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Carbon-Based and TMDs-Based Materials as Catalyst Support for Fuel Cells
Global energy consumption and environmental pollution caused by the extensive use of fossil fuels have increased the need to look forward to more renewable energy sources. Fuel cell, one of the promising energy conversion devices, has the potential to outsmart the existing devices but has several setbacks to be employed on a larger scale. One of the hindrances is the sluggish oxygen reduction reaction kinetics at the cathode and hence requires electrocatalysts to improve its overall performance. This chapter provides a brief overview of graphene and transition metal dichalcogenides (TMDs)- based composites that have the potential to be used as a catalyst support. 2024 World Scientific Publishing Company. -
Carbon-Based Nanomaterials for Cancer Treatment and Diagnosis: A Review
Early detection and treatment is a successful method to fight against cancer. The use of conventional methods often limits the early-stage detection of the same. Nanotechnology, especially nanomedicine, has become an evolving field in research today. Nanoparticles, with their diverse application in medicine, have grown tremendously. Despite their physicochemical properties, the toxicity of nanoparticles in living organisms has helped the scientific community develop these nanoparticles for cancer treatment. In this review, we highlight the two different types of synthesis of nanoparticles: Top-down and Bottom-up approaches, many instances of various techniques and other inorganic nanoparticles that are good platforms for bioimaging and biosensing applications, drug delivery nanocarriers for specific tumor targeting, thereby reducing the toxicity to healthy tissues and Photodynamic therapy (PDT) and Photo Thermal Therapy (PTT), their mechanism and nanoparticles and examples of their surface functionalization used in cancer treatment. 2022 Wiley-VCH GmbH. -
Carboplatin-loaded zeolitic imidazolate framework-8: Induction of antiproliferative activity and apoptosis in breast cancer cell
The challenge with breast cancer is its ongoing high prevalence and difficulties in early detection and access to effective care. A solution lies in creating tailored metalorganic frameworks to encapsulate anticancer drugs, enabling precise and targeted treatment with less adverse effects and improved effectiveness. Zeolitic imidazolate framework-8 (ZIF-8) and carboplatin (CP)-loaded ZIF-8 were synthesized and characterized using various analytical techniques. High Resolution-transmission electron microscopy of ZIF-8 and CP@ZIF-8 indicates that the particles had a spherical shape and were nanosized. The drug release rate of CP is 98% under an acidic medium (pH 5.5) because of the dissolution of ZIF-8 into its coordinating ions, whereas 35% in a physiological medium (pH 7.4) with the addition of CP, the high porosity, and pore diameter of ZIF-8 decrease from 1243 to 1041m2/g. Breast cancer MCF-7 cells were shown greater IC50 in CP@ZIF-8 (15.013.03g/mL) than free CP (34.984.25g/mL) in an in vitro cytotoxicity assessment. The cytotoxicity of the CP@ZIF-8 against MCF-7 cells was studied using the methylthiazolyldiphenyl-tetrazolium bromide method. The morphological changes were examined using fluorescent staining (acridine orangeethidium bromide and Hoechst 33258) methods. The comet assay assessed the DNA fragmentation (single-cell gel electrophoresis). The results from the study revealed that CP@ZIF-8 can be used in the treatment of breast cancer. 2024 International Union of Biochemistry and Molecular Biology, Inc. -
Carcinogens in Food: Evaluating the Presence of Cadmium, Lead, in Poultry Meat in South India
Objective: Local chickens were spontaneously sampled and slaughtered in the central markets of Coimbatore, Erode, and Namakkal districts, South India. Materials and Methods: Wet digestion was used to extract lead (Pb), cadmium (Cd), and zinc (Zn) in their blood and selected different organs (intestine, breast, liver, and gizzard), and their concentrations were measured using an atomic absorption spectrophotometer. Results: Apart from the blood of chickens from Coimbatore and Namakkal, where Pb was not found, the concentrations of Pb in the blood and organs of chickens from the three towns ranged from 1.8 to 8.33 mg/kg, exceeding the maximum tolerance thresholds (0.1 mg/kg) in internal organs of poultry birds. Except for the intestine of chickens from the three areas, Cd was only found in the heart, blood, and gizzard of Erode chickens, as well as the liver and gizzard of Namakkal chickens, in concentrations ranging from 0.13 to 0.58. According to threshold level, the upper limit met the maximum limits (0.5 mg/kg). Zn was found in all sections of chickens from the three selected districts, with concentrations ranging from 4.96 to 174.17 mg/kg. Conclusion: Its concentrations were within the permissible limits (10-50 mg/kg) in some areas of certain chickens, but it surpassed the permissible limit in the liver of chicken from Coimbatore. Any organs and blood from local chickens sold in Coimbatore, Erode, and Namakkal areas can be hazardous to ones health. This work is licensed under a Creative Commons Attribution-Non Commercial 4.0 International License -
Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging
Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images. 2022 Sibu Cyriac, Sivakumar R. and Nidhin Raju. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Cardiovascular Disease Prediction Using Machine Learning-Random Forest Technique
Cardiovascular diseases (CVDs) pose a significant global health challenge. Early and accurate diagnosis is crucial for effective treatment. This research focuses on developing a robust classification system for CVDs using machine learning techniques. This study proposes an enhanced Random Forest (RF) model optimized for big data environments and explore the potential of CNN-based classification. By leveraging medical imaging data and employing these advanced algorithms, we aim to improve the accuracy and efficiency of CVD diagnosis. 2024 IEEE. -
Cardless Society: Assessing the Role of Cardless ATMs in Shaping the Future of Financial Transactions
The ubiquitous ATM faces a critical crossroads in a world where the digital pulse is becoming more and more ingrained. The sound of plastic clicking, which used to be a comforting symbol of financial independence, is becoming less audible in the background noise of near-field communication and the Erie silence of digital scans. This study goes beyond the physical card and explores the unexplored world of cardless ATM technology, where security, convenience meet and innovation completely reimagines the process of getting cash. The meticulous analysis and potential use of technology can completely twist the dynamic rhythm of this world. 2024 IEEE. -
Carmelight Trends in Social Sector Expenditure
The Multidisciplinary National Journal, Vol-10 (1), pp. 77-96. ISSN-0975-9484 -
Carrying capacity assessment for religious crowd management - An application to sabarimala mass gathering pilgrimage, India
Crowd Management is always a challenging task when people gather in large numbers. Crowd disasters in India, including recurring incidents at religious venues, demands a crowd management system developed on the characteristics of the place, event, and participants. Assessment of carrying capacity is the prime process to design crowd management protocols and regulations. Carrying capacity assessment of religious gathering venues in India is often an overlooked process. The present study assessed the crowd carrying capacity of Sabarimala pilgrimage, Kerala, India. Physical carrying capacity assessment methods used for tourism venues have been applied and contextualised for crowd carrying capacity assessment. Characteristics of the venue, pilgrimage and pilgrims were studied to map the active crowd area and space utilisation zones. The physical carrying capacity was estimated based on the comfortable crowd density and threshold crowd density assessments. The study identified two factors influencing pilgrim movement within the venue viz. service level at the holy step and capacity of the darshan facility. Service level at the holy step is the prime factor that regulates the flow of the pilgrim within the venue including the pilgrim movement for deity darshan and hence the comfortable capacity of the holy step was distinguished as the effective carrying capacity of the venue. Physical carrying capacity at the comfortable crowd density has to be maintained throughout the event to avoid the triggering of crowd crushes. The crowd carrying capacity assessment (CCCA) method applied in this study is a simple process. Considering the crowd density and crowd regulation factors, the CCCA method can be applied to design crowd management protocols of other religious pilgrimage destinations in India. International Journal of Religious Tourism and Pilgrimage -
Case study: Impact of Industry 4.0 and its impact on fighting COVID-19
