Search Results (116)

Surface subsidence, a major geological hazard induced by mining activities, severely compromises the sustainable economic development of mining areas and the safety and stability of residents’ livelihoods. Consequently, long-term and effective monitoring and prediction of mining areas are essential. Aiming to identify the key characteristic of narrow and elongated mining areas—where the strike length is significantly greater than the dip length—this study proposes a surface subsidence monitoring method integrating Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Improved Probability Integral Method (IPIM). Specifically, this method utilizes SBAS-InSAR technology to acquire cumulative subsidence results of low-gradient deformation zones in mining areas. To address the issue of excessively fast edge convergence in traditional Probability Integral Method (PIM) applications for narrow and elongated mining areas, the traditional PIM is adjusted by modifying the dip-direction influence radius parameter; this adjustment alters the shape of the dip-direction subsidence curve at the edge of the subsidence basin, thereby resolving the convergence problem. Meanwhile, based on the InSAR deformation gradient theory, the subsidence edge and subsidence center are identified, and the corresponding threshold is determined. The results of SBAS-InSAR and IPIM are then fused via the inverse distance squared weighting (IDSW) method to eliminate discontinuous boundaries in fused results and obtain complete surface subsidence data of the mining area. Taking the 31109-1 working face of the Lijiahao Coal Mine as the study area, 14 scenes of Sentinel-1A imagery and field leveling data of the working face were used to validate the feasibility and accuracy of the proposed method. The results indicate that a distinct rectangular subsidence basin was formed in the working face during the monitoring period. The maximum subsidence measured by the integrated method is 3453 mm, and the location, subsidence curve, and variation trend of the monitored subsidence basin are basically consistent with actual mining conditions. The maximum relative errors of subsidence in the strike and dip directions are 5.2% and 4.1%, respectively. This method can effectively compensate for the limitations of SBAS-InSAR and PIM when applied individually to surface subsidence monitoring in narrow and elongated mining areas, enabling the acquisition of refined subsidence information for the entire mining basin. The research results provide a scientific basis for subsidence monitoring and early warning, disaster prevention and mitigation, and the rational development and utilization of resources in mining areas. Full article

The recent global spread of the SARS-CoV-2 pathogen, which causes COVID-19, and its rapid mutation, requires the fast development of effective preventive and treatment measures. According to WHO reports, over 778 million confirmed cases of COVID-19 have been reported, including approximately 7 million deaths. The androgen-regulated cell-surface serine protease TMPRSS2 interacts with the SARS-CoV-2 spike protein. Therefore, directly inhibiting TMPRSS2 will negatively impact the activation of coronaviruses and, consequently, disease progression. That is why TMPRSS2 is a very important target in current drug discovery. On the other hand, it is known that C₆₀ fullerene (a nearly spherical molecule consisting of 60 carbon atoms) exhibits activity against various protein targets. Here, for the first time, the potential binding of C₆₀ fullerene with TMPRSS2 was investigated using different computer simulation methods, including p2Rank, PCA, gmx_MMPBSA analysis, molecular docking, and molecular dynamics simulations. As a result, four potential binding pockets on the TMPRSS2 surface that could interact with C₆₀ fullerene were identified. Among all “C₆₀ fullerene-TMPRSS2” complexes, one was selected as the most promising binding site based on the results of computational modeling evaluations. This opens up the prospect of creating new anticoronavirus drugs based on these carbon nanoparticles. Full article

Water pipeline leak detection in a fast and accurate way is of much importance for water utility companies and the general public. At present, the rapid development of remote sensing and computer technologies makes it possible to detect water pipeline leaks on a large scale efficiently and timely. The leakage will cause an increase in the water content and dielectric constant of the soil around the pipeline, so it is feasible to determine the leakage site by measuring the subsurface soil relative dielectric constant (SSRDC). In this paper, we combine the SAOCOM-1A L-band synthetic-aperture radar (SAR) and the ground-penetrating radar (GPR) data to develop regression models that predict the SSRDC values. The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. We evaluate multiple linear regression (MLR), random forest (RF), and multi-layer perceptron neural network (MLPNN) models for their ability to predict the SSRDC values using the selected features. The experimental results show that the MLPNN model ($R^2$ = 0.705, RMSE = 1.936, MAE = 1.664) can better estimate the SSRDC values. Further, in the main urban area of Tianjin, China, which has a large water pipeline system, the SSDRC values of the area are obtained with the best model, and the locations where the predicted SSDRC values exceeded a certain threshold were considered potential leak locations. The empirical results indicate an encouraging potential of the proposed method to locate the pipeline leaks. This will provide a new avenue for the monitoring and treatment of water pipeline leaks. Full article

