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ObjectiveIn recent years, there has been a steady increase in the occurrence and fatality rates of shrimp diseases, causing substantial impacts in shrimp aquaculture. These diseases are marked by their swift onset, high infectivity, complex control requirements, and elevated mortality rates. With the continuous growth of shrimp factory farming, traditional manual detection approaches are no longer able to keep pace with the current requirements. Hence, there is an urgent necessity for an automated solution to identify shrimp diseases. The main goal of this research is to create a cost-effective inspection method using computer vision that achieves a harmonious balance between cost efficiency and detection accuracy. The improved YOLOv8 (You Only Look Once) network and multiple features were employed to detect shrimp diseases.MethodsTo address the issue of surface foam interference, the improved YOLOv8 network was applied to detect and extract surface shrimps as the primary focus of the image. This target detection approach accurately recognizes objects of interest in the image, determining their category and location, with extraction results surpassing those of threshold segmentation. Taking into account the cost limitations of platform computing power in practical production settings, the network was optimized by reducing parameters and computations, thereby improving detection speed and deployment efficiency. Additionally, the Farnberck optical flow method and gray level co-occurrence matrix (GLCM) were employed to capture the movement and image texture features of shrimp video clips. A dataset was created using these extracted multiple feature parameters, and a Support Vector Machine (SVM) classifier was trained to categorize the multiple feature parameters in video clips, facilitating the detection of shrimp health.Results and DiscussionsThe improved YOLOv8 in this study effectively enhanced detection accuracy without increasing the number of parameters and flops. According to the results of the ablation experiment, replacing the backbone network with FasterNet lightweight backbone network significantly reduces the number of parameters and computation, albeit at the cost of decreased accuracy. However, after integrating the efficient multi-scale attention (EMA) on the neck, the mAP0.5 increased by 0.3% compared to YOLOv8s, while mAP0.95 only decreased by 2.1%. Furthermore, the parameter count decreased by 45%, and FLOPs decreased by 42%. The improved YOLOv8 exhibits remarkable performance, ranking second only to YOLOv7 in terms of mAP0.5 and mAP0.95, with respective reductions of 0.4% and 0.6%. Additionally, it possesses a significantly reduced parameter count and FLOPS compared to YOLOv7, matching those of YOLOv5. Despite the YOLOv7-Tiny and YOLOv8-VanillaNet models boasting lower parameters and Flops, their accuracy lags behind that of the improved YOLOv8. The mAP0.5 and mAP0.95 of YOLOv7-Tiny and YOLOv8-VanillaNet are 22.4%, 36.2%, 2.3%, and 4.7% lower than that of the improved YOLOv8, respectively. Using a support vector machine (SVM) trained on a comprehensive dataset incorporating multiple feature, the classifier achieved an impressive accuracy rate of 97.625%. The 150 normal fragments and the 150 diseased fragments were randomly selected as test samples. The classifier exhibited a detection accuracy of 89% on this dataset of the 300 samples. This result indicates that the combination of features extracted using the Farnberck optical flow method and GLCM can effectively capture the distinguishing dynamics of movement speed and direction between infected and healthy shrimp. In this research, the majority of errors stem from the incorrect recognition of diseased segments as normal segments, accounting for 88.2% of the total error. These errors can be categorized into three main types: 1) The first type occurs when floating foam obstructs the water surface, resulting in a small number of shrimp being extracted from the image. 2) The second type is attributed to changes in water movement. In this study, nanotubes were used for oxygenation, leading to the generation of sprays on the water surface, which affected the movement of shrimp. 3) The third type of error is linked to video quality. When the video's pixel count is low, the difference in optical flow between diseased shrimp and normal shrimp becomes relatively small. Therefore, it is advisable to adjust the collection area based on the actual production environment and enhance video quality.ConclusionsThe multiple features introduced in this study effectively capture the movement of shrimp, and can be employed for disease detection. The improved YOLOv8 is particularly well-suited for platforms with limited computational resources and is feasible for deployment in actual production settings. However, the experiment was conducted in a factory farming environment, limiting the applicability of the method to other farming environments. Overall, this method only requires consumer-grade cameras as image acquisition equipment and has lower requirements on the detection platform, and can provide a theoretical basis and methodological support for the future application of aquatic disease detection methods.

Central heating system faults affect building energy consumption and indoor thermal comfort significantly. To aim at the balance between thermal comfortable and energy-saving of the heating system for high-rise residential buildings, this paper proposes a method for the central heating system of high-rise residential buildings based on distributed model predictive control. The method analyzes the coupling factors between adjacent rooms’ temperature. Based on the state space method, a multivariable indoor temperature model is established and verified. The distributed model predictive control method is used to control and optimize the indoor temperature, and the load distribution of the circulating water pump in the heat exchange station is optimized according to the predicted heat demand. The results demonstrate that the indoor temperature after distributed model predictive control can stable near the set value. Compared with the centralized control methods, the proposed methodology can reduce energy consumption by 14.28%. Meanwhile, the efficiency of water pumps is increased by 16.74% after using the distributed control strategy.

