Antimicrobial ion-imprinted chitosan-derived hydrogel with quaternary ammonium and thermoresponsive components for UO22+ adsorption.

Uranium recovery from wastewater or seawater is important for both pollution control and uranium supply. Due to the complexity of the water body, it requires that the adsorbent should not only be highly efficient for selective adsorption but also have good antimicrobial properties. In this study, an antimicrobial thermosensitive hydrogel (UITAC) for uranium adsorption was prepared by one-step ion-imprinted polymerization using chitosan as a substrate and allyl trimethylammonium chloride as the antimicrobial modifier. UITAC showed excellent antibacterial rate against Escherichia coli and Staphylococcus aureus, being 98.8 % and 89.1 %, respectively. Endothermic and exothermic peaks respectively showed up at 36.3-38.5 °C and 30.5-34.1 °C in the DSC curves. UITAC quickly achieved its adsorption equilibrium in 30.0 min at 50 °C, pH 5.0 in the 0.8 mg/mL UO22+ solution, with an adsorption capacity of 81.2 mg/g. The adsorption capacity could remain at 80 % after 5 cycles of repeated use. UITAC showed better adsorption selectivity to UO22+ than vanadium and other metal ions, with selectivity coefficients α(UO22+/Mn+) being 1.4-10.3. The pseudo-second-order kinetics and Langmuir adsorption model had a better fit for UO22+ adsorption by UITAC. The adsorption was a spontaneous process. The Gibbs Free Energy change, enthalpy change, and entropy change at 323.2 K were - 16.0 kJ/mol, 64.3 kJ/mol, and 248.4 J/mol·K, respectively. UITAC showed high potential in practical application environment.

A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild.

Accurate counting of maize tassels is essential for monitoring crop growth and estimating crop yield. Recently, deep-learning-based object detection methods have been used for this purpose, where plant counts are estimated from the number of bounding boxes detected. However, these methods suffer from 2 issues: (a) The scales of maize tassels vary because of image capture from varying distances and crop growth stage; and (b) tassel areas tend to be affected by occlusions or complex backgrounds, making the detection inefficient. In this paper, we propose a multiscale lite attention enhancement network (MLAENet) that uses only point-level annotations (i.e., objects labeled with points) to count maize tassels in the wild. Specifically, the proposed method includes a new multicolumn lite feature extraction module that generates a scale-dependent density map by exploiting multiple dilated convolutions with different rates, capturing rich contextual information at different scales more effectively. In addition, a multifeature enhancement module that integrates an attention strategy is proposed to enable the model to distinguish between tassel areas and their complex backgrounds. Finally, a new up-sampling module, UP-Block, is designed to improve the quality of the estimated density map by automatically suppressing the gridding effect during the up-sampling process. Extensive experiments on 2 publicly available tassel-counting datasets, maize tassels counting and maize tassels counting from unmanned aerial vehicle, demonstrate that the proposed MLAENet achieves marked advantages in counting accuracy and inference speed compared to state-of-the-art methods. The model is publicly available at https://github.com/ShiratsuyuShigure/MLAENet-pytorch/tree/main.

Integrating multiple factors to optimize watchtower deployment for wildfire detection.

