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Copper Nanoparticles: A Review on Synthesis, Characterization and Applications
An emerging field of science Nanotechnology which is involved in manipulation of atoms and molecules has shown great potential in all fields of sciences. Nanotechnology deals with nanoparticles ranging from size 1 to 100 nm in diameter, due to small size and high surface area eventually increases the state of activity. This review focuses on metal and metal oxide nanoparticles and mainly on green synthesis, characterization and application of copper nanoparticles. Green synthesis of copper and copper oxide (Cu and CuO) is economically beneficial and ecofriendly. Copper nanoparticles are used in diverse fields such as biomedicine, pharmaceuticals, bioremediation, molecular biology, bioengineering, genetic engineering, dye degradation, catalysis, cosmetics and textiles. Structural properties and biological effects of copper nanoparticles have promising effectivity in field of life sciences 2020. All rights reserved. -
Copper immobilized on a layered magnetite-based nanocatalyst for sustainable Ullmann cross-coupling reaction
This study demonstrates the efficient synthesis of diarylthioethers via CS cross-coupling between diverse aryl halides and arylthiols utilizing a magnetically retractable Fe3O4@SiO2PrNH2SACu(ii) nanocatalyst using K2CO3 as a base in DMF. The heterogeneous nanocatalyst was fabricated through a multistep process. The designed catalyst was characterized using various techniques, such as XRD, HRTEM, FESEM, STEM, EDAX, elemental mapping, TGA, VSM, XPS, ICP-OES and FT-IR. The catalyst design provides a dual role of the Schiff base-anchoring copper ions, to accelerate the oxidative addition and reductive elimination steps. This method makes use of ligand-free synthesis of diarylsulfides, enabling magnetic recovery and reuse of the catalyst for up to 6 cycles. The nanocatalyst exhibited high catalytic activity and a broad substrate scope. The magnetic nature of the nanocatalyst enabled easy separation from the reaction mixture using an external magnet, thus simplifying the workup. The synthesized nanocatalyst was then utilized for the synthesis of diarylthioethers and heterodiarylthioethers. The pure compounds were characterized using 1H and 13C NMR. This catalytic system offers a cost-effective, efficient, and simple protocol for the formation of the CS bond. This journal is The Royal Society of Chemistry, 2026. -
Coping with Public and Private Face-to-Face and Cyber Victimization among Adolescents in Six Countries: Roles of Severity and Country
This study investigated the role of medium (face-to-face, cyber) and publicity (public, private) in adolescents perceptions of severity and coping strategies (i.e., avoidant, ignoring, helplessness, social support seeking, retaliation) for victimization, while accounting for gender and cultural values. There were 3432 adolescents (ages 1115, 49% girls) in this study; they were from China, Cyprus, the Czech Republic, India, Japan, and the United States. Adolescents completed questionnaires on individualism and collectivism, and ratings of coping strategies and severity for public face-to-face victimization, private face-to-face victimization, public cyber victimization, and private cyber victimization. Findings revealed similarities in adolescents coping strategies based on perceptions of severity, publicity, and medium for some coping strategies (i.e., social support seeking, retaliation) but differential associations for other coping strategies (i.e., avoidance, helplessness, ignoring). The results of this study are important for prevention and intervention efforts because they underscore the importance of teaching effective coping strategies to adolescents, and to consider how perceptions of severity, publicity, and medium might influence the implementation of these coping strategies. 2022 by the authors. -
Coping with Burnout Across Cultures
The well-being of employees is impacted by numerous factors within their work realm. These factors consist of internal elements, such as the work environment, relationships with coworkers, and satisfaction with their jobs, as well as external factors like job security, working conditions, pay, and growth opportunities. Unfortunately, the COVID-19 pandemic has introduced significant changes that have greatly disrupted the factors that were crucial for employees to maintain a healthy and productive career. These changes include the global economic downturn, shifts in workplace culture, and a decline in worklife balance, all contributing to increased job insecurity among employees. The weight of unemployment and job insecurity often materialises as burnout and enduring fatigue among employees, consequently lessening their peak efficiency level. However, while exploring coping techniques for burnout, cultural practices are persistently overlooked. However, each culture possesses distinctive norms that shape an individuals way of handling workplace stress