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A metal-A nd base-free domino protocol for the synthesis of 1,3-benzoselenazines, 1,3-benzothiazines and related scaffolds
Efficient protocols have been described for the synthesis of 1,3-benzoselenazines, 1,3-benzothiazines, 2-aryl thiazin-4-ones and diaryl[b,f][1,5]diazocine-6,12(5H,11H)-diones. These transformations were successfully driven towards the product formation under mild acid catalyzed reaction conditions at room temperature using 2-amino aryl/hetero-aryl alkyl alcohols and amides as substrates. The merits of the present methods also rely on the easy access of rarely explored bioactive scaffolds like 1,3-benzoselenazine derivatives, for which well-documented methods are rarely known in the literature. A broad range of substrates with both electron-rich and electron-deficient groups were well-tolerated under the developed conditions to furnish the desired products in yields up to 98%. The scope of the devised method is not only restricted to the synthesis of 1,3-benzoselenazines, but it was also further extended towards the synthesis of 1,3-benzothiazines, 1,3-benzothiazinones and the corresponding eight membered N-heterocycles such as diaryl[b,f][1,5]diazocine-6,12(5H,11H)-diones. 2018 The Royal Society of Chemistry. -
A Metal-Free KOtBu-Mediated Protocol towards the Synthesis of Quinolines, Indenoquinolines and Acridines
An expeditious strategy has been developed for the synthesis of diverse quinolines, indenoquinolines and acridines using KOtBu-mediated reaction conditions. The designed process utilizes 2-aminoaryl carbaldehydes/2-aminoaryl ketones and methyl/methylene group containing ketones as readily available feedstock. The chemical transformation was affected at room temperature within a short duration of time to obtain diverse N-heterocycles yields up to 92 %. The established process also exhibits considerable functional group tolerance with an operational simplicity. 2024 Wiley-VCH GmbH. -
A method for identification of restarted radio sources from large radiosurveys
Active galaxies hosting radio jets can exhibit distinct active phases marked by two sets of radio lobes. Typically, these episodic radio sources have been identified through morphological observations. In addition, spectral characteristics-based methods are also employed wherever multi-frequency deep radio observations are available. However, these methods are inefficient in detecting restarted radio sources that do not exhibit a clear morphology. To address this, a method of using the spectral curvature (SPC=?150MHz1400MHz-?74MHz150MHz) to identify restarted radio sources is presented. This is based on the fact that restarted radio sources with significant remnant emission are expected to have concave spectra in contrast to the convex or straight spectra observed in most radio sources. We use available wide area radio surveys in the range of frequencies from 74MHz to 1.4GHz to search for episodic radio sources and to shortlist 9,405 sources based on the criteria of SPC?0.5. The candidates thus identified can be followed up for detailed morphological and spectral index studies. This method will find application in the automated identification of episodic radio sources in large radio sky surveys from telescopes like LOFAR and SKA. Indian Academy of Sciences 2025. -
A method to secure FIR system using blockchain
In India, we can see that technology has touched in every aspect of our life. There exist technology in all the fields e.g. education, agricultural, business, government etc. and we can also understand how beneficial it is, as it saves the time, money and human power. In spite of being technologically advanced, the system lacks in security perspective. When we talk about today, India has moved to the era of digitalization after the launch of the campaign Digital India, the Indian Police Department has replaced the manual system with the centralized online process to register the complaint. The main objective of this paper is to provide a method to secure the FIR system using blockchain technology. This introduces to the essential principal of blockchain technology and its future in the police department of India. Blockchain technology will also explain to protect the FIR from the malfeasance. BEIESP. -
A Methodological Framework for Descriptive Phenomenological Research
