Search Results (1,102)

Channeling in cementing causes interlayer interference, severely restricting oilfield recovery. Existing channeling plugging agents, such as cement and gels, often lead to reservoir damage or insufficient strength. Oil-soluble resin (OSR) particles show great potential in selective plugging of channeling fractures due to their excellent oil solubility, temperature/salt resistance, and high strength. However, their application is limited by the efficient filling and retention in deep fractures. This study innovatively combines the OSR particle plugging system with the mature rapid filtration loss plugging mechanism in drilling, systematically exploring the influence of particle size and sorting on their filtration, packing behavior, and plugging performance in channeling fractures. Through API filtration tests, visual fracture models, and high-temperature/high-pressure (100 °C, salinity 3.0 × 10⁵ mg/L) core flow experiments, it was found that well-sorted large particles preferentially bridge in fractures to form a high-porosity filter cake, enabling rapid water filtration from the resin plugging agent. This promotes efficient accumulation of OSR particles to form a long filter cake slug with a water content <20% while minimizing the invasion of fine particles into matrix pores. The slug thermally coalesces and solidifies into an integral body at reservoir temperature, achieving a plugging strength of 5–6 MPa for fractures. In contrast, poorly sorted particles or undersized particles form filter cakes with low porosity, resulting in slow water filtration, high water content (>50%) in the filter cake, insufficient fracture filling, and significantly reduced plugging strength (<1 MPa). Finally, a double-slug strategy is adopted: small-sized OSR for temporary plugging of the oil layer injection face combined with well-sorted large-sized OSR for main plugging of channeling fractures. This strategy achieves fluid diversion under low injection pressure (0.9 MPa), effectively protects reservoir permeability (recovery rate > 95% after backflow), and establishes high-strength selective plugging. This study clarifies the core role of particle size and sorting in regulating the OSR plugging effect based on rapid filtration loss, providing key insights for developing low-damage, high-performance channeling plugging agents and scientific gradation of particle-based plugging agents. Full article

Aloin, a kind of active phenolic component, is sourced from Aloe vera. Recently, the determination of aloin has received enormous attention, owing to its positive performance (including anti-tumor, antibacterial, detoxification, liver protection, anti-stomach damage, and skin protection activities) and painful side effects (increased carcinogenicity caused by excessive use of aloin) impacting human health. This investigation was inspired by the good fluorescence properties of carbon dots (CDs); CD-based sensors have aroused a great deal of interest due to their excellent sensitivity and selectivity. Thus, it is of great significance to develop novel CD-based sensors for aloin determination. Herein, N,F-CDs were designed and synthesized through a convenient hydrothermal strategy; the synthesized N,F-CDs possessed good fluorescence performance and a small particle size (near 4.3 nm), which demonstrated the successful preparation of N,F-CDs. The resulting N,F-CDs possessed a large Stokes shift and could emit a highly stable green fluorescence. The fluorescence of the N,F-CDs could be effectively quenched by aloin through the inner filter effect. Furthermore, the synthesis procedure was easy to operate. Finally, the N,F-CD-coated test strips were fabricated and combined with a miniaturized fluorimeter for the fluorescence detection of aloin via the inner filter effect for the first time. The N,F-CD-coated test strips were fabricated and used for the fluorescence sensing of aloin, and the results were compared with a typical ultraviolet (UV) method. The N,F-CD-coated test strips exhibited high recovery (96.9~106.1%) and sensitivity (31.8 nM, n = 3), good selectivity, low sample consumption (1 μL), high speed (5 min), good stability, and anti-interference properties. The results indicate that N,F-CD-coated test strips are applicable for the quantitative determination of aloin in bovine serum, orange juice, and urine samples. Full article

