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Catalytic Conversion of 2- Methyl Phenol to Salicylaldehyde Using Manganese-Oxide Doped Cellulose-Derived Carbon Spheres
In this study, we report on the hydrothermal synthesis of MnOx-anchored carbon spheres as an effective catalyst. A new method is employed to prepare carbon spheres from cellulose. Using a solvent-free process, the prepared catalyst is used to convert o-cresol to salicylaldehyde. Methods including XRD, Raman spectroscopy, FE-SEM, EDS mapping, and HR-TEM are used to examine the structural and morphological characteristics of the catalyst. It is confirmed from the BET analysis that doping with MnOx increases the surface area of the carbon spheres. At standard atmospheric pressure, the conversion of o-cresol to salicylaldehyde is highly selective due to the enhanced surface area and active sites of MnOx-doped carbon spheres. Under atmospheric pressure, the MnOx/CS catalysts show excellent efficiency, yielding 96% salicylaldehyde in under 1 h. This study underscores the feasibility of using MnOx-doped carbon spheres as a robust catalyst for the controlled oxidation of o-cresol. The results show that the catalyst has a great deal of activity and effectiveness, as well as being cheap and reusable, which greatly increases its potential for real-world catalytic applications. 2025 Wiley-VCH GmbH. -
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 -
Catalysts of Change: The Transformative Journey from HR 1.0 to HR 5.0 Innovations, Challenges, and Strategies in Human Resource Management with Technology and Data-Driven Integration
Human Resource Management (HRM) has evolved significantly, transitioning from HR 1.0 to HR 5.0 due to technological advancements, shifting demographics, and the demands of the global business environment. This chapter highlights the evolution of HRM discussions, focusing on the key changes, concerns, and approaches that have characterized this transformation. In the HR 1.0 phase, the emphasis was on paperwork and routine clerical tasks. This laid the basis for the kind of advancement that was HR 2.0, which brought into account computerization and rudimentary data interconnection. In HR 3.0, SHRM became dominant, meaning that organizational HR practices were oriented only to achieving strategic objectives. There was a shift in the application of digital technology in performing human resource activities under the emergence of HR 4.0. The present phase is called Human Resources 5.0 (HR 5.0), which marked the strain referred to as ''People First." This approach is a mixture of Technologies Focus and Humanity Focus with an ample concern on the experience and emotional state of the employees fused with the capacity of the organizations to deal with competitive issues common to most firms. It also explores the influences that have led to these changes such as; technological developments, geographic expansion, and prospective staff demands. It also looks at possible risks linked to each shift among them, resistance to change in organization cultures, threats to data privacy amongst others plus the need to acquire higher skills. Moreover, the triple-layered paper maps actionable approaches toward the transformation of HR that are supported by cases that focus on learning culture, diversification, inclusion, and the utilization of big data. Reading through this paper to understand how HR has evolved from 1.0 to 5.0, will help HR managers, and organizations get ideas on how to meanwhile shape their human resource management strategies and advancement in the ever-growing global economy. 2025, The Research Publication. 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. -
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. -
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. -
Caste, Cricket, and Community Fraternal Intersections in Blue Star
[No abstract available] -
Cassava (Manihot esculenta Crantz)A potential source of phytochemicals, food, and nutrition-An 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. -
CaSi2O5:Sm3+ Orange -Red Emitting Phosphors for Latent Fingerprint Detection Application
Orange-red emitting CaSi2O5:xSm3+ (x = 0.1, 0.2, 0.5, 1, 1.5, 2, and 2.5mol% of Sm3+) phosphors were synthesized by a high-temperature solid-state reaction. In this study, the crystal structure, phase purity, functional group presence, and their bending and stretching vibrations, photoluminescence (PL) spectra, thermoluminescence (TL) spectra, and colour purity was systematically investigated. The phosphor exhibits a strong excitation with the charge transfer band (CTB) of O2? and Sm3+ at 263nm. Under 263nm excitation, the CaSi2O5:Sm3+ phosphor shows characteristic peaks at 595nm and 629nm, which are attributed to the characteristic 4G5/2?6H7/2 and 4G5/2?6H9/2 transitions of the Sm3+ ions, respectively. The doping concentration x = 2mol% is found to be the optimal doping concentration. The CIE coordinates of the optimal concentration phosphor CaSi2O5:2Sm3+ are found to be (0.589, 0.41) in the orange-red region with a colour purity percentage of 96.93%. Judd-Ofelt analysis was also carried out with the photoluminescence emission spectrum, in order to investigate the transition dynamics. Fingerprints were developed on non-porous glass and aluminium foil substrates. The experimental results display that the CaSi2O5:xSm3+ phosphors have a huge potential for practical applications. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Cashless Confidence: Exploring the Influence of Digital Payments on Gen Z Consumption Patterns
