Bastar University

Outbreak of fowlpox (FP) was investigated in unvaccinated commercial layer farm in Durg and surrounding areas where significant morbidity was observed. The flocks exhibited typical clinical manifestation characterized by nodular and proliferative lesion on comb, wattle, eyelid. All ages of layers birds were affected. Epidemiological investigation revealed overall morbidity and mortality was 70 percent and 3.3 percent respectively. Hens were house in deep liter system. Affected birds showed depression, reduced feed intake and decreased egg production. Laboratory confirmation of the disease was performed using agar gel immunodiffusion (AGID) test, counter immunoelectrophoresis (CIE) test and histopathology of virus infected chorioallantoic membrane (CAM) of embryonated chicken egg (ECE), all of which yielded positive results for the presence of the fowl pox virus. One field isolated of fowlpox virus was also subject to HA property and compared with vaccine strain of fowlpox virus. The aim of the present study is to investigate fowlpox infection in poultry farm near Durg district.

This study was carried out to show variation in soil nutrients under the different land use types of the upper catchment area of Narmada River Amarkantak in Central India. The soil sample was collected from 0-20 cm and 20-40 cm using a soil auger and under six land use types. The land use were dense mixed forest, sal mixed forest, mixed forest, open land, rangeland and agriculture land. A total of 60 composite samples were obtained (6 land use types x 5 quadrates x 2 depths) and analysed for pH, EC, N, P, K and SOC. The results revealed that the average pH ranged from 5.75±0.15 to 6.79±0.58, average EC values ranged from 0.13±0.03 to 0.31±0.08 dS m-1 statistically significant in different land use types. The average nitrogen ranged from 115.83±22.74 to 523.75±161.54 kg ha-1. The average nitrogen was highest measured in dense mixed forest and lowest was in open land. The average potassium content varied from 240.97±31.20 to 474.56±90.69 kg ha-1. The highest potassium was observed in dense mixed forests and the lowest was in open land. The average phosphorus contents were lies from 6.77±1.50 to 23.03±9.42 kg ha-1. The maximum average P was measured in dense mixed forest and the minimum was in open land. The average SOC and SOM varied from 0.58±0.35 to 2.38±0.70% and 0.99±0.61 to 4.11±1.21%, respectively. The highest SOC and SOM were recorded in mixed forest and the lowest was recorded in open land. The highly positive correlation was observed between nitrogen to phosphorus, potassium, soil organic carbon and soil organic matter with R2 value was 0.96, 0.96, 0.91 and 0.91, respectively. The phosphorus was showing highly positive correlation with nitrogen, potassium, soil organic carbon and soil organic matter with R2 value was 0.96, 0.94, 0.93 and 0.93, respectively. The soil organic carbon and soil organic matter was statistically positive correlation was performed of nitrogen, phosphorus and potassium with R2 value 0.91, 0.93 and 0.92, respectively. The electrical conductivity was negatively correlation of nitrogen, phosphorus, potassium, soil organic carbon and soil organic matter with R2 value -0.39, -0.32, -0.34, -0.17 and -0.17, respectively. The present study indicated that the soil nutrient (NPK) contents were statistically significant relation to different land use types. The soil nutrient was highest in forest vegetation as compared to agricultural land, rangeland, and open land due to the forest area’s large no. of plant vegetation are present and higher litter decomposition rate.

The green chemistry approach in synthesizing nanoparticles, particularly using plant extract, has gained booming demand for eco-friendly nanotechnology, evaluating their multifunctional properties, and exploring potential applications. Hence, addressing the need for sustainable nanomaterials in biomedical and environmental fields. This work describes the synthesis of silver nanoparticles (AgNPs) using Manilkara zapota (M. zapota) leaf extract as a reducing agent through a simple bio-combustion method. The synthesized AgNPs were characterized by various analytical techniques such as UV-visible spectroscopy, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and so on. The optical studies of the AgNPs reveal a bandgap energy of about 3.31 eV. The nanoparticles synthesized in this work were found to crystallize in a cubic phase, corresponding to JCPDS #96-9013046, in the Fm-3 m space group. The crystallite size was 22.38 nm and was independently confirmed through the W-H plot (24.75 nm). Infrared spectroscopy results revealed the characteristics of O-H stretching at 3335 cm⁻¹ and a metal peroxide peak at 849 cm⁻¹, respectively. It was seen that the SEM and TEM images depict a clustered agglomeration of the nanoparticles, while EDAX confirmed the elemental composition. The as-synthesized AgNPs showed high catalytic efficiency towards degradation of MB dye (83.33%) following 0th order kinetics with an R² value of 0.98 and rate constant k₀ of 0.007, under acidic conditions at pH-2 being optimal for their performance. The antibacterial assays showed that AgNPs were effective against both Staphylococcus aureus (gram-positive) and Escherichia coli (gram-negative) bacterial strains. Activity was observed at a low concentration of 400 µg/mL. Hence, the obtained AgNPs prepared via bio-combustion route resulted in a potential multifunctional nanomaterial with strong catalytic properties coupled with antibacterial activity.

At the application layer intelligent real-time load balancing is more crucial than ever as microservices-based architectures take center stage in today's cloud applications. We are pleased to present Flask-AMDLNR (Application-level Machine-learning Driven Load balancer for Networked Requests) a state-of-the-art artificial intelligence framework that optimizes request routing in dynamic microservices networks. Flask-AMDLNR intelligently blends real-time data such as service response times system load and user demand trends with machine learning models to generate context-aware well-informed routing decisions. As a cloud-native service it integrates seamlessly with container orchestration platforms like Kubernetes and fully utilizes attributes like fault tolerance distributed tracing and auto-scaling. When load balancing is implemented in real-time it can swiftly adapt to changes in traffic patterns and service performance which improves system responsiveness throughput and overall reliability in comparison to more antiquated static balancing techniques. Because of this Flask-AMDLNR is essential to creating intelligent scalable and robust microservices architectures in the cloud.