Shri Govindram Seksaria Institute of Technology and Science

Polymer gears are increasingly used as an alternative to metal gears in power transmission due to their advantages such as lightweight, low noise and vibration. However, crack induced failures remain a critical concern. Vibration signal analysis, governed by time varying mesh stiffness (TVMS), is an effective tool for fault detection. Hertzian contact stiffness (HCS) plays a critical role in TVMS estimation, and several models exist to estimate it. This study proposes a modified Hertzian contact stiffness (MHCS) model and a TVMS estimation model that accounts for pitch point cracks. The proposed MHCS formulation is incorporated into a TVMS model to estimate the mesh stiffness of cracked polymer gear. Finite Element Method (FEM)simulations are performed to validate the developed model. Further, dynamic modelling of a polymer gear pair has been done by employing proposed TVMS for a cracked gear and the vibration response is experimentally validated. The proposed MHCS model shows improved accuracy in predicting contact stiffness. The results demonstrate that pitch point cracks cause significant fluctuations in TVMS, which are effectively captured by the developed model. The dynamic response analysis reveals distinct vibration signatures corresponding to crack presence, and experimental validation confirms good agreement with the predicted results. The proposed model effectively predicts the mesh stiffness and vibration characteristics of a cracked gear system, providing valuable insights through vibration analysis for detecting tooth cracks.

To avoid project overruns, it is critical in the construction business to inspect and supervise progress at each step of development. In an efficient circumstance, accomplishing a building project in the minimum amount of time and money is critical. The current research focuses on Genetic Algorithm (GA) for scheduling and optimization solvers in the MATLAB platform for bridge construction. The bridge construction activities are planned and scheduled using Primavera software. In general, bridge construction projects are iterative, large, and complex, and thus necessitate a thorough examination of performance data under extreme conditions. The GA optimization solvers’ performance is directly related to the GA parameters that have been determined based on the project’s aim and actual duration project for a viable solution. Shortening the duration of any construction project typically involves hiring additional labour, using more construction equipment, and adopting specialized building procedures, all of which increase costs and time accountability for the client. Finally, it is concluded that Genetic Algorithms (GA) provide fitness functions for time cost optimization of bridge construction projects by computing objective functions and constraints. It is also concluded that Time Cost Optimization was successful in reducing the total costs of the bridge project.

The paper provides a rather comprehensive overview of movie recommendation systems and demonstrates the importance of these systems in enhancing content delivery in digital entertainment. The models they consider are collaborative filtering, content-based filtering and hybrid architectures. Collaborative filtering is terrific at identifying latent patterns of user behavior, but it falters when the data is sparse, and when there is the cold-start problem. Content-based filtering provides highly personalized suggestions by exploring movie characters, but tends to reduce diversity. As a compromise between personalization, novelty, and robustness, hybrid models attempt to find a happy medium between the two. The paper also explores the key issues surrounding construction of efficient recommendation systems, such as the absence of user interactions, data biases, scaling headaches and instant privacy concerns. They systematically analyze the contemporary streaming services and present the primary performance metrics that measure the effectiveness of a system. The experiments support that optimally designed recommendation systems are significant drivers of enhancing the user experience, engagement, and retention. Finally, the paper recommends the continuous adjustment of the algorithm to enhance flexibility, accuracy, and reliability in dynamic and real-life environments.

In this article, we introduce the design, simulation, and layout implementation of a two-stage CMOS operational amplifier employed as a comparator, using SCL 180 nm technology, targeting low-frequency biomedical applications. The design approach emphasizes transistor-level width and length optimization, aiming for an optimal balance between gain, stability, and power efficiency, keeping simplicity in design structure. The proposed design offers an impressive gain of 100 dB, ensuring proper detection of low-amplitude differential signals, and boasts an exceptional phase margin of 179°, verifying extremely stable operation, irrespective of oscillation and noise effects. Transient analysis verifies proper operation of the proposed comparator design, while a mere measured peak transient power of 1.955 µW verifies its compatibility for implementation in power-restricted biomedical applications. The proposed design has been implemented at the layout level employing standard CMOS layout design principles, verifying DRC and LVS checks, ensuring proper correlation of the layout layout and technology consistency. The results obtained verify the efficacy of our proposed approach, and it proves that our proposed design qualifies as an apt, stable, and energy-efficient candidate for biomedical analog front-end circuits and process for evaluation.

Data Envelopment Analysis (DEA) is a non-parametric method used to evaluate the efficiency of decision-making units (DMUs) such as district hospitals. Ensuring the effective allocation of sustainable healthcare resources is crucial for hospitals to provide high-quality care to patients, so it is essential to closely monitor efficiency. This research focuses on assessing the efficiency of 51 district hospitals in Madhya Pradesh, India with reference to the Maternal Mortality Rate (MMR) and Infant Mortality Rate (IMR). Using DEA, input and output data from various hospitals is analyzed to determine their relative efficiency. Results show that while most district hospitals' efficiency scores fluctuated, a few had an increased trend during the period. The study identifies best practices and areas for improvement, providing valuable insights for healthcare management and policy-making.