Search Results (20)

This study investigated the conversion of cellulose from rice husk (RH) and straw (RS), two types of agricultural waste, into Carboxymethyl cellulose (CMC). Cellulose was extracted using KOH and NaOH, hydrolyzed, and bleached to increase purity and fineness. The cellulose synthesis yielded a higher net CMC content for RH-CMC (84.8%) than for RS-CMC (57.7%). Due to smaller particle sizes, RH-CMC exhibited lower NaCl content (0.77%) and higher purity. FT-IR analysis confirmed similar functional groups to commercial CMC, while XRD analysis presented a more amorphous structure and a higher degree of carboxymethylation. A biodegradable film preparation of starch-based CMC using citric acid as a crosslinking agent shows food packaging properties. The biodegradable film demonstrated good swelling, water solubility, and moisture content, with desirable mechanical properties, maximum load (6.54 N), tensile strength (670.52 kN/m²), elongation at break (13.3%), and elastic modulus (2679 kN/m²), indicating durability and flexibility. The RH-CMC film showed better chemical and mechanical properties and complete biodegradability in soil within ten days. Applying the biodegradable film for tomato preservation showed that wrapping with the film reduced weight loss more efficiently than dip coating. The additional highlight of the work was a consumer survey in Thailand that revealed low awareness but significant interest in switching to alternative uses, indicating commercial potential for eco-friendly packaging choices and market opportunities for sustainable materials. Full article

Due to the limitations of farming space, fish cage aquaculture is gradually expanding into offshore deep-sea areas, where the environmental conditions surrounding deep-sea fish cages are more complex and harsher compared to those in shallower offshore locations. Conventional multi-point moored gravity flexible fish cages are prone to damage in the more hostile environments of the deep sea. In this paper, we present a design for a single-point mooring vessel-shaped fish cage that can quickly adjust its bow direction when subjected to waves from various angles. This design ensures that the floating frame consistently responds effectively to wave impacts, thereby reducing the wave forces experienced. The dynamic response of the floating frame and the mooring forces were simulated by coupling the Smoothed Particle Hydrodynamics method with the Moordyn numerical model for mooring analysis. The three degrees of freedom (heave, surge, and pitch) and the mooring forces of a scaled-down vessel-type ship cage model under wave conditions were investigated both numerically and experimentally. The results indicate that the error between the simulation data and the experimental results is maintained within 6%. Building on this foundation, the motion response and mooring force of a full-sized ship-shaped net box under wave conditions off the southeast coast of China were simulated. This study examined the effects of varying mooring lengths and buoy configurations on the motion response and mooring force of the fish cage. Finally, we constructed the fish cage and tested it under the influence of a typhoon. The results demonstrate that the fish cage could operate stably without structural damage, such as mooring failure or floating frame breakage, despite the significant deformation of the floating frame. Full article

The present review focuses on the theoretical model developments made in floating flexible net fish cages and the floating bodies application to offshore aquaculture. A brief discussion of the essential mathematical equations related to various theoretical models of flexible net cages in the frequency domain is presented. The single and array of floating or submerged flexible net cages connected with or without mooring lines are discussed. Further, as the combined effect of the hydroelastic behaviour of floaters and the flexible behaviour of fish cages are necessary to assess their efficiency and survivability from structural damages, the issues and the knowledge gap between the recent and future models are also discussed. In conclusion, the practical suggestions concerning advancements in future research and directions within floating flexible net cages and the hydroelastic response of elastic floaters are highlighted. Full article