The emerging development in industrial technology for automation and data sharing is known as Industry 4.0. It incorporates the Internet of Things, Cyber-physical systems, and Cloud computing, all of which contribute to the development of a "smart factory". Customers, distributors, vendors, and stakeholders in the supply chain would be capable of connecting and can exchange data easily through Industry 4.0. The COVID-19 pandemic is quickly spreading and posing a threat to people all over the world. Employment and activities in all markets have been disrupted, putting economies all over the world in serious jeopardy. To combat the pandemic, retailers will benefit from Industry 4.0 because it will help to mitigate the impact of identified risks. I4.0 executives were focused on gaining a competitive edge, rising efficiency, lowering prices, and, ensuring profitability as their primary aim was to enhance the productivity of business during the time before the COVID-19 crisis. Our Government has imposed new behavioral trends including social distancing, isolation and, lockdown. The Government needs additional financial resources to combat pandemics as a result of these actions, there has been a global economic slowdown. This chapter enlightens the significance and technologies of Industry 4.0, showing how those technologies and applications help in attaining a better society. It also explains how Industry 4.0 helps in accomplishing sustainable manufacturing and the management tactics it used to boost the company's efficiency, as well as the effects of COVID-19. 2023 Bentham Science Publishers. All rights reserved. -
Cassava (Manihot esculenta Crantz)A potential source of phytochemicals, food, and nutritionAn updated review
Cassava (Manihot esculenta Crantz) is believed to be an important staple food crop providing potential valuable food source as well as variety of phytoconstituents. Its starchy tubers provide a significant source of energy for around 500 million individuals. Among staple crops, it is regarded to be one of the top suppliers of carbohydrates. Its physicochemical qualities, as well as its availability, have made it a captivating food component. Cassava starch is a valuable raw material used to make a variety of both native and modified starch for cooking purposes. They have also been used for a variety of industrial uses. Cassava starch and flour have the potential to be valuable alternatives to rice, maize, and wheat crops. The advantages included being a staple diet for humans, a component of animal feeds, a raw ingredient for food processing, edible coatings, locally produced alcoholic beverages, and ethanol manufacturing. The roots consist of cyanogenic glycosides, which can lead to lethal cyanide poisoning if tubers arse not properly detoxified using different processing methods include washing, fermentation, boiling, peeling and chemical processing to escape toxin content. The current review summarizes cassava's bioactive components which could be a potential source of various pharmaceutical drugs as well as a source of traditional and modern food applications. 2024 The Authors. eFood published by John Wiley & Sons Australia, Ltd on behalf of International Association of Dietetic Nutrition and Safety. -
Caste, Cricket, and Community Fraternal Intersections in Blue Star
[No abstract available] -
Casual nexus between firm ownership structure and market liquidity /
Asian Journal of Research in Banking and Finance, Vol.4, Issue 12, pp.12-22, ISSN No: 2249-7323. -
Cat Swarm Optimization Algorithm Tuned Multilayer Perceptron for Stock Price Prediction
Due to the nonlinear and dynamic nature of stock data, prediction is one of the most challenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of the hybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired by the behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variables for the proposed model. The model's performance is validated using historical data not used for training. The model's prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model's results are compared with other models optimized by various bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the best performance compared to other models taken for analysis. 2022 Polish Academy of Sciences. All rights reserved. -
Cataloging of happy facial affect using a radial basis function neural network
The paper entitled "Cataloging of Happy facial Affect using a Radial Basis Function Neural Network" has developed an affect recognition system for identifying happy affect from faces using a RBF neural network. The methodology adapted by this research is a four step process: image preprocessing, marking of region of interest, feature extraction and a classification network. The emotion recognition system has been a momentous field in human-computer interaction. Though it is considerably a challenging field to make a system intelligent that is able to identify and understand human emotions for various vital purposes, e.g. security, society, entertainment but many research work has been done and going on, in order to produce an accurate and effective emotion recognition system. Emotion recognition system can be classified into facial emotion recognition and speech emotion recognition. This work is on facial emotion recognition that identifies one of the seven basic emotions i.e. happy affect. This is carried out by extracting unique facial expression feature; calculating euclidean distance, and building the feature vector. For classification radial basis function neural network is used. The deployment was done in Matlab. The happy affect recognition system gave satisfactory results. 2013 Springer. -
