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Education suffering within structural inequalities: A Critical Discourse Analysis of a policy framework
Education acts as an important catalyst for socioeconomic and democratic evolution in society and is a critical tool for building an equitable system. In our paper, we have historicized one of the most important educational policies, viz. Samagra Shiksha Abhiyan (SAMSA) in India that carries large expectations to minimize the educational divide. We have studied the policy through the lens of Political Economy and have further critiqued it through the framework of Critical Discourse Analysis. We find in our paper that the budget allocated to SAMSA was revised in 2022, from its preceding years with a 28 per cent slash. We critically reflect on the principles mentioned in the policy and find that although there has been an attempt to mitigate the hazards of banking education the Public-Private Partnership initiative reinforces struggles for equitable education, and further, the privatization sets the government free from any accountability. Moreover, a constitutional right like the Right to Education (RTE) is not sufficient enough to meet the goals of universalisation of education. Besides, we analyse the principles such as Education for All, Equity, Equal Opportunity, Access, Gender Concern, Centrality of teacher, Moral Compulsion, and Convergent and integrated system of education management, and argue that although some of the facets of societal structural inequalities are addressed, however, there exists hardly a proper roadmap that could be monitoring the process of creating an inclusive educational paradigm. 2023, Institute for Education Policy Studies. All rights reserved. -
Synthesis of 4H-3,1-Benzothiazin-4-Ones via C-N/C-S Bond Forming Reactions
A Phosphine-free and effective process has been expressed for the formulation of N,S-heterocycles following a C-N/C-S bond forming reactions. The described process operates through EDC-HCl-mediated heterocyclization of diverse isothiocyanates and bis-nucleophiles to deliver 1,3-thiazinone derivatives, which eliminates the use of hazardous reagents. The developed protocol was found applicable over a wide range of substrates in delivering N,S-heterocycles in excellent yields at room temperature and short reaction time. 2022 Taylor & Francis Group, LLC. -
Improved Security of the Data Communication in VANET Environment Using ASCII-ECC Algorithm
Now-a-days, with the augmenting accident statistics, the Vehicular Ad-hoc Networks (VANET) are turning out to be more popular, helping in prevention of accidents in addition to damage to the vehicles together with populace. In VANET, message can well be transmitted within a pre-stated region to attain systems safety and also improveits efficacy. Ensuring authenticity of messages is a challenge in such dynamic environment. Though few researchers worked on this, security level is very less. Hence enhanced communicationsecurity on the VANET environment utilizing the American Standard Code for Information Interchange centred Elliptic Curve Cryptography (ASCII-ECC) is proposedin this paper. The network design is definedinitially. Subsequently, the entire vehicles get registered to the Trusted Authority (TA); similarly, all vehicle users areregistered with their On-Board Unit (OBU). This is followed byMedian-centred K-Means (MKM) performs the cluster formation together with Cluster Head Selection (CHS). Next, TA takes care of the verification procedure. Modified Cockroach Swarm Optimization (MCSO) calculates the shortest path and the ASCII-ECC carries out the secure data communication if the vehicle is an authorized one. If not, TA sends the alert message for discarding the request. The system renders better performance when it was weighed against the top-notch methods. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Exploring the effect of Covid-19 on herding in Asian financial markets
We examine herding behavior before, during, and after the Covid-19 pandemic in eight prominent Asian stock markets. Daily stock returns for the period Jan- 2018 to July- 2022 in the markets were investigated using the models prescribed by Chang et al., (2000) and Chiang and Zheng (2010). The empirical results provide strong support to earlier studies by providing robust evidence of herding in Vietnam, Indonesia, India, South Korea, and Singapore when the market is bullish and Indonesia and Vietnam also exhibit herding when the market is bearish. Herding tendency is dominant for Vietnam, India, and Indonesia during the pandemic with the post-pandemic time being more potent for China and Vietnam. Notably, an anti-herding tendency is found in China, Hong Kong, and Singapore. As a policy measure, efficient information dissemination, deterrence of insider trading, and regulation of mispricing can be undertaken. 2022 -
An in-silico pharmacophore-based molecular docking study to evaluate the inhibitory potentials of novel fungal triterpenoid Astrakurkurone analogues against a hypothetical mutated main protease of SARS-CoV-2 virus
