The results revealed that land use and forest cover changes were influenced by various factors, including ethnicity, educational attainment, household size, distance, and forest management. Forest management emerged as a key factor in protecting forests and minimizing deforestation and degradation. Additionally, distance and ethnicity significantly impacted land use and forest cover change in Phongsaly Province. These factors should be prioritized and strengthened within local communities, particularly in areas experiencing high rates of deforestation and forest degradation. Changes in land use and forest cover have had a significant impact on carbon storage and greenhouse gas (GHG) emissions in Phongsaly Province, reducing the ecosystem’s capacity for carbon sequestration and diminishing its ability to absorb carbon dioxide. The primary driver of GHG emissions was the conversion of forested areas into agricultural land, particularly for upland crops. Conversely, a positive trend has been observed with the restoration of agricultural land back into forested areas, contributing to an increase in carbon sinks.

One of the most important ways to achieve the goals stipulated by the Paris (2015) Agree-ment on climate change is to solve a two-fold task: 1) the adsorption of CO2 by the forest communities fcom the atmosphere during global warming and 2) their adaptation to these climate changes, which should ensure the effectiveness of adsorption itself. Report presents the regional experience of the numerical solution of this task. Calculations of the carbon balance of forests in the Oka-Volga River basin were carried out for global forecasts of moderate and extreme warming. The proposed index of labile elastic-plastic stability of forest ecosystems, which characterizes their succession-restorative po-tential, was used as an indicator of adaptation. A numerical experiment was conducted to assess the effect of the elastic-plastic stability of forest formations and the predicted climatic conditions on the carbon balance. In the upcoming 100-year forecast period, the overall stability of forest formations should increase, and to the greatest extent with extreme warming. Accordingly, one should expect a significant increase in the ability of boreal forests to ab-sorb greenhouse gases. It is determined unambiguous picture of a significant increase in the adsorption capacity of boreal forests with a rise in their regenerative potential.

Heat transfer fluids (HTFs) are critical in numerous industrial processes, enabling efficient heat exchange and precise temperature control. HTF degradation, primarily from thermal cracking and oxidation, negatively impacts system performance, reducing fluid lifespan and increasing operational costs, thus necessitating regular monitoring and proactive management. This review assesses optimal sampling frequencies for organic and synthetic HTFs, considering degradation mechanisms, relevant analytical parameters, and the economic advantages of proactive monitoring. The objective of this review is to examine HTF degradation mechanisms, compare organic and synthetic fluid properties and their impact on sampling frequency, and discuss strategies for optimising system performance and extending fluid life through effective HTF condition management. The article highlights the importance of fluid management, including appropriate fluid selection, to optimise system and fluid health, which is crucial for maximising their lifespans, ensuring safe operation, and minimising costs.

Disaster Risk Management benefits from innovative techniques including AI and Multi Sensor Fusion. The Firefguard Approach uses such technologies to improve the Wildfire Management works in Saxony, Eastern Germany by supporting standing efforts in Early Warning, Disaster Response and Monitoring. Unmanned Aerial Systems (UAS) play a vital role in providing real-time information via a 5G network to a central information management system that delivers geospatial information to response teams. This study highlights the potential of combining UAS, AI, geospatial solutions and existing data for real-time wildfire monitoring and risk assessment systems.

Land use as for human-circumstance interaction is as we all know changed the global land surface sharply and continuously. Farmland abandonment is the phenomenon of going extreme of marginal of land use, which exert positive and negative impacts on our living circumstances. In order to map the extent of farmland abandonment of Zhejiang Province, we try to use the geo-big data analysis platform to perform the massive data preprocessing and map the extent of farmland abandonment of the study area based on multi-source land use and land cover data. Then we execute landscape pattern analysis using landscape pattern analysis software and spatial auto-correlation (Moran’s I) analysis based on ArcGIS and Fragstats software. We found that the area of farmland is about 16.32% on account of all land use types, which is 1.89104 km2. While the whole area of FA is 1.72 × 108m2, and the farmland abandonment ratio is 1.65%. AF’s area is about 1.95 × 109m2, and the continuous cultivation ratio is 18.69%. The landscape fragmentation, landscape aggregation and landscape diversity of FA, AF and FL are different. At the same time, the spatial auto-correlation of FA and AF are dominant high congregation and low discrete. At last, we compared our calculated results with the existed research results which demonstrate our research does scientific convincible. We also make futural prospects prediction and show the research deficiency as well as bring out some policy implications based on our research, which means build proper land use management regulation and decrease the farmland abandonment on account of the premise of suitable land use policies.

This research underscores the importance of enhancing the early detection of diabetic retinopathy and glaucoma, two prominent culprits behind vision loss. Typically, retinal diseases lurk without symptoms until they inflict severe vision impairment, underscoring the critical need for early identification. The research is centered on the potential of leveraging fundus images, which offer invaluable insights by analyzing various attributes of retinal blood vessels, such as their length, width, tortuosity, and branching patterns. The conventional practice of manually segmenting retinal vessels by medical professionals is both intricate and time-consuming, demanding specialized expertise. This approach, reliant on pathologists, grapples with limitations related to scalability and accessibility. To surmount these challenges, the research introduces an automated solution employing computer vision. It conducts an evaluation of diverse retinal vessel segmentation and classification methods, including machine learning, filtering-based, and model-based techniques. Robust performance assessments, involving metrics like the true positive rate, true negative rate, and accuracy, facilitate a comprehensive comparison of these methodologies. The ultimate goal of this research is to create more efficient and accessible diagnostic tools, consequently enhancing the early detection of eye diseases through automated retinal vessel segmentation and classification. This endeavor combines the capabilities of computer vision and deep learning to pioneer new benchmarks in the realm of biomedical imaging, thereby addressing the pressing issues surrounding eye disease diagnosis.