Browse Items (16481 total)
Sort by:
-
How Are We Surviving the Pandemic, COVID-19?: Perspectives from Hospitality Industry Workers in Australia
The COVID-19 pandemic has been disastrous and has affected the hospitality industry worldwide, and the people working in the sector were impacted immensely. The purpose of this study is to understand the viewpoints of hospitality workers in Australia on how lockdowns have impacted professional and personal well-being. The case study methodology is adopted for this study. Viewpoints from Australian hospitality workers were collected through semi-structured interviews. With the pandemic taking surprising turns with the rise of new infections and in turn new pandemic waves, the industry is facing a constant lurking fear of lockdowns. Changing variants of COVID-19 creates a profound effect on the psychological and personal well-being of the people employed in the hospitality sector. This chapter would reflect upon the viewpoints of hospitality workers in Australia after two years of the COVID-19 crisis. A real-time assessment is required to understand the vulnerability of hospitality industry workers in a developed country. 2023 Priyakrushna Mohanty, Anukrati Sharma, James Kennell and Azizul Hassan. -
Redefining Disease Detection: Innovative Machine Learning and Wearable Sensor Integration
Wearable sensor technology is considered to be one of the fastest growing fields of information and communication technologies and it has revolutionized the healthcare delivery by enabling continuous and real-time physiological monitoring. This research presents a novel approach that allows an early onset disease detection instigated with the prowess of advanced Graph Neural Network (GNNs) matched with the body streams gathered from wearable machines using its implementation technology - Pythonline of programming named Awesome Geometric libraries referred to as Aztec PyTorch. Graph neural networks (GNNs) are especially suitable within the scope of modeling complex relationships among multivariate inputs of the sensors for modeling the temporal and spatial subjacent dependence of the physiological signs with regards to reality. The proposed system analyzes the data acquired from the various wearable sensors such as heart rate, accelerometers and bio sensors, which help in anomaly detection and hence the detection of the patient having cardiovascular, metabolic and neurological diseases. The synergy between innovative deep learning models and sensors as ubiquitous technologies offers great promise to transform the provision of personalised healthcare services and dealing with disease in its early stages. 2025 IEEE. -
Transition to an Empty Nest: A Phenomenological Exploration of Homemaker Mothers of Out-of-State College Students
This phenomenological study explores the lived experiences of Indian homemaker mothers transitioning to an empty nest, focusing on middle-aged mothers emotional, psychological, and cultural dimensions. Five homemaker mothers, aged between 46 and 50, with at least one child attending an out-of-state college in Bengaluru, participated in semi-structured interviews. Data was analysed using Colaizzis descriptive phenomenological method, which revealed 95 minor themes organised into 9 major themes and 4 overarching categories. The findings highlight a complex emotional intersectionality where feelings of pride and joy in their childrens achievements coexisted with sadness, loneliness, and loss. Cultural expectations surrounding motherhood in India, which emphasises maternal self-sacrifice, further emphasised the emotional challenges the participants face. Coping strategies such as spirituality, social support, and technology emerged as key elements in navigating the transition, with participants often using prayer and digital communication to maintain emotional bonds with their children. The study also found that the empty nest transition triggered a redefinition of parental roles and personal identity. Participants with higher education levels were better equipped to embrace this phase as an opportunity for self-growth, while others struggled with their diminished caregiving role. Technology played a dual roleoffering emotional comfort through digital connection and fostering dependency and frustration. Overall, the empty nest experience for Indian homemaker mothers is emotionally challenging and a potential period of personal rediscovery, shaped by cultural norms and the evolving role of family dynamics. This research provides a culturally specific perspective on a largely Western-studied phenomenon, offering insights for further investigation into the changing maternal identity in India. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Generative AI in Action: Empirical Case Studies on Startup Innovation and Entrepreneurial Decision-Making
