Browse Items (16481 total)
Sort by:
-
Smart and Digital Wellbeing Initiatives in Healthcare: The Way Forward
Smart and digital wellbeing initiatives play a significant role in modern healthcare. This chapter explores current initiatives, their use and effectiveness, as well as their challenges and future directions, with a special focus on mental health. It explores digital wellbeing, the growing need for initiatives, and the tools available in India and the world. These initiatives enhance accessibility, reduce stigma, offer personalized interventions, and improve psychological and workplace wellbeing. However, current technology, especially AI-based chatbots, have limited user interaction and authentic therapeutic support. Additionally, biased datasets can lead to flawed responses and issues with inclusive support. Current initiatives face privacy concerns due to a lack of regulatory bodies, emphasizing the need for robust regulation and oversight. Collaborative development, appropriate marketing and improved security are key to improving existing initiatives. Future research needs to address and explore these concerns to ensure the development of effective and ethical initiatives. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Mike Davisthe Storyteller (1946-2022)
This short note is a recapturing of what Mike Davis stood for and how we all can pay homage to such a great figure who cannot be merely disciplined into any academic specializations. His wonderful marriage of theory and practice is a case in point emphasized throughout this note. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Design, Synthesis, and Applications of Carbon Dots with Controlled Physicochemical Properties
Modification of carbon dots (CDs) is essential to enhance their photophysical newlineproperties and applicability. Physical (e.g., composite material blending, coreshell architecture) and chemical (e.g., doping, surface passivation) methods exist to modify CDs. Different precursors can impart varied functionalities and heteroatomic dopants on CDs. Despite several modification strategies, the reproducibility and scalability of CDs still need to be improved. Newer approaches for modifying CDs are thus essential to ensure lab-to-lab and batchto-batch consistency. Our study focused on developing novel strategies for the physicochemical modifications of CDs. The theoretical simulation we performed for surface-functionalised CDs with the aid of density functional theory and time-dependent density functional theory helped to predict the mechanism of photoluminescence (PL) and to analyse the effect of hydrogen bonding on the newlineproperties of CDs (Chapter 3). We have developed a novel and general method for preparing amine functionalized CDs from modified paper precursors (Chapter 4). This strategy allows us to prepare CDs with customized functionalities, alleviating the post-synthesis modification. A novel ionimprinting strategy involving CDs synthesised from modified paper precursors newlinewas also developed through our research (Chapter 5). In another work, we utilized silk fibers as a matrix for immobilising CDs (Chapter 6). CDs prepared from mulberry leaves were fed to silkworms to produce CD-embedded silk fibres, which could be used to detect dopamine. In addition, we prepared CDs newlinefrom an unreported natural source (frankincense), which were used to detect lead ions (Chapter 7). We demonstrated the heavy metal sensing application of these newlineCDs in combination with a UV-light LED chip and a smartphone, which is relevant in resource-limited areas. The research results presented in the thesis are expected to inspire further investigations and applications related to CDs. -
A Verilog-Based Design Framework for Real-Time Edge Detection in Image Processing
This article details the design process of a real-time image processing system developed in Verilog. The design has proven highly effective in real-time image acquisition, buffering, and processing, with a focus on hardware and performance optimization. The principal modules are a line buffer for image frame storage, a convolution engine featuring edge detection filters such as Sobel and Prewitt, and a control unit responsible for data flow and synchronization. The architecture facilitates the transmission of image data from a camera, with processed images transmitted via VGA/HDMI interfaces. Focus is placed on attaining low latency, high throughput, and optimal utilization of FPGA platform resources. The technology is particularly relevant for autonomous systems, medical imaging, industrial automation, and surveillance, where real-time edge detection is crucial for decision-making. 2025 IEEE. -
Negative consumer engagement via food delivery applications: A real concern for generation Z?