and pressure. This paper will predominantly look at secondary data published in online databases to explore previously existing literature on differences in culture while coping with burnout. Through the literature review, the authors compare the coping mechanisms employees adhere to between individualistic cultures and collectivist cultures. The paper highlights employees routines at a broader level, emphasising the need for organisations to be aware of diverse coping styles, especially on sites that act as a melting pot of cultures. It aims to promote safer work environments by articulating the differences in coping mechanisms of employees in different cultures. The paper explores sustainable practices for employees and workers to enhance their job satisfaction and well-being. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Cop-edge critical generalized Petersen and Paley graphs
Cop Robber game is a two player game played on an undirected graph. In this game, the cops try to capture a robber moving on the vertices of the graph. The cop number of a graph is the least number of cops needed to guarantee that the robber will be caught. We study cop-edge critical graphs, i.e. graphs G such that for any edge e in E(G) either c(G?e) < c(G) or c(G?e) > c(G). In this article, we study the edge criticality of generalized Petersen graphs and Paley graphs. 2023 Azarbaijan Shahid Madani University. -
Cooperative Social Entrepreneurship Among Rural Artisans: A YouTube-Based NLP Analysis of Environmental Practices and Livelihood Outcome
This paper presents a novel data-driven study of rural artisans in Karnataka by leveraging YouTube video transcripts and natural language processing (NLP) to examine how cooperative social entrepreneurship (CSE) relates to environmental practices and livelihood outcomes. CSE initiatives in India typically rely on primary surveys to understand how artisanal groups adopt eco-friendly practices and how this affects their livelihoods, but such data are costly to collect and difficult to scale. We investigate whether publicly available video narratives can serve as a scalable secondary data source for studying CSE among rural artisans. We compile a corpus of YouTube videos on banana-fibre craft, Anegundi/Hampi artisan collectives, and Karnataka handicrafts. Audio is transcribed using an automatic speech recognition pipeline, and the resulting bilingual/multilingual text (English-Hindi-Kannada) is processed with a rule-based NLP tagger to identify three constructs central to our CSE perspective: (i) artisan and community references (CSE signals), (ii) environmental practices (e.g., "banana waste to fibre,""eco-friendly,""sustainable"), and (iii) livelihood/product mentions (e.g., baskets, mats, runners) as observable proxies for livelihood outcomes. On top of this, we apply text mining techniques topic modeling, sentiment analysis, and supervised classification with fine-tuned transformer models (BERT) to classify transcript segments (e.g., environmental focus vs livelihood focus) and extract key thematic topics. Experimental results show that our BERT-based classifier achieves over 90% accuracy, substantially outperforming traditional baselines such as TF-IDF+SVM and LSTM. The videos frequently encode both CSE signals and explicit environmental practices, and a non-trivial subset articulates marketable products, suggesting that platform narratives can partially capture the CSE-environment-outcome chain without questionnaires. However, explicit statements about market, seasonality, or constraint variables remain sparse, revealing limitations of video-based secondary data. The study contributes methodologically by integrating digital media analytics into rural development research, offers complexity and performance analysis of the employed algorithms, and stresses reproducibility through transparent documentation of data sources, model architectures, training configurations, and evaluation metrics. 2025 IEEE. -
Cooperative Federalism in South Asia and Europe: Contemporary Issues and Trends
This book explores the challenges, opportunities, and trends impacting the working of federations in South Asia and Europe. It deliberates on the changing socio-economic realities, challenges facing the existing structures of governance, degrees of consociationalism, and the growing aspirations of people in South Asia and Europe.Through case studies from Greece, Germany, Austria, Switzerland, Spain, France, Sri Lanka, Pakistan, Nepal, Maldives, Bhutan, and India, the volume focuses on critical issues relating to cooperative federalism its complexities, institutional dilemmas, and trends in South Asia and Europe. It discusses a variety of themes relevant to Cooperative Federalism including federal-state relations; cooperative governance; constitution; multiculturalism, fiscal relations, democratization, devolution of powers, consociationalism, and global citizenship in South Asia and Europe. The book further emphasizes the need to strike a balance between the federal government and the constituent units in these two regions. Topical and lucid, this book will be of interest to teachers, scholars, and researchers of political science, comparative government and politics, federalism, South Asian politics, European politics, governance studies, and political studies. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
