Search Results (61,979)

Antimicrobial susceptibility tests, achieved through the use of antibiotic-impregnated disks in a suitable laboratory environment, are conducted to determine which antibiotics are effective against the bacteria present in the body of an infected patient. The Kirby–Bauer method, a type of disk diffusion antimicrobial susceptibility test, is currently widely applied in microbiology laboratories due to its proven effectiveness. In our study, we developed an algorithm that utilizes image processing techniques to detect the inhibition zones of bacteria. A certain color depth acts as the threshold for the inhibition zone, with its radius determined according to the size of the reference object. This approach facilitates the measurement of inhibition zones and employs machine learning and deep learning to categorize antibiograms, followed by determination of whether a bacterium on the disk is sensitive or resistant to the antibiotics applied. The focus of this research is creating an automated interpretation system for antimicrobial susceptibility testing using the disk diffusion technique, thus simplifying the measurement and interpretation of inhibition zone sizes. Full article

Convolutional neural networks (CNNs) have achieved promising results in many tasks, and evaluating the model’s generalization ability based on the trained model and training data is paramount for practical applications. Although many measures for evaluating the generalization of CNN models have been proposed, the existing works are limited to small-scale or simplified model sets, which would result in poor accuracy and applicability of the derived methods. This study addresses these limitations by leveraging ResNet models as a case study to evaluate the model’s generalization ability. We utilized Intersection over Union (IoU) as a method to quantify the ratio of task-relevant features to assess model generalization. Class activation maps (CAMs) were used as a representation of the distribution of features learned by the model. To systematically investigate the generalization ability, we constructed a diverse model set based on the ResNet architecture. A total of 2000 CNN models were trained on the ImageNet subset by systematically changing commonly used hyperparameters. The results of our experiments revealed a strong correlation between the IoU-based evaluation method and the model’s generalization performance (Pearson correlation coefficient more than 0.8). We also performed extensive experiments to demonstrate the feasibility and robustness of the evaluation methods. Full article

Jumping robots possess the capability to surmount formidable obstacles and are well-suited for navigating through complex terrain environments. However, most of the existing jumping robots face challenges in achieving stable jumping and they also have low energy utilization efficiency, which limits their practical applications. In this work, a two-module jumping robot based on tensegrity structure is put forward. Firstly, the structural design and jumping mechanism of the robot are elaborated in the article. Then, dynamic models, including the two modules’ simultaneous jumping and step-up jumping process of the robot, are established utilizing the Lagrange dynamic modeling method. On this basis, the effects of parameters, including the stiffness of elastic cables and the initial tilt angle of the robot, on the jumping performance of the robot can be obtained. Finally, simulations are carried out and a prototype is developed to verify the rationality of the tensegrity-based jumping robot proposed in this work. The experiment results show that our jumping robot can achieve a stable jumping process and the step-up jumping of each module of the prototype can have higher energy efficiency than that of simultaneous jumping of each module, which enables the robot a better jumping performance. This research serves as a valuable reference for the design and analysis of jumping robots. Full article

This paper investigates the effect of spatial variability of soil elastic modulus on the longitudinal responses of the existing shield tunnel to the new tunnel undercrossing using a random two-stage analysis method (RTSAM). The Timoshenko–Winkler-based deterministic method considering longitudinal variation in the subgrade reaction coefficient and the random field of the soil elastic modulus discretized by the Karhunen–Loeve expansion method are combined to establish the RTSAM. Then, the proposed RTSAM is applied to carry out a random analysis based on an actual engineering case. Results show that the increases in the scale of fluctuation and the coefficient of variation of the soil elastic modulus lead to higher variabilities of tunnel responses. A decreasing pillar depth and mean value of the soil elastic modulus and an increasing skew angle strengthen the effect of the spatial variability of the soil elastic modulus on tunnel responses. The variabilities of tunnel responses under the random field of the soil elastic modulus are overestimated by the Euler–Bernoulli beam model. The results of this study provide references for the uncertainty analysis of the new tunneling-induced responses of the existing tunnel under the random field of soil properties. Full article

