Detection Criteria Research Articles

Spatiotemporal Evolution of Marine Heatwaves Globally

Abstract The spatiotemporal evolution of marine heatwaves (MHWs) is explored using a tracking algorithm called Ocetrac that provides objective characterization of MHW spatiotemporal evolution. Candidate MHW grid points are defined in detrended gridded sea temperature data using a seasonally varying temperature threshold. Identified MHW points are collected into spatially distinct objects using edge detection with weak sensitivity to edge detection and size percentile threshold criteria at each timestep. Ocetrac then uses 3D connectivity to determine if these objects are part of the same event, but Ocetrac only defines the full MHW event after all timesteps have been processed, limiting its use in predictability studies. Here Ocetrac is applied to monthly satellite sea surface temperature data from September 1981 through January 2021. The resulting MHWs are characterized by their intensity, duration, and total area covered. The global analysis shows that MHWs in the Gulf of Maine and Mediterranean Sea are spatially isolated, while major MHWs in the Pacific and Indian Oceans are connected in space and time. The largest and most long lasting MHW using this method lasts for 60 months from November 2013 to October 2018, encompassing previously identified MHW events including those in the Northeast Pacific (2014-2015), the Tasman Sea (2015-2016, 2017-2018), and the Great Barrier Reef (2016).

Natural Cover suppression patterns used in PRODES Caatinga mapping

Abstract. Since 2022, we have mapped native vegetation removal for all Brazilian biomes within the scope of the PRODES Project. In this study, our main objective was to present interpretation criteria for feature detection used to identify natural vegetation removal throughout the Caatinga. To this end, we created a catalog of typical patterns that can be used for supervised and unsupervised deforestation mapping. We used PRODES data from 2023 year's PRODES Caatinga based on the composition of top of atmosphere (TOA) reflectance images from MSI/Sentinel-2 satellite. The original reflectance data was used to generate false color images in the following combination: band 8 (Red), band 11 (Green) and band 4 (Blue). This image was then visually classified into different natural vegetation removal classes. These are good examples of what PRODES interpreters have encountered throughout this diverse region and can help any future mapping efforts of the Caatinga.

Detection of Crack Location and Crack Depth in Leaf Spring

The objective of this work is to find the crack location and crack depth in leaf spring of an army jeep. The spring is subjected to heavy jerks and vibration during the military operation. Therefore it is necessary to find the crack in a leaf spring. Crack in a leaf spring introduces local flexibility that would affect the vibration response of the leaf spring. Main problem is to detect existence of a crack together with its location and depth in the leaf spring .We used a natural frequency a basic criteria for crack detection. Analytical calculations are done to find out natural frequency of leaf spring by using Euler's beam theory. Ansys 16.0 is used to find out natural frequency along with experimentation on FFT analyzer. Now, We propose a method based on some set of fuzzy rules obtained from the information supplemented by Numerical Analysis. Fuzzy controller use comprises of three input variables (first, second and third natural frequency) and two output variables (relative crack length and relative crack depth) are generated with Triangular MF.

Detecting Differential Item Functioning Using Response Time

This study investigated uniform differential item functioning (DIF) detection in response times. We proposed a regression analysis approach with both the working speed and the group membership as independent variables, and logarithm transformed response times as the dependent variable. Effect size measures such as Δ[Formula: see text] and percentage change in regression coefficients in conjunction with the statistical significance tests were used to flag DIF items. A simulation study was conducted to assess the performance of three DIF detection criteria: (a) significance test, (b) significance test with Δ[Formula: see text], and (c) significance test with the percentage change in regression coefficients. The simulation study considered factors such as sample sizes, proportion of the focal group in relation to total sample size, number of DIF items, and the amount of DIF. The results showed that the significance test alone was too strict; using the percentage change in regression coefficients as an effect size measure reduced the flagging rate when the sample size was large, but the effect was inconsistent across different conditions; using Δ R2 with significance test reduced the flagging rate and was fairly consistent. The PISA 2018 data were used to illustrate the performance of the proposed method in a real dataset. Furthermore, we provide guidelines for conducting DIF studies with response time.

Criteria for early detection of slag in steel casting

Criteria for early detection of slag in steel casting

A Novel Ultrasonic Leak Detection System in Nuclear Power Plants Using Rigid Guide Tubes with FCOG and SNR.

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments.

