The rapid development of cities and urbanization in China has forced the growth of new channels for buying agricultural products. The purpose of this research is to examine how Internet of Things (IoT’s) technologies can digitize a traditional fresh food supply chain. Comparative and descriptive analysis methods are used to highlight the major pain points in the traditional supply chains and assess how digital transformation could help. We delve into every part of digital transformation, which includes establishing an information platform based on IoT and developing smart storage options. Our findings revealed that through end-to-end digital integration, supply chain efficiency is improved with shorter lead times and leaner inventories that yield reduced costs as well as fewer losses while ensuring product quality and traceability. In sum, such an approach would enhance sustainability within the fresh food value chain. As such, our article highlights key aspects of transitioning towards a digital environment in this sector for those planning similar ventures.

Abrupt changes in environmental temperature, wind and humidity can lead to great threats to human life safety. The Gansu marathon disaster of China highlights the importance of early warning of hypothermia from extremely low apparent temperature (AT). Here a deep convolutional neural network model together with a statistical downscaling framework is developed to forecast environmental factors for 1 to 12 h in advance to evaluate the effectiveness of deep learning for AT prediction at 1 km resolution. The experiments use data for temperature, wind speed and relative humidity in ERA-5 and the results show that the developed deep learning model can predict the upcoming extreme low temperature AT event in the Gansu marathon region several hours in advance with better accuracy than climatological and persistence forecasting methods. The hypothermia time estimated by the deep learning method with a heat loss model agrees well with the observed estimation at 3-hour lead. Therefore, the developed deep learning forecasting method is effective for short-term AT prediction and hypothermia warnings at local areas.

The problem of the synthesis of new type nanomaterials in the form of nano-coatings with sub-nanometric heterogeneity has been formulated. It has been presented an analysis of influences of physical vapor deposition in ultrahigh vacuum on the process of intermixing a film with a substrate, including the results, which has been obtained under the formation of transition metal – silicon interface. The generalization of the obtained experimental results develops an approach to the development of new nano-coatings with low-dimensional heterogeneity. The principles of constructing such low-dimensional nano-coatings, their properties and possible applications are considered. 

Plum (Prunus domestica) is a seasonal nutraceutical fruit rich in many functional food nutrients such as vitamin C, antioxidants, total phenolic content, and minerals. Recently, researchers have focused on improvised technologies for the retention of bioactive compounds during the processing of perishable fruits; plum is one of these fruits. This study looked at how the percentage of moisture content and percentage of acidity were affected by conventional drying and osmotic dehydration. Total phenolic content (mg GA/100 g of plum), total anthocyanin content (mg/100 g), and vitamin C (mg/100 g) Conventional drying of fruit was carried out at 80.0 ℃ for 5 h. At various temperatures (45.0 ℃, 50.0 ℃, and 55.0 ℃) and hypertonic solution concentrations (65.0 B, 70.0 B, and 75.0 B), the whole fruit was osmotically dehydrated. It was observed that the osmotically treated fruit retains more nutrients than conventionally dried fruit. The total phenolic content of fruit significantly increased with the increase in process temperature. However, vitamin C and total anthocyanin content of the fruit decreased significantly with process temperature, and hypertonic solution concentration was observed. Hence, it was concluded that osmodehydration could be employed for nutrient retention in plum fruit over conventional drying. This process needs to be further refined, improvised, and optimised for plum processing.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

Forest is the main carbon sink of terrestrial ecosystem. Due to the unique growth characteristics of plants, the response of their growth status and physiological activities to climate change will affect the carbon cycle process of forest ecosystem. Based on the local scale CO₂ flux and temperature observation data recorded by the FLUXNET registration site and Harvard Forest FLUX observation tower from 2000 to 2012, combined with the phenological model, this paper analyzes the impact of temperature changes on CO₂ flux in temperate forest ecosystems. The results show that: (1) the maximum NEE in 2000–2012 was 298.13 g·m^-2·a^-1, which occurred in 2010. Except in the 2010 and 2011, the annual NEE in other years was negative. (2) NEE, GPP, temperature and phenology models have good fitting effects (R² > 0.8), which shows that the stable period of photosynthesis in temperate mixed forest ecosystem is mainly concentrated in summer, and vegetation growth is the dominant factor of carbon cycle in temperate mixed forest ecosystem. (3) The linear fitting results of the change time points of air temperature (maximum point, minimum point and 0 point date) and the change time points of NEE and GPP (maximum point, minimum point and 0 point date) show that there is a significant positive correlation between air temperature and CO₂ flux (P < 0.01), and the change of air temperature affects the carbon cycle process of temperate mixed forest ecosystem.