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Challenges To Democratic Consolidation In Ecuador - Space For Opposition And Indigenous Representation Under Rafael Correa And Lenin Moreno
The illiberal democratic trend currently sweeping the world has emerged as a major obstacle for democratic consolidation, leading to its acceptance as the new normal of democracy. This trend has been successfully reversed in Ecuador, but the country has encountered and still grapples with several obstacles that must be overcome in order to return to the democratic consolidation route. The study focuses on the issues of consolidation, emphasizing the space allotted for participatory democracy by the ruling elites. The study examines Rafael Correas and Lenin Morenos governments in the context of the democratic consolidation framework to determine their strategic actions, behavior, and interests. The scope of the investigation will be limited with the focus made-on the space allowed for the opposition and indigenous community representation, from 2008 to 2021, to determine the extent to which Ecuadors liberal democratic process is being consolidated. 2021 Taylor & Francis Group, LLC. -
Bacterial diversity of geochemically distinct hot springs located in Maharashtra, India
Bacterial diversity of four thermally different hot springs of Ratnagiri district, Maharashtra, India, was investigated using culture-dependent and culture-independent approaches. A total of 144 bacterial cultures were isolated and identified using MALDI-TOF MS (matrix-assisted laser desorption ionization-time of flight mass spectrometry) and 16S rRNA gene sequencing. Culture-independent analysis by Ion Torrent sequencing targeting the V3 region of the 16S rRNA gene revealed the predominance of Firmicutes across all the hot springs, followed by Chloroflexi, Bacteroidetes, Cyanobacteria, Proteobacteria, Armatimonadetes, Actinobacteria, Nitrospirae, Acidobacteria, and DeinococcusThermus, with subtle differences in their abundance. At the lower taxonomic rank of genus, we noted the prevalence of Acinetobacter followed by Clostridium, Planomicrobium, Bacillus, Streptomyces, and Leptolyngbya. Metagenomics imputation using in silico approach revealed divergence in the metabolic capabilities of bacterial communities along the thermal gradient of host springs, with site TS (63C) featuring the abundant functional gene families. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Plant Identification Using Fitness-Based Position Update in Whale Optimization Algorithm
Since the beginning of time, humans have relied on plants for food, energy, and medicine. Plants are recognized by leaf, flower, or fruit and linked to their suitable cluster. Classification methods are used to extract and select traits that are helpful in identifying a plant. In plant leaf image categorization, each plant is assigned a label according to its classification. The purpose of classifying plant leaf images is to enable farmers to recognize plants, leading to the management of plants in several aspects. This study aims to present a modified whale optimization algorithm and categorizes plant leaf images into classes. This modified algorithm works on different sets of plant leaves. The proposed algorithm examines several benchmark functions with adequate performance. On ten plant leaf images, this classification method was validated. The proposed model calculates precision, recall, F-measurement, and accuracy for ten different plant leaf image datasets and compares these parameters with other existing algorithms. Based on experimental data, it is observed that the accuracy of the proposed method outperforms the accuracy of different algorithms under consideration and improves accuracy by 5%. 2022 Tech Science Press. All rights reserved. -
Green accounting and its application: A study on reporting practices of environmental accounting in India
Green Accounting is an important device for understanding the role of business ventures in the economy towards environmental security and welfare. It is a well-known term for environment and natural resources accounting. Many companies all over the world have initiated the practices of making environmental disclosures in their annual reports. However, these practices are still largely voluntary in nature. The objective of this research paper is to study the environment-related disclosures of companies taken from Nifty 50 based on the summary of Global Reporting Standards. Content Analysis, both sector-wise and keyword-wise is used on the annual reports of 29 sample companies using MAXQDA software. A high count of the formulated keywords is observed in some relevant sectors of Energy, Cement and Metals. 2022 Inderscience Enterprises Ltd. -
A simple and efficient synthesis of imidazoquinoxalines and spiroquinoxalinones via pictect-spengler reaction using Wang resin
An efficient approach for the synthesis of various imidazoquinoxalines and spiroquinoxalinones has been reported from 2-(1H-imidazol-1-yl) aniline and different aldehydes using Wang-OSO3H as a reusable catalyst to get in good yields. The reaction condition has been optimized by screening in various solvents and a gram scale experiment has also demonstrated. Further, the substrate scope of the reaction has also been well demonstrated. 2021 Taylor & Francis Group, LLC. -
