The increasing metal release into the environment warrants investigating their impact on plants, which are cornerstones of ecosystems. Here, Lactuca sativa L. (lettuce) seedlings were exposed hydroponically to different concentrations of silver ions and nanoparticles (Ag NPs) for 25 days to evaluate their impact on plant growth. Seedlings taking Ag⁺ ions showed an increment of 18% in total phenolic content and 12% in total flavonoid content, whereas under Ag NPs, 7% free radical scavenging activity, 12% total phenolic contents (TPC), and 10% total reducing power are increased. An increase in 31% shoot length, 25% chlorophyll, 11% carbohydrate, and 16% protein content of the lettuce plant is observed in response to Ag NPs, while silver nitrate (AgNO₃) has a reduced 40% growth. The lettuce plant was most susceptible to toxic effects of Ag+ ions at a lower concentration, i.e., 0.01 mg/L, while Ag NPs showed less toxicity, only when higher concentrations >100 mg/L were applied. Further, biomolecules other than antioxidant enzymes showed higher phytotoxicity for Ag⁺ ions, followed by Ag NPs with the concentration of 25, 50, and 100 mg/L compared to the control. Thus, moderate concentrations of Ag NPs have a stimulatory effect on seedling growth, while higher concentrations induced inhibitory effects due to the release of Ag⁺ ions. These results suggest that optimum metallic contents are desirable for the healthier growth of plants in a controlled way.

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.

Smoke detection based on video surveillance is important for early fire warning. Because the smoke is often small and thin in the early stage of a fire, using the collected smoke images for the identification and early warning of fires is very difficult. Therefore, an improved lightweight network that combines the attention mechanism and the improved upsampling algorithm has been proposed to solve the problem of small and thin smoke in the early fire stage. Firstly, the dataset consists of self-created small and thin smoke pictures and public smoke pictures. Secondly, an attention mechanism module combined with channel and spatial attention, which are attributes of pictures, is proposed to solve the small and thin smoke detection problem. Thirdly, to increase the receptive field of the smoke feature map in the feature fusion network and to solve the problem caused by the different smoke scenes, the original upsampling has been replaced with an improved upsampling algorithm. Finally, extensive comparative experiments on the dataset show that improved detection model has demonstrated an excellent effect.

Accurate feeding control in aquaculture using fish feeding behavior state is key to improving feed utilization efficiency and reducing water pollution. A hybrid method based on ResNet34-CBAM and migration learning is proposed in this paper to achieve tilapia feeding behavior classification. In order to enhance the diversity of samples, pre-processing, such as scaling and cropping, and adding noise, was performed on the images. An underwater camera was used for taking pictures of tilapia behavior, followed by feature extraction using a pre-trained ResNet34 residual network, training with an improved classification for identifying feeding behavior. The testing findings demonstrate that the suggested technique in this article is 99.72% accurate in an actual environment. The model also outperforms other recognition models based on MobileNetV2, AlexNet, VGG11, ShuffleNet_v2_x0_5, and ResNet regarding accuracy, precision, and recall compared with other convolutional neural networks. Compared to the original model, the method shows a 7.84 percent improvement, indicating that it can correctly identify the feeding behavior of real fish in aquaculture and then scientifically determine the amount of baiting, which can improve feed utilization, increase aquaculture production, and decrease aquaculture costs.

In intensive aquaculture systems, accurate water temperature prediction is crucial for aquaculture efficiency. Traditional prediction models often have limitations in dealing with strongly coupled, nonlinear, and time-varying water temperature data. A novel hybrid model for temperature prediction is proposed to improve prediction generalization ability and robustness. The model integrates advanced data processing and prediction techniques. Firstly, the VMD method is utilized to achieve effective data decomposition and noise reduction. Secondly, the CNN algorithm is applied to achieve feature extraction of the data. Finally, the bi-directional LSTM and self-concerned combination are used to obtain the final prediction results. The experimental results show that the MAE, RMSE, MSE, MAPE, and R ² of the VMD-CNN-BILSTM-SA combination prediction model proposed in this paper are 0.016, 0.143, 0.020, 0.035, and 0.978, respectively. Compared with other deep learning models, the BiLSTM model presented in this paper improves the R ² by 13.2% compared with LSTM and 13.7% over the GRU model. This study can be applied in fishery farming, which can reduce the risk of farming and promote the modernization of fishery.