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.

Facing the digital economy era, considerable attention is paid to the importance of understanding the fundamental impact on the information and development of blended teaching methods regarding the higher education. For this reason, the purpose of this study is to answer the challenges brought by the digital economy era, identify the effective teaching methods which would be used in English Correspondence course in the era of digital economy, aiming to form the patterns of learning, provide high motivation, strength and knowledge, and most importantly contribute to the complex competences of future working. For further research, it is expected to be able to prove that using the blended teaching methods will effectively improve students’ communication skills and learning efficiency, enhance students’ learning experience and critical thinking skills.

There are diverse effects in consequence of exposure to radiofrequency electromagnetic fields (RF-EMF). The interactions of fields and the exposed body tissues are related to the nature of exposure, tissue comportment, field strength and signal frequency. These interactions can crop different effects.

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%.