Numeric prediction of dissolved oxygen status through two-stage training for classification-driven regression

Dissolved oxygen of aquaculture is an important measure of the quality of culture environment and how aquatic products have been grown. In the machine learning context, the above measure can be achieved by defining a regression problem, which aims at numerical prediction of the dissolved oxygen status. In general, the vast majority of popular machine learning algorithms were designed for undertaking classification tasks. In order to effectively adopt the popular machine learning algorithms for the above-mentioned numerical prediction, in this paper, we propose a two-stage training approach that involves transforming a regression problem into a classification problem and then transforming it back to regression problem. In particular, unsupervised discretization of continuous attributes is adopted at the first stage to transform the target (numeric) attribute into a discrete (nominal) one with several intervals, such that popular machine learning algorithms can be used to predict the interval to which an instance belongs in the setting of a classification task. Furthermore, based on the classification result at the first stage, some of the instances within the predicted interval are selected for training at the second stage towards numerical prediction of the target attribute value of each instance. An experimental study is conducted to investigate in general the effectiveness of the popular learning algorithms in the numerical prediction task and also analyze how the increase of the number of training instances (selected at the second training stage) can impact on the final prediction performance. The results show that the adoption of decision tree learning and neural networks lead to better and more stable performance than Naive Bayes, K Nearest Neighbours and Support Vector Machine

Underwater image quality assessment: subjective and objective methods

Underwater image enhancement plays a critical role in marine industry. Various algorithms are applied to enhance underwater images, but their performance in terms of perceptual quality has been little studied. In this paper, we investigate five popular enhancement algorithms and their output image quality. To this end, we have created a benchmark, including images enhanced by different algorithms and ground truth image quality obtained by human perception experiments. We statistically analyse the impact of various enhancement algorithms on the perceived quality of underwater images. Also, the visual quality provided by these algorithms is evaluated objectively, aiming to inform the development of objective metrics for automatic assessment of the quality for underwater image enhancement. The image quality benchmark and its objective metric are made publicly available

Development of novel microemulsion-based hydrogel for topical delivery of sinomenium

The objective of the present investigation was to develop and evaluate microemulsion-based hydrogel (MBH) for the topical delivery of sinomenium. The solubility of sinomenium in oils and surfactants was evaluated to identify components of the microemulsion, the pseudo-ternary phase diagrams were developed to identify the area of microemulsion existence and obtain the optimization Km (the weight ratio of surfactant to cosurfactant). The transdermal ability of various microemulsion formulations were evaluated in vitro using Franz diffusion cells fitted with rat skins and sinomenium was analyzed by HPLC. The permeation of microemulsions accorded with the Fick’s first diffusion law and the optimal formulation of the microemulsion was obtained. The MBH formulation containing 2 % sinomenium was prepared with Carbomer 940 as the gelling matrix. Stability test showed that MBH stored at 4°C and 25 °C for 3 months had no significant change in physicochemical properties. Pharmacokinetic study in vivo was conducted using rabbits, and the area under curve of plasma concentration-time (AUC0→∞) of MBH was 1.27 times greater than that of the hydrogel. These results indicated that MBH might be a promising vehicle for the transdermal delivery of sinomenium.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Comparison of harmonic limits and evaluation of the international standards

The paper takes the Chinese standard GB/T 14549-1993, the British Engineering Recommendation G5/4-1, the Institute of Electrical and Electronics Engineers IEEE Std 519-2014 and the part of IEC 61000-3 series standard as an example. Then summarize the difference of harmonic limits, the different processing methods of harmonic impedance, and the different summation law of each standard. Compare the harmonic limit calculation and evaluation methods of Chinese GB/T 14549-1993, British G5/4-1 and IEEE Std 519-2014 standard in detail, summarize the characteristics of the three standards in disturbance emission evaluation and introduce examples to verify the conclusion. Finally, summarize the characteristics and differences of mentioned harmonic standards

Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges

Concern about food safety has become a hot topic, and numerous researchers have come up with various effective solutions. To ensure the safety of food and avoid financial loss, it is important to improve the safety of food information in addition to the quality of food. Additionally, protecting the privacy and security of food can increase food harvests from a technological perspective, reduce industrial pollution, mitigate environmental impacts, and obtain healthier and safer food. Therefore, food traceability is one of the most effective methods available. Collecting and analyzing key information on food traceability, as well as related technology needs, can improve the efficiency of the traceability chain and provide important insights for managers. Technology solutions, such as the Internet of Things (IoT), Artificial Intelligence (AI), Privacy Preservation (PP), and Blockchain (BC), are proposed for food monitoring, traceability, and analysis of collected data, as well as intelligent decision-making, to support the selection of the best solution. However, research on the integration of these technologies is still lacking, especially in the integration of PP with food traceability. To this end, the study provides a systematic review of the use of PP technology in food traceability and identifies the security needs at each stage of food traceability in terms of data flow and technology. Then, the work related to food safety traceability is fully discussed, particularly with regard to the benefits of PP integration. Finally, current developments in the limitations of food traceability are discussed, and some possible suggestions for the adoption of integrated technologies are made

A Review of Measurement Methods of Dissolved Oxygen in Water

Part 1: GIS, GPS, RS and Precision FarmingInternational audienceSome kinds of checking methods and principle of dissolved oxygen in water were summarized. Such as: iodometric method, current determination method (Clark dissolved oxygen electrode), conductance measurement and fluorescence quenching. The advantages and disadvantages of each method were compared, and fluorescence quenching was discussed. The method uses Ruthenium complex as fluorescence sensitive reagent, which emits fluorescence under the excited light. The quenching accords with Stem-Volmer formula, and the density of oxygen could be deduced by checking fluorescence spectrum

Deep Convolutional Generative Adversarial Network and Convolutional Neural Network for Smoke Detection

Real-time smoke detection is of great significance for early warning of fire, which can avoid the serious loss caused by fire. Detecting smoke in actual scenes is still a challenging task due to large variance of smoke color, texture, and shapes. Moreover, the smoke detection in the actual scene is faced with the difficulties in data collection and insufficient smoke datasets, and the smoke morphology is susceptible to environmental influences. To improve the performance of smoke detection and solve the problem of too few datasets in real scenes, this paper proposes a model that combines a deep convolutional generative adversarial network and a convolutional neural network (DCG-CNN) to extract smoke features and detection. The vibe algorithm was used to collect smoke and nonsmoke images in the dynamic scene and deep convolutional generative adversarial network (DCGAN) used these images to generate images that are as realistic as possible. Besides, we designed an improved convolutional neural network (CNN) model for extracting smoke features and smoke detection. The experimental results show that the method has a good detection performance on the smoke generated in the actual scenes and effectively reduces the false alarm rate