Search Results (28)

When addressing the issue that the current market sales of fresh aquatic products, which are segmented into sections, mainly rely on manual operations, there are problems such as high labor intensity, low cutting efficiency, and uneven cutting. This paper designs an automated cutting device based on the commonly used grass carp as the object. Firstly, two efficient cutting schemes were proposed and compared. The optimized cutting scheme was designed using the SolidWorks software 2021. Then, three optimized schemes for the automatic clamping module, transmission module, and pushing module were designed. Subsequently, the structural design and calculation analysis of the key internal components were carried out to build the three-dimensional model of the automatic grass carp cutting device, and the cutting prototype was manufactured. Finally, an automated cutting prototype was fabricated based on the above-mentioned optimized design schemes, and relevant tests and analyses were conducted. The results showed that the prototype device can cut grass carp with a length of 300 ± 5 mm, with a processing capacity of 200 kg/h. The surface damage rate is less than 5%, and the cutting efficiency has been improved while the surface damage rate has significantly decreased. Therefore, the device manufactured through this design can meet the rapid processing, cutting, and various specific processing requirements of grass carp and other fresh aquatic products, providing a feasible solution for aquatic product cutting manufacturing equipment. Full article

Modern computational models tend to become more and more complex, especially in fields like computational biology, physical modeling, social simulation, and others. With the increasing complexity of simulations, modern computational architectures demand efficient parallel execution strategies. This paper proposes a novel approach leveraging the reactive stream paradigm as a general-purpose synchronization protocol for parallel simulation. We introduce a method to construct simulation graphs from predefined transition functions, ensuring modularity and reusability. Additionally, we outline strategies for graph optimization and interactive simulation through push and pull patterns. The resulting computational graph, implemented using reactive streams, offers a scalable framework for parallel computation. Through theoretical analysis and practical implementation, we demonstrate the feasibility of this approach, highlighting its advantages over traditional parallel simulation methods. Finally, we discuss future challenges, including automatic graph construction, fault tolerance, and optimization strategies, as key areas for further research. Full article

The articles presented in this Special Issue epitomize the convergence of cutting-edge sensor technologies, innovative data processing techniques, and advanced algorithmic approaches in ocean remote sensing. Through studies ranging from sensor calibration and data fusion to the application of deep learning and transformer models, the research showcased here pushes the boundaries of what can be achieved in ocean observation. A recurring theme among these contributions is the importance of integrating data from multiple sources and employing state-of-the-art computational methods. Deep learning and the transformer architecture highlight a paradigm shift in remote sensing data analysis. These advanced techniques help extract complex features from high-dimensional datasets and can process large amounts of data quickly and automatically. Furthermore, research focusing on spatiotemporal dynamics and environmental monitoring highlights the critical role of remote sensing in addressing global challenges. By capturing the dynamic interactions between atmospheric, oceanic, and terrestrial processes, these studies provide important insights into the drivers of climate and environmental change. This information is valuable for developing predictive models and informing policy decisions related to climate change mitigation and adaptation. Full article

With a focus on the problems of complex structure and accumulated lateral clearance in the single degree of freedom non-circular wheel system seedling-picking mechanism, which leads to poor motion accuracy, trajectory, and attitude, this study developed a 2R open-chain chili plug seedling-picking mechanism (SPM) constrained by a differential non-circular wheel system. The picking arm was driven by a single-stage non-uniform speed transmission mechanism to reproduce the seedling-picking trajectory and attitude. A protruding seedling-picking device, SPM control system, and test bench were designed. A kinematic model of a differential non-circular gear system was established, and an optimization design software for the SPM was developed based on kinematic analysis. The kinematic characteristics of the SPM were analyzed under optimal parameters. This study completed the seedling-picking performance test of the SPM on the control panel. The test showed that the designed chili SPM can sequentially complete the processes of seedling picking, conveying, retracting, pushing, and returning under the automatic control of the test bench without damaging the main root. The lateral root damage rate was 15.7%, effectively ensuring the integrity of the seedling bowl substrate. Full article