Background: The aim of this study was to evaluate whether increased body mass index (BMI) and biochemical and lifestyle parameters linked to obesity and smoke exposure disrupt immune responses of children and adolescents following vaccination with the mRNA BNT162b2 vaccine. Methods: A prospective, single-center, cohort study was conducted. Participants were assigned to receive two doses of the mRNA vaccine. Anti-SARS-CoV-2 IgG and neutralizing antibodies (AB) were measured before vaccination (T0) and 14 days after the second dose (T1). BMI and biochemical parameters were evaluated at T0. A questionnaire on lifestyle characteristics was filled in. Results: IgG optical density (OD) ratio at T1 was lower in the overweight–obese group regardless of COVID-19 disease positive history [p = 0.028 for the seronegative group, p = 0.032 for the seropositive group]. Neutralizing AB were lower in overweight–obese participants in the seronegative group at T1 [p = 0.008]. HDL, fasting glucose/insulin ratio (FGIR), C-reactive protein (CRP), HBA1c, uric acid, and smoke exposure were significantly correlated with BMI [p = 0.006, p < 0.001, p < 0.001, p = 0.006, p = 0.009, p < 0.001, respectively]. The main biochemical parameters that were inversely correlated with IgG and neutralizing AB titers at T1 were uric acid [p = 0.018, p = 0.002], FGIR [p = 0.001, p = 0.008] and HBA1C [p = 0.027, p = 0.038], while smoke exposure negatively affected the humoral immune responses at T0 in the convalescent group [p = 0.004, p = 0.005]. Conclusions: Current data suggests that uric acid, insulin resistance (IR), and smoke exposure could adversely affect the immune responses in overweight–obese vaccinated children, highlighting the need for actions to enhance the protection of this particular subgroup. Full article

Drone-based synthetic aperture radar (SAR) systems have increasingly gained attention due to their potential for rapid surveillance in localized areas. This paper presents a novel approach to SAR processing for subsurface target detection from a lightweight drone platform. The limited processing capacity and memory resources of small SAR platforms demand efficient recovery performance for high-resolution imaging. Compressed sensing (CS) algorithms are widely used to mitigate data storage requirements, yet they often suffer from challenges related to computational burden and detection errors. CS theory exploits signal sparsity and the incoherence of sensing matrices to reconstruct target information from reduced data measurements. Although random sensing matrices are commonly employed to ensure the independence of measured data, they incur high computational cost and memory resources. While deterministic sensing matrices provide fast data recovery, they suffer from increased internal interference, leading to degraded performance in noisy environments. This paper proposes a novel hybrid sensing matrix and recovery algorithm for efficient target detection in small drone-based SAR platforms. After establishing the principles of signal sampling and recovery, SAR imaging simulations are conducted to evaluate the performance of the proposed method with respect to data compression, processing speed, and recovery accuracy. For verification, a custom-built drone SAR platform is utilized to recover subsurface targets obscured by high-clutter backgrounds. Experimental results demonstrate the effective recovery of buried target images, highlighting the potential of the proposed method for practical applications in high-clutter environments. Full article

Biosensors are transforming point-of-care diagnostics by simplifying the detection process and enabling rapid, accurate testing. This study introduces a novel, reusable biosensor designed for direct viral RNA detection from unfiltered saliva, targeting SARS-CoV-2. Unlike conventional methods requiring filtration, our biosensor leverages a unique electrode design that prevents interference from saliva debris, allowing precise measurements. The biosensor is based on electrochemical principles, employing oligonucleotide probes immobilized on a hydrophobic-coated electrode, which prevents air bubbles and salt crystal formation. During validation, the biosensor demonstrated a sensitivity and specificity of 100%, accurately identifying SARS-CoV-2 in saliva samples without false positives or negatives. Cross-validation with RT-qPCR, the gold standard for COVID-19 diagnostics, confirmed the reliability of our device. The biosensor’s performance was tested on 60 participants, yielding 12 true positive results and 48 true negatives, aligning perfectly with RT-qPCR outcomes. This reusable, easy-to-use biosensor offers significant potential for point-of-care applications in various healthcare settings, providing a fast, efficient, and cost-effective method for detecting viral infections such as COVID-19. Its robust design, minimal sample preparation requirements, and multiple-use capability mark a significant advancement in biosensing technology. Full article

