Economics of organic versus chemical farming for three crops in Andhra Pradesh, India

To tackle the challenge of food grain production and food security, chemical agriculture advocates call for the continuing or higher use of chemical fertilizers and synthetic pesticides. However, the continuous use and higher reliance on these inputs can lead to a reduction in crop productivity, deterioration in the quality of natural resources and the eco-system. Organic farming offers a solution for sustainable agricultural growth and safeguarding the ecosystem. A conversion from chemical farming to organic farming can be a lengthy process, and during its course the farmer may incur a loss in income. The farmer will switch over only when he is convinced that in the long run, the benefits from organic farming are more than from chemical farming. A study of the economics of organic versus chemical farming may help policy makers to take appropriate measures for the spread of organic farming, which in turn has a bearing on the incomes of farmers, health conditions of the people and the environment. The present study compared the economics of organic farmers (N=350) and chemical farmers (N=200) for three crops, paddy, redgram, and groundnuts, in the state of Andhra Pradesh, a south eastern coastal state of India. It was found that organic farmers are earning a gross income of 5%, 10% and 7% more compared to the chemical farmers of paddy, redgram and groundnut, respectively, and with lower input costs the profits earned by the organic farmers are higher by 37%, 33% and 59% for the selected crops respectively. Organic farming is generally more profitable in terms of financial costs and returns than chemical farming, irrespective of the crop or the size of farm (the exceptions being small redgram farms and large goundnut farms). An analysis of the farmers’ perception of organic farming reveals that electronic media (television) is the prime motivator for farmers to adopt organic practices. Farmers believed that organic farming improves soil fertility and their profits in the long run

Characterization, Stability, and Transport Through Defects in Graphene Nanoribbons

Graphene nanoribbons (GNRs) constitute a new class of nanostructured materials with unique properties and significant potential for applications. During production of GNRs, defects are generally introduced within the lattice. Assessment of defects\u27 stability and characterization of their effects on GNRs are therefore very important for predicting GNRs performance under realistic circumstances. Here we consider various possible defects, namely the ones caused by removal/addition of carbon atoms from/to the lattice as well as those caused by bond rotation/rearrangement. Our study is based on ab initio geometry optimization and electronic structure calculations. We determine which defects can be stable in graphene nanoflakes and/or GNRs, by calculating the corresponding vibration modes. We further investigate how the presence of defects would modify electronic transport through defected GNRs. Among the defects considered, only some turn out to be stable within the GNR lattice. Transport in presence of defects is generally less compared to the pristine case, however, different defects cause different levels of conductance reduction. We also investigate the effects of a spin-polarized defect on transport characteristics

Effects of n-acetylcysteine amide in preventing/treating cataracts

Cataract, the opacification of an eye lens, is a common pathological abnormality of the lens accounting for approximately 50% of all blindness. The only effective treatment currently available for a cataract is the surgical removal of the affected lens and replacement with an artificial lens for the restoration of vision. Although, cataract surgery is considered to be a very successful procedure in terms of visual outcome, the cost of surgery, need for trained personnel and surgeons, and postsurgical complications, limit the worldwide availability and accessibility of this procedure. Hence, alternative preventive and treatment procedures are worthy of investigation. The lens depends on a balanced redox state for maintaining its transparency, and a high content of glutathione (GSH) in the lens is believed to play a key role in doing so. Several studies have reported that oxidative stress plays an important role in the etiology of cataract development and, therefore, the present study has sought to evaluate the efficacy of a thiol antioxidant, (R) -N-acetylcysteine amide (NACA), in preventing/reversing cataracts. To investigate NACA\u27s ability to provide therapeutic benefits for cataracts, three different experimental models were utilized. The first was an ex-vivo cataract model, where culturing the rat lenses in dexamethasone resulted in posterior cataracts. The second was an in vivo mouse model, where injection of acetaminophen caused cataracts. The third model was a rat in vivo model where injection of sodium selenite generated nuclear cataracts. Treatment with NACA in each model helped to decrease the severity of cataracts. In summary, the results from this study suggest that NACA can potentially be developed into a promising therapeutic option for prevention and reversal of cataract formation --Abstract, page iii

Linear and non-linear analyses of convection in a micropolar fluid occupying a porous medium

