Search Results (4,182)

Nanoporous copper (np-Cu) has attracted much more attention due to its lower cost compared to other noble metals and high functionality in practical use. Herein, Al_100−xCu_x(x = 13–88 at.%) precursor films with thicknesses of 0.16–1.1 μm were fabricated by varying magnetron co-sputtering parameters. Subsequently, utilizing a one-step dealloying strategy, a series of np-Cu films with ligament sizes ranging from 11.4–19.0 nm were synthesized. The effects of precursor composition and substrate temperature on the microstructure of np-Cu films were investigated. As the atomic ratio of Cu increases from 15 to 34, the np-Cu film detached from the substrate gradually transforms into a bi-continuous ligament-channel structure that is well bonded to the substrate. Furthermore, the novel bi-layer hierarchical np-Cu films were successfully prepared based on single-layer nanoporous films. Our findings not only contribute to the systematic understanding of the modification of the morphology and structure of np-Cu films but also offer a valuable framework for the design and fabrication of other non-noble nanoporous metals with tailored properties. Full article

As the world shifts towards a low-carbon economy, the strategic deployment of renewable energy sources (RESs) is critical for maximizing energy output and ensuring sustainability. This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). A key innovation of GREENIA is its ability to provide natural language explanations (NLEs), enabling transparent and interpretable insights for both technical and non-technical stakeholders. Applied in Greece, the framework addresses the challenges posed by the interplay of meteorological factors from 10 different meteorological stations across the country. Validation against real-world data demonstrates improved prediction accuracy using metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHAP analysis enhances transparency by identifying key meteorological influences, such as temperature and humidity, while NLE translates these insights into actionable recommendations in natural language, improving accessibility for energy planners and policymakers. The resulting strategic plan offers precise, intelligent, and interpretable recommendations for deploying RES technologies, ensuring maximum efficiency and sustainability. This approach not only advances renewable energy optimization but also equips stakeholders with practical tools for guiding the strategic deployment of RES across diverse regions, contributing to sustainable energy management. Full article

The Industrial Internet of Things (IIoT) revolutionizes both industrial and residential operations by integrating AI (artificial intelligence)-driven analytics with real-time monitoring, optimizing energy usage, and significantly enhancing energy efficiency. This study proposes a secure IIoT framework that simultaneously predicts both active and reactive loads while also incorporating anomaly detection. The system is optimized for real-time deployment on an edge server, such as a single-board computer (SBC), as well as on a cloud or centralized server. It ensures secure and reliable industrial operations by integrating smart data acquisition systems with real-time monitoring, control, and protective measures. We propose a Temporal Convolutional Networks-Gated Recurrent Unit-Attention (TCN-GRU-Attention) model to predict both active and reactive loads, which demonstrates superior performance compared to other conventional models. The performance metrics for active load forecasting are 0.0183 Mean Squared Error (MSE), 0.1022 Mean Absolute Error (MAE), and 0.1354 Root Mean Squared Error (RMSE), while for reactive load forecasting, the metrics are 0.0202 (MSE), 0.1077 (MAE), and 0.1422 (RMSE). Furthermore, we introduce an optimized Isolation Forest model for anomaly detection that considers the transient conditions of appliances when identifying irregular behavior. The model demonstrates very promising performance, with the average performance metrics for all appliances using this Isolation Forest model being 95% for Precision, 98% for Recall, 96% for F1 Score, and nearly 100% for Accuracy. To secure the entire system, Transport Layer Security (TLS) and Secure Sockets Layer (SSL) security protocols are employed, along with hash-encoded encrypted credentials for enhanced protection. Full article

