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This study explores how magnetization manipulation occurs in a flowing medium over a pipe geometry using nuclear magnetic resonance methods. In particular, the experimental conditions on radio frequency pulsing and the signal detection are studied in an ultra-low frequency regime of a few kilohertz. An analytical model of the magnetization preparation is presented, which calculates the magnetization distribution of an excited flowing segment. The effective flip angle distribution is mapped to simulate the temporal development of the magnetization in the segment. The simulation results are compared to the experimental data from a flow metering setup. As a result, the quantitative impact of the flow profile on the magnetic signal is shown. [ABSTRACT FROM AUTHOR]

Semiconductor nanocrystals (NCs) can function as efficient gain materials with chemical versatility because of their surface ligands. Because the properties of NCs in solution are sensitive to ligand–environment interactions, local chemical changes can result in changes in the optical response. However, amplification of the optical response is technically challenging because of colloidal instability at NC concentrations needed for sufficient gain to overcome losses. This paper demonstrates liquid lasing from plasmonic lattice cavities integrated with ligand-engineered CdZnS/ZnS NCs dispersed in toluene and water. By taking advantage of calcium ion-induced aggregation of NCs in aqueous solutions, we show how lasing threshold can be used as a transduction signal for ion detection. Our work highlights how NC solutions and plasmonic lattices with open cavity architectures can serve as a biosensing platform for lab-on-chip devices. [ABSTRACT FROM AUTHOR]

The remarkable signal-detection capabilities of the auditory and vestibular systems have been studied for decades. Much of the conceptual framework that arose from this research has suggested that these sensory systems rest on the verge of instability, near a Hopf bifurcation, in order to explain the detection specifications. However, this paradigm contains several unresolved issues. Critical systems are not robust to stochastic fluctuations or imprecise tuning of the system parameters. Further, a system poised at criticality exhibits a phenomenon known in dynamical systems theory as critical slowing down, where the response time diverges as the system approaches the critical point. An alternative description of these sensory systems is based on the notion of chaotic dynamics, where the instabilities inherent to the dynamics produce high temporal acuity and sensitivity to weak signals, even in the presence of noise. This alternative description resolves the issues that arise in the criticality picture. We review the conceptual framework and experimental evidence that supports the use of chaos for signal detection by these systems, and propose future validation experiments.

In this article, we present the first demonstration of a distance-based paper analytical device (dPAD) for uric acid quantification in human urine samples with instrument-free readout and user-friendliness for the rapid diagnosis and prognosis of various related diseases. By employing gold nanoparticles (AuNPs) as a peroxidase-like nanozyme, our proposed technique eliminates the utilization of horseradish peroxidase (HRP), making the device cost-effective and stable. In our dPAD, uric acid in the sample is oxidized by the uricase enzyme and subsequently catalysed with AuNPs in the sample zone, generating hydroxyl radicals (˙OH). Then, the produced ˙OH reacts with 3,3′-diaminobenzidine (DAB) to form poly DAB (oxDAB), resulting in a coloured distance signal in the detection zone of the dPAD. The variation of the distance of the observed red-brown colour is directly proportional to the uric acid concentration. Our sensor exhibited a linear range from 0.50 to 6.0 mmol L−1 (R2 = 0.9922) with a detection limit (LOD) of 0.25 mmol L−1, covering the clinical range of uric acid in urine. Hence, there is no need for additional sample preparation or dilution. Additionally, this assay is highly selective, with no interferences. We also found that this approach could accurately and precisely determine uric acid in human control samples with the recovery ranging from 99.37 to 100.35 with the highest RSD of 4.05%. Our method is comparable with the use of a commercially available uric acid sensor at a 95% confidence interval. Consequently, the developed dPAD offers numerous advantages such as cost-effectiveness, simplicity, and ease of operation with unskilled individuals. Furthermore, this concept can be applied for extensive biosensing applications in monitoring other biomarkers as an alternative analytical point-of-care (POC) device. [ABSTRACT FROM AUTHOR]

The detection and identification of epileptic seizures attracted considerable relevance for the neurophysiologists. In order to accomplish the detection of epileptic seizures or equivalently ictal EEG states, this paper proposes the use of nonlinear and chaos features not computed over the raw EEG signals as it was commonly experienced, but instead over intrinsic mode functions (IMFs) extracted subsequently to the application of newly time-frequency signal decomposition methods on the basis of empirical mode decomposition (EMD) and variational mode decomposition (VMD) methods. The first step within the proposed methodology is to excerpt the various components of the IMFs by EMD and VMD decomposition methods on time EEG segments. The Hjorth parameters, the Hurst exponent, the Recurrence Quantification Analysis (RQA), the detrended fluctuation analysis (DFA), the Largest Lyapunov Exponent (LLE), The Higuchi and Katz fractal dimensions (HFD and KFD), seven nonlinear and chaos features computed over the IMFs were investigated and their classification performances evaluated using the k-nearest neighbor (KNN) and the multilayer perceptron neural network (MLPNN) classifiers. Furthermore, the combination of the best nonlinear features has also been examined in terms of sensitivity, specificity and overall classification accuracy. The publicly available Bonn EEG dataset has been has been employed to validate the efficiency of the proposed method for detecting ictal EEG signals from normal or interictal EEG segments. Among the several experiments involved in the current study, the ultimate results establish that the overall classification accuracy can achieve 100%, 99.45%, 99.8%, 99.8%, 98.6% and 99.1% for six different epileptic seizure detection case problems studied, confirming the ability of the proposed methodology in helping the clinic practitioners in the epilepsy detection care units to classify seizure events with a great confidence. [ABSTRACT FROM AUTHOR]

In this work, we reported an electrochemical biosensor with target-assisted self-cleavage DNAzyme function for signal amplified detection of miRNA. The target-recycling amplification led to significant signal enhancement and thus offers high detection sensitivity. [ABSTRACT FROM AUTHOR]

Spoofed signal interference poses a serious threat to the security of Global Navigation Satellite Systems (GNSS). In order to effectively detect spoofing signals, this paper proposes a spoofing signal detection method based on the Transformer model in the signal capture phase. When a spoofed signal is added, the capture matrix of the receiver changes. The method takes the capture matrix near the relevant peak as the data set for training the Transformer model to improve the model's ability to recognize the features of the capture matrix, and then uses the trained model to identify the capture results and get the discriminative result of whether there is deception joining. Subsequently, the trained model is embedded into the navigation receiver, and the receiver configuration is modified so that it continuously performs signal capture over the whole data and detects spoofing signals on the capture results. The experimental results show that the spoofing signal detection method based on the Transformer model has a higher detection accuracy compared to other deep learning models. For data with different search steps, its detection accuracy can reach more than 95%. When the chip delay of the spoofed signal is greater than half a chip, the detection accuracy tends to be close to 100%. For online open-source spoofing datasets, the detection algorithm can still obtain excellent detection results. The spoofing signal detection technique based on the Transformer model is of great significance to improve the security and robustness of the navigation system and has the prospect of wide application. [ABSTRACT FROM AUTHOR]

