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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. -
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). -
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. -
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. -
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. -
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. -
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. -
Idiosyncratic Deals: Understanding Effect of Intrinsic and Extrinsic Motivation I-Deals on Innovative Work Behaviour
I-deals or Idiosyncratic deals are specialised, adaptable work patterns by mutual agreement between employees and their managers to meet demands of a dynamic work place. Innovative work behaviour also known as IWB is referred to as the employee behaviour that intends to create and introduce novel and valuable products, processes, innovations and ways of working within a job-role or work-group of an organization. This research discovers the connection between various types of intrinsic and extrinsic motivational deals such as the work responsibility idiosyncratic deals, flexibility deals and financial ones and innovative behaviour, specially within the purview of the working women. It also provides an overview on the outcome of these deals on innovation at a workplace. Our study adopted descriptive research to assess the association of Idiosyncratic deals with IWB using a quantitative study across 352 female employees of Indian Corporate sector. It was found that there exists a direct and positive association amid intrinsically and extrinsically motivated Idiosyncratic deals and an innovative mind-set, in the context of Indian IT sector. This study establishes the influence of idiosyncratic deals and the motivational factors within them in driving an innovative mind set. Thus, the study helps to recognize the value that I-deals brings in establishing an effective innovative environment for employees playing a vital role in the growth of the organization. 2024, Iquz Galaxy Publisher. All rights reserved. -
Social Media Dependency and Facebook Usage among the Older Adults of Kerala
Social media has become an integral part of modern society, with people of all ages using various platforms to connect, share information, and stay up-to-date on current events. However, there has been a recent trend of increased Facebook usage among older adults, which has raised concerns about social media dependency. The current study explores the trends in social media dependency and Facebook intensity among older adults of Kerala. The present study employed a quantitative research design and the sample consisted of 416 older adults, aged above 60 years. Two scales were used to collect data: the Social Media Dependency Scale (SMDS) and the Facebook Intensity Measure (FBI). Frequency and percentage analysis, Spearman's Rho, Kruskal-Wallis H test, and Mann-Whitney U test were carried out using SPSS (Version 23) for deriving results. Those individuals who are more dependent on social media are also more likely to engage in high levels of Facebook activity. A good majority of the participants were found to be using social media and Facebook for more than 3 hours in a day and having more than 400 friends. Social media dependency and Facebook intensity were reported to be high in urban localities, South Kerala having significantly higher rates of social media dependency when compared to North and Central regions. Social media dependency was found to be high among males, whereas no difference was in Facebook intensity among male and females. Implications: Given the importance of social connections for the well-being of older adults, it is critical to understand the impact of social media use on their lives and develop strategies to promote healthy social media behaviour. 2023 Redfame Publishing Inc.. All Rights Reserved. -
Experimental evaluation of image segmentation for heart images
The cardiac death is the principal reason of the death in the world.The research work focuses on finding an efficient image segmentation technique for the computer aided detection and also to decrease the noise in the image.The segmentation is implemented with the help of fuzzy C-means clustering along with dual inverse thresholding function and Otsu thresholding.Experimental proof is evaluated with the help of morphological functions and with Gaussian function.The result of the work provides an accurate segmentation for myocardial ischemia in the human heart photo image as well as magnetic resonance imaging. Copyright 2021 Inderscience Enterprises Ltd. -
An Integrated Segmentation Techniques for Myocardial Ischemia
Abstract: Myocardial Ischemia segmentation is a challenging task for basic and translational research on cardiovascular, as it provides ultimately realistic in heart muscle model. The main objective of the research work is to find an efficient segmentation technique for the myocardial ischemia based on the myocardial infarcted MRI data set for the accurate classification of scar volume. The paper will give an insight about the segmentation technique based on myocardial ischemia and discusses essential cellular components. The paper provides an integrated approach which comprises of fuzzy c-means and morphological operations along with median filtering enhancement technique help in detecting the myocardial ischemia. The developed model is tested with 2D and 3D enhanced myocardial ischemia MRI and also with normal heart. The purpose of segmentation in myocardial ischemia is to identify the scar region in the heart. The integrated model is evaluated based on statistical measures and validated based on manual segmentation done by clinical expert. The scar classification is done based on the myocardial ischemia segmentation which leads to better prediction of arrhythmia in heart patient. The integrated model is considered as one of the best model for segmenting myocardial ischemia. 2020, Pleiades Publishing, Ltd. -
Classification of myocardial ischemia in delayed contrast enhancement using machine learning
