Sensitivity Characteristics Research Articles

Development of a time-resolved immunochromatographic test strip for rapid and quantitative determination of GFAP in serum.

Glial fibrillary acidic protein (GFAP) in serum has been shown as a biomarker of traumatic brain injury (TBI) which is a significant global public health concern. Accurate and rapid detection of serum GFAP is critical for TBI diagnosis. In this study, a time-resolved fluorescence immunochromatographic test strip (TRFIS) was proposed for the quantitative detection of serum GFAP. This TRFIS possessed excellent linearity ranging from 0.05 to 2.5 ng/mL for the detection of serum GFAP and displayed good linearity (Y = 598723X + 797198, R2 = 0.99), with the lowest detection limit of 16 pg/mL. This TRFIS allowed for quantitative detection of serum GFAP within 15 min and showed high specificity. The intra-batch coefficient of variation (CV) and the inter-batch CV were both < 4.0%. Additionally, this TRFIS was applied to detect GFAP in the serum samples from healthy donors and patients with cerebral hemorrhage, and the results of TRFIS could efficiently discern the patients with cerebral hemorrhage from the healthy donors. Our developed TRFIS has the characteristics of high sensitivity, high accuracy, and a wide linear range and is suitable for rapid and quantitative determination of serum GFAP on-site.

‘One size does not fit all’

Online learning is immensely popular and attracts learners from diverse cultural backgrounds. Given learning is situated in culture, the diversity of online students presents a great challenge to course designers, but remains largely unaddressed. To complicate matters, Learning Management Systems (LMS), though efficient and easy to use for course design, promote a one-size-fits-all’ approach. The PhD study referred to in this paper seeks to make inroads to cultural sensitivity in LMS. A beginning point for the project is to explore Instructional designers’ perspectives on culturally sensitive online learning design features. We report on the early findings from this first step and aim to incite rich discussion around culture, instructional design and online learning.

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

A milling tool wear predicting method with processing generalization capability

Tool Wear Prediction (TWP) is crucial to ensure machining quality. Compared to traditional prediction methods based on full-life or failure data, labelled data are limited or nonexistent when a new tool or similar tools have emerged. This makes it extremely challenging to predict wear using limited tracking data. This work aims to provide an innovative method for accurate long-term wear prediction of milling processes using limited monitoring data of individual tools. First, the Variational Modal Decomposition (VMD) and Pearson Correlation Threshold (PCT) are combined to adaptively filter the corresponding machining signals. Second, fitness analysis is performed by selecting features strongly related to tool degradation using feature monotonicity metrics, and sensitive information is fused based on Kernel Principal Components Analysis (KPCA). Third, a Stacked Bi-directional Long Short-Term Memory with attention mechanism (AT-SBiLSTM) model is constructed to ascertain the connection between sensitive feature and the future wear state of the tool by multi-step advance rolling prediction. The results of the various milling experiment show that the proposed approach can provide accurate forecasts of tool wear using limited monitoring data.

-wave-based exploration of sensitive EEG features and classification of situation awareness

Abstract The purpose of this study was to explore the electroencephalogram (EEG) features sensitive to situation awareness (SA) and then classify SA levels. Forty-eight participants were recruited to complete an SA standard test based on the multi-attribute task battery (MATB) II, and the corresponding EEG data and situation awareness global assessment technology (SAGAT) scores were recorded. The population with the top 25% of SAGAT scores was selected as the high-SA level (HSL) group, and the bottom 25% was the low-SA level (LSL) group. The results showed that (1) for the relative power of $\beta$ 1 (16–20Hz), $\beta$ 2 (20–24Hz) and $\beta$ 3 (24–30Hz), repeated measures analysis of variance (ANOVA) in three brain regions (Central Central-Parietal, and Parietal) × three brain lateralities (left, midline, and right) × two SA groups (HSL and LSL) showed a significant main effect for SA groups; post hoc comparisons revealed that compared with LSL, the above features of HSL were higher. (2) for most ratio features associated with $\beta$ 1 ∼ $\beta$ 3, ANOVA also revealed a main effect for SA groups. (3) EEG features sensitive to SA were selected to classify SA levels with small-sample data based on the general supervised machine learning classifiers. Five-fold cross-validation results showed that among the models with easy interpretability, logistic regression (LR) and decision tree (DT) presented the highest accuracy (both 92%), while among the models with hard interpretability, the accuracy of random forest (RF) was 88.8%, followed by an artificial neural network (ANN) of 84%. The above results suggested that (1) the relative power of $\beta$ 1 ∼ $\beta$ 3 and their associated ratios were sensitive to changes in SA levels; (2) the general supervised machine learning models all exhibited good accuracy (greater than 75%); and (3) furthermore, LR and DT are recommended by combining the interpretability and accuracy of the models.