Supermolecule refers to two or more molecules that are bonded together by secondary bonds between molecules to form a multimolecular system at the level of more than molecules. At this point, the molecules form an orderly aggregation, showing its unique structure and function. Supramolecular catalysts can form structures with specific functions through non‐covalent interactions (e. g., hydrogen bonding, π‐π stacking, etc.) to mimic the catalytic mechanism of natural enzymes and achieve susceptible and specific detection of biomolecules. The application of supramolecules is mainly focused on the self‐assembly of components for the construction of sensing platform. Therefore, supramolecular catalysts have a huge potential in biomolecule detection. This article focuses on the application of cyclodextrins(CD), cucurbit[n]uril(CB), porphyrins(PP) and Calix[n]arenes(CA) in the field of catalysis and in improving detection sensitivity. Research progress of supramolecular catalysts in polymerization amplification is summarized in this paper. [ABSTRACT FROM AUTHOR]

AbstractIn spatial analysis, it is essential to understand and quantify spatial or temporal heterogeneity. This paper focuses on the generalized spatially varying coefficient model (GSVCM), a powerful framework to accommodate spatial heterogeneity by allowing regression coefficients to vary in a given spatial domain. We propose a penalized bivariate spline method for detecting local signals in GSVCM. The key idea is to use bivariate splines defined on triangulation to approximate nonparametric varying coefficient functions and impose a local penalty on L2 norms of spline coefficients for each triangle to identify null regions of zero effects. Moreover, we develop model confidence regions as the inference tool to quantify the uncertainty of the estimated null regions. Our method partitions the region of interest using triangulation and efficiently approximates irregular domains. In addition, we propose an efficient algorithm to obtain the proposed estimator using the local quadratic approximation. We also establish the consistency of estimated nonparametric coefficient functions and the estimated null regions. The numerical performance of the proposed method is evaluated in both simulation cases and real data analysis. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study is to use non-destructive testing of corrosion on coated aluminium alloys using differential eddy current detection (DECD), with the aim of elucidating the relationship between the characteristics of corrosion defects and the detection signal. Design/methodology/approach: Pitting corrosion defects of varying geometrical dimensions were fabricated on the surface of aluminium alloy plates, and their impedance signals were detected using DECD to investigate the influence of defect diameter, depth, corrosion products and coating thickness on the detection signals. Furthermore, finite element analysis was used to ascertain the eddy current distributions and detection signals under different parameters. Findings: The size of the defect is positively correlated with the strength of the detection signal, with the defect affecting the latter by modifying the distribution and magnitude of the eddy current. An increase in the diameter and depth of corrosion defects will enhance the eddy current detection (ECD) signal. The presence of corrosion products in the corrosion defects has no significant effect on the eddy current signal. The presence of a coating results in a decrease in the ECD signal, with the magnitude of this decrease increasing with the thickness of the coating. Originality/value: The objective is to provide experimental and theoretical references for the design of eddy current non-destructive testing equipment and eddy current testing applications. [ABSTRACT FROM AUTHOR]

Over the past 45 years, psychological scientists have invested a tremendous amount of effort into increasing the accuracy of suspect identifications from eyewitness lineup procedures. Those efforts have paid dividends, leading to the development of several practices that increase the accuracy of suspect identifications. Meanwhile, lineup rejections have largely been written off as lacking diagnostic value and little effort has been placed into understanding (1) why lineup rejections are less diagnostic than suspect identifications, and (2) how to increase the diagnostic value of lineup rejections. We show that preventing innocent-suspect identifications is not equivalent to demonstrating innocence and highlight several consequences of not being able to clear innocent suspects from police suspicion. We then review recent evidence, grounded in Signal Detection Theory, suggesting that lineup rejections are less diagnostic than suspect identifications because it is harder to reject a lineup of several faces than it is to identify a single face. When lineups are modified so that witnesses provide rejection ratings for each non-identified lineup member, rejection accuracy drastically increases. We highlight several questions for future research examining the potential for eyewitness memory to demonstrate innocence and question what impact suspect rejections might play in multiple-witness cases. [ABSTRACT FROM AUTHOR]

This study aims to identify potential exoplanet signals from nearby stars with resolved debris discs. However, the high activity of many stars with debris discs limits the detection of periodic signals. Our study is constrained to a sample of 29 stars that have appropriate radial velocity data and debris disc measurements sufficient to resolve their inclination. Our results confirm and update previous findings for exoplanets around HD 10647, HD 115617, HD 69830, GJ 581, HD 22049, and HD 142091, and we identify long-term activity signals around HD 207129 and HD 202628. We utilize the inclination angles of the debris discs, assuming co-planarity between debris disc and exoplanet orbit, to determine the 'disc-aligned' masses of radial velocity exoplanets in this study. The 'disc-aligned' masses of HD 69830 b, HD 69830 c, and 61 Vir b suggests that they may be classified as 'hot' or 'warm' Jupiters and so might be nearby examples of planets that have undergone recent type-II disc migration. [ABSTRACT FROM AUTHOR]

The detection of sub-ppm levels of nanoplastics using a surface-enhanced Raman scattering sensor was demonstrated. The proposed Au nanograting as surface-enhanced Raman scattering sensor, fabricated using nanoimprint lithography and oblique angle deposition, was designed to contain nanoplastics with enhanced local plasmonic fields. Electromagnetic simulations and Raman measurements show that the plasmonic field enhanced inside the nanograting enables the detection of Raman signal from 50-nm-sized polystyrene nanoparticles in 3-μL-droplets at a concertation of 0.1 ppm. Furthermore, the proposed sensor was applied to detect polystyrene in microwave-heated water in a disposable bowl. The results show that the proposed sensor offers promising potential for detecting nanoplastics released from plasticware. [ABSTRACT FROM AUTHOR]