The China Loess Plateau (CLP) is the world’s most extensive and thickest region of loess deposits. The inherently loose structure of loess makes the CLP particularly vulnerable to geohazards such as landslides, collapses, and subsidence, resulting in substantial geological and environmental challenges. Xining City, situated at the northwest edge of the CLP, is especially prone to frequent geological hazards due to intensified human activities and natural forces. Synthetic Aperture Radar Interferometry (InSAR) has become a widely used tool for identifying landslide hazards and displacement monitoring because of its high accuracy, low cost, and wide coverage. In this study, we utilized the small baseline subset (SBAS) InSAR technique to derive the line of sight (LOS) displacements of Xining City using Sentinel-1 datasets from ascending and descending orbits between October 2014 and September 2022. By integrating LOS displacements from the two datasets, we retrieved the eastward and vertical displacements to characterize the kinematics of active slopes. To identify the active areas semi-automatically, we applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster InSAR measurement points (IMPs). Forty-eight active slopes with areas ranging from 0.0049 to 0.5496 km² and twenty-five subsidence-dominant areas ranging from 0.023 to 3.123 km² were identified across Xining City. Kinematics analysis of the Jiujiawan landslide indicated that acceleration started in August 2016, likely triggered by rainfall, and continued until the landslide. The extreme rainfall in August 2022 may have pushed the Jiujiawan landslide beyond its critical threshold, leading to instability. Additionally, the study identified nine active slopes that threaten the normal operation of the Lanzhou–Xinjiang High-Speed Railway, with kinematic analysis suggesting rainfall-related accelerations. The influence of anthropogenic activities on ground displacements in loess areas was also confirmed through time series displacement analysis. Our results can be leveraged for geohazard prevention and management in Xining City. As SAR image data continue to accumulate, InSAR can serve as a regular tool for maintaining up-to-date landslide inventories, thereby contributing to more sustainable geohazard management. Full article

Fire alarm systems are an important part of the safety concept in complex buildings. For this reason, there are high availability requirements for the systems, which must be sustained by a maintenance concept. A shortage of skilled workers and rising costs in the construction and operation of buildings are pushing these concepts ever further. This study deals with proposed changes to the maintenance strategies to achieve cost and time savings in addition to an improvement in quality. As a first part of the work, the current state of research on developments in fire alarm systems and their maintenance and inspection concepts is analyzed within a literature review. The results serve as a basis for further research, which is based on a qualitative content analysis of expert interviews and standardized surveys to identify the weaknesses in current inspection strategies and future factors influencing the methods and technology of inspections through technical innovations. As a data basis for this study, expert interviews were conducted with experts from manufacturers, industry associations, and standards bodies in order to determine the possible influencing factors. To determine their relevance for the inspection, more than 40 experts were surveyed about testing the systems. The presented results show that new technical risks, such as cyber threats and networked plant structures, are insufficiently covered by current inspection strategies. Furthermore, inspection steps can be substituted by new technologies. The most important influencing factors that can be identified here are automatic self-test functions of components and remote inspection techniques of the systems. Finally, the results are discussed within the framework of a PESTEL analysis. In conclusion, it can be stated that the integration of identified impacts in future inspection strategies brings time and efficiency benefits in the operation of systems. Full article

Digital twin technology has evolved from a theoretical concept to practical application, facilitating seamless data exchange between virtual and physical domains. Although there has been progress, the infrastructure industry, which is recognized for its intricate nature and the need for timely action, is still in the first phases of digital twin advancement. A significant obstacle in this field is the absence of established definitions and modeling standards, which impede the precise depiction of infrastructure systems. To address these challenges, this paper proposes a high-precision digital twin modeling method tailored for pumping stations. The method focuses on two key scenarios: first, we construct an overall digital twin model that contains both physical entities and operational processes of pumping stations; second, we design a modeling process applicable to pumping stations by analyzing the deficiencies of the existing standard system. Additionally, we selected the East–West Water Transfer Project in China as a case study to demonstrate the high-precision digital twin model of a pumping station. This model will include essential components, such as the modeling of pumping stations, the operational processes of pumping stations, and the modeling of system operation analysis. Serving as the database for the digital twin, it can complete the automatic inspection of the pumping station, optimization of scheduling, prediction and regulation of energy and carbon emissions, and visualization of results for display and other applications. The model realized the benefits of 100% automatic inspection rate, reduction of eight corresponding operating personnel, and comprehensive cost saving of RMB 2.25 million. The objective of this research is to narrow the divide between theoretical concepts and real-world implementations by pushing the boundaries of digital twin modeling and offering valuable insights for its utilization in the infrastructure industry. It establishes the foundation for progress in the field of digital twin technology in the specific context of intricate infrastructure projects. This project aims to improve the practicality of digital twin technology in real-world situations, namely in the infrastructure industry. Full article