COOPERATIVE FEDERALISM IN A MULTINATIONAL COUNTRY: Examining the Case of Pakistan
Pakistan, as a multilingual and multiethnic country, has had to deal with issues of ethnic conflict and separatism. Cooperative federalism is used as a device by countries across the world to accommodate and manage the immense diversities they possess. This chapter examines the need for cooperative federalism in a multinational country like Pakistan to strengthen its federal model, ensuring that ethnic groups in the country do not feel insecure and alienated from the union, demanding secession. Beyond national security concerns, cooperative federalism in Pakistan will ensure economic security, human rights, social security, effective policymaking and much more, which form the basis of a welfare state. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
Cooperation affects NGO staff performance patterns
In order to optimise employee productivity and overall profitability, non-profits must invest heavily in their human resources. Contrarily, the focus of this study will be on the value of cooperation and the strategies the non-governmental organisation (NGO) should use to improve the performance of the bank as a whole. Once the data have been collected using quantitative and qualitative techniques, SPSS descriptive statistics will be utilised to maintain the findings and support the research hypothesis. According to the study, qualities like trust, camaraderie, job happiness, and benefits directly impact employees productivity at the bank. The degree of teamwork among co-workers directly affects how productive an employee is. Using the statistical program SPSS, managers and staff of NGOs were surveyed; the results revealed a favourable correlation between employee performance and NGO cooperation. When employees cooperate at work, their productivity increases, and the efficacy of the organisations they work for rises. Good news for charitable organisations. Because of this, the collaborative NGO outperforms the non-collaborative NGO in terms of productivity. It was found that better communication results in greater cooperation amongst NGOs. Copyright 2023 Inderscience Enterprises Ltd. -
Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an F 1score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is 9 6. 1 %. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices. 2024 IEEE. -
Convolutional neural network for stock trading using technical indicators
Stock market prediction is a very hot topic in financial world. Successful prediction of stock market movement may promise high profits. However, an accurate prediction of stock movement is a highly complicated and very difficult task because there are many factors that may affect the stock price such as global economy, politics, investor expectation and others. Several non-linear models such as Artificial Neural Network, fuzzy systems and hybrid models are being used for forecasting stock market. These models have limitations like slow convergence and overfitting problem. To solve the aforementioned issues, this paper intends to develop a robust stock trading model using deep learning network. In this paper, a stock trading model by integrating Technical Indicators and Convolutional Neural Network (TI-CNN) is developed and implemented. The stock data investigated in this work were collected from publicly available sources. Ten technical indicators are extracted from the historical data and taken as feature vectors. Subsequently, feature vectors are converted into an image using Gramian Angular Field and fed as an input to the CNN. Closing price of stock data are manually labelled as sell, buy, and hold points by determining the top and bottom points in a sliding window. The duration considered over a period from January 2009 to December 2018. Prediction ability of the developed TI-CNN model is tested on NASDAQ and NYSE data. Performance indicators such as accuracy and F1 score are calculated and compared to prove effectiveness of the proposed stock trading model. Experimental results demonstrate that the proposed TI-CNN achieves high prediction accuracy than that of the earlier models considered for comparison. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Convolutional Neural Network based Di-Strategy Cheetah Optimization Algorithm for Automatic Diabetes Prediction
