Search Results (437)

The mitochondrial genome and 5S rDNA are powerful tools for exploring and confirming species identity and understanding evolutionary trajectories. In addition, evaluating the nutritional value of Cambaroides dauricus by determining and analyzing the nutritional components of its muscles could provide essential data for relevant artificial breeding and food processing. In this study, for the first time, we characterized the mitochondrial genome of Cambaroides dauricus using Illumina sequencing technology. The results showed that the mitochondrial genome was a circular genome of 16,215 bp in length. The average sequencing depth of the mitochondrial genome was 100X. The nucleotide composition of the mitochondrial genome was 33.4% A, 39.1% T, 11.0% C and 16.5% G. Phylogenetic analyses showed that Cambaroides dauricus has a very close relationship with Cambaroides wladiwostokiensis. The results of 5S rDNA showed that the genetic relationship between Procambarus clarkii and Cambaroides dauricus is quite close. After determining the nutritional components of Cambaroides dauricus’ muscles with normal analysis methods, the results revealed that it has a crude protein content of 18.47 ± 0.87 per 100 milligrams, a crude fat content of 0.83 ± 0.12 per 100 milligrams, a crude ash content of 0.63 ± 0.06 per 100 milligrams and a moisture content of 79.8 ± 0.72 per 100 milligrams. There are eight essential amino acids in its protein, accounting for 41.59% of the total amino acids, and the proportion of umami amino acids is 36.27%. This study will provide a valuable basis for further studies of taxonomy, phylogenetic analyses and artificial breeding in Cambaroides dauricus. Full article

Breast cancer is one of the leading causes of death among women of all ages and backgrounds globally. In recent years, the growing deficit of expert radiologists—particularly in underdeveloped countries—alongside a surge in the number of images for analysis, has negatively affected the ability to secure timely and precise diagnostic results in breast cancer screening. AI technologies offer powerful tools that allow for the effective diagnosis and survival forecasting, reducing the dependency on human cognitive input. Towards this aim, this research introduces a deep meta-learning framework for swift analysis of mammography images—combining a Siamese network model with a triplet-based loss function—to facilitate automatic screening (recognition) of potentially suspicious breast cancer cases. Three pre-trained deep CNN architectures, namely GoogLeNet, ResNet50, and MobileNetV3, are fine-tuned and scrutinized for their effectiveness in transforming input mammograms to a suitable embedding space. The proposed framework undergoes a comprehensive evaluation through a rigorous series of experiments, utilizing two different, publicly accessible, and widely used datasets of digital X-ray mammograms: INbreast and CBIS-DDSM. The experimental results demonstrate the framework’s strong performance in differentiating between tumorous and normal images, even with a very limited number of training samples, on both datasets. Full article

In this study, the effects of plasma assistance on the electron beam physical vapour deposition (EB-PVD) process were investigated using an industrial coater (Smart Coater ALD Vacuum Technologies GmbH) equipped with a dual hollow cathode system. This configuration enabled the generation of a plasma environment during the deposition of the ceramic top coat onto a metallic substrate. The objective was to assess how plasma assistance influences the microstructure and thickness distribution of 7% wt. yttria-stabilised zirconia (YSZ) thermal barrier coatings (TBCs). Coatings were deposited with and without plasma assistance to enable a direct comparison. The thickness uniformity and columnar morphology of the 7YSZ top coats were evaluated by scanning electron microscopy (SEM) and X-ray diffraction (XRD). The mechanical properties of the deposited coatings were verified by the scratch test method. The results demonstrate that, in the presence of plasma, columnar grains become more uniformly spaced and exhibit sharper, well-defined boundaries even at reduced substrate temperatures. XRD analysis confirmed that plasma-assisted EB-PVD processes allow for maintaining the desired tetragonal phase of YSZ without inducing secondary phases or unwanted texture changes. These findings indicate that plasma-assisted EB-PVD can achieve desirable coating characteristics (uniform thickness and optimised columnar structure) more efficiently, offering potential advantages for high-temperature applications in aerospace and power-generation industries. Continued development of the EB-PVD process with the assistance of plasma generation could further improve deposition rates and TBC performance, underscoring the promising future of HC-assisted EB-PVD technology. Full article