Background: Descriptive phenomenological research is crucial in nursing for understanding individuals experiences, perceptions, and relationships, which are essential for person-centered healthcare. However, a common critique is that researchers often use phenomenological methods without fully comprehending their historical and philosophical foundations. Existing literature highlights discrepancies in the application of phenomenological principles by nurse researchers, particularly in their presentation of philosophical underpinnings and methodological details. Aim: This article aims to provide a comprehensive methodological framework for descriptive phenomenological research in nursing, addressing both theoretical and practical aspects to guide novice researchers. Data Sources: This framework synthesizes existing scholarship on descriptive phenomenology. Discussion: Starting from Husserl, this article provides a detailed overview of the history, foundations, and philosophical assumptions of the methodology. It also includes key terms and a comprehensive detailing of all aspects of the research process. Conclusion: This framework enriches existing scholarship by offering a streamlined, step-by-step methodological guide for researchers embarking on descriptive phenomenological studies. It emphasizes the importance of establishing minimum, yet critical criteria for publishing research employing this methodology. Implications for Research: Future nurse researchers are encouraged to enhance methodological transparency in their descriptive phenomenological studies to facilitate rigorous evaluation of method effectiveness and study quality. This framework aims to alleviate potential apprehensions and provide clarity and structure to novice researchers in the field. The Author(s) 2024. -
A mini review on recent advancements in inclined solar still
Water shortage is a global problem, and the demand for fresh water is growing at an ever-increasing rate. The only method to meet the demand for water is via water filtration. Water purification may be done in a variety of methods, including cleaning saltwater or holding rainfall and then releasing it into the environment. There are still several kinds of solar still are available, which may be utilized to improve the amount of water that is generated. The inclined solar still (ISS) is a particularly successful option because it has a large outer water surface to supplement the normal potable water production, as well as because it has a shallow depth of water to increase the overall efficacy of the inclined solar still. Increasing the water's surface area has been the subject of much investigation. As a result of this study, an evaluation was conducted on the present state of various ISS designs in order to make advanced adjustments and research to increase the productivity of the ISS in order to meet the rising need for potable water. According to this analysis, active ISS and hybrid ISS are shown to be the most successful ISS methods. 2022 The Author(s) -
A Mixed-Methods Study of Training in Evidence-Based Practice in Psychology Among Students, Faculty, and Practitioners in India and the United States
The current mixed-method study in India and the United States assessed understanding of what evidencebased practice in psychology (EBPP) is, how EBPP training and implementation occurs, and perceived barriers and needs related to EBPP training. Graduate students (India, n = 282; United States, n = 214), faculty (India, n = 24; United States, n = 67), and practitioners (India, n = 24; United States, n = 49) were surveyed, and focus groups with students (India, n = 31; United States, n = 12), faculty (India, n = 10, United States, n = 9), and practitioners (India, n = 28; United States, n = 17) were held. Individuals across countries and across the professional continuum were only somewhat aware of EBPP, largely equating it to just using empirically supported treatments. In both the United States and India, EBPP training was largely infused across the curriculum, though a sizable percentage of participants did report only limited exposure to EBPP training. Participants perceived themselves as engaging in EBPP. The biggest barriers to EBPP training (largely shared across countries) were hesitancy about EBPP, investing the time in training, and being wedded to a single school of thought. Indian participants also noted a limitation in primarily relying on data from Western countries. EBPP training needs identified included desire for greater flexibility within EBPP, receiving more theoretical foundation in EBPP, and more applied EBPP training. Results demonstrated advances in EBPP training in the past 15 years since the release of American Psychological Associations task force report but also provide areas for growth in training, specifically surrounding balancing research evidence with clients cultural context as well as ways to promote lifelong EBPP learning. 2024 American Psychological Association -
A Mixed-Methods Study on Experiencing in Indian Couples During Gottman's Intervention of Dreams-Within-Conflict