Diabetes is a chronic metabolic disease characterized by elevated blood sugar levels. Diabetes prediction leverages patient data to assess the risk of developing the condition, facilitating early diagnosis and intervention. However, existing models struggle to capture the complex interactions between risk factors due to limited feature representation, leading to inaccurate predictions. This research proposes a Convolutional Neural Network-based Di-Strategy Cheetah Optimization Algorithm (CNN-DS-COA) for automatic diabetes prediction using patient data. The COA is enhanced with tent chaotic mapping and an adaptive search agent, which improves population diversity distribution and convergence speed. Initially, the Pima Indians Diabetes Database (PIMA) and Germany datasets are employed to evaluate the performance of CNN-DS-COA. Min-max normalization is applied to scale the data within a uniform range while preserving relationships among values. The CNN is then used for automatic diabetes prediction, with DS-COA fine-tuning the CNNs parameter values effectively using two strategies. The proposed CNN-DS-COA achieves superior accuracy, with 99.90% and 99.72% on the PIMA and Frankfurt Hospital, Germany datasets, respectively, outperforming existing methods such as stacked ensemble approaches and statistical predictive models. 2025, Research Institute of Intelligent Computer Systems. All rights reserved. -
Convolutional bi-directional autoencoder assisted generative adversarial de-blurring framework for palm leaf character analysis
Palm leaf manuscripts have historically educated people on a variety of topics, including astronomy, mathematics, astrology and medicine. These manuscripts are constructed from dried palm leaves, which contain a wealth of information that remains largely untapped due to the challenges of digitalization and transcription. Recognizing the characters found in palm leaf manuscripts is a complex problem because blurred images of these manuscripts often conceal critical information. The present study proposes an automated de-blurring model to effectively identify exact Malayalam characters in palm leaf manuscripts. The input images are gathered from a real-time dataset to address this challenge. The Weighted Guided Image Filtering (WG_IF) method is employed to extract the detail layer, which not only reveals important information about the characters but also eliminates unwanted noise. The detailed layer is then input into the proposed Convolutional Bi-directional AutoEncoder assisted Generative Adversarial De-blurring (CBiAE_GADeblur) framework. This framework comprises two main blocks: the generator and the discriminator. The generator block uses the Convolutional Bi-directional Long Short Term Memory with AutoEncoder (CBLSTM_AE) method to produce de-blurred images. The discriminator block classifies these images as real or fake, enhancing the prediction accuracy of the palm characters. The proposed method demonstrates a superior accuracy rate of 98.25% in Prasavachikilsa and Vishavydyam datasets and exhibits lower time complexity. The motivation behind this research is to overcome the significant barriers posed by blurred palm leaf images, thereby unlocking and preserving the invaluable knowledge contained within these historical documents. Indian Academy of Sciences 2025. -
Convolutional Autoencoder Based Feature Extraction and KNN Classifier for Handwritten MODI Script Character Recognition
Character recognition is the process of identifying and classifying the images of printed or handwritten text and the conversion of that into machine-coded text. Deep learning techniques are efficiently used in the character recognition process. A Convolutional Autoencoder based technique for the character recognition of handwritten MODI script is proposed in this paper. MODI script was used for writing Marathi until the twentieth century. Though at present, Devnagari is taken over as the official script of Marathi, the historical importance of MODI script cannot be overlooked. MODI character recognition will not be an easy feat because of the various complexities of the script. Character recognition-related research of MODI script is in its initial stages. The proposed method is aimed to explore the use of a deep learning-based method for feature extraction and thereby building an efficient character recognition system for isolated handwritten MODI script. At the classification stage, the features extracted from the autoencoder are categorized using KNN classifier. Performance comparison of two different classifiers, such as KNN and SVM, is also carried out in this work. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Conversion of alkynes into 1,2-diketones using HFIP as sacrificial hydrogen donor and DMSO as dihydroxylating agent
A metal-free and hypervalent iodine free conversion of internal alkynes into 1,2-diketo compounds has been described. The efficacy of the present protocol rely on the use of HFIP (1,1,1,3,3,3-Hexafluoro-2-propanol) as reducing agent of alkynes and DMSO as dihydroxylating agent of olefins to acquire the desired chemical transformations. The obtained 1,2-diketones were further transformed into useful derivatives. 2020 Elsevier Ltd -
Conversational Agents and Chatbots: Current Trends