Structural equation based model to investigate the moderating effect of fear of COVID using partial least square method
This study assesses the magnitude of work life integration among health care workers with the help of positive psychology constructs in COVID-19 crisis. The effect of optimistic approach and sense of belongingness is studied on the performance-oriented healthcare workers and how it influenced their withdrawal cognition. The moderating effect of fear of corona disease is also analysed on performance orientor and withdrawal cognition. Empirical data derived through face-to-face interactions of 357 health care professionals using partial least squares-structural equation modelling PLS-SEM 3.3.3 provides the detailed analysis of the model (measurement and structural). The results indicate that optimistic approach and sense of belongingness contribute towards performance-oriented health care work with R2 value of 79% (? =.533; t = 7.042; p< 0.00) and (? =.0.391; t = 5.43; p< 0.00) respectively. Performance orientor show negative relation with withdrawal cognition (? = -0.122.; t = 2.11; p< 0.00) and R2-value of 74.8%. The moderation effect of fear of corona disease shows negative affect on performance orientor (? = -0.044.; t = 26.10; p< 0.01); R2-value of 79.3% and positive interaction on the withdrawal cognition (? = 0.844; t = 38.42; p< 0.00) and R2-value of 76.4%. 2022 Taru Publications. -
Detection and localization for watermarking technique using LSB encryption for DICOM Image
Watermarking is an effective way of transferring hidden data from one place to another, or proving ownership of digital content. The hidden data can be text, audio, images GIF etc., the data is embedded in a cover object usually an image or a video sequence. Usually the watermarking system(s) rely on their hidden aspect, as their primary security measure, once this is established that the cover object is counting some hidden data, then it is generally possible to recover the hidden information. The author proposed an in-genuine technique for DICOM color image water marking by combining Multi Quadrant LSB with truly random mixed key cryptography. This system provides a high level of security by just the water marking technique, as it breaks the cover image into up to four quadrants, & does LSB replacement of two bytes each quadrant. The bit sequence as the quadrant sequence can be randomized to increase the randomness, use of truly random mixed key cryptography, by using a pre shared, variable length, truly random, private key, turns hidden data into noise, which can only be recovered by having the private key. Thus, the proposed technique truly diminishes the probability of recovering hidden data, even if it is detected that something is hidden in cover object. 2022 Taru Publications. -
Covid-19 ct lung image segmentation using adaptive donkey and smuggler optimization algorithm
COVID'19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a newinf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the CT images automatically. For the worst segmentation results, the Edge-Attention Representation (EAR) is optimized using Adaptive Donkey and Smuggler Optimization (ADSO). The edges which are identified by the ADSO approach is utilized for calculating dissimilarities. An IFCM (Intuitionistic Fuzzy C-Means) clustering approach is applied for computing the similarity of the EA component among the generated edge maps and Ground-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation (SSS) structure is designed using the Randomly Selected Propagation (RP) technique and Inf-Net, which needs only less number of images and unlabelled data. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed using a Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all the advantages of the disease segmentation done using Semi Inf-Net and enhances the execution of multi-class disease labelling. The newly designed SSMCS approach is compared with existing U-Net++, MCS, and Semi-Inf-Net. factors such as MAE (Mean Absolute Error), Structure measure, Specificity (Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-Alignment Measure are considered for evaluation purpose. 2022 Tech Science Press. All rights reserved. -
Machine Learning Technology-Based Heart Disease Detection Models
At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Nae Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared. Copyright 2022 Umarani Nagavelli et al. -
Super strongly perfect graphs
Some of the published results on super strongly perfect graphs are found to be erroneous. We provide some examples and counter examples on the concepts associated with super strongly perfects. 2022 World Scientific Publishing Company. -
Product specific determinants of electronic gadget purchase intention - a case of the purchase behaviour of Indian youth
This study investigated the impact of product specific features of electronic gadgets on the purchase intention on the Indian youth. The study was quantitative in nature and data was collected from 650 young electronic gadget consumers in Bengaluru, India using structured questionnaires. Descriptive statistics and structural equation modelling (SEM) were used for data analysis. Brand image, product design, and country of origin are referred as product evaluation attributes; and corporate identity were identified as the determinants of purchase intention. Respondents were neutral regarding the role of product evaluation attributes and corporate identity in their purchases, but acknowledged these factors' importance. Findings implied a positive and significant influence of product evaluation attributes on the corporate identity of companies, and purchase intention of the youth. However, corporate identity did not influence purchase intention, clearly indicating that only product specific features, such as brand, design and country of origin are considered when youngsters purchase gadgets. Copyright 2022 Inderscience Enterprises Ltd. -
