Main Detection Methods Research Articles

Predictive value of the Wells score combined with D-dimer level in identifying acute pulmonary embolism in patients with coronary heart disease with chest pain.

Predictive value of the Wells score combined with D-dimer level in identifying acute pulmonary embolism in patients with coronary heart disease with chest pain.

A new attributed graph clustering by using label propagation in complex networks

The diffusion method is one of the main methods of community detection in complex networks. In this method, the use of the concept that diffusion within the nodes that are members of a community is faster than the diffusion of nodes that are not in the same community. In this way, the dense subgraph will detect the graph in the middle layer. The LPA algorithm, which mimics epidemic contagion by spreading labels, has attracted much attention in recent years as one of the most efficient algorithms in the subcategory of diffusion methods. This algorithm is one of the detection algorithms of most popular communities in recent years because of possessing some advantages including linear time order, the use of local information, and non-dependence on any parameter; however, due to the random behavior in LPA, there are some problems such as unstable and low quality resulting from larger monster communities. This algorithm is easily adaptable to attributed network. In this paper, it is supposed to propose a new version of the LPA algorithm for attributed graphs so that the detected communities solve the problems related to unstable and low quality in addition to possessing structural cohesiveness and attribute homogeneity. For this purpose, a weighted graph of the combination of node attributes and topological structure is produced from an attributed graph for nodes which have edges with each other. Also, the centrality of each node will be calculated equal to the influence of each node using Laplacian centrality, and the steps of selecting the node are being enhanced for updating as well as the mechanism of updating based on the influence of nodes. The proposed method has been compared to other primary and new attributed graph clustering algorithms for real and artificial datasets. In accordance with the results of the experiments on the proposed algorithm without parameter adjusting for different networks of density and entropy criteria, the normalized mutual information indicates that the proposed method is more efficient and precise than other state-of-the-art attributed graph clustering methods.

Study on Acoustic Detection Method of Gas Pipeline Leakage

Gas pipeline leakage will not only threaten people’s life and property, but also lead to serious environmental pollution and waste of resources. Based on the analysis and summary of domestic and foreign literature in recent years, the main causes of gas pipeline leakage are summarized, and the current main gas pipeline leakage detection methods are studied, hoping to provide certain reference for pipeline operation safety management.

Agenesia da veia cava caudal com drenagem pela veia ázigos em cão da raça Scott Terrier: relato de caso

The caudal vena cava agenesis is a malformation that occurs during the embryogenesis period, characterized by partial or total absence of the organ. Because it is a sometimes-asymptomatic abnormality, they are rare findings on imaging exams, which can range from simple ultrasound to computed tomography, and are the main methods for detection and closure of the diagnosis. This paper aims to report a case of a Scott Terrier dog, aged 8 years and 4 months, treated at a Veterinary Hospital in the city of Salvador-BA, which, after performing ultrasound and tomography, confirmed the agenesis of the caudal vena cava.

Dynamic Radar Networks of UAVs: A Tutorial Overview and Tracking Performance Comparison With Terrestrial Radar Networks

In the incoming years, the low-aerial space will be crowded by unmanned aerial vehicles (UAVs), which will be providing different services. In this expected context, an emerging problem is to detect and track unauthorized or malicious mini/micro UAVs. In contrast to current solutions mainly based on fixed terrestrial radars, this article will put forth the idea of a dynamic radar network (DRN) composed of UAVs able to smartly adapt their formation-navigation control to best track malicious UAVs in real-time, with high accuracy, and in a distributed fashion. To this end, some technological solutions and the main methods for target detection and tracking will be described. Further, an optimized navigation scheme will be developed according to an information-seeking approach. Some examples of simulation results and future directions of work will be finally presented highlighting the advantages of dynamic and reconfigurable networks over static ones.

Characterization and pro-inflammatory potential of indoor mold particles.

