Search Results (48)

Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. Physical models are constrained by modeling complexity and parameterization errors, while data-driven models lack interpretability and depend on high-quality data. To address these challenges, this study proposes OKG-ConvGRU, a domain knowledge-guided remote sensing prediction framework for ocean elements. This framework integrates knowledge graphs with the ConvGRU network, leveraging prior knowledge from marine science to enhance the prediction performance of ocean elements in remotely sensed images. Firstly, we construct a spatio-temporal knowledge graph for ocean elements (OKG), followed by semantic embedding representation for its spatial and temporal dimensions. Subsequently, a cross-attention-based feature fusion module (CAFM) is designed to efficiently integrate spatio-temporal multimodal features. Finally, these fused features are incorporated into an enhanced ConvGRU network. For multi-step prediction, we adopt a Seq2Seq architecture combined with a multi-step rolling strategy. Prediction experiments for chlorophyll-a concentration in the eastern seas of China validate the effectiveness of the proposed framework. The results show that, compared to baseline models, OKG-ConvGRU exhibits significant advantages in prediction accuracy, long-term stability, data utilization efficiency, and robustness. This study provides a scientific foundation and technical support for the precise monitoring and sustainable development of marine ecological environments. Full article

In recent years, Facility Management has undergone significant technological and methodological advancements, primarily driven by Building Information Modelling (BIM), Computer-Aided Facility Management (CAFM), and Computerized Maintenance Management Systems (CMMS). These innovations have improved process efficiency and risk management. However, challenges remain in asset management, maintenance, traceability, and transparency. This study investigates the potential of blockchain technology and non-fungible tokens (NFTs) to address these challenges. By referencing international (ISO, BOMA) and European (EN) standards, the research develops an asset management process model incorporating blockchain and NFTs. The methodology includes evaluating the technical and practical aspects of this model and strategies for metadata utilization. The model ensures an immutable record of transactions and maintenance activities, reducing errors and fraud. Smart contracts automate sub-phases like progress validation and milestone-based payments, increasing operational efficiency. The study’s practical implications are significant, offering advanced solutions for transparent, efficient, and secure Facility Management. It lays the groundwork for future research, emphasizing practical implementations and real-world case studies. Additionally, integrating blockchain with emerging technologies like artificial intelligence and machine learning could further enhance Facility Management processes. Full article

The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. This enhances the recognition accuracy of subtle surface defects and large-scale damages on cherries. Second, a channel attention feature fusion mechanism (CAFM) is incorporated at the front end of the detection head, which enhances the model’s ability to identify fine defects on the cherry surface. Additionally, to improve bounding box regression accuracy, powerful-IoU (PIoU) replaces the traditional CIoU loss function. Finally, self-distillation technology is introduced to further improve the mode’s generalization capability and detection accuracy through knowledge transfer. Experimental results show that the YOLOv8-DCPF model achieves precision, mAP, recall, and F1 score rates of 92.6%, 91.2%, 89.4%, and 89.0%, respectively, representing improvements of 6.9%, 5.6%, 6.1%, and 5.0% over the original YOLOv8n baseline network. The proposed model demonstrates high accuracy in cherry defect detection, providing an efficient and precise solution for intelligent cherry sorting in agricultural engineering applications. Full article

Accurate estrus detection and optimal insemination timing are crucial for improving sow productivity and enhancing farm profitability in intensive pig farming. However, sows’ estrus typically lasts only 48.4 ± 1.0 h, and interference from false estrus further complicates detection. This study proposes an enhanced YOLOv8 model, Integrated Convolution and Attention Enhancement (ICAE), for vulvar detection to identify the estrus stages. This model innovatively divides estrus into three phases (pre-estrus, estrus, and post-estrus) and distinguishes five different estrus states, including pseudo-estrus. ICAE-YOLO integrates the Convolution and Attention Fusion Module (CAFM) and Dual Dynamic Token Mixing (DDTM) for improved feature extraction, Dilation-wise Residual (DWR) for expanding the receptive field, and Focaler-Intersection over Union (Focaler-IoU) for boosting the performance across various detection tasks. To validate the model, it was trained and tested on a dataset of 6402 sow estrus images and compared with YOLOv8n, YOLOv5n, YOLOv7tiny, YOLOv9t, YOLOv10n, YOLOv11n, and the Faster R-CNN. The results show that ICAE-YOLO achieves an mAP of 93.4%, an F1-Score of 92.0%, GFLOPs of 8.0, and a model size of 4.97 M, reaching the highest recognition accuracy among the compared models, while maintaining a good balance between model size and performance. This model enables accurate, real-time estrus monitoring in complex, all-weather farming environments, providing a foundation for automated estrus detection in intensive pig farming. Full article