Optimized Load Balancing Technique for Software Defined Network
Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission media loads. These loads also contribute to decreasing the performance of Software- Defined Networks. This work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the load. We proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network load. The simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques. 2022 Tech Science Press. All rights reserved. -
Blockchain for Securing Healthcare Data Using Squirrel Search Optimization Algorithm
The Healthcare system is an organization that consists of important requirements corresponding to security and privacy, for example, protecting patients medical information from unauthorized access, communication with transport like ambulance and smart e-health monitoring. Due to lack of expert design of security protocols, the healthcare system is facing many security threats such as authenticity, data sharing, the conveying of medical data. In such situa-tion, block chain protocol is used. In this manuscript, Efficient Block chain Network for securing Healthcare data using Multi-Objective Squirrel Search Optimization Algorithm (MOSSA) is proposed to generate smart and secure Healthcare system. In this the block chain is a decentralized and the distributed ledger device that consists of various blocks linked with digital signature schemes, consensus mechanisms and chain of hashing, offers highly reliable storage capabilities. Further the block chain parameters, such as block size, transac-tion size and number of block chain channels are optimized with the help of MOSSA. With the evolution of the MOSSA provide new features for enhancing security and scalability. The simulation process is executed in the JAVA platform. The experimental result of the proposed method shows higher throughput of 26.87%, higher efficiency of 34.67%, lowest delay of 22.97%, lesser computational overhead of 37.03%, higher storage cost of 34.29% when compared to the existing method such as Block chain-ECIES-HSO, Block chain-hybrid GO-FFO, Block chain-SDN-HSO algorithm for healthcare technologies. 2022, Tech Science Press. All rights reserved. -
Promoting Sustainability Through Corporate Social Responsibility: Insights and Barriers of Medium-Sized Manufacturing Enterprises in India
The current study aims to explore if effective corporate social responsibility leads to corporate sustainability in medium-sized manufacturing enterprises. Using the factors, an exploratory examination was performed to assess their suitability in Indian context, and data were collected from 121 manufacturing companies using structured questionnaire based on pretested scale, and the proposed relationships were tested through partial least square structural equation modelling (PLS-SEM). The results show overall model fit and empirical examinations support causal relationships between effective corporate social responsibility and corporate sustainability (CS). The results indicated that effective CSR mediated the relationships between corporate sustainability and integration of CSR into corporate policy and priority of the board towards CSR. The results of this study are useful for medium-sized enterprises to establish a formal approach towards CSR and meet the needs of business and society in the 21st century. Copyright 2022, IGI Global. -
The influences of lateral groups on 4-cyanobiphenyl-benzonitrile- based dimers
Cyanobiphenyl-based compounds are known to display RT or low melting liquid crystals in a single-component system or composites. Herein, we discuss the influence of laterally substituted groups (-CN, -F, -H) on 4-[?-(4-cyanobiphenyl-4-yloxy)alk-1-yloxy]benzonitrile. Three series of new dimers were synthesised by using 4-cyano-4-hydroxybiphenyl connected via flexible spacers with different number of carbon atoms to 4-hydroxyphthalonitrile/ 2-fluoro-4-hydroxy benzonitrile/ 4-hydroxy benzonitrile. Their self-assembly in LC phases assessed by polarising optical microscopy (POM), differential scanning calorimetry (DSC) and X-ray diffraction studies, and their behaviours are compared with related non-substituted (-H) model compound. UV-Visible and fluorescent experiments confirm the strong aggregation, the intensities of emission decrease as we move from CN?F?H substitutions. A representative dimer from each series covering the aspect of polarity and flexibility have been simulated using 1000 minimisation steepest descent and CHARMM force filed to examine their self-assembly. This work helps to understand the influence of lateral groups, connecting spacers on the LC behaviour of dimers. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Machine Learning Technique to Detect Radiations in the Brain
