Search Results (38)

Offshore floating photovoltaic (OFPV) is an important renewable energy technology, and assessing the reliability of mooring systems is of great significance for promoting the large-scale commercial deployment of OFPV. However, owing to the complexity of the system structure, relevant reliability research has not been extensively carried out. With this in view, this work focuses on the systematic reliability analysis of a novel parallel mooring system composed of elastic ropes and anchor chains under the ultimate limit state (ULS), accidental limit state (ALS) and fatigue limit state (FLS), considering both long-term cyclic and extreme environmental conditions. The first-order second moment (FOSM), first-order reliability method (FORM) and Monte Carlo simulation have been employed to calculate the failure probabilities. By applying the series-parallel model to integrate multimode failures, it is confirmed that the failure probability of the entire mooring system is significantly greater than that under any single limit state. The results indicate that anchor chain is the main fatigue-critical component, and the Monte Carlo simulation based on extensive random sampling data is more conservative in reliability estimation than FOSM and FORM which cannot fully capture all distribution characteristics. This work could provide essential theoretical support for the safe design of subsequent OFPV mooring systems. Full article

With the acceleration of industrialization, the problems of water resource pollution and shortage caused by oil spills and industrial wastewater discharge have become increasingly severe, posing a major threat to ecological sustainable development. Therefore, efficient oil–water separation technology has become a key breakthrough to alleviate this crisis. In this study, Janus membranes with asymmetric wettability were prepared by layer-by-layer electrospinning. The influence of the thickness ratio between the hydrophobic layer and the hydrophilic layer on the mechanical properties, separation flux, and oil–water mixture efficiency of the Janus membranes was examined, and an optimized membrane configuration was determined: the optimal thickness ratio between hydrophobic and hydrophilic layers was 4:6. Under these conditions, the fracture stress of the fiber membranes reached 99% MPa, the fracture strain was 55.63 ± 4.77%, the separation flux values were 1888.22 and 1042.66 L m⁻² h⁻¹ for the oil–water mixture and water-in-oil emulsion, respectively, with the separation efficiencies all exceeding 99%. After 50 cycles of separation for two different oil-in-water emulsions, the separation flux and separation efficiency of the optimal sample remained relatively stable, demonstrating strong practicability. In general, the Janus fiber membranes met the expected requirements, laying a good foundation for future applications in oil–water separation, floating oil collection in water, and other fields. Full article

This paper presents a comprehensive methodology to forecast the daily energy demand associated with recharging private electric vehicles in urban areas. The approach is based on plausible scenarios regarding the penetration of battery-powered vehicles and the availability of charging infrastructure. Accurate space and time forecasting of charging activities and power requirements is a critical issue in supporting the transition from conventional to battery-powered vehicles for urban mobility. This technological shift represents a key milestone toward achieving the zero-emissions target set by the European Green Deal for 2050. The methodology leverages Floating Car Data (FCD) samples. The widespread use of On-Board Units (OBUs) in private vehicles for insurance purposes ensures the methodology’s applicability across diverse geographical contexts. In addition to FCD samples, the estimation of charging demand for private electric vehicles is informed by a large-scale, detailed survey conducted by ENEA in Italy in 2023. Funded by the Ministry of Environment and Energy Security as part of the National Research on the Electric System, the survey explored individual charging behaviors during daily urban trips and was designed to calibrate a discrete choice model. To date, the methodology has been applied to the Metropolitan Area of Rome, demonstrating robustness and reliability in its results on two different scenarios of analysis. Each demand/supply scenario has been evaluated in terms of the hourly distribution of peak charging power demand, at the level of individual urban zones or across broader areas. Results highlight the role of the different components of power demand (at home or at other destinations) in both scenarios. Charging at intermediate destinations exhibits a dual peak pattern—one in the early morning hours and another in the afternoon—whereas home-based charging shows a pronounced peak during evening return hours and a secondary peak in the early afternoon, corresponding to a decline in charging activity at other destinations. Power distributions, as expected, sensibly differ from one scenario to the other, conditional to different assumptions of private and public recharge availability and characteristics. Full article