Background: The main protease is an important structural protein of SARS-CoV-2, essential for its survivability inside a human host. Considering current vaccines' limitations and the absence of approved therapeutic targets, Mpro may be regarded as the potential candidate drug target. Novel fungal phytocompound Astrakurkurone may be studied as the potential Mpro inhibitor, considering its medicinal properties reported elsewhere. Methods: In silico molecular docking was performed with Astrakurkurone and its twenty pharmacophore-based analogues against the native Mpro protein. A hypothetical Mpro was also constructed with seven mutations and targeted by Astrakurkurone and its analogues. Furthermore, multiple parameters such as statistical analysis (Principal Component Analysis), pharmacophore alignment, and drug likeness evaluation were performed to understand the mechanism of protein-ligand molecular interaction. Finally, molecular dynamic simulation was done for the top-ranking ligands to validate the result. Result: We identified twenty Astrakurkurone analogues through pharmacophore screening methodology. Among these twenty compounds, two analogues namely, ZINC89341287 and ZINC12128321 showed the highest inhibitory potentials against native and our hypothetical mutant Mpro, respectively (?7.7 and ?7.3 kcal mol?1) when compared with the control drug Telaprevir (?5.9 and ?6.0 kcal mol?1). Finally, we observed that functional groups of ligands namely two aromatic and one acceptor groups were responsible for the residual interaction with the target proteins. The molecular dynamic simulation further revealed that these compounds could make a stable complex with their respective protein targets in the near-native physiological condition. Conclusion: To conclude, Astrakurkurone analogues ZINC89341287 and ZINC12128321 can be potential therapeutic agents against the highly infectious SARS-CoV-2 virus. 2022 Elsevier Ltd -
Modelling of critical success factors for blockchain technology adoption readiness in the context of agri-food supply chain
The agri-food supply chain is continuously facing several challenges; the most severe are food quality and safety issues. These issues debilitate the performance of the supply chain and often harm the consumer's health. Therefore, there is an urgent need to address food quality and safety assurance in the supply chain. Blockchain technology (BCT) holds the potential to resolve these issues by enhancing security and transparency. The present study explores the critical success factors (CSFs) of BCT adoption readiness in the AFSC. Initially, CSFs are identified through a literature survey and finalised by experts' opinion. The finalised factors are prioritised using the fuzzy best-worst method, followed by sensitivity analysis. The results reflect that 'food quality control', 'provenance tracking and traceability', and 'partnership and trust' as the top three success factors. The study's findings will assist policymakers, managers, and practitioners in strategising the decision-making process while BCT dissemination. Copyright 2023 Inderscience Enterprises Ltd. -
Toxic heavy metal ion detection by fluorescent nanocarbon sensor derived from a medicinal plant
In the twenty-first century, the importance of environmental pollution sensing cannot be overstated. Cadmium is a well-known poisonous heavy metal that seriously endangers human health. In terms of screening for poisons and diagnosing illnesses, the sensitive and focused detection of cadmium in cells is crucial. In this work, we developed Green fluorescent Carbon nanomaterial (Carbon nanomaterial) synthesized from a novel precursor, Justicia Wynaadensis, by the most eco-friendly, cost-effective hydrothermal method, which acts as a fluorescent probe for Cadmium fluorescence sensing technique with the concentration range of 1 nM1 M. The sensor displays remarkable linear detection with a 5.235 nM detection limit. 2022 The Author(s) -
A Hybrid AES with a Chaotic Map-Based Biometric Authentication Framework for IoT and Industry 4.0
The Internet of Things (IoT) is being applied in multiple domains, including smart homes and energy management. This work aims to tighten security in IoTs using fingerprint authentications and avoid unauthorized access to systems for safeguarding user privacy. Captured fingerprints can jeopardize the security and privacy of personal information. To solve privacy- and security-related problems in IoT-based environments, Biometric Authentication Frameworks (BAFs) are proposed to enable authentications in IoTs coupled with fingerprint authentications on edge consumer devices and to ensure biometric security in transmissions and databases. The Honeywell Advanced Encryption Security-Cryptography Measure (HAES-CM) scheme combined with Hybrid Advanced Encryption Standards with Chaotic Map Encryptions is proposed. BAFs enable private and secure communications between Industry 4.0s edge devices and IoT. This works suggested schemes evaluations with other encryption methods reveal that the suggested HAES-CM encryption strategy outperforms others in terms of processing speeds. 2023 by the authors. -
