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Scholar@UOC is the primary academic repository of the University of Calicut.

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Plant ecological studies on the arborescent vegetation of Sholayar Reserve forest, Southern Western Ghats of India
(Sn College, Nattika, 2024) Sreeja P Parayil; Abhilash,E.S.; Anitha, C. T.
An attempt has been made to study the tree species diversity in the Sholayar Reserve forest of Southern Western Ghats of India. The main objectives of the study are to analyze the structural status of the permanent arborescent vegetation, to assess the tree species diversity of different forest types and to evaluate the status of tree species belongs to the category rare, endangered and threatened. For the convenience of the study, the entire study area was divided into two zones based on altitude and vegetation types. The area above 700m altitude was considered as medium elevation zone, and below 700m altitude was considered as low elevation zone. Stratified random sampling technique was adopted for vegetation sampling. Various diversity indices were calculated and compared with other forests of the Western Ghats. Study sites were selected on the vegetation map and located on the ground by using Global Positioning System. In each plot using the census quadrat method (Oosting, 1956), all the tree species of 30cm or above girth at breast height or 1.37m above from the ground were recorded (Roy, 1993). A total of 280 sample plots of 0.1 ha. area was established in the study area. 28 study sites were selected, and 10 quadrats of 0.1ha was laid out in each study site. 10,946 trees belonging to 156 species out of 89 genera and 49 families were enumerated from the total sampling area. From the dense evergreen medium elevation forest 118 species of trees and from the low elevation degraded forest 94 species were recorded. Among the families, Euphorbiaceae is the largest one with 20 species, followed by Lauraceae (11 species), Meliaceae (11 species), Moraceae (9 species), Fabaceae (6 species) and Ebenaceae (6 species). Among 156 tree species recorded 3% of the species belongs to the rare category, 6% are endangered, 6% are vulnerable, 2% are critically endangered and 31% species belongs to the endemic category. A unique riparian vegetation is also observed in the study area. To protect this diverse forest ecosystem, proper conservation efforts, climate change mitigation strategies, and sustainable forest management practices are necessary.
Culture shock among employees in the IT sector: A study with special reference to South India
(Christ College Irinjalakuda., 2025) Culture shock among employees in the IT sector: A study with special reference to South India; Josheena Jose
India's Information Technology (IT) sector has emerged as a global powerhouse, contributing significantly to the nation's growth and placing India as one of the leading IT outsourcing destinations. However, this rapid growth has brought significant challenges for IT professionals, particularly new employees who experience culture shock while adapting to workplace environments, manifesting as disorientation, confusion, and anxiety that leads to serious mental health concerns, including stress-related conditions. With physicians in major IT hubs like Bengaluru reporting approximately ten daily patients with stress-related issues, predominantly software engineers, this phenomenon has become a critical concern affecting employee well-being and organizational performance. This study aimed to examine factors leading to culture shock among IT sector employees in South India, analyze employee morale, job performance, emotional labour, job satisfaction, and turnover intention; explore culture shock effects on employee sentiments through multiple mediation analysis, examine moderating effects of hybrid working, and analyze mitigating strategies against culture shock. The research employed a descriptive and analytical cross-sectional design, collecting quantitative data from IT professionals in NASSCOM-listed companies across the five South Indian states of Kerala, Tamil Nadu, Karnataka, Telangana, and Andhra Pradesh, focusing on employees with a maximum upto two years of work experience. The study reveals that IT employees in South India experience significant levels of culture shock, particularly regarding social connectedness and emotional intelligence factors, leading to moderate levels of employee morale while job performance remains notably low. Path analysis demonstrates that culture shock negatively impacts employee morale, intensifies emotional labour, reduces job performance and satisfaction, and increases turnover intention, with emotional labour and employee morale partially mediating the relationship between culture shock and turnover intention. Significantly, hybrid working arrangements amplify negative effects of culture shock, particularly reducing employee morale and job satisfaction, challenging conventional assumptions about remote work benefits. The study identifies social support and self-control as the most effective coping strategies among employees. These findings have crucial implications for multiple stakeholders: employers should implement mandatory buddy systems, enhanced onboarding processes, and team-building activities addressing social connectedness gaps; employees should conduct thorough organizational culture research before joining and familiarize themselves with global IT practices; and policymakers should revamp educational curricula to include social-emotional learning, mindfulness, and stress management components. The research highlights culture shock as a significant challenge with far-reaching implications for employee wellbeing, organizational performance, and industry sustainability, emphasizing that addressing this phenomenon is crucial not only for employee welfare but also for maintaining India's competitive advantage in the global technology landscape.
