Open Innovation Web-Based Platform for Evaluation of Water Quality Based on Big Data Analysis

There are many models presented that assess water quality. However, the applications of the models are limited due to the difficulty of preparing input data and interpreting model output. In this paper, we developed a Web-based platform to assist researchers in analyzing water quality. The data from sensors can be automatically imported to the platform according to the configured information of data structures. The platform also provides conventional methods and big data methods for the users to analyze water quality. Moreover, the users can choose the water quality parameters according to the water usage. The presented platform can show the model output in a text format and a graphic format, which allows for the analysis to be better understood by the user. The platform integrates the input, analysis, and output together well and brings great convenience to the research on water quality

CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions

Traffic sign detection is an important research direction in intelligent driving. Unfortunately, existing methods often overlook extreme conditions such as fog, rain, and motion blur. Moreover, the end-to-end training strategy for image denoising and object detection models fails to utilize inter-model information effectively. To address these issues, we propose CCSPNet, an efficient feature extraction module based on Transformers and CNNs, which effectively leverages contextual information, achieves faster inference speed and provides stronger feature enhancement capabilities. Furthermore, we establish the correlation between object detection and image denoising tasks and propose a joint training model, CCSPNet-Joint, to improve data efficiency and generalization. Finally, to validate our approach, we create the CCTSDB-AUG dataset for traffic sign detection in extreme scenarios. Extensive experiments have shown that CCSPNet achieves state-of-the-art performance in traffic sign detection under extreme conditions. Compared to end-to-end methods, CCSPNet-Joint achieves a 5.32% improvement in precision and an 18.09% improvement in [email protected]

Robust Multiple-View Geometry Estimation Based on GMM

Given three partially overlapping views of the scene from which a set of point or line correspondences have been extracted, 3D structure and camera motion parameters can be represented by the trifocal tensor, which is the key to many problems of computer vision on three views. Unlike in conventional typical methods, the residual value is the only rule to eliminate outliers with large value, we build a Gaussian mixture model assuming that the residuals corresponding to the inliers come from Gaussian distributions different from that of the residuals of outliers. Then Bayesian rule of minimal risk is employed to classify all the correspondences using the parameters computed from GMM. Experiments with both synthetic data and real images show that our method is more robust and precise than other typical methods because it can efficiently detect and delete the bad corresponding points, which include both bad locations and false matches

Availability, Pharmaceutics, Security, Pharmacokinetics, and Pharmacological Activities of Patchouli Alcohol

Patchouli alcohol (PA), a tricyclic sesquiterpene, is one of the critical bioactive ingredients and is mainly isolated from aerial part of Pogostemon cablin (known as guanghuoxiang in China) belonging to Labiatae. So far, PA has been widely applied in perfume industries. This review was written with the use of reliable information published between 1974 and 2016 from libraries and electronic researches including NCKI, PubMed, Reaxys, ACS, ScienceDirect, Springer, and Wiley-Blackwell, aiming at presenting comprehensive outline of security, pharmacokinetics, and bioactivities of PA and at further providing a potential guide in exploring the PA and its use in various medical fields. We found that PA maybe was a low toxic drug that was acquired numerously through vegetable oil isolation and chemical synthesis and its stability and low water dissolution were improved in pharmaceutics. It also possessed specific pharmacokinetic characteristics, such as two-compartment open model, first-order kinetic elimination, and certain biometabolism and biotransformation process, and was shown to have multiple biological activities, that is, immunomodulatory, anti-inflammatory, antioxidative, antitumor, antimicrobial, insecticidal, antiatherogenic, antiemetic, whitening, and sedative activity. However, the systematic evaluations of preparation, pharmaceutics, toxicology, pharmacokinetics, and bioactivities underlying molecular mechanisms of action also required further investigation prior to practices of PA in clinic

Thinking on the Reasons and Countermeasures of the Failure and Misrepresentation of Science and Technology Transmission

This paper makes a comparative analysis of the phenomena of communication failure and misrepresentation in the East and the West history of science and technology communication. Based on the analysis, the authors find out the causes of this phenomenon and puts forward their own thinking and countermeasures. They expect to arouse the attention of scientific and technological media workers, so as to better inherit the knowledge and wisdom of human beings

Network Pruning via Feature Shift Minimization

Channel pruning is widely used to reduce the complexity of deep network models. Recent pruning methods usually identify which parts of the network to discard by proposing a channel importance criterion. However, recent studies have shown that these criteria do not work well in all conditions. In this paper, we propose a novel Feature Shift Minimization (FSM) method to compress CNN models, which evaluates the feature shift by converging the information of both features and filters. Specifically, we first investigate the compression efficiency with some prevalent methods in different layer-depths and then propose the feature shift concept. Then, we introduce an approximation method to estimate the magnitude of the feature shift, since it is difficult to compute it directly. Besides, we present a distribution-optimization algorithm to compensate for the accuracy loss and improve the network compression efficiency. The proposed method yields state-of-the-art performance on various benchmark networks and datasets, verified by extensive experiments. Our codes are available at: https://github.com/lscgx/FSM

A Novel Chaotic Neural Network Using Memristive Synapse with Applications in Associative Memory

Chaotic Neural Network, also denoted by the acronym CNN, has rich dynamical behaviors that can be harnessed in promising engineering applications. However, due to its complex synapse learning rules and network structure, it is difficult to update its synaptic weights quickly and implement its large scale physical circuit. This paper addresses an implementation scheme of a novel CNN with memristive neural synapses that may provide a feasible solution for further development of CNN. Memristor, widely known as the fourth fundamental circuit element, was theoretically predicted by Chua in 1971 and has been developed in 2008 by the researchers in Hewlett-Packard Laboratory. Memristor based hybrid nanoscale CMOS technology is expected to revolutionize the digital and neuromorphic computation. The proposed memristive CNN has four significant features: (1) nanoscale memristors can simplify the synaptic circuit greatly and enable the synaptic weights update easily; (2) it can separate stored patterns from superimposed input; (3) it can deal with one-to-many associative memory; (4) it can deal with many-to-many associative memory. Simulation results are provided to illustrate the effectiveness of the proposed scheme