In Gottman Couple Therapy (GCT), the intervention of Dreams-within-Conflict (DWC) helps break down a gridlocked issue between couples through deeper emotional expression and experiencing (in-counseling exploration of emotions). The current study examined experiencing in a single session of DWC for N = 30 individuals (15 couples) using multiple methods such as self-assessment questionnaires, observation rating and coding of the video recording, and feedback interviews. The before and during DWC best experiencing video segments were selected and rated by two raters independently on the experiencing scale (ES) for partners. The changes in experiencing mode and peak scores (ESM and ESP) during DWC were investigated in the presence of individual characteristics of attachment (anxiety and avoidance) and relationship mindfulness traits. A paired-samples t-test showed a significant increase in experiencing for both partners. Hierarchical linear modeling analysis indicated that gender (women) significantly and positively predicted ESM. ESP was predicted positively by gender (women) and negatively by avoidance, though the results were not conclusive. Thematic analysis was used to look at the Indian couples' experiencing as shared by them in order to better grasp the therapeutic implications. The qualitative findings confirm the quantitative results that couples outside of intervention utilized experiencing levels 13 predominantly and moved to 34 levels during best experiencing segments of intervention. Couples reviewed positively to the emotional experiencing techniques used during the DWC intervention. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
A Model for Detecting Type 2 Diabetes Using Mixed Single-Cell RNA Sequencing with Optimized Data
Diabetes is a critical disease and is crucial to personage agility. Type 2 Diabetes (T2D) accounts for 92% of epithetical cases. This paper proposes an optimized type 2 diabetes detection model using mixed single-cell RNA sequencing (scRNA-seq) technology. Diabetes is a chronic metabolic disorder affecting millions of people worldwide. Early detection of the disease can greatly improve treatment outcomes, but current diagnostic methods have limitations. Our proposed model integrates scRNA-seq data from both human pancreatic beta cells to identify gene expression patterns associated with diabetes. Our study shows that the proposed model is highly accurate in identifying diabetes, achieving an area under the curve (AUC) of 0.98. We employed an optimized model to improve the detection of diabetes at an early stage, leading to better treatment outcomes and an improved quality of life for patients. We initially incorporated optimal features from the dataset using the Monte Carlo (MC) feature selection method. This method helped us to estimate the relative importance (RI) score of each gene or feature, which is then used to rank the features. Further, we proposed an optimized deep belief network (ODBN) as a classification model to classify T2D and non-diabetes. To improve the performance of ODBN, an adaptive chimp optimization algorithm (AChOA) is introduced to optimize the weight parameters and achieved a performance accuracy of 96.57%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
A model to predict the influence of inconsistencies in Thermal Barrier Coating ( TBC) thicknesses in pistons of IC engines /
Materials Today Proceedings, Vol.5, Issue 5, Part 2, pp.12623-12631 -
A modified fuzzy approach to prioritize project activities
Project management is an important task in business although project is not just confined to business. Due to the uncertainty of the various variables involved in a project, over several past decades research is going on in the search for an efficient project management model. Although numerous crisp models are easily implementable, the potential of fuzzy models are huge. In the case of software development, the variables involved are highly dynamic. In this paper, we propose a ranking based fuzzy model that can prioritize various activities. We use a popular crisp model to test the effectiveness of the fuzzy model proposed. Simulation is done through Java Server Pages (JSP). There is considerable computational and managerial advantage in implementing the fuzzy model. 2018 Authors. -
A modified invasive weed optimization for MPPT of PV based water pumping system driven by induction motor
A novel approach called Modified Invasive Weed Optimization (MIWO) technique has been developed and combined with the Perturb and Observes (P&O) algorithm to enhance the extraction of maximum power from photovoltaic (PV) panels in the presence of partial shading conditions. The conventional P&O algorithm falls short in extracting the maximum power from PV systems under partial shading conditions due to the existence of multiple maximum points. In such scenarios, optimization techniques can be employed to search for the global maximum point. The proposed MIWO-based P&O algorithm updates the reference voltage to ensure that the PV system operates at the Maximum Power Point (MPP) based on the prevailing weather conditions. This MIWO based PV system is further fed to water pumping system. A PV-based water pumping system is utilized for both irrigation and domestic purposes. Additionally, a sensorless vector control-based induction motor is employed in this study to drive the pump. The objective of this research is to demonstrate the achievement of an efficient PV-based water pumping system without the need for battery storage. Various results based on MIWO are compared with PSO and GWO. The results are presented based on various water pumping applications and the availability of solar irradiance during rapid climate changes. MATLAB/Simulink simulations, along with hardware-based experiments, are provided to validate the effectiveness of the proposed method under both transient and steady-state conditions. 2024 IOP Publishing Ltd. -
