Search Results (59)

Understanding how ecological factors shape viral evolution is essential for predicting adaptation in RNA viruses. In this study, we investigated the evolutionary dynamics of bacteriophage Qβ under varying host densities, focusing on two nonsynonymous mutations—A1930G and C2011A—located in the A1 protein. Using experimental evolution, phenotypic assays, and competition experiments, we found that C2011A is consistently selected at low bacterial densities, enhancing viral entry but reducing burst size. In contrast, A1930G is fixed at high densities, despite similar phenotypic effects, suggesting its advantage arises from interactions with additional mutations. Clonal analysis revealed that compensatory or beneficial mutations modulate the fitness of A1930G, enabling its fixation. The absence of both mutations in the same genome points to negative epistasis, confirmed by the poor performance of the double mutant generated by site-directed mutagenesis. Sequencing of intermediate transfers showed early emergence of A1930G, but its fixation was prevented by clonal interference with C2011A. These findings highlight how host availability, fitness trade-offs, epistasis, and competition among variants shape the adaptive landscape of RNA viruses. Full article

Tungsten powder/polytetrafluoroethylene (W/PTFE) composites have the potential to replace traditional metallic materials as casings for controllable power warheads. Under explosive loading, they generate high-density and relatively uniformly distributed metal powder particles, thereby enhancing close-range impact effects while reducing collateral damage. To characterize the material’s response under impact loading, plate impact tests were conducted to investigate the effects of tungsten content (70 wt%, 80 wt%, and 90 wt%) and tungsten particle size (200 μm, 400 μm, and 600 μm) on the impact behavior of the composites. The free surface velocity histories of the target plates were measured using a 37 mm single-stage light gas gun and a full-fiber laser interferometer (DISAR), enabling the determination of the shock velocity–particle velocity relationship to establish the equation of state. Experimental data show a linear relationship between shock velocity and particle velocity, with the 80 wt% and 90 wt% composites exhibiting similar shock velocities. The fitted slope increases from 2.792 to 2.957 as the tungsten mass fraction rises from 70 wt% to 90 wt%. With particle size increasing from 200 μm to 600 μm, the slope decreases from 3.204 to 2.756, while c₀ increases from 224.7 to 633.3. Comparison of the Hugoniot pressure curves of different specimens indicated that tungsten content significantly affects the impact behavior, whereas variations in tungsten particle size have a negligible influence on the Hugoniot pressure. A high tungsten content with small particle size (e.g., 90 wt% with ~200 μm) improves the overall compressive properties of composite materials. Based on the experimental results, a mesoscale finite element model consistent with the tests was developed. The overall error between the numerical simulations and experimental results was less than 5% under various conditions, thereby validating the accuracy of the model. Numerical simulations revealed the coupling mechanism between tungsten particle plastic deformation and matrix flow. The strong rarefaction unloading effect initiated at the composite’s free surface caused matrix spallation and jetting. Multiple wave systems were generated at the composite–copper interface, whose interference and coupling ultimately resulted in a nearly uniform macroscopic pressure field. Full article

Ketones, which are key biomarkers of fat oxidation, are relevant for metabolic health maintenance and disease development, making continuous monitoring essential. In this study, we introduce a novel colorimetric sensor designed for potential continuous acetone detection in biological fluids. The sensor features a polydimethylsiloxane (PDMS) shell that encapsulates a sensitive and specific liquid-core acetone-sensing probe. The microsphere sensors were characterized by evaluating their size, PDMS shell thickness, colorimetric response, and sensitivity under realistic conditions, including 100% relative humidity (RH) and CO₂ interference. The microsphere size and sensor sensitivity can be controlled by modifying the fabrication parameters. Critically, the sensor showed high selectivity for acetone detection, with negligible interference from CO₂ concentrations up to 4%. In addition, the sensor displayed good reproducibility (CV < 5%) and stability under realistic storage conditions (over two weeks at 4 °C). Finally, the accuracy of the microsphere sensor was validated against a gold standard gas chromatography-mass spectrometry (GC-MS) method using simulated and real breath samples from healthy individuals and type 1 diabetes patients. The correlation between the microsphere sensor and GC-MS produced a linear fit with a slope of 0.948 and an adjusted R-squared value of 0.954. Therefore, the liquid-core microsphere-based sensor is a promising platform for acetone body fluid analysis. Full article

