Processing Unit Research Articles

IoT Based Sign to Speech Converter System

The idea introduces a system for facilitating communication between the deaf and the non-signing community through a wearable device called IoT based Sign-to-Speech Converter System. The System consist of glove & it utilizes Internet of Things (IoT) technology to bridge the communication gap by translating sign language (gestures) into spoken language in real-time. The core functionality of the system is augmented by seamless integration with an Android application, enhancing user experience and accessibility. The proposed system leverages a combination of sensors embedded within the glove to capture intricate hand movements and gestures commonly used in sign language. These data are transmitted wirelessly to a central processing unit, to interpret the gestures and generates corresponding spoken language output. The Android application serves as a user interface, allowing for customization of settings and additional features such as text-based communication. Effectiveness and practicality of the proposed system have been demonstrated, opening-up new avenues for inclusive communication and empowerment of the deaf community.

Underground Cable Fault Detection

Underground cables play a crucial role in delivering electricity, ensuring a dependable power supply in both urban and industrial regions. However, detecting and resolving faults in these cables can be difficult due to their hidden placement. This project aims to develop an effective underground cable fault detection system that accurately identifies fault locations, reducing downtime. The system will combine electrical principles with modern sensing technologies to detect faults such as short circuits, open circuits, and insulation issues. By injecting a small current into the cable and analyzing the resulting voltage drop, the system will calculate the distance to the fault from the measurement point. Using a microcontroller along with sensors and signal processing units, it will determine and display the fault location in kilometers. Real-time fault detection will significantly decrease maintenance time and costs by quickly locating problem areas, enabling faster repairs and reducing power outages. This project aims to improve the efficiency and reliability of power transmission systems. Additionally, the system can be integrated with communication technologies for remote monitoring, allowing utility companies to detect and manage faults from centralized control centers.

Value Chain Analysis of Apple in Shimla District of Himachal Pradesh, India

The study sought to acquire extensive insights into the markets and associated marketing strategies within the Shimla district during the year 2021-22. Employing a random sampling technique, a representative sample comprising five traders, five wholesalers, five retailers, and thirty actively engaged consumers in the apple marketing sector was selected. The investigation identified the presence of five distinct marketing channels in the study area. Channel-V, which involves the processing of apples into value-added forms such as jam, wine, and juice, exhibited the most substantial degree of value addition, contributing 55.80 percent to the overall value addition. In the study area, the majority of farmers acquire their inputs from private institutes. A methodically prepared value chain map illustrates the continuous transfer of produce from farmers to consumers. Notably, the wholesale stage experienced the most significant loss, amounting to 4.11 kg per quintal. In the storage of apples, the storage unit gained a profit of 140 Rs per kg, while in the processing of apples, the processing unit it was 13333.59 Rs. per quintal.

Co-designing ab initio electronic structure methods on a RISC-V vector architecture

Ab initio electronic structure applications are among the most widely used in High-Performance Computing (HPC), and the eigenvalue problem is often their main computational bottleneck. This article presents our initial efforts in porting these codes to a RISC-V prototype platform leveraging a wide Vector Processing Unit (VPU). Our software tester is based on a mini-app extracted from the ELPA eigensolver library. The user-space emulator Vehave and a RISC-V vector architecture implemented on an FPGA were tested. Metrics from both systems and different vectorisation strategies were extracted, ranging from the simplest and most portable one (using autovectorisation and assisting this by fusing loops in the code) to the more complex one (using intrinsics). We observed a progressive reduction in the number of vectorised instructions, executed instructions and computing cycles with the different methodologies, which will lead to a substantial speed-up in the calculations. The obtained outcomes are crucial in advancing the porting of computational materials and molecular science codes to (post)-exascale architectures using RISC-V-based technologies fully developed within the EU. Our evaluation also provides valuable feedback for hardware designers, engineers and compiler developers, making this use case pivotal for co-design efforts.