A Modified Seven-Level Inverter with Inverted Sine Wave Carrier for PWM Control
The conventional multilevel inverter necessitates more active switching devices and high dc-link voltages. To minimalize the employment of switching devices and dc-link voltages, a novel topology has been proposed. In this paper, a novel minimum switch multilevel inverter is established using six switches and two dc-link voltages in the proportion of 1: 2. In addition, the proposed topology is proficient in making seven-level voltages by appropriate gate signals. The PWM signals were produced using several inverted sine carriers and a single trapezoidal reference. When compared to other existing inverters, this configuration needs fewer components, as well as fewer gate drives. Furthermore, this module can generate a negative level without the use of a supplementary circuit such as an H-Bridge. As a result, overall cost and complexity are greatly reduced. The proposed minimum switch multilevel inverter operation is validated through simulations followed by experimental results of a prototype. 2022 Arun Vijayakumar et al. -
A molecular docking study of SARS-CoV-2 main protease against phytochemicals of Boerhavia diffusa Linn. for novel COVID-19 drug discovery
SARS-CoV-2, the causative virus of the Corona virus disease that was first recorded in 2019 (COVID-19), has already affected over 110 million people across the world with no clear targeted drug therapy that can be efficiently administered to the wide spread victims. This study tries to discover a novel potential inhibitor to the main protease of the virus, by computer aided drug discovery where various major active phytochemicals of the plant Boerhavia diffusa Linn. namely 2-3-4 beta-Ecdysone, Bioquercetin, Biorobin, Boeravinone J, Boerhavisterol, kaempferol, Liriodendrin, quercetin and trans-caftaric acid were docked to SAR-CoV-2 Main Protease using Molecular docking server. The ligands that showed the least binding energy were Biorobin with ? 8.17kcal/mol, Bioquercetin with ? 7.97kcal/mol and Boerhavisterol with ? 6.77kcal/mol. These binding energies were found to be favorable for an efficient docking and resultant inhibition of the viral main protease. The graphical illustrations and visualizations of the docking were obtained along with inhibition constant, intermolecular energy (total and degenerate), interaction surfaces and HB Plot for all the successfully docked conditions of all the 9 ligands mentioned. Additionally the druglikeness of the top 3 hits namely Bioquercetin, Biorobin and Boeravisterol were tested by ADME studies and Boeravisterol was found to be a suitable candidate obeying the Lipinskys rule. Since the main protease of SARS has been reported to possess structural similarity with the main protease of MERS, comparative docking of these ligands were also carried out on the MERS Mpro, however the binding energies for this target was found to be unfavorable for spontaneous binding. From these results, it was concluded that Boerhavia diffusa possess potential therapeutic properties against COVID-19. 2021, Indian Virological Society. -
A Multi-Modal Approach to Digital Document Stream Segmentation for Title Insurance Domain
In the twenty-first century, storing and managing digital documents has become commonplace for all corporate and public sectors around the world. Physical documents are scanned in batches and stored in a digital archive as a heterogeneous document stream, referred to as a digital package. To make Robotic Process Automation (RPA) easier, it's necessary to automatically segment the document stream into a subset of independent, coherent multi-page documents by detecting the appropriate document boundary. It's a common requirement of a TI company's Automated Document Management Systems (ADMS), where business operations are automated using RPA and the goal is to extract information from digital documents with minimal user intervention. The current study proposes, evaluates, and compares a multi-modal binary classification network incorporating text and picture aspects of digital document pages to state-of-the-art baseline methodologies. Image and textual features are extracted simultaneously from the input document image by passing them through Visual Geometry Group 16 - Convolutional Neural Network (VGG16-CNN) and pre-trained Bidirectional Encoder Representations from Transformers (Legal-BERT {}_{base} ) model through transfer learning respectively. Both features are finally fused and passed through a fully connected layer of Multi Layered Perceptron (MLP) to obtain the binary classification of the pages as the First Page (FP) and Other Page (OP). Real-time document image streams from production business process archive were obtained from a reputed Title Insurance (TI) company for the study. The obtained F_{1} score of 97.37% and 97.15% are significantly higher than the accuracies of the considered two baseline models and well above the expected Straight Through Pass (STP) threshold defined by the process admin. 2013 IEEE. -