Languages facilitate the communication and interaction process among people. Computers learn to communicate with humans intelligently with the help of conversational agents and chatbots based on Natural Language Processing (NLP). Conversational agents and chatbots are gaining popularity in various applications. The development of chatbots or conversational agents is tightly coupled with an organizations customer service requirement. However, the background procedures that power the bots brain are more or less dependent on Artificial Intelligence-based processes. NLP mechanisms powered by various Deep Learning techniques are often used in the training and development of such intelligent agents. These bots inevitably become more competent as they interact with more people. The interactions between a customer and the bot are usually used as data in further training iterations. Chatbots are likely to respond with faster and more precise suggestions leading to solutions for frequently asked questions. Therefore, the current trends indicate the need for a supplementary system rather than substituting human agents existing customer service. The customer experience and intelligence of the chatbots are improved with the help of data analysis and training with the use of Deep Learning techniques. The chapter covers the current trends of conversational agents and chatbots, how the various Artificial Intelligence techniques have transformed the development of multiple architectures of these intelligent systems, and it compares the different state-of-the-art NLP-based chatbot architectures. 2024 selection and editorial matter, Anitha S. Pillai and Roberto Tedesco. -
Converging Deep Learning and Cloud Computing: A Scalable and Efficient Approach for Modern AI Infrastructure
Deep learning has proven to be a powerful approach to solving challenging problems, ranging from natural language processing, speech recognition, to computer vision. The proposed model has capable of matching the expanding amount of facts as well as complexity of the algorithms is demands of deep researching methodologies. These demands cannot be met with traditional computing environments. This is why cloud computing technologies have developed, offering a scalable and affordable alternative for executing deep learning algorithms. Cloud computing platforms provide the resources necessary to run deep learning workloads including compute, storage, and networking. This means you no longer have to spend lots of money on expensive hardware and makes it easier for teams and researchers to deploy and train deep learning models faster. Cloud computing has ushered in a new era of deep learning developments by mobilizing the power of specialized hardware, notably GPUs, to accelerate the training and performance of deep learning models. 2025 IEEE. -
Convergent replicated data structures that tolerate eventual consistency in NoSQL databases
The Eventual consistency is a new type of database approach that has emerged in the field of NoSQL, which provides tremendous benefits over the traditional databases as it allows one to scale an application to new levels. The consistency models used by NoSQL database is explained in this paper. It elaborates how the eventually consistent data structure ensures consistency on storage system with multiple independent components which replicate data with loose coordination. The paper also discusses the eventually consistent model that ensures that all updates are applied in a consistent manner to all replicas. 2013 IEEE. -
Convergence of retail banking interest rates to households in euro area: Time-varying measurement and determinants /
International Economics and Economic Policy, Vol.17, Issue 1, pp.25-65 -
Convergence of retail banking interest rates to households in euro area: time-varying measurement and determinants
This study measures time-varying progress of retail banking (to households) interest rates convergence and examines its determinants for twelve countries of the euro area, between 2003 and 2014. First, we measure convergence of interest rates using five different time-varying indicators, namely asymmetric dynamic conditional correlation (ADCC), beta convergence, sigma convergence, variance ratio, and dynamic cointegration. We then estimate panel regressions for each type of interest rate to identify the determinants of convergence over pre-crisis and crisis periods. The estimated ADCC is employed as the dependent variable and explanatory variables measure potential macroeconomic, external linkages, industry-specific, institutional and sociological determinants. The results reveal that convergence is weak and heterogeneous across sub-periods (pre-crisis and crisis), economic groups (core and periphery), product type (savings and credit) and products maturities (short, medium and long). Among the fundamental determinants, inflation, output correlation, and sociological factors strongly impact convergence, however, the explanatory power of determinants weakens during the crisis period. 2019, Springer-Verlag GmbH Germany, part of Springer Nature.