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Comprehending algorithmic bias and strategies for fostering trust in artificial intelligence
Fairness is threatened by algorithm bias, systematic and unfair disparities in machine learning results. Amazon's AI-driven hiring tool favoured men. AI promised data-driven, impartial decision-making, but it has revealed sector-wide prejudice, perpetuating systematic imbalances. The algorithm's bias is data and design. Biassed historical data and feature selection and pre-processing can bias algorithms. Development is harmed by human biases. Algorithm prejudice impacts money, education, employment, and crime. Diverse and representative data collection, understanding complicated "black box" algorithms, and legal and ethical considerations are needed to address this bias. Despite these issues, algorithm bias elimination techniques are emerging. This chapter uses secondary data to study algorithm bias. Algorithm bias is defined, its origins, its prevalence in data, examples, and issues are discussed. The chapter also tackles bias reduction and elimination to make AI a more reliable and impartial decision-maker. 2024, IGI Global. All rights reserved. -
Facile fabrication of dasatinib laden multifunctional polymeric micelles: Evaluation of anti-proliferative and apoptotic activities in human cancer cells
Dasatinib (DAS) has recently gained significant interest for its anticancer potential. Yet, the lipophilicity inherent in DAS limited its potential use as a chemotherapeutic drug. This study aimed to examine the effectiveness of polyethylene glycol-polycaprolactone (PEG-PCL) as a nanocarrier for DAS to increase its anticancer capabilities. The DAS-loaded PEG-PCL nanoparticles (termed as DAS@PEG-PCL NPs) were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Morphological staining and MTT tests were employed to investigate drug-loaded nanoparticles' apoptotic and anti-proliferative effects. The MTT assay demonstrated that incorporating DAS onto PEG-PCL NPs resulted in a dose-dependent increase in cytotoxicity in A549 (lung cancer) and HeLa (cervical cancer) cells. The A549 cancer cells were analyzed for their morphology using the acridine orange/ethidium bromide (AO/EB) and DAPI staining techniques. Overall, these findings demonstrate that the polymeric PEG-PCL nanoparticle systems hold great potential as a novel therapeutic strategy for cancer treatment. 2024 Wiley Periodicals LLC. -
TiO2-sodium alginate core-shell nanosystem for higher antimicrobial wound healing application
Wounds that are not properly managed can cause complications. Prompt and proper care is essential, to prevent microbial infection. Growing interest in metal oxide nanoparticles (NPs) for innovative wound treatments targeting healing and microbial infections. In this research, sodium alginate-coated titanium dioxide (TiSA) NPs are synthesized through a green co-precipitation method, combining inorganic TiO2 (Titanium dioxide) and SA (sodium alginate). Analysis via XRD and TEM revealed that the resulting TiSA NPs possessed an anatase phase and polygonal structure, respectively. Biomedical investigations demonstrated that TiSA NPs exhibited enhanced antimicrobial activity compared to the positive control, as well as its counterparts, and showed higher wound healing capabilities compared to TiO2 NPs. The antimicrobial effectiveness of TiSA NPs relied on various physicochemical factors, including small particle size, an altered band gap, and the presence of oxygen vacancies, resulting in microbial cell death. Moreover, TiSA NPs treatment demonstrated higher wound healing activity (98 1.09 %) compared to its counterparts after 24 h of incubation. Assessment of cytotoxicity on healthy fibroblast cells (L929) revealed that TiSA NPs exhibited lower toxicity compared to TiO2 NPs. These findings support the potential of TiSA NPs as promising agents for antimicrobial activity and wound healing. 2025 Elsevier B.V. -
Construction of multifunctional hyaluronic acid modified gold nanoparticles clocked with Irinotecan and indocyanine green: Investigation of chemotherapy and cancer cell imaging
To overcome the inherent limits of conventional cancer therapy, there is an immediate need to establish multifunctional drugs that combine accurate diagnosis with treatment. The work describes a small nanocomposite's mild and easy fabrication, including Irinotecan, folic acid, hyaluronic acid, and indocyanine green-integrated gold nanoparticles. The gold nanoparticles with indocyanine green integrated (HA@ICG/Au) were developed in one step for photodynamic treatment and biological fluorescence imaging. Both the drug delivery of Irinotecan and the enhancement of cellular selectivity are achieved by the hyaluronic acid-altered ICG/Au (HA@ICG/Au). To regulate the release of Irinotecan during tumour chemotherapy, the dual-targeted and pH-responsive system known as HA@ICG/Au:FA@IRI was developed. The nanocomposite composed of HA@ICG/Au:FA@IRI had a tiny surface area and was highly efficient at encapsulation and loading drugs. In an acidic milieu, the nanocomposite showed excellent biocompatibility, colloidal stability, photostability, and a rapid cumulative release rate. The improved cellular uptake of HA@ICG/Au:FA@IRI for fluorescence imaging was validated by fluorescence microscopy in vitro. The nanocomposite showed impressive cancer cell death when exposed to laser irradiation using a combination of synergistic chemotherapy and photodynamic treatment (PDT). Taken as a whole, the results show that the nanocomposite was successfully developed to target tumors in two different ways, resulting in a potentially helpful theranostics agent. 2025 Elsevier B.V. -
Analysis of determinants of voter turnout in Indian states for election years 19912019
