Need to Know the Tribes of Tamil Nadu

Tribes are those who have seen the evolving changes from the ancient ways of life and have regulated their lives. Their social organization culture and culture is found according to the context in which they live. The so-called tribes are variously called in Tamil. Politicians refer to them as savages, literati, hill people, rain people, Tolkudi, Mudukudi, Adigudi, Purvagudi, and Gandhians refer to Gandhi as Kirijan. The tribe is an educated social group. These are people who can consistently live in a common place. Speakers of the common dialect are those who have a common one-sided social morality. Followers of common ancestral beliefs panchayat systems and religious belief systems. The Fifth Principle of Sangam Literature shows that it was the hill people who formed the way of life and culture of the ancient Tamil community according to their landscape. Thus, the majority of the indigenous people are indigenous peoples with traditional cultural identities

A Supervised Machine Learning Model for Tool Condition Monitoring in Smart Manufacturing

In the current industry 4.0 scenario, good quality cutting tools result in a good surface finish, minimum vibrations, low power consumption, and reduction of machining time. Monitoring tool wear plays a crucial role in manufacturing quality components. In addition to tool monitoring, wear prediction assists the manufacturing systems in making tool-changing decisions. This paper introduces an industrial use case supervised machine learning model to predict the turning tool wear. Cutting forces, the surface roughness of a specimen, and flank wear of tool insert are measured for corresponding spindle speed, feed rate, and depth of cut. Those turning test datasets are applied in machine learning for tool wear predictions. The test was conducted using SNMG TiN Coated Silicon Carbide tool insert in turning of EN8 steel specimen. The dataset of cutting forces, surface finish, and flank wear is extracted from 200 turning tests with varied spindle speed, feed rate, and depth of cut. Random forest regression, Support vector regression, K Nearest Neighbour regression machine learning algorithms are used to predict the tool wear. R squared, the technique shows the random forest machine learning model predicts the tool wear of 91.82% of accuracy validated with the experimental trials. The experimental results exhibit flank wear is mainly influenced by the feed rate followed by the spindle speed and depth of cut. The reduction of flank wear with a lower feed rate can be achieved with a good surface finish of the workpiece. The proposed model may be helpful in tool wear prediction and making tool-changing decisions, which leads to achieving good quality machined components. Moreover, the machine learning model is adaptable for industry 4.0 and cloud environments for intelligent manufacturing systems

Subsurface Hydrogeochemical Processes in Lower Bhavani River Basin, Tamil Nadu, India

Bhavani River is one of the important tributaries of Cauvery River, and originates in the Silent Valley range of Kerala State, India. The Lower Bhavani River Basin lies between 11 15' N and 11 45' N latitudes and 77 00' E and 77 40' E longitudes with an area of 2,475 km2. Variation of groundwater quality in an area is a function of physical and chemical parameters that are greatly influenced by geological formations, recharge-discharge mechanisms of groundwater and anthropogenic activities. The correlation of groundwater chemistry with hydrologic and geologic environments gives valuable information to understand the effect of these processes and to properly manage aquifer systems. A detailed study has been carried out to understand the subsurface hydrogeochemical processes that are responsible for the quality variation of groundwater. Residence time of groundwater was also considered to be an important parameter to study groundwater evolution. The NETPATH computer code was used to model the major subsurface processes contributing to the evolution of groundwater chemistry. The occurrence of such chemical processes as silicate weathering, carbonate dissolution, ion exchange and dilution due to rain were verified by performing inverse mass balance modeling using the same code. The net geochemical mass balance reactions between initial and final water were identified and quantified based on the flow in selected well pairs. The model output shows that dilution, ion exchange and illite precipitation are the dominant processes that control the chemistry of the groundwater along the flow paths. Calcite and NaCl dissolution are also involved to a certain extent. Reverse ion exchange process is also observed in two models

A solid life-cycle approach to control the content of sludge in wastewater

A solid life-cycle approach to control the content of sludge in wastewate

Preliminary phytochemical studies for the quantification of secondary metabolites of medicinal importance in the plant, Acalypha fruticosa Forssk

The medicinal plant, Acalypha fruticosa Forssk for the treatment of dyspepsia, stomachache, fever, jaundice, skin diseases and even as an antidote is generally distributed in different environments of tropical region in Coimbatore district of Tamil Nadu. However, its occurrence is more common in lower hills of Western Ghats and other habitats in this region where the soil is stony with low moisture. So far, there was no study on the influence of habitat conditions on the change in the content of secondary metabolites of medicinal importance in this plant. Hence to know the changes in the content of such secondary metabolites in the leaves of A. fruticosa, the present study was undertaken in three different habitats. Thin layer chromatography revealed the presence of phytochemical compounds viz., alkaloids, flavonoids and saponins in the leaves of all the three populations. Further the content of all these compounds are found to be higher in the population of dry habitats

Effect of harmones on callus induction in Maize (Zea mays L.)

