, 95%CI = 1.68-10.29], and participants with difficulty with sight/vision [AOR = 1.56, 95%CI = 1.16-2.10] had higher odds of difficulty with mobility. The aged in Ghana had relatively higher prevalence (40.3%) of difficulty with mobility which is influenced by demographics, health status, and degeneration associated with ageing. This highlights the need to provide social support and strengthen social capital for the aged in Ghana, especially those with difficulty with vision, experiencing bodily pains and had poor health status. Additionally, the Government and stakeholders should provide assistive devices for the aged and geriatric care including recreational fields and care homes to address the health and physical needs of the aged in Ghana.

Persymmetric direction detector for range‐spread target in subspace interference plus homogeneous and partially homogeneous clutter

AbstractThe problem of detecting the direction of range‐spread targets in the presence of subspace interference, along with homogeneous and partially homogeneous Gaussian clutter backgrounds characterised by an unknown persymmetric covariance matrix, is being investigated. It is assumed that the interference subspace is linearly independent of the signal subspace, and the coordinates of both subspaces are unknown. Leveraging the property of persymmetry, the two‐step Generalised Likelihood Ratio Test and the two‐step Wald test are proposed to formulate three detectors in the presence of subspace interference, homogeneous, and partially homogeneous Gaussian clutter. Theoretical derivations and simulations demonstrate that three proposed detectors maintain a constant false alarm rate with respect to the unknown clutter covariance matrix. In comparison to existing detectors in similar backgrounds, particularly in scenarios with limited training data, these three detectors exhibit superior detection performance.

PCB plug-in solder joint defect detection method based on coordinated attention-guided information fusion

Printed Circuit Boards (PCBs) are the foundational component of electronic devices, and the detection of PCB defects is essential for ensuring the quality control of electronic products. Aiming at the problem that the existing PCB plug-in solder defect detection algorithms cannot meet the requirements of high precision, low false alarm rate, and high speed at the same time, this paper proposes a method based on spatial convolution pooling and information fusion. Firstly, on the basis of YOLOv3, an attention-guided pyramid structure is used to fuse context information, and multiple convolutions of different size are used to explore richer high-level semantic information; Secondly, a coordinated attention network structure is introduced to calibrate the fused pyramidal feature information, highlighting the important feature channels, and reducing the adverse impact of redundant parameters generated by feature fusion; Finally, the ASPP (Atrous Spatial Pyramid Pooling) structure is implemented in the original Darknet53 backbone feature extraction network to acquire multi-scale feature information of the detection targets. With these improvements, the average detection accuracy of the enhanced network has been elevated from 94.45 to 96.43%. This experiments shows that the improved network is more suitable for PCB plug-in solder defect detection applications.

Predictive Maintenance Based on Identity Resolution and Transformers in IIoT

The burgeoning development of next-generation technologies, especially the Industrial Internet of Things (IIoT), has heightened interest in predictive maintenance (PdM). Accurate failure forecasting and prompt responses to downtime are essential for improving the industrial efficiency. Traditional PdM methods often suffer from high false alarm rates and inefficiencies in complex environments. This paper introduces a predictive maintenance framework using identity resolution and a transformer model. Devices receive unique IDs via distributed identifiers (DIDs), followed by a state awareness model to assess device health from sensor signals. A sequence prediction model forecasts future signal sequences, which are then used with the state awareness model to determine future health statuses. Combining these predictions with unique IDs allows for the rapid identification of facilities needing maintenance. Experimental results show superior performance, with 99% accuracy for the state awareness model and a mean absolute error (MAE) of 0.062 for the sequence prediction model, underscoring the effectiveness of the framework.

Research on Network Intrusion Detection Technology Based on Artificial Intelligence

As network intrusion becomes more and more complicated and network characteristics become more and more diverse, the traditional network intrusion detection technology has some problems such as high false alarm rate, poor adaptability and low detection rate. The article provides an in-depth analysis of network intrusion detection techniques based on artificial intelligence to better solve these problems. In recent years, we have been witnessing great progress in image recognition, speech recognition, and natural language processing. The powerful advantage of artificial intelligence in processing large-scale and complex data provides new ideas for the processing of multi-attribute intrusion data. The introduction of AI technology in network intrusion detection can effectively improve the detection accuracy and reduce the false alarm rate and omission rate. Based on this, the article analyses the artificial intelligence network intrusion detection technology and discusses the network security management based on artificial intelligence for the reference of related personnel.

Optimized search for a binary black hole merger population in LIGO-Virgo O3 data

Maximizing the number of detections in matched filter searches for compact binary coalescence (CBC) gravitational wave signals requires a model of the source population distribution. In previous searches using the framework, sensitivity to the population of binary black hole (BBH) mergers was improved by restricting the range of filter template mass ratios and use of a simple one-dimensional population model. However, this approach does not make use of our full knowledge of the population and cannot be extended to a full parameter space search. Here, we introduce a new ranking method, based on kernel density estimation with adaptive bandwidth, to accurately model the probability distributions of binary source parameters over a template bank, both for signals and for noise events. We demonstrate this ranking method by conducting a search over LIGO-Virgo O3 data for BBHs with unrestricted mass ratio, using a signal model derived from previous significant detected events. We achieve over 10% increase in sensitive volume for a simple power-law simulated signal population, compared to the previous BBH search. Correspondingly, with the new ranking, eight additional candidate events above an inverse false alarm rate threshold 0.5 yr are identified. Published by the American Physical Society 2024

Evaluation of neutrophil gelatinase-associated lipocalin (NGAL), hypoxia-induced factor-1 alpha (HIF-1α) and apelin 13 levels as new potential biomarkers for pulmonary thromboembolism: A prospective clinical study

AimThe objective was to evaluate the serum levels of neutrophil gelatinase-associated lipocalin (NGAL), hypoxia-induced factor-1 alpha (HIF-1α), and apelin 13 in patients with acute pulmonary thromboembolism (PE) and to investigate their diagnostic and prognostic role in PE patients with different mortality risk groups. Material and methodsThis study was conducted in a tertiary referral center and included 124 subjects with 94 cases of PE and 30 cases of healthy control group. All subjects were 18 years of age or older. The diagnosis of PE was done with computed tomography angiography of the thorax. After the diagnosis of acute PE, the serum levels of neutrophil gelatinase-associated lipocalin (NGAL), hypoxia-induced factor-1 alpha (HIF-1α), and apelin 13 levels were measured with a commercial enzyme-linked immunosorbent assay (ELISA) kit. ResultsThe median and IQR (interquartile range) age of patients and control groups were 68 (56–76) and 61.5 (56–67) years, respectively. The majority of patients with PE had risk factors (97.88 %), and only two (2.12 %) had no known risk factors. HIF-1 alpha level was found to be higher in the patient group than in the control group (p = 0.03). At the same time, the HIF-1 alpha level was found to be higher in the high mortality risk group than in the control group, low mortality risk group and intermediate-low mortality risk group (p = 0.000, 0.011, 0.002, respectively). While there was no significant difference in NGAL level between the patient group and the control group, a significant difference was observed between the mortality groups. NGAL level was found to be higher in the high mortality risk group than the control group, low mortality risk group, and medium-low mortality risk group (p = 0.001, 0.000, 0.010, respectively). Apelin 13 levels did not differ significantly in all groups. ConclusionHIF-1 alpha is a promising biomarker in distinguishing between patients and control groups and in identifying those with high mortality risk in the patient group. At the same time, NGAL can be used as a successful biomarker in determining the group with high mortality risk in cases of PE.