A number of epidemiological studies find an association between indoor air dampness and respiratory health effects. This is often suggested to be linked to enhanced mold growth. However, the role of mold is obviously difficult to disentangle from other dampness-related exposure including microbes as well as non-biological particles and chemical pollutants. The association may partly be due to visible mycelial growth and a characteristic musty smell of mold. Thus, the potential role of mold exposure should be further explored by evaluating information from experimental studies elucidating possible mechanistic links. Such studies show that exposure to spores and hyphal fragments may act as allergens and pro-inflammatory mediators and that they may damage airways by the production of toxins, enzymes, and volatile organic compounds. In the present review, we hypothesize that continuous exposure to mold particles may result in chronic low-grade pro-inflammatory responses contributing to respiratory diseases. We summarize some of the main methods for detection and characterization of fungal aerosols and highlight in vitro research elucidating how molds may induce toxicity and pro-inflammatory reactions in human cell models relevant for airway exposure. Data suggest that the fraction of fungal hyphal fragments in indoor air is much higher than that of airborne spores, and the hyphal fragments often have a higher pro-inflammatory potential. Thus, hyphal fragments of prevalent mold species with strong pro-inflammatory potential may be particularly relevant candidates for respiratory diseases associated with damp/mold-contaminated indoor air. Future studies linking of indoor air dampness with health effects should assess the toxicity and pro-inflammatory potential of indoor air particulate matter and combined this information with a better characterization of biological components including hyphal fragments from both pathogenic and non-pathogenic mold species. Such studies may increase our understanding of the potential role of mold exposure.

Ternary nanocube-based “off-on” blinking-type electrochemiluminescence towards enzyme-free detection of hepatitis B virus (HBV)-related DNA

Hepatitis B virus (HBV) infection remains a global public health problem, which is directly associated with a very high risk of evolving into liver cirrhosis and hepatocellular carcinoma (HCC). So far, enzyme-linked immunosorbent assay (ELISA) is still a main detection method for HBV infection in hospital, by which it enables to identify HBV infection in vitro through detection of circulating hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg). However, ELISA requires antibody-dependent and enzyme-labeling. What’s worse, ELISA is silent to the type of occult HBV infection, which does not express HBsAg. Thus, it is of significance to establish a favorable alternative to detect HBV-related specific DNA rather than proteins, as well as to avoid antibody and enzyme-labeling assay for unambiguous screening of HBV infection. Herein, we have exploited a ternary nanocube (AgAuPt NCs)-based “off-on” blinking-type electrochemiluminescence (ECL) biosensor towards detection of HBV-related DNA for the first time, integrated with Pb2+-requiring DNAzyme recycling amplification. By this way, the detection limit down to 65 aM is achieved. The unique “off-on” blinking-type ECL biosensor not only overcomes the drawbacks of ELISA towards detection for HBV infection but also provides a potential for clinical application.

Emerging Invasive Fungal Infections: Clinical Features and Controversies in Diagnosis and Treatment Processes.

BackgroundThe diagnosis and treatment of invasive fungal infection (IFI) are still challenging due to its complexity and non-specificity. This study was aimed to investigate the clinical features, diagnosis process, and outcomes of patients with emerging IFIs.MethodsA retrospective review of emerging IFIs in adult patients at a university hospital in China was conducted; diagnoses were based on the criteria of EORTC/MSG 2008.Results145 IFI patients (pulmonary, intestinal and urinary) were enrolled in this study, including 80 proven (55.2%), 59 probable (40.7%), or 6 possible IFIs (4.1%). Among the 126 pulmonary IFIs, the positivity rate for sputum microscopy, sputum culture, and 1.3-ß-D-glucan (BG) test was 54.0%, 44.4%, and 37.3%, respectively. Among the 19 intestinal and urinary IFIs, routine examination of stool or urine and their culture were the main methods of detection. Positive results of 75 detected fungal strains from the samples showed that 30 cases were complicated with one or more bacterial infections. The average length of hospital stay of IFI patients was 14.0 (10.0, 20.0) days. The time from admission to antifungal therapy initiation (P<0.001), liver cirrhosis (P<0.001), hematological tumor (P<0.001), coinfection (P=0.019) and immune diseases (P=0.025) were independent predictors of prolonged hospitalization.ConclusionDelayed time was the primary predictor of prolonged hospitalization. This prediction is suggested to improve IFI diagnostic and therapeutic process of IFI to promote prognosis.

Experimental study and application of magnetic bridge double-loop main flux detection method

In this study, to perform online non-destructive testing (NDT) of mine wire rope damage and achieve effective and accurate flaw detection results, a magnetic bridge double-loop main flux detection method was proposed and the testing equipment was developed. The flaw detection magnetic field was simulated using a Maxwell module. The magnetic field intensity distribution and the vector direction were compared and then analysed. The practicality and feasibility of the detection method were verified by field experiments. The results show that the magnetic bridge double-loop main flux detection method is highly practicable and produces highly accurate test results. The mine wire rope non-destructive flaw detector developed using this method presents a stable detection signal and an obvious damage characteristic signal, which is more consistent with the requirements of wire rope damage detection under various working conditions. This technology has a wide range of applications.

