Search Results (95)

Mining is a branch of industry that continuously implements new technologies to optimise processes, in terms of increasing performance and decreasing the negative impact on the environment. To enhance the efficiency of the rock material fragmentation process, it is proposed to replace the existing hydraulic hammer with a fully electric alternative. This study used simple data-acquisition methods, including acoustic signals, vibrations, electric currents, and video recordings, to compare the efficiency of both hammers. Measurements were taken for hydraulic and electric hammers under comparable conditions, using the same boom, operator, and rock material. Two methodologies are proposed: the first one is based on acoustic data measurement and parametrisation, and the second one, for validation purposes, is based on visual (video) data analysis. The acoustic approach analyses the stability parameter ($\alpha$) of the $\alpha$-stable distribution, the shape parameter ($\nu$) of the t Location–Scale distribution that can assess signal impulsiveness, and three additional basic parameters (number of peaks, sum of amplitudes of impulses, and duration of the cycle for each truck/loader). The visual approach, used for validation, detects individual rock sizes and roughly evaluates the number of oversized pieces. However, it is a manual approach, due to challenging conditions. The results of both methods demonstrate that the electric hammer is more than three times more efficient. This increased efficiency is attributable to adaptive impact frequency control, a feature that is unavailable in hydraulic hammers. Full article

The basic theoretical properties of the approximate symmetric chordal metric (ASCM) for two real or complex numbers are studied, and reliable, accurate, and efficient algorithms are proposed for its computation. ASCM is defined as the minimum between the moduli of the differences of the two numbers and of their reciprocals. It differs from the chordal metric by including the modulus of the difference of the numbers. ASCM is not a true mathematical distance, but is a useful replacement for a distance in some applications. For instance, sensitivity analysis or block diagonalization of matrix pencils benefit from a measure of closeness of eigenvalues and also of their reciprocals; ASCM is ideal for this purpose. The proposed algorithms can be easily implemented on various architectures and compilers. Extensive numerical tests were performed to assess the performance of the associated implementation. The results were compared to those obtained in MATLAB, but with appropriate modifications for numbers very close to the bounds of the range of representable values, where the usual formulas give wrong results. Full article

Wingtip devices like winglets and other types have been created to improve the aerodynamic efficiency of aircraft based on minimizing the induced drag of tip vortices. This study aims to investigate the aerodynamic characteristics of these devices at low Reynolds numbers. In the present study, the models of a basic non-swept tapered wing and a wing with arc-shaped wingtips are examined. For this purpose, the basic model is equipped with replaceable tips with different geometries. The measurements are performed in a low-speed wind tunnel at a Reynolds number of around 100,000. The analysis of the collected data shows that the best aerodynamic characteristics have a configuration with a 45-degree dihedral angle at the tips of the wing. These results can be used in the conceptual design of small unmanned aerial vehicles (UAVs) to improve their performance in terms of range and endurance. Full article

The application of intelligent video monitoring for natural resource protection and management has become increasingly common in recent years. To enhance safety monitoring during the grazing prohibition and rest period of grassland, this paper proposes a multi-object segmentation algorithm based on mid-infrared images for all-weather surveillance. The approach integrates the Image Timing Features–Gaussian Mixture Model (ITF-GMM) and Convolutional-UNet (Con-UNet) to improve the accuracy of target detection. First, a robust background modelling, i.e., the ITF-GMM, is proposed. Unlike the basic Gaussian Mixture Model (GMM), the proposed model dynamically adjusts the learning rate according to the content difference between adjacent frames and optimizes the number of Gaussian distributions through time series histogram analysis of pixels. Second, a segmentation framework based on Con-UNet is developed to improve the feature extraction ability of UNet. In this model, the maximum pooling layer is replaced with a convolutional layer, addressing the challenge of limited training data and improving the network’s ability to preserve spatial features. Finally, an integrated computation strategy is designed to combine the outputs of ITF-GMM and Con-UNet at the pixel level, and morphological operations are performed to refine the segmentation results and suppress noises, ensuring clearer object boundaries. The experimental results show the effectiveness of proposed approach, achieving a precision of 96.92%, an accuracy of 99.87%, an intersection over union (IOU) of 94.81%, and a recall of 97.75%. Furthermore, the proposed algorithm meets real-time processing requirements, confirming its capability to enhance small-target detection in complex outdoor environments and supporting the automation of grassland monitoring and enforcement. Full article