A Representation-Based Query Strategy to Derive Qualitative Features for Improved Churn Prediction
The effectiveness of any Machine Learning process depends on the accuracy of annotated data that is used to train a learner. However, manual annotation is expensive. Hence, researchers adopt a semi-supervised approach called active learning that aims to achieve state-of-the-art performance using minimal number of samples. Although it boosts classifier performance, the underlying query strategies are unable to eliminate redundancy in selected samples. Redundant samples lead to increased cost and sub-optimal performance of learner. Inspired by this challenge, the study proposes a new representation-based query strategy that selects highly informative and representative subsets of samples for manual annotation. Data comprises messages of a set of customers sent to a service provider. Series of experiments are conducted to analyze the effectiveness of the proposed query strategy, called 'Entropy-based Min Max Similarity' (E-MMSIM), in the context of topic classification for churn prediction. The foundation of E-MMSIM is an algorithm that is popularly used to sequence proteins in protein databases. The algorithm is modified and utilized to select the most representative and informative samples. The performance is evaluated using F1-score, AUC and accuracy. It is observed that 'E-MMSIM' outperforms popular query strategies, and improves performance of topic classifiers for each of the 4 topics of churn prediction. The trained topic classifiers are used to derive qualitative features. These features are further integrated with structured variables for the same group of customers to predict churn. Experiments provide evidence that inclusion of qualitative features derived using E-MMSIM, enhance the performance of churn classifiers by 5%. 2013 IEEE. -
Unravelling the potential plant growth activity of halotolerant Bacillus licheniformis NJ04 isolated from soil and its possible use as a green bioinoculant on Solanum lycopersicum L.
Immensely expanding world population and narrowing arable land for agriculture is a mighty concern faced by the planet at present. One of the major reasons for decline in arable lands is the increased soil salinity, making it unfavourable for crop cultivation. Utilisation of these saline land for agriculture is possible with suitable invention for improving the soil quality. Biofertizers manufactured out of Plant Growth Promoting Rhizobacteria is one such innovation. In the present study, Bacillus licheniformis NJ04 strain was isolated and studied for its halotolerance and other effective plant growth promoting traits. The NJ04 strain was able to tolerate salt up to 10% and highlighted remarkable antifungal activity against common fungal phytopathogens. The preliminary seed germination test in Solanum lycopersicum seeds revealed a significant increase in root length (16.29 0.91 cm) and shoot length (9.66 0.11 cm) of treated plants as compared with the control plants and thereby shows its possible use as a green bioinoculant in agriculture and an ideal candidate to compete with salt stress. 2022 Elsevier Inc. -
Analgesic and Anti-Inflammatory Potential of Indole Derivatives
Some indole analogues show a good analgesic activity but on the other hand, it has some serious side effects like gastric ulcer. Therefore, there is still a need to develop derivatives of non-steroidal anti-inflammatory drugs (NSAIDs) with fewer side effects. For this purpose, some indole derivatives were prepared with objectives to develop new derivatives with maximum efficacy and minimum side effects. 1-(1H-indol-1-yl)-2-(sstituephenoxy)-ethan-1-one derivatives (M1M4) were analyzed further by thin-layer chromatorgarphy (TLC), melting point, IR, and 1H-NMR. The synthesized compounds then underwent oral toxicity studies that include hematological, biochemical, and histopathological findings. The compound was then evaluated for invivo anti-inflammatory and analgesic activities on carrageenan-induced rat paw edema and acetic acid-induced writhing methods. As a result of the biological activities, promising results were obtained in the compound M2 (2-(2-aminophenoxy)-1-(1H-indol-1-yl)ethanone) and it was subjected to further studies. It was found that compound M2 was practically nontoxic, and no clinical abnormalities were found in hematology and biochemistry, correlated with histopathological observation. It also showed significant anti-inflammatory and analgesic activities at its oral high dose (400 mg/kg). The study suggested that compound M2 was found to have significant anti-inflammatory and analgesic activities. The possible mechanism of M2 might suggest being act as a central anti-nociceptive agent and peripheral inhibitor of painful inflammation. The possible mechanism of action of the compounds whose biological activity was evaluated was explained by molecular docking study against COX-1 and COX-2, and the most active compound M2 formed ?9.3 and ?8.3 binding energies against COX-1 and COX-2. In addition, molecular dynamics (MD) simulation of both M2s complexes with COX-1 and COX-2 was performed to examine the stability and behavior of the molecular docking pose, and the MM-PBSA binding free energies were measured as ?153.820 11.782 and ?172.604 9.591, respectively. Based on computational ADME studies, compounds comply with the limiting guidelines. 2022 Taylor & Francis Group, LLC. -
