Kumaraguru College of Technology

This article introduces a compact four-port MIMO antenna specifically developed for use in 5G midband and ultra-wideband communication systems. The proposed antenna features a compact structure with four symmetrical monopole radiating elements arranged in a closely spaced configuration (less than λmax/2), enabling efficient space utilization without using any decoupling structures in between the radiating elements. Machine learning (ML) algorithms namely random forest, decision tree and K-nearest neighbour (KNN) were employed to predict S-parameters from antenna design features, with KNN achieving the best accuracy and lowest mean squared error. Unlike traditional HFSS-based parametric sweeps, this work integrates ML regression with grid search to optimize element spacing, minimizing total S-parameter error across the frequency range. This approach significantly reduces simulation effort while ensuring optimal performance for compact, high-isolation MIMO antennas. The antenna is fabricated using FR4 substrate (εr = 4.4) and measures only 60 × 60 × 1.6 mm3. An operational bandwidth of 9.9 GHz (2.1–12 GHz) and strong isolation of 15 dB are offered. The MIMO characteristics are validated using diversity parameters such as envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG) and total active reflection coefficient (TARC). The designed antenna demonstrates excellent MIMO performance exhibiting an average ECC of 0.0304, DG close to 9.99 and MEGij difference below 0.94 dB. It offers TARC under − 10 dB and exhibits a signal group delay below 0.25 ns confirming strong diversity and MIMO performance across the intended wide frequency band.

This study investigates the prevalence and determinants of repeat breeding and infertility among small dairy farms in a district with intensive crossbred and traditional dairy farming systems in India. Data were collected from 2,254 animals and 579 farmers representing diverse herd compositions and management systems. Results revealed that repeat breeding affected 20.4% of animals, while infertility was observed in 44.7%, exceeding national averages. Species, breed type, age, milk yield, and farmer type were significant factors influencing reproductive outcomes. Crossbred cows (based on Holstein-Friesian, Jersey, and indigenous breeds) exhibited higher reproductive disorders than indigenous cattle breeds, while buffaloes recorded the highest infertility rates (62.5%). The consequences of reproductive inefficiencies include involuntary extended calving intervals, reduced lifetime milk yield, and hence an increased carbon footprint through increased methane emission intensity. Poor reproductive efficiency therefore impacts both farm profitability and environmental sustainability. The study emphasizes evidence-based reproductive management, digital herd monitoring, and farmer training as essential strategies for improving fertility and advancing climate-smart dairy practices in Tamil Nadu.

Enhancing aerodynamic efficiency and structural reliability remains a critical challenge in the design of horizontal axis wind turbine (HAWT) blades. This study presents a comprehensive aerodynamic–structural optimization framework incorporating five distinct design methodologies to improve lift generation, torque output, and material performance. Aerodynamic characteristics were evaluated using computational fluid dynamics (CFD), while structural behavior was examined through fluid–structure interaction (FSI) simulations. Among the investigated configurations, a profile-modified three-blade model demonstrated the highest aerodynamic performance, achieving a 38.05

This study presents the design and evaluation of eleven Blended Wing Unmanned Aerial Vehicle (BWUAV) configurations developed to enhance aerodynamic performance and structural stability. All models were created in SOLIDWORKS using the MH-91 airfoil and analyzed in ANSYS 2022 with a pressure-based solver and the k-epsilon enhanced wall treatment model at an inlet velocity of 75 m/s. Aerodynamic forces, pressure behavior, velocity distribution, and lift-to-drag characteristics were examined for each configuration. Among all designs, Hybrid Model 2 produced the highest aerodynamic efficiency, generating a lift of 108.99 N and a drag of 51.85 N, resulting in the highest L/D ratio among the eleven models. Other high-performing geometries included Blunt Nose Model 4, Pointed Nose Model 1, and Hybrid Model 1, but Hybrid Model 2 consistently demonstrated superior flow stability and favorable pressure gradients. Structural analysis was carried out using conventional composite materials, where CFRP GY-70 exhibited the lowest total deformation and elastic strain, while S-GFRP produced the lowest equivalent stress. To combine the advantages of both materials, a hybrid composite was developed using ANSYS Material Designer. When applied to the selected models, the hybrid composite further reduced deformation and improved stress distribution, establishing Hybrid Model 2 as the most structurally efficient configuration. The combined aerodynamic and structural results confirm that the proposed BWUAV framework provides a stable, lightweight, and reliable platform suitable for diverse unmanned applications and future development.

The mechanical and functional performances of epoxy-based composites reinforced with cluster bean stem microfibers and balloon vine stem-derived lignin were evaluated through tensile, flexural, impact, hardness, fatigue, creep, and water absorption tests, conducted in accordance with relevant ASTM standards. The primary objective of the study was to investigate the effect of silane surface treatment on the overall performance of the composites. Reinforcing materials were treated using 3-glycidyloxypropyl trimethoxysilane (3-GPTMS), and composites were fabricated via the conventional hand lay-up technique. The performance of both untreated and silane-treated composites was compared to a reference sample made with 100 vol