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Mesoporous iron aluminophosphate: An efficient catalyst for one pot synthesis of amides by ester-amide exchange reaction
A series of metal aluminophosphates (MAlP: M = V, Fe, Co, Ni & Cu) were prepared by co-precipitation method. All the materials were characterized by various physico-chemical techniques. The materials were found to be mesoporous and moderately acidic. The catalytic activity of the materials was investigated in the synthesis of benzamides in a single pot reaction under solvent free refluxing conditions from methyl benzoate and different amines. Iron aluminophosphate was found to be the most effective catalyst for the synthesis of benzamides with 100% selectivity. The isolated yield of benzamide varied from 46% to 100% depending on the nature of amine. A possible reaction mechanism has been proposed which correlates the surface acidity and catalytic activity of the catalyst. The catalyst could be recycled for about three times without any appreciable loss in activity, thus making the method ecofriendly and economical. -
Mesoporous onion-like carbon nanostructures from natural oil for high-performance supercapacitor and electrochemical sensing applications: Insights into the post-synthesis sonochemical treatment on the electrochemical performance
Although onion-like carbon nanostructures (OLCs) are attractive materials for energy storage, their commercialization is hampered by the absence of a simple, cost-effective, large-scale synthesis route and binder-free electrode processing. The present study employs a scalable and straightforward technique to fabricate sonochemically tailored OLCs-based high-performance supercapacitor electrode material. An enhanced supercapacitive performance was demonstrated by the OLCs when sonicated in DMF at 60 C for 15 min, with a specific capacitance of 647 F/g, capacitance retention of 97% for 5000 cycles, and a charge transfer resistance of 3 ?. Furthermore, the OLCs were employed in the electrochemical quantification of methylene blue, a potential COVID-19 drug. The sensor demonstrated excellent analytical characteristics, including a linear range of 100 pM to 1000 pM, an ultralow sensitivity of 64.23 pM, and a high selectivity. When used to identify and quantify methylene blue in its pharmaceutical formulation, the sensor demonstrated excellent reproducibility, high stability, and satisfactory recovery. 2021 The Author(s) -
Message efficient ring leader election in distributed systems
Leader Election Algorithm, not only in distributed systems but in any communication network, is an essential matter for discussion. Tremendous amount of work are happening in the research community on election as network protocols are in need of co-ordinator process for the smooth running of the system. These so called Coordinator processes are responsible for the synchronization of the system otherwise, the system loses its reliability. Furthermore, if the leader process crashes, the new leader process should take the charge as early as possible. New leader is one among the currently running processes with the highest process id. In this paper we have presented a modified version of ring algorithm. Our work involves substantial modifications of the existing ring election algorithm and the comparison of message complexity with the original algorithm. Simulation results show that our algorithm minimizes the number of messages even in worst case scenario. 2013 Springer Science+Business Media. -
Message framing and COVID-19 vaccine acceptance among millennials in South India
Vaccine hesitancy and refusal remain a major concern for healthcare professionals and policymakers. Hence, it is necessary to ascertain the underlying factors that promote or hinder the uptake of vaccines. Authorities and policy makers are experimenting with vaccine promotion messages to communities using loss and gain-framed messages. However, the effectiveness of message framing in influencing the intention to be vaccinated is unclear. Based on the Theory of Planned Behaviour (TPB), this study analysed the impact of individual attitude towards COVID-19 vaccination, direct and indirect social norms, perceived behavioural control and perceived threat towards South Indian millennials intention to get vaccinated. The study also assessed the effect of framing vaccine communication messages with gain and loss framing. Data was collected from 228 Millennials from South India during the COVID-19 pandemic from September to October 2021 and analysed using PLS path modelling and Necessary Condition Analysis (NCA). The findings reveal that attitudes towards vaccination, perceived threat and indirect social norms positively impact millennials intention to take up vaccines in both message frames. Further, independent sample t-test between the framing groups indicate that negative (loss framed message) leads to higher vaccination intention compared to positive (gain framed message). A loss-framed message is thus recommended for message framing to promote vaccine uptake among millennials. These findings provide useful information in understanding the impact of message framing on behavioural intentions, especially in the context of vaccine uptake intentions of Millennials in South India. Copyright: 2022 Prakash et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. -
Message from General Chair
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Message from IEEE InC4 2023 General Chair
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Message from IEEE InC4 2023 General Co-Chair
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Message from IEEE InC4 2023 Program Chair
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Message from IEEE InC4 2024 General Chair
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Message from IEEE InC4 2024 Program Chair
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Message from IEEE InC4 2024 Publication Chair
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Messaging service for business and operations and inquiries /
Patent Number: 202111036684, Applicant: Dr. Akhilesh Tiwari.
