Detection Scheme Research Articles

Incipient fault detection and isolation with Cauchy–Schwarz divergence: A probabilistic approach

To monitor the dynamics and non-stationarity inherent in industrial processes, we propose a novel incipient fault detection and isolation scheme grounded in a probabilistic perspective, using the Cauchy–Schwarz (CS) divergence. Our innovation lies in the utilization of marginal CS divergence for incipient fault detection and the conditional CS divergence for fault isolation. This approach neither require prior parametric assumptions about the underlying data distribution nor depend on historical fault data, while simultaneously providing explanatory diagnostics. Beyond this, we develop a change point detection-base diagnosis technique for practical engineering applications. This online process monitoring technique guarantees timely intervention to uphold process stability and safety. We demonstrate the compelling performance, higher detection rate and lower alarm rate, of the CS divergence over prevalent divergence-based approaches, such as Kullback–Leibler divergence, Wasserstein distance and Mahalanobis distance. We also illustrate the explanatory insights offered by conditional CS divergence in fault isolation on synthetic data, benchmarks of continuous stirred-tank reactor process and continuous stirred-tank heater process and even a real-world continuous catalytic reforming process. Code of this CS divergence based fault detection and isolation is available at https://github.com/Feiya-Lv/Incipient-Fault-Detection-and-Isolation-with-Cauchy--Schwarz-Divergence.

Attack Detection and Mitigation Scheme of Load Frequency Control Systems Against False Data Injection Attacks

Attack Detection and Mitigation Scheme of Load Frequency Control Systems Against False Data Injection Attacks

Meta-analysis of prevalence of paratuberculosis in cattle using published estimates under serum and milk ELISA

Johne's disease (JD), caused by Mycobacterium avium subspecies paratuberculosis (MAP), poses significant challenges to the global livestock industry, particularly affecting bovine populations. To better understand the prevalence of paratuberculosis and its diagnostic nuances, a comprehensive meta-analysis was conducted. This analysis encompassed 21 studies involving 632,767 cows for milk enzyme-linked immunosorbent assay (ELISA) and 51 studies involving 256,409 cows for serum ELISA. The pooled prevalence estimate for paratuberculosis on a cow-basis was found to be 16% (95% CI: 14%; 18%) for milk ELISA and 8% (95% CI: 7%; 8%) for serum ELISA. Notably, higher confidence intervals (CI) were observed in milk ELISA, the Europe and Asia groups, suggesting variability in prevalence estimates within these regions. Conversely, lower CIs were noted in the USA and Canada groups, indicating greater consistency in prevalence estimates within these countries. However, serum ELISA exhibited high CI values across all regions, underscoring potential variability in diagnostic performance. These findings provide valuable insights for veterinarians, researchers, policymakers, and livestock producers in optimizing paratuberculosis detection and control strategies to mitigate its impact on bovine health and agricultural productivity.

An Update of Extracellular Vesicle Involvement in Different Steps of Cancer Metastasis and Targeting Strategies.

Cancer metastasis is the deadliest event in tumorigenesis. Despite extensive research, there are still unsolved challenges regarding early metastasis detection and targeting strategies. Extracellular vesicles (EVs) and their impact on tumorigenic-related events are in the eye of current investigations. EVs represent a plethora of biomarkers and information, and they are considered key determinants in tumor progression and for tumor prognosis and monitoring. EVs are one of the key mediators for inter-cellular communications between tumor cells and their nearby stroma. They are involved in different steps of metastasis from invasion toward formation of pre-metastatic niches (PMNs), and final growth and colonization of tumor cells in desired organ/s of the target. Membrane components of EVs and their cargo can be traced for the identification of tumor metastasis, and their targeting is a promising strategy in cancer therapy. In this review, we aimed to discuss the current understanding of EV-based metastatic predilection in cancer, providing updated information about EV involvement in different metastatic steps and suggesting some strategies to hamper this devastating condition.

