There are several factors that generate postharvest losses of Citrus sinensis, but none have been focused on the central jungle of the Junín region of Peru. The objective of this research was to evaluate postharvest losses of Citrus sinensis in the province of Satipo, Junín region of Peru, considering the stages of the production chain. The methodology was applied to descriptive and cross-sectional design. A sample of 10 orange trees, 3 transport intermediaries and 5 traders selected for compliance with minimum volume and quality requirements were used. The °Brix, pH and acidity characteristics of the fruit were determined. Subsequently, absolute and percentage losses were quantified through direct observation, surveys and interviews. The main postharvest losses of Citrus sinensis were 1.50% in harvesting and detaching, 1.75% in transport to the collection center, 2.23% in storage and transport by intermediaries, and 2.90% in storage and sale by retailers. The overall loss was 8.12% throughout the production chain and US$5.75 per MT of C. sinensis harvested. The main damages found were mechanical and biological, caused by poor harvesting and packaging techniques, precarious storage and careless transport of the merchandise.

Aiming at the current problems of poor dynamic reconstruction of UAV aerial remote sensing images and low image clarity, the dynamic reconstruction method of UAV aerial remote sensing images based on compression perception is proposed. Construct a quality reduction model for UAV aerial remote sensing images, obtain image feature information, and further noise reduction preprocessing of UAV aerial remote sensing images to better improve the resolution, spectral and multi-temporal trends of UAV aerial remote sensing images, and effectively solve the problems of resource waste such as large amount of sampled data, long sampling time and large amount of data transmission and storage. Maximize the UAV aerial remote sensing images sampling rate, reduce the complexity of dynamic reconstruction of UAV aerial remote sensing images, and effectively obtain the research requirements of high-quality image reconstruction. The experimental results show that the proposed dynamic reconstruction method of UAV aerial remote sensing images based on compressed sensing is correct and effective, which is better than the current mainstream methods.

In order to explore the influence of the ferroelectric surface on the structure and properties of semiconductor oxides, the growth of CdS nanocrystals was regulated and controlled by taking single-crystal perovskite PbTiO₃ nanosheets as the substrate through a simple hydrothermal method. Through composition design, a series of PbTiO₃-CdS nanocomposite materials with different loading concentrations were prepared, and their microstructure and photocatalytic properties were systematically analyzed. Studies show that in the prepared product, CdS nanoparticles selectively grow on the surfaces of PbTiO₃ nanosheets, and their morphology is affected by the exposed surfaces of PbTiO₃ nanosheets. There is a clear interface between the PbTiO₃ substrate and CdS nanoparticles. The concentration of the initial reactant and the time of hydrothermal reaction also significantly affect the crystal morphology of CdS. Photocatalysis studies have shown that the prepared PbTiO₃-CdS nanocomposite material has a significant degradation effect on 10 mg/L of Rhodamine B aqueous solution. The degradation efficiency rises with the increase of CdS loading concentration. When degrading 10 mg/L Rhodamine B aqueous solution, the PbTiO₃-CdS sample with a mass fraction of 3% can reach a degradation rate of 72% within 120 min.

Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.

Based on Landsat–7ETM + images of 2007 and 2012 and Landsat–8 images of 2018, this study took Fuyang City, Anhui Province (Yingzhou District, Yingdong District, Yingquan District) as the research object, and made a quantitative analysis of land use/cover change in Fuyang City from 2007 to 2018 with the Environment for Visualizing Images (ENVI) software. According to the data of land use types in three phases, the article analyzes the development trend of various land use types and the main reasons for the changes of land use, which provides a certain basis for the urban planning and environmental construction of Fuyang City. The results show that with the rapid economic development and continuous improvement of the urbanization level in Fuyang City during 11 years, the area of various land types in the study area has changed greatly. The area of construction land area changed by 448.27 km², with an increase of 543.57%; the area of arable land changed by 597.52 km², with a decrease of 34.74%; the area of bare land changed by 26.00 km², with a decrease of 80.68%. The changes were closely related to the rapid economic and social development in the study area. Under the influence of environmental protection policies and environmental awareness, the area of forest land changed by 85.00 km², with an increase of 97.58%; the water area changed by 84.35 km², with an increase of 201.39%.