The present inventions is about a method and system by which the entity can interact with each other in a manufacturing channel by using a messaging system (11). The said messaging system to perform status inquiry and functional processing steps with respect to data stored (1) at the resource management system of the other. That information is transmitted through a messaging system (11) to the other party such as the seller. -
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. -
Metaheuristic Machine Learning Algorithms for Liver Disease Prediction
In machine learning, optimizing solutions is critical for improving performance. This study explores the use of metaheuristic algorithms to enhance key processes such as hyperparameter tuning, feature selection, and model optimization. Specifically, we integrate the Artificial Bee Colony (ABC) algorithm with Random Forest and Decision Tree models to improve the accuracy and efficiency of disease prediction. Machine learning has the potential to uncover complex patterns in medical data, offering transformative capabilities in disease diagnosis. However, selecting the optimal algorithm for model optimization presents a significant challenge. In this work, we employ Random Forest, Decision Tree models, and the ABC algorithmbased on the foraging behaviours of honeybeesto predict liver disease using a dataset from Indian medical records. Our experiments demonstrate that the Random Forest model achieves an accuracy of 85.12%, the Decision Tree model 76.89%, and the ABC algorithm 80.45%. These findings underscore the promise of metaheuristic approaches in machine learning, with the ABC algorithm proving to be a valuable tool in improving predictive accuracy. In conclusion, the integration of machine learning models with metaheuristic techniques, such as the ABC algorithm, represents a significant advancement in disease prediction, driving progress in data-driven healthcare. 2024, Iquz Galaxy Publisher. All rights reserved. -
Metaheuristicsbased Task Offloading Framework in Fog Computing for Latency-sensitive Internet of Things Applications
The Internet of Things (IoT) applications have tremendously increased its popularity within a short span of time due to the wide range of services it offers. In the present scenario, IoT applications rely on cloud computing platforms for data storage and task offloading. Since the IoT applications are latency-sensitive, depending on a remote cloud datacenter further increases the delay and response time. Most of the IoT applications shift from cloud to fog computing for improved performance and to lower the latency. Fog enhances the Quality of service (QoS) of the connected applications by providing low latency. Different task offloading schemes in fog computing are proposed in literature to enhance the performance of IoT-fog-cloud integration. The proposed methodology focuses on constructing a metaheuristic based task offloading framework in the three-tiered IoT-fog-cloud network to enable efficient execution of latency-sensitive IoT applications. The proposed work utilizes two effective optimization algorithms such as Flamingo search algorithm (FSA) and Honey badger algorithm (HBA). Initially, the FSA algorithm is executed in an iterative manner where the objective function is optimized in every iteration. The best solutions are taken in this algorithm and fine tuning is performed using the HBA algorithm to refine the solution. The output obtained from the HBA algorithm is termed as the optimized outcome of the proposed framework. Finally, evaluations are carried out separately based on different scenarios to prove the performance efficacy of the proposed framework. The proposed framework obtains the task offloading time of 71s and also obtains less degree of imbalance and lesser latency when compared over existing techniques. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Metal and Ligand-Free Approach Towards the Efficient One-Pot Synthesis of Dipyridopyrimidinimine Derivatives
We report a facile, expeditious, user-friendly, and convenient metal-free synthesis employing base catalysis in a one-pot procedure to construct 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives. This protocol involves a domino process leading to the formation of double C?N bonds utilising KOtBu as the base and DMAc as the superior solvent at 25 C for 2 h. The versatility of this methodology was demonstrated by its successful application to substrates with both electron-withdrawing and electron-donating functional groups, yielding novel functionalized stable 11H-dipyrido[1,2-a : 3?,2?-d]pyrimidin-11-imine derivatives in good to excellent yields. Additionally, we have discussed a plausible reaction pathway for the synthesis. 2024 Wiley-VCH GmbH. -
Metal organic frameworks in biomedicine: Innovations in drug delivery
Metal-organic frameworks (MOFs) have emerged as a class of versatile materials, finding extensive applications in drug delivery because of their unique properties and flexible design. This comprehensive review aims to give a broad perspective on the recent advancements in the area of drug delivery applications using MOFs. The fundamental characteristics of MOFs, highlighting their exceptional porosity, high surface area, and tuneable framework structures, enable MOFs to serve as ideal drug carriers, allowing efficient drug loading and controlled release. The review delves into the various ligands and metal ions employed for drug encapsulation. These include physical encapsulation, covalent bonding, and host-guest interactions, each offering distinct advantages for diverse types of drugs and therapeutic applications. The importance of tailoring MOF properties to optimize drug loading capacity, stability, and release kinetics has been emphasized. Additionally, the explorations involve delving into the mechanisms of drug release from MOFs, with factors such as pH, temperature, and external stimuli that can be harnessed to trigger controlled drug release. The utilization of MOFs in combination therapies, such as co-delivery of multiple drugs or integrating imaging agents, has also been examined. Numerous examples of MOFs used for drug delivery, encompassing both in-vitro and in-vivo studies, covering a wide range of therapeutic areas, including cancer treatment, antimicrobial therapy, and targeted drug delivery, are included. Additionally, the review addresses the challenges and future perspectives in the development of MOFs for drug delivery. Strategies to improve MOF stability, biocompatibility, and scalability are discussed, along with the understanding of MOF-drug interaction and potential toxicity concerns. With their tuneable properties, high loading capacities, and controlled release capabilities, MOFs hold exceptional capabilities that promise to enhance the efficacy of therapeutic interventions. Continued research and development in this area can pave way for the translation of MOFs into clinical applications in the near future. 2024 The Author(s)