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Efficient chemical fixation of CO2from direct air under environment-friendly co-catalyst and solvent-free ambient conditions
The capture and conversion of CO2from direct air into value-added products under mild conditions represents a promising step towards environmental remediation and energy sustainability. Consequently, herein, we report the first example of a Mg(ii)-based MOF exhibiting highly efficient fixation of CO2from direct air into value-added cyclic carbonates under eco-friendly co-catalyst and solvent-free mild conditions. The bifunctional MOF catalyst was rationally constructed by utilizing an eco-friendly Lewis acidic metal ion, Mg(ii), and a nitrogen-rich tripodal linker, TATAB. The MOF possesses a high BET surface area of 2606.13 m2g?1and highly polar 1D channels decorated with a high density of CO2-philic sites which promote a remarkably high CO2uptake of 50.2 wt% at 273 K with a high heat of adsorption value of 55.13 kJ mol?1. The high CO2-affinity combined with the presence of a high density of nucleophilic and Lewis acidic sites conferred efficient catalytic properties to the Mg-MOF for chemical fixation of CO2from direct air under environment-friendly mild conditions. The remarkable performance of the Mg-MOF for the fixation of CO2from direct air was further supported by in-depth theoretical calculations. Moreover, the computational studies provided an insight into the mechanistic details of the catalytic process in the absence of any co-catalyst and solvent. Overall, this work represents a rare demonstration of carbon capture and utilization (CCU) from direct air under eco-friendly mild conditions. The Royal Society of Chemistry 2021. -
Engineering the functionality of porous organic polymers (POPs) for metal/cocatalyst-free CO2 fixation at atmospheric conditions
Carbon dioxide (CO2) utilization as C1 feedstock under metal/co-catalyst-free conditions facilitates the development of eco-friendly routes for mitigating atmospheric CO2 concentration and producing value-added compounds. In this regard, herein, we designed a bifunctional porous organic polymer (POP-1) by incorporating acidic (-CONH) and CO2-philic (-NH/N) sites by judicious choice of organic precursors. Indeed, POP-1 exhibits high heat of interaction for CO2 (40.2 kJ/mol) and excellent catalytic performance for transforming carbon dioxide to cyclic carbonates, a high-value commodity chemical with high selectivity and yield under metal/cocatalyst/solvent-free atmospheric pressure conditions. Interestingly, an analogous polymer (POP-2) that lacks basic (-NH/N) sites showed lower CO2 interaction energy (31.6 kJ/mol) and catalytic activity than that of POP-1. The theoretical studies further supported the superior catalytic activity of POP-1 in the absence of Lewis acidic metal and cocatalyst. Notably, POP-1 showed excellent reusability with retention of catalytic performance for multiple cycles of usage. Overall, this work presents a novel approach to metal/cocatalyst/solvent-free utilization of CO2 under eco-friendly atmospheric pressure conditions. 2024 Elsevier Ltd -
Therapeutic potential of marine macrolides: An overview from 1990 to 2022
The sea is a vast ecosystem that has remained primarily unexploited and untapped, resulting in numerous organisms. Consequently, marine organisms have piqued the interest of scientists as an abundant source of natural resources with unique structural features and fascinating biological activities. Marine macrolide is a top-class natural product with a heavily oxygenated polyene backbone containing macrocyclic lactone. In the last few decades, significant efforts have been made to isolate and characterize macrolides' chemical and biological properties. Numerous macrolides are extracted from different marine organisms such as marine microorganisms, sponges, zooplankton, molluscs, cnidarians, red algae, tunicates, and bryozoans. Notably, the prominent macrolide sources are fungi, dinoflagellates, and sponges. Marine macrolides have several bioactive characteristics such as antimicrobial (antibacterial, antifungal, antimalarial, antiviral), anti-inflammatory, antidiabetic, cytotoxic, and neuroprotective activities. In brief, marine organisms are plentiful in naturally occurring macrolides, which can become the source of efficient and effective therapeutics for many diseases. This current review summarizes these exciting and promising novel marine macrolides in biological activities and possible therapeutic applications. 2022 The Authors -
The importance of strategic agility and resilience in work-life balance