Under dynamic conditions, the response time of traditional voltage detection methods is relatively lengthy, leading to overshoots in the DC-link voltage of single-phase power converters, which significantly degrades system performance. This study proposes a rapid voltage transient detection method based on reduced-order generalized integrator (ROGI) aimed at improving the response speed of bus voltage peak detection. By reducing the order of the algorithm, this method shortens the generation time of orthogonal signals and accelerates the detection speed of voltage changes. This enables grid power to rapidly compensate for the load power under dynamic conditions while maintaining a high-precision complementarity of the split capacitor voltage. As a result, it effectively diminishes severe fluctuations in DC-link voltage, which increases the stability of the system under dynamic load changes. This paper meticulously derives the relationship between capacitor voltage changes and grid response delays, and constructs the ROGI algorithm and its control scheme, ensuring the robustness of the method in the presence of system parameter variations. Finally, to validate the effectiveness of the proposed method, a hardware experimental platform is established and tested. Results of load step response experiments indicate that the ROGI algorithm, in comparison to traditional detection methods, reduces the detection time by 40.9%. [ABSTRACT FROM AUTHOR]

Laser desorption/ionization mass spectrometry (LDI-MS) techniques have emerged as powerful tools for analyzing compounds of natural and synthetic origins. However, conventional methods like matrix-assisted laser desorption/ionization (MALDI) often suffer from background signals and limited detection capabilities, particularly for low molecular weight compounds. Surface-assisted laser desorption/ionization (SALDI) methodologies, utilizing nanomaterials, have shown promise in overcoming these limitations. Gold nanoparticles (AuNPs) are among the most widely utilized nanomaterials in SALDI-MS applications. AuNPs offer several advantages over traditional MALDI matrices, such as reduced interference, internal calibration potential, and enhanced stability. They have been successfully employed for analyzing a diverse range of analytes, from low molecular weight (LMW) compounds to peptides, proteins, and biological entities. This paper provides a comprehensive overview of various synthesis methods, morphological properties, and comparisons with conventional MALDI matrices. Furthermore, it explores the application of AuNPs for analyzing LMW compounds and imaging various surfaces using LDI-MS techniques. [ABSTRACT FROM AUTHOR]

Background and Objectives: Statistical methods for signal detection of adverse drug reactions (ADRs) in electronic health records (EHRs) need information about optimal significance levels and sample sizes to achieve sufficient power. Sauzet and Cornelius proposed tests for signal detection based on the hazard functions of Weibull type distributions (WSP tests) which use the time-to-event information available in EHRs. Optimal significance levels and sample sizes for the application of the WPS tests are derived. Method: A simulation study was performed with a range of scenarios for sample size, rate of event due (ADRs), and not due to the drug and random time to ADR occurrence. Based on the area under the curve of the receiver operating characteristic graph, we obtain optimal significance levels of the different WSP tests for the implementation in a hypothesis free signal detection setting and approximate sample sizes required to reach a power of 80% or 90%. Results: The dWSP–pPWSP (combination of double WSP and power WSP) test with a significance level of 0.004 was recommended. Sample sizes needed for a power of 80% were found to start at 60 events for an ADR rate equal to the background rate of 0.1. The number of events required for a background rate of 0.05 and an ADR rate equal to a 20% increase of the background rate was 900. Conclusion: Based on this study, it is recommended to use the dWSP–pWSP test combination for signal detection with a significance level of 0.004 when the same test is applied to all adverse events not depending on rates. [ABSTRACT FROM AUTHOR]

Introduction: There is a need to strengthen the evidence base regarding medication use during pregnancy and to facilitate the early detection of safety signals. EudraVigilance (EV) serves as the primary system for managing and analysing information concerning suspected adverse drug reactions (ADRs) within the European Economic Area. Despite its various functionalities, the current format for electronic submissions of safety reports lacks a specific data element indicating medicine exposure during pregnancy. Objective: This paper aims to address the limitations of existing approaches by developing a rule-based algorithm in EV that more reliably identifies cases that are truly representative of an ADR during pregnancy. Methods: The study utilised the standardised MedDRA query (SMQ) 'Pregnancy and neonatal topics' (PNT) as a benchmark for comparison. Recognising that the SMQ PNT also retrieves healthy pregnancy outcomes, contraceptive failure, failed abortifacients as well as ADRs not associated with pregnancy, a novel algorithm was tailored to improve the accuracy of identifying suspected ADRs occurring during pregnancy. Results: Upon testing, the algorithm demonstrated superior performance, correctly predicting 90% of cases reporting an ADR during pregnancy, compared to 54% achieved by the SMQ PNT. The implementation of the algorithm in EV led to the retrieval of 202,426 cases. Conclusion: The development and successful testing of the novel algorithm represents a step forward in pregnancy-specific signal detection in EV. Because signals associated with pregnancy may be diluted in a large database such as EV, this study lays the groundwork for future research to evaluate the effectiveness of disproportionality methods on a more refined subset of pregnancy-related ADR reports. [ABSTRACT FROM AUTHOR]

Background: Monoclonal antibodies (mAbs) are pivotal in treating various diseases, including cancers and autoimmune disorders. Despite their therapeutic benefits, mAb therapy has been associated with neurological toxicity. Objectives: This study aimed to assess the occurrence of neuronal toxicity associated with mAbs, utilizing data from the FDA Adverse Event Reporting System (FAERS) safety database. The study also sought to delineate the medical characteristics of the reported cases. Methods: A comprehensive analysis of neurological adverse events reported in the FAERS database was conducted, employing computational methodologies such as proportional relative risk (PRR), information component (IC025), and chi-square (χ2). Individual case safety reports (ICSRs) pertaining to neurological disorders linked to mAbs from the date of first global marketing authorization until June 30, 2023, were meticulously examined. Results: The FAERS safety database contains 79,022 ICSRs linking mAbs to nervous system disorders. Rituximab, bevacizumab, denosumab, nivolumab, and trastuzumab were frequently cited. Reported adverse events include headache, peripheral neuropathy, dizziness, and cerebrovascular accident. Most ICSRs (85.81%) were serious, mainly affecting females (57.04%) with a 14.09% fatality rate. Panitumumab, atezolizumab, bevacizumab, and trastuzumab showed strong drug-event associations. Signal disproportionate reporting (SDR) analysis flagged myasthenia gravis, peripheral neuropathy, and neurotoxicity across multiple mAbs, suggesting potential signals. Conclusions: Interdisciplinary collaboration between oncologists and neurologists is crucial for safe mAb use. Our study enhances understanding of mAb neurological safety. Disproportionality signal analysis provides valuable evidence for risk mitigation. [ABSTRACT FROM AUTHOR]