IDENTIFICATION AND ANALYSIS OF GENETIC EPILEPSY AND EPILEPTIC ENCEPHALOPATHIES IN THE OUTPATIENT PRACTICE OF EPILEPSY SPECIALISTS

Background : Given the significant share of gene mutations in the etiology of epilepsy, it is important for the practitioners to evaluate progress in this area. Objective : To describe the spectrum of being detected gene mutations in patients with epilepsy or epileptic encephalopathy in clinical practice of neurologists specializing in epilepsy with an analysis of diagnosed epileptic syndromes, the characteristics of seizures, the timing of a genetic diagnosis, options and treatment efficacy. Methods : The study included 100 patients (40 boys, 60 girls) with epilepsy/epileptic encephalopathy and a gene mutation identified. The average age was 6.9±5.1 years. Through remote access, epilepsy specialists filled out a specially designed unified table containing information from outpatient case history. Results: In the outpatient practice of epilepsy specialists, there are patients with a wide range of gene mutations, the leading of which is a mutation in the SCN1A gene (15%). Nowadays, the main method (85%) of detection remains the next generation sequencing in the “Hereditary Epilepsy” panel. Years pass from the onset of the disease to the genetic diagnosis (Me - 3 years). In most cases, patients with severe (52% have epileptic encephalopathy, 88% have developmental disorders) and pharmacoresistant (mean amount of anti-epileptic drugs - 3,8±2,2, multitherapy -70%) syndromes have undergone genetic testing. In the treatment of these patients epileptologists are increasingly (52%) use alternative methods: steroids, ketogenic diet and others. The absence of seizures was observed only in 46% of patients. Conclusion: Thus, in the outpatient practice of epileptologists of Russia, patients with a wide range of gene mutations are found. As a rule, these are patients with severe, therapy-resistant epileptic syndromes.

Molecular outflows in local galaxies: Method comparison and a role of intermittent AGN driving

We report new detections and limits from a NOEMA and ALMA CO(1-0) search for molecular outflows in 13 local galaxies with high far-infrared surface brightness, and combine these with local universe CO outflow results from the literature. The CO line ratios and spatial outflow structure of our targets provide some constraints on the conversion steps from observables to physical quantities such as molecular mass outflow rates. Where available, ratios between outflow emission in higher J CO transitions and in CO(1-0) are typically consistent with excitationRi1 ≲ 1. However, for IRAS 13120−5453,R31 = 2.10 ± 0.29 indicates optically thin CO in the outflow. Like much of the outflow literature, we useαCO(1 − 0)= 0.8, and we present arguments for usingC = 1 in deriving molecular mass outflow ratesṀout=CMoutvout/Rout. We compare the two main methods for molecular outflow detection: CO millimeter interferometry andHerschelOH-based spectroscopic outflow searches. For 26 sources studied with both methods, we find an 80% agreement in detectingvout≳ 150 km s−1outflows, and non-matches can be plausibly ascribed to outflow geometry and signal-to-noise ratio. For a published sample of 12 bright ultraluminous infrared galaxies with detailed OH-based outflow modeling, CO outflows are detected in all but one. Outflow masses, velocities, and sizes for these 11 sources agree well between the two methods, and modest remaining differences may relate to the different but overlapping regions sampled by CO emission and OH absorption. Outflow properties correlate better with active galactic nucleus (AGN) luminosity and with bolometric luminosity than with far-infrared surface brightness. The most massive outflows are found for systems with current AGN activity, but significant outflows in nonAGN systems must relate to star formation or to AGN activity in the recent past. We report scaling relations for the increase of outflow mass, rate, momentum rate, and kinetic power with bolometric luminosity. Short flow times of ∼106yr and some sources with resolved multiple outflow episodes support a role of intermittent driving, likely by AGNs.

