Cd-Ni-Ti alloy plating is a widely used electroplating technique for enhancing the corrosion resistance and mechanical properties of metal substrates. However, the plating process parameters and electrolytic conditions play a crucial role in determining the quality and performance of the plated alloy. In this study, we investigated the effect of different electrolytic conditions on the Cd-Ni-Ti alloy plating. Various electrolyte compositions, bath temperatures, current densities, and plating times were examined to assess their impact on the alloy's composition, microstructure, and surface characteristics. The results showed that variations in electrolytic conditions significantly influenced the deposition rate, composition, and microstructural features of the Cd-Ni-Ti alloy. Optimized electrolytic conditions were identified that resulted in improved alloy quality and enhanced corrosion resistance. These findings provide valuable insights for controlling and optimizing the Cd-Ni-Ti alloy plating process, thereby facilitating the development of high-performance coated materials.

This article presents the design and implementation of an Arduino-based laboratory Phasor Measurement Unit (PMU) for power system analysis. The PMU is a crucial device used in power systems to measure and monitor phasors, frequencies, and voltages at various locations. The proposed laboratory PMU offers a cost-effective and accessible solution for educational and research purposes. The article describes the design methodology, hardware components, and software implementation of the Arduino-based PMU. Experimental results demonstrate the accuracy and reliability of the developed PMU, making it a valuable tool for power system analysis and education.

Cost overrun risk is a persistent challenge in construction projects, leading to significant financial implications and project delays. Accurate forecasting of cost overrun probability is crucial for effective project planning, risk management, and decision-making. This study proposes a fuzzy model for forecasting the probability of cost overrun risk in Indian construction projects. The fuzzy model incorporates multiple input variables, including project characteristics, economic factors, and external influences, to assess the uncertainty and imprecision associated with cost overrun risk. Historical project data is utilized to develop the fuzzy model, and a comprehensive validation process is conducted to assess its accuracy and reliability. The findings demonstrate the effectiveness of the proposed fuzzy model in providing probabilistic forecasts of cost overrun risk, enabling project stakeholders to proactively identify and mitigate potential risks.

The increasing complexity and heterogeneity of web pages pose significant challenges for template detection and extraction. In this study, we propose an efficient methodology for accurately identifying and extracting templates from heterogeneous web pages. Our approach combines machine learning techniques, feature extraction algorithms, and clustering methods to achieve robust template detection. We evaluate the performance of our methodology on a diverse dataset of web pages and demonstrate its effectiveness in accurately capturing templates. The proposed methodology has the potential to streamline web content extraction and facilitate various web mining tasks.