This chapter addresses the classification of myocardial ischemia in delayed contrast enhancement using machine-learning techniques for magnetic resonance imaging which solves the social issue of a sudden cardiac death. To automate the classification of myocardial ischemia, the computer-aided design has a crucial path on the mixture ensemble of machine learning. The mixture ensemble of machine learning can partition a high-dimensional image in a simultaneous and competitive way. The detection and the segmentation processes are carried out through Fuzzy C-Means multispectral and single-channel algorithms along with a morphological filtering technique for feature extraction. Furthermore, the feed forward neural network (FFNN) technique is trained through the Levenberg-Marquardt Back Propagation algorithm for the classification of myocardial ischemia in delayed contrast enhancement. The proposed classification model performs well for the classification of myocardial ischemia. The rigorous process of the proposed result reveals that the FFNN classifier produces 99.9% accuracy on the classification of myocardial ischemia and also shows that the given classifier is considered one of the best methods in classifying medical myocardial ischemia. 2019 Elsevier Inc. All rights reserved. -
Medical image classification using MRI: An investigation
The main objective of the paper is to review the performance of various machine learning classification technique currently used for magnetic resonance imaging. The prerequisite for the best classification technique is the main drive for the paper. In magnetic resonance imaging, detection of various diseases might be simple but the physicians need quantification for further treatment. So, the machine learning along with digital image processing aids for the diagnosis of the diseases and synergizes between the computer and the radiologist. The review of machine learning classification based on the support vector machine, discrete wavelet transform, artificial neural network, and principal component analysis reveals that discrete wavelet transform combined with other highly used method like PCA, ANN, etc., will bring high accuracy rate of 100%. The hybrid technique provides the second opinion to the radiologist on taking the decision. Springer Nature Switzerland AG 2019. -
Segmentation technique for medical image processing: A survey
Segmentation is one of the popular and efficient technique in context to medical image analysis. The purpose of the segmentation is to clearly extract the Region of Interest from the medical images. The main focus of this paper is to review and summarize an efficient segmentation method. While doing the comparison study on segmentation methods using the Support Vector Machine, K-Nearest Neighbors, Random Forest and the Convolutional Neural Network for medical image analysis identifies that Convolutional Neural Network works efficiently for doing in-depth analysis. The Convolutional Neural Network can be used as segmentation technique for achieving the high accuracy on medical image analysis. 2017 IEEE. -
Harnessing transition metal oxide?carbon heterostructures: Pioneering electrocatalysts for energy systems and other applications
Exponential demand for energy resources and fossil fuel substitution with green alternatives are essential to bringing sustainable development and a solution to the energy crisis. Transition metal oxides (TMOs) and their composites (TMOCs) as promising electrocatalysts to develop potential energy conversion and storage devices contribute to the solution to this crisis. The productivity of green fuels such as hydrogen from water-splitting reactions, the efficiency of energy storage and harvesting devices including supercapacitors and batteries, and the performance of electrochemical sensors can be remarkably enhanced with TMOs and their composites. Excellent electrochemical attributes, stability, abundant reserves, low cost, environment-friendly, and low toxicity make TMOs and their composites an excellent choice. The tunability of the physical and chemical properties of TMOs makes them attractive for research in designing different energy storage devices. This review presents a concise overview of the unique physical and electrochemical aspects of various TMOs and TMOCs, such as spinels, perovskites, and TMO-integrated carbon-based compounds, and their relevance for specific applications, emphasizing energy-related fields. The recent research advancements of TMOs-based functional materials for emerging applications, such as water splitting, fuel cells, supercapacitors, batteries, and sensing, are discussed. This review also highlights the advantageous properties and pertinent fabrication methods of TMOs and TMOCs for electrocatalysis, along with the methods to enhance their electrocatalytic abilities, which improve the overall efficiency of the desired applications. 2024 -
Discovery of inverse-Compton X-ray emission and estimate of the volume-averaged magnetic field in a galaxy group
Observed in a significant fraction of clusters and groups of galaxies, diffuse radio synchrotron emission reveals the presence of relativistic electrons and magnetic fields permeating large scale systems of galaxies. Although, these non-thermal electrons are expected to upscatter cosmic microwave background photons up to hard X-ray energies, such inverse-Compton (IC) X-ray emission has so far not been unambiguously detected on cluster/group scales. Using deep, new proprietary XMM-Newton observations (?200 ks of clean exposure), we report a 4.6 ? detection of extended IC X-ray emission in MRC 0116 +111, an extraordinary group of galaxies at z = 0.131. Assuming a spectral slope derived from low frequency radio data, the detection remains robust to systematic uncertainties. Together with low frequency radio data from the Giant Metrewave Radio Telescope (GMRT), this detection provides an estimate for the volume-averaged magnetic field of (1.9 0.3) ?G within the central part of the group. This value can serve as an anchor for studies of magnetic fields in the largest gravitationally bound systems in the Universe. 2023 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