Generative artificial intelligence (AI) is quickly reshaping the world of entrepreneurship and offers startups more opportunities than ever before to innovate and make strategic decisions, as well as to operate more effectively. The chapter is a synthesis of 50 recent academic articles that critically examine how generative AI, specifically large language models and creative automation systems, is transforming the business model design process and venture execution. It discusses two significant directions, one the effect of generative AI in startup innovation of quick product ideation, bespoke customer service and scalable solutions and the other the effect that AI will have on entrepreneurial decision-making, with AI-based analytics and support systems informing resource allocation and market perspective. Based on empirical evidence and practical case studies, the chapter offers practical recommendations to successful adoption and sets the research directions in the future to allow the full implementation of the transformative abilities of generative AI in the startup world. 2026 by IGI Global Scientific Publishing. All rights reserved. -
DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre
The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches. 2021 M. S. Mekala et al. -
Applications of brain-computer interfaces in automated financial services
The purpose of this study is to evaluate the possibility that brain-computer interfaces (BCIs) could bring about a revolutionary transformation in the realm of automated financial services. Brain-computer interfaces (BCIs) hold the promise of revolutionizing the way financial transactions are carried out, increasing security measures, and bodying stoner gestures. This is to be accomplished by providing direct communication between the brain and external bias. Among the subjects that are covered in this study are verification methods, real-time decision-making, and client participation in financial services. The study delves into the intricate workings of BCIs. Through the use of neural data, brain-computer interfaces (BCIs) can supply an unknown position of intelligence into the gestures and preferences of stoners. Because of this, financial institutions can offer services that are more effective and more efficiently adapted to the specific needs of each client. This inquiry emphasizes key breakthroughs in BCI technology. 2025, IGI Global Scientific Publishing. -
Nonlinear analysis of the effect of viscoelasticity on ferroconvection
Thispaper concerns a nonlinear analysis of the effects of viscoelasticity on convection in ferroliquids. We consider the Oldroyd model for the constitutive equation of the liquid. The linear stability analysis yields the critical value of the Rayleigh number for the onset of oscillatory convection in Maxwell and Jeffrey ferroliquids. The use of a minimal mode double Fourier series in the nonlinear perturbation equations yields a KhayatLorenz model for the ferromagnetic liquid, and that is scaled further to get the classical Lorenz model as a limiting case. The scaled KhayatLorenz model thus obtained is solved numerically and the solution is used to compute the time-dependent Nusselt number, which quantifies the heat transport. The results are analyzed for the dependence of the time-averaged Nusselt number on different parameters. 2021 Wiley Periodicals LLC -
Brain Tumor Detectin Using Deep Learning Model
Brain tumor is a life-threatening disease that can disrupt normal brain functioning and have a significant impact on a patient's quality of life. Early detection and diagnosis are crucial for effective treatment. In recent years, deep learning techniques for image analysis and detection have played a vital role in the medical field, supplying more accurate and reliable results. Segmentation, the process of distinguishing between normal and abnormal brain cells or tissues, is a critical step in the detection of brain tumors. In this research, we aim to investigate various techniques for brain tumor detection and segmentation using Magnetic Resonance Imaging (MRI) images. The detection process begins by analyzing the symmetric and asymmetric shape of the brain to identify abnormalities. We will then classify the cells as either Tumored or non-Tumored. This research is aimed at finding a more accurate and efficient method for detecting brain tumors. Four Keras models are compared side by side to find out the best deep learning model for providing a suitable outcome. The models are ResNet50, DenseNet201, Inception V3 and MobileNet. These models gave training accuracy of 85.30%, 78%, 78%, and 77.12% respectively. 2023 IEEE. -
Development and characterization of carbon fiber reinforcement in Aluminium metal matrix composites
Carbon fibers (CF) possess exceptional mechanical properties and the highest degree of chemical stability. However, carbon reinforcement in metal matrix composites is extremely scarce due to production difficulties, particularly in obtaining a uniform distribution. Carbon fiber reinforced composites are typically made using high temperature processing processes. However, the fibers must be coated with Ni or Cu in order to achieve effective particle dispersion; otherwise, there is a larger likelihood of intermetallic compound formation, which reduces the chances for enhanced properties. In this work, the metallurgical, mechanical, and tribological characteristics of the carbon fiber reinforcement in AA 7050 are examined. Uncoated carbon fibers are reinforced into the Aluminium matrix using a low temperature processing technique known as powder metallurgy. The AA 7050 matrix reinforced with carbon fibers at various weight percentages between 0 and 1.5. The samples undergone mechanical and metallurgical testing in accordance with ASTM guidelines. The findings indicate that the 0.25 weight percent carbon fiber reinforcement in the matrix increased the material's hardness by 30% over the monolithic alloy, making it an excellent alternative for structural applications. Published under licence by IOP Publishing Ltd. -
