Search

Abstract The Paliyar tribe of Kadamalaikundu, Theni District of Tamil Nadu, India, has been deeply associated with forestry and ethnomedicinal practices for ages. The study aimed to identify the important medicinal plant species used by this tribe for ethnomedicinal purposes in the local vicinity. Eighty-nine informants were interviewed using the ‘specimen display’ and forest walk method, semi-structured open-ended questionnaires, interviews, and personal observations. The people from the region reported ninety-three plant species having medicinal properties. The Solanaceae family was recorded as the most dominant plant family, reporting seven different species of medicinal plants, followed by six species of the Acanthaceae family. In the study, Coccinia grandis had the highest use value (2.5), followed by Withania somnifera (2). In contrast, Cardiospermum halicacabum, Gossypium hirsutum, and Ocimum tenuiflorum with use value (1) and the ICF (Informant Consensus Factor) of value 1 accounted for 15 diseases out of forty-one categorized ailments. This study confirmed that the Paliyar tribe was dependent on a large number of medicinal plants for their primary healthcare and veterinary well-being, which require immediate conservation owing to their population fragmentation and the reluctance of the younger generation to adopt ethnomedicinal practices as occupation, which could lead to the loss of traditional knowledge as well. The study also documented the various important medicinal plant uses that could pave the way to the new pharmacological dimensions for modern wellness programs.

The concept of DioNSBSs is introduced in this study. This paper is focused on the following: The introduction is in Section 1. The preliminary definitions and results are found in Section 2. Section 3 introduces the notion of DioNSBS and its several illustrative examples.

Neutosophic vague subbisemirings (NSVSBS) are discussed here, as well as their level sets. Subbisemirings are a generalization of bisemirings, and NSVSBSs are a generalization of sub-bisemirings. A number of illustrative examples are provided to illustrate the theory for (ξ, τ )-NSVSBS over bisemiring theory. Following is an outline of the preliminary definitions and results presented in Section 2. The concept of a NSVSBS is introduced in Section 3.

The QCA is a revolutionary dominating transistor-less computational nanotechnology based on quantum dots. As such, it might be used in the next generation of quantum computational nano-electronics devices. New molecular materials like QCA are being developed for use in nanoscale devices and cables. The major objective of this research is to optimize the design and investigate numerous properties of a QCA reversible logic circuit design. These locations include, limited Toffoli Gate (TG) and Peres Gate (PG). To address the key problems associated with the physical integration of digital circuits is power consumption and power dissipation which leads to synchronization issues, this research proposes a novel reversible logic gates (RLG-TG) a single layer coplanar approach. RLG designs with minimal design area, latency, and quantum cell count (QCA) are given and implemented using a Bijection functional method. Using the QD-E (Energy) tool, the first-order energy dissipation of the proposed shape and the impact of output bias cell temperature are also investigated. The proposed circuit designs were tested using the CVSE parameters, which had high clock signal saturation energies of 9.8e-22 d (Jules), recovery times for damping factors of 1e-15 s, and relative dielectric constants of 12.90 for GaAs and AlGaAs. The number of quantum cells used by the described new RLG-TG and RLG-PG designs is decreased by 38.23 % and 21.14 %, respectively, when compared to the optimal RLG designs employed in the state-of-the-art RLG designs. In this investigation of the proposed four-bit EPG and OPG circuits occupies 18.91 % and 38.27 % less design area, requires 46.15 % and 46.25 % less number of cells, and both designs has been 66.66 % improvement in delay.

In today's digital era, the rise of hate speech has emerged as a critical concern, driven by the rapid information-sharing capabilities of social media platforms and online communities. As the internet expands, the proliferation of harmful content, including hate speech, presents considerable obstacles in ensuring a secure and inclusive online environment. In response to this challenge, researchers have embraced machine learning and deep learning methods to create automated systems that can effectively detect hate speech and conduct sentiment analysis, offering potential solutions to address this pressing issue. This survey article provides a comprehensive overview of recent advancements in hate speech detection and sentiment analysis using machine learning and deep learning models. We present an in-depth analysis of various methodologies and datasets employed in this domain. Additionally, we explore the unique challenges faced by these models in accurately identifying and classifying hate speech and sentiment in online text. Finally, we outline areas where more study is needed and suggest potential new avenues for exploration in the field of hate speech identification and sentiment analysis. Using the results of this survey, we hope to encourage the development of more effective machine learning and deep learning-based solutions to curb hate speech and promote a more inclusive online environment.