The rapid advancement of industrial automation in the automotive manufacturing sector has heightened demand for welding quality, particularly in critical component welding, where traditional manual inspection methods are inefficient and prone to human error, leading to low defect recognition rates that fail to meet modern manufacturing standards. To address these challenges, an enhanced YOLOv8-based algorithm for steel defect detection, termed YOLOv8-WD (weld detection), was developed to improve accuracy and efficiency in identifying defects in steel. We implemented a novel data augmentation strategy with various image transformation techniques to enhance the model’s generalization across different welding scenarios. The Efficient Vision Transformer (EfficientViT) architecture was adopted to optimize feature representation and contextual understanding, improving detection accuracy. Additionally, we integrated the Convolution and Attention Fusion Module (CAFM) to effectively combine local and global features, enhancing the model’s ability to capture diverse feature scales. Dynamic convolution (DyConv) techniques were also employed to generate convolutional kernels based on input images, increasing model flexibility and efficiency. Through comprehensive optimization and tuning, our research achieved a mean average precision (map) at IoU 0.5 of 90.5% across multiple datasets, contributing to improved weld defect detection and offering a reliable automated inspection solution for the industry. Full article

Leaf diseases such as Mosaic disease and Black Rot are among the most common diseases affecting apple leaves, significantly reducing apple yield and quality. Detecting leaf diseases is crucial for the prevention and control of these conditions. In this paper, we propose incorporating rotated bounding boxes into deep learning-based detection, introducing the ProbIoU loss function to better quantify the difference between model predictions and real results in practice. Specifically, we integrated the Plant Village dataset with an on-site dataset of apple leaves from an orchard in Weifang City, Shandong Province, China. Additionally, data augmentation techniques were employed to expand the dataset and address the class imbalance issue. We utilized the EfficientNetV2 architecture with inverted residual structures (FusedMBConv and S-MBConv modules) in the backbone network to build sparse features using a top–down approach, minimizing information loss. The inclusion of the SimAM attention mechanism effectively captures both channel and spatial attention, expanding the receptive field and enhancing feature extraction. Furthermore, we introduced depth-wise separable convolution and the CAFM in the neck network to improve feature fusion capabilities. Finally, experimental results demonstrate that our model outperforms other detection models, achieving 93.3% mAP@0.5, 88.7% Precision, and 89.6% Recall. This approach provides a highly effective solution for the early detection of apple leaf diseases, with the potential to significantly improve disease management in apple orchards. Full article

In recent years, deep learning-based multi-source data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, has gained significant attention in the field of remote sensing. However, the traditional convolutional neural network fusion techniques always provide poor extraction of discriminative spatial–spectral features from diversified land covers and overlook the correlation and complementarity between different data sources. Furthermore, the mere act of stacking multi-source feature embeddings fails to represent the deep semantic relationships among them. In this paper, we propose a cross attention-based multi-scale convolutional fusion network for HSI-LiDAR joint classification. It contains three major modules: spatial–elevation–spectral convolutional feature extraction module (SESM), cross attention fusion module (CAFM), and classification module. In the SESM, improved multi-scale convolutional blocks are utilized to extract features from HSI and LiDAR to ensure discriminability and comprehensiveness in diversified land cover conditions. Spatial and spectral pseudo-3D convolutions, pointwise convolutions, residual aggregation, one-shot aggregation, and parameter-sharing techniques are implemented in the module. In the CAFM, a self-designed local-global cross attention block is utilized to collect and integrate relationships of the feature embeddings and generate joint semantic representations. In the classification module, average polling, dropout, and linear layers are used to map the fused semantic representations to the final classification results. The experimental evaluations on three public HSI-LiDAR datasets demonstrate the competitiveness of the proposed network in comparison with state-of-the-art methods. Full article