Nanopore-based DNA sequencing has emerged as a transformative biosensing technology, enabling real-time molecular diagnostics in compact and mobile form factors. However, the computational complexity of the basecalling process—the step that translates raw nanopore signals into nucleotide sequences—poses a critical energy challenge for mobile deployment. While deep learning (DL) models currently dominate this task due to their high accuracy, they demand substantial power budgets and computing resources, making them unsuitable for portable or field-scale biosensor platforms. In this work, we propose an embedded hardware–software framework for DNA sequence detection that leverages a Viterbi-based Hidden Markov Model (HMM) implemented on a custom 64-bit RISC-V core. The proposed HMM detector is realized on an off-the-shelf Virtex-7 FPGA and evaluated against state-of-the-art DL-based basecallers in terms of energy efficiency and inference accuracy. From one side, the experimental results show that our system achieves an energy efficiency improvement of 6.5×, 5.5×, and 4.6×, respectively, compared to similar HMM-based detectors implemented on a commodity x86 processor, Cortex-A9 ARM embedded system, and a previously published Rocket-based system. From another side, the proposed detector demonstrates 15× and 2.4× energy efficiency superiority over state-of-the-art DL-based detectors, with competitive accuracy and sufficient throughput for field-based genomic surveillance applications and point-of-care diagnostics. This study highlights the practical advantages of classical probabilistic algorithms when tightly integrated with lightweight embedded processors for biosensing applications constrained by energy, size, and latency. Full article

Spectroscopic observations of Earth-like exoplanets and ultra-faint galaxies–top scientific priorities for the coming decades–involve measuring broadband signals at rates of only a few photons per square meter per hour. This imposes exceptional requirements on the detector performance, necessitating dark currents below 1 e⁻/pixel/kilo second, read noise under 1 e⁻/pixel/frame, and the ability to handle large-format arrays–capabilities that are not yet met by most existing infrared detectors. In addition, spaceborne LiDAR systems require photodetectors with exceptional sensitivity, compact size, low power consumption, and multi-channel capability to facilitate long-range range finding, topographic mapping, and active spectroscopy without increasing the instrument burden. MCT Avalanche photodiodes arrays offer high internal gain, pixelation, and photon-counting performance across SW to MW wavelengths needed for multi-beam and multi-wavelength measurements, marking them as a critical enabling technology for next-generation planetary and Earth science LiDAR missions. This work reports the latest progress in developing Hg_1−xCd_xTe linear-mode e-APDs at premier industrial research institutions, including relevant experimental data, simulations and major project planning. Related studies are summarized to demonstrate the practical and iterative approach for device fabrication, which have a transformative impact on the evolution of this discipline. Full article

This script discusses a qualitative analysis of the characteristics of coals burned in the combustion chambers of thermal power plants in Serbia. The study includes the following coal characteristics (mass fraction): moisture ($W\%$) ash ($A\%$), combustible materials ($V_g\%$) and lower heating power ($H_d(\mathrm{kJ}·{\mathrm{kg}}^{-1})$). Based on the collected data, statistical modeling was conducted, which included the calculation of the mean value ($\overline{X}$), standard deviation ($S$), and coefficient of variation ($C_v$) for each of the listed characteristics. The results indicate that all analyzed characteristics exhibit significant deviations from their mean values, as confirmed by the high values of the coefficient of variation (moisture 70.20%, ash 62.21%, combustible matter 43.33%, and lower heating value 44.10%). Large mass fraction deviations ($W$), ($A$), $(V_g)$ and $H_d$ around the mean value may negatively impact the operation of boiler plants and electrostatic precipitators of thermal power plants in Serbia, where the considered coals are burned. Large oscillations of ash (62.21%) around the mean value (17.00%) suggests that it is not feasible to implement dry flue-gas desulfurization (FGD) processes, due to the additional amount of ash. Distribution testing confirmed that all examined parameters can be reasonably approximated by a normal distribution. Subsequent statistical modeling using Student’s t-test at a 0.05 significance level demonstrated strong agreement between the coal characteristics from Serbia and corresponding parameters of coals from Bosnia and Herzegovina and Montenegro. The obtained results enable reliable quality comparison of coals, particularly lignites, across different basins. These findings establish a solid foundation for further energy and technological valorization of these fuel resources. Full article