Cardiovascular disease (CVD), generally called heart illness, is a collective term for various ailments that affect the heart and blood vessels. Heart disease is a primary cause of fatality and morbidity in people worldwide, resulting in 18 million deaths per year. By identifying those who are most vulnerable to heart diseases and ensuring they receive the appropriate care, premature demise can be prevented. Machine learning algorithms are now crucial in the medical field, especially when using medical databases to diagnose diseases. Such efficient algorithms and data processing techniques are applied to predict various diseases and offer much potential for accurate heart disease prognosis. Therefore, this study compares the performance logistic regression, decision tree, and support vector machine (SVM) methods with and without Boruta feature selection. The Cleveland Clinic Heart Disease Dataset acquired from Kaggle, which consists of 14 features and 303 instances, was used for the investigation. It was found that the Boruta feature selection algorithm, which selects six of the most relevant features, improved the results of the algorithms. Among these classification algorithms, logistic regression produced the most efficient result, with an accuracy of 88.52 %.

Clean energy sources like wind and solar have a huge potential to lessen reliance on fossil fuels. Due to the stochastic nature of various energy sources, dependable hybrid systems have recently been developed. This paper's major goal is to use the existing wind and solar resources to provide electricity. A 6 kWp solar-wind hybrid system installed on the roof of an educational building is studied and optimized using HOMER (Hybrid Optimization of Multiple Energy Resources) software at different levels of reliability. At an average annual Cost of Energy (COE) of $1.156 per kWh, the system generates 1996 kWh of power overall. Investigations are made on the techno-economic characteristics of real and ideal hybrid system topologies with maximum capacity shortfalls of 0 %, 5 %, 10 %, and 20 %. The hybrid system's sensitivity analysis looks at how a capacity gap affects overall net present costs and excess power generation. A 2 kWp PV system with one string of ten 12V batteries is shown to be more cost-effective than the existing system with a COE of $0.575/kWh. The most effective configuration for utilizing the site's solar and wind resources is demonstrated to be a 5 kWp wind turbine, a 2 kWp PV system, and battery storage. A wind-solar hybrid system is more expensive than the current system. Despite this, an additional 1 kWp solar PV system may be added to the current system due to the reduction in the limit deficit from 22.3 % to 3.1 %. The findings show that solar-wind hybrid energy systems may efficiently use renewable energy sources for dispersed applications. Additionally, new research directions are noted.

Glaucoma is the leading cause of blindness worldwide. Glaucoma being a chronic eye disease results in irreversible loss of vision. Everyone is at danger for glaucoma. But, certain groups are greater risk. Hence, accurate Classification of normal and glaucoma images is significant one. In the screening and early detection of glaucoma, Cup to Disc Ratio (CDR) method plays a significant role. The automatic segmentation for early detection from Optimal Coherence Tomography (OCT) images is a fundamental task. Several research works based on OCT were contributed for early detection of glacuoma. However, inherent feature extraction and over fitting issues while performing early glaucoma detection have been researched by the research community for long time. In this paper, we propose Artificial Intelligence architecture, called Phase Quantized Polar Transformed Cellular Automaton (PQPT-CA) method that use the displacement gradients and distance gradients for early glaucoma detection. The proposed PQPT-CA method comprises of inherent feature extraction, robust feature segmentation and early glaucoma detection based on OCT. To start with, First Order Phase Quantization model is applied to the raw input image for extracting inherent features. Next, with the extracted features, Differential Polar Transformation is applied to generate robust segmentation map. Finally, glaucoma detection is performed for three different state employing Cellular Automaton model. The experiments show that our PQPT-CA method achieves state-of-the-art early detection on ACRIMA database. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated diagnosis accuracy on ACRIMA database. The PQPT-CA method increases the Diagnosis Accuracy by 24% and minimizes computational time and false positive rate by 21% and 54% respectively.

This project is aimed to reduce the risk involved in vehicle theft. So in order to overcome these challenges, we have developed.The scope of this project are outlined - Capture human faces from web cam, Create data set to train AI,Analyse face data with bank server to identify person If both data matches, then person will solution that will leverage the ML & AI to restrict card access to only the authorized users those are identified by face recognition algorithm be allowed to proceed transaction,If data mismatch, then email will be triggered to the person’s email that is configured in bank system. And transaction will be denied.

A facile and green method for synthesizing Zinc Oxide nanoparticles (ZnO NPs) was successfully carried out using unutilized sweet lime; i.e., Citrus Limetta rind pulp (U-CLRP) extract. The structural, morphological and optical studies were elucidated to confirm the crystallinity, size, and shape of the synthesized NPs. Copper doping on ZnO NPs (CZnO NPs) was carried out to enhance the optical properties. The as-prepared and doped nanoparticle's potential for efficient degradation of a commercial dye, Methylene Blue (MB), was tested under Ultraviolet (UV) and visible light radiation. ZnO NPs showed promising results for dye degradation while an improved result was witnessed for CZnO NPs. ZnO NPs showed 74% of degradation of MB dye under UV irradiation and 57% degradation under visible light radiation. CZnO NPs presented 85 and 90% degradation in the UV and visible radiation, respectively. This green reduction method utilizing biological waste sources shows a promising path for photocatalysts to be developed economically as well as efficiently in the future. These CZnO/ ZnO NPs proved their potential for embedding them as efficient catalysts for wastewater treatment plants of textile industries. HIGHLIGHTS Citrus Limetta fruit's unutilized rind pulp extract is utilized for the green synthesis of ZnO NPs.; The extract acted both as a reducing and stabilizing agent.; ZnO and Cu doped ZnO NPs were characterized and tested for their dye degradation potential.; Cu doping on ZnO NPs enhanced its photocatalytic activity under both UV and visible radiation.; This extract paves a green-reduction way for many metal oxide nanostructures.;