Utilizing supplementary cementitious materials is an effective way to fabricate low-carbon cement-based materials. In this paper, the composite mortars with good properties were prepared by mixing them with basic oxygen furnace slag (BOFS), Bayer red mud (BRM), and phosphogypsum (PG). The influences of the replacement amounts of BRM and PG on the mechanical properties, hydration characteristic, chloride corrosion resistance, and microstructure of the materials were investigated. The results showed that simply adding 10 wt% BRM slightly modified the properties of the composite mortars. With the increase in PG, the mechanical strength and corrosion resistance coefficient K_C of the mortars first increased and then decreased, in contrast to the chloride migration coefficient D_RCM and electric flux Q. Among the samples, sample S3, with 6 wt% BRM and 4 wt% PG, had the best properties, a flexural strength of 6.6 MPa, and a compressive strength of 43.5 MPa at a curing age of 28 d. And the values of D_RCM and Q of the sample, respectively, decreased by 44.06% and 22.83% compared with the control sample, along with the value of K_C corroded after 120 d increasing by 16.33%. The microstructure analysis indicated that the alkali activation of BRM promoted the generation of lamellar portlandite and reticular and granular C-S-H gel. The free aluminum in BRM could dissolve into C-S-H gel to induce the generation of C-A-S-H gel. Furthermore, the generated amount of ettringite increased by adding PG. The aforementioned improvement in mechanical properties is primarily attributed to BRM promoting the hydration of the composite mortars and inducing the transformation of the C-S-H gel into C-A-S-H gel, and PG promoting the generation of ettringite. Moreover, the filling effects of BRM and PG decreased the porosity and number of harmful pores. It increased the compactness of the microstructure to endow the composite mortars with excellent chloride corrosion resistance. Full article

Maceral identification in images obtained with an immersive microscopy is one of the most important techniques for coal quality characterization. The objective of this paper is to explore the potential of semantic segmentation for the classification of liptinite macerals within microscope images. The following U-Net-based architectures were proposed for the task: a U-Net with a varying depth and feature map numbers, a U-Net extended with a proposed feature map attention mechanism, and a U-Net architecture with an encoder part replaced with a ResNet backbone. Two resolutions of input images were examined: 256 × 256 and 512 × 512 pixels. The training was conducted using constant and scheduled learning rate values. The results show a superior performance of the networks using a ResNet-based encoder, with the best IoU measure, equal 0.91, obtained with ResNet50. The other networks achieved worse results, but attention-supported U-Nets were considerably better than the basic versions. Both training approaches (constant and scheduled learning rates) yielded comparable results. The best results were better than those reported in the literature for other architectures of deep neural networks. It was also observed that the images presenting the greatest challenges to the networks were highly imbalanced, with the liptinite present only in a small area of the image. The architectures employing ResNet-based encoders were the only ones capable of surmounting these challenges. Full article

China has a large number of transmission lines laid in the mountains and forests and other regions, and these transmission lines enable national strategic projects such as the west-east power transmission project. However, the occurrence of mountain fires in the corresponding areas will seriously affect these transmission projects. At the same time, these mountain fires yield fewer image samples and complex backgrounds. Based on this, this paper proposes a transmission line hill fire detection model with YOLOv11 as the basic framework, named meta-learning attention YOLO (MA-YOLO). Firstly, the feature extraction module in it is replaced with a meta-feature extraction module, and the scale of the detection head is adjusted to detect smaller-sized hill fire targets. After this, the re-weighting module learns class-specific re-weighting vectors from the support set samples and uses them to recalibrate the mapping of meta-features. To enhance the model’s ability to learn target hill fire features from complex backgrounds, adaptive feature fusion (AFF) is integrated into the feature extraction process of YOLOv11 to improve the model’s feature fusion capabilities, filter out useless information in the features, and reduce the interference of complex backgrounds in detection. The experimental results show that the accuracy of MA-YOLO is improved by 10.8% in few-shot scenarios. MA-YOLO misses fewer hill fire targets in different scenarios and is less likely to be affected by complex backgrounds. Full article