This chapter's objective is to analyze agility and resilience which are essential qualities of work-life balance. Similarly, both enable individuals to play the important role of both professional and personal responsibilities effectively. In this chapter, the author has mentioned the importance and the role of strategic agility which describes the ability to predict and respond quickly to changes and challenges in the work environment. Also, all these involve being acceptable, adaptable, flexible, and open to new ideas and approaches. In the work-life balance framework, strategic agility supports individuals to be proactive, positive, and efficient enough to manage their time and energy. It helps individuals prioritize their tasks, allocate resources properly, and enhance their understanding of where to invest their efforts. 2024, IGI Global. All rights reserved. -
Crowd Monitoring System Using Facial Recognition
The World Health Organization (WHO) suggests social isolation as a remedy to lessen the transmission of COVID-19 in public areas. Most countries and national health authorities have established the 2-m physical distance as a required safety measure in shopping malls, schools, and other covered locations. In this study, we use standard CCTV security cameras to create an automated system for people detecting crowds in indoor and outdoor settings. Popular computer vision algorithms and the CNN model are implemented to build up the system and a comparative study is performed with algorithms like Support Vector Machine and KNN algorithm. The created model is a general and precise people tracking and identifying the solution that may be used in a wide range of other study areas where the focus is on person detection, including autonomous cars, anomaly detection, crowd analysis, and manymore. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Fractional and memory effects on wave reflection in pre-stressed microstructured solids with dual porosity
The present work investigates the influence of fractional-order derivative and memory-dependent derivative on the behavior of various waves reflected at the free surface of a size-dependent, pre-stressed, microstructured thermoelastic solid with a dual porosity framework. A generalized MooreGibsonThomson (MGT) model, incorporating higher-order terms and memory effects, is adopted to describe the complex heat transfer behavior within the material. A nonlocal framework based on Eringen's theory is utilized to derive the basic relations of the considered medium. An examination of the non-dimensionalized governing equations is conducted employing the normal mode technique to provide accurate solutions. The research demonstrates the presence of six separate wave modes that travel at varying speeds within the medium. The energy and amplitude ratios of reflected waves are determined by applying suitable boundary conditions. The influence of varying incidence angles on the reflected wave energy distribution is investigated numerically and visualized using MATLAB software. The study reveals that the energy ratios of the reflected waves are sensitive to the fractional-order parameter, kernel functions, initial stress, and nonlocality parameter. The analysis suggests a conservative reflection process, indicating minimal energy loss during reflection. Key findings and their implications for relevant scenarios are presented in the conclusion. Comparisons with existing models for certain cases demonstrate good agreement, supporting the validity of the present model. 2025 Elsevier Masson SAS -
Advancements in e-Governance Initiatives: Digitalizing Healthcare in India
In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve ones health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someones general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patients clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within Indias overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of Indias EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
ICT Policy Reforms for Innovation and Economic Development: A Comparative Study of India and China
The widespread adoption of Information and Communication Technologies (ICTs) has become essential for economic and social growth across the world. This paper aims to examine the impact of ICT policies and reforms on the level of economic development and adoption of ICTs in two countries, India and China. Previous studies have shown the positive impact of ICT adoption on economic growth, productivity, and innovation. However, the effectiveness of specific policy measures in promoting ICT adoption and economic development remains ambiguous to the users of ICT. This paper presents a comparative analysis of the ICT policies and reforms implemented in India and China from 2010 to 2021 and their impact on GDP per capita and internet usage. The study aims to identify and analyze the key ICT policies and reforms implemented in the two countries and examine their impact on economic development. The data for this study have been collected from the World Bank indicators database. The sample consists of the two fastest-growing economies in the world, India and China. The data analysis involves conducting descriptive statistics, correlation, and regression analysis to examine the relationship between ICT policies and reforms and their impact on GDP per capita, internet usage, and research and development expenditure. The findings of this study will contribute to the existing literature on the relationship between ICTs and economic development and provide insights into the policy measures that can promote ICT adoption and economic growth in different contexts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Effecient Approach to Detect Fraud Instagram Accounts Using Supervised ML Algorithms
