Landslides pose significant threats to life, property and sustainable development in mountainous regions worldwide, with their occurrence increasingly influenced by climate change. This study addresses the critical need for accurate landslide susceptibility models in the Western Province of Rwanda, where traditional methods have shown limitations. It employed and compared three deep learning models: convolutional neural network (CNN), deep neural network (DNN), and multi-layer perceptron (MLP), to assess the landslide risks, incorporating climate change considerations. The study utilised 16 conditioning factors, carefully selected to avoid multicollinearity, with the digital surface model (DSM) showing the highest variance inflation factor (VIF) of 3.9730. The CNN model demonstrated superior performance, achieving the highest overall accuracy (93.7
Due to the rapid population growth, industrialization, and climate change, freshwater scarcity is turning out to be one of the most acute global problems. Traditional desalination methods such as reverse osmosis (RO) and multi-stage flash distillation are efficient; nevertheless, traditional desalination methods are energy consuming and unaffordable in remote and low-income areas. The solar distillation stills offer an alternative that is sustainable and environmentally friendly since they utilize the large amount of solar energy to make potable water by using natural evaporation and condensing mechanisms. The present review paper provides a critical analysis of the latest advances in the solar distillation technologies with a particular focus on the design innovations, the approaches toward the performance improvement, and the way to integrate the technology with other renewable energy sources. Many design configurations such as active, passive, stepped, tubular, and multi effect systems are examined to determine their characteristics of operation. The use of advanced materials, including nanofluids, phase-change materials, and selective coatings, to enhance thermal performance and yield water is also explained. The review also assesses economic viability, environmental advantages and practical implementation in rural arid and disaster-prone areas. The solar distillation with passive techniques improves efficiency of 10–30
This work investigates the subject of wormholes with spherical symmetry in a recently proposed curvature-based gravitational framework, namely the & Fouriertrf;(R,Lm,T) theory. This framework has gained much popularity due to its promising feature of direct/indirect matter and curvature interaction. As a first case, we assume the background matter as anisotropic fluid along with a particular wormhole shape model and perform the graphical analysis of the respective energy constraints. This graphical examination is conducted by considering two choices of redshift function: constant and variable radial-dependent forms. Possible constraints on the model parameters are then listed, which refer to the validity of energy bounds. Further, by taking isotropic fluid and a specific EoS parameter as separate cases, the form of the wormhole shape function is computed for both constant and variable redshifts. To comprehend the proposed wormhole geometries, basic axioms regarding wormhole shape models are verified for each case. Further, some crucial measures like complexity factor, total and active gravitational energies, and volume integral quantifier are examined graphically and the 2D as well as 3D embedding visualizations are also provided. Lastly, to reveal the stability of these solutions, we examine the behavior of TOV forces, adiabatic index and speed of sound parameters graphically. It is found that wormhole solution corresponding to isotropic fluid does not satisfy the basic wormhole criteria while the solutions, in other two cases, exhibit valid and physically stable behavior.
Various feed ingredients including omega oils and oil seeds, have been used worldwide to improve intake among the birds. In this research paper, feed ingredients were analyzed for fat, fiber, protein, tannins and cyanogenic glycosides (HCN) contents along the fatty acid characterization. Different supplemented enriched experimental diets were also developed to estimate the Crude fat, protein, fatty acids content and gross energy estimation. These experimental iso caloric and isonitrogenous diets were evaluated through mathematical model for characterization. TOPSIS technique is applied in feed ingredients, protein, tannins and cyanogenic glycosides. PIS and NIS solution proposed in different protein. To define the collective index of the ranking
This study attempts to investigate the value relevance of intangible assets reported in the financial statements of pharmaceutical companies listed on the BSE between 2011 and 2022. We investigate using panel data how intangible asset reporting, with the adoption of Indian Accounting Standards/International Financial Reporting Standards (IFRS) by Indian pharmaceutical firms, affects the market value of their shares. The unbalanced panel data set of 1,009 firm-year observations are used for analysis. Closing prices are taken as a proxy for firm value, gross intangible assets are the primary independent variable, while Earnings per share, net worth, cash flow, book value, firm size and market-to-book value, firm and year dummies are used as control variables. The fixed-effect model and difference-in-difference estimation are used for empirically investigating the linkage. Additionally, endogeneity issues are addressed, and robustness checks are done using various econometric tests. Based on empirical investigation, we discover that although value relevance of intangible assets has decreased in the post-IFRS period relative to the pre-IFRS, it still holds significance in forecasting the market value of the shares. The results will help policymakers evaluate the benefits and drawbacks of establishing accounting standards by adding to the body of knowledge already available on the value relevance of gross intangible accounting information.