Piezothermoelasticity and wave interaction studies hold immense significance in designing functional devices ranging from transducers to sensors for a variety of purposes like energy harvesting and structural health monitoring. These applications catalyze interest in this article which addresses the problem of reflection of plane wave at the boundary of piezothermoelastic half-space. Through this study, the effect of impedance parameter on amplitude and energy ratios of the reflected waves is studied. Four wave modes are indicated upon reflection and a linear system of equations is formed to obtain a closed-form expression for amplitude and energy ratios. These equations are solved by suitable mathematical tools leading to expression for amplitude ratios as a function of incidence angle. For a suitable piezothermoelastic medium, the ratios are plotted against incidence angle and the findings are compared for two well-known theories of thermoelasticity, namely, Lord–Shulman (LS theory) and Green–Lindsay (GL theory). The analytical outcomes suggest approximate values of impedance and incidence angle for preferred energy division between reflected waves. It is recognized that adding impedance increases the amplitude of the quasi-longitudinal (qP) wave and decreases that of the quasi-transverse wave, making it suitable for devices that require a more robust qP wave signal detection. [ABSTRACT FROM AUTHOR]

Factors such as the memory effect of viscoelastic materials and the influence of microcantilever deflection still pose challenges in the mechanical modeling, mathematical solution, and material parameter identification for AFM indentation-relaxation experiments to characterize the viscoelastic properties of cells. This paper aims to provide a rational mechanical model for interpreting the detection signals in AFM indentation-relaxation experiments. Considering the contribution of microcantilever deflection under the Euler beam assumption to the viscoelastic indentation force, the Lee-Radok's model was updated. Combining a piecewise integration method and a time-domain differential method, we derived the implicit/explicit governing equations of the probe-cell viscoelastic indentation forces under ramp-hold protocol. After building the finite element (FE) model, we examined the effect of microcantilever deflection on the acquired data in FE simulation results and the updated model predictions, and compared our updated model with relevant experiments. Then, we proposed a new two-stage (TS) approach for parameter extraction and compared the differences between this new approach and the classic single-stage (SS) approach, thus the strategy for suppressing parameter extraction error was presented. The results validate that the transient modulus extracting by the classical SS approach is essentially an equivalent transient modulus dependent on the ramp loading history, which incurs a divergence in identification of cellular viscoelastic parameter; whereas the new TS approach is valid at a broader range of loading conditions due to explicitly reflecting the dependence of viscoelastic material parameters on ramp loading history. These conclusions provide a theoretical foundation and reference for the advancement of AFM-based detection techniques for cellular mechanical properties. [ABSTRACT FROM AUTHOR]

In practical applications of wavelet transform, engineers and practitioners encounter challenges that arise due to the disparity between wavelet theory, which deals with continuous functions, and the digital nature of signals in engineering contexts. In particular, wavelet transform theory does not consider the effect of changes in digital signals on the result of the wavelet transform. This paper emphasizes the influence of the type of digital signals on the accuracy of wavelet transform in engineering applications and proposes an efficient wavelet function based on the derivative of the signal for better damage detection in beam structures. For this purpose, the obtained signals from the mode shapes of the steel beam are used to examine the efficiency of the proposed derivative-based wavelet transform. The effects of changes in boundary conditions, location of damage, and level of damage on the performance of the proposed method, are evaluated. Findings show that when we use the derivate of the signal in the wavelet transform, the location of damage in all damage scenarios is detected with high accuracy. This research demonstrates the importance of the type of signal used in the wavelet transform for enhancing the precision of fault and damage detection in signals. [ABSTRACT FROM AUTHOR]

Background: Safety analyses play a pivotal role in drug development, ensuring the protection of patients while advancing innovative pharmaceuticals to market. A single study generally does not have sufficient sample size to evaluate all important safety events with reasonable precision and may not cover the full target population for the investigational treatment. Integrated analyses (pooled or meta-analysis) over several studies may be helpful in that regard. But without a structured conscious workflow accompanied with appropriate statistical methods for the integrated analysis, this can easily take a route compromising the interpretation. Methods: In this article we apply the ICH estimand framework to clinical trial integration and summarize respective critical statistical assumptions to ensure the integrated analyses are interpretable. Results: The estimand framework is valuable for developing principles for a deeper understanding of the critical statistical aspects of planning an integrated safety analysis. Our principles address the clinical question of interest, estimand and estimation. Special focus was given to the criteria for inclusion and exclusion of the component studies in the integrated analysis, and to integration of estimates pertaining to signal detection. Conclusion: Performing an integrated analysis and its preparatory steps calls for a good understanding of the clinical question of interest and its estimand, care and sound practice, to enable interpretation and avoid introducing unnecessary bias. It is valuable to use the estimand framework not only for efficacy evaluations, but also for safety evaluations in clinical trials and for integrated safety analyses. [ABSTRACT FROM AUTHOR]

This research introduced a microfluidic kit for rapid and economic detection of melamine (MLM) and sodium hypochlorite (NaOCl) in milk samples. The kit allows for simultaneous identification of MLM and NaOCl by reacting with cyanuric acid-modified Au nanoparticles (Au@Ca) and N, N-diethyl-p-phenylenediamine, respectively. The resulting color changes are used as the analytical signal for detection. All of the effective operational parameters were optimized by a one-factor-at-a-time approach. Under the optimum conditions (pH 6.5, path length = 50 mm, flow rate = 0.1 ml/min) a linear relationship between the concentrations of MLM and NaOCl and the blue component of the analyzed RGB pixels of the images captured with a smartphone camera was observed. The linear ranges for MLM and NaOCl were obtained in the concentration ranges of 0.1–1 and 0.1–4.5 µM, respectively, with 0.040 µM and 0.078 µM detection limits, consecutively. The results indicate that the proposed microfluidic kit provides strong selectivity and high reproducibility for detecting MLM and NaOCl in milk samples. To validate the performance of the method, the microfluidic kit was employed for analyzing five different milk samples, yielding recovery values between 94.0% and 105.0% and relative standard deviation values lower than 4.12% indicating the proposed system has admissible accuracy for both analytes. Besides, the results were compared to those obtained using high-performance liquid chromatography, as a standard reference method, and a good agreement between the two approaches was found. [ABSTRACT FROM AUTHOR]