Natural gas leakage detection from offshore platform by OGI camera and unsupervised deep learning

Natural gas leak from offshore platform poses a potential to cause explosion disaster and bring significant causalities and economic losses. Existing deep learning-based leak detection approaches are limited by the requirement of a large number of labeled leak datasets, and also has worse performance in the complex and changeable marine environment. This study proposes a detection approach of natural gas leakage from offshore platform by integrating optical gas imaging (OGI) camera and unsupervised deep probability learning. In this approach, unsupervised deep learning is applied to learn the changeable infrared features of offshore platform, and variational Bayesian inference is integrated to provide the larger epistemic uncertainty contour corresponding to the infrared natural gas plume. An epistemic uncertainty-based detection score is proposed as the detection criterion to improve the accuracy of natural gas plume detection and localization. An OGI imaging experiment of natural gas leak from offshore platform is conducted to construct the benchmark dataset. With such datasets, two pre-defined parameters, namely Monte Carlo sampling number m = 100 and dropout probability p=0.1 are determined to guarantee the detection accuracy and efficiency. Comparison between the proposed approach and prevalent unsupervised deep learning approach is also conducted. The results demonstrate that our proposed approach has the higher detection accuracy with AUC = 0.9753, as well as the real-time capability with inference time of 4s/frame. Overall, our proposed approach provides a more accurate and generalized approach of natural gas detection for safety monitoring and detection management of offshore platforms.

Revolutionizing LVH detection using artificial intelligence: the AI heartbeat project.

Many studies have shown the utility and promise of artificial intelligence (AI), for the diagnosis of left ventricular hypertrophy (LVH). The aim of the present study was to conduct a meta-analysis to compare the accuracy of AI tools to electrocardiographic criteria, including Sokolow-Lyon and the Cornell, most commonly used for the detection of LVH in clinical practice. Nine studies meeting the inclusion criteria were selected, comprising a sample size of 31 657 patients in the testing and 100 271 in the training datasets. Meta-analysis was performed using a hierarchal model, calculating the pooled sensitivity, specificity, accuracy, along with the 95% confidence intervals (95% CIs). To ensure that the results were not skewed by one particular study, a sensitivity analysis using the 'leave-out-one approach' was adopted for all three outcomes. AI was associated with greater pooled estimates; accuracy, 80.50 (95% CI: 80.4-80.60), sensitivity, 89.29 (95% CI: 89.25-89.33) and specificity, 93.32 (95% CI: 93.26-93.38). Adjusting for weightage of individual studies on the outcomes, the results showed that while accuracy and specificity were unchanged, the adjusted pooled sensitivity was 53.16 (95% CI: 52.92-53.40). AI demonstrates higher diagnostic accuracy and sensitivity compared with conventional ECG criteria for LVH detection. AI holds promise as a reliable and efficient tool for the accurate detection of LVH in diverse populations. Further studies are needed to test AI models in hypertensive populations, particularly in low resource settings.

Post-marketing safety concerns with rimegepant based on a pharmacovigilance study

PurposeThis study aimed to comprehensively assess the safety of rimegepant administration in real-world clinical settings.MethodsData from the Food and Drug Administration Adverse Event Reporting System (FAERS) spanning the second quarter of 2020 through the first quarter of 2023 were retrospectively analyzed in this pharmacovigilance investigation. This study focuses on employing subgroup analysis to monitor rimegepant drug safety. Descriptive analysis was employed to examine clinical characteristics and concomitant medication of adverse event reports associated with rimegepant, including report season, reporter country, sex, age, weight, dose, and frequency, onset time, et al. Correlation analysis, including techniques such as violin plots, was utilized to explore relationships between clinical characteristics in greater detail. Additionally, four disproportionality analysis methods were applied to assess adverse event signals associated with rimegepant.ResultsA total of 5,416,969 adverse event reports extracted from the FAERS database, 10, 194 adverse events were identified as the “primary suspect” (PS) drug attributed to rimegepant. Rimegepant-associated adverse events involved 27 System Organ Classes (SOCs), and the significant SOC meeting all four detection criteria was “general disorders and administration site conditions” (SOC: 10018065). Additionally, new significant adverse events were discovered, including “vomiting projectile” (PT: 10047708), “eructation” (PT: 10015137), “motion sickness” (PT: 10027990), “feeling drunk” (PT: 10016330), “reaction to food additive” (PT: 10037977), etc. Descriptive analysis indicated that the majority of reporters were consumers (88.1%), with most reports involving female patients. Significant differences were observed between female and male patients across age categories, and the concomitant use of rimegepant with other medications was complex.ConclusionThis study has preliminarily identified potential new adverse events associated with rimegepant, such as those involving the gastrointestinal system, nervous system, and immune system, which warrant further research to determine their exact mechanisms and risk factors. Additionally, significant differences in rimegepant-related adverse events were observed across different age groups and sexes, and the complexity of concomitant medication use should be given special attention in clinical practice.