Confluence Effect of Debris-Filled Damage and Temperature Variations on Guided-Wave Ultrasonic Testing (GWUT)

Continuous monitoring of structural health is essential for the timely detection of damage and avoidance of structural failure. Guided-wave ultrasonic testing (GWUT) assesses structural damages by correlating its sensitive features with the damage parameter of interest. However, few or no studies have been performed on the detection and influence of debris-filled damage on GWUT under environmental conditions. This paper used the pitch–catch technique of GWUT, signal cross-correlation, statistical root mean square (RMS) and root mean square deviation (RMSD) to study the combined influence of varying debris-filled damage percentages and temperatures on damage detection. Through experimental result analysis, a predictive model with an R2 of about 78% and RMSE values of about 7.5×10−5 was established. When validated, the model proved effective, with a comparable relative error of less than 10%.

Development of a time-resolved immunochromatographic test strip for rapid and quantitative determination of retinol-binding protein 4 in urine.

Urine retinol-binding protein 4 (RBP4) has recently been reported as a novel earlier biomarker of chronic kidney disease (CKD) which is a global public health problem with high morbidity and mortality. Accurate and rapid detection of urine RBP4 is essential for early monitor of impaired kidney function and prevention of CKD progression. In the present study, we developed a time-resolved fluorescence immunochromatographic test strip (TRFIS) for the quantitative and rapid detection of urine RBP4. This TRFIS possessed excellent linearity ranging from 0.024 to 12.50 ng/mL for the detection of urine RBP4, and displayed a good linearity (Y = 239,581 × X + 617,238, R2 = 0.9902), with the lowest visual detection limit of 0.049 ng/mL. This TRFIS allows for quantitative detection of urine RBP4 within 15min and shows high specificity. The intra-batch coefficient of variation (CV) and the inter-batch CV were both < 8%, respectively. Additionally, this TRFIS was applied to detect RBP4 in the urine samples from healthy donors and patients with CKD, and the results of TRFIS could efficiently discern the patients with CKD from the healthy donors. Thedeveloped TRFIS has the characteristics of high sensitivity, high accuracy, and a wide linear range, and is suitable for rapid and quantitative determination of urine RBP4.

A Low-Frequency Fiber Bragg Grating Acceleration Sensor Based on Spring Support and Symmetric Compensation Structure with Flexible Hinges

To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor’s structure, the expressions for sensitivity and resonant frequency of the sensor are derived. The structural parameters of the sensor are optimized, and a simulation analysis is conducted using ANSYS 19.2 software. According to the results of simulation analysis and size optimization, the sensor prototype is constructed. Subsequently, its amplitude-frequency response, sensitivity, and temperature characteristics are investigated through vibration experiments. The experimental results show that the resonant frequency of the sensor is 73 Hz, the operating frequency range is 0~60 Hz, and the sensitivity measures 24.24 pm/g. This design meets the requirements for measuring vibration signals at low frequencies.

