Ethanol mediated As(Ⅲ) adsorption onto Zn-loaded pinecone biochar：Experimental investigation,modeling,and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters（OMs） and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol（EtO H）-mediated As（Ⅲ） adsorption onto Zn-loaded pinecone（PC） biochar through batch experiments conducted under Box–Behnken design. The effect of EtO H on As（Ⅲ） adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtO H and pH on As（Ⅲ） adsorption,whereas neural network revealed the stronger influence of Et OH（64.5%） followed by pH（20.75%）and As（Ⅲ） concentration（14.75%） on the adsorption phenomena. Besides, the interaction among process variables indicated that Et OH enhances As（Ⅲ） adsorption over a pH range of2 to 7, possibly due to facilitation of ligand–metal（Zn） binding complexation mechanism.Eventually, hybrid response surface model–genetic algorithm（RSM–GA） approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As（Ⅲ）（10.47 μg/g） is facilitated at 30.22 mg C/L of Et OH with initial As（Ⅲ） concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As（Ⅲ） species in the presence of OM.

Camouflaged target detection based on multimodal image input pixel-level fusion

Camouflaged targets are a type of nonsalient target with high foreground and background fusion and minimal target feature information,making target recognition extremely difficult.Most detection algorithms for camouflaged targets use only the target’s single-band information,resulting in low detection accuracy and a high missed detection rate.We present a multimodal image fusion camouflaged target detection technique (MIF-YOLOv5) in this paper.First,we provide a multimodal image input to achieve pixel-level fusion of the camouflaged target’s optical and infrared images to improve the effective feature information of the camouflaged target.Second,a loss function is created,and the K-Means++clustering technique is used to optimize the target anchor frame in the dataset to increase camouflage personnel detection accuracy and robustness.Finally,a comprehensive detection index of camouflaged targets is proposed to compare the overall effectiveness of various approaches.More crucially,we create a multispectral camouflage target dataset to test the suggested technique.Experimental results show that the proposed method has the best comprehensive detection performance,with a detection accuracy of 96.5%,a recognition probability of92.5%,a parameter number increase of 1×10^(4),a theoretical calculation amount increase of 0.03 GFLOPs,and a comprehensive detection index of 0.85.The advantage of this method in terms of detection accuracy is also apparent in performance comparisons with other target algorithms.