The traits of Generation Z are notable for how they define and reshape themselves in the 21st century. The current study investigates the factors that Generation Z users consider when deciding on a food application. The respondents in the current study were divided into working and nonworking groups, and the results imply that since college students are still supported financially by their parents or guardians, their main concerns are higher discounts, prices, and quantities. An in-depth and semistructured questionnaire was addressed to both groups of 15 respondents belonging to Generation Z. The stakeholders who would benefit from this research would primarily be online delivery restaurants, food application developers, academic researchers, and Generation Z cohorts. 2024, IGI Global. All rights reserved. -
Spectral and type I X-ray burst studies of 4U 1702?429 using AstroSat observations
4U 1702?429, an atoll-type neutron star low-mass X-ray binary, was observed twice by the AstroSat/Soft X-ray Telescope (SXT) and Large Area X-ray Proportional Counters (LAXPC-20) on 2018 April 27 and 2019 August 8. Persistent emission spectra of the source were well fitted with the model combination - constant tbabs (thcomp diskbb+powerlaw). The parameters obtained from the spectral analysis revealed the source to be in a hard spectral state during the observations. Time-resolved spectral analyses were performed on the three type I X-ray bursts detected from the source. Burst analysis showed that the source underwent a photospheric radius expansion. Consequently, the radius of the neutron star and distance to the source (with isotropic and anisotropic burst emission) were obtained as 12.65+?008690 km and 6.92+?000916 and 8.43+?001020 kpc, respectively. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. -
A Review on Synchronization and Localization of Devices in WSN
Wireless sensor networks are communication networks that deal with sensor devices that are wirelessly interconnected in order to collect and forward data between different environments. Network scaling of small sensor devices with all its limitations has a foolproof scope for future applications. The advantage of minimal infrastructural cost and applicability within challenging environments make it an attractive choice. Statistics have been shown to prove the demand for research for synchronization and localization as a research problem. WSNs are capable of dynamically building virtual infrastructure and getting synchronized with the rhythm of communication setup. Limitations in the amount of energy that can be utilized make it a necessity for the networks to be more optimal in terms of energy consumption. These challenges necessitate the need to study and analyze the recent advancements implemented in approaching synchronization and localization problems. This paper reviews recent research proposals and methodologies to identify related attributes and their relation to the system. A detailed comparative study is conducted to identify relevant patterns that influence the performance of the networks in terms of energy consumption. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Spectral and type I X-ray burst studies of M15 X-2 using NICER observations
In this work, we present spectral and burst analyses of three thermonuclear type I X-ray bursts (B1, B2, and B3) detected from the ultracompact neutron star low-mass X-ray binary M15 X-2, using data from the Neutron Star Interior Composition Explorer (NICER). Time-averaged spectral fitting with the model tbabsthcompiskbb) suggests that the source was in a soft or soft-intermediate spectral state, characterized by a photon power law index of ??2 and an average mass accretion rate of ?0.09 m?Edd. The type I X-ray bursts exhibited rapid rise times of 1.25?2.75 s, followed by longer decay phases lasting 14.50?23.25 s, with characteristic burst timescales (?) of ?11 s, which are consistent with pure helium burning. Notably, burst B3 displayed a double-peaked profile indicative of a photospheric radius expansion event, from which we inferred the neutron star radius to be 10.8+2.4?2.2 km. Based on the peak flux of the burst, we estimated the source distance to be 10.54+1.43?1.26 kpc under the assumption of isotropic emission, and 14.06+1.90?1.68 kpc for anisotropic emission geometry. A strong ?420 Hz burst oscillation candidate was detected in the cooling tail of burst B1. 2025 Elsevier B.V. -
Discovery of a 459 Hz Burst Oscillation in XTE J1810?189 with NICER
We present a detailed temporal study of a type I X-ray burst from the neutron star low-mass X-ray binary (NS-LMXB) XTE J1810?189, observed on 2023 April 27 using the Neutron Star Interior Composition Explorer. The burst exhibited a rapid rise time of 2.55 s, followed by an exponential decay lasting for 7.5 s, with a total duration of ?13 s. Type I X-ray bursts are driven by thermonuclear burning on the surface of a neutron star in an NS-LMXB. As these bursts originate from the stellar surface they can exhibit highly coherent signals known as burst oscillations, which serve as probes of the neutron stars spin frequency. We report the detection of a burst oscillation signal at ?459 Hz at the cooling tail of the burst. The oscillation showed a strong Leahy-normalized power of PL = 35.95 at 458.92 Hz, corresponding to a single-trial significance of 5.53? and a multiple-trial corrected significance of 3.14?. The folded pulse profile in the 0.212 keV band is well described by a constant plus sinusoid with a fractional rms amplitude of 14.63%. These results suggest that the burst oscillation frequency of XTE J1810-189 directly reflects on the neutron stars spin, measured here to be ?2.18 ms, placing it among the rapidly rotating NS-LMXBs. This burst oscillation signal at the cooling tail of the burst can be interpreted through surface mode model or the asymmetric cooling wake model. 2025. The Author(s). Published by the American Astronomical Society. -