Antibiotic tolerance among clinical isolates: mechanisms, detection, prevalence, and significance.

SUMMARYAntibiotic treatment failures in the absence of resistance are not uncommon. Recently, attention has grown around the phenomenon of antibiotic tolerance, an underappreciated contributor to recalcitrant infections first detected in the 1970s. Tolerance describes the ability of a bacterial population to survive transient exposure to an otherwise lethal concentration of antibiotic without exhibiting resistance. With advances in genomics, we are gaining a better understanding of the molecular mechanisms behind tolerance, and several studies have sought to examine the clinical prevalence of tolerance. Attempts have also been made to assess the clinical significance of tolerance through in vivo infection models and prospective/retrospective clinical studies. Here, we review the data available on the molecular mechanisms, detection, prevalence, and clinical significance of genotypic tolerance that span ~50 years. We discuss the need for standardized methodology and interpretation criteria for tolerance detection and the impact that methodological inconsistencies have on our ability to accurately assess the scale of the problem. In terms of the clinical significance of tolerance, studies suggest that tolerance contributes to worse outcomes for patients (e.g., higher mortality, prolonged hospitalization), but historical data from animal models are varied. Furthermore, we lack the necessary information to effectively treat tolerant infections. Overall, while the tolerance field is gaining much-needed traction, the underlying clinical significance of tolerance that underpins all tolerance research is still far from clear and requires attention.

The potential of decision trees as a tool to simplify broiler chicken welfare assessments

To simplify fast-growth broiler welfare assessments and use them as a benchmarking tool, decision trees were used to identify iceberg indicators discriminating flocks passing/failing welfare assessments as with the complete AWIN protocol. A dataset was constructed with data from 57 flocks and 3 previous projects. A final flock assessment score, previously not included in the dataset, was calculated and used as the benchmarking assessment classifier (pass/fail). A decision tree to classify flocks was built using the Chi-square Automatic Interaction Detection (CHAID) criterion. Cost-complexity pruning, and tenfold cross-validation were used. The final decision tree included cumulative mortality (%), immobile, lame birds (%), and birds with back wounds (%). Values were (mean ± se) 2.77 ± 0.14%, 0.16 ± 0.02%, 0.25 ± 0.02%, and 0.003 ± 0.001% for flocks passing the assessment; and 4.39 ± 0.49%, 0.24 ± 0.05%, 0.49 ± 0.09%, and 0.015 ± 0.006% for flocks failing. Cumulative mortality had the highest relative importance. The validated model correctly predicted 80.70% of benchmarking assessment outcomes. Model specificity was 0.8696; sensitivity was 0.5455. Decision trees can be useful to simplify welfare assessments. Model improvements will be possible as more information becomes available, and predictions are based on more samples.

The Predictive Value of Revised Petersen’s Criteria in Detection of Mild Cognitive Impairment in A Sample of Community-Dwelling Egyptians

The Predictive Value of Revised Petersen’s Criteria in Detection of Mild Cognitive Impairment in A Sample of Community-Dwelling Egyptians

Secure distributed estimation via an average diffusion LMS and average likelihood ratio test

Secure distributed estimation algorithms are designed to protect against a spectrum of attacks by exploring different attack models and implementing strategies to enhance the resilience of the algorithm. These models encompass diverse scenarios such as measurement sensor attacks and communication link attacks, which have been extensively investigated in existing literature. This paper, however, focuses on a specific type of attack: the multiplicative sensor attack model. To counter this, the paper introduces the Average diffusion least mean square (ADLMS) algorithm as a viable solution. Furthermore, the paper introduces the Average Likelihood Ratio Test (ALRT) detector, which provides a straightforward detection criterion. In the presence of communication link attacks, the paper considers the manipulation attack model and presents an ALRT adversary detector. The analysis extends to these ALRT detectors, encompassing the calculation of adversary detection probability and false alarm probability, both achieved in closed form. The paper also provides the mean convergence analysis of the proposed ADLMS algorithm. Simulation results reveal that the proposed algorithms exhibit enhanced performance compared to the DLMS algorithm, while the incremental complexity remains only marginally higher than that of the DLMS algorithm.