Combined wavelet transforms and neural network feed-forward model for ECG peak detection and classification

We have focused on development of a combined approach for electrocardiogram (ECG) signal filtering and various ECG peak detection. The filtering model is based on the combination of wavelet transform and neural network where after computing the wavelet coefficients the neural network feed-forward model is used to update the weights. The filtered signal is processed through the convolution layers and bidirectional long short-term memory (Bi-LSTM) architecture to perform the ECG peak detection. Further, we apply a combined feature extraction strategy where wavelet transform and morphological feature are extracted to classify the ECG beats as classify 5 different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC) to examine the heart condition. The feature extraction phase uses wavelet transform, morphological features and high-order statistics to generate the robust features. The obtained feature vector is processed through the principal component analysis (PCA) module to reduce the dimension of feature vector. These features are trained by using support vector machine (SVM) and k-nearest neighbor (KNN) supervised model. The proposed approach is tested on publicly available MIT-BIH dataset where performance analysis shows that the proposed approach obtained average precision, sensitivity and error as 99.98%, 99.96%, and 0.101 which outperforms the existing filtering and peak detection schemes.

Integrated interferometers as a new platform for low cost gas chromatography detection

Gas chromatography is a reference method for gas analysis. As part of efforts to miniaturize gas chromatography systems, the miniaturization of detectors is essential. In this work, we report a new integrated photonic platform for gas chromatography analyte detection. The fabricated silicon die integrates Mach-Zehnder interferometers into low dead volume microfluidic channels, with coherent cost-effective detection scheme with a fixed 850 nm wavelength laser. A proof of concept is demonstrated with the separation and detection of three volatile organic compounds: heptane, octane, and toluene. Peaks’ widths at half height range from 1 to 5 seconds. Peaks are very well resolved by our system, which acquires more than 100 points per second. From a heptane dilution range, we evaluate the limit of detection of our system to be the headspace of a 0.22% heptane concentration solution. To our knowledge, these are the first integrated Mach-Zehnder interferometers reported for gas chromatography detection. This work could open new strategies for fast low cost and low limit of detection specific gas chromatography silicon micro-detectors.

Smoothed-Interface SPH Model for Multiphase Fluid-Structure Interaction

Numerical simulation of multiphase flows with large density ratios presents considerable challenges. Traditional Smoothed Particle Hydrodynamics (SPH) methods, while efficient in tracking multi-phase interfaces, often suffer from non-physical gaps near interfaces, compromising accuracy. In this paper, we develop an advanced multiphase model with enhanced interface continuity and stability, offering a reliable approach to both multiphase flows and fluid-structure interaction. The model employs equivalent continuity equations across different phases and refines the treatment of pressure and its gradients at interfaces through a smoothing technique, which effectively eliminates the non-physical gaps prevalent in the conventional SPH multiphase models. Moreover, an efficient and accurate method for calculating interface normal vectors and identifying interfaces is proposed. Based on the method, we introduce a series of techniques, including an improved interface force model, a particle displacement strategy, and a new particle penetration detection scheme, all of which significantly improve the interface continuity and the overall simulation accuracy of multiphase flows. Finally, the proposed multiphase SPH model is validated through several numerical examples, including two-dimensional static water, standing waves, dam break, oscillating multi-fluid, and wedge entry, as well as three-dimensional sphere entry. Comparative results with the traditional model indicate that the multiphase SPH model in this work ensures interface continuity and stability in long-period simulations, even under intense flow conditions. These results suggest that the smoothed-interface SPH multiphase model can eliminate non-physical gaps at interfaces, greatly enhancing interface continuity and stability, and highlighting its potential for accurate multiphase flow simulations.

An Enhanced Multi-Sensor Detection Scheme With Drift Calibration and Array Optimization

An Enhanced Multi-Sensor Detection Scheme With Drift Calibration and Array Optimization

Fiber-Optics Quench Detection Schemes in HTS Cables for Fusion Magnets

Fiber-Optics Quench Detection Schemes in HTS Cables for Fusion Magnets

Evaluating the effectiveness of various diversity and combining techniques on an RF-FSO link