Biosensors harness biological materials as receptors linked to transducers, enabling the capture and transformation of primary biorecognition signals into measurable outputs. This study presents a novel carboxylation method for synthesizing carboxylated graphene (CG) under acidic conditions, enhancing biosensing capabilities. The characterization of the CG was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Raman spectroscopy, thermogravimetric analysis (TGA), and X-ray diffraction (XRD). We modified screen-printed carbon electrodes (SPCEs) with CG to immobilize the SARS-CoV-2 N-protein, facilitating targeted detection of IgA antibodies (IgA-SARS-CoV-2). The analytical performance was assessed via electrochemical techniques such as cyclic voltammetry and electrochemical impedance spectroscopy, confirming CG synthesis effectiveness and biosensor functionality. The developed biosensor efficiently detects IgA-SARS-CoV-2 across a dilution range of 1:1000 to 1:200 v/v in a phosphate-buffered saline (PBS) solution, with a limit of detection calculated at 1:1601 v/v. This device shows considerable potential because of its fast response time, miniaturized design facilitated by SPCEs, reduced sample volume requirements, high sensitivity and specificity, low detection limits, and signal enhancement achieved through nanomaterial integration. Full article

Effective noise reduction strategies are crucial for improving the precision of Ground-Based Synthetic Aperture Radar (GB-SAR) technology in bridge deflection measurement, particularly in mitigating the signal noise introduced by complex environmental factors, and thereby ensuring reliable structural health assessments. This study presents an innovative gradual de-noising method that integrates an Improved Second-Order Blind Identification (I-SOBI) algorithm with Fast Fourier Transform (FFT) featuring Adaptive Cutoff Frequency Selection (A-CFS) for reducing the complex environmental noises. The novel method is a two-stage process. The first stage employs the proposed I-SOBI to preserve the contribution of effective information in separated signals as much as possible and to recover pure signals from noisy ones that have nonlinear characteristics or are non-Gaussian in distribution. The second stage utilizes the FFT with the A-CFS method to further deal with the residual high-frequency noises still within the signals, which is conducted under a proper cutoff frequency to ensure the quality of de-noised outputs. Through meticulous simulation and practical experiments, the effectiveness of the proposed de-noising method has been comprehensively validated. The experimental results state that the method performs better than the traditional Second-Order Blind Identification (SOBI) method in terms of noises reduction capabilities, achieving a higher accuracy of bridge deflection measurement using GB-SAR. Additionally, the method is particularly effective for de-noising nonlinear time-series signals, making it well-suited for handling complex signal characteristics. It significantly contributes to the provision of reliable bridge dynamic-behavior information for infrastructure assessment. Full article

Efficient estimation methods for simultaneous autoregressive (SAR) models with missing data in the response variable have been well explored in the literature. A common practice is introducing measurement error into SAR models to separate the noise component from the spatial process. However, prior studies have not considered incorporating measurement error into SAR models with missing data. Maximum likelihood estimation for such models, especially with large datasets, poses significant computational challenges. This paper proposes an efficient likelihood-based estimation method, the marginal maximum likelihood (ML), for estimating SAR models on large datasets with measurement errors and a high percentage of missing data in the response variable. The spatial autoregressive model (SAM) and the spatial error model (SEM), two popular SAR model types, are considered. The missing data mechanism is assumed to follow a missing-at-random (MAR) pattern. We propose a fast method for marginal ML estimation with a computational complexity of $O\left(n^{3/2}\right)$, where n is the total number of observations. This complexity applies when the spatial weight matrix is constructed based on a local neighbourhood structure. The effectiveness of the proposed methods is demonstrated through simulations and real-world data applications. Full article

