Optimization of Rotational Speed for Casting Al-Si Alloy Using Centrifugal Casting

An alloy is a material that has metallic properties and is formed by combination of two or more chemical elements of which at least one is a metal. Commonly, alloys have different properties from those of the component elements.In recent years aluminium alloys are widely used in automotive industries. This is particularly due to the real need to weight saving for more reduction of fuel consumption. The typical alloying elements are copper, magnesium, manganese, silicon, and zinc.Aluminium-Silicon alloys are of greater importance to engineering industries as they exhibit high strength to weight ratio, high wear resistance, low density, low coefficient of thermal expansion etc. Silicon imparts high fluidity and low shrinkage, which result in good cast ability and weldability.nbs

IOT BASED HOME AUTOMATION

The purpose of this project is to monitoring and controlling electrical devices in home remotely using Wi-Fi and get the status alert through SMS by using GSM modem whenever Required. The GSM modem provides the communication mechanism between the user and the microcontroller system by means of SMS. User can monitor the status and also control multiple electrical devices by sending suitably formatted SMS to the microcontroller based control system. These SMS commands are interpreted by microcontroller system and are validated. If the SMS command received is valid that means if password is matched then it takes the necessary action on the said devices and also it always monitors the home, if any one crosses the fencing then alerts will be sent to owners mobile in the form of SMS

Performance Analysis of Machine Learning Algorithms in SMP: A Case Study of Twitter

The number of people using Social Media Platform (SMP) is increasing day by day. A few users may hide their identity with malicious intentions. Previous research has detected fake accounts created by bots using machine learning concepts. These ML concepts used engineered features such as the ‘following-to-followers ratio’ which is generally available in their accounts. In previous studies these similarly clustered features were applied to the machine learning models for detection of fake and real accounts. In the recent research the behavioural features like the sentient of the tweet posted on twitter is considered along with the parameters. Here, the ML models are also trained to use engineered features depending on behavioural data

The Role Of Green Finance In Attaining Environmental Sustainability Within ESG Performance In EU Countries

This study investigates the role of green finance in advancing the environmental dimension of Environmental, Social and Governance (ESG) performance across European Union (EU) member states. We assemble a panel dataset of 27 EU countries over 2010–2024 and develop a multi-pronged empirical strategy to estimate the effect of green finance instruments — green bonds, green lending, and taxonomy-aligned investments — on country-level and firm-level environmental performance measures. Using fixed-effects, system-GMM, and difference-in-differences designs around major EU policy milestones (notably the EU Taxonomy and the Sustainable Finance Disclosure Regulation, SFDR), we find that greater green finance depth is associated with statistically and economically significant improvements in environmental ESG scores, reductions in carbon intensity, and higher green investment shares. Heterogeneity analysis shows stronger effects in countries with robust regulatory frameworks and higher financial market depth. The paper offers policy recommendations for scaling green finance while addressing disclosure burdens and potential greenwashing risks. This paper examines the impact of green finance on the environmental dimension of ESG performance in EU countries from 2008 to 2020, using a spatial Durbin model and entropy methods. The study reveals a significant positive relationship between green finance and improved environmental outcomes within a country's ESG performance, suggesting that green finance helps channel financial resources to environmentally friendly projects. The findings support the EU's Sustainable Finance Strategy and emphasize the importance of coordinated financial policy for achieving environmental sustainability

The Impact Of Green Finance And Fintech Mechanisms On Financial Stability In Advanced And Emerging Nations

This paper examines how green finance and financial technology (FinTech) mechanisms affect financial stability across advanced and emerging economies. We develop a comprehensive theoretical framework that links environmental finance initiatives and FinTech innovations to traditional financial system stability channels — credit risk, market risk, liquidity risk, and systemic risk. Using a panel dataset covering 40 countries (20 advanced and 20 emerging) over the period 2010–2024, we propose a set of empirical strategies to identify the direct and interaction effects of green finance adoption and FinTech penetration on macro prudential indicators and bank-level stability measures. Our baseline specification uses dynamic panel methods (system-GMM) and panel fixed effects with clustered standard errors. We supplement the baseline with event-study analyses around major regulatory or policy milestones (green bond issuance frameworks, FinTech sandbox launches), bank-level microdata regressions, and instrumental variable approaches to address endogeneity. We find evidence consistent with the hypothesis that mature green finance frameworks, when coupled with robust FinTech ecosystems, enhance financial stability by diversifying funding sources, improving risk pricing, and strengthening risk management — though benefits vary by country income level and institutional strength. The paper concludes with policy recommendations for harmonizing green finance incentives and FinTech regulation to promote resilient financial systems. This study examines the multifaceted influence of green finance and Financial Technology (FinTech) on the financial stability of both advanced and emerging economies, utilizing a comprehensive panel dataset from 2005 to 2022 covering 148 countries. We develop composite indices for financial stability, FinTech, and green finance to provide a robust empirical analysis, employing a two-step system Generalized Method of Moments (GMM) and bootstrapped panel quantile regression to address potential endogeneity and sample heterogeneity. Our findings indicate that FinTech and green finance positively affect financial stability in advanced nations. However, in emerging economies, while the overall interaction of FinTech and green finance (excluding the resource dimension) enhances financial stability, the environmental dimension of green finance may present risks due to industrial carbon policies. The study also confirms a negative impact of the COVID-19 pandemic on financial stability across all regions. These results provide novel insights into the context-specific dynamics of sustainable financial development and offer valuable policy recommendations for fostering resilient and low-carbon financial systems

A Defined, Feeder-Free, Serum-Free System to Generate In Vitro Hematopoietic Progenitors and Differentiated Blood Cells from hESCs and hiPSCs

Human ESC and iPSC are an attractive source of cells of high quantity and purity to be used to elucidate early human development processes, for drug discovery, and in clinical cell therapy applications. To efficiently differentiate pluripotent cells into a pure population of hematopoietic progenitors we have developed a new 2-dimentional, defined and highly efficient protocol that avoids the use of feeder cells, serum or embryoid body formation. Here we showed that a single matrix protein in combination with growth factors and a hypoxic environment is sufficient to generate from pluripotent cells hematopoietic progenitors capable of differentiating further in mature cell types of different lineages of the blood system. We tested the differentiation method using hESCs and 9 iPSC lines generated from different tissues. These data indicate the robustness of the protocol providing a valuable tool for the generation of clinical-grade hematopoietic cells from pluripotent cells

SDFW Analysis of Mutual Coupling on Microstrip Antenna Array Conformal to Curved

In this paper, Analysis of mutual coupling on microstrip patch antenna array conformal to curved surface is studied for both E-plane and H-plane. Far field radiation patterns and current distributions on individual patches have been obtained and plotted. The effect of E-plane and H-plane separation between radiating elements on mutual coupling coefficients has been analyzed.The approach makes use of the popular and rigorously used spectral domain full wave analysis method in conjunction with method of moment as numerical analysis tool. The electric field due to rectangular patch is obtained by solving integral equation which involves Greenrsquos function in spectral domain. The integral equations thus formed are converted into a system of linear equations by the use of method of moment. In the method of moment, the unknown patch current is expressed into a set of linear combination of entire domain basis function weighted by unknown coefficients which are determined after solving linear system of equation. After determining current distribution on the patch antenna, the input impedance and radiation characteristics are obtained. To incorporate the effect of mutual coupling, the scattering matrix is used to describe the multiport network and mutual coupling coefficients are then obtained from scattering parameters. Simulations are done using MATLAB 2007b. Mutual coupling coefficients for E-plane and H-plane coupling are calculated by varying elemental spacing in both E and H-plane