Nowadays social media plays a vital role in different fields including business, economic communication and personal. Many person get profit from the different origins of availability of data from these social media, but cyber-crimes are increasing day by day. A person can generate many fake accounts and hence pretenders can easily be made. Instagram, as one of the popular types of online social media site, carries big information and messages through the posts. Most of the person use Instagram as a digital life marketing place because it is a one of the big social media site. The goal of the research paper is to recognize and stop fake IDs and pages. Because through the professional pages of Instagram, many fake cases and things are occurring present days. So the main thing is to recognize fake pages and fake accounts also. In this paper, we work on various IDs of Instagram. We want to observe an ID is real or not using Machine Learning techniques namely Logistic Regression, Naive Bayes, Support vector machine, Decision tree, Random Forest. 2022 IEEE. -
Meta-analysis of EMF-induced pollution by COVID-19 in virtual teaching and learning with an artificial intelligence perspective
Concerns about the health effects of frequent exposure to electromagnetic fields (EMF) emitted from mobile towers and handsets have been raised because of the gradual increase in usage of cell phones and frequent setting up of mobile towers. The present study is targeted to detrimental effects of EMF radiation on various biological systems mainly due to online teaching and learning processes by suppressing the immune system. During the COVID-19 pandemic, the increased usage of internet due to online education and online office leads to more detrimental effects of EMF radiation. Further inculcation of soft computing techniques in EMF radiation has been presented. A literature review focusing on the usage of soft computing techniques in the domain of EMF radiation has been presented in the article. An online survey has been conducted targeting Indian academic stakeholders (specially teachers, students, and parents termed as population in the paper) for analyzing the awareness towards the biohazards of EMF exposure. 2022 IGI Global. All rights reserved. -
Web Platforms for Fintech Products
Internet marketing and digital marketing are not synonymous in the minds of the majority of the population, yet this may not be true. Given the rise in popularity of digital marketing as a marketing tactic, it is critical to comprehend the distinctions between the two methods. Even while it should be evident that they might be connected, there is very little difference between them. Internet marketing is merely a subclass of digital marketing, as well as the extent of digital marketing encompasses much more than internet marketing. This paper discussed digital marketing technologies, as well as the advantages and disadvantages of employing digital marketing and digital finance tools in general. In order to remain competitive, businesses must overcome obstacles and seize possibilities presented by digital marketing technologies. Lastly, it's critical to prioritise digital marketing and make use of digital finance techniques in order to maintain a good performance without wasting time or money. 2022 IEEE. -
Recent Progress on the Development of Chemosensors
Chemosensors are the chemical structures which convert chemical stimuli into responsive form that can be easily detected, such as change of colour, fluorescence, and other electronic signal. Recently, chemosensors development for detection and monitoring of gases has been growing interest due to the significant importance in environmental and biological systems. Subsequently, the development of chemosensors for detection of various gases is considered to be a significant goal in science and among the all gases, carbon dioxide (CO2) is a major public concern due to its role in global greenhouse warming with environmental pollution. Moreover, quite critical level of CO2 in the modern agricultural, food, environmental, oil and chemical industries is dangerous for living beings to survive such high concentration levels of CO2. Therefore, rapid and selective detection and monitoring of CO2 in the gaseous as well as in the liquid phases provides an incentive for development of new methods. The coverage of this book chapter is divided into different sections according to the use of different types of molecular backbones and the detection pathways. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Deprotection induced modulation of excited state intramolecular proton transfer for selective detection of perborate and ammonia