Experimental Investigation of Nano Hexagonal Boron Nitride Reinforcement in Aluminum Alloys Through Casting Method
Aluminum metal matrix composites (AlMMCs) have a significant impact on a variety of industries that seek for innovation, efficiency, and sustainability. AlMMCs are substantial because of the special combination of properties that make them an essential part of contemporary production and design. Custom made properties of the AlMMCs can be obtained by the reinforcing different ceramic particles. Among the reinforcements, nano hexagonal boron nitride were rarely used. Hexagonal boron nitride particles have self-lubrication properties and it is one of the promising substitutes of graphite. The incorporation of hexagonal boron nitride (hBN) as a reinforcement material in aluminum alloys has garnered significant attention in recent years. This paper provides an overview of the reinforcement of nano hBN in aluminum alloys through casting method and highlights the mechanical and thermal properties of these alloys. The results show that the wear rate of the composite at 2wt.% is 9.91% lower for a load of 40 N when compared to unreinforced composite. Furthermore, the impact of hBN content, dispersion, and processing parameters on the properties of the composites is analyzed. The unique structural and thermal properties of hBN, along with excellent lubricating abilities, make it a promising candidate for reinforcing aluminum composites. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Processing of nanoreinforced aluminium hybrid metal matrix composites and the effect of post-heat treatment: a review
The demand for cutting-edge materials with a high strength-to-weight ratio and economic considerations is steadily increasing. Lightweight materials such as aluminium (Al) and its alloys are attractive, but some properties such as low thermal stability and high wear rate limit the application of aluminium alloys (AA) to some extent. Many researchers have developed various composites to get around these restrictions and increase the performance of aluminium and its alloy. Metal matrix composites (MMCs) with nanoparticles have revealed greater mechanical and tribological properties compared with micron-sized reinforcements. Most engineering applications require materials with excellent multidimensional properties, which are difficult to achieve using single reinforced MMCs. Hybrid metal matrix composites (HMMCs) with superior properties are the latest trends in composite technology. The choice of reinforcement selection has a vibrant role in the manufacturing of hybrid metal matrix composites. Researchers face a major challenge in finding optimum reinforcement combinations and their corresponding concentrations. The manufacturing of nanocomposites is difficult due to their high surface area and energy. To determine the most effective reinforcement combinations for hybrid composites, this article addresses several nanoreinforcements, their effects, and the appropriate processing methods for aluminium and its alloys. Researchers have paid less attention to the impact of precipitation hardening in aluminium and its alloys; thus, this paper also considers the effect of post-heat treatment ofaluminium composites. 2022, King Abdulaziz City for Science and Technology. -
Characterization and Analysis of Carbon Fiber and Nano hBN Reinforced Hybrid Aluminium Metal Matrix Composites by Conventional Sintering
As mono composites focus solely on improving one property at a time, the significance of hybrid composites grows day by day. The incorporation of carbon fiber (CF) and nano hexagonal boron nitride (hBN) particles as reinforcement for Aluminium has gained significant popularity because of their superior properties. In this work, the AA 7050 is reinforced with carbon fiber and nano hBN and fabricated by powder metallurgy method. To achieve an effective nanoparticle distribution in the matrix, premixing of the particles was done. The reinforcements were effectively dispersed by ball milling, and the composite was created using a traditional powder metallurgy. Dispersion of the particles in the matrix was analyzed by optical microscope. The effect of adding reinforcement to the matrix was investigated using properties such as micro hardness and compression test, and wear characterization. A significant increase in mechanical and wear properties was achieved for the combination of 0.25 wt. % carbon fiber and 0.5 wt. % hBN addition due to the uniform dispersion of nano particles along with the carbon fiber presence. The microhardness, compressive strength and wear rate is improved by 33%, 66% and 54 % respectively than the bare alloy. This study provides insight into the importance of hybrid Aluminium nano composites for high strength applications. Author(s) 2024. -
Effect of Premixing Process on the Uniform Distribution of Nano hBN and Carbon Fiber Reinforcements in AA7050 Matrix