The utilization of ocean wave energy through environmentally sustainable technologies plays a pivotal role in the transition toward renewable energy sources. Among such technologies, the Submerged Horizontal Plate (SHP) stands out as a viable option for clean power production. This study focuses on the system’s application in a region on the southern coast of Brazil, identified as a potential site for future installation. To investigate this system, a three-dimensional numerical wave tank was developed to simulate wave behavior and hydrodynamic loads using the Navier–Stokes framework in the computational fluid dynamics software ANSYS FLUENT 2022 R2. The volume of fluid approach was adopted to track the free surface. The setup for wave generation in the numerical wave tank was verified against analytical solutions to ensure precision and validated under the SHP’s non-oscillating condition. To represent the oscillating condition, boundary conditions constrained motion along the x- and y-axes, allowing movement exclusively along the z-axis. A parametric analysis of 54 cases, with varying geometric configurations, wave characteristics, and submersion depths, indicated that the oscillating SHP configuration elongated perpendicular to wave propagation, combined with specific wave conditions, achieved a theoretical mean efficiency of 76.61%. Full article

Sign language serves as a vital way to communicate with individuals with hearing loss, deafness, or a speech disorder, yet accessibility remains limited, requiring technological advances to bridge the gap. This study presents the first real-time Greek Sign Language recognition system utilizing deep learning and embedded computers. The recognition system is implemented using You Only Look Once (YOLO11X-seg), an advanced object detection model, which is embedded in a Python-based framework. The model is trained to recognize Greek Sign Language letters and an expandable set of specific words, i.e., the model is capable of distinguishing between static hand shapes (letters) and dynamic gestures (words). The most important advantage of the proposed system is its mobility and scalable processing power. The data are recorded using a mobile IP camera (based on Raspberry Pi 4) via a Motion-Joint Photographic Experts Group (MJPEG) Stream. The image is transmitted over a private ZeroTier network to a remote powerful computer capable of quickly processing large sign language models, employing Moonlight streaming technology. Smaller models can run on an embedded computer. The experimental evaluation shows excellent 99.07% recognition accuracy, while real-time operation is supported, with the image frames processed in 42.7 ms (23.4 frames/s), offering remote accessibility without requiring a direct connection to the processing unit. Full article

Silicon carbide (SiC) is widely utilized in semiconductors, microelectronics, optoelectronics, and other advanced technologies. However, its inherent characteristics, such as its hardness, brittleness, and high chemical stability, limit the processing efficiency and application of SiC wafers. This study explores the use of plasma etching as a pre-treatment step before chemical mechanical polishing (CMP) to enhance the material removal rate and improve CMP efficiency. Experiments were designed based on the Taguchi method to investigate the etching rate of plasma under various processing parameters, including applied power, nozzle-to-substrate distance, and etching time. The experimental results indicate that the etching rate is directly proportional to the applied power and increases with nozzle-to-substrate distance within 3–5 mm, while it is independent of etching time. A maximum etching rate of 5.99 μm/min is achieved under optimal conditions. And the etching mechanism and microstructural changes in SiC during plasma etching were analyzed using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), white light interferometry, and ultra-depth-of-field microscopy. XPS confirmed the formation of a softened SiO₂ layer, which reduces hardness and enhances CMP efficiency; SEM revealed that etching pits form in relation to distance; and white light interferometry demonstrated that etching causes a smooth surface to become rough. Additionally, surface defects resulting from the etching process were analyzed to reveal the underlying reaction mechanism. Full article