Disagreement amongst counterfactual explanations: how transparency can be misleading

AbstractCounterfactual explanations are increasingly used as an Explainable Artificial Intelligence (XAI) technique to provide stakeholders of complex machine learning algorithms with explanations for data-driven decisions. The popularity of counterfactual explanations resulted in a boom in the algorithms generating them. However, not every algorithm creates uniform explanations for the same instance. Even though in some contexts multiple possible explanations are beneficial, there are circumstances where diversity amongst counterfactual explanations results in a potential disagreement problem among stakeholders. Ethical issues arise when for example, malicious agents use this diversity to fairwash an unfair machine learning model by hiding sensitive features. As legislators worldwide tend to start including the right to explanations for data-driven, high-stakes decisions in their policies, these ethical issues should be understood and addressed. Our literature review on the disagreement problem in XAI reveals that this problem has never been empirically assessed for counterfactual explanations. Therefore, in this work, we conduct a large-scale empirical analysis, on 40 data sets, using 12 explanation-generating methods, for two black-box models, yielding over 192,000 explanations. Our study finds alarmingly high disagreement levels between the methods tested. A malicious user is able to both exclude and include desired features when multiple counterfactual explanations are available. This disagreement seems to be driven mainly by the data set characteristics and the type of counterfactual algorithm. XAI centers on the transparency of algorithmic decision-making, but our analysis advocates for transparency about this self-proclaimed transparency.

Predicting bladder cancer survival with high accuracy: insights from MAPK pathway-related genes

The mitogen-activated protein kinase (MAPK) pathway plays a critical role in tumor development and immunotherapy. Nevertheless, additional research is necessary to comprehend the relationship between the MAPK pathway and the prognosis of bladder cancer (BLCA), as well as its influence on the tumor immune microenvironment. To create prognostic models, we screened ten genes associated with the MAPK pathway using COX and least absolute shrinkage and selection operator (LASSO) regression analysis. These models were validated in the Genomic Data Commons (GEO) cohort and further examined for immune infiltration, somatic mutation, and drug sensitivity characteristics. Finally, the findings were validated using The Human Protein Atlas (HPA) database and through Quantitative Real-time PCR (qRT-PCR). Patients were classified into high-risk and low-risk groups based on the prognosis-related genes of the MAPK pathway. The high-risk group had poorer overall survival than the low-risk group and showed increased immune infiltration compared to the low-risk group. Additionally, the nomograms built using the risk scores and clinical factors exhibited high accuracy in predicting the survival of BLCA patients. The prognostic profiling of MAPK pathway-associated genes represents a potent clinical prediction tool, serving as the foundation for precise clinical treatment of BLCA.

Diagnosis of allergen components in fungi and microbial communities: patient sensitization characteristics and main sensitizing proteins

The human body, as a highly complex ecosystem, harbors diverse microbial communities, with major factors triggering allergic reactions encompassing the skin microbiome and fungi. The global diversity of fungi is estimated to range from approximately 600 000 to 1 million species, and theoretically, IgE-mediated sensitization may occur to any fungal species. As of now, the World Health Organization/IUIS official database records 113 fungal allergens originating from 30 different fungi species, covering 42 allergen families. Regarding the skin microbiome, 14 distinct Malassezia allergens have been identified, all derived from three different Malassezia fungi species--M. furfur, M. sympodialis, and M. globosa. The conditions of patients with these allergies are exceptionally complex. This article extensively discusses the latest research advancements and clinical applications related to skin microbiome and fungal allergies from the European Academy of Allergy and Clinical Immunology (EAACI) publication, "Molecular Allergology User's Guide 2.0". Additionally, it compiles information on the sources of fungal allergens, characteristics of allergen component protein families, clinical relevance, and management strategies, both domestically and internationally. The aim is to enhance the profound understanding of allergen components among relevant professionals. Through the application of advanced allergen component diagnostic techniques, the goal is to achieve precise diagnosis and treatment of fungal allergy patients and explore the mechanisms underlying fungal sensitization and pathogenesis, laying the foundation for studying the fungal allergen protein sensitization spectrum in the Chinese population.