Mechanical failures frequently occur in On-Load Tap Changers (OLTCs) during operation, potentially compromising the reliability and stability of power systems. The goal of this study is to develop an intelligent and accurate diagnostic approach for OLTC mechanical fault identification, particularly under the challenge of non-stationary vibration signals. To achieve this, a novel hybrid method is proposed that integrates the Gazelle Optimization Algorithm (GOA), Feature Mode Decomposition (FMD), and a Transformer-based classification model. Specifically, GOA is employed to automatically optimize key FMD parameters, including the number of filters (K), filter length (L), and number of decomposition modes (N), enabling high-resolution signal decomposition. From the resulting intrinsic mode functions (IMFs), statistical time domain features—peak factor, impulse factor, waveform factor, and clearance factor—are extracted to form feature vectors. After feature extraction, the resulting vectors are utilized by a Transformer to classify fault types. Benchmark comparisons with other decomposition and learning approaches highlight the enhanced performance of the proposed framework. The model achieves a 95.83% classification accuracy on the test set and an average of 96.7% under five-fold cross-validation, demonstrating excellent accuracy and generalization. What distinguishes this research is its incorporation of a GOA–FMD and a Transformer-based attention mechanism for pattern recognition into a unified and efficient diagnostic framework. With its high effectiveness and adaptability, the proposed framework shows great promise for real-world applications in the smart fault monitoring of power systems. Full article

Respiratory diseases present significant global health challenges. Recent advances in respiratory sound analysis (RSA) have shown great potential for automated disease diagnosis and patient management. The International Conference on Biomedical and Health Informatics 2017 (ICBHI2017) database stands as one of the most authoritative open-access RSA datasets. This review systematically examines 135 technical publications utilizing the database, and a comprehensive and timely summary of RSA methodologies is offered for researchers and practitioners in this field. Specifically, this review covers signal processing techniques including data resampling, augmentation, normalization, and filtering; feature extraction approaches spanning time-domain, frequency-domain, joint time–frequency analysis, and deep feature representation from pre-trained models; and classification methods for adventitious sound (AS) categorization and pathological state (PS) recognition. Current achievements for AS and PS classification are summarized across studies using official and custom data splits. Despite promising technique advancements, several challenges remain unresolved. These include a severe class imbalance in the dataset, limited exploration of advanced data augmentation techniques and foundation models, a lack of model interpretability, and insufficient generalization studies across clinical settings. Future directions involve multi-modal data fusion, the development of standardized processing workflows, interpretable artificial intelligence, and integration with broader clinical data sources to enhance diagnostic performance and clinical applicability. Full article

For the gas injection process in the underground natural gas storage, it is of great significance to separate lubricating oil droplets from natural gas to ensure the capacity of gas injection. However, the interaction mechanism, among various factors, is still unclear in real field conditions. In this study, the filtration performances of filter elements were investigated in a range of gas temperatures from 20 °C to 70 °C, aerosol concentrations from 400 mg/m³ to 1200 mg/m³ and flowrates from 37.2 m³/h to 74.4 m³/h. The results show that at the same flowrate, the steady-state filtration efficiency decreases with increasing temperature, resulting from a shift in the upstream aerosol towards the smaller size and an increase in the concentration for droplets within the range of 0.3 to 2 μm. The filtration efficiency only increases by 0.02% at 20 °C, while the steady-state efficiency increases by more than 0.10% at 70 °C and all the flowrates. At the same gas temperature and aerosol concentration, the steady-state filtration efficiency increases with the increase in flowrate. In practical applications, the filtration performance can be improved by lowing the gas temperature, setting up fewer filter elements in the separator, and appropriately reducing the injection amount of lubricating oil. Full article