The optical characteristics and electrical behavior of zinc oxide nanorods (ZnO-NRs) and silver nanoparticles (Ag-NPs) were investigated using advanced scanning probe microscopy techniques. The study revealed that the ZnO nanorods had a length of about 350 nm, while the Ag nanoparticles were spherical with heights ranging from 5 to 14 nm. Measurements with Kelvin probe force microscopy (KPFM) showed that the work functions of ZnO nanorods were approximately 4.55 eV, higher than that of bulk ZnO, and the work function of Ag nanoparticles ranged from 4.54 to 4.56 eV. The electrical characterization of ZnO nanorods, silver nanoparticles, and their hybrid was also conducted using conductive atomic force microscopy (C-AFM) to determine the local current-voltage (I-V) characteristics, which revealed a characteristic similar to that of a Schottky diode. The current-voltage characteristic curves of ZnO nanorods and Ag nanoparticles both showed an increase in current at around 1 V, and the hybrid ZnONRs/AgNP exhibited an increase in turn-on voltage at around 2.5 volts. This suggested that the presence of Ag nanoparticles enhanced the electrical properties of ZnO nanorods, improving the charge carrier mobility and conduction mechanisms through a Schottky junction. The investigation also explored the optical properties of ZnO-NRs, AgNPs, and their hybrid, revealing absorption bands at 3.11 eV and 3.18 eV for ZnO-NRs and AgNPs, respectively. The hybrid material showed absorption at 3.13 eV, indicating enhanced absorption, and the presence of AgNP affected the optical properties of ZnO-NR, resulting in increased photoluminescence intensity and slightly changes in peak positions. Full article

This study investigates the integration of Building Information Models (BIMs) and Virtual Tour (VT) environments in the Architecture, Engineering and Construction (AEC) industry, focusing on Computer-Aided Facility Management (CAFM), Computerized Maintenance Management Systems (CMMSs), and data Life-Cycle Assessment (LCA). The interconnected nature of tasks throughout a building’s life cycle increasingly demands a seamless integration of real-time monitoring, 3D models, and building data technologies. While there are numerous examples of effective links between IoT and BIMs, as well as IoT and VTs, a research gap exists concerning VT-BIM integration. This article presents a technical solution that connects BIMs and IoT data using VTs to enhance workflow efficiency and information transfer. The VT is developed upon a pilot based on the Controlled Environments for Living Lab Studies (CELLS), a unique facility designed for flexible monitoring and remote-control processes that incorporate BIMs and IoT technologies. The findings offer valuable insights into the potential of VTs to complement and connect to BIMs from a life-cycle perspective, improving the usability of digital twins for beginner users and contributing to the advancement of the AEC and CAFM industries. Our technical solution helps complete the connectivity of BIMs-VT-IoT, providing an intuitive interface (VT) for rapid data visualisation and access to dashboards, models and building databases. The practical field of application is facility management, enhancing monitoring and asset management tasks. This includes (a) sensor data monitoring, (b) remote control of connected equipment, and (c) centralised access to asset-space information bridging BIM and visual (photographic/video) data. Full article

The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth. Full article

The OHIO (Odin Hospital Indoor cOmpass) project received funding from the European Union’s Horizon 2020 research and innovation action program, via ODIN–Open Call, which is issued and executed under the ODIN project and focuses on enhancing hospital safety, productivity, and quality by introducing digital solutions, such as the Internet of Things (IoT), robotics, and artificial intelligence (AI). OHIO aims to enhance the productivity and quality of medical equipment maintenance activities within the pilot hospital, “Le Scotte” in Siena (Italy), by leveraging internal informational resources. OHIO will also be completely integrated with the ODIN platform, taking advantage of the available services and functionalities. OHIO exploits Bluetooth Low Energy (BLE) tags and antennas together with the resources provided by the ODIN platform to develop a complex ontology-based IoT framework, which acts as a central cockpit for the maintenance of medical equipment through a central management web application and an indoor real-time location system (RTLS) for mobile devices. The application programmable interfaces (APIs) are based on REST architecture for seamless data exchange and integration with the hospital’s existing computer-aided facility management (CAFM) and computerized maintenance management system (CMMS) software. The outcomes of the project are assessed both with quantitative and qualitative methods, by evaluating key performance indicators (KPIs) extracted from the literature and performing a preliminary usability test on both the whole system and the graphic user interfaces (GUIs) of the developed applications. The test implementation demonstrates improvements in maintenance timings, including a reduction in maintenance operation delays, duration of maintenance tasks, and equipment downtime. Usability post-test questionnaires show positive feedback regarding the usability and effectiveness of the applications. The OHIO framework enhanced the effectiveness of medical equipment maintenance by integrating existing software with newly designed, enhanced interfaces. The research also indicates possibilities for scaling up the developed methods and applications to additional large-scale pilot hospitals within the ODIN network. Full article

