Due to the short cost-effective heat transportation distance, the existing geothermal heating technologies cannot be used to develop deep hydrothermal-type geothermal fields situated far away from urban areas. To solve the problem, a new multi-energy source coupling a low-temperature sustainable central heating system with a multifunctional relay energy station is put forward. As for the proposed central heating system, a compression heat pump integrated with a heat exchanger in the heating substation and a gas-fired water/lithium bromide single-effect absorption heat pump in the multifunctional relay energy station are used to lower the return temperature of the primary network step by step. The proposed central heating system is analyzed using thermodynamics and economics, and matching relationships between the design temperature of the return water and the main line length of the primary network are discussed. The studied results indicate that, as for the proposed central heating system, the cost-effective main line length of the primary network can approach 33.8 km, and the optimal design return temperature of the primary network is 23 ℃. Besides, the annual coefficient of performance and annual energy efficiency of the proposed central heating system are about 3.01 and 42.7%, respectively.

UAVs, also known as unmanned aerial vehicles, have emerged as an efficient and flexible system for offering a rapid and cost-effective solution. In recent years, large-scale mapping using UAV photogrammetry has gained significant popularity and has been widely adopted in academia as well as the private sector. This study aims to investigate the technical aspects of this field, provide insights into the procedural steps involved, and present a case study conducted in Cesme, Izmir. The findings derived from the case study are thoroughly discussed, and the potential applications of UAV photogrammetry in large-scale mapping are examined. The study area is divided into 12 blocks. The flight plans and the distribution of ground control point (GCP) locations were determined based on these blocks. As a result of the data processing procedure, average GCP positional errors ranging from 1 to 18 cm have been obtained for the blocks.

Under the background of green economic transformation, the sustainable utilization of ecological resources has become a trend, and bamboo all-for-one tourism has become a new development direction for bamboo-resource-rich areas. Based on the all-for-one tourism model and characteristics of bamboo resources, this paper puts forward a bamboo all-for-one tourism model, which shows the relationship between resources, products, and markets, and elaborates on the joint effect mechanism of industrial environment, governance environment, and external environment. Taking Yibin City, Sichuan Province as an example, this paper also analyzes existing problems of developing bamboo all-for-one tourism and then proposes suggestions to provide effective analytical ideas and reference, such as establishing a market-oriented all-product development model, introducing the sustainable development concept of bamboo management, establishing the management concept of sharing by all people, and driving all industries developing in a coordinated way.

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 women’s sector in the academe is one of the most affected profiles during the COVID-19 pandemic which directly ravages their livelihood and other economic activities. Thus, this research project investigated the economic situations of 30 private and public-school teachers who were displaced from their occupations or were forcibly deprived of income-generating activities. In-depth interviews as research instruments were employed in the study to extract responses on how the educators creatively apply adaptive economic strategies and how government should aid them during a global crisis. The research findings showed that the pandemic has affected the economic activities of the respondents including the loss of their livelihood and other economic sidelines. They responded to these economic effects through adaptive strategies using diversifying and analyzing trends, using digital technology resources, data-driven, acquiring new alternative skills, pricing strategy, and becoming an expert. Results dictated that government could support affected women by initiating training options, homepreneurship support, encouraging independent income-earners, financial management and tax breaks, and industry compatibility endorsement. This study is important to map out the specific economic effects of the pandemic and aid them with initiatives by providing them with concrete economic tools and programs.