Stochastic error propagation with independent probability distributions in LCA does not preserve mass balances and leads to unusable product compositions—a first quantification

Abstract Purpose To mitigate the effects of the triple planetary crisis of climate change, pollution and biodiversity loss, a system-based approach to estimating environmental impacts—such as life cycle assessment (LCA)—is critical. International standards recommend using uncertainty analysis to improve the reliability of LCA, but there has been debate about how to do this for many years. In particular, in order to characterise uncertainty in the inputs and outputs of each unit process in an LCA, a prevalent approach is to represent each one by an independent probability distribution. Thus, any physical relationships between inputs and outputs are ignored, which causes two potential errors during Monte Carlo simulation (a popular method for propagating uncertainty through an LCA model). First, the sum of the inputs to a unit process may not equal the sum of the outputs (i.e. there may be a mass imbalance), and second, the proportions of each input and output may be unrealistic (e.g. too much cement in a concrete production unit process). However, while some literature has discussed the problem, it has not yet been quantified. Methods Therefore, this paper investigates the extent to which existing uncertainty characterisation approaches, where there is a lack of parameterisation or correlations in databases, lead to mass imbalances and unrealistic variations in unit process compositions when performing uncertainty analysis. The matrix-based structure of LCA and the standard uncertainty analysis procedure using Monte Carlo (MC) simulation to propagate uncertainty are described. We apply the procedure to a concrete production process. Two uncertainty characterisation approaches are also explored to assess the effect of data quality scoring on mass imbalances and the mass contribution of each exchange (i.e. production compositions). Results and discussion For median data quality scores and using a typical (basic + additional uncertainty) uncertainty characterisation approach, the 1000-iteration MC simulation leads to mass imbalances ranging from − 49 to + 30% of the original mass and found that the mass imbalance exceeded existing prescribed plausibility limits on 62.7% of MC runs. On average across all exchanges, the exchange mass exceeded the 5% plausible variation limit on 77.7% of MC runs. This means that the final concrete product compositions are unlikely to be realistic or functionally equivalent to one another. We discuss the appropriateness of using universal variances for the underlying normal distribution for data quality scores (“additional uncertainty”) when input exchange quantities are of different scales. Additionally, we discuss potential solutions to the mass imbalance problem and their suitability for implementation at a database scale. Conclusions We have quantified, for the first time, the significant impact that uncertainty characterisation via independent probability distributions has on maintaining mass balances and plausible product compositions in unit processes. To overcome these challenges, databases would need to be parameterised and have the ability to sum quantities to perform mass balance checks during uncertainty analysis.

Improved modularity and new features in ipie: Toward even larger AFQMC calculations on CPUs and GPUs at zero and finite temperatures.

ipie is a Python-based auxiliary-field quantum Monte Carlo (AFQMC) package that has undergone substantial improvements since its initial release [Malone et al., J. Chem. Theory Comput. 19(1), 109-121 (2023)]. This paper outlines the improved modularity and new capabilities implemented in ipie. We highlight the ease of incorporating different trial and walker types and the seamless integration of ipie with external libraries. We enable distributed Hamiltonian simulations of large systems that otherwise would not fit on a single central processing unit node or graphics processing unit (GPU) card. This development enabled us to compute the interaction energy of a benzene dimer with 84 electrons and 1512 orbitals with multi-GPUs. Using CUDA and cupy for NVIDIA GPUs, ipie supports GPU-accelerated multi-slater determinant trial wavefunctions [Huang et al. arXiv:2406.08314 (2024)] to enable efficient and highly accurate simulations of large-scale systems. This allows for near-exact ground state energies of multi-reference clusters, [Cu2O2]2+ and [Fe2S2(SCH3)4]2-. We also describe implementations of free projection AFQMC, finite temperature AFQMC, AFQMC for electron-phonon systems, and automatic differentiation in AFQMC for calculating physical properties. These advancements position ipie as a leading platform for AFQMC research in quantum chemistry, facilitating more complex and ambitious computational method development and their applications.

Geometallurgical modeling and reconciliation with production data in a fhosphate mine

The characterization of the ore and its performance prediction in the processing unit are essential for the success of mining projects and should be part of the resource model. The greatest difficulties encountered in the elaboration of these models are the characteristics of the metallurgical variables, such as non-additivity and non-linearity, lack of uniformity of the database and the few samples of process tests. The generalization of geometallurgical models from primary attributes is a valid option when the relationships between geological characteristics and metallurgical responses are well established and will be tested in this work. Bench tests that simulate the mineral concentration process were performed and compared with geological and chemical information. Multivariate statistical analyses and computational algorithms allowed the prediction of metallurgical attributes from the properties of the rocks simulated by Gaussian sequential simulation. The results were reconciled with the plant data over a period of one year and proved to be consistent.