Catalytic Activity and Reusability of Mesoporous Iron Aluminophosphate Catalyst in Pharmacologically Important Organic Transformations
Journal Atoms and Molecules an International Online Journal, Vol-4 (1), pp. 675-681. ISSN-2277-1247 -
Catalytic potential of fluorescein under visible light irradiation: Enabling single-pot open flask synthesis of novel pyrazolyl methanesulfonamides
This groundbreaking study introduces a novel and efficient method for synthesizing a range of substituted pyrazolyl methanesulfonamides through a five-component cyclocondensation reaction. This reaction incorporates five different components, such as ethyl acetoacetate, hydrazine, dimedone, benzaldehydes, substituted phenyl acetonitriles, and methyl sulfonyl chloride was made to react under visible light irradiation, with fluorescein serving as an effective catalyst and ethanol as solvent for 30 mintues. This method offers significant advantages, including simplified handling, higher yields of target products with shorter reaction times, and easier purification processes. We successfully synthesized around 15 novel pyrazolyl methanesulfonamide derivatives with high efficiency. Comprehensive spectral characterization confirmed the structural integrity and purity of these derivatives, demonstrating the robustness and versatility of this approach. Facilitated by visible light and utilizing fluorescein as a bio-friendly catalyst, this methodology is both green and sustainable. This innovative approach not only streamlines the synthesis of pyrazolyl methanesulfonamides but also holds considerable promise for advancing research and applications in fields such as medicinal chemistry and materials science. 2024 The Author(s) -
Catalyzing Green Mobility: Consumer Preferences for Green Energy Vehicles
Due to growing urbanization and the increase of vehicles, most Indian cities endure traffic congestion and significant air pollution. As a result, alternate technology in autos, such as electric vehicles, may become necessary (EV). This study aims to identify consumer preferences toward electric vehicles in the Indian market. This research conducted a survey and analyzed the opinions of people regarding their preferences for electric vehicles, demographics, and some of the demotivation which might be stopping them to switch to electric vehicles altogether. This research will help in determining different factors influencing the perception of consumers toward electric vehicles and what they expect when they think about purchasing a new electric vehicle. It is important to understand that electric vehicles are really getting popular now because of the rising fuel prices and environmental concerns. People are thinking about electric vehicles and replacing them with their regular petrol or diesel vehicles. In this research there might be some challenges or roadblocks in switching to electric vehicles. This research found out that despite a favorable attitude toward electric vehicles, individuals are hesitant to transition to electric vehicles due to different hurdles connected with them. This research found out that mostly the preferences of the consumers are good charging infrastructure, a good range of the electric vehicle, pocket-friendly vehicles are the most common preferences of consumers buying an electric vehicle. 2023 EDP Sciences. All rights reserved. -
Catalyzing Security and Efficiency: Blockchains Integration with IoT and Cloud Computing
Blockchain technology is a system that combines a number of computer technologies, encryption, shared storage, namely intelligent contracts, consensus processes, and peer-to-peer (P2P) networks. This research project begins with a description of the architecture of blockchains, followed by a comparison of the various consensus techniques used across various blockchain implementations. This studys scope includes a thorough analysis of the entire blockchain ecosystem. Our investigation also explores the complexity of the consensus models built into different blockchain platforms. This research painstakingly dissects these elements to pinpoint crucial elements that are essential for propelling the adoption and development of blockchain technology. In conclusion, our research corrects misconceptions about blockchains expansive potential and helps to direct the development of the technology across a wide range of industries. These results are significant for determining the future direction of blockchains enduring influence. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.