The migration of hindered phenolic antioxidants from food contact materials (FCMs) into foodstuffs poses health risks due to endocrine disruption and organ toxicity. Hence, the development of a high-efficiency analytical method for hindered phenolic antioxidants is of great importance for food safety. This study developed a novel ultrasound-assisted dispersive solid-phase filter extraction (d-SPFE) coupled with green supercritical fluid chromatography (SFC) method for the simultaneous determination of six representative hindered phenolic antioxidants. Under optimized conditions, the method achieved high extraction efficiency, with the complete separation of all analytes within 10 min. A wide linearity range (0.02–2.0 μg/mL) was achieved, with coefficients of determination all greater than 0.9996. The limits of detection (LOD, S/N = 3) and limits of quantification (LOQ, S/N = 10) were 2.4–3.6 ng/mL and 8–12 ng/mL, respectively. Validation tests demonstrated precise spiked recoveries (89.4–101.6%), with intra-day and inter-day relative standard deviations (RSDs) all less than 10%. The d-SPFE-SFC synergy significantly outperforms conventional techniques in terms of analysis speed and eco-efficiency. Successful application to food simulants confirms its reliability in monitoring hindered phenolic antioxidant migration from FCMs. This green and rapid methodology will enable the direct assessment of migration risks. Full article

Precision seeding plays a critical role in optimizing crop yield and resource efficiency. This study evaluates the application of a Seed Miss Prevention System (SMPS) integrated with a spoon-wheel precision metering device to mitigate seed misses and enhance its performance. A combination of Discrete Element Method (DEM) simulations, electrical hardware design, mechanical retrofitting, software development and laboratory experiments was employed to assess the effectiveness of the system across multiple seed cultivars and operating speeds. Experimental results demonstrated that the SMPS significantly reduced seed misses at lower operational speeds (3–10 rpm), with the implementation of a dual-sensor configuration further improving detection accuracy by filtering out false positives. At higher speeds (≥15 rpm), however, seed miss rates increased, particularly for irregularly shaped seeds like white beans ‘Great Northern’, due to the mechanical limitations of the metering device. Statistical analyses, including Tukey’s HSD test, confirmed the effectiveness of the SMPS in reducing miss rates across different seed types. Despite these improvements, complete elimination of seed misses was not achieved, highlighting the need for further optimization in seed miss detection. Future research should explore adaptations for higher-speed metering devices and field-scale validations. The findings underscore the potential of SMPS technology in advancing precision agriculture by improving seeding accuracy and operational efficiency. Full article

Traditional multilevel inverter topologies, such FC, NPC, and CHB, have a few significant disadvantages. They need a great number of parts, which raises the complexity, expense, and switching losses. Furthermore, their intricate control schemes make voltage balancing and synchronization challenging. Lastly, under some circumstances, they experience severe harmonic distortion, necessitating the inclusion of expensive filters to enhance signal quality. This paper proposes a novel multilevel converter topology that uses the phase-disposition PWM (PD-PWM) technique to control a 19-level output. This new configuration maintains performance comparable to the CHB-MLI reference while using fewer switches, simplifying control, and reducing costs. Our approach is based on extensive simulations conducted in the MATLAB Simulink environment, with results compared to the CHB-MLI. A low-pass filter is added to improve the output voltage quality, reducing the THD% to 1.33%. This strategy offers several advantages, including simpler control, lower costs, increased reliability, and higher-quality output. The system was replicated using MATLAB Simulink and validated through hardware-in-the-loop (HIL) testing. The HIL method ensures real-world testing without causing damage to the hardware. The integrated system includes sensors and necessary hardware for a comprehensive energy management solution. Full article

Background: QSAR (Quantitative Structure–Activity Relationships) methods have been the basis for the design of new molecules with a certain activity. The great advantage of QSAR methods is that they can predict the pharmacological activity of compounds without the need to obtain or synthesize them previously. Currently, the development of antibiotic resistance by microorganisms is the most important issue in the treatment of infectious diseases. This elevated resistance is associated with expanded morbidity and mortality, as well as an increase in healthcare costs. The development of new molecules with antibacterial activity is therefore urgently needed. Methods: By means of molecular topology, we developed discriminant functions (DF1 and DF2) capable of predicting antibacterial activity. When applied to a database with 6373 chemicals, they selected 266 molecules as candidates, from which 41% have this activity, according to the bibliography. Regression equations determining pharmacokinetic properties such as mean residence time (MRT), volume of distribution (V_D), and clearance (CL) were applied to the selected molecules. Results: We have observed that most antibacterial compounds have pharmacokinetic theoretical values in the intervals 20 > MRT > 0, 3 > V_D > 0, and 500 > CL > 0. We have applied these intervals to our antibacterial model with the objective of finding new antibacterials with a good pharmacokinetic profile. We show that they are an effective tool for discriminating antibacterial compounds, increasing the bibliographic success rate to 50.8, 59, and 61.5%, respectively. When drug-like filters are applied to these new models, the vast majority (89.9–100%) of the selected molecules present antibacterial activity. Conclusions: Considering these results, these new models could avoid the application of drug-likeness filters when searching for new potential antibacterials. All of this proves the usefulness of these mathematical–topological models. Full article

