Remote sensing technologies have revolutionized forestry analysis by providing valuable information about forest ecosystems on a large scale. This review article explores the latest advancements in remote sensing tools that leverage optical, thermal, RADAR, and LiDAR data, along with state-of-the-art methods of data processing and analysis. We investigate how these tools, combined with artificial intelligence (AI) techniques and cloud-computing facilities, enhance the analytical outreach and offer new insights in the fields of remote sensing and forestry disciplines. The article aims to provide a comprehensive overview of these advancements, discuss their potential applications, and highlight the challenges and future directions. Through this examination, we demonstrate the immense potential of integrating remote sensing and AI to revolutionize forest management and conservation practices.

One-dimensional unsteady theoretical models of three different photovoltaic module installation modes are established. Through MATLAB modeling and simulation, the influence of photovoltaic modules on roof heat transfer in different layout modes is compared. Comparing with ordinary roof, the shading effect of photovoltaic roof in summer and heat preservation effect in winter was analyzed. The results show that the PV roof layout with ventilation channel is better in summer. The proof layout with closed flow channel is better in winter.

An investigation is conducted into how radiation affects the non-Newtonian second-grade fluid in double-diffusive convection over a stretching sheet. When fluid is flowing through a porous material, the Lorentz force and viscous dissipation are also taken into account. The flow equations are coupled partial differential equations that can be solved by MATLAB’s built-in bvp4c algorithm after being transformed into ODEs using appropriate similarity transformations. Utilizing graphs and tables, the impact of a flow parameter on a fluid is displayed. On velocity, temperature, and concentration profiles, the effects of the magnetic field, Eckert number, and Schmidt number have been visually represented. Calculate their inaccuracy by comparing the Nusselt number and Sherwood number values to those from earlier investigations.

An α, α^′-dipyridyl adduct of a complex compound hexaaquatribenzene-1,2,4,5-tetracarbonatotetra iron (III) with porous structure was synthesized for the first time. According to the results of elemental, X-ray, IR-spectroscopic and differential-thermal analyses the individuality, chemical formula, thermal destruction, and form of coordination of acidic anion and dipyridyl were established. During interaction of a complex compound with dipyridyl, it completely loses all crystallization molecule of water resulting in a compound with a chemical formula of Fe₄(C₆H₂(COO)₄)₃(dpy)₂(dipyridyl). Using the identification of diffraction pattern the parameters of lattice cell of the complex compound were determined.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

In order to understand the finishing effect of Waterborne Acrylic Paint under different painting methods and amount, bamboo-laminated lumber for furniture was coated with waterborne acrylic paint, then the effects of different painting methods and amount on the drying rate, smoothness, hardness, adhesion and wear resistance of the paint film were investigated. Further, the mechanism of film formation was described by thermal property analysis using thermogravimetry and differential scanning calorimeter. The results show that different painting methods have little effect on film properties, the drying time of primer and topcoat are not affected by them, which is 8/8.5 min for primer surface/solid and 6.5/7 min for topcoats. The film surface hardness and adhesion can reach B and 0 grade, the best wear resistance of the film is 51.24 mg·100 r⁻¹ when using one-layer primer one-layer topcoat. Different coating amount has great influence on film properties, the drying speed of the film increases with the increase of the painting amount. The film properties reach the best when the painting amount is 80 g/m², while too little painting amount leads to the decrease of hardness, and too much leads to the wear resistance weaken. Thermal analysis of the primer and topcoat show that water decomposition occurs at 100 ℃ and thermal decomposition of organic components occur at 350 ℃. Topcoats have better thermal stability than primers higher than that of topcoat, the topcoat displayed better thermal stability than the primer.