Ritesh Kumar

India's real estate sector is undoubtedly one of the strongest pillars of the current growth story of the country.

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Every tutorial shows you how to build an AI agent. Nobody shows you what happens when it does...

A new class of side-chain-fluorinated cyclic ethers was studied for high voltage lithium metal batteries. Compared to linear fluorinated ethers, cyclic ethers maintain higher ionic conductivity despite increased ion pairing, while enhanced fluorination suppresses undesired solvent polymerization, improves ox

Hey everyone, As a frontend developer, I love sharing quick code snippets and learning moments on...

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Northern leaf blight (NLB) caused by Exserohilum turcicum is a devastating foliar disease of maize. Deploying resistant varieties is considered a sustainable and eco-friendly option for long-term agriculture sustainability. However, challenges arise with the durability of host resistance, prompting the need for integrated approaches. While NLB management in Nepal involves the use of traditional fungicides, our study focuses on evaluating the efficacy of newly registered fungicides concerning’ disease control and post-harvest seed infestation levels in the inner Terai region of Nepal. Altogether four chemical fungicides, two commonly used (Mancozate and SAAF) and two newly registered (Tilt, and Nativo) were evaluated under in vitro (Poisoned food technique) and field conditions (Randomized complete block design). Tilt and Nativo at 50 ppm were found strikingly effective in completely inhibiting the fungal pathogen’s mycelial growth. Also under field conditions, the treatment (Seed treatment with carbestin-50 + Foliar application of Tilt (0.1%) showed the lowest disease incidence (81.95%), percent disease index (PDI) (33.37%), sporulation (0.52 × 104 spores/ml), AUDPC (1316.98) with highest yield (4.39 t/ha) which was at par with Nativo (0.05%). These results revealed that Tilt or Nativo could be applied in knee high or before the tasseling stage and can also be used alternatively to overcome fungicide resistance issues. No incidence of E. turcicum was detected in any treatment using the freezing blotter technique, indicating either very low natural seed infection or limitations in detection sensitivity.

In order to ensure sustainable agricultural productivity, a reliable diagnostic framework to identify mango leaf diseases through interpretable visual symptoms is necessary. Deep learning models have high classification accuracy, but the “black box” nature of the deep learning models often makes it difficult to understand the underlying rationale of a prediction. To overcome this limitation, an explainable artificial intelligence (XAI) agent is computationally developed based on a two-stage diagnostic strategy. The proposed framework first employs hybrid vision transformer architecture for leaf-level classification, and employs local interpretability methods to determine the specific image patches that influence the decision. In the second stage, a feature detection model scans the identified regions to link the classification to visible pathological indicators such as necrotic regions, holes, and discoloration. By bridging the gap between global predictions and local geometric causes, this dual-model approach mimics the selective attention of a human specialist. The result analysis demonstrates that this strategy effectively transforms opaque diagnostic processes into a transparent and human-understandable format, thereby enhancing the reliability of automated systems for early crop management in hazardous or large-scale agricultural environments. The empirical results reveal that the suggested framework is capable of accurately classifying leaves while simultaneously localizing symptoms in an interpretable way, which can further be applied to diagnose diseases in different plants.

Introduction: Low-carbon steels are traditionally valued for their structural applications. However, engineering steel's microstructure for tailoring novel magnetic properties has not been extensively explored before. We investigate how common processing techniques—thermal treatments and mechanical strain—significantly influence the magnetic properties of low-carbon steels.

This article presents a compact quad-port MIMO antenna for 5G NR mm-Wave applications. Initially, an ICE-POP structured single-element antenna is designed and then transformed into a four-port MIMO configuration. The proposed antenna provides an ultra-wide bandwidth of 23.5–45 GHz (21.5 GHz), covering the 5G NR bands n257, n258, n259, and n260. Fabricated on Rogers RT/Duroid 5880 with overall dimensions of 24 × 24 × 0.8 mm³, the antenna incorporates a 3 × 3 circular-ring defected ground structure (DGS) to achieve isolation better than − 25 dB. It exhibits a peak gain of 6.75 dBi, radiation efficiency of 98%, and satisfactory MIMO performance in terms of ECC, DG, CCL, and TARC. The antenna was designed and analyzed using HFSS, followed by fabrication and experimental validation.