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This study is an attempt to understand the socio-economic characteristics of farmers and relationship between education and farming experience in Prayagraj district of Eastern-Uttar Pradesh. For, the present study primary data were collected on various parameters by using personal interview/enquiry method for the period of 2019-2020 from the selected 12 villages and 240 respondents spread over two blocks Jasra (Jamunapar) and Soraon (Gangapar) of Prayagraj district by survey method using interview schedule. Also chi square test has been applied to see the relationship between education and farming experience as dependent and independent variables in the study area.The result of the research study indicated thatmore than half of the respondents (57.08%) were in middle age groups (36 to 50 years), which shows that they are in their productive and economically active age and capable of producing major crops, which increases their growth. Majority of 92.08 per cent of the respondents surveyed were found married, 41.66 per cent of the respondents had high school level of education. Whereas, 47.50 per cent households belongs to other backward caste, 58.75 per cent of the respondents had more than 15 to 30 years of farming experiences, 21.20 per cent of farmers doing Farming+ Animal husbandry as their occupation, less than half of the respondents (43.33%) was found in medium size family (6-8 members ), more than half of the respondents (57.91%) living in nuclear family, majority of farmers 79.58 per cent have pucca houses, more than half of the respondents (57.91%) holding marginal land holding (< 1 hectare) and there is significant relationship between the education of farmers and farming experience.

The proposed work is to design an Arduino-controlled two-wheeled Bluetooth robotic arm for control purposes. The arm mechanism works either by an outdoor user or by preset command. The medication and trade fields are the most developed field regarding the usage of this technology. The design will allow our robotic arm to exercise in four different axes (directions) with four servomotors. It can also take the objects from one place to other with the help of the geared motors and wheels attached to the holder. And jointly combines and works it with the fabric it receives. This mechanism is controlled with the help of the automation application controlled with the help of the HC-05 Bluetooth module linked to the Arduino Uno. The code is programmed to manage the working of servomotors. This design is similar to some robotic cranes, or we can turn it into a robotic crane with some adjustments. It is highly used in the manufacturing plants, Industrial sector for Automation, Agricultural and medical fields.

We introduce the notion of lattice path matroidal subdivisions, or LPM subdivisions for short, and show that these subdivisions are regular and hence the weight vectors for them lie in the Dressian. This leads us to explore the structure of the set of these weights inside the Dressian and owing to the fact that Lattice path matroids are positroids, we move to the positive Dressian which in turn is equal to the positive tropical Grassmannian, an object of immense interest currently in Physics. This is related to the amplituhedron and positive configuration space, which we describe here and wish to explore these connections further., Comment: Comments are welcome

Many Types of powder available in the market to identify the latent fingerprint some powders are toxic in our body to affect the body organ. I have attempt to use new powder for develop the latent fingerprint which are simple, non-toxic, less expensive than the commercially used fingerprint powder. These are easily available in every fruit shops. This type of work has not been reported previously and can provide the information to the investigators in cases of scarcity or non-availability of regular conventional fingerprint development powders. This is use simple and novice in the field in the Forensic fingerprint can also do it.

Among the main challenges that persons with disabilities encounter on a regular schedule is interaction. The advancement of recognizing signing communicative patterns and techniques made possible by modern technological advancements has significantly contributed to the resolution of this issue. Systems for the identification of hand signals utilizing human intelligence have shown excellent reliability. However, the modeling learning algorithm might take a lot of time because of the vast amount of information needed. We suggest an expedited Indian Signature Identification System that makes use of dispersed intelligence to address this issue. This same concept uses Convolutional Neural Network (CNN) for identifying indications and switching them to English speech. The modeling proposed technique has been distributed over Temporal Unit Operations and Graphics Processors Elements.

its letter to editor , so no abstract is needed

This paper presents a star-of-star topology for internet-of-things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probability (OP) analysis is performed, assuming imperfect channel state information at the GS. The paper employs stochastic geometry to model the random locations of satellites, making the analysis general and independent of any specific constellation. Furthermore, the paper examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. The paper also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks.

Given a lattice polygon $P$ with $g$ interior lattice points, we associate to it the moduli space of tropical curves of genus $g$ with Newton polygon $P$. We completely classify the possible dimensions such a moduli space can have. For non-hyperelliptic polygons the dimension must be between $g$ and $2g+1$, and can take on any integer value in this range, with exceptions only in the cases of genus $3$, $4$, and $7$. We provide a similar result for hyperelliptic polygons, for which the range of dimensions is from $g$ to $2g-1$. In the case of non-hyperelliptic polygons, our results also hold for the moduli space of algebraic curves that are non-degenerate with respect to $P$., Comment: 17 pages, 15 figures

Iron is an essential element for survival of most organisms. One mechanism of host defense is to tightly chelate iron to several proteins to limit its extracellular availability. This has forced pathogens such as Acinetobacter baumannii to adapt mechanisms for the acquisition and utilization of iron even in iron-limiting conditions. A. baumannii uses a variety of iron acquisition strategies to meet its iron requirements. It can lyse erythrocytes to harvest the heme molecules, use iron-chelating siderophores, and use outer membrane vesicles to acquire iron. Iron acquisition pathways, in general, have been seen to affect many other virulence factors such as cell adherence, cell motility, and biofilm formation. The knowledge gained from research on iron acquisition led to the synthesis of the antibiotic cefiderocol, which uses iron uptake pathways for entry into the cell with some success as a novel cephalosporin. Understanding the mechanisms of iron acquisition of A. baumannii allows for insight into clinical infections and offer potential targets for novel antibiotics or potentiators of current drugs.

Polymer nanocomposites (PNCs) offer a broad range of thermophysical properties that are linked to their compositions. However, it is challenging to establish a universal composition-property relation of PNCs due to their enormous composition and chemical space. Here, we address this problem and develop a new method to model the composition-microstructure relation of a PNC through an intelligent machine learning pipeline named nanoNET. The nanoNET is a nanoparticles (NPs) distribution predictor, built upon computer vision and image recognition concepts. It integrates unsupervised deep learning and regression in a fully automated pipeline. We conduct coarse-grained molecular dynamics simulations of PNCs and utilize the data to establish and validate the nanoNET. Within this framework, a random forest regression model predicts the NPs distribution in a PNC in a latent space. Subsequently, a convolutional neural network-based decoder converts the latent space representation to the actual radial distribution function (RDF) of NPs in the given PNC. The nanoNET predicts NPs distribution in many unknown PNCs very accurately. This method is very generic and can accelerate the design, discovery, and fundamental understanding of composition-microstructure relations of PNCs and other molecular systems.