Acetate protected Naphthalene Coupled Benzothiazole (NCB) has been designed and synthesized for selective detection of perborate (BO3) and ammonia (NH3) based on modulation of excited-state intramolecular proton transfer (ESIPT) process by chemodosimetric deacetylation pathway. In presence of nucleophilic species like BO3 and NH3, acetyl group deprotection of NCB resulted ESIPT within the molecule exhibiting a significant enhancement of absorption and emission signals at 425 nm and 472 nm respectively. The emission enhancement of NCB has been observed by 31-folds and 14-folds in presence of BO3 and NH3 respectively. The selectivity and fast sensitivity of NCB have been shown by the lower detection limit (1.32 M for BO3, 1.74 M for NH3 in UVvis study and 0.60 M for BO3 and 4.39 M for NH3 in fluorescence study) and fast response (rate constants: 12.36 s?1 and 5.54 s?1 for BO3 and NH3 respectively). Analytes induced deacetylation pathway of NCB followed by ESIPT has been clearly demonstrated by theoretical calculation. The test strips based on NCB with BO3 and NH3 are fabricated, which can act as a convenient and efficient test kits for both these analytes. In the practical applications, the sensor NCB can be utilized as low cost food spoilage indicator and soil analysis by fluorometric method. 2024 Elsevier B.V. -
Vehicular Propagation Velocity Forecasting Using Open CV
This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety. 2023 IEEE. -
Evaluating the performance of machine learning using feature selection methods on dengue dataset
Dengue fever is a mosquito-borne disease transmitted by the bite of an Aedes mosquito infected with a dengue virus. The bites of an infected female Aedes mosquito which gets the virus while feeding on the infected persons blood, transmits the virus to others. Dengue transmission is climate sensitive for several reasons such as temperature, humidity, rainfall, etc. Areas having higher vapor pressure and rainfall rate are most vulnerable to the spreading of the dengue disease. So to find the important features responsible for spreading the dengue we have used the classification algorithms. Machine learning is one of the key methods used in modern day analysis. Many algorithms have been used for medical purposes. Dengue disease is one of the serious contagious diseases. To find the features related to spreading of dengue disease, we have used popular machine learning algorithms. This proposed work focuses on evaluating the performances of the various machine learning techniques like-Random Forest Classifier (RFC), Decision Tree Classifier (DTC) and Linear Support Vector Machine (LSVM). Predictive Mean Matching is applied for preprocessing of the data and percentage split is applied for resampling of the data. Information gain values for each of the attributes are calculated. The attributes are sorted on the basis of information gain values. Feature selection methods (FSMs) such as Forward Selection (FS) and Backward Elimination (BE) are applied to choose the finest subset of the attributes, so that the algorithm runs more efficiently with a lower run time. It also results in the improvement of the accuracy. The attributes selected by the Feature Selection Methods are the main attributes which results in the probable effects of global weather change on human healthiness. BEIESP. -
The changing paradigm - Gender dimensions of watershed management in Hosadurga Taluk, Chitradurga District, Karnataka, India /
Intenational Journal Of Science And Research, Vol.4, Issue 7, pp.280-285, ISSN No: 2319-7064 (Online). -
Magnetic property applications of microwave method prepared zinc ion modified CoAl2O4 nanoparticles
Employing Microwave combustion technique and utilizing L-arginine as fuel pure Cobalt Aluminate and Zn doped Cobalt Aluminate nanoparticles (NPs) were prepared. XRD, DRS-UV, HRSEM and VSM techniques were used to investigate the structural, optical, morphological, and magnetic properties. The average crystallite size is found in the range of 15-24 nm. Elemental confirmation is done by aid of EDX spectra. The band gap values of the produced samples were discovered to be between 2.57 and 2.45 eV. At room temperature, the prepared samples showed diamagnetic magnetic characteristics, which were corroborated by MagnetizationField (MH) hysteresis curves. 2021, S.C. Virtual Company of Phisics S.R.L. All rights reserved. -