Linear and weakly non-linear analyses of convection in a micropolar fluid occupying a high-porosity medium are performed. The Brinkman-Eringen momentum equation is considered. The linear and non-linear analyses are, respectively, based on the normal mode technique and truncated representation of Fourier series. The linear theory for a two-phase system reiterates that the preferred mode of convection is stationary as in the case of a single-phase system. An autonomous system of differential equations representing cellular convection arising in the study is considered to analyse the critical points. The Nusselt number is obtained as a function of micropolar and porous medium parameters. © 2002 Elsevier Science Ltd. All rights reserved

Detection of Bundle Branch Block using Adaptive Bacterial Foraging Optimization and Neural Network

AbstractThe medical practitioners analyze the electrical activity of the human heart so as to predict various ailments by studying the data collected from the Electrocardiogram (ECG). A Bundle Branch Block (BBB) is a type of heart disease which occurs when there is an obstruction along the pathway of an electrical impulse. This abnormality makes the heart beat irregular as there is an obstruction in the branches of heart, this results in pulses to travel slower than the usual. Our current study involved is to diagnose this heart problem using Adaptive Bacterial Foraging Optimization (ABFO) Algorithm. The Data collected from MIT/BIH arrhythmia BBB database applied to an ABFO Algorithm for obtaining best(important) feature from each ECG beat. These features later fed to Levenberg Marquardt Neural Network (LMNN) based classifier. The results show the proposed classification using ABFO is better than some recent algorithms reported in the literature

Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models

Fashionable image generation aims to synthesize images of diverse fashion prevalent around the globe, helping fashion designers in real-time visualization by giving them a basic customized structure of how a specific design preference would look in real life and what further improvements can be made for enhanced customer satisfaction. Moreover, users can alone interact and generate fashionable images by just giving a few simple prompts. Recently, diffusion models have gained popularity as generative models owing to their flexibility and generation of realistic images from Gaussian noise. Latent diffusion models are a type of generative model that use diffusion processes to model the generation of complex data, such as images, audio, or text. They are called "latent" because they learn a hidden representation, or latent variable, of the data that captures its underlying structure. We propose a method exploiting the equivalence between diffusion models and energy-based models (EBMs) and suggesting ways to compose multiple probability distributions. We describe a pipeline on how our method can be used specifically for new fashionable outfit generation and virtual try-on using LLM-guided text-to-image generation. Our results indicate that using an LLM to refine the prompts to the latent diffusion model assists in generating globally creative and culturally diversified fashion styles and reducing bias.Comment: Third Workshop on Ethical Considerations in Creative applications of Computer Vision (EC3V) at CVPR 2023. arXiv admin note: substantial text overlap with arXiv:2301.02110 by other author

MACHINE LEARNING BASED CYBER SECURITY OF THREATS DETECTION USING INTRUSION SYSTEM

Cyber-security has recently resulted in significant changes in operations and technology, and data science is also developing in the context of computing. It is crucial to draw patterns or insights about security incidents from cyber-security data and create models based on the essential data in order to automate and make security systems useful. Security issues increase along with increased internet usage. Due to system security problems, malware degrades system performance and affects data privacy. Attacks can be detected and reported using intrusion detection systems (IDS). It is determined by an Intrusion Detection System (IDS) whether network traffic behaviour is typical, unusual, or suggestive of a specific type of attack. Machine Learning is being used more and more in cyber security. Making the process of detecting malware more realistic, scalable, and efficient than current methodologies is the main objective of applying Machine Learning to cyber security. As a result, Machine Learning-based intrusion system security against threats is presented in this study. This system use the Support Vector Machine (SVM) classifier to detect threats in a highly accurate and efficient manner. Accuracy, sensitivity, and specificity will be used as metrics to assess the performance of the system being presented

Efficient two-dimensional magnetotellurics modelling using implicitly restarted Lanczos method

This paper presents an efficient algorithm, FDA2DMT (Free Decay Analysis for 2D Magnetotellurics (MT)), based on eigenmode approach to solve the relevant partial differential equation, for forward computation of two-dimensional (2D) responses. The main advantage of this approach lies in the fact that only a small subset of eigenvalues and corresponding eigenvectors are required for satisfactory results. This small subset (pre-specified number) of eigenmodes are obtained using shift and invert implementation of Implicitly Restarted Lanczos Method (IRLM). It has been established by experimentation that only 15-20% smallest eigenvalue and corresponding eigenvectors are sufficient to secure the acceptable accuracy. Once the single frequency response is computed using eigenmode approach, the responses for subsequent frequencies can be obtained in negligible time. Experiment design results for validation of FDA2DMT are presented by considering two synthetic models from COMMEMI report, Brewitt-Taylor and Weaver (1976) model and a field data based model from Garhwal Himalaya