Dyadic social interactions evoke complex dynamics between two agents that, while exchanging unequal levels of body autonomy and motor control, may find a fine balance to synergize, take turns, and gradually build social rapport. To study the evolution of such complex interactions, we currently rely exclusively on subjective pencil and paper means. Here, we complement this approach with objective biometrics of socio-motor behaviors conducive to socio-motor agency. Using a common clinical test as the backdrop of our study to probe social interactions between a child and a clinician, we demonstrate new ways to streamline the detection of social readiness potential in both typically developing and autistic children by uncovering a handful of tasks that enable quantification of levels of motor autonomy and levels of motor control. Using these biometrics of autonomy and control, we further highlight differences between males and females and uncover a new data type amenable to generalizing our results to any social setting. The new methods convert continuous dyadic bodily biorhythmic activity into spike trains and demonstrate that in the context of dyadic behavioral analyses, they are well characterized by a continuous Gamma process that can classify individual levels of our thus defined socio-motor agency during a dyadic exchange. Finally, we apply signal detection processing tools in a machine learning approach to show the validity of the streamlined version of the digitized ADOS test. We offer a new framework that combines stochastic analyses, nonlinear dynamics, and information theory to streamline and facilitate scaling the screening and tracking of social interactions with applications to autism. [ABSTRACT FROM AUTHOR]

Recent advancements in bacterial two-component systems (TCS) have spurred research into TCS-based biosensors, notably for their signal amplification and broad input responsiveness. The CusRS system in Escherichia coli (E. coli), comprising cusS and cusR genes, is a copper-sensing module in E. coli. However, due to insufficient sensing performance, CusRS-based biosensors often cannot meet practical requirements. To address this issue, we made improvements and innovation from several aspects. CusR and CusS expression were adjusted to enhance the Cu(II) biosensor's performance. A copy-number inducible plasmid was used for signal amplification, while removing copper detox genes cueO and cusCFBA improved sensitivity and lowered detection limits. Ultimately, in the optimized biosensor of Cu26, the fold-change (I/I0) increased from 1.5-fold to 18-fold at 1 μM, rising to 100-fold after optimizing the cell culture procedure. The biosensor's high fluorescence enabled rapid, instrument-free detection and an improved analysis strategy reduced the detection limit to 0.01 μM, surpassing traditional methods. Optimizing the CusRS copper-sensing system in E.coli by adjusting CusR/CusS expression and deleting detox genes, achieving a 100-fold signal increase and a detection limit of 0.01 μM, enabling rapid, instrument-free detection. [ABSTRACT FROM AUTHOR]

Background: In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships. Methods: We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings. Results: We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used. Conclusions: We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations. [ABSTRACT FROM AUTHOR]

ABSTRACT To tackle the challenge of non‐destructively analyzing complex oxide layers formed by atmospheric corrosion on active metals, we investigate the potential of inverse spatially offset Raman spectroscopy (inverse‐SORS) to detect corrosion products and elucidate how the spectroscopic features of SORS vary. This study explores the utilization of inverse‐SORS technology for longitudinal structural analysis of opaque CeO2/La2O3 laminates, which serve as a proxy for corrosion products. Through a combination of experimental measurements and Monte Carlo simulation, it demonstrates the feasibility of inverse‐SORS in non‐destructively elucidating the layered structure of these laminates. With increasing the spatial offset of this technology, the Raman intensity ratio of La2O3‐to‐CeO2 increases from 0.23 to 3.2 and then decreases to 0.27 for the CeO2/La2O3 sample with a 34‐μm‐thick CeO2 layer under 532‐nm excitation, whereas the bottom La2O3 layer is hardly detected when the thickness of the upper La2O3 layer increases to 225 μm. The results reveal that a thinner CeO2 layer facilitates the escape and detection of Raman signals originating from the bottom La2O3 layer, leading to an enhanced La2O3‐to‐CeO2 signal ratio and a reduced offset at the characteristic peak. The research also sheds light on the intricate interplay among multiple variables, such as spatial offset, laser wavelength, the corresponding absorption factor, the thickness of the oxide layer, and the compactness. These insights suggest that SORS is a valuable and non‐destructive approach for dissecting the structures of highly turbid composites of metallic compounds and semi‐quantitatively deducing their thickness. [ABSTRACT FROM AUTHOR]

Physical insult to the central nervous system (CNS) such as during electrode insertion leads to reactive astrogliosis which in turn contributes to glial scarring (GS). To date, reducing GS in these settings has focused on pharmacological agents and variations in electrode material composition or implantation procedures, and the role of electrode surface topography has remained unexplored. Since proteoglycans, a major component of GS tissue, possesses very well‐defined (nano) topography, a role for stochastic nanoroughness in glial scar formation is theorized. Using an in vitro system, we provide proof of concept that on substrates possessing stochastic nanoroughness corresponding to that of healthy astrocytes, glial scar formation is significantly inhibited, and more importantly, can be even reversed, and it involves signaling via the stretch‐activated cation channel Piezo‐1. In vivo studies reveal an absence of astrocytes aggregation along the electrode track of chronically implanted electrodes modified with stochastic surface nanoroughness, compared to non‐modified electrodes, while signal detection within the superior colliculus remains unaffected. These findings shedlight on the crucial role of stochastic biophysical cues in modulating GS formation; and offer a promising non‐chemical approach for engineering neural biomaterials interface for the CNS. [ABSTRACT FROM AUTHOR]

Periodograms are widely employed for identifying periodicity in time series data, yet they often struggle to accurately quantify the statistical significance of detected periodic signals when the data complexity precludes reliable simulations. We develop a data-driven approach to address this challenge by introducing a null-signal template (NST). The NST is created by carefully randomizing the period of each cycle in the periodogram template, rendering it non-periodic. It has the same frequentist properties as a periodic signal template, and we show with simulations that the distribution of false positives is the same as with the original periodic template, regardless of the underlying data. Thus, performing a periodicity search with the NST acts as an effective simulation of the null (no-signal) hypothesis, without having to simulate the noise properties of the data. We apply the NST method to the supermassive black hole binaries (SMBHB) search in the Palomar Transient Factory (PTF), where Charisi et al. had previously proposed 33 high signal-to-noise candidates utilizing simulations to quantify their significance. Our approach reveals that these simulations do not capture the complexity of the real data. There are no statistically significant periodic signal detections above the non-periodic background. To improve the search sensitivity, we introduce a Gaussian quadrature based algorithm for the Bayes Factor with correlated noise as a test statistic. We show with simulations that this improves sensitivity to true signals by more than an order of magnitude. However, the Bayes Factor approach also results in no statistically significant detections in the PTF data. [ABSTRACT FROM AUTHOR]

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains. [ABSTRACT FROM AUTHOR]