Abstract Diversity and combining is one of the robust techniques to mitigate multi-path fading in RF links and turbulence-induced scintillation in FSO links. This article evaluates the effect of different diversity and combining techniques on outage and BER performance of decode and forward RF-FSO link. We have utilized diversity and combining techniques over source to relay RF link and/or relay to destination FSO link. The selection combining, transmit antenna selection, maximal ratio combining, and maximum ratio transmission schemes are considered. The Monte-Carlo simulation results of outage probability and bit error rate are compared by considering different parameters affecting link quality like the strength of atmospheric turbulence, pointing error, FSO link detection scheme, RF link fading strength, diversity order, etc. It is found that configurations like SIMO-SISO, MISO-SISO, SISO-SIMO, and SISO-MISO, where diversity is applied in either source to relay link or relay to destination link, are improving the overall link performance provided that diversity is applied at the link experiencing stronger fading compared to another link. The link configurations SIMO-MISO or MISO-SIMO also improve the link performance and it can achieve BER of 10−5 at less than 15 dB SNR in favourable link conditions.

A sensor platform based on SERS detection/janus textile for sweat glucose and lactate analysis toward portable monitoring of wellness status

Herein we report a wearable sweat sensor of a Janus fabric based on surface enhanced Raman scattering (SERS) technology, mainly detecting the two important metabolites glucose and lactate. Janus fabric is composed of electrospinning PU on a piece of medical gauze (cotton), working as the unidirectional moisture transport component (R = 1305%) to collect and transfer sweat efficiently. SERS tags with different structures act as the probe to recognize and detect the glucose and lactate in high sensitivity. Core-shell structured gold nanorods with DTNB inside (AuNRs@DTNB@Au) are used to detect lactate, while gold nanorods with MPBA (AuNRs@MPBA) are used to detect glucose. Through the characteristic SERS information, two calibration functions were established for the concentration determination of glucose and lactate. The concentrations of glucose and lactate in sweat of a 23 years volunteer during three-stage interval running are tested to be 95.5, 53.2, 30.5 μM and 4.9, 13.9, 10.8 mM, indicating the glucose (energy) consumption during exercise and the rapid accumulation of lactate at the early stage accompanied by the subsequent relief. As expected, this sensing system is able to provide a novel strategy for effective acquisition and rapid detection of essential biomarkers in sweat.

A new strategy for volatile organics detection – Molecularly imprinted polymers-enabled headspace extracting and electrochemical sensing

The widely accepted method for detecting trace odor compounds such as 2-methylisoborneol (2-MIB) requires high-cost solid-phase microextraction coupled with gas chromatography-tandem mass spectrometry (SPME-GC/MS). As for now, cases that directly used SPME-GC/MS in the field are unseen, and most detection activities are still carried out in rigorous laboratory conditions. However, lengthy logistics and storage issues can compromise sample conditions, underscoring the significance of on-site detection methods. Here, we reported molecularly imprinted polymers-enabled headspace extracting and electrochemical sensing (MHEES) with the core feature of using the same piece of MIP for both headspace extracting and electrochemical sensing, allowing rapid on-site detection of trace 2-MIB in natural water bodies with a total cost of less than $2.10 per test. MHEES demonstrated exceptional sensitivity during extensively repeated laboratory experiments, achieving a detection limit of 3.7 ng∙L−1 and a standard deviation of 4.58 %. The practicality of MHEES was confirmed by conducting two rounds of on-site analyses on different reservoirs with fluctuating seasonal concentrations of 2-MIB. The developing flow of MHEES, from molecules docking to on-site application, was rigorous and could be cross-referenced to related research. The proposed MHEES design can potentially revolutionize the field of analytical chemistry by providing a versatile and affordable platform for on-site volatile compounds detection. It can readily adapt to various organic compounds and effectively address real-world challenges such as monitoring emerging pollutants, tracing disease biomarkers in body fluids, inspecting food safety, identifying chemical reaction intermediates, and supporting healthcare applications.