This study investigated a 100% waste-derived material system, using bottom ash (BA) and recycled concrete aggregate (RCA) for sustainable pavement base applications. This innovative approach diverts both construction and power plant waste from landfills while replacing conventional natural aggregates and cement-based binders. Five RCA:BA replacement ratios (90:10 to 50:50) were evaluated with three Na₂SiO₃:NaOH alkaline activator ratios (1:1, 1:1.5, and 1:2) through unconfined compressive strength (UCS) testing, scanning electron microscopy, energy dispersive X-ray spectroscopy (SEM-EDX), and X-ray diffraction (XRD) analysis. The RCA90BA10 composition with a G/N ratio of 1:2 achieved exceptional performance, reaching 9.14 MPa UCS at 7 days while exceeding the Department of Highways, Thailand, requirement of 2.413 MPa. All geopolymer-stabilized mixtures substantially surpassed minimum specifications, validating the technology for high-traffic pavement applications. Toughness evaluation confirmed superior energy absorption capacity of 107.89 N·m for the optimal formulation. Microstructural characterization revealed that higher G/N ratios promoted extensive sodium aluminosilicate hydrate and calcium silicate hydrate gel formation, creating dense, well-integrated matrices. XRD patterns confirmed successful geopolymerization through pronounced amorphous gel development between 20° and 35° 2θ, correlating directly with mechanical performance improvements. The RCA90BA10 formulation demonstrated optimal balance between reactive aluminosilicate content and structural aggregate framework. This technology offers significant environmental benefits by diverting construction and power plant waste from landfills while achieving mechanical properties superior to conventional materials, providing a scalable solution for sustainable infrastructure development. Full article

Industrial catalysts are accelerating the global transition toward renewable energy, serving as enablers for innovative technologies that enhance efficiency, lower costs, and improve environmental sustainability. This review explores the pivotal roles of industrial catalysts in hydrogen production, biofuel generation, and biomass conversion, highlighting their transformative impact on renewable energy systems. Precious-metal-based electrocatalysts such as ruthenium (Ru), iridium (Ir), and platinum (Pt) demonstrate high efficiency but face challenges due to their cost and stability. Alternatives like nickel-cobalt oxide (NiCo₂O₄) and Ti₃C₂ MXene materials show promise in addressing these limitations, enabling cost-effective and scalable hydrogen production. Additionally, nickel-based catalysts supported on alumina optimize SMR, reducing coke formation and improving efficiency. In biofuel production, heterogeneous catalysts play a crucial role in converting biomass into valuable fuels. Co-based bimetallic catalysts enhance hydrodeoxygenation (HDO) processes, improving the yield of biofuels like dimethylfuran (DMF) and γ-valerolactone (GVL). Innovative materials such as biochar, red mud, and metal–organic frameworks (MOFs) facilitate sustainable waste-to-fuel conversion and biodiesel production, offering environmental and economic benefits. Power-to-X technologies, which convert renewable electricity into chemical energy carriers like hydrogen and synthetic fuels, rely on advanced catalysts to improve reaction rates, selectivity, and energy efficiency. Innovations in non-precious metal catalysts, nanostructured materials, and defect-engineered catalysts provide solutions for sustainable energy systems. These advancements promise to enhance efficiency, reduce environmental footprints, and ensure the viability of renewable energy technologies. Full article

Unmanned aerial vehicles (UAVs) are commonly used in various fields and industries, but their limited battery life has become a key constraint for their development. Wireless Power Transmission (WPT) technology, with its convenience, durability, intelligence, and unmanned features, significantly enhances UAVs’ battery life and operational range. However, the variety of UAV models and different sizes pose challenges for designing couplers in the WPT system. This paper presents a design method for an array-type coupler in a UAV WPT system that uses a deep neural network. By establishing an electromagnetic 3D structure of the array-type coupler using electromagnetic simulation software, the dimensions of the transmitting and receiving coils are modified to assess how changes in the aperture of the transmitting coil and the length of the receiving coil affect the mutual inductance of the coupler. Furthermore, deep learning methods are utilized to train a high-precision model using the calculated data as the training and testing sets. Finally, taking the FAIRSER-X model UAV as an example, the transmitting and receiving coils are wound, and the feasibility and accuracy of the proposed method are verified through an LCR meter, which notably enhances the design efficiency of UAV WPT systems. Full article