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning. Single-cell RNA sequencing data analysis is limited by noise and high dimensionality. Here, authors present scLENS, a tool that automates accurate signal detection without manual input, particularly in complex datasets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a low‐complexity expectation propagation (EP) detector for orthogonal time frequency space (OTFS) system with practical rectangular waveforms. In the high‐mobility scenario, OTFS is becoming a potential scheme for the sixth‐generation (6G) wireless communication system. However, the large size of the effective delay‐Doppler (DD) domain channel matrix brings unbearable computational complexity to the signal detection algorithm based on the matrix inversion. We propose a low‐complexity EP detector based on the sparsity and the block circulant structure of the effective channel covariance matrix in the DD domain. The proposed algorithm only requires log‐linear complexity. In addition, simulation results show that the proposed algorithm not only has the advantage of low complexity but also has good performance, which achieves a tradeoff between performance and complexity. [ABSTRACT FROM AUTHOR]

Detection of colorimetric signals is commonly used in various analytical methods and for testing in non‐laboratory and resource‐limited settings. The performance of colorimetric assays is largely based on nanoparticles and their unique optical properties. Multifunctional nanoparticles combining optical and enzyme‐like catalytic properties—known as nanozymes—hold great promise for analytical applications as signal‐generating labels. However, the extensive focus on the catalytic properties leaves their unique optical properties overlooked. In this article, the use of the optical and catalytic properties of nanozymes is reviewed for analytical applications relying on the inherent optical properties of nanozymes, the colorimetric detection of a catalytically‐formed product, and colorimetric changes of nanoparticles caused by the catalytically‐formed product. The impact of the extinction coefficient of nanozymes and reaction products, as well as the kinetic parameters of nanozymes on the sensitivity and limit of detection of assays, are quantitatively evaluated. Finally, the existing limitations and prospects of nanozymes for colorimetric biosensors are summarized. [ABSTRACT FROM AUTHOR]

The safety of vancomycin in children requires special attention. The evidence from active pharmacovigilance systems about vancomycin-related adverse drug reactions (ADRs) was rare. We aimed to investigate the association between doses of vancomycin and ADRs among children. This retrospective cohort study included a total of 643 inpatient children. Children who received vancomycin were regarded as exposed groups and classified into low-dose, normal-dose, and high-dose groups. Those who did not receive vancomycin and had similar severity of infection were classified into the unexposed group. ADR signal detection was performed by the China Hospital Pharmacovigilance System. Logistic regression analyses were conducted to explore the effect of intravenous vancomycin on ADRs. This study included 156 patients (178 ADR cases) in the exposed group and 487 patients (364 ADR cases) in the unexposed group. The top three reported ADRs were eosinophilia (n = 75, 48.1%), hemoglobin decreased (n = 72, 46.2%), and blood bilirubin increased (n = 13, 8.3%) in the exposed group. Patients in the low-dose, normal-dose, and high-dose groups showed a higher risk of hemoglobin decreased when compared with those in the unexposed group, with the fully adjusted ORs (95% CIs) of 6.91 (3.61, 13.20), 7.80 (3.87, 15.76), and 8.80 (4.08, 19.01), respectively. Besides, compared with patients in the unexposed group, those in the normal-dose and high-dose groups show a significantly higher risk of eosinophilia and blood bilirubin increased, respectively. It is important to monitor eosinophilia, hemoglobin decreased, and blood bilirubin increased in pediatrics receiving vancomycin, especially those receiving normal-dose and high-dose vancomycin. [ABSTRACT FROM AUTHOR]

Hydrophobic, elastic, and conductive (HEC) aerogels have significant potential in electronic devices. Herein, we propose a new method to fabricate an HEC aerogel with excellent mechanical and sensing performances from TEMPO-oxidized bacterial cellulose (O-BC) and conductive MXene nanosheets via directional freeze-drying and silanization modification. O-BC with a high aspect ratio can interweave with each other to form continuous layers, while MXene can induce a regular and flat structure and provide good conductivity. The silanization modification ensures high hydrophobicity and high elasticity, which can prevent the aerogel from structural collapse by avoiding adhesion among lamellae. The resulting aerogel can withstand compressive strain high up to 90% and long-term compression for 10,000 cycles at 50% strain due to the elastic and hydrophobic lamellar structure. It also offers a precise electrical response to stress signals in a broad detection range of 0–40 kPa and can accurately detect biological signals from humans. These structural and mechanical performance benefits make the HEC aerogel valuable in the field of pressure sensing. [ABSTRACT FROM AUTHOR]

In the realm of automation systems, multi-function radars serve as essential sensory components for self-driving vehicles and airbornes. Effective resource allocation management is crucial, requiring a high level of versatility to accomplish multiple tasks, especially, for increasingly miniaturized hardware. Here, we advance a balanced protocol for detecting, positioning, and tracking moving targets in real-time. Our protocol integrates efficient data processing methods with robust hardware. Specifically, detection signals are modulated by optical vortices for imaging, and real time processing of the image field facilitates target positioning and tracking. Moreover, the protocol extends its utility to serve as a topographic laser profiling system for natural landscapes, highlighting its adaptability. This adaptability and versatility well position the proposed protocol to support a wide range of applications, spanning self-driving vehicles and aerial systems, underscoring its potential significance across multiple platforms. [ABSTRACT FROM AUTHOR]

Purpose: A human cadaveric model combining standard lung protective mechanical ventilation and modified cardiac bypass techniques was developed to allow investigation into automated modes of detection of venous air emboli (VAE) prior to in vivo human or animal investigations. Methods: In this study, in order to create an artificial cardiopulmonary circuit in a cadaver that could mimic VAE physiology, the direction of flow was reversed from conventional cardiac bypass. Normal saline was circulated in isolation through the heart and lungs as opposed to the peripheral organs by placing the venous cannula into the aorta and the arterial cannula into the inferior vena cava with selective ligation of other vessels. Results: Mechanical ventilation and this reversed cardiac bypass scheme allowed preliminary detection of VAE independently but not in concert in our current simulation scheme due to pulmonary edema in the cadaver. A limited dissection approach was used initially followed by a radical exposure of the great vessels, and both proved feasible in terms of air signal detection. We used electrical impendence as a preliminary tool to validate detection in this cadaveric model however we theorize that it would work for echocardiographic, intravenous ultrasound or other novel modalities as well. Conclusion: A cadaveric model allows monitoring technology development with reduced use of animal and conventional human testing. [ABSTRACT FROM AUTHOR]