Automatic speech recognition (ASR) technology faces the dual challenges of model complexity and noise robustness when deployed on terminal devices (e.g., mobile devices, embedded systems). To meet the demand for lightweight and high-performance models in terminal devices, we propose a lightweight end-to-end speech recognition model, OS-Denseformer (Omni-Scale-Denseformer). The core of this model lies in its lightweight design and noise adaptability: multi-scale acoustic features are efficiently extracted through a multi-sampling structure to enhance noise robustness; the proposed OS-Conv module improves local feature extraction capability while significantly reducing the number of parameters, enhancing computational efficiency, and lowering model complexity; the proposed normalization function, ExpNorm, normalizes the model output, facilitating more accurate parameter optimization during model training. Finally, we employ distinct loss functions across different training stages, using Minimum Bayes Risk (MBR) joint optimization to determine the optimal weighting scheme that directly minimizes the character error rate (CER). Experimental results on public datasets such as AISHELL-1 demonstrate that, under a high-noise environment of −15 dB, the CER of the OS-Denseformer model is reduced by 9.95%, 7.97%, and 4.85% compared to the benchmark models Squeezeformer, Conformer, and Zipformer, respectively. Additionally, the model parameter count is reduced by 53.35%, 10.27%, and 27.66%, while the giga floating-point operations per second (GFLOPs) are decreased by 67.51%, 66.51%, and 13.82%, respectively. Deployment on resource-constrained mobile devices demonstrates that, compared to Conformer, OS-Denseformer reduced memory usage by 10.79% and decreased inference latency by 61.62%. Full article

Background/Objectives: Increasing the bioavailability of poorly absorbed drugs is a continuous challenge in modern pharmaceutical technology. This is due to the problematic nature of BCS class IV active pharmaceutical ingredients: these drugs possess poor solubility and membrane permeability. Moreover, many undergo immediate efflux and/or rapid systemic metabolism after absorption. This project aimed to improve the bioavailability of BCS class IV drugs by formulating gastroretentive self-emulsifying systems using curcumin as a model drug. Methods: The base of the systems was created by melting emulsifying agents, dissolution retardants, and PEGs together. Curcumin was added after the mixture was cooled slightly. Aqueous dispersions of several compositions were characterized by dynamic light scattering. After screening these results, the viscosities of the selected formulations were evaluated. Dissolution retardants were selected and added to the most superior samples, and their dissolution profiles were compared. Gastroretention of the final formulation was achieved by dispersing air in the molten system through melt foaming; internal structure was assessed by microCT, and physicochemical properties by PXRD and DSC. Cytotoxicity was measured in Caco-2 cells using MTT and Neutral Red assays, and transcellular transport was also studied. Results: Based on these results, a homogeneous gastric floating system was developed. We observed an advantageous cytotoxic profile and increased bioavailability. Conclusions: Overall, we were able to create a self-emulsifying gastroretentive formulation displaying extended release and gastric retention with a low amount of cost-efficient excipients. Full article

The subject of ship structural dynamics has faced new technological obstacles due to scientific and technological advancements, and one of the main concerns in related sectors is how to effectively reduce the vibration levels of different ships. This article focuses on the application scenarios of ship floating raft isolation systems, establishing a wave propagation model for acoustic black hole (ABH) structures based on the idea of the ABH effect. Then, a transfer matrix model for serially connected ABH structures is derived, which serves as a basis for subsequent structural designs. Second, the finite element method is used to study the energy distribution and vibration characteristics of a symmetrically distributed periodic non-uniform multi-level ABH structure. Meanwhile, it examines its bandgap distribution under a one-dimensional periodic arrangement and then investigates the vibration properties of non-uniform multi-level ABH thin-plate constructions with different periods from the perspective of engineering applications. Moreover, parameter optimization studies of non-uniform multi-level ABH structures with finite periods are carried out with an emphasis on engineering applications. The first step is to use the design space to determine the range of values for the parameters that need to be optimized. The hyper Latin cubic sampling method is then employed to select samples, and the EI criterion and PSO optimization algorithm are applied to add new samples to improve the Kriging surrogate model’s accuracy. When the optimal structural parameters have been determined, they are applied to the raft rib plate to verify the isolation effect of the non-uniform multi-level ABH structure by analyzing the vibration level difference at specific raft positions before and after embedding it. Full article

Accurate and rapid aquatic organism recognition is a core technology for fisheries automation and aquatic organism statistical research. However, due to absorption and scattering effects, images of aquatic organisms often suffer from poor contrast and color distortion. Additionally, the clustering behavior of aquatic organisms often leads to occlusion, further complicating the identification task. This study proposes a lightweight object detection model, Mobile-YOLO, for the recognition of four representative aquatic organisms, namely holothurian, echinus, scallop, and starfish. Our model first utilizes the Mobile-Nano backbone network we proposed, which enhances feature perception while maintaining a lightweight design. Then, we propose a lightweight detection head, LDtect, which achieves a balance between lightweight structure and high accuracy. Additionally, we introduce Dysample (dynamic sampling) and HWD (Haar wavelet downsampling) modules, aiming to optimize the feature fusion structure and achieve lightweight goals by improving the processes of upsampling and downsampling. These modules also help compensate for the accuracy loss caused by the lightweight design of LDtect. Compared to the baseline model, our model reduces Params (parameters) by 32.2%, FLOPs (floating point operations) by 28.4%, and weights (model storage size) by 30.8%, while improving FPS (frames per second) by 95.2%. The improvement in mAP (mean average precision) can also lead to better accuracy in practical applications, such as marine species monitoring, conservation efforts, and biodiversity assessment. Furthermore, the model’s accuracy is enhanced, with the mAP increased by 1.6%, demonstrating the advanced nature of our approach. Compared with YOLO (You Only Look Once) series (YOLOv5-12), SSD (Single Shot MultiBox Detector), EfficientDet (Efficient Detection), RetinaNet, and RT-DETR (Real-Time Detection Transformer), our model achieves leading comprehensive performance in terms of both accuracy and lightweight design. The results indicate that our research provides technological support for precise and rapid aquatic organism recognition. Full article