The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with ’ImageNet’ weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a ’normal’ class with 2494 images and an ’OSCC’ (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis. Full article

With information technology pushing the development of intelligent teaching environments, the online teaching platform emerges timely around the globe, and how to accurately evaluate the effect of the “any-time and anywhere” teacher–student interaction and learning has become one of the hotspots of today’s education research. Bullet chatting in online courses is one of the most important ways of interaction between teachers and students. The feedback from the students can help teachers improve their teaching methods, adjust teaching content, and schedule in time so as to improve the quality of their teaching. How to automatically identify the sentiment polarity in the comment text through deep machine learning has also become a key issue to be automatically processed in online course teaching. The traditional single-layer attention mechanism only enhances certain sentimentally intense words, so we proposed a sentiment analysis method based on a hierarchical attention mechanism that we called HAN. Firstly, we use CNN and LSTM to extract local and global information, gate mechanisms are used for extracting sentiment words, and the hierarchical attention mechanism is then used to weigh the different sentiment features, with the original information added to the attention mechanism concentration to prevent the loss of information. Experiments are conducted on China Universities MOOC and Tencent Classroom comment data sets; both accuracy and F1 are improved compared to the baseline, and the validity of the model is verified. Full article

In manufacturing companies, manual material handling (MMH) involves lifting, pushing, pulling, carrying, moving, and lowering objects, which can lead to musculoskeletal disorders (MSDs) among workers, resulting in high labor costs due to excessive overtime incurred for manual product preparation. The aim of this study was to show how ergonomic measures were used to reduce the risk of MSDs and to reduce operating costs in the warehouse department of an oil and gas service company. A preliminary study using the Nordic Body Map survey showed that the workers experienced pain in various parts of the body, indicating the presence of MSDs. The researchers then used methods such as the Rapid Upper Limb Assessment (RULA), Rapid Entire Body Assessment (REBA), and National Institute for Occupational Safety and Health (NIOSH) assessments to verify whether the MMH activities had an acceptable level of risk. The results revealed that certain manual material handling (MMH) activities were assessed as low–very high risk, with RULA scores ranging from 3 to 7 and REBA scores ranging from 4 to 11. An immediate solution was to replace the manual process with a semi-automatic process using a vacuum lifter. A feasibility study was conducted using the net present value (NPV), internal rate of return (IRR), and payback period to justify the economic viability of the solution. The analysis indicated that implementing the vacuum lifter not only mitigated the risk of MSDs but also reduced the operating costs, demonstrating its viability and profitability. Overall, this study suggests that implementing a vacuum lifter as an assistive device in the warehouse would be a beneficial investment for both the workers and the company, improving both well-being and finances. Full article

Dynamic detection, as one of the core tenets in tumor diagnosis, relies on specific recognition, rapid reaction, and significant signal output. Circulating tumor cells that carry the most complete information of neoplastic lesions are analytes of interest for sensor designer. To overcome the deficiencies in the use of antibodies, some antibody-like structures were used to integrate chemosensors, such as molecularly imprinted polymers (MIPs) and aptamer conjugates that may perform specific detection of analytes. The sensors inherited from such structurally stable molecular recognition materials have the advantage of being cost-effective, rapid-to-fabricate and easy-to-use, whilst exhibiting specificity comparable to antibody-based kits. Moreover, the fabricated sensors may automatically perform quantitative analysis via digital and microfluidic devices, facilitating advances in wearable sensors, meanwhile bringing new opportunities and challenges. Although most MIPs-based sensors for tumor diagnosis to date have not been practically used in hospitals, with no doubt, material innovation and artificial intelligence development are the driving forces to push the process forward. At the same time, the challenges and opportunities of these sensors moving forward to intelligent and implantable devices are discussed. Full article