Exploring the agency of policy through ecological urbanism for climate action: water and sanitation systems of Bengaluru
The cities of the world have been the exploiters of resources and the largest generators of waste. This paper explores the concept of Ecological Urbanism as a framework to convert cities from being waste generators to resource producers. The example of the wastewater from Bengaluru going into the lakes of Kolar is studied. The treated wastewater of the city reaches Kolar to fill its lakes, which subsequently recharges the groundwater. One citys waste becomes anothers resource in this process. The case of Kolar-Bengaluru is studied while asking critical questions of urban-rural planning with ecology as a main premise. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Sesquiterpenoid-rich Java Ginger rhizome extract prompts autophagic cell death in cervical cancer cell SiHa mainly by modulating cellular redox homeostasis
Java Ginger or Curcuma zanthorrhiza Roxb. has long gained focus among tribal people of Java, for its medicinal properties mainly against gynaecological challenges. The present study aims to identify the most potent phytocompound present in the extract and determine primary mode of action accountable for cytotoxic activity of Curcuma zanthorrhiza rhizome extract against HPV16-positive SiHa cervical cancer cells. The phytochemically-rich extract of rhizome (CZM) was capable to inhibit proliferation of target cells in a dose-dependent manner with an IC50 of 150?g/ml. Dysregulation of intercellular antioxidant defence system resulted to surges in ROS and RNS level, increased calcium concentration and compromised mitochondrial membrane potential. Nucleus got affected, cell cycle dynamics got impaired while clonogenicity and migration ability diminished. Expression of viral oncogenes E7 and E6 decreased significantly. Accumulation of toxic cell metabolite and decrease in level of essential ones continued. Finally, alteration in PI3K/AKT/mTOR signalling route was followed by onset of autophagic cell death concomitant with the upregulated expression of Beclin1, Atg5-12 and LC3II. Curcumin and a novel crystal as well as few phyto-fractions were isolated by column chromatography. Of these, curcumin was found to be most potent in inducing cytotoxicity in SiHa while two other fractions also showed significant activity. Thus, CZM acted against SiHa cells by inducing autophagy that commences in compliance to the changes in PI3K/AKT/mTOR pathway mainly in response to oxidative stress. To the best of our knowledge this is the first report of Curcuma zanthorrhiza Roxb. inducing autophagy. 2022, King Abdulaziz City for Science and Technology. -
In Silico Identification of 1-DTP Inhibitors of Corynebacterium diphtheriae Using Phytochemicals from Andrographis paniculata
A number of phytochemicals have been identified as promising drug molecules against a variety of diseases using an in-silico approach. The current research uses this approach to identify the phyto-derived drugs from Andrographis paniculata (Burm. f.) Wall. ex Nees (AP) for the treatment of diphtheria. In the present study, 18 bioactive molecules from Andrographis paniculata (obtained from the PubChem database) were docked against the diphtheria toxin using the AutoDock vina tool. Visualization of the top four molecules with the best dockscore, namely bisandrographolide (?10.4), andrographiside (?9.5), isoandrographolide (?9.4), and neoandrographolide (?9.1), helps gain a better understanding of the molecular interactions. Further screening using molecular dynamics simulation studies led to the identification of bisandrographolide and andrographiside as hit compounds. Investigation of pharmacokinetic properties, mainly ADMET, along with Lipinskis rule and binding affinity considerations, narrowed down the search for a potent drug to bisandrographolide, which was the only molecule to be negative for AMES toxicity. Thus, further modification of this compound followed by in vitro and in vivo studies can be used to examine itseffectiveness against diphtheria. 2023 by the authors. -
Robust Deep Learning Empowered Real Time Object Detection for Unmanned Aerial Vehicles based Surveillance Applications