Detection and prediction of anomalous behaviors of enterprise’s employees based on data-mining and optimization algorithm

Internal employees have always been at the core of organizational security management challenges. Once an employee exhibits behaviors that threaten the organization, the resulting damage can be profound. Therefore, analyzing reasonably stored behavioral data can equip managers with effective threat monitoring and warning solutions. Through data-mining, a knowledge graph for internal threat data is deduced, and models for detecting anomalous behaviors and predicting resignations are developed. Initially, data-mining is employed to model the knowledge ontology of internal threats and construct the knowledge graph; subsequently, using the behavioral characteristics, the BP neural network is optimized with the Sparrow Search Algorithm (SSA), establishing a detection model for anomalous behaviors (SBP); additionally, behavioral sequences are processed through data feature vectorization. Utilizing SBP, the LSTM network is further optimized, creating a predictive model for employee behaviors (SLSTM); ultimately, SBP detects anomalous behaviors, while SLSTM predicts resignation intentions, thus enhancing detection strategies for at-risk employees. The integration of these models forms a comprehensive threat detection technology within the organization. The efficacy and practicality of detecting anomalous behaviors and predicting resignations using SBP and SLSTM are demonstrated, comparing them with other algorithms and analyzing potential causes of misjudgment. This work has enhanced the detection efficiency and update speed of abnormal employee behaviors, lowered the misjudgment rate, and significantly mitigated the impact of internal threats on the organization.

A Simultaneous Calibration and Detection Strategy for Electrochemical Sensing with High Accuracy in Complex Water.

The electrochemical sensors loaded with nanomaterials have exhibited a great sensitivity. Nonetheless, the field detection for complex waterbodies can be affected by cross-sensitivity, environmental conditions such as temperature and pH value, as well as the relatively low reproducibility and stability of nanomaterials. In this paper, a simultaneous calibration and detection (SCD) strategy is proposed to introduce a simultaneous and precise calibration during field electrochemical detection, which is composed of a linear regression algorithm and a compact electrochemical sensor containing a series of identical sensing cells. This design can significantly mitigate cross-sensitivity in complex water and the inconsistency of sensing materials. Applied in the NO2- detection for practical waterbodies, the SCD strategy has exhibited a relative error of no more than 9.6% for the measurement compared to the results obtained by the standard Griess method and higher accuracy than the normal electrochemical method. The SCD strategy is independent of sensing materials, indicating that it can be widely applied to various detections by just switching the corresponding sensing material.

Innovative Colorimetric NQO1 Detection Strategy via Substrate Competitive and Biomimetic Cascade Reactions with a Highly Active NADH Oxidase Mimic.

NAD(P)H: quinone oxidoreductase-1 (NQO1) plays critical roles in antioxidation and abnormally overexpresses in tumors. Developing a fast and sensitive method of monitoring NQO1 will greatly promote cancer diagnosis in clinical practice. This study introduces a transformative colorimetric detection strategy for NQO1, harnessing an innovative competitive substrate mechanism between NQO1 and a new NADH oxidase (NOX) mimic, cobalt-nitrogen-doped carbon nanozyme (CoNC). This method ingeniously exploits the differential consumption of NADH in the presence of NQO1 to modulate the generation of H2O2 from CoNC catalysis, which is then quantified through a secondary, peroxidase-mimetic cascade reaction involving Prussian blue (PB) nanoparticles. This dual-stage reaction framework not only enhances the sensitivity of NQO1 detection, achieving a limit of detection as low as 0.67 μg mL-1, but also enables the differentiation between cancerous and noncancerous cells by their enzymatic activity profiles. Moreover, CoNC exhibits exceptional catalytic efficiency, with a specific activity reaching 5.2 U mg-1, significantly outperforming existing NOX mimics. Beyond mere detection, CoNC serves a dual role, acting as both a robust mimic of cytochrome c reductase (Cyt c) and a cornerstone for enzymatic regeneration, thereby broadening the scope of its biological applications. This study not only marks a significant step forward in the bioanalytical application of nanozymes but also sets the stage for their expanded use in clinical diagnostics and therapeutic monitoring.