Production of bioactive compounds from cell and organ cultures of Centella asiatica
Centella asiatica, commonly known as mandukaparni, has garnered recognition for its efficacy in addressing a spectrum of health concerns. Its diverse pharmacological properties encompass roles in treating neuro-related issues, gastrointestinal problems, and cardiovascular conditions. Furthermore, it exhibits multifaceted therapeutic effects, including antioxidant, antidiabetic, wound healing, skin protective, and anti-osteoporotic properties. This herbaceous plant is rich in bioactive compounds such as centellosides (triterpene saponins) including madecassoside, madecassic acid, asiatic acid, and asiaticoside. These compounds, crucial for their pharmacological potential, are biosynthetically produced through the mevalonate and methylerythritol phosphate pathways. However, the challenge lies in the production of these important secondary metabolites, given the adverse impact on the availability of mandukaparni due to increasing demand. To address this concern, this chapter emphasizes the biotechnological interventions for the production of bioactive phytochemicals. These include plant tissue culture techniques, such as cell and organ cultures, along with elicitation strategies, genetic engineering approaches, and bioreactor-scale production. These methods aim to enhance the sustainable production of centellosides, providing valuable insights for researchers and paving the way for future opportunities in the field of plant-based therapeutics. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Role of Antibiotics in Animal Feed: Prospects and Future Challenges
Antibiotics were discovered more than 50 years ago and are now commonly employed in cattle and poultry production to prevent animal illnesses and increase livestock productivity. They were quickly embraced as an integral part of livestock feeding programs after their benefits were recognized. To meet the increasing demand for animal protein for human consumption, antibiotics in animal feed are also highly sought after because they enhance the quality and quantity of meat as well as growth efficiency. Antibiotic overuse, however, has led to the emergence of resistant strains and presented health risks to people. The most significant negative impact of antibiotic residues entering the food chain has been the spread of antibiotic-resistant organisms to humans due to the transportable nature of resistance. Because of these drawbacks, there are many alternate strategies suggested for combating resistance. These include vaccines, bacteriocins, antimicrobial peptides, and phage therapy, to name a few. Even though they are not yet as effective as antibiotics, they can be utilized in preventive and management strategies. Overall, the combination of suggested alternative interventions with limited antibiotic use appears to be promising in combating antimicrobial resistance. In general, antibiotic usage in animal foods should be regulated because of these adverse repercussions, along with a need to concentrate on improving animal nutrition and the production of high-quality animal products. 2026 selection and editorial matter, Arti Gupta and Ram Prasad; individual chapters, the contributors. -
Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network
Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%. 2023 World Scientific Publishing Company. -
The role of behavioral finance in shaping economic decision-making: Insights from AI and big data integration
This chapter examines how behavioral biases and institutional factors influence investment decisions, bridging gaps in behavioral finance literature. It explores cognitive biases like overconfidence, loss aversion, and herd behavior alongside institutional elements such as regulations and market stability. A mixed-methods approach combines survey data from 320 investors and 30 interviews. Findings show a strong link between biases and decisions, with overconfidence and loss aversion as key drivers. Regulatory transparency reduces bias impact, while uncertainty amplifies emotional reactions. The study highlights the need for clear policies, financial education, and future research on AI's role in mitigating biases. 2025, IGI Global Scientific Publishing. -
Internet of Things Enhancing Sustainability of Business
