Correct drugs should be administered in ealing ith the disease appropriately. Since allopathic medicine accelerates relief, it is not meant to deal radically with the root causes of disease manifestation. Ayurvedic drugs take steps directed at the root cause by establishing long-term solutions to restore health holistically. Accurate identification and classification of these plants represent a key factor for improving Ayurvedic medicinal products in modern health care delivery. It deals with the concern of classification and identification of Ayurvedic medicinal plants using advanced deep learning techniques. In this case, we utilized the methodology of Convolutional Neural Networks (CNNs), which analyze image data about medicinal plants, thereby allowing accurate classification to take place. This paper also integrates several methods-FCM clustering and H-Gabor filters-for enhanced feature extraction and segmentation. By taking a comprehensive Medicinal Plant Data set, the approach facilitates identification and promotes the better usage of Ayurvedic resources. Some conclusions drawn from the conducted research on plant-based medicine, incorporating traditional knowledge, open prospects for future innovations for improvement.

Business Intelligence tools are crucial in transforming data into actionable insight to drive strategic decisions and operational efficiency. Power BI, one of the top business intelligence platforms, enables organizations through the integration of multiple data sources, the creation of dynamic visualizations, and real-time analytics. The following paper discusses the implementation of a Power BI project for Insight Inc. in support of project performance analysis for better decision-making. The project integrated interactive dashboards for data consolidation and visualization of key metrics, including project timelines, revenue distribution, delay reasons, and budget variances. The Project Timelines Dashboard depicts schedule discrepancies by comparing the plan versus actual timelines, thus enabling proactive resolution of delays. The Revenue by Application Dashboard underlined the profitability of different types of projects, which helped in prioritizing resources. The dashboards on reasons for delays and variance analysis provided actionable data on financial inefficiencies and operational bottlenecks. The KPI Monitoring Dashboard delivered real-time performance metrics that helped in fostering accountability and continuous improvement. Aggregation of these dashboards significantly enhanced the resource allocation capability of Insight Inc., project progress monitoring, and alignment of financial objectives with operational goals. It has brought to the fore the transformational job that Power BI does around complex business challenges and further fortifies this with future plans of enhancement, such as predictive analytics and IoT, to make it even more robust for data-driven decision-making.

Subgrade is the foundation layer which provide strength and stability to the structures above it. Subgrade soil often exhibits variability in strength, moisture susceptibility and compressibility, leading to potential failure like rutting and settlement. Various improvement techniques like chemical stabilization, mechanical stabilization and geosynthetics are used to enhance the strength and durability of the subgrade soil. 

This article summarizes the research on the use of  geosynthetics for the stabilization of subgrade soil. Geosynthetics act as a reinforcement and increases the load bearing capacity of the soil. It acts as an environment friendly, cost effective and sustainable solution for soil stabilization. An attempt is made herein to compare the effect of geosynthetic reinforced soil in terms of California Bearing Ratio (CBR) value, an key parameter in the design of pavement.

The mean (average) flow velocity is an important hydraulic parameter for river engineers. It is calculated by using the stream flow rate (discharge) and the sectional flow area. Several techniques are available to estimate the sectional average or mean flow velocity. Rectently introduced entropy theory technique is a reliable as well as a quick approach compared to the traditional techniques like the velocity-area method. The entropy theory technique can estimate the discharge by measuring the single point velocity measurement only. The estimated parameters are well compared with the observed ones. The obtained results were compared in terms of correlation coefficient and error percentage. The error percentage is well within the acceptable limits.

Reinforced concrete (RC) buildings resting on sloping ground are more vulnerable to damage under seismic excitation than buildings on flat ground due to the presence of short columns. Due to high stiffness, short columns attract more earthquake force than the long columns, resulting in more damage. The present study aims to review recommendations mentioned in the available literature to minimize the short column effect on RC buildings, investigate the performance of a building resting the sloping ground, and study the effect of spacing of column lateral ties in minimizing damage of short columns. For this purpose, a three-storey RC building is modeled considering flat ground and sloping ground using ETABS software. The behavior of both the models under seismic excitation is studied using nonlinear time history analysis. Acceleration time history of past earthquakes El Centro (1940) is applied to the RC frame and the response of both the structures are compared. The structural response under seismic loading is investigated in terms of number of plastic hinges formed, top-storey displacement, and inter-storey drift.

