Apple farming is a new production venture across the North Shewa Zone. Its production, harvest, postharvest handling, and marketing status are not well known. This study was conducted to assess the above-lined situations across the district. Four representative locations, Asabahir, Tsigereda, Tengego, and Godnamamas were selected based on their apple production status. Then, a total of 88 respondents were randomly selected and interviewed by a structured questionnaire. The data were analyzed by descriptive statistics of percentage, standard deviation, and chi-square tests. A larger percentage of farmers are male (82.9%), in their active production age (41.7%), and produce apples in their backyard (85.25%). The agronomic management of fertilization, pruning, training, and plant spacing deviate from the recommended practices of apple farming. Whereas varietal distribution, irrigation, and post-harvest treatments are better practiced. Loss of fruits by fruit drops and discrimination on the market due to small fruit size are serious problems across the locations. Regarding apple farming, the farmers think of it as a productive venture and got a better price per kg and single fruit sale. They sell mainly in local collectors (60.2%) and nearby cities. As for institutional support, the farmers got apple seedlings, training, and capacity buildings by Agriculture Offices and NGOs, even if the farmers are still in higher need of better support. Therefore, it can be concluded that if not outwaited by poor tree management, destructive product transportation, and higher loss of fruits from trees and in the market, the attitude of the farmers can be capitalized in better production of apples.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

The effects of climate change are already being felt, including the failure to harvest several agricultural products. On the other hand, peatland requires good management because it is a high carbon store and is vulnerable as a contributor to high emissions if it catches fire. This study aims to determine the potential for livelihood options through land management with an agroforestry pattern in peatlands. The methods used are field observation and in-depth interviews. The research location is in Kuburaya Regency, West Kalimantan, Indonesia. Several land use scenarios are presented using additional secondary data. The results show that agroforestry provides more livelihood options than monoculture farming or wood. The economic contribution is very important so that people reduce slash-and-burn activities that can increase carbon emissions and threaten the sustainability of peatland.

Idiomatic phrase are one of the lexical units.Many second-language learners showing great enthusiasm for using idiomatic expressions because of the rich cultural factors inherent in them and the vibrant,hilarious language that is close to life-like.However,  the idiomatic terms are so complicated that they frequently cause foreign learners to struggle with learning and comprehending Chinese.With its own advantages, the idea of lexical chunks has the potential to be a game changer in the teaching of idiomatic.

This paper provides a comprehensive review of SURF (speeded up robust features) feature descriptor, commonly used technique for image feature extraction. The SURF algorithm has obtained significant popularity because to its robustness, efficiency, and invariance to various image transformations. In this paper, an in-depth analysis of the underlying principles of SURF, its key components, and its use in computer vision tasks such as object recognition, image matching, and 3D reconstruction are proposed. Furthermore, we discuss recent advancements and variations of the SURF algorithm and compare it with other popular feature descriptors. Through this review, the aim is to provide a clear understanding of the SURF feature descriptor and its significance in the area of computer vision.