As a full tension structural system, the spoke-type single-layer cable net structure has a light graceful shape and superior mechanical properties. During construction, the structure will gradually be tensioned from the flexible unstressed state to the formed state with stiffness, and the structural configuration changes greatly, making construction difficult. This study focused on the spoke-type single-layer cable net structure of the Linyi Olympic Sports Center Stadium. The structural finite element model was established in ANSYS, and the construction scheme was selected and simulated using the nonlinear dynamic finite element method (NDFEM). Most of the existing structural automatic measuring systems are suitable for measuring points with gentle deformation. However, there is the lack of a stable and reliable automatic configuration monitoring system for the construction of single-layer cable net structures. Based on the Lecia TS16 robotic total station (RTS), the configuration automatic monitoring system (CAMS) was developed to obtain the coordinate data of key nodes on the ring cable and compression ring during the construction process. The original finite element model of clamps was refined to obtain the corresponding data in ANSYS. The results indicate that the selected construction scheme has a rational mechanical response according to the finite element simulation. The radial cable force when anchoring the traction cables is smaller than or equal to that in the formed state, which proves that the construction method of anchoring in batches is safe. The results of the ANSYS simulation is basically consistent with those obtained by CAMS, proving that the simulation method is credible. CAMS has good stability and measurement accuracy and can achieve the automatic monitoring of large structural deformation. The research findings provide valuable guidance for practical construction and other similar projects. Full article

In order to effectively solve the problems of the complex growth state of dragon fruit and how the picking process is mostly manual, this study designed a picking and selecting integrated remote-operation-type dragon-fruit-picking device. Based on SOLIDWORKS 2020 software for the three-dimensional digital design and overall assembly of key components, the structure and working theory of the machine are introduced. By improving the high-recognition-rate dragon fruit target detection algorithm based on YOLOv5, better recognition and locating effects were achieved for targets with a small size and high density, as well as those in bright-light scenes. Serial communication, information acquisition, and the precise control of each picking action were realized by building the software and hardware platforms of the picking device control system. By analyzing the working principle of the mechanical system and the mechanics of the machine picking process, the critical factors affecting the net picking rate and damage rate of the dragon-fruit-picking device were confirmed. Based on the force and parameter analysis of the test results, it was confirmed that the machine had an optimal picking influence when the flexible claw closing speed was 0.029 m/s, the electric cylinder extending speed was 0.085 m/s, and the mechanical arm moving speed was 0.15 m/s. The net picking rate of the device reached 90.5%, and the damage rate reached 2.9%. The picking device can complete the picking of a single dragon fruit, as well as a plurality of fruits grown at a growing point, and integrates the integration of picking fruits, removing bad fruits, and sorting fruits, which can improve the efficiency of dragon fruit harvesting and replace manual work. Full article

Over the past several decades, deep neural networks have been extensively applied to medical image segmentation tasks, achieving significant success. However, the effectiveness of traditional deep segmentation networks is substantially limited by the small scale of medical datasets, a limitation directly stemming from current medical data acquisition capabilities. To this end, we introduce AttEUnet, a medical cell segmentation network enhanced by edge attention, based on the Attention U-Net architecture. It incorporates a detection branch enhanced with edge attention and a learnable fusion gate unit to improve segmentation accuracy and convergence speed on small medical datasets. The AttEUnet allows for the integration of various types of prior information into the backbone network according to different tasks, offering notable flexibility and generalization ability. This method was trained and validated on two public datasets, MoNuSeg and PanNuke. The results show that AttEUnet significantly improves segmentation performance on small medical datasets, especially in capturing edge details, with F1 scores of 0.859 and 0.888 and Intersection over Union (IoU) scores of 0.758 and 0.794 on the respective datasets, outperforming both convolutional neural networks (CNNs) and transformer-based baseline networks. Furthermore, the proposed method demonstrated a convergence speed over 10.6 times faster than that of the baseline networks. The edge attention branch proposed in this study can also be added as an independent module to other classic network structures and can integrate more attention priors based on the task at hand, offering considerable scalability. Full article