Interior permanent magnet synchronous motors (IPMSMs) are widely applied as drive motors in electric vehicles because they have the advantages of high power density, high efficiency, and excellent dynamic performance. This paper introduces a framework for multi-objective optimization, tailored for the demands of V-Shaped IPMSMs, which involves high-dimensional variables. The framework is divided into three parts. Firstly, a proportional parametric finite element analysis (FEA) model for V-Shaped IPMSMs was established to reduce the probability of size interference among motor design parameters. Secondly, a surrogate model was trained using the design of experiments (DOE) approach and was utilized to substitute the FEA model. The accuracy of the surrogate model was then verified. Thirdly, the surrogate model was used as a fitness function, and a non-dominated sorting genetic algorithm II (NSGA-II) was employed as the optimization method to acquire the optimal goals rapidly. Based on the optimal design parameters, a prototype of the electrical motor was fabricated. Finally, the effectiveness of optimization was proven by experimental testing. Full article

Microplastics (MPs) are emerging pollutants that are ubiquitous in aquatic ecosystems and can affect the stability of aquatic food webs. They are intentionally produced in a size of less than 5 mm for specific purposes or are the result of the fragmentation of larger plastic debris. Zooplankton can be affected directly by the ingestion of MPs or indirectly by interference caused by suspended plastic particles. Various environmental agencies recommend the genus Moina for assessing risk from water pollutants. However, this genus has received less attention in research compared to non-indigenous cladocerans commonly used as test organisms. We evaluated the effects of artificially fragmented acrylonitrile butadiene styrene microplastics (ABS-MPs) on key demographic parameters such as survival, mortality, life expectancy, fecundity, and feeding rates of Moina macrocopa americana. We exposed M. macrocopa neonates to a diet consisting of the green microalgae Chlorella vulgaris and ABS-MP particles. Four treatments were set with different concentrations of ABS-MP particles (5, 10, and 20 mg L⁻¹). Survivorship, mortality, and reproduction were recorded daily until the last individual from the original cohort died. ABS-MPs significantly reduced M. macrocopa consumption rates of C. vulgaris, with an 85% decrease compared to the control. Although no statistically significant differences were found in life expectancy, net reproduction, or generation time among the toxic treatments, these parameters were drastically reduced compared to the control, even at the lowest concentration (5 mg L⁻¹); this resulted in a 34% reduction in average lifespan. The ABS-MPs interfere with the long-term population dynamics of M. macrocopa and change their consumption rates, potentially decreasing their fitness. Full article

The exposome represents the totality of endogenous and exogenous exposures across the lifespan. These exposures may result in DNA and RNA damage, in the form of adducts, which is a key factor in the etiology of a variety of human diseases, including cancer. It is understood that, following their repair, nucleic acid adducts are excreted into the urine, making urine an ideal, non-invasive matrix in which to study the whole-body nucleic acid adductome (the totality of nucleic acid adducts). However, the measurement of these adducts in urine presents challenges due to matrix interference and the variety of the chemical nature across the spectrum of nucleic adducts making their “one-size-fits-all” extraction by solid-phase extraction (SPE) challenging. Here, different types of SPE sorbents, and their combination, were evaluated for maximal recovery of nucleic acid adducts from urine. The SPE column combination of ENV+ coupled with PHE provided the best retention of a cocktail of 20 nucleic acid adduct standards. An untargeted high resolution mass spectrometry approach incorporating FeatureHunter 1.3 software was used to demonstrate the ability of this SPE method to successfully recover endogenous urinary nucleic acid adducts in addition to those represented by the cocktail of isotopically labeled standards. Using our approach, FeatureHunter 1.3 recognized approximately 500 adducts in both mouse and human urine samples. Isotopically labeled standards were used to identify a selection of the endogenous adducts and begin the characterization of the urinary nucleic acid adductome of mice and humans. Full article

In order to achieve rapid detection of oil quality, an innovative small-scale oil joint detection system is designed in this paper. The joint detection system combines a micro-distillation instrument and Raman spectrometer module through a combination module. The basic principle is to use a Raman spectrometer to detect the distillate in the micro-distillation instrument in order to predict the properties of the oil and determine its quality. The system uses Savitzy–Golay smoothing, baseline correction, normalization processing, partial least squares, and other methods for data fitting to ensure the authenticity and comparability of the measured data. Fourteen different sources and grades of diesel oil are selected in this paper for pre-experiments to verify the data fitting effect. Two oil samples, No. 1−10^# diesel oil 1 with strong fluorescence interference and No. 2−10^# diesel oil 2 with weak fluorescence interference, were used as test objects to compare the Raman spectra of undistilled oil and distilled oil, verify the testing effect of the joint detection system, and analyze the mechanism of fluorescence interference mainly existing in the heavy components of the oil. The test results show that the joint detection system can quickly detect the quality of different grades of diesel oil, with good smoothness, obvious characteristic bands, and good data fitting. The joint detection system designed in this paper has the advantages of high sensitivity, high integration, and small size, laying the foundation for research related to distillation ranges combined with Raman spectroscopy technology. Full article