Network Lifetime Enhancement by Elimination of Spatially and Temporally Correlated RFID Surveillance Data in WSNs
In wireless sensor networks (WSNs), radio frequency identification (RFID) plays an important role due to its data characteristics which are data simplicity, low cost, simple deployment, and less energy consumption. It consists of a series of tags and readers which collect a huge number of redundant data. It increases system overhead and decreases overall network lifetime. Existing solutions like Time-Distance Bloom Filter (TDBF) algorithm are inapplicable to the large-scale environment. Received Signal Strength (RSS) used in this algorithm is highly dependent on quality of tag and application environment. In this paper, we propose an approach for data redundancy minimization for RFID surveillance data which is a modified version of TDBF. The proposed algorithm is formulated by using the observed time and calculated distance of RFID tags. To overcome these problems, we design our approach to relevantly reduce the spatiotemporal data redundancy in the source level by adding the Received Signal Strength Indicator (RSSI) concept for energy-efficient RFID data communication in wireless sensor network scenario. We introduce in this paper the new improved idea of an existing algorithm which efficiently reduces the rate of data redundancy spatially and temporally. The implemented results overcome the limitations of existing algorithm for data redundancy reduction. Nevertheless, the performance evaluation shows the efficiency of proposed algorithm in terms of time and data accuracy. Furthermore, this algorithm supports multidimensional and large-scale environment suitable for sensor network nowadays. 2022 Lucy Dash et al. -
Recent developments in bandwidth improvement of dielectric resonator antennas
This article shows a compressed chronological overview of dielectric resonator antennas (DRAs) emphasizing the developments targeting to bandwidth performance characteristics in last three and half decades. The research articles available in open literature give strong information about the innovation and rapid developments of DRAs since 1980s. The sole intention of this review article is to, (a) highlight the novel researchers and to analyze their effective and innovative research carried out on DRA for the furtherance of its performance in terms of only bandwidth and bandwidth with other characteristics, (b) give a practical prediction of future of DRA as per the past and current state-of-art condition, and (c) provide a conceptual support to the antenna modelers for further innovations as well as miniaturization of the existing ones. In addition some of the significant observations made during the review can be noted as follows; (a) hybrid shape DRAs with Sierpinski and Minkowski fractal DRAs seems comfortable in obtaining wideband as well as multiband, (b) combination of multiple resonant modes (preferably lower modes) can lead to wider impedance bandwidth, (c) at proper matching wider patch with slotted dielectric resonator can exhibit better bandwidth. 2019 Wiley Periodicals, Inc. -
Climate predictors in Indian summer monsoon forecasting: a novel De-correlated RVFL ensemble strategy
Excessive rainfall and droughts harshly impact India's social and economic growth. Though several statistical methods have been used in literature to predict Indian monsoons, uncertainties cannot be ruled out. The accuracy prediction of ISMR (Indian Summer Monsoon Rainfall) is scientifically demanding. From this perspective, it is essential to explore exploiting machine learning techniques. In this paper, a novel De-correlated Regularized Random Vector Functional Link Neural Network Ensemble (DRRNE) prediction approach was proposed using Climate Predictors such as Southern Oscillation Index (SOI), Sea Surface Temperature Anomaly (SST), El-Ni Southern Oscillation (ENSO), and Dipole Mode Index (DMI) to predict ISMR. The proposed work has also investigated the predictability of climate above predictors using the DRRNE approach to predict ISMR. In addition to the predictors above, the data for an 8-year training window time series for June to September is combined and analyzed for four predictors (ENSO, DMI, SOI, and SST) to derive another predictor, ENSO-DMI-SOI-SST (EDSS). It is found that the combination of these four predictors- the EDSS- produces better accuracy than using any of the individual predictors in this study. Among the individual predictors (ENSO, DMI, SOI, and SST), the DMI predictor has shown the best predictability for ISMR prediction. Thus, the suggestedstudy concludes that the DRRNE technique with negative correlation learning may be a suitable tool for predicting the ISMR using the combined outcome of the four climate predictorsas mentioned above. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.