Quality of service (QoS) is a crucial requirement in distributed applications. Internet of Things architectures have become a widely used approach in many application domains, from Industry 4.0 to smart agriculture; thus, it is crucial to develop appropriate methodologies for managing QoS in such contexts. In an overcrowded spectrum scenario, cognitive radio technology could be an effective methodology for improving QoS requirements. In order to evaluate QoS in the context of a cognitive radio Internet of Things network, we propose a Petri net-based model that evaluates the cognitive radio environment and operates in a 200 kHz GSM/EDGE transponder band. The model is quite flexible as it considers several circuit and packet switching primary user network loads and configurations and several secondary user types of services (that involve semantic transparency or time transparency); furthermore, it is able to take into account mistakes of the spectrum sensing algorithm used by secondary users. Specifically, we derive the distribution of the response time perceived by the secondary users, where it is then possible to obtain an estimation of both the maximum throughput and jitter. The proposed cognitive radio scenario considers a secondary user synchronized access to the channel when using the GSM/EDGE frame structure. Full article

Timely and accurate acquisition of crop type information is significant for irrigation scheduling, yield estimation, harvesting arrangement, etc. The unmanned aerial vehicle (UAV) has emerged as an effective way to obtain high resolution remote sensing images for crop type mapping. Convolutional neural network (CNN)-based methods have been widely used to predict crop types according to UAV remote sensing imagery, which has excellent local feature extraction capabilities. However, its receptive field limits the capture of global contextual information. To solve this issue, this study introduced the self-attention-based transformer that obtained long-term feature dependencies of remote sensing imagery as supplementary to local details for accurate crop-type segmentation in UAV remote sensing imagery and proposed an end-to-end CNN–transformer feature-fused network (CTFuseNet). The proposed CTFuseNet first provided a parallel structure of CNN and transformer branches in the encoder to extract both local and global semantic features from the imagery. A new feature-fusion module was designed to flexibly aggregate the multi-scale global and local features from the two branches. Finally, the FPNHead of feature pyramid network served as the decoder for the improved adaptation to the multi-scale fused features and output the crop-type segmentation results. Our comprehensive experiments indicated that the proposed CTFuseNet achieved a higher crop-type-segmentation accuracy, with a mean intersection over union of 85.33% and a pixel accuracy of 92.46% on the benchmark remote sensing dataset and outperformed the state-of-the-art networks, including U-Net, PSPNet, DeepLabV3+, DANet, OCRNet, SETR, and SegFormer. Therefore, the proposed CTFuseNet was beneficial for crop-type segmentation, revealing the advantage of fusing the features found by the CNN and the transformer. Further work is needed to promote accuracy and efficiency of this approach, as well as to assess the model transferability. Full article

Carbon nanotubes (CNTs) exhibit high strength, Young’s modulus, and flexibility and serve as an ideal reinforcement for composite materials. Owing to their toughness against bending and/or twisting, they are typically used as fabric composites. The conventional multiaxial braiding method lacks tension and resultant strength in the thickness direction. Some braiding patterns are proposed; however, they may have shortcomings in flexibility. Thus, this study proposed three types of braiding pattern for fabrics based on natural products such as spider net and honeycomb, in accordance with thickness-direction strength. The spider-net-based structure included wefts with spaces in the center with overlapping warps. At both sides, the warps crossed and contacted the wefts to impart solidness to the structure and enhance its strength as well as flexural stability. In addition, box-type wefts were proposed by unifying the weft and warps into boxes, which enhanced the stability and flexibility of the framework. Finally, we proposed a structure based on rectangular and hexagonal shapes mimicking the honeycomb. Moreover, finite element calculations were performed to investigate the mechanisms through which the proposed structures garnered strength and deformation ability. The average stress in fabrics becomes smaller than half (43%) when four edges are restrained and sliding is inserted. Under three-dimensional forces, our proposed structures underwent mechanisms of wrapping, warping, sliding and doubling, and partial locking to demonstrate their enhanced mechanical properties. Furthermore, we proposed a hierarchical structure specialized for CNTs, which could facilitate applications in structural components of satellites, wind turbines, and ships. The hierarchical structure utilizing discontinuity and sliding benefits the usage for practical mechanical systems. Full article