Addressing the challenge of manual dependency and the difficulty in automating the online detection of height discrepancies following engine oil seal assembly, this paper proposes a composite vision-based method for the post-assembly size inspection of engine oil seals. The proposed method enables non-contact, online three-dimensional measurement of oil seals already installed on the engine. To achieve accurate positioning of the inner and outer ring regions of the oil seals, the process begins with obtaining the center point and the major and minor axes through ellipse fitting, which is performed using progressive template matching and the least squares method. After scaling the ellipse along its axes, the preprocessed image is segmented using the peak–valley thresholding method to generate an annular ROI (region of interest) mask, thereby reducing the complexity of the image. By integrating three-frequency four-step phase-shifting profilometry with an improved RANSAC (random sample consensus)-based plane fitting algorithm, the height difference between the inner and outer rings as well as the press-in depth are accurately calculated, effectively eliminating interference from non-target regions. Experimental results demonstrate that the proposed method significantly outperforms traditional manual measurement in terms of speed, with the relative deviations of the height difference and press-in depth confined within 0.33% and 1.45%, respectively, and a detection success rate of 96.35% over 1415 samples. Compared with existing methods, the proposed approach not only enhances detection accuracy and efficiency but also provides a practical and reliable solution for real-time monitoring of engine oil seal assembly dimensions, highlighting its substantial industrial application potential. Full article

Life tables are indispensable in IPM, offering an analysis of insect population dynamics. These tables record survival rates, fecundity, and other parameters at various developmental stages, enabling the identification of key factors that affect population numbers and the prediction of growth trajectories. This review discusses the application of life tables in agricultural pest management, including the assessment of the pest control capacity of natural enemies, the evaluation of biological agents, and the screening of insect-resistant plant species. In vector insect control, life tables are used to evaluate the transmission risks, model the population dynamics, and interfere with the life cycles of vector insects. For invasive pests, life tables help us to monitor population dynamics and predict future population sizes. In chemical pest control, life tables assist in evaluating the fitness costs of pesticide resistance, guiding insecticide selection, and optimizing application timing. In the final section, we explore future research directions, emphasizing the potential of integrating new technologies such as genomics, ethology, and satellite remote sensing to enhance life table analysis and improve IPM strategies. Full article

The piezoelectric grating voltage sensor has garnered significant attention in the realm of intelligent sensing, attributed to its compact size, cost-effectiveness, robust electromagnetic interference (EMI) immunity, and high network integration capabilities. In this paper, we propose a PZT-FBG (piezoelectric ceramic–fiber Bragg grating) voltage–temperature demodulation optical path architecture. This scheme effectively utilizes the originally unused temperature compensation reference grating, repurposing it as a temperature measurement grating. By employing FBGs with identical or similar parameters, we experimentally validate two distinct optical path connection schemes, before and after optimization. The experimental results reveal that, when the input voltage ranges from 250 V to 1800 V at a frequency of 50 Hz, the goodness of fit for the three fundamental waveforms is 0.996, 0.999, and 0.992, respectively. Furthermore, the sensor’s frequency response was tested across a frequency range of 50 Hz to 20 kHz, demonstrating that the measurement system can effectively respond within the sensor’s operational frequency range. Additionally, temperature measurement experiments showed a goodness of fit of 0.997 for the central wavelength of the FBG as the temperature increased. This research indicates that the improved optical path connection method not only accomplishes a synchronous demodulation of both temperature and voltage parameters but also markedly enhances the linearity and resolution of the voltage sensor. This discovery offers novel insights for further refining sensor performance and broadening the applications of optical voltage sensors. Full article

Aiming to address the problems of nonlinearity, a large time delay, poor adjustment ability, and a difficult parameter setting process of the tension control system of belt conveyor tensioning devices, an adaptive Proportional-Integral-Derivative (PID) parameter self-tuning algorithm based on an improved seeker optimization algorithm (ISOA) is proposed in this paper. The algorithm uses inertia weight random mutation to determine step size. An improved boundary reflection strategy avoids the defect of a large number of out-of-bound individuals gathering on the boundary in a traditional algorithm, and projects the individual reflection beyond the boundary into the boundary, which increases the diversity of the population and improves the convergence accuracy of the algorithm. To improve the system response speed and suppress the overshoot problem of the control target, coefficients related to the proportional term are introduced into the fitness function to accelerate the convergence of the algorithm. The improved algorithm is tested on three test functions such as Sphere and compared with other classical algorithms, which verify that the proposed algorithm is better in accuracy and stability. Finally, the interference and tracking performance of the ISOA-PID controller are verified in industrial experiments, which show that the PID controller optimized using the ISOA has good control quality and robustness. Full article