Fault3DNnet: A lightweight 3D seismic fault detection network with bidirectional decoding

Fault detection from seismic data is a critical component of hydrocarbon exploration and production. In recent years, deep-learning techniques have made tremendous achievements and breakthroughs in seismic fault detection. Especially U-nets, represented by FaultSeg3D and its variants, are a dominant method for 3D deep-learning-based seismic fault interpretation. The incorporation of state-of-the-art network modules or training strategies with U-nets continuously improves the performance of fault detection for field seismic data, usually at the cost of increased computational intensity and hardware consumption. We design a lightweight bidirectional decoding network, Fault3DNnet, for fault detection from 3D seismic data volume. To make it simple and concise, our Fault3DNnet is equipped with basic conventional convolution and regular operation modules. The two decoding branches in Fault3DNnet could provide complementary information and improve fault detection performance. The superiority of Fault3DNnet is verified using publicly available synthetic and four field seismic data sets. The experimental results indicate that the method predicts fault structures with improved completeness and continuity compared with FaultSeg3D. Meanwhile, the parameter and computational quantity of the network are only 1/7 and 1/5 of FaultSeg3D, respectively. With the same graphics processing unit memory consumption, Fault3DNnet can handle the size of approximately six times the volume of 3D seismic data during inferencing as that of FaultSeg3D. It helps to improve prediction stability and decrease discontinuous artifacts at the stitching boundaries when predicting the large field data in chunks. In general, our Fault3DNnet delivers superior fault detection results while greatly reducing computational intensity and memory usage.

Immersed boundary method for dynamic simulation of polarizable colloids of arbitrary shape in explicit ion electrolytes.

We develop a computational method for modeling electrostatic interactions of arbitrarily shaped, polarizable objects on colloidal length scales, including colloids/nanoparticles, polymers, and surfactants, dispersed in explicit ion electrolytes and nonionic solvents. Our method computes the nonuniform polarization charge distribution induced in a colloidal particle by both externally applied electric fields and local electric fields arising from other charged objects in the dispersion. This leads to expressions for electrostatic energies, forces, and torques that enable efficient molecular dynamics and Brownian dynamics simulations of colloidal dispersions in electrolytes, which can be harnessed to accurately predict structural and transport properties. We describe an implementation in which colloidal particles are modeled as rigid composites of small spherical beads that tessellate the surface of the particle. The electrostatics calculations are accelerated using a spectrally accurate particle-mesh-Ewald technique implemented on a graphics processing unit and regularized such that the electrostatic calculations are well-defined even for overlapping bodies. We illustrate the effectiveness of this approach with a comprehensive set of calculations: the induced dipole moments and forces for individual, paired, and lattice configurations of spherical colloids in an electric field; the induced dipole moment and torque for anisotropic particles subjected to an electric field; the equilibrium ion distribution in the double layer surrounding charged colloids; the dynamics of charged colloids; and the behavior of ions in the double layer of a polarizable colloid under the influence of an electric field.

Reducing the replication time for structural estimations: A successful replication of “An Anatomy of International Trade” using GPU computing

AbstractEaton, Kortum, and Kramarz (2011) (EKK) discovered empirical patterns from French manufacturing firms that a baseline firm heterogeneity model could not explain. The authors proposed and estimated a model that closely matches the patterns observed in French data. This paper successfully replicates their findings using author‐provided data, re‐implementing their algorithms in Python and leveraging graphics processing unit computing to significantly boost computational speed. Applying the model to Chinese manufacturing data, despite differences in context, showed that the model explains most observed patterns well.