Accurate identification of surface cracks is of great significance for the site selection of transmission towers, as it directly affects the safety and stability of power grid construction. Traditional manual inspection methods are labor-intensive and inefficient, making them inadequate for large-scale and high-precision applications. Consequently, intelligent crack recognition technologies are receiving increasing attention and adoption. This study proposes a novel surface crack identification method that integrates multiple neural networks, aiming to overcome the limitations of traditional crack identification approaches, such as low accuracy and poor generalization. The proposed framework incorporates a convolutional neural network (CNN)-based data filtering module and a crack segmentation module combining UNet and YOLOv8 architectures. Together, these components form a robust and accurate end-to-end crack identification system, which is further applied to the evaluation of rock fragmentation. To verify the effectiveness and accuracy of the proposed method, experiments were conducted on a rock crack dataset and compared against several existing approaches. The results demonstrate that the proposed method achieves superior performance in crack detection accuracy. Moreover, tests on various scenario datasets also yielded promising results, indicating the strong generalization ability and adaptability of the method. Full article

Acoustic energy penetrates into the Si substrate at cavity boundaries. Due to this, the air cavity-based bulk acoustic resonators experience higher harmonic mode, parasitic resonance, and spurious mode. To overcome these effects and enhance the performance parameters, a backside air cavity is fabricated using the deep reactive ion etching (DRIE) method. The DRIE method helps to achieve the optimized active area of the resonator. SiO₂ film on a silicon substrate as the support layer and ZnO as the piezoelectric (PZE) film are used for the resonator. The crystal growth and surface morphology of ZnO film were investigated with X-ray diffraction, scanning electron microscopy, and atomic force microscopy. FBAR is modeled in a 1-D modified Butterworth–Van Dyke (mBVD) equivalent circuit. As RF measurement results, we successfully demonstrated a FBAR with optimized active area of 320 × 320 μm², center frequency of 1.261 GHz, having a quality factor of 583.8. Overall, this suppression of higher harmonic mode shows the great potential for improving the selectivity of the sensor and also in RF filter design applications. This integration of DRIE-based cavity formation with ZnO-based FBAR architecture not only enables compact design but also effectively suppresses spurious and higher-order modes, which demonstrates a performance-enhancing fabrication strategy not fully explored in the current literature. Full article

Unmanned aerial vehicle (UAV) remote sensing has become an important tool for modern remote sensing technology with its low cost and high flexibility. Sun glitter (SG) remote sensing based on satellite platforms shows great potential in the fields of marine dynamic environment and marine oil spill, but the analysis and application of SG images based on UAV need to be further studied. In this study, we conduct a multi-angle water surface SG remote sensing experiment using multi-UAV and collect images under different observation parameters. Then, we analyze and discuss the SG signal in the multi-angle images, especially the distribution and intensity of SG. In addition, a model for extracting SG signals from images based on region-based dark pixel retrieval is proposed in this study. Since the current Cox-Munk model is only applicable to statistical SG, the extracted SG images are reduced in resolution by mean filtering. Based on the multi-angle SG remote sensing model, the water surface roughness and equivalent refractive index are estimated. The estimated results are compared with measured and literature data. Additionally, the influence of different observation angle combinations on the inversion results is also discussed. The results of the study show that multi-angle SG remote sensing of water surface based on UAVs provides a new idea for the analysis and application of image signals, which has an important role to play. Full article