Tellurium exhibits exceptional intrinsic electronic properties. However, investigations into the modulation of tellurium’s electronic properties through physical modification are notably scarce. Here, we present a comprehensive study focused on the evolution of the electronic properties of tellurium crystal flakes under plasma irradiation treatment by employing conductive atomic force microscopy and Raman spectroscopy. The plasma-treated tellurium experienced a process of defect generation through lattice breaking. Prior to the degradation of electronic transport performance due to plasma irradiation treatment, we made a remarkable observation: in the low-energy region of hydrogen plasma-treated tellurium, a notable enhancement in conductivity was unexpectedly detected. The mechanism underlying this enhancement in electronic transport performance was thoroughly elucidated by comparing it with the electronic structure induced by argon plasma irradiation. This study not only fundamentally uncovers the effects of plasma irradiation on tellurium crystal flakes but also unearths an unprecedented trend of enhanced electronic transport performance at low irradiation energies when utilizing hydrogen plasma. This abnormal trend bears significant implications for guiding the prospective application of tellurium-based 2D materials in the realm of electronic devices. Full article

The aim of this contribution is to present the R-WIM (Rainwater Information Management) tool, which was created based on an extensive database of territory parameters, weather, surface runoff, etc., and in accordance with the requirements of municipalities. This tool was created especially for the purpose of identifying places where it is appropriate to implement elements of blue-green infrastructure. This tool was created on the basis of the smart urbido s.r.o. software 2.0, which allows working with a wide range of graphic and non-graphic information so that it is possible to link them together functionally and computationally, and to model the necessary spatial phenomena within the environment of selected cities of the Moravian-Silesian Region of the Czech Republic. Full article

Carbon nanotubes (CNTs) are cylindrical nanostructures fabricated from carbon atoms that seem like seamless cylinders composed of rolled sheets of graphite. Owing to the unique properties of single-walled carbon nanotubes (SWCNTs), they are a promising candidate in various fields such as chemical sensing, hydrogen storage, catalyst support, electronics, nanobalances, and nanotubes. Because of their small size, large surface area, high sensitivity, and reversible behavior at room temperature, CNTs are ideal for measuring gas. They also show improved electron transfer when used as electrodes in electrochemical reactions and serve as solid media for protein immobilization on biosensors. SWCNTs can be metallic or semi-conductive, counting on their structural properties. In this study, an atomic force microscope (AFM) was used as a powerful tool to manipulate and disaggregate SWCNTs. By precisely controlling the AFM probe, it was possible to manipulate individual SWCNTs and separate them from the bundle structures. Next, the electrical transport of disaggregated SWCNTs was studied using the conductive atomic force microscope (cAFM) technique. Thus, current-voltage measurements on the unbundled branches of SWCNTs were carried out. Interestingly, these current-voltage measurements have allowed us to unravel the complex electrical characteristics of the nanotube bundle, which is a very crucial issue for gating effects as well as the resistance of the interconnects within carbon nanotube network devices. Full article

Triboelectric nanogenerators (TENGs) based on sliding metal–semiconductor junctions are an emerging technology that can efficiently convert mechanical into electrical energy. These miniature autonomous power sources can output large direct current (DC) densities, but often suffer from limited durability; hence, their practical scope remains uncertain. Herein, through a combination of conductive atomic force microscopy (C-AFM) and photocurrent decay (PCM) experiments, we explored the underlying cause of surface wear during the operation of DC-TENGs. Using monolayer-functionalized Si(211) surfaces as the model system, we demonstrate the extent to which surface damage develops during TENG operation. We reveal that the introduction of surface defects (oxide growth) during TENG operation is not caused by the passage of the rather large current densities (average output of ~2 × 10⁶ A/m²); it is instead mainly caused by the large pressure (~GPa) required for the sliding Schottky diode to output a measurable zero-bias current. We also discovered that the drop in output during operation occurs with a delay in the friction/pressure event, which partially explains why such deterioration of DC-TENG performance is often underestimated or not reported. Full article