Monitoring damage in concrete structures is crucial for maintaining the health of structural systems. The implementation of computer vision has been the key for providing accurate and quantitative monitoring. Recent development uses the robustness of deep-learning-aided computer vision, especially the convolutional neural network model. The convolutional neural network is not only accurate but also flexible in various scenarios. The convolutional neural network has been constructed to classify image in terms of individual pixel, namely pixel-level detection, which is especially useful in detecting and classifying damage in fine-grained detail. Moreover, in the real-world scenario, the scenes are mostly very complex with varying foreign objects other than concrete. Therefore, this study will focus on implementing a pixel-level convolutional neural network for concrete surface damage detection with complicated surrounding image settings. Since there are multiple types of damage on concrete surfaces, the convolutional neural network model will be trained to detect three types of damages, namely cracks, spallings, and voids. The training architecture will adopt U-Net and DeepLabV3+. Both models are compared using the evaluation metrics and the predicted results. The dataset used for the neural network training is self-built and contains multiple concrete damages and complex foregrounds on every image. To deal with overfitting, the dataset is augmented, and the models are regularized using L1 and Spatial dropout. U-Net slightly outperforms DeepLabV3+ with U-Net scores 0.7199 and 0.5993 on F1 and mIoU, respectively, while DeepLabV3+ scores 0.6478 and 0.5174 on F1 and mIoU, respectively. Given the complexity of the dataset and extensive image labeling, the neural network models achieved satisfactory results. Full article

To solve the problems of low net harvesting rate, high loss rate, and uneven stubble height during the harvest of laver, the laver (Porphyra yezoensis) was selected as the research object, the analysis of the cultivation mode, biomechanical characteristics, harvesting trajectory and force of laver were carried out. A sectional hob type harvesting device was designed. A rigid-flexible coupling model related to the interaction between the cutting mechanism and the laver was constructed based on ANSYS/LS-DYNA. The Box–Behnken design method was used to simulate the effects of different structural parameters and process parameters on the force of laver cutting, and the bench test of the laver harvesting device was carried out. The simulation results showed that the four factors that significantly affect the force exerted on the laver during cutting in proper order were cutter revolving speed, knife extension length, knife inclination angle and forward velocity. When the combination of the forward velocity, the cutter revolving speed, the knife extension length and inclination angle was 0.77 m/s, 900 r/min, 40 mm, and 110°, respectively, the cutting force on laver was the smallest, which was 4.21 N. The bench test of harvesting performance showed that the cutter revolving speed has a significant impact on the recovery rate, and the forward velocity has a significant impact on the loss rate. When the harvesting speed ratio was λ₄ (the cutter revolving speed was 900 r/min and the forward velocity was 0.77 m/s), the net harvesting rate and the loss rate were 97.45% and 3.38%, respectively, and the cutting proportion of laver can reach 77.5%. The results of the study provide a theoretical basis for the development of harvesting for laver. Full article

Recently, the local climate zone (LCZ) system has been presented to establish the connection between urban landscape and local thermal environment. However, LCZ entities are very difficult to be identified by pixel-based classifiers or object-oriented image analysis, as they are often a complicated combination of multiple ground objects (e.g., buildings, roads, grassland, etc.). Scene classifiers, especially deep learning methods can exploit the structure or contextual information of image scenes and then improve the performance of LCZ classification. However, the square and uniform-sized image patches often bring about extra challenges, as they cannot exactly match LCZ entities of diverse sizes and shapes in most cases. In this study, a sequential virtual scene method is presented to identify LCZ entities of diverse shapes and sizes, which consists of a small “core patch” for scanning diverse entities and sequential virtual scenes for providing abundant context. Specifically, the Bidirectional Long Short-Term Memory (Bi-LSTM) were used to learn the spatial relationship among virtual scenes, respectively. Importantly, a “self-attention” mechanism is designed to weigh the contribution of every virtual scene for alleviating the influences of mixed patches, according to the similarity between its hidden state and the final hidden state. Experiments prove SVS achieves better accuracies than random forest and ResNet and has the outstanding capacity of identifying irregular LCZ entities. It is a promising way to carry out LCZ mapping in cities of different types due to its flexibility and adaptability. Full article