Genomic selection serves as an effective way for crop genetic breeding, capable of significantly shortening the breeding cycle and improving the accuracy of breeding. Phenotype prediction can help identify genetic variants associated with specific phenotypes. This provides a data-driven selection criterion for genomic selection, making the selection process more efficient and targeted. Deep learning has become an important tool for phenotype prediction due to its abilities in automatic feature learning, nonlinear modeling, and high-dimensional data processing. Current deep learning models have improvements in various aspects, such as predictive performance and computation time, but they still have limitations in capturing the complex relationships between genotype and phenotype, indicating that there is still room for improvement in the accuracy of phenotype prediction. This study innovatively proposes a new method called DeepAT, which mainly includes an input layer, a data feature extraction layer, a feature relationship capture layer, and an output layer. This method can predict wheat yield based on genotype data and has innovations in the following four aspects: (1) The data feature extraction layer of DeepAT can extract representative feature vectors from high-dimensional SNP data. By introducing the ReLU activation function, it enhances the model’s ability to express nonlinear features and accelerates the model’s convergence speed; (2) DeepAT can handle high-dimensional and complex genotype data while retaining as much useful information as possible; (3) The feature relationship capture layer of DeepAT effectively captures the complex relationships between features from low-dimensional features through a self-attention mechanism; (4) Compared to traditional RNN structures, the model training process is more efficient and stable. Using a public wheat dataset from AGT, comparative experiments with three machine learning and six deep learning methods found that DeepAT exhibited better predictive performance than other methods, achieving a prediction accuracy of 99.98%, a mean squared error (MSE) of only 28.93 tones, and a Pearson correlation coefficient close to 1, with yield predicted values closely matching observed values. This method provides a new perspective for deep learning-assisted phenotype prediction and has great potential in smart breeding. Full article

Poyang Lake is the largest freshwater lake in China and plays a significant ecological role. Deep-learning-based video surveillance can effectively monitor bird species on the lake, contributing to the local biodiversity preservation. To address the challenges of multi-scale object detection against complex backgrounds, such as a high density and severe occlusion, we propose a new model known as the YOLOv8-bird model. First, we use Receptive-Field Attention convolution, which improves the model’s ability to capture and utilize image information. Second, we redesign a feature fusion network, termed the DyASF-P2, which enhances the network’s ability to capture small object features and reduces the target information loss. Third, a lightweight detection head is designed to effectively reduce the model’s size without sacrificing the precision. Last, the Inner-ShapeIoU loss function is proposed to address the multi-scale bird localization challenge. Experimental results on the PYL-5-2023 dataset demonstrate that the YOLOv8-bird model achieves precision, recall, [email protected], and [email protected]:0.95 scores of 94.6%, 89.4%, 94.8%, and 70.4%, respectively. Additionally, the model outperforms other mainstream object detection models in terms of accuracy. These results indicate that the proposed YOLOv8-bird model is well-suited for bird detection and counting tasks, which enable it to support biodiversity monitoring in the complex environment of Poyang Lake. Full article

To optimize rice yield and enhance quality through targeted field management at each growth stage, rapid and accurate identification of rice growth stages is crucial. This study presents the Mobilenetv3-YOLOv8 rice growth-stage recognition model, designed for high efficiency and accuracy using Unmanned Aerial Vehicle (UAV) imagery. A UAV captured images of rice fields across five distinct growth stages from two altitudes (3 m and 20 m) across two independent field experiments. These images were processed to create training, validation, and test datasets for model development. Mobilenetv3 was introduced to replace the standard YOLOv8 backbone, providing robust small-scale feature extraction through multi-scale feature fusion. Additionally, the Coordinate Attention (CA) mechanism was integrated into YOLOv8’s backbone, outperforming the Convolutional Block Attention Module (CBAM) by enhancing position-sensitive information capture and focusing on crucial pixel areas. Compared to the original YOLOv8, the enhanced Mobilenetv3-YOLOv8 model improved rice growth-stage identification accuracy and reduced the computational load. With an input image size of 400 × 400 pixels and the CA implemented in the second and third backbone layers, the model achieved its best performance, reaching 84.00% mAP and 84.08% recall. The optimized model achieved parameters and Giga Floating Point Operations (GFLOPs) of 6.60M and 0.9, respectively, with precision values for tillering, jointing, booting, heading, and filling stages of 94.88%, 93.36%, 67.85%, 78.31%, and 85.46%, respectively. The experimental results revealed that the optimal Mobilenetv3-YOLOv8 shows excellent performance and has potential for deployment in edge computing devices and practical applications for in-field rice growth-stage recognition in the future. Full article