Front loaders used in agriculture are characterized by a compact structure, which limits the scope of their application. The loading possibilities are expanded by designing front loaders equipped with telescopic arms. This design increases the loader’s working area, making it easier to load trucks. It is necessary to work on the arm extension drive and perform strength analyses on the new structures. This article presents a FEM numerical analysis of the structure of an extending front loader and an assessment of the state of stress and the value of displacements under the influence of load. This study discusses the advantages and disadvantages of front loaders compared to telehandlers and the legal requirements and standards for the design of front loaders in Europe. This work presents the concept of loader arm movement and assesses the effectiveness of using hydraulic motors coupled with a screw gear. The obtained results prove that the newly designed extending front loader system is safe and stable. Full article

Computer vision solutions have become widely used in various industries and as part of daily solutions. One task of computer vision is object detection. With the development of object detection algorithms and the growing number of various kinds of image data, different problems arise in relation to the building of models suitable for various solutions. This paper investigates the influence of parameters used in the training process involved in detecting similar kinds of objects, i.e., the hyperparameters of the algorithm and the training parameters. This experimental investigation focuses on the widely used YOLOv5 algorithm and analyses the performance of different models of YOLOv5 (n, s, m, l, x). In the research, the newly collected construction details (22 categories) dataset is used. Experiments are performed using pre-trained models of the YOLOv5. A total of 185 YOLOv5 models are trained and evaluated. All models are tested on 3300 images photographed on three different backgrounds: mixed, neutral, and white. Additionally, the best-obtained models are evaluated using 150 new images, each of which has several dozen construction details and is photographed against different backgrounds. The deep analysis of different YOLOv5 models and the hyperparameters shows the influence of various parameters when analysing the object detection of similar objects. The best model was obtained when the YOLOv5l was used and the parameters are as follows: coloured images, image size—320; batch size—32; epoch number—300; layers freeze option—10; data augmentation—on; learning rate—0.001; momentum—0.95; and weight decay—0.0007. These results may be useful for various tasks in which small and similar objects are analysed. Full article

SLAM (Simultaneous Localization and Mapping), primarily relying on camera or LiDAR (Light Detection and Ranging) sensors, plays a crucial role in robotics for localization and environmental reconstruction. This paper assesses the performance of two leading methods, namely ORB-SLAM3 and SC-LeGO-LOAM, focusing on localization and mapping in both indoor and outdoor environments. The evaluation employs artificial and cost-effective datasets incorporating data from a 3D LiDAR and an RGB-D (color and depth) camera. A practical approach is introduced for calculating ground-truth trajectories and during benchmarking, reconstruction maps based on ground truth are established. To assess the performance, ATE and RPE are utilized to evaluate the accuracy of localization; standard deviation is employed to compare the stability during the localization process for different methods. While both algorithms exhibit satisfactory positioning accuracy, their performance is suboptimal in scenarios with inadequate textures. Furthermore, 3D reconstruction maps established by the two approaches are also provided for direct observation of their differences and the limitations encountered during map construction. Moreover, the research includes a comprehensive comparison of computational performance metrics, encompassing Central Processing Unit (CPU) utilization, memory usage, and an in-depth analysis. This evaluation revealed that Visual SLAM requires more CPU resources than LiDAR SLAM, primarily due to additional data storage requirements, emphasizing the impact of environmental factors on resource requirements. In conclusion, LiDAR SLAM is more suitable for the outdoors due to its comprehensive nature, while Visual SLAM excels indoors, compensating for sparse aspects in LiDAR SLAM. To facilitate further research, a technical guide was also provided for the researchers in related fields. Full article

After the experience of the COVID-19 pandemic, it has become evident that efficient and secure interoperability of medical information is crucial for effective diagnoses and medical treatments. However, a significant challenge arises concerning the heterogeneity of the systems storing patient information in medical centers or hospitals. Memory management becomes a pivotal element for the effective operation of the proposed API, as it must seamlessly execute across various devices, ranging from healthcare units, such as mobile phones, to servers in cloud computing. This proposal addresses these issues through techniques designed to enhance the performance of the software architecture in creating a medical interoperability API. This API has the capacity to be cloned and distributed to facilitate the exchange of data related to a patient’s medical history. To tackle heterogeneity, efficient memory management was implemented by utilizing an object-oriented approach and leveraging design patterns like abstract factory and wrapper. Regarding the evaluation of the proposal, this study showed an estimated performance of 94.5 percent, which was indirectly demonstrated through the assessment of operation sequences. This result suggests a satisfactory level based on complexity and coupling. Full article