In Aluminium metal matrix composites, achieving a homogeneous dispersion of reinforcements remains a significant challenge, especially when mixing fibrous and nanoscale reinforcements. The effect of the premixing procedure on the homogeneous dispersion of carbon fiber (CF) and nano hexagonal boron nitride (hBN) reinforcements in AA7050 matrix is studied in this work. Before composites are prepared, a multi-stage process for premixing is used, which consists of ultra-sonication, magnetic stirring, and mechanical mixing in order to minimize particle clustering. This also improves the wetting between the reinforcement and the matrix. Field emission scanning electron microscope (FESEM) was used to characterize the premixed powders to assess agglomeration behaviour, interfacial integrity, and dispersion uniformity. Due to the premixing process, better densification of nearly 95.2% and enhancement of 33.3% of micro-hardness are reported for 0.25 wt.% CF and 0.5 wt.% hBN addition. The results reveal that after the premixing process, particle dispersion was improved, leading to high-quality composites in the subsequent sintering process. The premixing process offers a better way to disperse the nano reinforcement particles in the production of aluminium metal matrix nanocomposites, which directly influences the properties of the composites. -
MR Brain Tumor Classification and Segmentation Via Wavelets
Timely, accurate detection of magnetic resonance (MR) images of brain is most important in the medical analysis. Many methods have already explained about the tumor classification in the literature. This paper explains the method of classifying MR brain images into normal or abnormal (affected by tumor), abnormality segments present in the image. This paper proposes DWT-discrete wavelet transform in first step to extract the image features from the given input image. To reduce the dimensions of the feature image principle component Analysis (PCA) is employed. Reduced extracted feature image is given to kernel support vector machine (KSVM) for processing. The data set has 90 brain MR images (both normal and abnormal) with seven common diseases. These images are used in KSVM process. Gaussian Radial Basis (GRB) kernel is used for the classification method proposed and yields maximum accuracy of 98% compared to linear kernel (LIN). From the analysis, compared with the existing methods GRB kernel method was effective. If this classification finds abnormal MR image with tumor then the corresponding part is separated and segmented by thresholding technique. 2018 IEEE. -
Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs
In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. 2023 Tech Science Press. All rights reserved. -
The Psychosocial Experiences of Family Caregivers of Cancer Patients
Introduction: This study explores the psychosocial experiences of family caregivers of cancer patients, focusing on changes in their lives postdiagnosis, motivations for caregiving, challenges faced, coping mechanisms, and perceptions of caregiving. Understanding these experiences is crucial for enhancing support services for caregivers. Methods: A qualitative, exploratory study employing interpretative phenomenological analysis was conducted with 10 family caregivers. In?depth interviews were used to gather data on their lived experiences, which were analyzed to identify key themes. Results: Findings revealed significant emotional shifts, lifestyle adjustments, and changes in caregivers perceptions of the patient following the cancer diagnosis. Motivations for caregiving stemmed from external influences and personal attributes. Caregivers encountered multiple challenges, including lifestyle disruptions, emotional strain, financial difficulties, and a lack of support. Coping strategies involved prioritization, reliance on personal strengths, spirituality, family support, and rationalization, highlighting resilience and adaptability. Conclusion: Despite the difficulties, caregivers viewed their role as noble and transformative, maintaining a positive outlook and a deep concern for their loved ones well?being. These insights emphasize the need for healthcare providers to develop targeted support interventions to assist family caregivers in managing their responsibilities effectively 2025 Indian Journal of Social Psychiatry. -
Psychological capital in positive ageing :
Positive ageing is feeling good and maintaining a positive attitude, keeping healthy and being fully involved in life. Older adults add value to family and society by sharing of wisdom, gratitude,spirituality, resilience, optimism, hope and confidence (PsyCap). These are the mental resources that developed through their life experiences when things went well and when faced with challenges. The aim was to understand the process of development of psychological capital in positive ageing. The participants were chosen purposively, older adults 70-80 years, men and women, retired, tenth standard, middle socio-economic status, spouses have expired and living with family. They were interviewed with a validated semi structured interview schedule. Themes were analyzed using Interpretative Phenomenological Analysis, substantiated by verbatim from participant interviews and connections with existing theories and literature. Three super ordinate themes emerged, Factors that promote the development of PsyCap varies , Personal trauma and inadequacies as learning opportunities and Spiritual and philosophical ways of adaptation . Results indicated that support from family and friends and their internal strength helped them face adversity and aided in the development of optimism, hope, gratitude, confidence and self-belief. Challenges, lack of adequate resources and retirement were opportunities for learning as they facilitated the growth of PsyCap. Participants were grateful for effectual social support in time of grief. Their resilient attitude kept them positive and helped to prioritize goals effectively. Religion and spirituality provided solace and meaning to their lives, reflection led to the evolving of a philosophy that left them feeling fulfilled as they reached out to those in need. The study has implications for promoting a positive and healthy attitude towards older adults and sensitising family, caregivers and policy makers. -