Glide-symmetric double-corrugated parallel-plate waveguides (GS-DCPPWs) have essential technical properties such as an electromagnetic bandgap, lower dispersion, and the ability to control the equivalent refractive index. For this reason, a fast and simple analysis and design of GS-DCPPW structures have great importance to improve related microwave systems. This paper introduces a novel design methodology based on the auxiliary functions of generalized scattering matrix (AFGSM) for the dimensional synthesis of GS-DCPPWs. We test the applicability of the AFGSM method on a variety of numerical examples to determine the passband/stopband regions of single and GS-DCPPWs before applying the design procedure. Certain design specifications are chosen, and unit cell dimensions are constructed in accordance with the proposed design technique. Three design scenarios are considered to assess the success of how well the design criteria can be met with the proposed method. The designed unit cells have been periodically connected in a various finite numbers to create periodic filters as a test application for adjusting the electromagnetic bandgap. The success of the periodic GS-DCPPW filters obtained with the proposed design strategy in meeting the specified design requirements has been tested using full-wave electromagnetic simulators (CST Microwave Studio and HFSS). The results indicate that the combined use of the equivalent transmission line circuit and the root-finding routine provided by the proposed method facilitates rapid, efficient, versatile, and approximate designs for corrugated parallel-plate waveguides. Moreover, the design methodology provides the viability of developing a minimal unit cell and a compact periodic filter performance with respect to the literature counterparts. Full article

Particulate matter (PM) is recognized as a leading health risk factor worldwide, causing adverse effects for people in living and working environments. During the COVID-19 pandemic, it was shown that ultrafine particles (UFP) and PM concentrations, may have played an important role in the transmission of SARS-CoV-2. This study aims to investigate whether the mechanical ventilation system installed as a COVID-19 mitigation measure in a university dining hall can be effectively and sustainably used to improve indoor UFP exposure levels, integrated with a continuous low-cost sensor monitoring system. Measurements of particle number concentration (PNC), average diameter (D_avg), and Lung Deposited Surface Area (LDSA) were performed over three working days divided into ten homogeneous daily time slots (from 12:00 am to 11:59 pm) using high-frequency (1 Hz) real-time devices. PM and other indoor pollutants (CO₂ and TVOC) were monitored using low-cost handheld sensors. Indoor PNC (Dp < 700 nm) increased and showed great variability related to dining activities, reaching a maximum average PNC level of 30,000 part/cm³ (st. dev. 16,900). D_avg (Dp < 300 nm) increased during lunch and dinner times, from 22 nm at night to 48 nm during post-dinner recovery activities. Plasma-based filter technology reduced average PNC (Dp < 700 nm) by up to three times, effectively mitigating UFP concentrations in indoor environments, especially during dining hall access periods. It could be successfully adopted also after the pandemic emergency, as a sustainable health and safety control measure to improve UFPs exposure levels. Full article

New positioning solutions are a key factor in the pursuit of autonomous driving. Global Navigation Satellite System (GNSS) is the most common method; however, traditional systems may have high position errors due to the signal’s path between the satellite and the receiver’s antenna. In this work, we present a GNSS adaptive antenna with beamforming capabilities that can apply spatial filtering to mitigate interferences and improve satellite connectivity, reducing the positioning error. The array, developed with off-the-shelf GNSS antennas, was used to demonstrate the improvement of the gain comparatively to the traditional GNSS antenna, while maintaining circular polarization in all directions. A digital beamforming solution was employed with the software-defined platform based on a Xilinx ZCU216. The full system performance was tested in an anechoic chamber, where good results were obtained in both single- and multibeam scenarios, with great agreement between the simulated and measured data. The results presented in this paper validate the proposed Field-Programmable Gate Array (FPGA)-based antenna array and beamforming development platform, paving the way for the seamless and rapid design and testing of numerous antenna array geometries with up to 16 channels and beamforming algorithms, including adaptive ones. This powerful and versatile tool will accelerate research on the performance improvement of GNSS reception. Full article