FSDA: Framework for Secure Data Aggregation in wireless sensor network for enhancing key management
An effective key management plays a crucial role in imposing a resilient security technique in Wireless Sensor Network (WSN). After reviewing the existing approaches of key management, it is confirmed that existing approachs does not offer good coverage on all potential security breaches in WSN. With WSN being essential part of Internet-of-Things (IoT), the existing approaches of key management can definitely not address such security breaches. Therefore, this paper introduces a Framework for Secure Data Aggregation (FSDA) that hybridizes the public key encryption mechanism in order to obtain a novel key management system. The proposed system does not target any specific attacks but is widely applicable for both internal and external attacks in WSN owing to its design principle. The study outcome exhibits that proposed FSDA offers highly reduced computational burden, minimal delay, less energy consumption, and higher data transmission perforance in contrast to frequency used encryption schemes in WSN. Copyright 2018 Institute of Advanced Engineering and Science. All rights reserved. -
N-tier modelling of robust key management for secure data aggregation in wireless sensor network
Security problems in Wireless Sensor Network (WSN) have been researched from more than a decade. There are various security approaches being evolving towards resisting various forms of attack using different methodologies. After reviewing the existing security approaches, it can be concluded that such security approaches are highly attack-specific and doesnt address various associated issues in WSN. It is essential for security approach to be computationally lightweight. Therefore, this paper presents a novel analytical modelling that is based on n-tier approach with a target to generate an optimized secret key that could ensure higher degree of security during the process of data aggregation in WSN. The study outcome shows that proposed system is computationally lightweight with good performance on reduced delay and reduced energy consumption. It also exhibits enhanced response time and good data delivery performance to balance the need of security and data forwarding performance in WSN. 2019 Institute of Advanced Engineering and Science. -
RASK: Request authentication using shared keys for secured data aggregation in sensor network
Accomplishing a robust security features to resists lethal attacks is still an open research area in wireless sensor network. The present paper review existing security techniques to find that there is still a trade-off between cryptographic-based security incorporations and communication performance. Moreover, we have identified that majority of the existing system has not emphasized on first line of defense i.e. security the route discovery process that can act as a firewall for all forms of illegitimate nodes existing in the network. The proposed study introduced RASK i.e. Request Authentication using Shared Key, which is a novel concept developed using simple quadratic formulation of generating keys for encrypting the message during data aggregation. The study outcome has been significantly benchmarked with recent studies and existing cryptographic standards to find RASK outperform existing techniques. Springer International Publishing AG 2017. -
A genome wide association study of fast beta EEG in families of European ancestry
Background Differences in fast beta (2028Hz) electroencephalogram (EEG) oscillatory activity distinguish some individuals with psychiatric and substance use disorders, suggesting that it may be a useful endophenotype for studying the genetics of disorders characterized by neural hyper-excitability. Despite the high heritability estimates provided by twin and family studies, there have been relatively few genetic studies of beta EEG, and to date only one genetic association finding has replicated (i.e., GABRA2). Method In a sample of 1564 individuals from 117 families of European Ancestry (EA) drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), we performed a Genome-Wide Association Study (GWAS) on resting-state fronto-central fast beta EEG power, adjusting regression models for family relatedness, age, sex, and ancestry. To further characterize genetic findings, we examined the functional and behavioral significance of GWAS findings. Results Three intronic variants located within DSE (dermatan sulfate epimerase) on 6q22 were associated with fast beta EEG at a genome wide significant level (p<5נ10?8). The most significant SNP was rs2252790 (p<2.6נ10?8; MAF=0.36; ?=0.135). rs2252790 is an eQTL for ROS1 expressed most robustly in the temporal cortex (p=1.2נ10?6) and for DSE/TSPYL4 expressed most robustly in the hippocampus (p=7.3נ10?4; ?=0.29). Previous studies have indicated that DSE is involved in a network of genes integral to membrane organization; gene-based tests indicated that several variants within this network (i.e., DSE, ZEB2, RND3, MCTP1, and CTBP2) were also associated with beta EEG (empirical p<0.05), and of these genes, ZEB2 and CTBP2 were associated with DSM-V Alcohol Use Disorder (AUD; empirical p<0.05). Discussion In this sample of EA families enriched for AUDs, fast beta EEG is associated with variants within DSE on 6q22; the most significant SNP influences the mRNA expression of DSE and ROS1 in hippocampus and temporal cortex, brain regions important for beta EEG activity. Gene-based tests suggest evidence of association with related genes, ZEB2, RND3, MCTP1, CTBP2, and beta EEG. Converging data from GWAS, gene expression, and gene-networks presented in this study provide support for the role of genetic variants within DSE and related genes in neural hyperexcitability, and has highlighted two potential candidate genes for AUD and/or related neurological conditions: ZEB2 and CTBP2. However, results must be replicated in large, independent samples. 2016 Elsevier B.V.