Unknown Attack Detection Based on Zero-Shot Learning

In recent years, due to the frequent occurrence of network intrusions, more and more researchers have begun to focus on network intrusion detection. However, it is still a challenge to detect unknown attacks. Currently, there are two main methods of unknown attack detection: clustering and honeypot. But they still have unsolved problems such as difficulty in collecting unknown attack samples and failure to detect on time. Zero-Shot learning is proposed to deal with the problem in this article, which can recognize unknown attacks by learning the mapping relations between feature space and semantic space (such as attribute space). When the semantic descriptions of all attacks (including known and unknown attacks) are provided, the classifier built by Zero-Shot learning can extract common semantic information among all attacks and construct connections between known and unknown attacks. The classifier then utilizes the connections to classify unknown attacks although there are no samples for unknown attacks. In this article, we first propose to use Zero-Shot learning to overcome the challenge of unknown attack detection and illustrate the feasibility of this method. Secondly, we then propose a novel method of Zero-Shot learning based on sparse autoencoder for unknown attack detection. This method maps the feature of known attacks to the semantic space, and restores the semantic space to the feature space by constrains of reconstruction error, and establishes the feature to semantic mapping, which is used to detect unknown attacks. Verification tests have been carried out by using the public dataset NSL_KDD. From the experiments conducted in this work, the results show that the average accuracy reaches 88.3%, which performs better than other methods.

PC software-based portable cyclic voltammetry system with PB-MCNT-GNPs-modified electrodes for E. coli detection

PC software-based portable cyclic voltammetry (PCV) systems have the advantages of portability, high performance, and real-time detection. In this paper, the PCV system used cyclic voltammetry (CV) as the main detection and analysis method and contained the following components: a three-electrode unit, a portable potentiostat, and PC software. The PC software was used as the system control and display, and a dynamic peak position adjustment (DPPA) algorithm for E. coli measurements based on thick biofilm modification on electrodes was designed especially for this system to realize the real-time correspondence between the measured results and the modified electrodes. The performance test results obtained by setting different detection parameters in the PCV system were compared with those of commercial electrochemical workstations. The difference was less than 4.99%, with a relative standard deviation less than 0.20%. An electrochemical biosensor based on a Prussian blue-multiwalled carbon nanotube-gold nanoparticle composite was developed for E. coli detection. After constructing an antibody-BSA-E. coli electrode modification on the sensor, experimental data processed by the DPPA algorithm showed that the logarithm (lg DfE.coli) of the E. coli dilution factor and the peak current response had a linear relationship. The PCV system could quickly and accurately detect E. coli concentrations with dynamic adjustment algorithms for biofilm-modified electrodes. Furthermore, the system could detect the electrochemical activities of various high-sensitivity biomolecules, showing great detection potential for on-site monitoring and meeting the requirements of real-time and portable detection in various food safety fields.

A robust whitening algorithm to identify buried objects with similar attributes in correlation-based detection

Buried objects detection (BOD) is a complicated task that has been investigated by researchers in recent studies. Ground Penetrating Radar (GPR) is a precise method employed to measure the backscattered signal. The back-scattered wave can be used to extract the feature of the medium and buried objects. In landmine problem, the existence of unwanted buried objects such as building bricks or pipelines reflecting waves like landmines causes difficulties in the detection process. In this paper, different scenarios in presence and also in the absence of landmine have been implemented. These scenarios are compared with the main case in which there exist only one landmine in dry soil. Using correlation coefficient between the s-parameter of the main case and other cases, detection could be carried out. The motivation for this method stems from the fact that landmines and clutters often have different scattering parameters (s-parameter) because of different shapes and/or composition, especially in Ultra-wideband (UWB) signal. The s-parameter can be exploited for discrimination of these buried objects. The reduction of similarity among different s-parameters, which are collected as a database, is the main purpose of this paper, and results in better discrimination. To this end, here are two main processes. Firstly, cancellation of the reflected signal from ground surface, being the main clutter in landmine detection, results in better discrimination. It improves the false alarm and the miss detection rates. After clutter reduction, whitening algorithms can be applied in order to reduce similarity leading to better identification. In this paper, the main detection method is based on both simulation and experimental database which are collected from different scenarios.

Damage detection in sluice hoist beams subject to excitation at resonance frequency band based on local primary frequency

Cracks of sluice hoist beams due to the load and aging of the material threaten the safety of sluice structural system. As the one of the main methods of damage detection, the non-destructive detection method based on natural frequency is still insensitive to local damage. Therefore, this paper proposes a method for hoist beams damage detection driven by resonance frequency band based on local primary frequency in the local mode. Firstly, the possibility of damage detection based on local primary frequency is discussed and the procedure of determining resonance frequency band is explained. Then the damage identification index based on the change ratio of local primary frequency is provided. Finally, numerical results demonstrate the correctness and effectiveness of the proposed method. The proposed method can provide reference for damage detection of hoist beams and health monitoring of sluice structural system.