Single‐nitrogen‐vacancy (NV) centers in diamond are highly promising quantum nuclear magnetic resonance (NMR) sensors. However, their exposure to many sources of noise, such as surface impurities, shot noise from avalanche photodiode overlaps, and spin‐state projection errors inherent in quantum systems, limits their usefulness. Often, long measurement durations are required to accumulate sufficient data for NMR signal detection via fast Fourier transform spectrometry. For practical reasons, methods to shorten the necessary accumulation time for NMR signal detection are greatly desired. In this article, an on‐line formulation of maximum likelihood estimation (MLE) signal processing for quantum heterodyne NMR measurements is presented as a step toward this goal. This MLE method reduces the required data accumulation time by orders of magnitude and provides good estimates of target frequency locales in real time. These results are significant to practitioners of NMR detection with single‐NV centers in diamond who require a quick litmus test for potential signals when probing a wide area. [ABSTRACT FROM AUTHOR]

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. [ABSTRACT FROM AUTHOR]

In order to study the effect of laser-excited surface waves on the transmission of surface rectangular defects, the transmission information of surface waves is used to achieve the quantitative calculation of defect depth. In this paper, the finite element method is employed to analyse the transmission process of laser-excited surface waves on rectangular defects. Subsequently, the influence of the front and back edges of rectangular defects on the transmission of surface waves is examined, and the formation mechanisms of feature points in the time-domain signals at detection points are elucidated. Ultimately, the calculation formula for defect depth is derived on the basis of the propagation path of each component after transmission. The experimental results are consistent with the simulation results, and the relative error of the surface rectangular defect depth calculation is within 9%. The validation of the proposed method on homogeneous isotropic metallic materials demonstrates the effectiveness and applicability of the proposed method. These results will provide a reliable method for characterising the depth of rectangular defects on surfaces by laser ultrasonic technology. [ABSTRACT FROM AUTHOR]

With the rapid development of wind power generation in recent years, the demand for detecting weak signals of wind turbine faults has become more urgent. This paper introduces a novel memristor chaotic circuit constructed based on third-order magnetically memristors. The Melnikov chaotic condition of this circuit is analyzed, and its dynamical characteristics are studied through phase trajectory diagrams, bifurcation diagrams, Lyapunov exponent spectra, and Poincaré maps. Leveraging the initial value sensitivity and noise immunity of chaotic systems, the memristor chaotic circuit is employed for the detection of weak signals in wind turbine faults. Using the chaotic system state transition method, we find the threshold for the circuit state to transition from chaotic state to large-scale periodic state, adjust the parameters to make the system in a critical state, input the wind turbine fault vibration signal, and detect the fault signal based on its state transition. Subsequently, the chaotic resonance method is employed, introducing the signal under test, which contains high-intensity chaotic noise, into this novel memristive circuit. This results in chaotic resonance, causing the noise components to be concentrated toward the frequency region where the weak signal under test is located, thereby enhancing the fault signal and facilitating fault identification. The results indicate that this novel memristor chaotic circuit possesses advantages such as high accuracy, strong noise immunity, straightforward operation, and clear judgment in the field of weak signal detection. This circuit shows promising applications in the field of weak signal detection. [ABSTRACT FROM AUTHOR]

Socio-affective functioning, or the way we interact and relate to others, is one of the four dynamic sexual recidivism risk domains. Accurately recognizing emotions enables the inference of mental and affective states supporting social adaptation. As little attention has been paid to affective prosody recognition in forensic inpatients who have committed sexual offenses (FICSOs), this study assessed the accuracy and sensitivity scores of 111 male participants assigned into three groups: FICSOs (n = 35), forensic inpatients who have committed non-sexual offenses (FICNSOs, n = 26) and community members (CoM, n = 50). Collected data also include response bias, emotion labeling reflection time, task easiness and task easiness reflection time. Using non-parametric group comparisons (Kruskal–Wallis H and Mann–Whitney U), results highlight, overall, a pervasive impairment of affective prosody recognition in FICSOs and FICNSOs compared to CoM. However, there was no difference in disgust sensitivity scores between FICSOs and CoM. FICSOs and FICNSOs took significantly longer than CoM to select an emotional label, especially for happiness. In addition, a metacognitive impairment was found in FICSOs and FICNSOs as they found the task significantly easier than CoM while being less sensitive. [ABSTRACT FROM AUTHOR]

Reconfigurable phototransistors featuring bipolar photoresponses are favorable for manipulating high-performance neuromorphic vision sensory. Here, we present a momentum-conserved reconfigurable phototransistor based on the van der Waals heterojunction between methylammonium lead iodide perovskite and two-dimensional Bi2O2Se semiconductor, which exhibits a synergistic interplay of interband hot-carrier transitions and reconfigurable heterointerface band alignments, eventually achieving the ultrahigh bipolar optoelectronic performances with the photoresponsivity of 6×107 AW−1, accompanied by the specific detectivity of 5.2×1011 Jones, and the dynamic range of 110 dB. Moreover, A 3×3 heterotransistor array is fabricated to perform in-sensor analog multiply-accumulate operations even under the challenging dim illumination of 0.1 μWcm−2 that comparable to natural moonlight. The reconfigurable heterotransistor array can be further adopted to enhance the traffic-light detection under dim-light conditions. Our advancement in momentum-conserved reconfigurable heterotransistor signifies a leap forward in real-time, energy-efficient, and low-light image processing for neuromorphic vision sensors. Xu et al. report reconfigurable phototransistors based on MAPbI3/Bi2O2Se heterostructure, with momentum conservation promotes hot carrier extraction and interlayer carrier transport. Heterotransistor array enables traffic light signal detection under dim light, assisted by YOLOv4 neural network. [ABSTRACT FROM AUTHOR]

Accurately estimating the modulation parameters of pseudorandom binary code–pulse amplitude modulation (PRBC–PAM) signals damaged by strong noise poses a significant challenge in emitter identification and countermeasure. Traditionally, weak signal detection methods based on chaos theory can handle situations with low signal-to-noise ratio, but most of them are developed for simple sin/cos waveform and cannot face PRBC–PAM signals commonly used in ultra-low altitude performance equipment. To address the issue, this article proposes a novel adaptive detection and estimation method utilizing the in-depth analysis of the Duffing oscillator's behaviour and output characteristics. Firstly, the short-time Fourier transform (STFT) is used for chaotic state identification and ternary processing. Then, two novel approaches are proposed, including the adjusting zero value (AZV) method and the chaotic state ratio (CSR) method. The proposed weak signal detection system exhibits unique capability to adaptively modify its internal periodic driving force frequency, thus altering the difference frequency to estimate the signal parameters effectively. Furthermore, the accuracy of the proposed method is substantiated in carrier frequency estimation under varying SNR conditions through extensive experiments, demonstrating that the method maintains high precision in carrier frequency estimation and a low bit error rate in both the pseudorandom sequence and carrier frequency, even at an SNR of −30 dB. [ABSTRACT FROM AUTHOR]