Western Rajasthan, characterized by its arid climate and limited water resources, faces significant challenges in water resource management. The region heavily relies on groundwater extraction, leading to sustainability concerns and declining water tables. This article examines the current status of water resources, highlighting the reliance on traditional practices and the critical issues of water scarcity, over-extraction, and inadequate infrastructure. Climate change further exacerbates these challenges, impacting rainfall patterns and exacerbating drought conditions. To address these issues, the article proposes various management strategies, including groundwater recharge initiatives, promotion of sustainable agricultural practices, community engagement, investment in infrastructure, and stronger regulatory frameworks. By implementing these strategies, Western Rajasthan can enhance water availability and promote sustainable usage, securing vital resources for future generations.

With the rapid industrialization and modernization of power sector and increase in demand requires electrical supply without failure. In this regard, it is essential to have adance protection schemes for complete power system. It incorporates frequent swiching losses and has high response time against the faults. In this work, an advance current differential relay for better safety of equipment is presented in which Arduino is programmed like that it takes approximate 200 samples in a complete current cycle through which reliability, accuracy and efficiency of this protection scheme is increased and also it is more flexible for various applications by providing repogramming feature of Arduino.

This paper investigates the improvement of oscillatory stability in large scale power systems by optimizing Power System Stabilizer (PSS) parameters using the Multi-Verse Optimization (MVO) Algorithm. The PSS parameters are designed to utilize a multi-task eigenvalues function for concurrently manages the damping ratio & the real part of eigenvalues, aiming to shift unstable and/or weakly mechanical modes to a designated D-shape area within the s-plane. This algorithm is utilized on the IEEE-68, which consists of 16 machines and 68 buses. The effectiveness of MVO is evaluated through dynamic simulations, eigenvalue analysis, and performance error indicators across several operating scenarios under extreme conditions of critical turbulence, and compared to results derived from the Genetic Algorithm (GA). The solutions illustratie that the virtue of damping and overall dynamic stability of the suggested MVO-PSS is better than GA-PSS in alleviating low-frequency oscillations

In this paper, we examine the optimal solution to the thermal generation scheduling problem using three versions of Dynamic Programming (DP), Conventional DP (CDP), Sequential Combination DP (SC-DP), and Truncated Combination DP (TC-DP) to solve the Unit Commitment (UC) problem. The Unit Commitment (UC), one of the essential problems in power system applications, consists of producing a minimum cost output schedule for a setting horizon while satisfying the units constraints on the system generation as a whole. The lambda-iteration technique is applied to solve the embedded economic dispatch sub-problem. The presented techniques are implemented for 5-unit and 10-unit thermal systems for 24-hour. In comparative simulation results, it is shown that the CDP can find accurate solutions for smaller systems, but it is computationally expensive for large systems. In order to strike a better trade-off between solution quality and computational efficiency, TC-DP inevitably prunes non-promising decision paths, making it more efficient than classical DP by reducing the state-space explosion. The Results indicate that TC-DP is an efficient and scalable optimization framework for modern complex power systems.

Desertification and land degradation is occuring at an alarming rate in dry lands of the globe. Over 2 billion hectares of land worldwide are degraded. It is estimated that 12 million hectares of land is lost to degradation affecting 40 per cent of population especially rural population, small farmers and poor people in underdeveloped countries. There are large number of reports that indicate extinction of a sizable number of plant and animal species. Global warming and climate change due to excessive release of green house gases and carbon emissions in the environment and uncontrolled human activities exacerbate desertification and land degradation. Steadily increasing population particularly in the developing countries and the pursuit of better living conditions led to an increasing use of energy which released large amount of green house gases in the atmosphere causing more frequent and intense droughts and prolonged heat waves. This global problem has been recognized by the United Nations as life threatening to millions of people and therefore listed as goal no. 15 under Sustainable Development Goals and it was decided to restore 150 million hectares of the deforested and degraded land of the planet by 2020 and 350 million hectares by 2030, and revive native plant life to the landscape. Forests, mangroves, coral reefs, peat lands and other ecosystems that support life also store a large amount of carbon, hence their protection is equally important for mitigating and reversing climate change, which is essential for stopping of further spread of desertification. In this paper efforts initiated at International and National level to mitigate further land degradation to save Planet Earth are reviewed.