The COVID-19 pandemic has prompted the need for the development of new biosensors for SARS-CoV-2 detection. Particularly, systems with qualities such as sensitivity, fast detection, appropriate to large-scale analysis, and applicable in situ, avoiding using specific materials or personnel to undergo the test, are highly desirable. In this regard, developing an electrochemical biosensor based on peptides derived from the angiotensin-converting enzyme receptor 2 (ACE2) is a possible answer. To this end, an impedimetric detector was developed based on a graphite electrode surface modified with an ACE2 peptide-mimic. This sensor enables accurate quantification of recombinant 2019-nCoV spike RBD protein (used as a model analyte) within a linear detection range of 0.167–0.994 ng mL⁻¹, providing a reliable method for detecting SARS-CoV-2. The observed sensitivity was further demonstrated by molecular dynamics that established the high affinity and specificity of the peptide to the protein. Unlike other impedimetric sensors, the herein presented system can detect impedance in a single frequency, allowing a measure as fast as 3 min to complete the analysis and achieving a detection limit of 45.08 pg mL⁻¹. Thus, the proposed peptide-based electrochemical biosensor offers fast results with adequate sensitivity, opening a path to new developments concerning other viruses of interest. Full article

The clinical presentation, organ involvement, and severity of disease caused by SARS-CoV-2 are highly variable, ranging from asymptomatic or mild infection to respiratory or multi-organ failure and, in children and young adults, the life-threatening multisystemic inflammatory disease (MIS-C). SARS-CoV-2 enters cells via the angiotensin-converting enzyme-2 receptor (ACE-2), which is expressed on the cell surfaces of all organ systems, including the gastrointestinal tract. GI manifestations have a high prevalence in children with COVID-19. However, isolated terminal ileitis without other manifestations of COVID-19 is rare. In March 2023, two previously healthy boys (aged 16 months and 9 years) without respiratory symptoms presented with fever and diarrhea, elevated C-reactive protein levels, and low procalcitonin levels. Imaging studies revealed marked terminal ileitis in both cases. SARS-CoV-2 (Omicron XBB.1.9 and XBB.1.5 variants) was detected by nucleic acid amplification in throat and stool samples. Both patients recovered fast with supportive measures only. A differential diagnosis of acute abdominal pain includes enterocolitis, mesenteric lymphadenitis, appendicitis, and more. During SARS-CoV-2 epidemics, this virus alone may be responsible for inflammation of the terminal ileum, as demonstrated. Coinfection with Campylobacter jejuni in one of our patients demonstrates the importance of a complete microbiological workup. Full article

The positioning performance of the Global Navigation Satellite System (GNSS) network real-time kinematic (NRTK) depends on regional atmospheric error modeling. Under normal atmospheric conditions, NRTK positioning provides high accuracy and rapid initialization. However, fluctuations in atmospheric conditions can lead to poor atmospheric error modeling, resulting in significant atmospheric biases that affect the positioning accuracy, initialization speed, and reliability of NRTK positioning. Consequently, this decreases the efficiency of NRTK operations. In response to these challenges, this paper proposes a fast and reliable NRTK positioning method based on sequential ambiguity resolution (SAR) of multi-frequency combined observations. This method processes observations from extra-wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) measurements; performs sequential AR using the LAMBDA algorithm; and subsequently constrains other parameters using fixed ambiguities. Ultimately, this method achieves high precision, rapid initialization, and reliable positioning. Experimental analysis was conducted using Continuous Operating Reference Station (CORS) data, with baseline lengths ranging from 88 km to 110 km. The results showed that the proposed algorithm offers positioning accuracy comparable to conventional algorithms in conventional NRTK positioning and has higher fixed rate and positioning accuracy in single-epoch positioning. On two datasets, the proposed algorithm demonstrated over 30% improvement in time to first fix (TTFF) compared to conventional algorithms. It provides higher precision in suboptimal positioning solutions when conventional NRTK algorithms fail to achieve fixed solutions during the initialization phase. These experiments highlight the advantages of the proposed algorithm in terms of initialization speed and positioning reliability. Full article