(1) Objective: As light propagates through water, it undergoes significant attenuation and scattering, causing underwater images to experience color distortion and exhibit a bluish or greenish tint. Additionally, suspended particles in the water further degrade image quality. This paper proposes an improved U-Net network model for underwater image enhancement to generate high-quality images. (2) Method: Instead of incorporating additional complex modules into enhancement networks, we opted to simplify the classic U-Net architecture. Specifically, we replaced the standard convolutions in U-Net with our self-designed efficient basic block, which integrates a simplified channel attention mechanism. Moreover, we employed Layer Normalization to enhance the capability of training with a small number of samples and used the GELU activation function to achieve additional benefits in image denoising. Furthermore, we introduced the SK fusion module into the network to aggregate feature information, replacing traditional concatenation operations. In the experimental section, we used the “Underwater ImageNet” dataset from “Enhancing Underwater Visual Perception (EUVP)” for training and testing. EUVP, established by Islam et al., is a large-scale dataset comprising paired images (high-quality clear images and low-quality blurry images) as well as unpaired underwater images. (3) Results: We compared our proposed method with several high-performing traditional algorithms and deep learning-based methods. The traditional algorithms include He, UDCP, ICM, and ULAP, while the deep learning-based methods include CycleGAN, UGAN, UGAN-P, and FUnIEGAN. The results demonstrate that our algorithm exhibits outstanding competitiveness on the underwater imagenet-dataset. Compared to the currently optimal lightweight model, FUnIE-GAN, our method reduces the number of parameters by 0.969 times and decreases Floating-Point Operations Per Second (FLOPS) by more than half. In terms of image quality, our approach achieves a minimal UCIQE reduction of only 0.008 while improving the NIQE by 0.019 compared to state-of-the-art (SOTA) methods. Finally, extensive ablation experiments validate the feasibility of our designed network. (4) Conclusions: The underwater image enhancement algorithm proposed in this paper significantly reduces model size and accelerates inference speed while maintaining high processing performance, demonstrating strong potential for practical applications. Full article

From its inception, cell culture has been a great scientific tool for researchers in many diverse fields. The advancement from monolayer 2D cultures into three-dimensional cellular systems enabled a better experimental tool, as the 3D culture mimics in vivo environments more closely. Cells are aggregated in clusters, allowing for more cell-to-cell interactions, cell migration, and differences in nutrient and oxygen availability. Spheroids and organoids are most commonly used and have proven themselves as models for a large number of analytical purposes. The simplicity of spheroid production is often a good starting point. Because organoids are more complex, they can provide better and more complete data, but they can be difficult to grow and maintain. With increasing concern about the applicability of data obtained from animal studies and questions regarding animal welfare, these can replace a large proportion of these models and provide accurate and rapid results. In this overview, aimed at someone looking for an introductory summary of the requirements and possibilities of different 3D culture approaches, we give the basic information on various uses of spheroids and organoids in different fields of science. Assays based on spheroids and organoids can be adapted for a range of applications, and their use will continue to grow. Full article

A phenotype survey on soybean flower and pod drop conducted by agricultural experts revealed issues such as poor real-time performance and strong subjectivity. Based on the YOLOv5 detection model, a microscale detection layer is added and the size of the initial anchor box is improved to enhance feature expression ability. The CBAM attention mechanism is introduced in the backbone network to capture the information of direction and position, which helps the model to locate and recognize more accurately. The test results show that the accuracy rate of the soybean flower and pod recognition model reaches 98.4%, and the recall rate reaches 97.4%. Compared with the original network model, the accuracy rate and recall rate increase by 12.8% and 4.1%, respectively. Compared with manual counting, the average accuracy rate of field flower number is 80.32%, and the average accuracy rate of pod number is 82.17%. The research results show that models can effectively replace manual labor to complete the task of field soybean flower and pod identification and counting, and this application will promote the study of the basic laws of flower and pod fall and provide phenotypic investigation techniques. Full article

This study aimed to evaluate the use of oyster mushroom (Pleurotus ostreatus) powder (OMP) for producing rye bread. The raw materials were low-extract rye flour and OMP, which were analyzed in terms of their nutritional and health-promoting qualities. Mixtures of rye flour with OMP were prepared, replacing 5, 7.5, and 10% rye flour with OMP. The baking quality of the tested flour samples was assessed based on their water absorption, falling number, and amylograph and swelling curve tests. The laboratory baking test was carried out using the sourdough method, prepared based on LV2 starter cultures, and the bread samples were assessed in terms of their technological, sensory, and nutritional characteristics, as well as the antioxidant potential. The OMP was characterized by a high content of basic nutrition components and a higher antioxidant potential. The addition of OMP increased the nutritional value of the rye flour and its water absorption, significantly prolonged the starch gelatinization time, and increased the xylolytic activity of the flour. The OMP enhanced the bread’s dietary fiber, minerals, protein, and phenolic compounds, and boosted its antioxidant potential. Also, the starch present in the bread with OMP was characterized by a higher pro-health value due to a higher share of slowly digestible starch. Incorporating 7.5% OMP into the rye bread formula positively affected the bread’s sensory profile in contrast to the bread with a 10% addition of OMP. Full article