Elections, considered the flagship to the emergence of a new government and a new era is a platform replete with exuberance and vibrancy in all forms. No election is complete without its voters who form the backbone behind the success of democracy. Democracy means elections and free and fair elections mean democracy. The present study is a focus on economic determinants of voter turnout in India since 1991 till date (2019 elections). Economics of voting is a study that encompasses analysis of both economists and political scientists in an attempt to study the economic forces influencing political outcome of the country. In this study, relevant forces determining voter turnout and their impact on political outcomes have been emphasized upon. The data are collected across regions and is characterized using panel regression. Economic factors influencing voter turnout are explored using pooled regression and fixed effect model. Results suggest that as India goes to vote, factors such as income employment influence turnout. Literacy (GER) and urban voter turnout do not influence voter turnout. Lack of efficient governance, bureaucratic loopholes, corruption, large-scale migration and others are some of the potent causes of low turnout. 2022, The Author(s), under exclusive licence to Institute for Social and Economic Change. -
Parental Expectations and Fear of Negative Evaluation Among Indian Emerging Adults: The Mediating Role of Maladaptive Perfectionism
Background: Contrary to traditional notions of emerging adulthood as a period free from parental pressures, the prolonged transition to adulthood in contemporary society implies that parental influence remains a significant factor in the lives of emerging adults. This presents a potential challenge to emerging adults, as navigating independence while managing parental expectations can result in adverse psychological outcomes. The present study examined the relationship between perceived parental expectations and fear of negative evaluation (FNE) and the mediating role of maladaptive perfectionism. Method: This cross-sectional study was conducted on 466 emerging adults from India between 18 and 25 years old. They responded to the Perception of Parental Expectations Inventory, the Frost Multidimensional Perfectionism-Brief Scale, and the Brief Fear of Negative EvaluationStraightforward Items Scale. Results: Correlation analyses revealed significant, positive associations between perceived parental expectations, maladaptive perfectionism, and FNE. Findings from regression analyses indicated that increased perceptions of parental expectations and maladaptive perfectionism predicted increased levels of FNE. The relationship between perceived parental expectations and FNE was fully mediated by maladaptive perfectionism. Conclusion: A key reason for heightened perceptions of parental expectations associated with increased FNE is that emerging adults tend to adopt unrealistic perfectionistic standards. Maladaptive perfectionism represents a vital intervention target for individuals who perceive elevated parental expectations and are at risk for FNE, offering promising avenues for promoting well-being in emerging adults. 2024 The Author(s). -
Climate, agriculture, and farmer's mental health: Unravelling the nexus in Wayanad, Kerala
A sizable majority of the population works in the primary sector in Kerala's Wayanad district, where agriculture is the backbone of the local economy. However, dynamic issues including climate change, fluctuating soil quality, crop diseases, and related economic consequences pose difficulties for this industry. The complicated linkages between agricultural practices and climate change are discussed using qualitative data from in-depth interviews with 15 Wayanad farmers. Agricultural productivity and revenue are strongly impacted by unpredictable rainfall, which is exacerbated by strong winds, natural disasters, wildlife intrusions, and crop diseases. The failure of farmers to adjust to these climate changes is a remarkable finding, frequently brought on by fear and unstable financial situations. This resistance causes anxiety, a sense of powerlessness, and a sense of responsibility for circumstances that are out of their control. In order to help farmers manage the unforeseeable effects of climate change, the study emphasizes the urgent need for policy initiatives in areas like Wayanad. Cooperative farming and knowledge-sharing platforms are examples of strategies that could improve farmers' psychological resilience and general well-being. Given that agriculture accounts for a substantial portion of the region's income and that resources and knowledge are scarce, climate change has a considerable impact on agricultural outputs and farmers' psychological well-being. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Buffer zones in Wayanad: A social constructivist exploration into farmers mental health
Buffer zones are regions set aside to border protected areas to preserve biodiversity, control interactions between people and wildlife, and foster sustainable development. The majority of research on buffer zones focuses on ecological issues, and little is known about how they affect local communities mental health. This study explores buffer zones potential consequences on farmers mental health in Wayanad. Through purposive sampling, eleven participants residing in Wayanad were recruited for the study. The socio-demographics of participants were collected through printed translated questionnaires. The qualitative exploration of their lived experiences, perceptions, and coping strategies was conducted using semi-structured, in-depth interviews. Thematic analysis by Braun and Clarke was used to gain a clearer understanding of the data collected. Through in-depth analysis of the data, it was identified that Mental Health Factors, Communication Factors, Financial Impact, Operational Stress, Interference of Judiciary and Legislature, and Seclusion of the Tribal Community were the issues the farmers faced in Wayanad. The results will contribute to the expanding mental health field and give policymakers, conservationists, and mental health professionals information about the potential psychological effects of buffer zones and guide them in creating suitable interventions and support systems to improve mental health. The Author(s) 2024. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
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 -
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.