Time-Frequency Analysis and Identification method for weak Magnetic signal of Submarine in aeromagnetic Detection

As an important strategic weapon, submarine detection is very difficult. Aeromagnetic detection is one of the main detection methods, but there are some problems such as complex measurement environment and difficult recognition of target magnetic signals. To solve this problem, wavelet threshold denoising is used to suppress magnetic noise, and WVD is used to analyze the magnetic signal in time and frequency to improve the recognition ability of the unexploding target magnetic signal. The simulation results show that this method is feasible.

Memory and resource leak defects and their repairs in Java projects

Despite huge software engineering efforts and programming language support, resource and memory leaks are still a troublesome issue, even in memory-managed languages such as Java. Understanding the properties of leak-inducing defects, how the leaks manifest, and how they are repaired is an essential prerequisite for designing better approaches for avoidance, diagnosis, and repair of leak-related bugs. We conduct a detailed empirical study on 491 issues from 15 large open-source Java projects. The study proposes taxonomies for the leak types, for the defects causing them, and for the repair actions. We investigate, under several aspects, the distributions within each taxonomy and the relationships between them. We find that manual code inspection and manual runtime detection are still the main methods for leak detection. We find that most of the errors manifest on error-free execution paths, and developers repair the leak defects in a shorter time than non-leak defects. We also identify 13 recurring code transformations in the repair patches. Based on our findings, we draw a variety of implications on how developers can avoid, detect, isolate and repair leak-related bugs.

A Review of Non-Destructive Damage Detection Methods for Steel Wire Ropes

As an important load-bearing component, steel wire ropes (WRs) are widely used in complex systems such as mine hoists, cranes, ropeways, elevators, oil rigs, and cable-stayed bridges. Non-destructive damage detection for WRs is an important way to assess damage states to guarantee WR’s reliability and safety. With intelligent sensors, signal processing, and pattern recognition technology developing rapidly, this field has made great progress. However, there is a lack of a systematic review on technologies or methods introduced and employed, as well as research summaries and prospects in recent years. In order to bridge this gap, and to promote the development of non-destructive detection technology for WRs, we present an overview of non-destructive damage detection research of WRs and discuss the core issues on this topic in this paper. First, the WRs’ damage type is introduced, and its causes are explained. Then, we summarize several main non-destructive detection methods for WRs, including electromagnetic detection method, optical detection method, ultrasonic guided wave detection method, and acoustic emission detection method. Finally, a prospect is put forward. Based on the review of papers, we provide insight about the future of the non-destructive damage detection methods for steel WRs to a certain extent.

From exoplanets to exocomets

To date (June 2017), more than 3610 planets orbiting other stars than our Sun are known. We shall briefly review the main detection methods, together with some of the big surprises which arose since the discovery of the first exoplanet around a solar type star in 1995. It is now also possible to characterize the atmospheres of few extrasolar planets and exocomets become detectable.

A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection.

As a key candidate technique for fifth-generation (5G) mobile communication systems, non-orthogonal multiple access (NOMA) has attracted considerable attention in the field of wireless communication. Successive interference cancellation (SIC) is the main NOMA detection method applied at receivers for both uplink and downlink NOMA transmissions. However, SIC is limited by the receiver complex and error propagation problems. Toward this end, we explore a high-performance, high-efficiency tool—deep learning (DL). In this paper, we propose a learning method that automatically analyzes the channel state information (CSI) of the communication system and detects the original transmit sequences. In contrast to existing SIC schemes, which must search for the optimal order of the channel gain and remove the signal with higher power allocation factor while detecting a signal with a lower power allocation factor, the proposed deep learning method can combine the channel estimation process with recovery of the desired signal suffering from channel distortion and multiuser signal superposition. Extensive performance simulations were conducted for the proposed MIMO-NOMA-DL system, and the results were compared with those of the conventional SIC method. According to our simulation results, the deep learning method can successfully address channel impairment and achieve good detection performance. In contrast to implementing well-designed detection algorithms, MIMO-NOMA-DL searches for the optimal solution via a neural network (NN). Consequently, deep learning is a powerful and effective tool for NOMA signal detection.