Spin-bearing molecules as optically addressable platforms for quantum technologies

Abstract Efforts to harness quantum hardware relying on quantum mechanical principles have been steadily progressing. The search for novel material platforms that could spur the progress by providing new functionalities for solving the outstanding technological problems is however still active. Any physical property presenting two distinct energy states that can be found in a long-lived superposition state can serve as a quantum bit (qubit), the basic information processing unit in quantum technologies. Molecular systems that can feature electron and/or nuclear spin states together with optical transitions are one of the material platforms that can serve as optically addressable qubits. The attractiveness of molecular systems for quantum technologies relies on the fact that molecular structures of atomically defined nature can be obtained in endless diversity of chemical compositions. Crucially, by harnessing the molecular design protocols, the optical and spin (electronic and nuclear) properties of molecules can be tailored, aiding the design of optically addressable spin qubits and quantum sensors. In this contribution, we present a concise and collective discussion of optically addressable spin-bearing molecules – namely, organic molecules, transition metal (TM) and rare-earth ion (REI) complexes – and highlight recent results such as chemical tuning of optical and electron spin quantum coherence, optical spin initialization and readout, intramolecular quantum teleportation, optical coherent storage, and photonic-enhanced optical addressing. We envision that optically addressable spin-carrying molecules could become a scalable building block of quantum hardware for applications in the fields of quantum sensing, quantum communication and quantum computing.

Big data research is everyone's research-Making epilepsy data science accessible to the global community: Report of the ILAE big data commission.

Epilepsy care generates multiple sources of high-dimensional data, including clinical, imaging, electroencephalographic, genomic, and neuropsychological information, that are collected routinely to establish the diagnosis and guide management. Thanks to high-performance computing, sophisticated graphics processing units, and advanced analytics, we are now on the cusp of being able to use these data to significantly improve individualized care for people with epilepsy. Despite this, many clinicians, health care providers, and people with epilepsy are apprehensive about implementing Big Data and accompanying technologies such as artificial intelligence (AI). Practical, ethical, privacy, and climate issues represent real and enduring concerns that have yet to be completely resolved. Similarly, Big Data and AI-related biases have the potential to exacerbate local and global disparities. These are highly germane concerns to the field of epilepsy, given its high burden in developing nations and areas of socioeconomic deprivation. This educational paper from the International League Against Epilepsy's (ILAE) Big Data Commission aims to help clinicians caring for people with epilepsy become familiar with how Big Data is collected and processed, how they are applied to studies using AI, and outline the immense potential positive impact Big Data can have on diagnosis and management.

AUTOMATED TECHNOLOGY OF MEASUREMENT OF PHYSIOLOGICAL PARAMETERS OF FARM ANIMALS

Abstract. The development and application of automated technologies in the agricultural sector have significantly transformed the management and monitoring of farm animals. This paper presents a comprehensive analysis of existing technical systems for measuring the physiological parameters of farm animals, particularly focusing on cows. The primary goal of these technologies is to enhance the accuracy and efficiency of health monitoring and to optimize livestock management through continuous data collection and analysis. Key technological solutions include the use of wireless data transmission systems, radio monitoring devices, and unmanned aerial vehicles (UAVs). These technologies enable real-time tracking of animal health parameters such as body temperature, heart rate, movement patterns, and other critical indicators. The use of various sensors, including ear, neck, and intra-body sensors, plays a crucial role in collecting primary physiological data. These sensors are integrated into a broader system that includes radio modules with built-in controllers for converting raw data into digital formats, enabling wireless transmission to central systems for processing and storage. The radio monitoring of animals allows for the continuous tracking of physiological and behavioral parameters across large areas, providing crucial insights into their well-being. It is particularly beneficial in detecting early signs of disease or health issues, ensuring timely intervention, and preventing potential economic losses for farmers. The integration of unmanned aerial vehicles (UAVs) further expands the capabilities of monitoring systems, enabling the visual assessment of animals' behavior and movement across vast pastures. Equipped with video cameras and data relays, UAVs collect video footage that is transmitted to a central processing unit for real-time analysis and storage. The combined use of radio monitoring and UAV technology forms a comprehensive framework for livestock management, allowing farmers to make data-driven decisions, optimize resource allocation, and improve overall herd health and productivity. By leveraging these automated systems, it becomes possible to reduce the dependency on manual labor, minimize human error, and ensure that animals are closely monitored without the need for constant physical presence. This represents a significant advancement in modern animal farming, aligning with global trends toward smart farming and precision agriculture. In conclusion, the integration of automated physiological monitoring technologies in farm animal management presents an innovative solution for enhancing animal welfare, improving farm efficiency, and reducing operational costs. These systems hold considerable potential for further advancements in the field of animal health monitoring, ultimately contributing to more sustainable and productive farming practices.