In this manuscript, the VLSI implementation of the arithmetic unit of the floating point number algorithm is executed. Due to this operation, it has been optimized for adder, subtractor, and multiplier. The execution of this module can be utilized by the MAC unit. MAC Unit is further used in other applications like IIR & FIR filters. This arithmetic unit is based on a floating point standard format named as “IEEE 754”. Floating point numbers are used to represent the real numbers with point values. The linear convolution technique uses a series of addition and multiplication operations. Therefore, it is regarded as the essential building block of an FIR and IIR filters using a multiplier unit. Both multiplication and accumulation operations are carried out by the MAC unit. The Arithmetic Logic Unit (ALU), which executes arithmetic and logical operations, is the essential building block for arithmetic processors. This floating point arithmetic unit can also be used for the ALU of processor. The proposed hardware arithmetic unit is implemented on Nexys – 4 DDR (Artix – 7 FPGA family) trainer kit using VHDL language. Xilinx ISE 2014.4 EDA software is used for synthesis and simulation of the proposed design.

Recent technical developments, especially in the domains of personalised medicine, wearable technology, telemedicine, and artificial intelligence (AI), have thrown the healthcare business into a state of profound disruption. These technologies have the ability to improve the efficiency of spending management, the quality of treatment patients get, and the results they get. In this research, empirical data, case studies, and mathematical models are used in order to explore the impact that these technologies have had on society. Not only does the author give a comprehensive analysis of the effectiveness of various technologies, but they also discuss the challenges that they provide. Along with this, it delves into the ethical considerations and legal frameworks needed for these technologies to be used fairly and securely.

Plastics are widely used in various industries for their versatility, lightweight properties, and ease of manufacturing. Plastic injection molding is a key process in producing high-quality, complex plastic parts, allowing for mass production with precision and efficiency. However, challenges such as warpage and shrinkage defects during cooling can significantly affect the dimensional accuracy and performance of molded parts. Warpage occurs when uneven cooling causes internal stresses, leading to deformation, while shrinkage results from material contraction as it cools. These defects are influenced by factors like material properties, mold design, and process parameters. Various techniques, including optimization of molding parameters and innovative approaches like in-mold decoration and microcellular injection molding, have been developed to minimize these issues. This paper explores advancements in warpage control with emphasizing their impact on product quality and manufacturing efficiency. Case study shows the modelling of warpage defect with nonlinear regression analysis developed by full factorial design (FFD).

This study explores wind energy as a key power source in the renewable energy sector, focusing on the role of synthetic fiberglass and carbon fiber-reinforced polymer composites in manufacturing wind turbine blades. Adjusting the fiber layer arrangement has shown potential for enhancing the mechanical properties of these polymer composites. This article examines the impact of different fiber layer configurations on the mechanical characteristics of polymer composites. Two types of synthetic fibers and epoxy as a base material are used in the layer modifications. Composite structures with fiber layer configurations of C/C/C/C layer, G/G/G/G layer, C/G/C/G layer, C/G/G/C layer, and C/G/G/C layer are synthesized using the VARTM process. After successful fabrication, both physical (theoretical and experimental density, void content) and mechanical properties (tensile, flexural, hardness, and impact tests) are thoroughly characterized.

The results for void content reveal that it is lowest in panels with the same fiber type and higher in different-fiber panels. Across all measured properties, the C/C/C/C layer panel demonstrates superior mechanical performance compared to other configurations. Additionally, fiber layer alternation in C/G/C/G layer, C/G/G/C layer, and C/G/G/C layer panels improves mechanical properties compared to the G/G/G/G layer panel. These findings clearly indicate that altering fiber layers has a notable impact on the composite properties.

Pressure vessels are an essential component in industrial applications, where cost-effective design is critical. This paper presents the minimization of total cost of the pressure vessel with appropriate constraints using TLBO algorithm. The dimensions of the pressure vessel are considered as design variables. The optimized results achieved using the TLBO algorithm are evaluated against those derived from other optimization methods. The analysis concludes that TLBO demonstrates greater computational efficiency and ease of implementation.

India has experienced a lot of natural calamities since last decade. Accuacy of forecasting and selection of forecasting technique becomes relevant for predicting the occurance of calamity and inventory of  resuce equipments and other arragements to be needed post calamity. The calamities experienced in form of natural disasters such as earthquakes, cyclones, landslides, floods, draughts, wildfires, tsunamis and pandemics such as covid-19.The paper discusses on cyclones, their categorisation based on severity, and higlights the significance of choosing the most appropriate forecasting technique to predict the cyclone based on its severity.