Magnetic two-dimensional (2D) materials have garnered significant attention for their potential to revolutionize 2D spintronics due to their unique magnetic properties. However, their air-sensitivity and highly insulating nature of the magnetic semiconductors present substantial challenges for device fabrication with effective contacts. In this study, we introduce a polycarbonate (PC)-assisted transfer method that effectively forms van der Waals (vdW) contacts with 2D materials, streamlining the fabrication process without the need for additional lithography. This method is particularly advantageous for air-sensitive magnetic materials, as demonstrated in Fe₃GeTe₂. It also ensures excellent interface contact quality and preserves the intrinsic magnetic properties in magnetic semiconductors like CrSBr. Remarkably, this method achieves a contact resistance four orders of magnitude lower than that achieved with traditional thermally evaporated electrodes in thin-layer CrSBr devices and enables the observation of sharp magnetic transitions similar to those observed with graphene vdW contacts. Compatible with standard dry-transfer processes and scalable to large wafer sizes, our approach provides a straightforward and effective solution for developing complex magnetic heterojunction devices and expanding the applications of magnetic 2D materials. Full article

Printed batteries are increasingly being investigated for feeding small, wearable devices more and more involved in our daily lives, promoting the study of printing technologies. Among these, gravure is very attractive as a low-cost and low-waste production method for functional layers in different fields, such as energy, sensors, and biomedical, because it is easy to scale up industrially. Thanks to our research, the feasibility of gravure printing was recently proved for rechargeable lithium-ion batteries (LiBs) manufacturing. Such studies allowed the production of high-quality electrodes involving different active materials with high stability, reproducibility, and good performance. Going beyond lithium-based storage devices, our attention was devoted on the possibility of employing highly sustainable gravure printing for sodium-ion batteries (NaBs) manufacturing, following the trendy interest in sodium, which is more abundant, economical, and ecofriendly than lithium. Here a study on gravure printed anodes for sodium-ion batteries based on hard carbon as an active material is presented and discussed. Thanks to our methodology centered on the capillary number, a high printing quality anodic layer was produced providing typical electrochemical behavior and good performance. Such results are very innovative and relevant in the field of sodium-ion batteries and further demonstrate the high potential of gravure in printed battery manufacturing. Full article

Graphitic carbon nitride (g-C₃N₄) as a fascinating conjugated polymer has attracted considerable attention due to its outstanding electronic properties, high physicochemical stability, and unique structure. In this work, we reported the characterization of g-C₃N₄, which was simply synthesized by thermal polymerization of thiourea, the photocatalytic degradation kinetics, and the pathway of levofloxacin (LEV) using the prepared g-C₃N₄. The XRD and SEM results confirmed a crystalline graphite structure with a tri-s-triazine unit and stacked sheet-like layers of g-C₃N₄. The efficacy factor (EF) was compared to different photocatalytic processes to assess the LEV removal performance. g-C₃N₄ exhibits good stability as a photocatalyst during LEV photodegradation. Radical scavenger experiments revealed that in the oxidative degradation of LEV, ^•O₂^– and h⁺ played the determining roles. Moreover, based on the identification of intermediates using liquid chromatography with tandem mass spectrometry (LC-MS/MS), the degradation pathway of LEV was proposed. Full article