Application of Visual Transformer in Low-resolution Thermal Infrared Image Recognition

Abstract Addressing the challenges of inadequate accuracy and limited robustness exhibited by current lightweight object detection networks specifically tailored for low-resolution thermal infrared face detection scenarios, this paper delves into developing an ultra-lightweight thermal infrared face detection algorithm that leverages visual attention mechanisms. To ascertain the optimal neural network complexity, a series of comparative experiments are meticulously conducted. With Yolo-FastestDet serving as the benchmark, this study endeavors to compress the backbone network, striking a delicate balance between network depth and detection speed. Additionally, to bolster the network’s capacity for profound feature extraction and precise discrimination of target edges and small objects, a Transformer-Encoder-based visual attention module is seamlessly integrated. Consequently, a lightweight face detection algorithm, enriched with attention mechanisms, is formulated. Furthermore, to mitigate the scarcity of low-resolution infrared face image data, a self-constructed visible-infrared face dataset is employed for training and evaluation purposes. The experimental outcomes reveal that the proposed algorithm attains an impressive mAP@0.5 score of 0.953 on the test dataset while satisfying the stringent real-time detection criterion of 30 frames per second (FPS) when deployed on an embedded Raspberry Pi CPU.

ECG Biometric Verification Incorporating Different Physiological Conditions

The liveness detection criteria of biological signals have become one of the reasons it has been introduced as an ideal biometric recognition system. Electrocardiogram (ECG) is one of the biological signals that records the rhythms of human’s heart in the form of PQRST waves proves the uniqueness of the ECG itself making it suitable to be applied as biometric mechanisms. Previous research had shown the success of proving ECG as a biometric modality however most experimentations were done in normal conditions. Thus, to improve the current research, this work proposed a robust biometric identification by introducing ECG signals incorporating various physiological conditions. After the data collection of cycling, walking, climbing stairs, and jogging, the signals are pre-processed by using MODWT to remove unwanted noises produced during data collection process. Then, Pan Tompkins algorithm is used to segment the QRS complexes. The segmented signals are overlapped and align with each other to observe its pattern. Next, the QRS waveform is classified by using various class of SVM by considering two factors which are same physiological conditions and different physiological conditions. The subjects are compared between same and different physiological condition to validate the proposed method. The results show that the precision achieved up to 100%. In average, Gaussian SVM gives highest precision when compared to other type of SVM classifiers suggesting that Gaussian SVM is the most appropriate to be applied for person identification. Thus, the proposed method proves that biometric recognition can be performed regardless of different physiological conditions and can be applied in real life scenarios.

Using autonomous recording units to identify and monitor western yellow‐billed cuckoo habitat

AbstractAutonomous recording units (ARUs) paired with signal classification software can be used to detect species‐specific calls, making them useful for evaluating patterns in avian occurrence and activity. However, classification of target signals is not always reliable and may be especially challenging for species that vocalize infrequently. We assessed the use of ARUs to identify and monitor habitat for a cryptic and federally threatened distinct population segment, the western yellow‐billed cuckoo (Coccyzus americanus) in mountainous xeroriparian drainages. Using Kaleidoscope Pro, we developed a call‐classifier and processed acoustic data collected in sites also surveyed using traditional human‐observer methods, applying the same spatial and temporal detection criteria to estimate breeding territories for each method. The classifier detected a total of 4,061 true positive calls at 4 sites, had an overall precision score of 0.07, recall score of 0.09, and F‐score (beta = 1) of 0.08, indicating high false positive and false negative classification rates. Our results were, however, consistent with other ARU studies of rare and cryptic species and ARUs estimated occupancy as effectively as human surveys with as little as 2 hours of daily recording. Total detections varied among sites, likely due to differences in cuckoo population densities and the interaction between topography and ARU detection space. Our results suggest that despite performance shortcomings of call‐classifiers, ARUs can be effective for monitoring cuckoos, with potential for providing higher resolution temporal and spatial information on activity and habitat use and may be particularly effective in remote locations where cuckoos often occur.

Subgroup disproportionality analysis of dementia-related adverse events with sacubitril/valsartan across geographical regions

This study aimed to evaluate the association between sacubitril/valsartan and dementia-related adverse events (AEs) in geographical subpopulations using subgroup disproportionality analysis. Cases from the FDA adverse event reporting system involving patients aged 60 or older with sacubitril/valsartan or angiotensin receptor blockers (ARBs) were analyzed. The adjusted reporting odds ratios (RORs) for dementia-related AEs were calculated for each continent. A total of 61,518 AEs associated with sacubitril/valsartan or ARBs were identified. Among these, 1441 were dementia-related AEs. In Asia, Europe, and Africa, the reporting risk of dementia-related AEs associated with sacubitril/valsartan was lower compared to ARBs (adjusted ROR, 0.57 [95% CI 0.31–1.01]; adjusted ROR, 0.89 [95% CI 0.69–1.14]; adjusted ROR, 0.40 [95% CI 0.27–0.61], respectively). In Latin America and Oceania, the reporting risk of dementia-related AEs associated with sacubitril/valsartan was similar to that associated with ARBs (adjusted ROR, 1.04 [95% CI 0.75–1.44]; adjusted ROR, 1.02 [95% CI 0.31–3.37], respectively). On the contrary, in North America, the reporting risk associated with sacubitril/valsartan was higher compared to ARBs (adjusted ROR, 1.29 [95% CI 1.10–1.53]). Although the ROR value did not meet the criteria for signal detection, the significantly greater than 1 ROR observed in North America suggests that caution may be warranted regarding potential dementia-related adverse events associated with sacubitril/valsartan.