Background and Objectives: This study aimed to determine the prevalence of overweight, obesity, and the associated risk factors among medical students at Jazan University in Saudi Arabia. Materials and Methods: A cross-sectional study was conducted among 228 medical students from their second to sixth academic years at the Faculty of Medicine, Jazan University. A self-administered questionnaire was used to collect data regarding sociodemographic characteristics, physical activity, dietary habits, comorbidities, medication use, family history, and lifestyle factors. Anthropometric measurements including height, weight, and waist circumference were recorded. Chi-square test and binary logistic regression were used to identify the risk factors associated with obesity. Results: The prevalence of overweight and obesity among the participants was 13.3% and 15%, respectively. Hence, the combined prevalence of overweight and obesity is 28.3%. The mean weight was 63.39 ± 18.93 kg, and the mean height was 163.48 ± 9.78 cm. On the other hand, 17.3% of participants were underweight, whereas 54.4% had normal BMI. Most of the participants (61%) did not engage in regular exercise. A high proportion consumed fruits (82.9%) and vegetables (58.8%) 3 or fewer days per week, and 84.2% consumed 3 or fewer meals per day. Fast-food consumption more than 3 days per week was reported by 42.1% of participants. Obesity was not significantly associated with sociodemographic factors, physical activity, dietary habits, comorbidities, medication use, or family histories. However, those with a monthly family income of SAR 15,000–24,999 had significantly lower odds of obesity than those in the lowest income group (OR 0.230, p = 0.045). Conclusions: The prevalence of overweight and obesity among medical students at Jazan University is high. Although no significant associations were found between obesity and most risk factors, this study highlights the need for interventions that promote healthy lifestyles among medical students. Further research is needed to identify effective strategies for preventing and managing obesity in this population. Full article

Tomographic Synthetic Aperture Radar (TomoSAR) building object height inversion is a sparse reconstruction problem that utilizes the data obtained from several spacecraft passes to invert the scatterer position in the height direction. In practical applications, the number of passes is often small, and the observation data are also small due to the objective conditions, so this study focuses on the inversion under the restricted observation data conditions. The Analytic Learned Iterative Shrinkage Thresholding Algorithm (ALISTA) is a kind of deep unfolding network algorithm, which is a combination of the Iterative Shrinkage Thresholding Algorithm (ISTA) and deep learning technology, and it has the advantages of both. The ALISTA is one of the representative algorithms for TomoSAR building object height inversion. However, the structure of the ALISTA algorithm is simple, which has neither the excellent connection structure of a deep learning network nor the acceleration format combined with the ISTA algorithm. Therefore, this study proposes two directions of improvement for the ALISTA algorithm: firstly, an improvement in the inter-layer connection of the network by introducing a connection similar to residual networks obtains the Extragradient Analytic Learned Iterative Shrinkage Thresholding Algorithm (EALISTA) and further proves that the EALISTA achieves linear convergence; secondly, there is an improvement in the iterative format of the intra-layer iteration of the network by introducing the Nesterov momentum acceleration, which obtains the Fast Analytic Learned Iterative Shrinkage Thresholding Algorithm (FALISTA). We first performed inversion experiments on simulated data, which verified the effectiveness of the two proposed algorithms. Then, we conducted TomoSAR building object height inversion experiments on limited measured data and used the deviation metric P to measure the robustness of the algorithms to invert under restricted observation data. The results show that both proposed algorithms have better robustness, which verifies the superior performance of the two algorithms. In addition, we further analyze how to choose the most suitable algorithms for inversion in engineering practice applications based on the results of the experiments on measured data. Full article

This paper presents a low-power, energy-efficient, 12-bit incremental zoom analog-to-digital converter (ADC) for multi-channel bio-signal acquisitions. The ADC consists of a 7-stage ring voltage-controlled oscillator (VCO)-based incremental ΔΣ modulator (I-ΔΣM) and an 8-bit successive approximation register (SAR) ADC. The proposed VCO-based I-ΔΣM can provide fast phase-alignment of the ring-VCO to reduce the interval settling time; thereby, the I-ΔΣM can accommodate time-division-multiplexed input signals without phase leakage between consecutive measurements. The SAR ADC also adopts splitting unit capacitors that can support V_CM-free tri-level switching and prevent invalid states from the phase frequency detector with minimal logic gates and switches. The proposed ADC has been fabricated in a standard 180 nm standard 1P6M CMOS process, exhibiting a 67-dB peak signal-to-noise ratio, a 74-dB dynamic range, and a Walden figure of merit of 19.12 fJ/c-s, while consuming a power of 3.51 μW with a sampling rate of 100 kS/s. Full article