Deep learning (DL) has been demonstrated to be a valuable tool for classifying state of disorders of consciousness (DOC) using EEG signals. However, the performance of the DL-based DOC state classification is often challenged by the limited size of EEG datasets. To overcome this issue, we introduce multiple open-source EEG datasets to increase data volume and train a novel multi-task pre-training Transformer model named MutaPT. Furthermore, we propose a cross-distribution self-supervised (CDS) pre-training strategy to enhance the model's generalization ability, addressing data distribution shifts across multiple datasets. An EEG dataset of DOC patients is used to validate the effectiveness of our methods for the task of classifying DOC states. Experimental results show the superiority of our MutaPT over several DL models for EEG classification. [ABSTRACT FROM AUTHOR]

Intelligent construction robots, as the key equipment to implement the intelligent transformation of the construction industry, represent the level of intelligent construction with their autonomous construction capability, and local path planning is the key technology for efficient robot construction. Aiming at the environmental characteristics of the construction site, the difficulties of local path planning for intelligent construction robots are explored. Firstly, classical local path planning algorithms such as artificial potential field, time elasticity band, Bug, dynamic window, etc. are analyzed. Secondly, local path planning algorithms of artificial intelligence such as reinforcement learning, deep reinforcement learning, fuzzy control, swarm intelligence, etc. are summarized. Finally, the limitations of various methods on the application of intelligent construction robots in the construction site environment are analyzed, and the development trend of local path planning algorithms of intelligent construction robots is discussed, aiming to provide certain ideas and suggestions for the research on local path planning of intelligent construction robots in complex construction sites, improve the autonomous construction ability of robots, and promote the development of intelligent construction technology. [ABSTRACT FROM AUTHOR]

Building load prediction is one of the important means of saving energy and reducing emissions, and accurate cold load prediction is conducive to the realization of online monitoring and the optimal control of building air conditioning systems. Therefore, a joint prediction model was proposed in this paper. Firstly, by combining the Pearson correlation coefficient (PCC) method with sensitivity analysis, the optimal combination of parameters that influence building cooling load (BCL) were obtained. Secondly, the parameters of the support vector machine (SVM) model were improved by using the genetic algorithm (GA), and a GA-SVM prediction model was proposed to perform building hourly cold load prediction. Then, when there is a demand for the fluctuation prediction of BCL or extreme weather conditions are encountered, the information granulation (IG) method is used to fuzzy granulate the data. At the same time, the fluctuation range of the BCL was obtained by combining the prediction of the established GA-SVM model. Finally, the model was validated with the actual operational data of a large public building in Xi'an. The results show that the CV-RMSE and MAPE of the GA-SVM model are reduced by 58.85% and 68.04%, respectively, compared with the SVM for the time-by-time BCL prediction, indicating that the optimization of the SVM by using the GA can effectively reduce the error of the prediction model. Compared with the other three widely used prediction models, the R2 of the GA-SVM model is improved by 4.75~6.35%, the MAPE is reduced by 68.00~72.76%, and the CV-RMSE is reduced by 59.69~64.97%. This proved that the GA-SVM has higher prediction accuracy. In addition, the joint model was used for BCL fluctuation range prediction, and the R2 of the prediction model was 97.27~99.68%, the MAPE was 2.59~2.84%, and the CV-RMSE was only 0.0249~0.0319, which demonstrated the effectiveness of the joint prediction model. The results of the study have important guiding significance for building load interval prediction, daily energy management and energy scheduling. [ABSTRACT FROM AUTHOR]

For researching the influence of vertical component of near-field records on the seismic response of wind turbine towers, firstly, shaking table test of a 1/20 scaled 2.0 MW wind turbine is carried out. Second, the finite element (FE) model of the scaled tested model is established by ABAQUS. Finally, the dynamic stability is analysed based on the validated FE model. Moreover, the sensitivity of seismic response to different V/H ratios (peak vertical acceleration to horizontal acceleration) of near-field records is analysed through experimental and numerical studies. The results show that the near-field earthquakes have a greater effect on the acceleration and displacement response. The vertical acceleration, vertical displacement and the axial force of this tower are sensitive to the vertical ground motions and the V/H ratios. The larger contributions of vertical component of near-field ground motions lead to smaller safety factors, which is particularly more unfavourable for the dynamic stability of this tower. The middle and lower section of this tower is the dangerous position for local buckling. The influence of the near-field vertical ground motions and a reasonable value of V/H ratios should be better understood and studied, so as to provide guidance for structural seismic design. [ABSTRACT FROM AUTHOR]

Microbes play an important role in aquatic carbon cycling but we have a limited understanding of their functional responses to changes in temperature across large geographic areas. Here, we explored how microbial communities utilized different carbon substrates and the underlying ecological mechanisms along a space‐for‐time substitution temperature gradient of future climate change. The gradient included 47 lakes from five major lake regions in China spanning a difference of nearly 15°C in mean annual temperatures (MAT). Our results indicated that lakes from warmer regions generally had lower values of variables related to carbon concentrations and greater carbon utilization than those from colder regions. The greater utilization of carbon substrates under higher temperatures could be attributed to changes in bacterial community composition, with a greater abundance of Cyanobacteria and Actinobacteriota and less Proteobacteria in warmer lake regions. We also found that the core species in microbial networks changed with increasing temperature, from Hydrogenophaga and Rhodobacteraceae, which inhibited the utilization of amino acids and carbohydrates, to the CL500‐29‐marine‐group, which promoted the utilization of all almost carbon substrates. Overall, our findings suggest that temperature can mediate aquatic carbon utilization by changing the interactions between bacteria and individual carbon substrates, and the discovery of core species that affect carbon utilization provides insight into potential carbon sequestration within inland water bodies under future climate warming. [ABSTRACT FROM AUTHOR]