Underwater sonar target recognition is crucial in fields such as national defense, navigation, and environmental monitoring. However, it faces issues such as the complex characteristics of ship-radiated noise, imbalanced data distribution, non-stationarity, and bottlenecks of existing technologies. This paper proposes the MultiFuseNet-AID network, aiming to address these challenges. The network includes the TriFusion block module, the novel lightweight attention residual network (NLARN), the long- and short-term attention (LSTA) module, and the Mamba module. Through the TriFusion block module, the original, differential, and cumulative signals are processed in parallel, and features such as MFCC, CQT, and Fbank are fused to achieve deep multi-domain feature fusion, thereby enhancing the signal representation ability. The NLARN was optimized based on the ResNet architecture, with the SE attention mechanism embedded. Combined with the long- and short-term attention (LSTA) and the Mamba module, it could capture long-sequence dependencies with an O(N) complexity, completing the optimization of lightweight long sequence modeling. At the same time, with the help of feature fusion, and layer normalization and residual connections of the Mamba module, the adaptability of the model in complex scenarios with imbalanced data and strong noise was enhanced. On the DeepShip and ShipsEar datasets, the recognition rates of this model reached 98.39% and 99.77%, respectively. The number of parameters and the number of floating point operations were significantly lower than those of classical models, and it showed good stability and generalization ability under different sample label ratios. The research shows that the MultiFuseNet-AID network effectively broke through the bottlenecks of existing technologies. However, there is still room for improvement in terms of adaptability to extreme underwater environments, training efficiency, and adaptability to ultra-small devices. It provides a new direction for the development of underwater sonar target recognition technology. Full article

Laser wireless power transfer (LWPT) offers a transformative approach to wireless energy transmission, addressing critical limitations in unmanned aerial vehicles (UAVs) such as battery energy limitation. However, challenges like beam divergence, non-uniform irradiation, and alignment instability limit its practical application. Here, we present a lightweight air-floating metalens platform to overcome these barriers. This innovative lens focuses laser beams near the photovoltaic receiver with an energy distribution uniformity across a single spot at the focal plane that is 50 times greater than that of a conventional Gaussian beam spot, achieving a multi-spot energy distribution uniformity of up to 99% theoretically. Experimentally, we achieved 75% uniformity using a metalens sample. Simultaneously, our system maintains superior beam quality within a dynamic range of 4 m and enhances charging efficiency by 1.5 times. Our research provides a robust technical solution to improve UAV endurance, enabling efficient, long-range wireless power transfer and opening broader technological implications. Full article

With the increasing demand for wood in the wood market and the frequent trade of high-value wood, the accurate identification of wood varieties has become essential. This study employs two hyperspectral imaging systems—visible and near-infrared spectroscopy (VNIR) and short-wave infrared spectroscopy (SWIR)—in combination with a deep learning model to propose a method for wood species identification. Spectral data from wood samples were obtained through hyperspectral imaging technology, and classification was performed using a combination of convolutional neural networks (CNNs) and Transformer models. Multiple spectral preprocessing and feature extraction techniques were applied to enhance data quality and model performance. The experimental results show that the full-band modeling is significantly better than the feature-band modeling in terms of classification accuracy and robustness. Among them, the classification accuracy of SWIR reaches 100%, the number of model parameters is 1,286,228, the total size of the model is 4.93 MB, and the Floating Point Operations (FLOPs) is 1.29 M. Additionally, the Shapley Additive Explanation (SHAP) technique was utilized for model interpretability, revealing key spectral bands and feature regions that the model emphasizes during classification. Compared with other models, CNN-Transformer is more effective in capturing the key features. This method provides an efficient and reliable tool for the wood industry, particularly in wood processing and trade, offering broad application potential and significant economic benefits. Full article