The growing demand for sustainable and environmentally friendly fuels and the increasing need to diversify energy sources have stimulated significant research in the field of renewable motor fuels. Despite the progress made, there is still a need to expand the feedstocks, optimize technological pathways, and, in particular, conduct comprehensive studies of the compatibility of renewable components with traditional fuels. In light of the above, the authors propose optimizing the properties of renewable fuels by using new vegetable oils and alcohols for their synthesis. The work is focused on studying the basic physical–chemical properties of fatty acid esters and assessing the possibility of using them as renewable components of motor fuels. Renewable components were obtained via the esterification of selected plant oils (rapeseed oil, camelina oil, palm kernel oil, and coconut oil) with different alcohols (ethanol and isobutanol) with further vacuum distillation of esters. The influence of the structure and composition of renewable components on their physical–chemical properties was studied and substantiated. It shows how the carbon number distribution and double bonds in fatty acid radicals influence the properties of renewable components. The paper shows the impact of the type and structure of alcohol used for esterification on the properties of studied products. The regularities in the change in properties of renewable components depending on the composition of oils and alcohols are explained and substantiated from the point of view of physical chemistry and the basics of forces of intermolecular interactions. Renewable components were compared to the properties of conventional motor fuels (diesel fuel and jet fuel). Based on the level of component compatibility with petroleum fuels, recommendations for replacing or blending petroleum fuels with renewable components were proposed. Full article

In the medical field, blood analysis is a key method used to evaluate the health status of the human body. The types and number of blood cells serve as important criteria for doctors to diagnose and treat diseases. In view of the problems regarding difficult classification and low efficiency in blood cell detection, this paper proposes an improved YOLOv5-BS blood cell target detection algorithm. The purpose of the improvement is to enhance the real-time performance and accuracy of blood cell type recognition. The algorithm is based on YOLOv5s as the basic network, incorporating the advantages of both CNN and Transformer architectures. First, the BotNet backbone network is incorporated. Then the YOLOv5 head architecture is replaced with the Decoupled Head structure. Finally, a new loss function SIoU is used to improve the accuracy and efficiency of the model. To detect the feasibility of the algorithm, a comparative experiment was conducted. The experiment shows that the improved algorithm has an accuracy of 92.8% on the test set, an average precision of 83.3%, and a recall rate of 99%. Compared with YOLOv8s and PP-YOLO, the average precision is increased by 3.9% and 1%, and the recall rate is increased by 3% and 2%. This algorithm effectively improves the efficiency and accuracy of blood cell detection and effectively improves the problem of blood cell detection. Full article

The phase rule of Gibbs is one of the basic equations in phase equilibria. Although it has been with us for 150 years, discussions, interpretations and extensions have been published. Here, the following new content is provided: (i). the choice of independent components is discussed, and the component rule is introduced, (ii). independent state variables are divided into compositional and non-compositional ones, (iii). the generalized phase rule is derived replacing number two in the original phase rule by the number of independent non-compositional state variables introduced above, (iv). the degree of freedom is decreased by the number of compositional constraints in special points (azeotrope and congruent melting) of phase diagrams, (v). a rule is derived connecting the maximum number of coexisting phases with the dimensions of the phase diagram, (vi). examples show how to apply the phase rule to unary, binary and ternary phase diagrams and their sections, (vii). the same is extended with the discussion of calculable and not calculable phase fractions, (viii). it is shown that the current definition of the degree of freedom is not sufficient in the number of cases, (ix). the current definition of the degree of freedom is extended, (x). the application of the generalized phase rule is demonstrated when other non-compositional state variables are applied for nano-phase diagrams, and/or for phase diagrams under the influence of electric potential difference, external magnetic field, mechanical strain or the gravitational field. Full article

Endoscopic ultrasound (EUS)-guided tissue sampling includes the techniques of fine needle aspiration (FNA) and fine needle biopsy (FNB), and both procedures have revolutionized specimen collection from the gastrointestinal tract, especially from remote/inaccessible organs. EUS-FNB has replaced FNA as the procedure of choice for tissue acquisition in solid pancreatic lesions (SPLs) across various society guidelines. FNB specimens provide a larger histological tissue core (preserving tissue architecture) with fewer needle passes, and this is extremely relevant in today’s era of precision and personalized molecular medicine. Innovations in needle tip design are constantly under development to maximize diagnostic accuracy by enhancing histological sampling capabilities. But, apart from the basic framework of the needle, various other factors play a role that influence diagnostic outcomes, namely, sampling techniques (fanning, aspiration or suction, and number of passes), collection methods, on-site evaluation (rapid, macroscopic, or visual), and specimen processing. The choice taken depends strongly on the endoscopist’s preference, available resources at the disposal, and procedure objectives. Hence, in this review, we explicate in detail the concepts and available literature at our disposal on the topic of EUS-guided pancreatic tissue sampling to best guide any practicing gastroenterologist/endoscopist in a not-to-ideal set-up, which EUS-guided tissue acquisition technique is the “best” for their case to augment their diagnostic outcomes. Full article