Single-walled carbon nanotubes (SWCNTs) loaded with magnetic iron oxide nanoparticles were prepared by the arc discharge method and air heat treatment. The nanocomposite was characterized by X-ray diffraction, scanning electron microscopy, Raman spectroscopy, vibrating sample magnetometry, etc. The results showed that the heat-treated nanocomposites (SWCNTs/Fe_xO_y) had iron oxide phases and superparamagnetic properties with a saturation magnetization of 33.32 emu/g. Compared with the non-heat-treated materials, SWCNTs/Fe_xO_y had a larger specific surface area and pore volume. Using SWCNTs/Fe_xO_y to remove the organic contaminant (bisphenol A, BPA), it was found that under the conditions of pH = 3 and adsorbent dosage of 0.2 g/L, the maximum adsorption capacity of the composite was 117 mg/g, and the adsorption could reach more than 90% in only 5 min when the BPA content was below 0.05 mmol/L. The fitting results of the Langmuir and D-R models are more consistent with the experimental data, indicating a relatively uniform distribution of the adsorption sites and that the adsorption process is more consistent with physical adsorption. The kinetic calculations showed that the SWCNTs/Fe_xO_y exhibits chemical effects on both the surface and the gap, and the adsorption process is controlled by the π-π bonds and the hydrophobicity of the SWCNTs/Fe_xO_y. Full article

In this study, we report on the isolation, purification, and anti-inflammatory evaluation of compounds from the plant species Ageratina pichinchensis. Using open-column chromatography, 11 known compounds were purified, which chemical structures were elucidated by nuclear magnetic resonance techniques (1D and 2D). All compounds were evaluated in an in vitro model of RAW 264.7 mouse macrophage cells, measuring the nitric oxide inhibition to determine the anti-inflammatory effect. The compound betuletol 3-O-β-glucoside (11) inhibited nitric oxide with a half-maximal inhibitory concentration (IC₅₀) of 75.08 ± 3.07% at 75 µM; additionally, it inhibited the secretion of interleukin 6 (IL-6) and activation of the nuclear factor (NF-kβ). These results suggest that the anti-inflammatory effect attributed to A. pichinchensis species is promoted by compound 11, which could be considered a potential anti-inflammatory agent by suppressing the expression of NF-kβ target genes, such as those involved in the proinflammatory pathway and inducible nitric oxide synthase (iNOS). Full article

Context modeling has always been the groundwork for the dialogue response generation task, yet it presents challenges due to the loose context relations among open-domain dialogue sentences. Introducing simulated dialogue futures has been proposed as a solution to mitigate the problem of low history–response relevance. However, these approaches simply assume that the history and future of a dialogue have the same effect on response generation. In reality, the coherence between dialogue sentences varies, and thus, history and the future are not uniformly helpful in response prediction. Consequently, determining and leveraging the relevance between history–response and response–future to aid in response prediction emerges as a pivotal concern. This paper addresses this concern by initially establishing three context relations of response and its context (history and future), reflecting the relevance between the response and preceding and following sentences. Subsequently, we annotate response contextual relation labels on a large-scale dataset, DailyDialog (DD). Leveraging these relation labels, we propose a response generation model that adaptively integrates contributions from preceding and succeeding sentences guided by explicit relation labels. This approach mitigates the impact in cases of lower relevance and amplifies contributions in cases of higher relevance, thus improving the capability of context modeling. Experimental results on public dataset DD demonstrate that our response generation model significantly enhances coherence by 3.02% in long sequences (4-gram) and augments bi-gram diversity by 17.67%, surpassing the performance of previous models. Full article

Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources. Full article

An arbitrary Lagrangian-Eulerian lattice Boltzmann flux solver (ALE-LBFS) coupled with the mode superposition method is proposed in this work and applied to study two- and three-dimensional flutter phenomenon on dynamic unstructured meshes. The ALE-LBFS is applied to predict the flow field by using the vertex-centered finite volume method with an implicit dual time-stepping method. The convective fluxes are evaluated by using lattice Boltzmann solutions of the non-free D1Q4 lattice model and the viscous fluxes are obtained directly. Additional fluxes due to mesh motion are calculated directly by using local conservative variables and mesh velocity. The mode superposition method is used to solve for the dynamic response of solid structures. The exchange of aerodynamic forces and structural motions is achieved through interpolation with the radial basis function. The flow solver and the structural solver are tightly coupled so that the restriction on the physical time step can be removed. In addition, geometric conservation law (GCL) is also applied to guarantee conservation laws. The proposed method is tested through a series of simulations about moving boundaries and fluid–structure interaction problems in 2D and 3D. The present results show good consistency against the experiments and numerical simulations obtained from the literature. It is also shown that the proposed method not only can effectively predict the flutter boundaries in both 2D and 3D cases but can also accurately capture the transonic dip phenomenon. The tight coupling of the ALE-LBFS and the mode superposition method presents an effective and powerful tool for flutter prediction and can be applied to many essential aeronautical problems. Full article

Loads and strains in critical areas play a crucial role in aircraft structural health monitoring, the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct measurement of actual flight loads presents challenges. This process typically involves using load-strain stiffness matrices, derived from ground calibration tests, to map measured flight parameters to loads at critical locations. Presently, deep learning neural network methods are rapidly developing, offering new perspectives for this task. This paper explores the potential of deep learning models in predicting flight parameter loads and strains, integrating the methods of flight parameter preprocessing techniques, flight maneuver recognition (FMR), virtual ground calibration tests for wings, dimensionality reduction of flight data through Autoencoder (AE) network models, and the application of Long Short-Term Memory (LSTM) network models to predict strains. These efforts contribute to the prediction of strains in critical areas based on flight parameters, thereby enabling real-time assessment of aircraft damage. Full article

The geological conditions evaluation of coalbed methane (CBM) is of great significance to CBM exploration and development. The CBM resources in the Southern Sichuan Coalfield (SSC) of China are very abundant; however, the CBM investigation works in this area are only just beginning, and the basic geological research of CBM is seriously inadequate, restricting CBM exploration and development. Therefore, in this study, a representative CBM block (Dacun) in the SSC was selected, and the CBM geological conditions were evaluated based on field injection/fall-off well testing, gas content and composition measurements, and a series of laboratory experiments. The results show that the CH₄ concentrations of coal seams in the Dacun Block, overall, take on an increasing trend as the depth increases, and the CH₄ weathering zone depth is 310 m. Due to the coupled control of temperature and formation pressure, the gas content shows a “increase→decrease” trend as the depth increases, and the critical depth is around 700 m. The CBM is enriched in the hinge zone of the Dacun syncline. The moisture content shows a negative correlation with CBM gas content. The porosities of coal seams vary from 4.20% to 5.41% and increase with the R_o,max. The permeabilities of coal seams show a strong heterogeneity with values ranging from 0.001mD to 2.85 mD and present a decreasing trend with the increase in depth. Moreover, a negative relationship exists between coal permeability and minimum horizontal stress magnitude. The reservoir pressure coefficients are between 0.51 and 1.26 and show a fluctuation change trend (increase→decrease→increase) as the depth increases, reflecting that three sets of independent superposed gas-bearing systems possibly exist vertically in the Longtan Formation of the study area. The Langmuir volumes (V_L) of coals range from 22.67 to 36.84 m³/t, indicating the coals have strong adsorptivity. The V_L presents a parabolic change of first increasing and then decreasing with the increase in depth, and the turning depth is around 700 m, consistent with the critical depth of gas content. The gas saturations of coal seams are, overall, low, with values varying from 29.10% to 116.48% (avg. 68.45%). Both gas content and reservoir pressure show a positive correlation with gas saturation. The CBM development in the Dacun Block needs a large depressurization of reservoir pressure due to the low ratio (avg. 0.37) of critical desorption pressure to reservoir pressure. Full article