Tea bud localization detection not only ensures tea quality, improves picking efficiency, and advances intelligent harvesting, but also fosters tea industry upgrades and enhances economic benefits. To solve the problem of the high computational complexity of deep learning detection models, we developed the Tea Bud DSCF-YOLOv8n (TBF-YOLOv8n)lightweight detection model. Improvement of the Cross Stage Partial Bottleneck Module with Two Convolutions(C2f) module via efficient Distributed Shift Convolution (DSConv) yields the C2f module with DSConv(DSCf)module, which reduces the model’s size. Additionally, the coordinate attention (CA) mechanism is incorporated to mitigate interference from irrelevant factors, thereby improving mean accuracy. Furthermore, the SIOU_Loss (SCYLLA-IOU_Loss) function and the Dynamic Sample(DySample)up-sampling operator are implemented to accelerate convergence and enhance both average precision and detection accuracy. The experimental results show that compared to the YOLOv8n model, the TBF-YOLOv8n model has a 3.7% increase in accuracy, a 1.1% increase in average accuracy, a 44.4% reduction in gigabit floating point operations (GFLOPs), and a 13.4% reduction in the total number of parameters included in the model. In comparison experiments with a variety of lightweight detection models, the TBF-YOLOv8n still performs well in terms of detection accuracy while remaining more lightweight. In conclusion, the TBF-YOLOv8n model achieves a commendable balance between efficiency and precision, offering valuable insights for advancing intelligent tea bud harvesting technologies. Full article

Greece has mineral resources in which strategic elements such as nickel (Ni) and cobalt (Co) are present. In this study, characterizations of the metal concentrations of three laterite deposits, as well as minerals from a kiln originating from a mineral processing plant, were carried out. The aim of this research was to integrate technological characterization data on the quantities of cobalt (Co) and nickel (Ni) present in the lateritic profiles of three mineral deposits from Greece and in kiln dust. The study was conducted at the Mineral Processing Laboratory of UPC/Bases Manresa. Aliquots were constituted for particle-size distribution tests by wet sieving followed by laser granulometry. Scanning electron microscopy (SEM) was used to determine the mineralogical compositions. Sink–float tests were conducted to estimate the particle liberation. Mineral liberation analysis (MLA) was performed to understand the liberation and the association of minerals in the samples. Full article

Floating photovoltaic systems are rapidly gaining popularity due to their advantages in conserving land resources and their high energy conversion efficiency, making them a promising option for photovoltaic power generation. However, these systems face challenges in offshore environments characterized by high salinity, humidity, and variable irradiation, which necessitate effective maximum power point tracking (MPPT) technologies to optimize performance. Currently, there is limited research in this area, and few reviews analyze it comprehensively. This paper provides a thorough review of MPPT techniques applicable to floating photovoltaic systems, evaluating the suitability of various methods under marine conditions. Traditional algorithms require modifications to address the drift phenomena under uniform irradiation, while different GMPPT techniques exhibit distinct strengths and limitations in partial shading conditions (PSCs). Hardware reconfiguration technologies are not suitable for offshore use, and while sampled data-based techniques are simple, they carry the risk of erroneous judgments. Intelligent technologies face implementation challenges. Hybrid algorithms, which can combine the advantages of multiple approaches, emerge as a more viable solution. This review aims to serve as a valuable reference for engineers researching MPPT technologies for floating photovoltaic systems. Full article

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with “one-line knit to shape” capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry. Full article

In this work, a novel internal heat absorption technology using inorganic material rods is employed during the solidification process of steel ingots, aiming to control their solidification and improve the quality of the final product. The study investigates the effect of the insertion depth of inorganic materials on the solidification microstructure and macrosegregation of 2.5-ton 42CrMo ingots. The mechanical properties of samples from the product are also tested. A numerical simulation model for casting 2.5-ton ingots is established and implemented in Ansys Fluent fluid simulation software, with inorganic material rods set at different preset depths. The simulation explores the physical processes of the melting and floating of inorganic materials in molten steel, as well as their effects on the temperature and flow fields of the material. The results show that deeper insertion of inorganic materials (200 mm from the hot top) reduces the tendency for macrosegregation compared to that at the insertion depth of 100 mm. Specifically, the positive segregation area decreases by 10.35%, while the negative segregation area decreases by 15.32%. Moreover, deeper insertion results in a significant refinement of the solidification microstructure, ultimately enhancing the mechanical properties of the products machined from the ingots (i.e., the yield strength increased by 4.7%). The numerical simulation results indicate that as the placement depth of inorganic materials in the ingot mold increases, the cooling effect becomes more significant, the flow area of molten steel initiated by the inorganic materials expands, and the linear velocity of the double-circle flow increases. This further explains why the solidification quality of the ingots improves with the increasing placement depth of inorganic materials. Full article