Islanding detection technique of distribution generation system
Islanding is a condition in which the micro grid is disconnected from the main grid which consists of loads and distribution generation. Islanding is required whenever there is a fault and whenever the maintenance is required. Under normal condition or stable condition, the system works under constant current control mode. After islanding the system switched to voltage controlled mode. There are different methods that can be used to detect islanding situation such as active and passive methods. In this paper DQ-PLL detection technique used for detecting islanding condition is carried out. This paper also explains in detail the advantages of DQ-PLL method for islanding detection The implementation is validated by using MATLAB/SIMULINK software. 2016 IEEE. -
Negotiating Inclusion: Minority Institutions and Constitutional-Legal Dimensions in India
The chapter Negotiating Inclusion: Minority Institutions and Constitutional and Legal Dimensions in India is based on the premise that special provisions for inclusion of minority groups were one of the contested topics that have been negotiated in India since independence. The present chapter critically explores the two main sites of negotiation: Constituent Assembly Debates and the cases involving the question of minority rights to culture and education as adjudicated by the Indian courts. In doing so, the paper undertakes an examination of the logic of state recognition and reservations, voiced by nationalist leaders and members of the Constituent Assembly, who were apprehensive that the provisions on minority accommodation may not be compatible with Indias secular credentials. Constitutional provisions, specifically the fundamental rights embodied in Articles 29 and 30 were further debated and re-interpreted by the High Courts and Supreme Court. Further on, the issue of minority accommodation led to the establishment of institutional mechanisms in India, one such institution being the National Commission for Minority Educational Institutions (NCMEI)-a recent addition in the series of negotiating spaces of the religious and minority communities in India. A thorough examination of the functioning of the NCMEI, an institution which remains understudied, may inform new avenues into thinking about the sites of minority rights negotiations in India, given the shifting ideological positions at the national level. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Understanding Startup Valuation and its Impact on Startup Ecosystem
Startups play a substantial role in the economic growth of a nation, by introducing new technologies, ground-breaking innovation, creating jobs, etc. A couple of decades back, it was extremely difficult to start a business, but today new businesses pop up every day, all around the world. Recognizing the importance of a startup, governments across the globe are doing their best to provide an atmosphere where startups can bloom. Despite its importance and all the support, the startup failure rate is at 90%; about 10% of startups fail in the first year and 70% fail in two to five years. The startup boom saw the emergence of alternative sources of funding like Venture Capitalist, Angel Investors, etc. These investors (Venture Capitalist, Angel Investors, etc.) played a crucial role in startup success by providing easy access to funds which is a critical and scarce resource for any founder. Traditionally business success is linked with sustainable profitability but in the startup world most used method to define success is valuation. Based on CB Insights research, as of January 2022, there are more than 900 unicorns (startup with a valuation of over $1 billion) around the world and of these unicorns less than 10% are profitable. It's difficult to explain/comprehend how startups' which are neither profitable nor foresee profitability in near future are valued higher than traditional business with stable profitability. Current valuation methods have impacted the startup ecosystem. Today, founders start their business with exit in mind, the focus of founders is on growth/scale rather than profitability. There is a school of thought that believes that such valuations will soon result in the bursting of the startup bubble just like the dotcom bubble seen in late 1990s. The focus of this paper is to investigate the techniques used by investors for startup valuation and how these techniques are impacting the startup ecosystem and its founders. The paper looks at all stages of the investment cycle, from seed to IPO or takeover and understands the process of valuation at each stage and how it impacts all stakeholders in the ecosystem. 2022 Walter de Gruyter GmbH, Berlin/Boston.