In recent years, deep-learning methods have significantly improved the classification results in the field of plant-leaf recognition. However, limited by the model input, the original image needs to be compressed to a certain size before it can be input into the convolutional neural network. This results in great changes in the shape and texture information of some samples, thus affecting the classification accuracy of the model to a certain extent. Therefore, a minimum enclosing quadrate (MEQ) method is proposed to standardize the sample datasets. First, the minimum enclosing rectangle (MER) of the leaf is obtained in the original image, and the target area is clipped. Then, the minimum enclosing quadrate of the leaf is obtained by extending the short side of the rectangle. Finally, the sample is compressed to fit the input requirements of the model. In addition, in order to further improve the classification accuracy of plant-leaf recognition, an EC-ResNet50 model based on transfer-learning strategy is proposed and further combined with the MEQ method. The Swedish leaf, Flavia leaf, and MEW2012 leaf datasets are used to test the performance of the proposed methods, respectively. The experimental results show that using the MEQ method to standardize datasets can significantly improve the classification accuracy of neural networks. The Grad-CAM visual analysis reveals that the convolutional neural network exhibits a higher degree of attention towards the leaf surface features and utilizes more comprehensive feature regions during recognition of the leaf samples processed by MEQ method. In addition, the proposed MEQ + EC-ResNet50 method also achieved the best classification results among all the compared methods. This experiment provides a widely applicable sample standardization method for leaf recognition research, which can avoid the problem of sample deformation caused by compression processing and reduce the interference of redundant information in the image to the classification results to a certain degree. Full article

Industrial computed tomography (CT) is widely used in the measurement field owing to its advantages such as non-contact and high precision. To obtain accurate size parameters, fitting parameters can be obtained rapidly by processing volume data in the form of point clouds. However, due to factors such as artifacts in the CT reconstruction process, many abnormal interference points exist in the point clouds obtained after segmentation. The classic least squares algorithm is easily affected by these points, resulting in significant deviation of the solution of linear equations from the normal value and poor robustness, while the random sample consensus (RANSAC) approach has insufficient fitting accuracy within a limited timeframe and the number of iterations. To address these shortcomings, we propose a spherical point cloud fitting algorithm based on projection filtering and K-Means clustering (PK-RANSAC), which strategically integrates and enhances these two methods to achieve excellent accuracy and robustness. The proposed method first uses RANSAC for rough parameter estimation, then corrects the deviation of the spherical center coordinates through two-dimensional projection, and finally obtains the spherical center point set by sampling and performing K-Means clustering. The largest cluster is weighted to obtain accurate fitting parameters. We conducted a comparative experiment using a three-dimensional ball-plate standard. The sphere center fitting deviation of PK-RANSAC was 1.91 μm, which is significantly better than RANSAC’s value of 25.41 μm. The experimental results demonstrate that PK-RANSAC has higher accuracy and stronger robustness for fitting geometric parameters. Full article

Hybrid bonded–bolted composite material interference connections significantly enhance the collaborative load-bearing capabilities of the adhesive layer and bolts, thus improving structural load-carrying capacity and fatigue life. So, these connections offer significant developmental potential and application prospects in aircraft structural assembly. However, interference causes damage to the adhesive layer and composite laminate around the holes, leading to issues with interface damage. In this study, we employed experimental and finite element methods. Initially, different interference-fit sizes were selected for bolt insertion to analyze the damage mechanism of the adhesive layer during interference-fit bolt installation. Subsequently, a finite element tensile model considering damage to the adhesive layer and composite laminate around the holes post-insertion was established. This study aimed to investigate damage in composite bonded–bolted hybrid joints, explore load-carrying rules and failure modes, and reveal the mechanisms of interference effects on structural damage and failure. The research results indicate that the finite element prediction model considering initial damage around the holes is more effective. As the interference-fit size increases, damage to the adhesive layer transitions from surface debonding to local cracking, while damage to the composite matrix shifts from slight compression failure to severe delamination and fiber-bending fracturing. The structural strength shows a trend of initially increasing and then decreasing, with the maximum strength observed at an interference-fit size of 1.1%. Full article

This work focuses on the characterization of five palygorskite clays from the Brazilian state of Piaui and their feasibility as eco-friendly adsorbents for the removal of Fe³⁺ ions from aqueous solutions. For characterization, we applied the techniques of X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy with energy dispersive spectroscopy (SEM/EDS), size distribution measurements, density measurement by He pycnometry, superconducting quantum interference device (SQUID) magnetometry, Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA/DTA), zeta potential measurement, hydrophobicity determination by contact angle, Brunauer–Emmett–Teller surface area analysis (BET technique) and atomic force microscopy (AFM). Batch experiments were performed in function of process parameters such as contact time and initial concentration of Fe³⁺. The natural palygorskites (Palys) had excellent performance for the removal of Fe³⁺ from aqueous solutions by adsorption (around 60 mg/g), and the Langmuir is supposedly the best model fitted the experimental data. Full article