A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks
The past several decades of research into machine learning have been of great assistance to humanity in the diagnosis of a variety of ailments using various forms of automated diagnostic procedures. Machine learning, combined with smart health devices, has improved health monitoring, timely diagnoses, and treatment. This paper introduces a unified disease diagnosis framework, integrating cloud computing, machine learning, and IoT. The framework has three layers: physical (collects patient data), fog (intermediate layer with a domain identification unit to determine input and diagnosis type), and transmission (cloud server with a disease detection unit). The performance evaluation shows the robustness and efficiency of the model as compared to state-of-art models. 2023, Taru Publications. All rights reserved. -
A MULTI-OBJECTIVE HUNTER-PREY OPTIMIZATION FOR OPTIMAL INTEGRATION OF CAPACITOR BANKS AND PHOTOVOLTAIC DISTRIBUTION GENERATION UNITS IN RADIAL DISTRIBUTION SYSTEMS
This article put forward the determination of the optimal siting and sizing of capacitor banks and PV-DG (Photo-Voltaic Distribution Generation) units in a radial distribution system. A modern population-based optimization algorithm, Hunter-Prey Optimization (HPO), is applied to determine the optimal capacitor bank and PV-DG placement. This algorithm, HPO, got its motivation from the trapping behaviour of the carnivore (predator/hunter) like lions and wolves towards their target animal like deer. The typical IEEE-33 & 69 test bus systems are scrutinized for validating the effectiveness of the suggested algorithm using MATLAB software R2021b version. The acquired results are collated with the existing heuristic algorithms for the active power loss criterion. The nominal or base values for system losses and voltage profile were considered for the comparison, with the results from HPO. The HPO application has an efficient performance in figuring out the most favourable location and capacity of the capacitor banks and PV DGs compared with the other techniques. 2023 by authors and Galileo Institute of Technology and Education of the Amazon (ITEGAM). -
A multi-preference integrated algorithm for deep learning based recommender framework
Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short-term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi-preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1-score. Consequently, the MPIA can be integrated with real time recommender systems. 2022 John Wiley & Sons, Ltd. -
A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification
In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and ManMachine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
A Multi-Stimuli responsive organic luminogen with aggregation induced emission for the selective detection of Zn2+ ions in solution and solid state
Organic luminogens capable of excited state intramolecular electron transfer (ESIPT) have drawn prodigious attraction due to their enhanced emission in solid-state. A novel Schiff base molecule, 3,5-dibromo-2-hydroxybenzylidenenicotinohydrazide (DHN) exhibited stimuli-induced reversible fluorescence switching and selective binding propensity towards zinc in aqueous media, and the concentration-dependent studies showed a limit of detection of 9.135 nM. DHN was found to be weakly fluorescent in polar solvents with a quantum yield ranging between 0.0365 and 0.0789 but exhibited a very strong fluorescence in solid state (?exc = 370 nm) due to aggregation induced emission (AIE). The ESIPT fluorophore renders significant reversible halochromic properties in solution and solid-state. In addition, utilizing the solid-state fluorescence, we have prepared PVA-probe green-emitting composite films, which can be used for the on-site detection of Zn2+ in aqueous media. The practical applicability of DHN was proven by detecting Zn2+ in real drug samples. Finally, the ESIPT fluorophore was used for fluorescent imaging of intracellular zinc in the cells acquired from the nervous tissue of rats (N2a). The investigations carried out highlight the versatility of ESIPT Schiff bases used for the development of multi-responsive fluorescent materials for selective sensing of metal ions in both solid and solution states. 2022 Elsevier B.V.