Pawan Kumar

Series links Part 1: Everything You Know About Scaling Web Apps Breaks When You Serve an LLM Part...

Originally published at dheeth.blog. Series links Part 1: Everything You Know About Scaling Web...

Yttrium gallium oxide, Y3GaO6, (YGO) phosphors doped with varying concentrations of Sm3+ ions have been synthesized using a solution combustion approach. Various spectroscopic and structural analyses were performed on the prepared phosphors to investigate their structural and optical features. The crystal structure

Series links Part 1: Everything You Know About Scaling Web Apps Breaks When You Serve an LLM Part...

The rapid advancement of high-tech electronic, photonic, and data-storage technologies has created an urgent demand for organic materials exhibiting strong and tunable nonlinear optical (NLO) properties, as these materials enable ultrafast signal processing, optical modulation, frequency conversion, and high-density information storage. Triphenylamine was employed as the electron-donating unit, while various electron-withdrawing groups were positioned at the termini to construct a series of donor–π–acceptor push–pull chromophores (1–8). These chromophores were systematically characterized using UV–vis spectroscopy, Aggregation-induced Emission behavior, acidochromic, electrochemical studies, and Nonlinear optical and thermal analysis. Theoretical investigations using density functional theory at the B3LYP/6-31G(d,p) level revealed reduced HOMO–LUMO energy gaps, consistent with the observed electrochemical and optical behavior.

Deploy Qwen2.5-1.5B-Instruct on a Kubernetes GPU node with vLLM, expose it as an OpenAI-compatible API, and verify it with a real curl request.

River Ramganga, a tributary of river Ganga, plays a vital role in supporting industrial, domestic, and agriculture sectors in Uttarakhand and Uttar Pradesh, India. However, the water quality is degraded due to anthropogenic pressures and geohazard-induced activities, particularly in landslide-prone Himalayan terrain. Study aimed to assess the freshwater quality of Ramganga River and its tributaries (Dhela and Dhandi) with samples collected from selected seven sites over a year and analyzed using water quality index (WQI), heavy metal pollution index (HPI), heavy metal evaluation index (HEI), and principal component analysis (PCA). WQI ranged from 62.65 (poor) at the upstream Dhela site to 204.91 (unsuitable for drinking) at the Dhandi–Dhela confluence, whereas downstream sites were also classified as unsuitable (WQI > 140). HPI values exceed the critical level (>30) at all sites, whereas HEI values (2.93–9.83) placed the river in the low-risk class with cumulative metal concentrations. Increased BOD (up to 75.78 mg/L), COD (up to 223.10 mg/L), hardness (>200 mg/L), and Fe and Pb frequently exceeding Bureau of Indian Standard (BIS) limits highlight severe deterioration of water quality and potential health risks. HPI and HEI revealed significant metal contamination, whereas WQI suggested the unsuitability for consumption. PCA highlighted total dissolved solids (TDS) as the highest loading factor, whereas phosphate was the lowest, reflecting the interactions between geohazard-induced sediment mobilization and water quality. Similarly, PCA of heavy metals showed Zn and Fe as the principal contributors linked to geomorphic instability enhanced by erosion and landslide. These findings highlighted the urgency of strategic water stress management to enhance resilience, mitigate contamination, and ensure sustainable usage of river Ramganga system.

Agriculture is significant in world food production and global economic stability, but leaf disease and pest infection can cause a threat to crop quantity and quality. Thus, it became crucial to have timely and accurate identification of plant leaf disease to prevent loss in agriculture on a large scale and to have sustainable crop management. This paper introduces LeafLiteX, a lightweight mobile-based deep learning application for real-time detection and classification of leaf diseases. This application uses U-Net segmentation to precisely find leaf regions and MobileNetV3-Large to quickly classify diseases with less computation on the computer. The application performs end-to-end processing, from image acquisition to segmentation and disease prediction on mobile devices. An experiment was performed on publicly available crop disease datasets containing various leaf images having different disease types. The model obtained an accuracy of 98.85% showing improved generalization with minimal latency. The design of the model was such that it was suitable for inference on-device while still being robust enough despite changes in lighting conditions, background noise, and camera resolution. LeafLiteX is a low-cost, easy to use, offline-capable, and in-the-moment decision-making supportive diagnostic application that supports farmers and agrarians who require early detection. This paper demonstrates the capabilities that can be achieved using edge-optimized machine learning and computer vision to support the development of smart agriculture technologies. While traditional methods rely solely on classification, this research focuses more on practical implementation by incorporating segmentation, lightweight classification, and explainability to develop a mobile-friendly model.

Fall armyworm (FAW) is a major invasive pest causing significant damage to maize and other cereal crops, necessitating the development of sustainable management strategies. This study evaluated the toxicological and demographic effects of diet-incorporated silica nanoparticles (SiNPs) on FAW under laboratory conditions. SiNPs were incorporated into an artificial diet at concentrations of 100–500 ppm, along with an untreated control, and assessed for their effects on survival, development, reproduction, and population growth using age–stage, two-sex life table analysis. Larval mortality increased with concentration, reaching 85.77% at 500 ppm, with LC₅₀ and LC₉₀ values of 239.23 and 1438.61 ppm, respectively. SiNPs exposure significantly prolonged larval (18.60–24.59 days) and pupal (6.42–8.33 days) durations, while reducing pupal weight (male: 186.04–163.39 mg; female: 222.79–210.46 mg), adult longevity (8.62–7.26 days), female spawning (90.13% to 64.05%), and mating rate (92.81% to 66.13%), whereas mating frequency remained unaffected. Life table parameters showed declines in intrinsic rate of increase (0.158–0.064 day⁻¹), finite rate of increase (1.17–1.06 day⁻¹), and net reproductive rate (149.79–14.10 offspring per individual), along with increased mean generation time (31.67–41.16 days). These results demonstrate that dietary exposure to SiNPs adversely affects the growth, survival, and reproductive performance of FAW, leading to reduced population growth potential. Overall, the study provides clear evidence that SiNPs can suppress FAW populations under controlled conditions and highlights their potential as an eco-friendly component of integrated pest management, although further field-level validation is required.