This case study employs the Asset-Based Community Development (ABCD) theory as a conceptual framework, utilizing semi-structured interviews combined with focus group discussions to uncover the driving forces influencing rural revitalization and sustainable development within communities. ABCD is considered a transformative approach that emphasizes achieving sustainable development by mobilizing existing resources within the community. Conducted against the backdrop of rural revitalization in China, the study conducts on-site investigations in Yucun, Zhejiang Province. Through the analysis of Yucun’s community development and asset utilization practices, the study reveals successful experiences in various aspects, including community construction, industrial development, cultural heritage preservation, ecological conservation, organizational management, and open economic thinking. The results indicate that Yucun’s sustainable development benefits from its unique resources, leveraging policy advantages, collective financial organizations, and open economic thinking, among other factors. These elements collectively drive rural revitalization in Yucun, leading to sustainable development.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

A fresh interest has been accorded to metal iodides due to their fascinating physicochemical properties such as high ionic conductivity, variable optical properties, and high thermal stabilities in making micro and macro devices. Breakthroughs in cathodic preparation and metallization of metal iodides revealed new opportunities for using these compounds in various fields, especially in energy conversion and materials with luminescent and sensory properties. In energy storage metal iodides are being looked at due to their potential to enhance battery performance, in optoelectronics the property of the metal iodides is available to create efficient LEDs and solar cells. Further, their application in sensing devices, especially in environmental and medical monitoring has been quite mentioned due to their response towards environmental changes such as heat or light. Nevertheless, some challenges are still in question, including material stability, scale-up opportunities, and compatibility with other technologies. This work highlights the groundbreaking potential of metal iodide-based nanomaterials, emphasizing their transformative role in innovation and their promise for future advancements.

Our study evaluated the effect of vanadium (V) on the behavior of Zinnia elegans “double variegated”. In this experiment, Zinnia plants grown in a greenhouse were fed with a nutrient solution and two concentrations of vanadium (0, 6, and 10 μm) applied four times during the experiment. The V at its levels of 6 µm and 10 µm increased plant length, number of inflorescences and fresh weight. We observed that during the development and appearance of flower buds, and flowering were earlier with the addition of 6 µm and 10 µm. During harvest the changes in size and shape were homogeneous with the control treatment. With the addition of 6 µm, flowers of different sizes were induced, with non-uniform petals, but with different shades of color. With 10 µm the shape of the petals, the distance between them and changes in the shades of the flowers were modified. The postharvest life for the flowers of the control treatment was shorter (15 days), the petals, anthers and floral disc at this time were observed in a poor condition. While 6 µm and 10 µm had a longer postharvest life (20 days), the flowers had a good presentation, their colors were more intense compared to the harvest stage. The application of this beneficial element contributed to the development and flowering of Zinnia in the greenhouse. It is suggested that future research be carried out on the accumulation and/or concentration of vanadium in the different stages of growth or its effect on the concentration of other nutrients.