The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. However, the branches overlap each other during the growth of maize seedlings and cannot be used as an identifying feature. However, during the tasseling stage, its apical ear blooms and has distinctive features that can be used as an identifying feature. However, the sizes of the maize tassels are small, the background is complex, and the existing network has obvious recognition errors. Therefore, in this paper, unmanned aerial vehicle (UAV) RGB images and an improved YOLOv8 target detection network are used to enhance the recognition accuracy of maize tassels. In the new network, a microscale target detection head is added to increase the ability to perceive small-sized maize tassels; In addition, Spatial Pyramid Pooling—Fast (SPPF) is replaced by the Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) in the backbone network part to connect different levels of detailed features and semantic information. Moreover, a dual-attention module synthesized by GAM-CBAM is added to the neck part to reduce the loss of features of maize tassels, thus improving the network’s detection ability. We also labeled the new maize tassels dataset in VOC format as the training and validation of the network model. In the final model testing results, the new network model’s precision reached 93.6% and recall reached 92.5%, which was an improvement of 2.8–12.6 percentage points and 3.6–15.2 percentage points compared to the mAP₅₀ and F1-score values of other models. From the experimental results, it is shown that the improved YOLOv8 network, with high performance and robustness in small-sized maize tassel recognition, can accurately recognize maize tassels in UAV images, which provides technical support for automated counting, accurate cultivation, and large-scale intelligent cultivation of maize seedlings. Full article

Exopolysaccharides (EPSs) are large-molecular-weight, complex carbohydrate molecules and extracellularly secreted bio-polymers released by many microorganisms, including lactic acid bacteria (LAB). LAB are well known for their ability to produce a wide range of EPSs, which has received major attention. LAB-EPSs have the potential to improve health, and their applications are in the food and pharmaceutical industries. Several methods have been developed and optimized in recent years for producing, extracting, purifying, and characterizing LAB-produced EPSs. The simplest method of evaluating the production of EPSs is to observe morphological features, such as ropy and mucoid appearances of colonies. Ethanol precipitation is widely used to extract the EPSs from the cell-free supernatant and is generally purified using dialysis. The most commonly used method to quantify the carbohydrate content is phenol–sulfuric acid. The structural characteristics of EPSs are identified via Fourier transform infrared, nuclear magnetic resonance, and X-ray diffraction spectroscopy. The molecular weight and composition of monosaccharides are determined through size-exclusion chromatography, thin-layer chromatography, gas chromatography, and high-performance liquid chromatography. The surface morphology of EPSs is observed via scanning electron microscopy and atomic force microscopy, whereas thermal characteristics are determined through thermogravimetry analysis, derivative thermogravimetry, and differential scanning calorimetry. In the present review, we discuss the different existing methods used for the detailed study of LAB-produced EPSs, which provide a comprehensive guide on LAB-EPS preparation, critically evaluating methods, addressing knowledge gaps and key challenges, and offering solutions to enhance reproducibility, scalability, and support for both research and industrial applications. Full article

The gypsum crystals (CaSO₄·2H₂O) crystallizes in a low symmetry system (monoclinic) and shows a marked layered structure along with a perfect cleavage parallel to the {010} faces. Owing to its widespread occurrence, as a single or twinned crystal, here the gypsum equilibrium (E.S.) and growth shapes (G.S.) have been re-visited. In making the distinction among E.S. and G.S., in the present work, the basic difference between epitaxy and homo-taxy is clearly evidenced. Gypsum has also been a fruitful occasion to recollect the general rules concerning either contact or penetration twins, for free growing and for twinned crystals nucleating onto pre-existing substrates. Both geometric and crystal growth aspects have been considered as well, by unifying theory and experiments of crystallography and crystal growth through the intervention of ${\mathsf{\beta }}_{\mathrm{adh}}$, the physical quantity representing the specific adhesion energy between gypsum and other phases. Hence, the adhesion energy allowed us to systematically use the Dupré’s formula. In the final part of the paper, peculiar attention has been paid to sediments (or solution growth) where the crystal size is very small, in order to offer a new simple way to afford classical (CNT) and non-classical nucleation (NCNT) theories, both ruling two quantities commonly used in the industrial crystallization: the total induction times (${\mathrm{t}}_{\mathrm{i}\mathrm{n}\mathrm{d}}^{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}$) and crystal size distribution (CSD). Full article