Apply frequency response function schemes for damage detection in composite nanoscale-pipes under transient conditions

Knowledge of Nanoscale-structures sustainability is strongly associated with the reliability and the performances of Nano-electro-mechanical systems such as Nanoactuators and Nano-sensors that are widely used in bio-applications. Therefore, the damages in these types of infinitesimal systems must be quickly and accurately detected, located, and repaired. In this study, a novel damage detection framework in Carbon Fiber Reinforced Polymer (CFRP) composite Nanoscale-Pipes (CNP) subjected to the fatigue effect of water internal pressure and thermal effect via Nanoscale-electrical capacitance sensors (NECS). First, a finite element model (FEM) of CNP damage is established, then the electrical potential difference (EPD) between the Nanoscale-electrodes (NEs) pairs before and after damage is measured. The distributed NECS measuring nano-electrodes are installed around the CNP and load the transient external excitations between each other. It is proposed and applied the transfer function (TF) of the CNP as an "open loop control" system to reflect the evolution of damage. The accuracy and reliability of the suggested technology are verified through the experimental results available in the literature. The results show that when the damage under NEs pairs has begun to grow, the signal amplitude value will change suddenly and sensitively, which demonstrates the effectiveness of the proposed method and the good potential for engineering applications.

Findings on Age at Onset of Cancer in Diabetic and Non-diabetic Populations.

Background Diabetes mellitus and cancer are two associated chronic diseases. Despite being a widely researched topic, the underlying mechanisms of this association remain unclear. One of the poorly explored topics regarding diabetes and cancer is the relation between the age of cancer onset and diabetes mellitus status; therefore, this research exposes the difference in the age of cancer diagnosis in both groups. Methods We conducted a retrospective study by reviewing the clinical files on a secondary care hospital's database. Files from first-time consultations of patients over 18 diagnosed using a histopathological report were included.The present study aimed to determine whether there is a difference in age at the onset of cancer in diabetic and non-diabetic individuals. Moreover, we calculated the average BMI at the onset for both populations. Results Our study included 8,741 patients; 1,551 (17.8%) were diabetic, and 7,190 (82.2%) were non-diabetic. From 28 types of cancer, 27 showed a difference in the age at the onset of cancer when diabetic and non-diabetic subjects were compared. This difference is significant as it suggests a potential link between diabetes and cancer, which could have implications for early detection and prevention strategies. Out of the 27 types, 17 showed statistical significance with p-values ranging from 0.048 to <0.0001 considering a 95% CI. Among those, the most significant types of cancer were breast, cervical, lung, ovarian, rectal, thyroid, and sarcoma, reporting p-values <0.0001. The mean age at onset of cancer in diabetic and non-diabetic populations was 62.7 years (SD ± 3.9) and 55.3 years (SD ± 7.9), respectively, showing a difference of 7.4 years in both groups. The BMI was statistically significant in patients with breast (p = 0.006), endometrial (p = 0.007), head and neck (p=0.014), and thyroid (p = 0.022) cancer types. Conclusion The data offer a critical view of the relationship between cancer and diabetes. Since virtually no one has produced a similar report, there is a broad field for researching the causal factors implicated in the pathway of diabetic and non-diabetic individuals who develop cancer. Research regarding metformin, diabetic neuropathy, and other possible causes must be addressed to determine whether they are involved in this process.

Adaptive machine learning models: Concepts for real-time financial fraud prevention in dynamic environments