Features of pain sensitivity during premedication with xenon in patients before outpatient dental interventions

Features of pain sensitivity during premedication with xenon in patients before outpatient dental interventions

The influence of the rectangular dielectric grating filling factor on the grating-based sensor’s sensitive element characteristics

The influence of grating filling factor on the spectral sensitivity of sensors, characterized by full width at half maximum (FWHM) and a figure of merit (FOM), operating on the principle of waveguide mode resonance in dielectric gratings formed on a substrate, has been investigated. Resonant wavelengths were determined for varying grating thicknesses and filling factors. Sensitivity values were obtained for normally and obliquely incident plane waves, as well as for Gaussian beams. Additionally, a scaling rule is presented, allowing determination of sensor parameters using a calculator based on identical grating parameters and corresponding resonant wavelengths. This method holds when linear dimensions of the grating and tested wavelengths are scaled proportionally.

Clinical characteristics and risk factors for bacterial co-infections in COVID-19 patients: A retrospective study

ObjectiveThis study aimed to analyze the bacterial composition, distribution, drug sensitivity, and clinical characteristics of COVID-19 patients who develop bacterial co-infections. MethodsWe conducted a retrospective study of 184 COVID-19 patients admitted between December 2022 and January 2023. Data on gender, age, length of hospital stay, pneumonia classification, underlying diseases, invasive surgery, hormone therapy, inflammation indicators, and other relevant information were collected. Samples of sputum, bronchoscopy sputum, alveolar lavage fluid, middle urine, puncture fluid, wound secretions, and blood were collected for pathogen isolation, identification, and drug sensitivity testing. ResultsThe majority of COVID-19 patients with bacterial co-infection were elderly and had underlying diseases. Invasive surgery and hormone therapy were identified as risk factors for co-infections. Laboratory analysis showed reduced lymphocyte count and elevated levels of CRP and PCT. The most common pathogens in co-infections were S. aureus, E. coli, K. pneumoniae, A. baumannii, and P. aeruginosa. The detection rate of drug-resistant strains, including MRSA, CRKP, CRAB, CRPA, and CRECO, increased with the severity of pneumonia. ConclusionRespiratory tract infections were the most common site of bacterial co-infection in COVID-19 patients. Severe cases were more susceptible to multidrug-resistant pathogens, leading to a higher mortality rate. Timely control and prevention of co-infection are crucial for improving the prognosis of COVID-19 patients.

Automated estimation of early-age concrete compressive strength using EMI signature-driven deep learning technique

Concrete is a vital construction material for civil infrastructure, whose early-age compressive strength estimation plays a critical role in determining the proper time for formwork removal and the readiness of the structures for service. Electromechanical impedance (EMI) technique has been proven to be a non-destructive and effective method for monitoring concrete early strength gain. Nonetheless, such a technique heavily relies on effective frequency band selection and statistical parameter-based analysis, which may prevent the technique from a real-time operation. This study proposes an EMI signature-driven deep learning (DL) technique to enable the automated estimation of early-age concrete compressive strength. A novel DL framework called multi-scale learning residual denoising network (MLRD-Net) was developed to autonomously learn sensitive features from raw EMI signatures and ultimately estimate the compressive strength. The EMI signatures were collected during the strength development process of concrete specimens produced from two different mix designs based on strength. Two independent databases were constructed from recorded EMI signatures to test the effectiveness of the MLRD-Net. The results demonstrated the excellent performance of the proposed model with coefficients of determination exceeding 0.99 for both databases. In addition, the MLRD-Net shows superior performances over other methods in terms of prediction accuracy, noise resistance and fitness capacities, indicating its great potential for real-time and accurate estimation of the strength gain of early-age concrete.

Adaptive state-of-health temperature sensitivity characteristics for durability improvement of PEM fuel cells

To obtain the temperature sensitivity characteristics of the proton exchange membrane fuel cell under different degradation levels, accelerated durability tests and temperature sensitivity tests of the fuel cell are first performed, and the temperature characteristics under different degradation states are analyzed by electrochemical impedance spectroscopy and polarization curve. Besides, an adaptive state-of-health transient thermal model of fuel cells is developed to investigate the effect of operating temperature on the internal gas concentration and hydrothermal distribution characteristics of the fuel cell from a mechanistic perspective. The experimental results of temperature sensitivity validate that the adaptive state-of-health transient thermal model can effectively monitor the steady and transient performance of the fuel cell. Subsequently, a mathematical model is proposed to describe the relationship among the load current, state of health, and optimal temperature using the temperature sensitivity test data and the transient thermal model of the adaptive state of health, which provides important insights into the optimal temperature range tailored to the current state of health of fuel cells to ensure efficient and stable operation of fuel cells all the time and contributes to the fine design of an optimal degradation adaptive temperature control strategy.