Investigating brain area functions requires advanced technologies, but meaningful insights depend on correlating neural signals with behavior. Traditional mice behavior annotation methods, including manual and semi-automated approaches, are limited by subjectivity and time constraints. To overcome these limitations, our study employs the YOLO neural network for precise mice tracking and composite RGB frames for behavioral scoring. Our model, trained on over 10,000 frames, accurately classifies sitting, running, and grooming behaviors. Additionally, we provide statistical metrics and data visualization tools. We further combined AI-powered behavior labeling to examine hippocampal neuronal activity using fluorescence microscopy. To analyze neuronal circuit dynamics, we utilized a manifold analysis approach, revealing distinct functional patterns corresponding to transgenic 5xFAD Alzheimer’s model mice. This open-source software enhances the accuracy and efficiency of behavioral and neural data interpretation, advancing neuroscience research. Full article

Plow loosening machines are essential agricultural machinery in the agricultural production process. Improving the surface strengthening process and extending the working life of the plow teeth of the plow loosening machine are of great significance. In this paper, the preparation of Stellite6/Cu composite coating on the surface of 316L steel substrate intended for strengthening the plow teeth of a plow loosening machine using laser cladding technology was studied. The influence of different laser process parameters on the microstructure and properties of Stellite6/Cu composite coating was investigated. The composite coating powder was composed of Stellite6 powder with a different weight percent of copper. Microstructural analysis, phase composition, elemental distribution, microhardness, wear resistance, and corrosion resistance of the composite coatings on the plow teeth were analyzed using scanning electron microscopy (SEM), X-ray diffraction (XRD), microhardness testing, energy dispersive spectroscopy (EDS), friction and wear testing, and electrochemical workstation measurements. The results showed that (1) When the laser power was 1000 W, the average hardness of the prepared Stellite6/Cu composite layer achieved the highest hardness, approximately 1.36 times higher than the average hardness of the substrate, and the composite coating prepared exhibited the best wear resistance; (2) When the scanning speed was 800 mm/min, the composite coating exhibited the lowest average friction coefficient and the optimal corrosion resistance in a 3.5% wt.% NaCl solution with a self-corrosion current density of −7.55 µA/cm²; (3) When the copper content was 1 wt.%, the composite coating achieved the highest average hardness with 515.2 HV, the lowest average friction coefficient with 0.424, and the best corrosion resistance with a current density of −8.878 µA/cm². Full article

The low-carbon transition of the global energy system is an urgent necessity to address climate change and meet growing energy demand. As a major source of energy consumption and emissions, buildings play a key role in this transition. This study systematically analyzes the flexible resources of building energy systems and vehicle-to-grid (V2G) interaction technologies, and mainly focuses on the regulation characteristics and coordination mechanisms of distributed energy supply (renewable energy and multi-energy cogeneration), energy storage (electric/thermal/cooling), and flexible loads (air conditioning and electric vehicles) within regional energy systems. The study reveals that distributed renewable energy and multi-energy cogeneration technologies form an integrated architecture through a complementary “output fluctuation mitigation–cascade energy supply” mechanism, enabling the coordinated optimization of building energy efficiency and grid regulation. Electricity and thermal energy storage serve as dual pillars of flexibility along the “fast response–economic storage” dimension. Air conditioning loads and electric vehicles (EVs) complement each other via thermodynamic regulation and Vehicle-to-Everything (V2X) technologies, constructing a dual-dimensional regulation mode in terms of both power and time. Ultimately, a dynamic balance system integrating sources, loads, and storage is established, driven by the spatiotemporal complementarity of multi-energy flows. This paper proposes an innovative framework that optimizes energy consumption and enhances grid stability by coordinating distributed renewable energy, energy storage, and flexible loads across multiple time scales. This approach offers a new perspective for achieving sustainable and flexible building energy systems. In addition, this paper explores the application of demand response policies in building energy systems, analyzing the role of policy incentives and market mechanisms in promoting building energy flexibility. Full article