When one assumes that the current era is the era for digital revolution then the Internet of Things (IoT) is supposed to be one of the most significant among all. It is the IoT which is assisting the bussinesses. Current IoT applications, on the other hand, are still in their early stages, and the true capacity of viable business opportunities has yet to be realised. However, IoT adoption may need considerable integration and experienced personnel. It also frequently generates new requirements in terms of security and interoperability, or the ability for different computer hardware systems as well as software applications to "speak"to one another. 2022 IEEE. -
Analysis of MRI Images to Discover Brain Tumor Detection Using CNN and VGG-16
Brain tumor is a malignant illness where irregular cells, excess cells and uncontrollable cells are grown inside the brain. Now-a-days Image processing plays a main role in discovery of breast cancer, lung cancer and brain tumor in initial stage. In Image processing even the smallest part of tumor is sensed and can be cured in early stage for giving the suitable treatment. Bio-medical Image processing is a rising arena it consists of many types of imaging approaches like CT scans, X-Ray and MRI. Medical image processing may be the challenging and complex field which is rising nowadays. CNN is known as convolutional neural network it used for image recognition and that is exactly intended for progression pixel data. The performance of model is measured using two different datasets which is merged as one. In this paper two models are used CNN and VGG-16 and finding the best model using their accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Can bot be my mental health therapist? A pandemic panorama
[No abstract available] -
Brand awareness of 'generation y' customers towards doughnut retail outlets in India
The Research is all about knowing the customers acquiring top of mind recall about doughnut retail outlets in Bangalore city, India through various methods. Once the brand is established in the minds of the consumers, it occupies a unique position and special meaning and value is generated. Brand awareness is the consumer's conscious or unconscious decision, expressed through intention or behavior, to repurchase a brand continually. In order to create brand loyalty, advertisers must break consumer habits, help them to acquire new habits and reinforce those habits by reminding consumers of their purchase and encourage them to continue purchasing those products in the future. 'Generation Y' refers to customers millennial, the generation of people born during the 1980s and early 2000s. 'Generation Y' consumer's access social media on daily basis but they often ignore advertisements that are targeted to them. The previous research works on' Generation Y' customers emphasize that marketers must focus on social media marketing to draw the attention of these customers. Determining the brand awareness of 'Generation Y' customers was considered, in order to know the present level of awareness about the doughnut brands, increase the customer traffic and sales as 'Generation Y' customers are the target customers for doughnut retail outlets. -
Brand awareness of 'generation y' customers towards doughnut retail outlets in India /
The Journal Of Business And Retail Management Research, Vol.11, Issue 4, pp.108-113, ISSN: 2056-6271 (Online) 1751-8202 (Print). -
Burnout Among Academicians: a Review of Antecedent and Protective Factors of Burnout in University Teaching Faculties
The producers and intermediaries of knowledge are teachers. In addition to their teaching duties, faculty have another responsibility to generate new knowledge through scientific research. Teaching faculties in such a multi-tasking environment may feel equally stressed in their job as those in other highly demanding roles. Such unaddressed work-related stresses would at times increase burnout among faculty members. Hence, it is a pressing need to understand the causes of burnout among teaching staff. This systematic review aims to provide evidence-based recommendations to all stakeholders in education by summarising the academic publications on the determinants of academic burnout. In addition, the study also aimed to identify protective factors against burnout among university teaching faculties. To achieve this goal, a literature search was conducted using the PRISMA-P 2015 methodology, encompassing studies of empirical research published between January 2010 and October 2025. The search yielded 2,731 records, of which 24 studies were selected after undergoing several rounds of screening to meet the study criteria. Based on the results, excessive workloads, pressure to publish, lack of support, low pay, and negative cognitive styles were identified as key risk factors for academic burnout. Protective variables such as strong individual qualities, perceived supervisor support, psychological capital, and self-efficacy were frequently identified as effective in reducing burnout. This concludes that there are complex interrelationships between institutional stressors and personal susceptibility that contribute to academic burnout among university faculty members. The findings provide valuable information for policymakers, teachers, and researchers who are struggling to reconcile the requirement to publish with the duty to teach in modern institutions of higher learning. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026.