This research investigates the fluid flow characterization and thermohydraulic performances (THP) of rib surfaces, using computational and experimental methods. ANSYS computational fluid dynamics (CFD) software was used to predict and validate the findings in the experimental setup. Artificial rib surfaces, including polygonal and forward trapezoidal-shaped ribs, were placed in the absorber plate at different relative pitch distances (p/e) = 6.7, 10, 13.4 and relative height (e/d) = 20, and the mass flow rate of air (working fluid) varied at Reynolds numbers ranging from 2000 to 20,000. According to the validation results, the RNG renormalization k-ε model was selected for the investigation. The results show that strong turbulence occured closer to the wall surface and behind the rib surface, enhancing thermal performances due to the sharp edge shape of the rib. A polygonal rib with a pitch distance of p/e = 6.7 achieved a higher Nusselt number (Nu) and thermohydraulic performance of 2.95 at Re 4000. An empirical correlation between the Nusselt number (Nu) and friction factor (f) was developed using linear regression analysis and was compared with the predicted values. The comparison results show a close range of ±8% between the experimental and predicted values.

In this study, the combustion, performance, and emission characteristics of a multi-cylinder diesel engine using tea tree oil (TTO) blended with diethyl ether (DEE) and diesel are investigated. The blends considered are virgin diesel, B20 (80% vol. diesel +15% vol. TTO +5 vol% DEE), B30 (70% vol. diesel +22.5% vol. TTO +7.5 vol% DEE), and B40 (60% vol. diesel +30% vol. TTO +10 vol% DEE). The results show that the cetane numbers of B20, B30, and B40 blends are four to five times higher than that of TTO. Compared with diesel, those values are lower by 5.46%, 8.2%, and 10.9%, respectively. At 75% engine load, the brake power generated by B40 is comparable to that of virgin diesel, whereas those generated by B30 and B20 are 2.2% and 3.8% lower, respectively. Similarly, the average thermal efficiencies of B40, B30, and B20 are 2%, 7.4%, and 11.3% lower than that of virgin diesel, respectively. Meanwhile, all the TTO blends emit equal amounts of carbon dioxide, whereas their carbon monoxide and hydrocarbon emissions reduced significantly by 17%–35% and 13%–25%, respectively. However, the TTO blends emit 11%–19% more nitrogen oxide than diesel. It is demonstrated that B40 may be a viable replacement for virgin diesel that affords comparable performance and reduced emissions.

A wireless touch network is a distributed, self-organizing network of multiple sensors and actuators in combination with multiple sensors and a radio channel. Also, the security area of such a network can be several meters to several meters. The main difference between wireless sensor networks from traditional computer and telephone networks is the lack of a fixed infrastructure owned by a specific operator or provider. Each user terminal in a touch network is capable of acting as a terminal device only. Despite the long history of sensor networks, the concept of building a sensor network is not finally imposed and expressed in some software and hardware (platform) solutions. In this paper, the design and analysis of multicluster model of the sensor nodes in wireless sensor network with the help of solar energy. This proposed model provides the required energy to transmit the information between two end nodes in different cluster. The communication between the end to end clusters was increased based on this design. The implementation of sensory networks at the current stage depends largely on the specific needs of the industrial problem. The architecture, software, and hardware implementation technology is at an intensive development stage, attracting the attention of developers looking for a technological niche of future makers.

The separation of ethanol-water mixture is employed in the present work to produce pure ethanol, the present investigation on the separation of water from the ethanol to achieve pure ethanol by adsorption process. The different parameters like quantity of adsorbent, flow rate of feed mixture, and different adsorbents which are zeolite 3A, zeolite 4A, and silica gel are selected to study purification of ethanol by adsorption. The effect of process parameter for purification is also recorded and studied to evaluate the performance of adsorption equipment and adsorbent. The experiments are conducted at 30°C. The feed mixture is 95.6% (v/v) concentration of ethanol and 4.4% (v/v) of water. The designed adsorption column is suitable for purification of ethanol. The highest ethanol concentration 99.9443% obtained at 20 ml/min flow rate of feed mixture using 50 g of zeolite.