The rapid growth of digital payment systems has significantly changed financial behaviors, especially among Generation Z. This group consists of digital natives whose buying habits are closely linked to technology. Using qualitative thematic analysis, which identifies common themes in qualitative data, this research reviews various studies and reports on financial technology adoption. It looks at how mobile wallets, UPI platforms, QR-based payments, and new fintech solutions affect Gen Z's spending habits, views on money, and financial choices in India. Drawing from existing literature, empirical research, and theories like behavioral economics and the Technology Acceptance Model (TAM), the paper brings together evidence on the psychological, social, and cultural factors behind digital payment adoption. The findings show that ease of use, peer influence, gamification features, and social media-driven consumption greatly motivate Gen Z to use cashless transactions. However, these same factors may lead to risks such as impulsive buying, accumulating debt through Buy Now Pay Later (BNPL) services, and privacy issues. Gender dynamics, urban-rural divides, and differences in financial literacy further complicate these trends. Meanwhile, digital platforms are promising for improving financial management through budgeting tools and investment apps. The paper concludes that digital payments are not just replacing cash; they are transforming financial identities, consumption culture, and intergenerational attitudes toward money. Insights from this study have important implications for policymakers, financial institutions, and educators looking to promote responsible, inclusive, and sustainable digital payment systems for the future economy. 2026 by IGI Global Scientific Publishing. All rights reserved. -
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. -
Case studies: multimodal applications in natural language processing
This chapter explores the incorporation of natural language processing (NLP) with multimodal information sources, including text, speech, and visual information, towards the improvement of practical applications. NLP may very significantly enhance tasks such as sentiment analysis, image captioning, and cross-modal retrieval by combining these modalities. Two examples of deep learning approaches are neural networks and transformers, which are examples of critical approaches for developing robots that analyze and understand complex multimodal inputs. The chapter is full of case examples that illustrate how multimodal NLP can revolutionize many industries, including healthcare data analysis and voice-activated assistant development. These illustrations demonstrate how NLP can enhance user interactions and decision-making processes by offering deeper, more contextual insights. In fact, the chapter also covers issues and ways ahead of multimodal NLPas integrating data, handling faulty or missing data, and how to resolve ethical dilemmas. These ongoing changes will define future artificial intelligence systems with increased adaptability, intuitiveness, and applicability. 2026 Elsevier Inc. All rights reserved. -
Case Analysis of Evolving and Successful Practices in Water Governance in Asia
Water governance is a critical issue across Asia, where countries face escalating challenges such as water scarcity, pollution, and inefficient management systems. This paper contributes to the field by offering a comparative analysis of innovative and successful governance practices from multiple Asian countries, including Pakistan, Turkey, Kyrgyzstan, and Jordan. Unlike prior studies that focus on isolated national or local contexts, this research synthesizes diverse governance models to highlight scalable innovations and transferable strategies. Key insights include the effectiveness of decentralized governance frameworks, inclusive stakeholder engagement, and data-driven policy integration. These practices are evaluated for their potential to inform context-specific water management solutions that are both sustainable and resilient. The study also emphasizes the alignment of governance strategies with the United Nations Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). By bridging theoretical insights with practical applications, the research addresses critical gaps in the literature and provides actionable frameworks for policymakers to strengthen water security across the region. 2025 Policy Studies Organization. -
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 -
Carmelight Trends in Social Sector Expenditure
The Multidisciplinary National Journal, Vol-10 (1), pp. 77-96. ISSN-0975-9484 -
Career Resilience and Advancement: A Research Note on Women in Indian IT
Purpose: The representation of women in the Information Technology (IT) industry in India is higher than in other sectors. However, many women leave the industry after five years of employment. Organizations are concerned about the gender gap in leadership positions within this sector. The paper suggested that career resilience (CR) is a strategic method for retaining and advancing women in IT professions. Methodology/Approach: The paper employed an interpretive synthesis approach. Literature from peer-reviewed sources on CR, career development, and career advancement of women professionals was reviewed. Findings: The synthesis of literature highlighted the role of CR in improving women's continued participation in the industry. The findings suggested building resilience through organizational interventions as a way forward to creating a more gender-equitable workforce. Practical Implications: The strategies presented are practical and feasible for IT organizations to create a more inclusive workspace. These strategies are designed to empower organizations to take meaningful steps for an equitable workforce. Originality: The paper presented a comprehensive approach to sustaining and advancing women in the IT industry in India. 2025, Associated Management Consultants Pvt. Ltd. All rights reserved. -
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. -
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. -
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.