REVA University

Abstract Structural analysis and design of a reinforced concrete (RC) beam satisfying a reliable serviceability criterion demand precise calculation of the effective modulus of elasticity ( E RCC ) of a beam. Various international codes suggest an empirical equation for the modulus of elasticity of concrete ( E c ) dependent on the compressive strength of concrete ( f c k ) alone. However, real-life buildings are of RC material and not concrete only. The current study was undertaken to represent a simplified method to evaluate E RCC for doubly RC beams by developing empirical correction factors and design charts resulting entirely from experimental data. Six beams of a cross-section 250 mm × 500 mm × 2,100 mm for M35 concrete grade were tested, subjected to two-point loading. The reinforcement for a beam specimen was varied in the range of 0.193% to 0.535%. The regression analysis was carried out to express a compact equation of E RCC dependent on f c k , tension reinforcement ( p t ), and compression reinforcement ( p c ). A dimensionless correction factor ( k = E RCC / E c ) and a design chart for the M35 grade of concrete were calculated and plotted. The correction factor was found to be between 1.05 and 1.30 for beams with a reinforcement ratio ( ρ = p t / p c ) in the range of 0.2 to 0.55. The modified approach resulted in 15%–25% reduction in deflections due to serviceability loads that the conventional code estimates. Results indicate that stiffness correction increases systematically with reinforcement percentage, leading to improved prediction of beam deflection and serviceability response. The deflection reduction was recorded strictly adhering to reinforcement ratios, beam dimensions, and loading levels undertaken in the study. The charts are therefore recommended for use within the calibrated parameter domain and should not be extrapolated beyond the validated limits.

The increased global production of plastic materials and the presence of plastic waste in the environment have resulted in widespread environmental contamination despite the presence of restrictions from various governments. The breakdown of macroplastic materials has resulted in the formation of microplastics (MPs; ≤ 5 mm), which are now widespread in marine, freshwater, and terrestrial ecosystems. The widespread presence of MPs in the environment poses severe ecotoxicological risks to organisms at various trophic levels. Although there has been increased interest in the study of the impacts of MPs, critical knowledge gaps still exist regarding the application of remediation measures at various scales. This article aims to comprehend the present status of global MP contamination, with a special focus on the patterns of contamination and the difficulties associated with environmental monitoring in India. The study also examines advances over the last decade in the biodegradation of secondary MPs using bacterial and fungal consortia. The critical parameters associated with the degradation process, the efficiency of the microorganisms in various experimental setups, and the associated enzymatic processes are critically reviewed in the study.

The present work focuses on fabricating asymmetric supercapacitors (ASCs) using AC from coir fiber as the anode and nickel-cobalt double-layered hydroxide (NC-LDH) as the cathode material. The kinetic study of AC and NC-LDH was examined using a power law relationship and the Dunn model. The AC electrode showed a dominant surface-controlled charge storage mechanism of about 99.76%, with a very minimal diffusion-controlled process. The NC-LDH electrode exhibited the predominance of a pseudocapacitive process, with diffusion and surface-controlled charge storage mechanisms of about 93.50% and 6.49%, respectively. The surface areas of AC and NC-LDH were found to be 1330 and 52 m2/g, with total pore volumes of 0.81 and 0.38 cm3/g, respectively. The AC and NC-LDH demonstrated specific capacitances of 294 F/g and 586 F/g at current densities of 1 and 0.5 A/g, respectively, showing good cyclic performance and Coulombic efficiencies of about 99.61% and 98.96% over 5000 cycles in a three-electrode system. An asymmetric supercapacitor (ASC) device was fabricated, which displayed both diffusion- and surface-controlled processes of about 81.63 and 18.36% at a scan rate of 50 mV/s, and delivered a specific capacitance of 125.4 F/g at 0.5 A/g, offering an energy density of about 34 Wh/kg at a power density of 1300 W/kg. The device exhibited superior long-term cyclic stability, showing 92.38% capacitive retention and 99.85% Coulombic efficiency after 20,000 prolonged charge-discharge cycles. Thus, the kinetic study of the ASC device underscores the synergistic combination of AC and NC-LDH, thereby improving electrochemical performance through dual mechanisms (nonfaradaic and faradaic), which aid in increasing the overall capacitance of the ASC device.

In the advent of rapid climate change, the growing complexity of abiotic stress combinations poses a significant threat to global agricultural production and food security. Traditional approaches, such as univariate statistical models or isolated omics studies that focus on single stressors, are often inadequate for predicting crop performance in the contemporary multivariate stress conditions present in agriculture. Recent technological breakthroughs in high-throughput multi-omics, phenomics, and environmental monitoring have generated enormous datasets that clarify the intricate interplay between genetic and environmental factors affecting plant stress responses. Concurrently, machine learning (ML) and artificial intelligence (AI) methodologies have emerged as powerful tools for modeling these complex interactions; yet, their conventional “black box” nature limits biological interpretability and practical use in crop improvement. This review highlights recent developments in interpretable machine learning algorithms that anticipate multifactorial abiotic stress responses in climate-resilient crops. We analyze the integration of multi-omics data with high-throughput phenotyping and environmental factors using interpretable models, such as attention-based neural networks, SHAP value analysis, and decision tree ensembles. These techniques aim to enhance the predictive accuracy and clarify essential regulatory pathways and biological drivers influencing stress resilience. Additionally, we also shed light on the challenges in data integration, model transparency, and the translational properties of computational discoveries into practical breeding methodologies. Finally, we propose future research directions aimed at refining these AI-driven frameworks to expedite the creation of crop varieties with enhanced tolerance to multiple stressors. This review emphasizes the game-changing capability of interpretable machine learning to close the gap between computational predictions and operational, field-level applicability in precision agriculture in a changing climate.

The wear performance of additively manufactured (AM) AlSi10Mg components is critical for their deployment in tribological applications, yet a comparative analysis of the wear behavior under as-built, T6, and stress-relief (SR) conditions remains insufficiently explored. To broaden the industrial adoption of AM components, it is crucial to evaluate their wear behavior, as this underpins reliability and safety while promoting creativity in both design and material choices. This research examines the wear characteristics of the AlSi10Mg alloy created using Selective Laser Melting (SLM), evaluated in the as-built condition and following T6 and SR heat treatments. The microstructural variations under these three states were analyzed using scanning electron microscopy (SEM). The0020as-built specimens exhibited the highest hardness (137.3 HV), due to the presence of a refined alpha-Al cellular framework embedded with Si particles generated by rapid solidification. Heat treatment altered this structure, leading to Si phase coarsening and a corresponding reduction in hardness to 103.35 HV in the T6 condition and further down to 73.75 HV in the SR condition. Wear experiments were carried out under applied loads ranging from 5 to 15 N (max load 15 N) for a duration of 300 s, along with assessments of the coefficient of friction (COF), the surface morphology following wear, and the loss of material. The findings indicated that the as-built specimens consistently demonstrated lower wear volume loss across all load levels in comparison to the samples that underwent heat treatment. Additionally, the heat-treated specimens developed compressive residual stresses, while the as-built SLM parts primarily exhibited tensile stresses.