Adaptive machine learning models are revolutionizing real-time financial fraud prevention in dynamic environments, offering unparalleled accuracy and responsiveness to evolving fraud patterns. Financial institutions face constant threats from increasingly sophisticated fraud schemes that adapt and change over time. Traditional static models often fall short in addressing these rapidly shifting threats, necessitating the adoption of adaptive machine learning techniques. Adaptive machine learning models are designed to evolve continuously by learning from new data and adjusting to emerging fraud patterns. These models employ advanced algorithms, such as reinforcement learning, online learning, and deep learning, to maintain their effectiveness in detecting and preventing fraud. Reinforcement learning algorithms optimize detection strategies by receiving feedback from their actions, continually improving their decision-making processes. Online learning algorithms update models incrementally as new transaction data becomes available, ensuring that the models remain current and responsive. One of the key strengths of adaptive machine learning models is their ability to process vast amounts of data in real time. By leveraging technologies such as neural networks and ensemble learning, these models can analyze complex datasets, identify subtle anomalies, and detect fraudulent activities with high precision. Real-time data processing capabilities enable immediate detection and response to suspicious transactions, significantly reducing the risk of financial losses. Adaptive models also incorporate anomaly detection techniques to identify deviations from normal transaction behavior. By constantly learning from the latest data, these models can recognize previously unseen fraud patterns, providing a robust defense against novel threats. Additionally, the integration of explainable AI (XAI) techniques ensures that the decision-making processes of these models are transparent and interpretable, fostering trust and compliance with regulatory requirements. Implementing adaptive machine learning models for real-time fraud prevention involves addressing challenges such as data quality, computational efficiency, and model interpretability. Financial institutions must ensure the availability of high-quality data and invest in robust computational infrastructure to support real-time processing. Furthermore, adopting explainable AI techniques enhances model transparency and regulatory compliance. In conclusion, adaptive machine learning models offer a dynamic and effective solution for real-time financial fraud prevention. By continuously learning and adapting to new data, these models provide a resilient defense against evolving fraud schemes, enhancing the security and integrity of financial transactions. This adaptive approach not only mitigates financial risks but also strengthens the overall trustworthiness of financial systems.

An enzyme-free fluorescence sensing platform based on multiplex toehold-mediated strand displacement for point-of-care testing of single nucleotide polymorphisms

Single nucleotide polymorphisms (SNPs) are biomarkers for the prediction and diagnosis of biomedical diseases, as well as important reference markers for intelligent design and breeding of animals and plants. Therefore, the development of high-sensitivity, practicability, and convenient SNPs sensing strategies and detection platforms is of great significance. In this study, we pioneered an ultra-sensitive universal sensing strategy for single base mutation detection and an enzyme-free FRET fluorescence sensing point-of-care testing (POCT) platform for SNP detection. Based on the thermodynamically regulated strand replacement mechanism, the system rationally introduced extra base mismatches and combined multiple toehold-mediated strand displacement (TMSD) reactions to achieve enzyme-free recognition of the single base mutation and double amplification of the detection signal. Finally, the biological signal of the mutant target (MT) was recognized, amplified, and transduced into a fluorescence resonance energy transfer (FRET) signal between Cy3 and Cy5 for output. Overall, the proposed biosensing strategy demonstrated high sensitivity for MT, with a detection limit as low as 0.37 fM, and could achieve detection of targets with mutant abundances as low as 0.1 %. Moreover, a portable detection system was assembled in conjunction with this sensing strategy to form a POCT platform for SNP genotyping. This POCT platform was validated to accurately identify SNPs from human cancer cells and soybean leaf genome extracts. In summary, this study will provide new insights into the design of enzyme-free biosensors for SNP detection, and will provide important technical support tools for the prevention and diagnosis of major diseases, animal and plant breeding, etc.

A europium-based CP fluorescent probe for sensing malachite green, ascorbic acid and uric acid

Lanthanide (such as Eu and Tb) coordination polymers (CPs) having various coordination modes and unique fluorescence properties can be used as highly sensitive chemical sensors. We synthesized a europium-based CP of Eu2(PEDA)3(H2O)4 (referred to as Eu-PEDA, PEDA = 1,2-phenylenediacetic acid group), and investigated its application in fluorescence detection. Eu-PEDA can be considered as a multifunctional fluorescent probe for sensing malachite green (MG), ascorbic acid (AA) and uric acid (UA) to achieve rapid fluorescence quenching response, and Eu-PEDA exhibited high quenching efficiency Ksv (6.21 × 105 M−1 for MG; 1.16 × 104 M−1 for AA; 1.02 × 104 M−1 for UA), low detection limit (MG: 0.050 μM; AA: 0.705 μM; UA: 0.965 μM). Furthermore, Eu-PEDA suspension dipping test strips, the Eu-PEDA test strips are obtained. The resultant test strip, shows the potential for directly visual detection of MG, AA and UA just by dripping the analytes on it. This work thus offers a facile and efficient strategy for rapid and visual detection of MG, AA and UA by Eu-based coordination compounds. In addition, the possible mechanisms of fluorescence quenching were studied by absorption spectra, the emission and excitation spectra.