High-throughput software LDPC decoder on GPU

The new or future communication systems require much higher flexibility and scalability to support diverse scenarios. In this paper, a high-throughput low-density parity-check (LDPC) decoder on graphics processing unit is presented to meet the flexible and scalable requirements. A memory-reduced forward/backward approach for the check node update is proposed. Moreover, elaborate on-chip memory allocations are conducted to improve memory bandwidth. The proposed (26112, 8448) 5G LDPC decoder on RTX4090 achieves 27.6 Gbps decoding throughput with five layered iterations, while the latency is less than 1 ms. Compared with related works, the throughput speedups obtained by the presented LDPC decoder are from 1.18×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.18\ imes$$\\end{document} to 12.4×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$12.4\ imes$$\\end{document}.

An explicit time integration method for Boussinesq approximation

AbstractThis paper presents a novel approach to the discretization of the Boussinesq equation using finite elements, coupled with an explicit pressure correction scheme. The entire numerical scheme is formulated exclusively in terms of matrix‐vector products, enabling a highly efficient numerical solution. The key advantage of this approach lies in its suitability for fast computations and seamless parallelization on graphics processing units (GPUs). We present the numerical scheme and discuss different computational examples.

Electrochemical Drawdown of U3+ and Ce3+ in Molten LiCl-KCl Using a Liquid Cadmium Cathode

Abstract The electrorefining of spent nuclear fuels is a key step to recover uranium and transuranium on separated cathodes. However, the electrolyte salts become contaminated with fission products after batches of electrorefining, and therefore the two unit processes for the drawdown of actinide and lanthanide are suggested before treatment of the contaminated salts. We investigated the electrochemical drawdown of U3+, Ce3+, and U3+ from Ce3+ in molten LiCl-KCl electrolyte using a liquid cadmium cathode (LCC) at 773 K. The drawdown mechanism of U3+ and Ce3+ was determined by cyclic voltammograms and calculating the equilibrium potential. After galvanostatic electrolysis, high recovery yields were obtained for the drawdown of U3+ and Ce3+, but the current efficiency of Ce3+ was at least twice as high as that of U3+ due to the cyclic electrolysis of U3+/U4+. Despite significant underpotential deposition of Ce3+ in the LCC, an exceptional separation factor for Ce relative to U reached 84.67 ± 17.13, which was attributed to the formed pure uranium products hindering the subsequent deposition of Ce3+. Moreover, pure uranium products and Ce-Cd intermetallic compounds were characterized by X-ray diffraction and scanning electron microscope coupled with energy dispersive X-ray spectroscopy.

Organoleptic Evaluation of Aonla (Emblica officinalis G.) Ready-To-Serve during Storage

A research experiment was conducted during the year 2019-20 and 2020-21 in the Fruit and Vegetable Processing Unit Laboratory, Department of Horticulture, College of Agriculture, Gwalior with seven different varieties of aonla viz., NA-4, NA-5, NA-6, NA-7, NA-10, Laxmi and Chakaiya for preparation of aonla RTS. Hence an attempt was made to standardized non-alcoholic based products viz., ready-to-serve with different varieties. 9 hedonic scale, out of there. From the findings, Laxmi and Chakaiya varieties were found superior with respect to colour, appearance, taste and aroma as well as flavor. The overall acceptability of processed items has been found to be significantly higher with storage periods of 30, 60, 90, and 120 days. To overcome present national and international prohibitions on the use of chemical food additives in food processing and preservation, research into biological and plant-derived food additives has considerably increased. With respect to the sensory evaluation of different aonla products was concerned, the Laxmi and Chakaiya varieties are found to be superior for the purpose of the processing industry.