Ultra-high-pressure tubular reactors are crucial pieces of equipment for polyethylene production. Long-term operation under high temperature, high pressure, and other extremely harsh conditions can lead to various defects, with circumferential cracks posing a major safety risk. Detecting cracks is challenging, particularly when they are under a protective layer of a certain thickness. This study designed a pulsed eddy current differential probe to detect circumferential cracks in ultra-high-pressure tubular reactors, with the lift-off distance acting as a protective layer. Detection models for traditional cylindrical and semi-circular excitation differential probes were established using finite element simulations. Corresponding experiments under different lift-off conditions were carried out, and the model's accuracy was verified by the consistency between the simulation results and experimental data. The distribution of the eddy current field under different conditions and the disturbances caused by cracks at various positions to the detection signal were then calculated in the simulations. The simulation results showed that the cracks significantly disturbed the eddy current field of the semi-circular excitation differential probe compared with that of the traditional cylindrical probe. The designed differential probe effectively detected circumferential cracks of specific lengths and depths using the difference in the voltage signals. The experimental results were in agreement with the simulation results, showing that the designed probe could effectively detect 20 mm-long circumferential cracks at a lift-off of 60 mm. The experimental results also show that the probe's detection coverage area in the axial direction varied with the lift-off height. The probe design and findings are valuable for detecting cracks in ultra-high-pressure tubular reactors with protective layers. [ABSTRACT FROM AUTHOR]

In shipborne high-frequency surface wave radar (HFSWR), the change of platform speed or heading will cause variations in the extension of the Doppler spectrum for vessel targets located in different directions, subsequently resulting in alterations to the signal-to-noise ratio (SNR). These alterations in vessel target echo present challenges for manual parameter adjustments in traditional constant false alarm rate (CFAR) methods for shipborne HFSWR, thereby hindering the maintenance of stable target detection capabilities. In this paper, a self-attention-convolutional neural network (SA-CNN)-based CFAR detector is proposed, which transforms the detection problem into signal structure classification. First, the extension characteristics of vessel target echoes resulting from changes in speed or heading of the shipborne platform are quantitatively analysed, thereby guiding the selection of an optimal sliding window and constructing input vectors for the neural network. Subsequently, the SA-CNN is designed to efficiently extract the structural features of the signal and accurately predict the probability of target presence. Finally, the Monte Carlo method is used to control the false alarm rate effectively. Simulation and real dataset verification demonstrate that the proposed method exhibits superior detection performance compared to traditional methods in shipborne HFSWR, especially for detecting extended targets. [ABSTRACT FROM AUTHOR]

Food safety is a major public health issue of global concern. In recent years, the CRISPR/Cas system has shown promise in the field of molecular detection. The system has been coupled with various nucleic acid amplification methods and combined with different signal output systems to develop a new generation of CRISPR/Cas-based nucleic acid biosensor technology. This review describes the design concept of the CRISPR/Cas-based nucleic acid biosensor and its application in food analysis. A detailed overview of different CRISPR/Cas systems, signal amplification methods, and signal output strategies is provided. CRISPR/Cas-based nucleic acid biosensors have the advantages of high sensitivity, strong specificity, and timeliness, achieving fast analysis of a variety of targets, including bacteria, toxins, metal ions, pesticides, veterinary drugs, and adulteration, promoting the development of rapid food safety detection technology. At the end, we also provide our outlook for the future development of CRISPR/Cas-based nucleic acid biosensors. [ABSTRACT FROM AUTHOR]

The size and weight of Massive Multiple Input Multiple Output (mMIMO) significantly increase with the addition of more antennas. The 3rd Generation Partnership Project standardization group addressed these issues by incorporating the multi-panel feature into New Radio communications. The estimated value of the channel is essential for signal detection and precoding in mMIMO. Traditional channel estimation methods are ineffective in multi-panel mMIMO system because of its hybrid structure and non-uniform antenna array structure. This paper introduces a novel channel estimation technique for multi-panel mMIMO systems operating in multiuser scenario by transforming the channel estimation problem into an angular domain block sparse signal recovery problem. We propose a generalized block orthogonal matching pursuit (GB-OMP) method to solve the obtained sparse model. The proposed method incorporates a well-designed pilot sequence for optimal performance. The proposed GB-OMP method outperforms the reference method, as shown by the simulation results. [ABSTRACT FROM AUTHOR]

Pipelines are widely used as the main transportation method in petrochemical and metallurgical industries. However, the special nature of the materials transported in pipelines means they are often subjected to high temperature, high pressure and corrosive conditions. This can lead to defects, such as thinning and cracking, resulting in pipeline failure and causing serious economic losses. Therefore, regular non-destructive testing of pipelines is particularly important. This paper uses the non-destructive testing method with pulsed eddy current technology to characterise pipeline thinning and defects. A preliminary study is conducted on the pulsed eddy current detection signal patterns for pipeline crack defects with varying orientations and quantities. A more portable pulsed eddy current detection device with an extended wall thickness detection range is developed. It can achieve measurements with a maximum thickness of 30 mm for detections of pipelines with rectangular defects and thickness-stepped reduced. The experimental results are consistent with modelling results, and the accuracy for pipelines with insulation layer is also investigated. On-site measurements using the PEC system are conducted on equipment sections with pipe thickness within 20 mm in a petrochemical plant. It is confirmed that the developed PEC inspection device can accurately characterises the thickness of pipelines with 0–50 mm cladding. [ABSTRACT FROM AUTHOR]

Emotion-like states in animals are commonly assessed using judgment bias tests that measure judgements of ambiguous cues. Some studies have used these tests to argue for emotion-like states in insects. However, most of these results could have other explanations, including changes in motivation and attention. To control for these explanations, we developed a novel judgment bias test, requiring bumblebees to make an active choice indicating their interpretation of ambiguous stimuli. Bumblebees were trained to associate high or low rewards, in two different reward chambers, with distinct colours. We subsequently presented bees with ambiguous colours between the two learnt colours. In response, physically stressed bees were less likely than control bees to enter the reward chamber associated with high reward. Signal detection and drift diffusion models showed that stressed bees were more likely to choose low reward locations in response to ambiguous cues. The signal detection model further showed that the behaviour of stressed bees was explained by a reduction in the estimated probability of high rewards. We thus provide strong evidence for judgement biases in bees and suggest that their stress-induced behaviour is explained by reduced expectation of higher rewards, as expected for a pessimistic judgement bias. [ABSTRACT FROM AUTHOR]