A cable strut structure uses a tension cable net as the main load-bearing system, which can allow full play of the high-strength material characteristics of the cables, greatly reduce the burden on the lower supporting system, and increase the span in an economic and effective manner. The annular cable supported grid structure is a new type of cable strut structure, which uses rigid grids to replace the flexible cables at the top chord of the spoke cable truss to meet the requirements of laying a heavy rigid roof. In this paper, first the structural mechanical characteristics of the annular cable supported grid structure are introduced, showing that the structural characteristics derived from the cable truss are the basis of non-bracket construction, while the presence of the upper grid results in difficulties with structure installation configuration control. Second, considering the characteristics of the cable supported grid structure and the difficulties in construction, the non-bracket construction method for annular cable supported grid structure with the commonly used nonlinear dynamic finite element method (NDFEM) in construction simulations is proposed. Finally, a numerical example is given and analyzed to verify the accuracy and feasibility of this method. The results indicate that the non-bracket construction technology proposed in this paper is suitable for the construction of a cable supported grid structure, and has the advantages of convenient prestress flow control, no need for brackets, simple and economical construction equipment, flexible arrangement of construction period, safe and reliable construction, and high construction accuracy. Full article

Attention mechanisms are widely used for Convolutional Neural Networks (CNNs) when performing various visual tasks. Many methods introduce multi-scale information into attention mechanisms to improve their feature transformation performance; however, these methods do not take into account the potential importance of scale invariance. This paper proposes a novel type of convolution, called Calibrated Convolution with Gaussian of Difference (CCGD), that takes into account both the attention mechanisms and scale invariance. A simple yet effective scale-invariant attention module that operates within a single convolution is able to adaptively build powerful scale-invariant features to recalibrate the feature representation. Along with this, a CNN with a heterogeneously grouped structure is used, which enhances the multi-scale representation capability. CCGD can be flexibly deployed in modern CNN architectures without introducing extra parameters. During experimental tests on various datasets, the method increased the ResNet50-based classification accuracy from 76.40% to 77.87% on the ImageNet dataset, and the tests generally confirmed that CCGD can outperform other state-of-the-art attention methods. Full article

To overcome the inherent challenge of NO_x reduction in the net oxidizing environment of diesel engine exhaust, the NO_x storage and reduction (NSR) concept was proposed in 1995, soon developed and commercialized as a promising DeNO_x technique over the past two decades. Years of practice suggest that it is a tailor-made technique for light-duty diesel vehicles, with the advantage of being space saving, cost effective, and efficient in NO_x abatement; however, the over-reliance of NSR catalysts on high loadings of Pt has always been the bottleneck for its wide application. There remains fervent interest in searching for efficient, economical, and durable alternatives. To date, La-based perovskites are the most explored promising candidate, showing prominent structural and thermal stability and redox property. The perovskite-type oxide structure enables the coupling of redox and storage centers with homogeneous distribution, which maximizes the contact area for NO_x spillover and contributes to efficient NO_x storage and reduction. Moreover, the wide range of possible cationic substitutions in perovskite generates great flexibility, yielding various formulations with interesting features desirable for the NSR process. Herein, this review provides an overview of the features and performances of La-based perovskite in NO oxidation, NO_x storage, and NO_x reduction, and in this way comprehensively evaluates its potential to substitute Pt and further improve the DeNO_x efficiency of the current NSR catalyst. The fundamental structure–property relationships are summarized and highlighted to instruct rational catalyst design. The critical research needs and essential aspects in catalyst design, including poisoner resistance and catalyst sustainability, are finally addressed to inspire the future development of perovskite material for practical application. Full article