The genus Garcinia comprises c. 35 species in India many of which are endemic and economically important with immense medicinal properties. The two species viz., Garcinia imberti and G. travancorica are lesser-known endemic medium-sized straight-stemmed trees with horizontal branches. The trees are distributed in the restricted forest areas (700–1,500 m) of Agasthyamalai Biosphere Reserve. Both the species are dioecious trees and have male and female flowers in two different individuals, sometimes at a distance of a few kilometers from each other. A large number of mature individuals of these species have been over-exploited from Agasthyamalai Biosphere Reserve and therefore a few mature individuals and seedlings alone exist. The populations were severely fragmented and exist in a few locations of natural forest areas. The number of mature individuals recorded in G. imbertI and G. travancorica was 127±14 and 112±14 respectively in the entire distributional areas. There was an extreme fluctuation observed every year in the case of flowering and fruiting and also in the number of individuals due to the disturbance in the forest ecosystem. Both species have been included under IUCN threatened category and therefore they need effective conservation measures.

Two chalcones namely, 1,5-bis(4-hydroxy-3-methoxyphenyl)pentan-1,4-dien-3-one (BHMPD) and 2,5-bis(4-hydroxy-3-methoxybenzylidene)cyclopentanone (BHMBC) have been synthesised and characterized by microanalysis, FT-IR, mass spectra and NMR (1H and 13C) techniques. The thermal decomposition of these compounds was studied by TGA and DTA under dynamic nitrogen atmosphere at different heating rates of 10, 15 and 20 K min−1. The kinetic parameters were calculated using model-fitting (Coats–Redfern, CR) and model-free methods (Friedman, Kissinger–Akahira–Sunose, KAS and Flynn–Wall–Ozawa, FWO). The decomposition process of BHMPD and BHMBC followed a single step mechanism as evidenced from the data. Existence of compensation effect was noticed for the decomposition of these compounds. Invariant kinetic parameters are consistent with the average values obtained by Friedman and KAS isoconversional method in both compounds.

The non-isothermal decomposition properties of 2-(2-hydroxy- benzylideneamino)-3-phenylpropanoic acid [HBAPPA] have been studied using microanalysis, FT-IR, UV, DTA, DTG and TG techniques. The TG studies were carried out at different heating rates of 10, 15 and 20 K/min. The Schiff base decomposed in three stages. The kinetic parameters were deduced for each stage. A probable mechanism has been proposed for the decomposition process.

In this paper, the effect of various shapes of ribs used in Solar Air Heaters (SAHs) was discussed. The review is concentrated on the geometry of the rib and its location on the SAH panel. Both numerical and experimental works were considered for discussion with dry air and Nano fluids as a working fluid. The influence of various shapes, such as an L shape, W shape, V shape, Multiple V shape, V shape with a gap, detachable & attachable ribs etc., was analyzed. The common fact observed from this analysis is that the implementation of artificial roughness in the absorber plate results in a considerable increase in the rate of heat transfer. Further, it is observed that ‘Multiple V-shaped with open between the ribs’ results in the maximum thermal enhancement when compared to the other shapes.

The magnetite of the Fe3O4 sample was prepared using the hydrothermal autoclave method, and a prepared sample was analyzed with structural, morphological, magnetical, elements, and functional group analysis from the XRD, SEM, VSM, EDAX, and FT-IR methods, respectively. The diffraction result confirmed the prepared particle as FCC structure (Fe3O4), which corresponded to JCPDS card number 75–1372. The VSM result exhibited the hysteresis loop and loop confirmation that the prepared sample has magnetic (Ferri) properties at room temperature. From the hysteresis graph, the materials were prepared as a soft magnetic nature. SEM–EDS results confirmed that particles are highly agglomerated (octahedron) and that the material contains only the desired elements, such as iron and oxygen, with no other elements observed in the material. On the other hand, XPS has been observed with carbon due sample being exposed to the room atmosphere after preparation which may carbon contamination of the surface. TGA result confirms that the sample was highly stable up to 1250 K. FT-IR results confirmed that the Fe–O bond was observed at 600 Cm−1. The maximum specific capacitance of 302 F/g was attained in a low scan rate such as 5 mv, and it decreased up to 12 F/g. [ABSTRACT FROM AUTHOR]

This research introduces an efficacious model for incremental data clustering using Entropy weighted-Gradient Namib Beetle Mayfly Algorithm (NBMA). Here, feature selection is done based upon support vector machine recursive feature elimination (SVM-RFE), where the weight parameter is optimally fine-tuned using NBMA. After that, clustering is carried out utilizing entropy weighted power k-means clustering algorithm and weight is updated employing designed Gradient NBMA. Finally, incremental data clustering takes place in which centroid matching is carried out based on RV coefficient, whereas centroid is updated based on deep maxout network (DMN). Also, the result shows the better performance of the proposed method.. [ABSTRACT FROM AUTHOR]