The increase of low-cost technology for underwater filming has made quantitative analysis an affordable resource for swimming coaches on a frequent basis. In this context, a synthesis of the kinematic determinants of underwater undulatory swimming (UUS) seems to be lacking. The aim of the present study was to synthesise the scientific evidence on the kinematic characteristics of competitive swimmers during UUS and the main kinematic determinants of UUS performance, as well as to summarise the main methodological considerations for UUS kinematic analysis. A systematic literature search was performed through four electronic databases following the PRISMA guidelines and STROBE for evaluating the quality of the included studies. Twenty-three research studies from the first search and two from the second search were finally considered. In total, 412 competitive swimmers (321 males and 91 females) with a performance standard of international B (11%), national (51%), or regional (35%) level were analysed. Most studies focused on a two-dimensional analysis of the ventral UUS performed from a push start and filmed 6–12 m from the starting wall. Kinematic analysis of UUS included kicking parameters (kicking length, frequency, and amplitude) as well as selected segmental kinematics in 76% of studies and the analysis of UUS performance determinants in 36%. Information about the determinants of UUS performance was inconsistent due in part to inconsistencies in the definition of kinematic parameters. Further research studies where automatic motion capture systems are applied to the analysis of UUS on the aforementioned conditions should be conducted. Full article

Fluorescence microscopy has represented a crucial technique to explore the cellular and molecular mechanisms in the field of biomedicine. However, the conventional one-photon microscopy exhibits many limitations when living samples are imaged. The new technologies, including two-photon microscopy (2PM), have considerably improved the in vivo study of pathophysiological processes, allowing the investigators to overcome the limits displayed by previous techniques. 2PM enables the real-time intravital imaging of the biological functions in different organs at cellular and subcellular resolution thanks to its improved laser penetration and less phototoxicity. The development of more sensitive detectors and long-wavelength fluorescent dyes as well as the implementation of semi-automatic software for data analysis allowed to gain insights in essential physiological functions, expanding the frontiers of cellular and molecular imaging. The future applications of 2PM are promising to push the intravital microscopy beyond the existing limits. In this review, we provide an overview of the current state-of-the-art methods of intravital microscopy, focusing on the most recent applications of 2PM in kidney physiology. Full article

To alleviate the workload in prevailing expert-based onsite inspection, a vision-based method using state-of-the-art deep learning architectures is proposed to automatically detect ceiling damage in large-span structures. The dataset consists of 914 images collected by the Kawaguchi Lab since 1995 with over 7000 learnable damages in the ceilings and is categorized into four typical damage forms (peelings, cracks, distortions, and fall-offs). Twelve detection models are established, trained, and compared by variable hyperparameter analysis. The best performing model reaches a mean average precision (mAP) of 75.28%, which is considerably high for object detection. A comparative study indicates that the model is generally robust to the challenges in ceiling damage detection, including partial occlusion by visual obstructions, the extremely varied aspect ratios, small object detection, and multi-object detection. Another comparative study in the F1 score performance, which combines the precision and recall in to one single metric, shows that the model outperforms the CNN (convolutional neural networks) model using the Saliency-MAP method in our previous research to a remarkable extent. In the case of a large-area ratio with a non-ceiling region, the F1 score of these two models are 0.83 and 0.28, respectively. The findings of this study push automatic ceiling damage detection in large-span structures one step further. Full article

Aiming to solve the problems of poor performance and low stability in the automatic clip-feeding of a grafting machine, an automatic clip-feeding mechanism with a precise single-clip discharge mechanism was designed, and a clip-feeding performance test was carried out. Taking the grafting clip of the 2TJGQ-800 type of vegetable-grafting robot as the research object, the clamping-force analysis model of the grafting clip was constructed by ABUQUS finite-element analysis software, and the variation law of clamping force, steel wire diameter, and opening deformation, as well as the calculation equation of clamping force, were obtained. The grafting clip model was verified by mechanical test, and test results showed that the grafting clip with a steel wire diameter of 0.7 mm proved safe and reliable for grafted cucumber and watermelon seedlings; the grafting clip with steel wire diameter of 0.8 mm had a risk of producing injury to grafted cucumber and watermelon seedlings when clamping. The method of single-clip discharge in the inclined discharging slideway was put forward, and the components for clip discharge and clip pushing were designed. The critical thrust for sending out the grafting clip in the clip-feeding slideway was 0.603 N after analyzing the force status of the grafting clip in the clip-feeding slideway. Test results showed that the success rate of automatic clip-feeding reached 98.67% when inclination angle of row-discharging slideway was 50° and the thrust of clip-pushing cylinder (input air pressure of 0.4 MPa) was 8.04 N, which met the technical requirements of mechanical grafting. The inclination of the grafting clip and the damaged clip in the feeding slideway is the main reason for the failure of clip-feeding. The research results can provide theoretical and design references for the innovative research of the automatic clip-feeding mechanism of grafting robots. Full article