Adaptive distributed fault estimation observer design for nonlinear time-delay multi-agent systems with directed graphs

This paper focuses the development of an adaptive observer-based distributed fault estimation observer (DFEO) for multi-agent nonlinear time-delay systems under a directed communication topology. The process involves constructing a fault estimation observer for each agent based on their relative output estimation errors. An adaptive DFEO design is proposed for multi-agent systems with a directed communication topology. Furthermore, investigating the effect of faulty agents on the criteria of fault detection on healthy agents. The proposed design is represented with Linear Matrix Inequalities (LMIs) to establish a systematic design procedure. The study also includes simulation results to demonstrate the effectiveness of the proposed design techniques.

High impedance fault detection method based on improved Mayr Arc model and fitting curve coefficients

To solve the problem of weak transient fault currents and significant nonlinear characteristics caused by arcs in resonant grounding systems, proposing a high-impedance fault (HIF) detection method based on an improved Mayr arc model and fitting curve coefficients. Firstly, to address the issue of low accuracy in simulating fault waveforms in existing HIF models, construct an improved arc model based on Mayr and control the parameter values under different feature dimensions, achieving accurate simulation of arc waveforms. Secondly, based on the impedance characteristics of the feeder, analyze the phase frequency characteristics, determine the first capacitive frequency band, and then determine the cutoff frequency of the Chebyshev filter to improve the ability of waveform feature extraction and fault recognition. Finally, by fitting the voltage and current geometric characteristic curve within the characteristic frequency band, using the least squares method and the quadratic coefficient values of the fitting curve to construct detection criteria, accurate detection of HIF is possible. Research shows that the improved arc model can fully characterize the current arc waveform under various working conditions. Simulations and real-world measurements verify that HIF can be accurately detected using the quadratic coefficient value of the fitting curve.

Optimising eDNA analysis for urban otter monitoring: seasonal patterns, detection strategies and prey availability

Significant advancements in environmental DNA (eDNA) analysis technology have led to its widespread adoption for species monitoring. However, the low DNA concentration in eDNA often raises concerns about PCR bias, which is a primary issue in enhancing the reliability of eDNA survey results. This study aims to identify the suitable eDNA analysis method and detection strategy for obtaining the presence data of Eurasian otters in an urban stream and to investigate the seasonal distribution patterns of Eurasian otters and their potential prey species. The eDNA survey revealed the presence of six out of the 13 mammal species recorded in fieldwork and literature reviews, including Canis lupus, Felis silvestris and Mustela sibirica. Notably, Eurasian otters were not detected in the eDNA metabarcoding conducted in April and October, despite traditional surveys confirming their presence. In contrast, qPCR assays successfully amplified Eurasian otters from the same samples that were analysed using metabarcoding. When evaluating detection criteria, based on the number of positive samples in repetitions, Eurasian otters were detected at 42.9% to 85.7% of the sampling sites in April and at all sampling sites in October. This suggests a higher detection probability of Eurasian otters in October, indicating a potential expansion of their home range during that season compared to April. Metabarcoding results revealed similar findings regarding fish species as traditional surveys, with Cyprinidae accounting for the largest proportion of fish species at the family level (April, 54.57%; October, 43.58%), followed by Gobiidae (April, 16.90%; October, 22.88%). At the species level, P. parva was the dominant fish species in Saetgang, constituting 5.68% and 6.35% of relative abundance in April and October, respectively. This implies that Eurasian otters as opportunistic predators, may increasingly take advantage of the availability of species within the family Cyprinidae, notably Pseudorasbora parva, as a food source within the study area during the months of April and October. This study highlights that qPCR is the more effective approach in urban areas to offer insights into otters’ distribution patterns, while metabarcoding is useful to provide the properties of the biological environment. Furthermore, this study indicated that it is necessary to determine the suitable eDNA analysis methods depending on the research purpose to obtain detection results effectively.