Traditional human-vision-based watchtower systems are being gradually replaced by the machine-vision-based watchtower system. The visual range of machine-vision-based watchtower is smaller than the range of traditional human-vision-based watchtower, which has led to a sharp increase in the number of towers that should be deployed. Consequently, the overlapping area between watchtowers is larger and overlaps are more frequent than in conventional watchtower networks. This poses an urgent challenge: identifying the optimal locations for deployment. If the number of required watchtowers must be increased to extend the detection coverage, overlaps among watchtowers are inevitable and result in viewshed redundancy. However, this redundancy of the viewshed resources of the watchtowers has not been utilized in the design of fire detection systems. Moreover, fire ignition factors, such as climatic factors, fuels, and human behaviour, cause the fire occurrence risk to differ among forest areas. Thus, the fire risk map of the area should also be considered in watchtower deployment. A fire risk model is used as the first step in producing the fire risk map, which is used to propose a new watchtower deployment model for optimizing the watchtower system by integrating viewshed analysis, location allocation, and multi-coverage of the high-fire-risk area while considering the budget constraints for providing optimal coverage. We use a real dataset of a forest park to evaluate the applicability of our approach. The proposed approach is evaluated against the FV-NB (Full coVerage with No Budget constraint) algorithm and the XV-B (maXimum possible coVerage with a Budget constraint) algorithm in terms of performance. The evaluation results demonstrate that our approach realizes higher coverage gain and excellent multiple-coverage of the fire risk area by integrating the viewshed and the fire risk level into location allocation while satisfying requirements on the overall coverage and budget. The proposed approach is more suitable in the environments with moderate watchtower density, in which overlapping areas are frequent. It offers as much as 8.9-17.3% improvement of multiple-coverage of the high-fire-risk area.

Flexible non-greedy discriminant subspace feature extraction.

Recently, L1-norm-based non-greedy linear discriminant analysis (NLDA-L1) for feature extraction has been shown to be effective for dimensionality reduction, which obtains projection vectors by a non-greedy algorithm. However, it usually acquires unsatisfactory performances due to the utilization of L1-norm distance measurement. Therefore, in this brief paper, we propose a flexible non-greedy discriminant subspace feature extraction method, which is an extension of NLDA-L1 by maximizing the ratio of Lp-norm inter-class dispersion to intra-class dispersion. Besides, we put forward a powerful iterative algorithm to solve the resulted objective function and also conduct theoretical analysis on the algorithm. Finally, experimental results on image databases show the effectiveness of our method.

L1-Norm Distance Minimization-Based Fast Robust Twin Support Vector $k$ -Plane Clustering.

Twin support vector clustering (TWSVC) is a recently proposed powerful k-plane clustering method. It, however, is prone to outliers due to the utilization of squared L2-norm distance. Besides, TWSVC is computationally expensive, attributing to the need of solving a series of constrained quadratic programming problems (CQPPs) in learning each clustering plane. To address these problems, this brief first develops a new k-plane clustering method called L1-norm distance minimization-based robust TWSVC by using robust L1-norm distance. To achieve this objective, we propose a novel iterative algorithm. In each iteration of the algorithm, one CQPP is solved. To speed up the computation of TWSVC and simultaneously inherit the merit of robustness, we further propose Fast RTWSVC and design an effective iterative algorithm to optimize it. Only a system of linear equations needs to be computed in each iteration. These characteristics make our methods more powerful and efficient than TWSVC. We also conduct some insightful analysis on the existence of local minimum and the convergence of the proposed algorithms. Theoretical insights and effectiveness of our methods are further supported by promising experimental results.

Changes of manifestations of 122 patients with rheumatic fever in South China during last decade.

The main objective of this study is to investigate changes of features of rheumatic fever (RF) in recent 10 years. A total of 315 patients with RF during 1985­1995 (group 1) and 1997­2007(group 2) were selected. Their manifestations were compared. Results show that the female/male ratio was 2.0. Group 2 had higher rate of low-grade fever and carditis, and lower rate of heart failure, lower positive rate of C-reactive protein and antistreptolycin o than group 1. In group 2, 61.4% patients fulfilled the updated Jones criteria, however, 76.2% fulfilled 2002­2003 WHO criteria. Diagnosing rheumatic carditis, sensibility and specificity of lymphocyte procoagulant activity (PCA) were 79.1 and 71.4%, respectively, and those of antibody to streptococcal polysaccharide (ASP) were 70.3 and 70%, respectively. Follow-up data of 35 cases were available. Recurrent rate of RF was 62.8%. Only 1/3 cases received regular secondary prevention. In conclusion, mild carditis was increasing. PCA and ASP were valuable tests for diagnosing rheumatic carditis. Atypical cases and secondary prevention need more attention.