Surveillance is a major stream of research in the field of Unmanned Aerial Vehicles (UAV), which focuses on the observation of a person, group of people, buildings, infrastructure, etc. With the integration of real time images and video processing approaches such as machine learning, deep learning, and computer vision, the UAV possesses several advantages such as enhanced safety, cheap, rapid response, and effective coverage facility. In this aspect, this study designs robust deep learning based real time object detection (RDL-RTOD) technique for UAV surveillance applications. The proposed RDL-RTOD technique encompasses a two-stage process namely object detection and objects classification. For detecting objects, YOLO-v2 with ResNet-152 technique is used and generates a bounding box for every object. In addition, the classification of detected objects takes place using optimal kernel extreme learning machine (OKELM). In addition, fruit fly optimization (FFO) algorithm is applied for tuning the weight parameter of the KELM model and thereby boosts the classification performance. A series of simulations were carried out on the benchmark dataset and the results are examined under various aspects. The experimental results highlighted the supremacy of the RDL-RTOD technique over the recent approaches in terms of several performance measures. 2022 River Publishers. -
Dependence of Eigen frequency on the output performance of a piezoelectric nano sensor: A comparative study
Energy harvesting is an approach to generating electricity that uses the energy of the local environment directly. Instead of relying on batteries or power generated elsewhere, the world needs a new generation of energy-producing products. Generation or accumulation and energy consumption must be balanced when designing such a system. Before implementation into the real world, simulations are available for optimizing device designs, comparing them, predicting them, and formulating methodologies. In this comparative study, using a finite element simulation software COMSOL Multiphysics, an Eigen frequency analysis is performed to validate the relationship between Eigen frequency and output voltage and also shows how much the selection of a piezo electric base material depends on the natural frequency, excitation frequency, and output voltage relationship. A piezoelectric sensor is constructed with an initial base material and using material sweeps, another six materials are added and switched for the purpose. After applying allowable stress and frequency analysis, measured the output electric potential for the first five eigenmodes. Selecting seven different piezo electric base materials that possess unique properties from traditional lead zirconate titanate (PZT) to upcoming polymer material polyvinylidene fluoride (PVDF), there reveals the role of selecting suitable energy harvesting medium in generating proper output. From the experimented materials, zinc oxide (ZnO), aluminum nitride (AlN), and PVDF are found to be reliable towards the resonance concept and attaining optimum electric potential. Thus, our study strongly supports previous works carried out by the researchers regarding the effect of various piezo electric base materials on output response. 2023 -
Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis
Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individuals sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individuals tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset. 2023 Tech Science Press. All rights reserved. -
Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems
The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches. 2023 CRL Publishing. All rights reserved. -
Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence
In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods. 2023 Authors. All rights reserved. -
Resilience in Children from Different Socioeconomic Backgrounds: An Exploratory Study
Poverty, violence, substance abuse, family dissonance and illness represent a few potential vulnerabilities in the lives of children who are at risk of failing in their future prospects. It is thus essential to explore resilience in children, owing to the excess or deficit of exposure and access in a childs life. This study aims at exploring the resilience of children of the age group 710years, from different socioeconomic backgrounds. The socioeconomic status was determined using the Kuppuswamy socioeconomic scale and these children had parents with authoritarian and permissive parenting styles which were screened through the Parenting Styles and Dimensions Questionnaire which act as risk factors for the children. Data was collected through individual semi-structured interviews with the participants and was analysed using thematic analysis. For the lower socioeconomic status group, the main themes identified were social interaction and competence, overcoming distress and future focus, and for the upper socioeconomic status group, the main themes identified were social interaction and competence and emotional management. The study paves the way for further exploration of the impact of economic status on childrens wellbeing and might inform changes at a clinical, research and policy level. 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.