Printed Circuit Boards (PCBs) are essential components in electronic devices, making defect detection crucial. PCB surface defects are diverse, complex, low in feature resolution, and often resemble the background, leading to detection challenges. This paper proposes the YOLOv8_DSM algorithm for PCB surface defect detection, optimized based on the three major characteristics of defect targets and feature map visualization. First, to address the complexity and variety of defect shapes, we introduce CSPLayer_2DCNv3, which incorporates deformable convolution into the backbone network. This enhances adaptive defect feature extraction, effectively capturing diverse defect characteristics. Second, to handle low feature resolution and background resemblance, we design a Shallow-layer Low-semantic Feature Fusion Module (SLFFM). By visualizing the last four downsampling convolution layers of the YOLOv8 backbone, we incorporate feature information from the second downsampling layer into SLFFM. We apply feature map separation-based SPDConv for downsampling, providing PAN-FPN with rich, fine-grained shallow-layer features. Additionally, SLFFM employs the bi-level routing attention (BRA) mechanism as a feature aggregation module, mitigating defect-background similarity issues. Lastly, MPDIoU is used as the bounding box loss regression function, improving training efficiency by enhancing convergence speed and accuracy. Experimental results show that YOLOv8_DSM achieves a mAP (0.5:0.9) of 63.4%, representing a 5.14% improvement over the original model. The model’s Frames Per Second (FPS) reaches 144.6. To meet practical engineering requirements, the designed PCB defect detection model is deployed in a PCB quality inspection system on a PC platform. Full article

The precise extraction of building footprints using remote sensing technology is increasingly critical for urban planning and development amid growing urbanization. However, considering the complexity of building backgrounds, diverse scales, and varied appearances, accurately and efficiently extracting building footprints from various remote sensing images remains a significant challenge. In this paper, we propose a novel network architecture called ME-FCN, specifically designed to perceive and optimize multi-scale features to effectively address the challenge of extracting building footprints from complex remote sensing images. We introduce a Squeeze-and-Excitation U-Block (SEUB), which cascades multi-scale semantic information exploration in shallow feature maps and incorporates channel attention to optimize features. In the network’s deeper layers, we implement an Adaptive Multi-scale feature Enhancement Block (AMEB), which captures large receptive field information through concatenated atrous convolutions. Additionally, we develop a novel Dual Multi-scale Attention (DMSA) mechanism to further enhance the accuracy of cascaded features. DMSA captures multi-scale semantic features across both channel and spatial dimensions, suppresses redundant information, and realizes multi-scale feature interaction and fusion, thereby improving the overall accuracy and efficiency. Comprehensive experiments on three datasets demonstrate that ME-FCN outperforms mainstream segmentation methods. Full article

Autonomous vehicles operating in public transportation spaces must rapidly and accurately detect all potential hazards in their surroundings to execute appropriate actions such as yielding, lane changing, and overtaking. This capability is a prerequisite for achieving advanced autonomous driving. In autonomous driving scenarios, distant objects are often small, which increases the risk of detection failures. To address this challenge, the MST-YOLOv8 model, which incorporates the C2f-MLCA structure and the ST-P2Neck structure to enhance the model’s ability to detect small objects, is proposed. This paper introduces mixed local channel attention (MLCA) into the C2f structure, enabling the model to pay more attention to the region of small objects. A P2 detection layer is added to the neck part of the YOLOv8 model, and scale sequence feature fusion (SSFF) and triple feature encoding (TFE) modules are introduced to assist the model in better localizing small objects. Compared with the original YOLOv8 model, MST-YOLOv8 demonstrates a 3.43% improvement in precision (P), an 8.15% improvement in recall (R), an 8.42% increase in mAP_0.5, a reduction in missed detection rate by 18.47%, a 70.97% improvement in small object detection AP, and a 68.92% improvement in AR. Full article