Pollen allergens sensitization characteristics and risk factors among allergy rhinitis of children in mainland china: A multicenter study

Pollen allergens sensitization characteristics and risk factors among allergy rhinitis of children in mainland china: A multicenter study

Pollen allergens sensitization characteristics and risk factors of allergy rhinitis of children in mainland china: A multicenter study

Pollen allergens sensitization characteristics and risk factors of allergy rhinitis of children in mainland china: A multicenter study

Annual extent of prescribed burning on moorland in Great Britain and overlap with ecosystem services

AbstractIn the UK uplands, prescribed burning of unenclosed heath, grass and blanket bog (‘moorland’) is used to support game shooting and grazing. Burning on moorland is contentious due to its impact on peat soils, hydrology and habitat condition. There is little information on spatial and temporal patterns of burning, the overlap with soil carbon and sensitive habitats and, importantly, whether these patterns are changing. This information is required to assess the sustainability of burning and the effectiveness of new legislation. We developed a method for semi‐automated detection of burning using satellite imagery – our best performing model has a balanced accuracy of 84.9%. We identified annual burn areas in Great Britain in five burning seasons from 2017/18 to 2021/22 of 8333 to 20 974 ha (average 15 250 ha year−1). Annual extent in England in 2021/22 was 73% lower than the average of the four previous seasons. Burning was identified over carbon‐rich soils (mean 5150 ha or 34% by area of all burning annually) and on steep slopes – 915 ha across the five seasons (1.3%), contravening guidance. Burning (&gt;1 ha) was recorded in 14% of UK protected areas (PAs) and, within these, the percentage area of moorland burned varied from 2 to 31%. In England in some years, the percentage area of moorland burned inside PAs was higher than outside, while this was not the case in Scotland. Burning in sensitive alpine habitats totalled 158 ha across the five seasons. The reduction in burned area in England in 2021/22 could relate to England‐specific legislation, introduced in May 2021, to prohibit burning on deep peat in PAs. This suggests that regulation can be effective. However, the continued overlap with sensitive features suggests that burning falls short of sustainable practices. Our method will enable repeatable re‐assessment of burning extents and overlap with ecosystem services.

Harnessing energy from hand-shaking vibration for electronics through a magnetic rolling pendulum bistable energy harvester

Converting the kinetic energy of human movement into electricity provides a solution for the power supply of portable electronics for communication, medical treatment, positioning, search-and-rescue, etc. Due to the characteristics of high sensitivity and snap-through, a magnetic rolling pendulum bistable energy harvester (MRP-BEH) based on magnetic-spring mechanism is proposed for scavenging energy from handshaking vibration. The harvester possessing the advantage of small damping exploits the rolling oscillation of a suspended magnet. By establishing the electromechanical model, numerical outcomes of the MRP-BEHs with different potential well depths are investigated through the response under constant and sweep frequency excitations. Particularly, the complex dynamic behaviors are characterized by basins of attraction and bifurcation diagrams. A prototype is fabricated, and experiments are undertaken under harmonic and handshaking excitations. Experimental results are consistent with numerical simulations, and excitation of 0.5 g witnesses a broadband frequency range from 5.18 Hz to 10.02 Hz for the harvester with proper system parameters. Regarding the excitation of handshaking, an instantaneous maximum power of 21.9 mW and an average power of 4.21 mW is achieved with matched load resistance. By boosting the output voltage with a transformer, the charging and discharging experiments of capacitors for powering various electronics demonstrate the broad application prospects of the harvester. This research, in a nutshell, broadens the methodology for achieving bistable oscillation and introduces a fresh perspective for designing high-efficiency energy harvesters attuned to human motion.