The brain of humans and other organisms is affected in various ways through the electromagnetic field (EMF) radiations generated by mobile phones and cell phone towers. Morphological variations in the brain are caused by the neurological changes due to the revelation of EMF. Cellular level analysis is used to measure and detect the effect of mobile radiations, but its utilization seems very expensive, and it is a tedious process, where its analysis requires the preparation of cell suspension. In this regard, this research article proposes optimal broadcasting learning to detect changes in brain morphology due to the revelation of EMF. Here, Drosophila melanogaster acts as a specimen under the revelation of EMF. Automatic segmentation is performed for the brain to attain the microscopic images from the prejudicial geometrical characteristics that are removed to detect the effect of revelation of EMF. The geometrical characteristics of the brain image of that is microscopic segmented are analyzed. Analysis results reveal the occurrence of several prejudicial characteristics that can be processed by machine learning techniques. The important prejudicial characteristics are given to four varieties of classifiers such as nae Bayes, artificial neural network, support vector machine, and unsystematic forest for the classification of open or nonopen microscopic image of D. melanogaster brain. The results are attained through various experimental evaluations, and the said classifiers perform well by achieving 96.44% using the prejudicial characteristics chosen by the feature selection method. The proposed system is an optimal approach that automatically identifies the effect of revelation of EMF with minimal time complexity, where the machine learning techniques produce an effective framework for image processing. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -
Pt Nanospheres Decorated Graphene-?-CD Modified Pencil Graphite Electrode for the Electrochemical Determination of Vitamin B6
An electrochemical sensor for Vitamin B6 determination has been prepared by the electrochemical deposition of Pt nanospheres on graphene-?-CD coated Pencil Graphite Electrode (PGE). Cyclic voltammetric (CV) and electrochemical impedance spectroscopic (EIS) studies were employed to explore the electrochemical properties of the modified electrode. The physicochemical properties of the modified electrodes were characterized by X-ray photoelectron spectroscopy (XPS), Scanning electron microscopy (SEM), Transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and optical profilometric studies. The experimental conditions such as effect of scan rate, concentration and pH were optimized. The linear dynamic range for the determination of Vitamin B6 was found to be 5nM to 205nM. The low level of detection limit (1.2nM) implies the high sensitivity of the process. The suggested method was effectively employed for the electrocatalytic evaluation of Vitamin B6 in different juice samples. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Wireless Sensor Data Acquisition and Control Monitoring Model for Internet of Things Applications
This article focuses on providing solutions for one important application termed as agriculture. In India, one major occupation for people living in urban and rural areas is agriculture where an economic rate depends only on the crops they yield. In such cases, if an intelligent monitoring device is not integrated then it becomes difficult for the farmers to grow their crops and to accomplish marginal income from what they have invested. Also existing methods have been analyzed in the same field where some devices have been installed and checked for increasing the productivity of horticulture crops. But existing methods fail to install an intelligent monitoring device that can provide periodic results within short span of time. Therefore, a sensor based technology with Internet of Things (IoT) has been implemented in the projected work for monitoring major parameters that support the growth and income of farmers. Also, an optimization algorithm for identifying the loss in different crops has been incorporated for maximizing the system boundary and to transmit data to farmers located in different areas. To prove the cogency of proposed method some existing methods have been compared and the results prove the projected technique produces improved results for about 58%. 2022 SulaimaLebbe Abdul Haleem et al. -
Brain image classification using time frequency extraction with histogram intensity similarity
Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done. 2022 CRL Publishing. All rights reserved. -
Photocatalytic degradation of methylene blue and metanil yellow dyes using green synthesized zinc oxide (Zno) nanocrystals
In this work, ZnO nanocrystals (NCs) have been effectively synthesized by a simple, efficient and cost-effective method using coconut husk extract as a novel fuel. The synthesized NCs are characterized by UV-Vis, XRD, FT-IR, SEM, EDX, Raman and PL studies. The obtained ZnO were found to be UV-active with a bandgap of 2.93 eV. The X-ray diffraction pattern confirms the crystallinity of the ZnO with hexagonally structured ZnO with a crystallite size of 48 nm, while the SEM analysis reveals the hexagonal bipyramid morphology. Photocatalytic activities of the synthesized ZnO NCs are used to degrade methylene blue and metanil yellow dyes. 2021 by the authors. Licensee MDPI, Basel, Switzerland.