ZnO nanoparticles (NPs) were synthesized using a hydrothermal method. Scanning electron microscope (SEM) and X-ray diffraction have been used for characterizing the synthesized ZnO NPs. An electrochemical sensor was fabricated using ZnO NPs–modified glassy carbon electrode for simultaneous determination of ascorbic acid (AA), dopamine (DA), and uric acid (UA). The proposed electrochemical sensor exhibited excellent detection performance toward three analytes, demonstrating that it can potentially be applied in clinical applications. The results indicated the ZnO NPs–modified electrode can detect AA in the concentrations range between 50 and 1,000 μM. The ZnO NPs–modified electrode can detect DA in the concentrations range between 2 and 150 μM. The ZnO NPs–modified electrode can detect UA in the concentrations range between 0.2 and 150 μM. The limits of detections of AA, DA, and UA using ZnO NPs–modified electrode were calculated to be 18.4, 0.75, and 0.11 μM, respectively.

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The livestock of Pakistan includes different animal breeds utilized for milk farming and exporting worldwide. Buffalo have a high milk production rate, and Pakistan is the third-largest milk-producing country, and its production is increasing over time. Hence, it is essential to recognize the best Buffalo breed for a high milk- and meat yield to meet the world’s demands and breed production. Pakistan has the second-largest number of buffalos among countries worldwide, where the Neli-Ravi breed is the most common. The extensive demand for Neli and Ravi breeds resulted in the new cross-breed “Neli-Ravi” in the 1960s. Identifying and segregating the Neli-Ravi breed from other buffalo breeds is the most crucial concern for Pakistan’s dairy-production centers. Therefore, the automatic detection and classification of buffalo breeds are required. In this research, a computer-vision-based recognition framework is proposed to identify and classify the Neli-Ravi breed from other buffalo breeds. The proposed framework employs self-activated-based improved convolutional neural networks (CNN) combined with self-transfer learning. Moreover, feature maps extracted from CNN are further transferred to obtain rich feature vectors. Different machine learning (Ml) classifiers are adopted to classify the feature vectors. The proposed framework is evaluated on two buffalo breeds, namely, Neli-Ravi and Khundi, and one additional target class contains different buffalo breeds collectively called Mix. The proposed research achieves a maximum of 93% accuracy using SVM and more than 85% accuracy employing recent variants.

The temperature and humidity control of a pig house is a complex multivariable control problem. How to keep the temperature and humidity in a pig house within a normal range is the problem to be solved in this paper. The traditional threshold-based environmental control system cannot meet this requirement. In this paper, an intelligent control system of temperature and humidity in a pig house based on machine learning and a fuzzy control algorithm is proposed. We use sensors to collect the temperature and humidity in the pig house and store these data in chronological order. Then, we use these time series data to train the GRU model and then use the GRU model to predict the temperature and humidity change curve in the pig house in the next 24 hours. Finally, the mathematical model of the pig house and related equipment is established, and the output power of the related equipment is calculated based on the prediction results of GRU so as to effectively regulate the indoor temperature and humidity. The experimental results show that compared with the threshold-based environmental control system, our system reduces the abnormal temperature and humidity by about 90%.