Conceptual Design and Economic Optimization of Different Valorization Routes for Orange Peel Waste: The Application of the Biorefinery Concept for an Integral Use of Raw Material

Biorefineries are novel biotechnological routes designed to generate sustainable processes from renewable raw materials. The valorization of orange peel waste (OPW) provides high-value products based on their composition. The economic optimization of biorefineries through conceptual design and generation of superstructures based on the analysis of processing units is a topic of great interest. This work aimed to obtain the most profitable biorefinery through economic optimization strategies based on high-value-added products from OPW. Two stages were considered: The first stage consisted of the conceptual design of multiple OPW processing units (production of essential oil, mucic acid, phenolic compounds, biogas, among others). An OPW flow rate of 140 kg/h was selected as the base case. From the stand-alone units, a biorefinery superstructure (second stage) was designed. Finally, the units with the best mass and energy results were selected in order to maximize the net present value (NPV) and obtain an optimal biorefinery configuration. The results evidenced that the production of essential oil and biogas presented the best yields (2.61 mL and 0.028 m3 per kg OPW, respectively). This biorefinery configuration obtained an NPV of −7.7 mUSD from the base case. Through the evaluation of the different superstructure configurations, the combined production of essential oil, biogas, and mucic acid and a scale-up of over 22 times the base case generated the minimum processing scale. Under a Colombian context, the implementation of the biorefineries analyzed are promising since the minimum processing scale contemplated only 8.8% of the OPW production. Efforts to increase yields and decrease capital and operating expenses while keeping environmental impacts low should be pursued.

Black powder and mineral deposits in the LPG production and processing line: Analytical study and formation mechanisms

The accumulation of mineral deposits such as black powder causes severe impacts throughout the gas production and transport process. The phenomenon takes place from gas wells to LNG/LPG treatment plants and terminals. The complexity lies in understanding the mechanisms by which these materials are formed in random quantities, as well as their varied nature. The aim of this study is to monitor black powder and other mineral deposits formation in a chain of gas production and processing situated in the south of Algeria and unravel their root causes. To that extent, an analytical study was carried out on four samples of both mineral deposits and black powder taken from an LPG processing and production unit. The adopted analytical approach included an exploration of the textural and structural properties. It also involves a composition characterizations related to the content of metallic elements, moisture, organic and mineral matter. The particle distribution is also determined. The analytical study was conducted using gravimetric determination, XRD, XRF, SEM/EDX and Raman spectroscopy. Particle distribution was given by Laser granulometry technique. The results of the DRX/FRX and EDX characterization primarily revealed that the samples contain a variety of mineral components such as iron oxides; magnetite Fe3O4 and hematite Fe2O3, iron oxy-hydroxide FeO(OH), iron sulphide FeS2, calcite CaCO3, SiO2 and halite NaCl. The presence distribution of these components depends on the location of the sample in gas line. Granulometry study shows that black powder contamination can be spread over a wide spectrum of particle sizes ranging from 1.51 µm to 678.50 µm. This study has allowed to elucidate the nature and distribution of particles deposited at different positions in the LPG production chain and to propose probable mechanisms for their formation.

Seasonal Behaviour of Arrivals and Prices of Major Crops in Krishi Upaj Mandis of Chhattisgarh, India

Chhattisgarh state is famous for its forest wealth and mineral resources and there is abundant production of crops. According to total area of crops, Paddy (77.47 percent) and Chickpea (5.43 percent) are major crops of Chhattisgarh state. Arrivals and price data (Year 2014-15 to 2023-24) of major crops were collected from website of Chhattisgarh state Mandi Board. The study was observed that the peak arrivals & price of paddy were started in the same months November to January in both market (KUM, Bemetra & KUM, Bhatapara) except peak price season starts in August to October with narrow difference in KUM, Bhatapara. In case of Chickpea, the peak arrivals and price started in the month of March to May & September to November, respectively. The Price stability of both crops (paddy & Chickpea) was higher in KUM, Bhatapara as compared to KUM, Bemetra. The less price fluctuation of paddy as compared to chickpea in Chhattisgarh state due to purchasing directly to farmers by the Government with Minimum Support Price (MSP) and also provided the Bonus. The study was suggested that the economic development of farmers in state, the government should purchase agricultural products other than paddy and cooperate in setting up dal mills & processing units. Necessary steps should be taken to increase of marketable surplus and development of community level threshing area. The study was importance in knowing the market behaviour and price fluctuation during the peak season and the major reason behind. The studies also throw light on the aspect how to enrich the economic condition of the farmers.