Callus induction from explants is a critical process in regeneration, micropropagation and transformation of maize (Zea mays L.) plants. Formation of callus from plant tissues on culture is affected by several factors. This study revealed to establish the effect of genotype, source of explants and auxin concentration on callus induction from five genotypes UMI 757 (G1), UMI 615 (G2), UMI 112 (G3), UMI 285 (G4) and CO 1 (G5) and one hybrid CO H (M) 5 (G6). Callus induction of the six maize varieties was investigated using immature embryos (E1), leaf bits (E2), root tips (E3), hypocotyls (E4) and seeds (E5) as explants with different concentrations of hormones. In this study, immature embryo was taken from 10 to 12 days after pollination (DAP) to get maximum response. The highest percentage of callus induction was observed (99.10) in immature embryo culture and seed culture gave the highest percentage of rhizogenic callus formation when compare to immature embryo. Among the genotypes tested, CO H (M) 5 recorded the highest callus induction percentage on (2D2K2) medium composition

Design And Evaluation Of Hydraulic Suspension Without Spring In LMV

The suspension is the backbone of all vehicles its principle function is to safely carry the maximum load for all designed operating conditions. This project defines design and evaluation of hydraulic suspension without spring in LMV. Shock reduction is an important characteristic which reduces the vibration of the vehicle and carries the load safely. In this project a hydraulic suspension is used to produce hydraulic pressure that negates external forces acting on the vehicle. As a result, the suspension system is able to control vehicle movement freely and continuously. This control capability makes it possible to provide higher levels of ride comfort and vehicle dynamics which obtained with conventional suspension systems. The design was done using CREO PARAMETRIC 2.0 and the model is imported to Proficy / SCADA (IFix version 4.0) for evaluation. The major features of the hydraulic system includeActive bouncing control using by this system,A frequency-sensitive damping mechanism and active control over roll dive

Effect of drought on gas exchange and chlorophyll fluorescence of groundnut genotypes

Drought is one of the major threats to groundnut productivity, causing a greater loss than any other abiotic factor. Water stress conditions alter plant photosynthetic activity, impacting future growth and assimilating mobilization towards sink tissues. The purpose of this study was to investigate how drought impacts the photosynthesis of plants and its links to drought tolerance. The influence of reproductive stage drought on photosynthetic activity and chlorophyll fluorescence of groundnut is well studied. The experiment was conducted in Kharif 2019 (Jul-Sep), where recent series in groundnut genotypes (60 nos) sown under rainfed conditions and water stress was created by withholding irrigation for 20 days between 35-55 days after sowing in the field to simulate drought conditions. Imposition of water deficit stress reduced PS II efficiency, which significantly altered the photosynthetic rate in the leaf. Observation of gas exchange parameters viz., photosynthetic rate, stomatal conductance and transpiration rate after 20 days of stress imposition revealed that of all 60 genotypes, 20 genotypes (VG 17008, VG 17046VG 18005, VG 18102, VG 18077, VG 19572, VG 19709, VG 18111, VG19561, VG19576, VG 19620, VG 19681, VG 19688, etc.,) had better Photosynthetic rate, Stomatal conductance. Similarly, PS II efficiency analyzed through fluorescence meter revealed that among the 60 and all the genotypes given above recorded higher value in Fv/Fm. Results obtained from Cluster analysis and PCA confirmed that photosynthetic rate and Fv/Fm is useful parameter in screening adapted cultivars under drought stress. These findings lay the groundwork for a future study to decipher the molecular pathways underpinning groundnut drought resistance

A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

Sleep plays a crucial role in the well-being of human lives. Traditional sleep studies using Polysomnography are associated with discomfort and often lower sleep quality caused by the acquisition setup. Previous works have focused on developing less obtrusive methods to conduct high-quality sleep studies, and ear-EEG is among popular alternatives. However, the performance of sleep staging based on ear-EEG is still inferior to scalp-EEG based sleep staging. In order to address the performance gap between scalp-EEG and ear-EEG based sleep staging, we propose a cross-modal knowledge distillation strategy, which is a domain adaptation approach. Our experiments and analysis validate the effectiveness of the proposed approach with existing architectures, where it enhances the accuracy of the ear-EEG based sleep staging by 3.46% and Cohen's kappa coefficient by a margin of 0.038.Comment: Code available at : https://github.com/Mithunjha/EarEEG_KnowledgeDistillatio

Multilayer vectorization to develop a deeper image feature learning model

Computer-Aided Diagnosis (CAD) approaches categorise medical images substantially. Shape, colour, and texture can be problem-specific in medical imagery. Conventional approaches rely largely on them and their relationship, resulting in systems that can\u27t illustrate high-issue domain ideas and have weak prototype generalization. Deep learning techniques deliver an end-to-end model that classifies medical photos thoroughly. Due to the improved medical picture quality and short dataset size, this approach may have high processing costs and model layer restrictions. Multilayer vectorization and the Coding Network-Multilayer Perceptron (CNMP) are merged with deep learning to handle these challenges. This study extracts a high-level characteristic using vectorization, CNN, and conventional characteristics. The model\u27s steps are below. The input picture is vectorized into a few pixels during preprocessing. These pixel images are delivered to a coding network being trained to create high-level classification feature vectors. Medical imaging fundamentals determine picture properties. Finally, neural networks combine the collected features. The recommended technique is tested on ISIC2017 and HIS2828. The model\u27s accuracy is 91% and 92%