Tannic acid, as a surroguate of natural organic matter (NOM), were used as model compound. A study was conducted to evaluate the main disinfection byproducts of sequential use ozone and monochloramine to disinfection in the presence of tannic acid. Results of Disinfection byproduct formation potential show that the main disinfection byproduct include TCM (trichloromethane) and DCAA(dichloroacetic acid). Smaller anounts of TCAA(trichloroacetic acid) were also found. The production of TCM and DCAA increase significantly with the increase of disinfectant dosage(ozone and monochloramine)and temperature. From pH 7-8, more disinfectant byproducts are produced.
Abstract Background Spinal infections such as pyogenic spondylitis, spinal tuberculosis, and brucellar spondylitis are severe conditions that can lead to significant spinal damage and chronic pain. Whole-slide imaging (WSI) provides valuable visual information in pathological diagnoses. However, owing to the complexity and high dimensionality of WSI data, traditional manual diagnostic methods are often time-consuming and prone to errors. Therefore, developing an automated image analysis method is crucial to enhance the diagnostic accuracy and efficiency of WSI for spinal infections. Methods This study employed a novel framework that combines Graph Convolutional Networks (GCNs) with uncertainty quantification techniques to classify WSI images of spinal infections. A graph was constructed from segmented regions of the WSI, where nodes represented segmented pathological features and edges represented spatial relationships. The model was trained using a dataset of 422 cases from a provincial center for disease control and prevention and annotated for tuberculosis, brucellosis, and purulent spondylitis. The performance metrics were accuracy, precision, recall, and F1 scores. Results The integrated GCN model demonstrated a classification accuracy of 87%, recall of 85%, and F1 score of 0.86. Comparative analyses revealed that the GCN model exhibited a 10% higher performance than that of traditional CNN models. Moreover, the GCN model effectively quantified uncertainty and enhanced confidence in diagnostic decisions. Conclusions Integrating GCNs with model uncertainty enhances the accuracy and reliability of WSI image classification in pathology. This method significantly improves the capture of spatial relationships and identification of pathological features of spinal infections, offering a robust framework for supporting diagnostic and therapeutic decisions in medical practice.
Wastewater reuse is an effective solution to water resource shortage problems, which commonly exists in urban areas. And disinfection is necessary to ensure the quality of the reclaimed water. Trihalomethanes (THMs) and haloacetic acids(HAAs) are two main disinfection byproducts(DBPs) in the water treatment process. Nitrogenous organic compounds are important disinfection byproducts precursors in the water. Tryptophan(Trp) is one kind of elementary amino acids, which are typical nitrogenous organic compounds. In this paper, the characteristics of THMs and HAAs formation from tryptophan under different chlorination disinfection conditions has been researched. The influencing factors were investigated, such as reaction time, initial concentration of tryptophan, chlorine dosage, pH and temperature. The results show that the yields of chloroform(TCM), dichloracetic acid(DCAA) and trichloracetic acid(TCAA) are all increased with the reaction time increasing, and the formation rates are all accelerated at the initial period; With the increase of initial tryptophan concentration, the yield of TCM, DCAA and TCAA are all increased at first and then reduced; With the increase of chlorine dosage, the yields of DCAA and TCAA both increased with ; The pH influences the formation of TCM and HAAs greatly. The yield of HAAs are low in the acidic condition and high in the conditions of neutral and alkaline conditions. The yield of HAAs is bigger at 20°C than other temperature.
The geometric correction is a bottleneck problem which baffles the remote sensing images' application.Geometric rectification is a nonlinear,uncertain,complex dynamic function and hard to be described completely,so a method of remote sensing image geometric correction based on the RBF neural networks and Ground Control Point(GCP) is made in this paper.Firstly,the algorithm determines the control point used to correct.Then,use the characteristic of RBF neural network can represent all functions at any accuracy to simulated surface of this complex spatial distribution function of the nonlinear.From the experimental results,it comes to some conclusions: The algorithm's principle is simple,and easy to be realized.Usually,the algorithm has an error less than one-pixel,which is accurate enough for geometric correction.It is a kind of more practical algorithm of the remote sensing image correction.
This paper presents a GPU based Range-Doppler radar imaging algorithm. It is implemented in OpenCL, which is deemed to be the industrial standard in the field of heterogeneous computing. Intensive floating-point calculations, for instance, large-scale FFTs, are effectively parallelized in this algorithm. The performance is promoted by orders of magnitude compared with CPU based implementation. It is able to process the data in real time by a single GTX 280 GPU under a practical condition.
The digital beamforming (DBF) technique discussed is based on a two-step adaptive scheme. A main beam is formed by a conventional digital beamforming algorithm. The space positions of M jammers are estimated and then M beams pointing at the directions of M jammers are formed. An adaptive processing of M+1 order is complemented with some adaptive algorithms. A comparison between partial adaptive arrays and two-step adaptive arrays is made.
The character of the eigenvalues of the constraint matrix was aimed,and the character is that the matrix has the same number of larger eigenvalues as number of the nulls that the matrix denoted.The bound of the eigenvalues of constraint matrix was estimated with one null by using gersgorin theorem accurately.Otherwise,on the aspect of mathematics,the perturbation was explained between eigenvalues of constraint matrixes of multiple nulls for one null by geometrical description.
In this paper we formally define an operational semantics framework RTL++ for modeling behavioral RTL hardware IP. The semantics we define is neutral to existing HDLs and extends traditional sense RTL by natively supporting pipelined and multi-cycled operations with a unified register variable type. We believe this formalization help to guide the design of new HDLs or extensions of existing HDLs in terms of elevating RTL design abstraction level and also bridging the current HDL semantic gap among synthesis, simulation and formal verification tools. The intra-module and inter-module execution of RTL++ semantics are specified in Plotkin-style structural operational semantics framework. An example of implementing the RTL++ extension of SystemC is presented along with experimental results showing the benefit of modeling in RTL++.
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