Ujjain Engineering College is an engineering college in Ujjain in the state of Madhya Pradesh, India. The college was established by the government of Madhya Pradesh in 1966.
Structures that are built on soft soil are more likely to be affected by earthquakes. Standard seismic assessment techniques do not take into account the interaction between the soil and the structure; as a result, the estimations of resilience are inaccurate. The Soil-Structure Interaction (SSI) analysis and Light Gradient Boosting Machine with pre-trained transformer embeddings are the two methods that are recommended for the prediction of significant seismic events in this paper. The numerical simulation framework and soil parameters associated with OpenSeesPy are included in this collection. Seismic Immediate Occupancy, Life Safety, and Collapse Prevention performance was categorized with the use of machine learning. There are a number of factors that influence earthquake performance, including soil stiffness, concrete modulus of elasticity, beam and column cross-sectional areas. The use of advanced machine learning in conjunction with SSI helps to enhance seismic evaluations, hence increasing earthquake safety and resilience.
The integration of renewable-based distributed units into distributed systems has been aided by recently developed technologies based on renewable energy, changes to utility infrastructure, and progressive government regulations. In this paper, an improved version of the golden jackal optimization (IGJO) is implemented to incorporate distributed generators (DGs) and capacitor banks (CBs) into the distribution system. The existing studies give only DG unit insertion, but in this work, simultaneous integration of different kinds of DG with a capacitor bank is used to analyze the impact. The main emphasis of this study is to minimize power loss along with the upgradation of the voltage profile. Improvement in voltage stability index and minimization of total voltage deviation (TVD) were also achieved by placing the DG and CB units in a suitable position. Load modeling is also considered here to validate the results. Seven types of loading, including constant power (half load and heavy load), constant current, constant impedance, residential, industrial, and commercial loads, are used to show the effect of integration of DG and capacitor bank into a 33-bus and 118-bus radial distribution system. Comparison of the proposed method with previous studies shows the better performance of the implemented method over other techniques.
Conventional transverse reinforcement in reinforced cement concrete (RCC) columns, typically in the form of steel ties, often provides limited confinement efficiency, thereby restricting the achievable ductility and load-carrying capacity of structural members. In recent years, various confinement techniques and materials, such as welded wire fabric (WWF) and fiber-reinforced polymers (FRP), have been investigated to enhance the mechanical performance of concrete columns. Among these, welded wire mesh (WWM) presents a promising alternative due to its uniform distribution, ease of application, and effective confinement characteristics. This study experimentally investigates the influence of WWM confinement on the strength and axial deformation behavior of circular RCC columns subjected to axial compression. The primary objective is to evaluate the enhancement in load-bearing capacity and ductility resulting from improved confinement. A series of column specimens were cast and divided into two groups: (i) conventionally reinforced columns with standard steel ties, and (ii) columns additionally confined using welded wire mesh. All specimens were tested under axial loading, and their performance was assessed in terms of ultimate load capacity, axial displacement, and failure modes. The results demonstrate that the inclusion of WWM significantly improves confinement effectiveness, leading to increased strength and enhanced ductility compared to conventional reinforcement. The study highlights the potential of WWM as a practical and efficient technique for improving the structural performance of circular RCC columns.
Smart farming devices give out sensor data, which enhance outputs. Predictions of crop and sustainable agriculture are important. Linear Regression popularity is utilized in the Data sequencing use. Comparison article between smart farming sensor information and LSTM networks. The temperature, humidity and soil moisture of crops are extracted using Ag sensors. The nonlinearity of LSTM agriculture is indicated by time series sensors. Linear regression is more convenient to apply in I/O balancing. It determined accuracy, precision, recall, F 1 and $\mathrm{R}^{2}$. Linear regression Seasonality predictions and the bad interaction of nonlinearity predictions lower the output. Linear regression is effective and efficient in situations where there is shortage of resources, as it is short in training and it can be read. The comparison made between deep learning and statistics show the merits and demerits of smart agriculture. In complex data prediction, LSTM is better than the low-cost linear regression that is fast. This paper is a comparative analysis of LSTM and Linear regression in their accuracy in prediction, the interpretability as well as their computational efficiency of edge-cloud smart farming system as opposed to an algorithm. The results can be applied in the choice of resource-constrained and high-performance agroclimatic model. The feature engineering and modelling would be able to improve the estimates on agricultural production.
