Conceptual Approach Towards Automation and Design of Three Axis Trolley Dumper used in the Industrial Applications

Number of vehicles on our roads is increasing day by day, also the technology has developed but the safety factor is always needed to be considered. Now a day’s vehicles come fitted with lots of safety features. One of the essential safety feature that need to be installed is automatic upper-dipper control of headlight, this feature can mainly use during night time driving. This feature can be installed in three axis trolley which are mainly used for industrial purpose

Molecular Basis of Plant Adaptation against Aridity

Environment fluctuations have become the greatest threat to global food security. Of various abiotic stress factors, aridity hampers the most yield contributing attributes. In the context of agriculture, term “aridity” refers to a protracted period of insufficient precipitation, having detrimental influence on crop development and overall biological output. A sustained drought has considerable negative effects on crops and livestock, including the reduced production, destruction of property, and livestock sell-offs. Consequently, plants themself exert various kinds of defensive mechanisms to combat the ill effects of climate change. For example, plants with small leaves, benefit from aridity as part of their strategy for modifying the soil to water shortages and nutrient restrictions. Furthermore, low genetic diversity among significant crop species, together with ecological productivity limits, must be addressed in order to adapt crops to episodic drought spells in the coming days. A deeper understanding of the molecular and genetic underpinnings of the most important intrinsic adaptation responses to drought stress seems to be beneficial for gene engineering as well as gene-based expression investigations in plant systems under hostile environment. Recently, molecular markers and “omics” have opened a huge opportunity to identify and develop specific gene constructs governing plant adaptation to environmental stress

EdgeAISim: A toolkit for simulation and modelling of AI models in edge computing environments

To meet next-generation Internet of Things (IoT) application demands, edge computing moves processing power and storage closer to the network edge to minimize latency and bandwidth utilization. Edge computing is becoming increasingly popular as a result of these benefits, but it comes with challenges such as managing resources efficiently. Researchers are utilising Artificial Intelligence (AI) models to solve the challenge of resource management in edge computing systems. However, existing simulation tools are only concerned with typical resource management policies, not the adoption and implementation of AI models for resource management, especially. Consequently, researchers continue to face significant challenges, making it hard and time-consuming to use AI models when designing novel resource management policies for edge computing with existing simulation tools. To overcome these issues, we propose a lightweight Python-based toolkit called EdgeAISim for the simulation and modelling of AI models for designing resource management policies in edge computing environments. In EdgeAISim, we extended the basic components of the EdgeSimPy framework and developed new AI-based simulation models for task scheduling, energy management, service migration, network flow scheduling, and mobility support for edge computing environments. In EdgeAISim, we have utilized advanced AI models such as Multi-Armed Bandit with Upper Confidence Bound, Deep Q-Networks, Deep Q-Networks with Graphical Neural Network, and Actor-Critic Network to optimize power usage while efficiently managing task migration within the edge computing environment. The performance of these proposed models of EdgeAISim is compared with the baseline, which uses a worst-fit algorithm-based resource management policy in different settings. Experimental results indicate that EdgeAISim exhibits a substantial reduction in power consumption, highlighting the compelling success of power optimization strategies in EdgeAISim. The development of EdgeAISim represents a promising step towards sustainable edge computing, providing eco-friendly and energy-efficient solutions that facilitate efficient task management in edge environments for different large-scale scenarios

Estimation of Genetic Variability, Heritability and Genetic Gain for Wood Density and Fibre Length in 36 Clones of White Willow (Salix Alba L.) International Journal of Agriculture, Environment & Biotechnology

Abstract Variability of wood density and fibre length was determined in 36 genotypes of Salix alba L. procured from seven different European countries namely Italy, Hungary, U.K, Netherlands, Turkey, Yugoslavia and Croatia. Genetic parameters were worked out with regards to estimate of heritability (broad sense), genetic advance, genetic gain as per cent of mean and correlation coefficient among them. Wood density was recorded in the range of 0.30-0.53 with mean value 0.40gcm -3 whereas fibre length ranged from 0.45-0.65 with mean 0.55mm. High heritability values show that the genetic control is stronger for wood density (h 2 =90.30) than for fibre length (h 2 =78.20). Both the characters were having high heritability with good genetic gain. Clone 84/22 from Turkey had given best performance in view of both the character. Further control crossing is underway to produce ideotype with regard to different end users. Highlight 1. Variability of wood density and fibre length was determined in 36 genotypes of Salix alba L. 2. Significant differences existed among the clones for fibre length 3. High heritability value (90.30) for wood density and fibre length (78.20) coupled with high genetic gain (33.33%) for wood density was recorded