Alzheimer’s disease diagnosis and risk prediction using advanced machine learning models
(Sullamussalam Science College, University of Calicut., 2025) Haulath, K.; Mohamed Basheer K.P
Alzheimer's Disease (AD), which impairs memory, cognition, and behavior, is a progressive Neurodegenerative Disorder (ND). Accounting for 60-80% of dementia cases, AD advances from mild memory loss to complete loss of interaction with the environment. As there is no cure for AD, early detection is vital to mitigate disease progression. Currently, Machine Learning (ML) and Deep Learning (DL) have exhibited promise in neuroimaging-based AD prediction by employing approaches, namely Lasso Regression (LR), Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and Deep Neural Networks (DNN). Nevertheless, these models face limitations, comprising overfitting, high error rates, and limited small dataset performance. To address these challenges, a two-part framework is introduced in this study. The AD Neuroimaging Initiative (ADNI) MRI dataset is used by the first implementation to enhance prediction and classification accuracy through the GELU Swish Radial Basis Function Network (GS-RBFN). MRI images undergo preprocessing steps, such as normalization, skull stripping, and spatial smoothing, followed by essential brain tissue segmentation with Brownian Log Scaling Archimedes Optimization-centric Watershed Segmentation (BLSAOWS) and Feature Selection (FS) using the Base Switch Rule Infimum and Supremum-based Rock Hyrax Swarm Optimization (BSRISRHSO) approach. AD classification is further performed by the GS-RBFN classifier. GS-RBFN (Gaussian—Swish Radial Basis Function Network) and TT Self-Weighted Deep-AD3-Net—for early detection and staging of AD. The proposed models employ novel optimization algorithms (BLSAOWS and BSRISRHSO) for improved segmentation and feature selection from MRI datasets. The performance analysis of GS-RBFN model learns the features efficiently, it achieves superior accuracy (98.45%), precision (98.44%), F-measure (98.44%), Sensitivity (98.44%), Recall (98.45%), and specificity (98.45%). In the second implementation, AD risk scoring and stage prediction are focused on using The AD Prediction Of Longitudinal Evolution (TADPOLE) dataset. This methodology involves ranking critical variables, Risk Score (RS) calculation, and brain shrinkage analysis. Essential variables are selected by the Recursive Hypothesis-Creation Algorithm (RHCA), with the True True Self-Weighting Mechanism (TT-SWM) calculating RS. By using the Queue-Boltzmann-ConstantSphere (QBCS) technique, brain shrinkage in the hippocampus is measured, whereas Gray-Level Co-occurrence Matrix (GLCM) features facilitate staging through the Deep-AD3-Net classifier. Experimental evaluation using the TADPOLE dataset achieved an accuracy of 98.45%, sensitivity of 97.82%, and AUC of 0.981, outperforming recent state-of-the-art models. The frameworks demonstrate robustness and clinical potential for early intervention and treatment planning. The study advances existing AD diagnostic approaches by improving interpretability, computational efficiency, and predictive reliability. Keywords: Alzheimer’s disease, Data augmentation, Brownian Log Scaling Archimedes Optimization-based Watershed Segmentation (BLSAOWS), Base Switch Rule Infmum and Supremum-based Rock Hyrax Swarm Optimization (BSRISRHSO), GELU and SWISH-based Radial Basis Function Network (GSRBFN), Recursive Hypothesis-Creation Algorithm (RHCA), Phylogenetic Method (PM), one Gray-Level Co-occurrence Matrix (GLCM), Gray-Level Co-occurrence Matrix (GLCM Genetic Algorithm (GA), Deep-AD3-Net classifier.
Writing the City: Exploring Urban Space in the Narratives of Select Indian Women Writers
(English Department, Vimala College (Autonomous), Thrissur, 2025) Keerthy Sophiya Ponnachan; Joycee, O. J.; Sijo Varghese C
Mathematical Modelling of Biological Variations due to Application of Nanofluids in Body Fluids
(Research and Postgraduate Department of Mathematics St. Thomas’ College, Thrissur., 2022) Sujesh A. S.; Alphonsa Mathew
A substance capable of flowing is termed as a fluid. Fluids are of two types, namely liquids and gases. The study of fluid's behaviour at rest (termed fluid statics) and in motion (termed fluid dynamics) is combinedly known as fluid mechanics. The fluid produced and circulated within the human body or secreted outside the human body is known as body fluid. Blood, saliva, urine, tears, sweat, and breast milk are a few examples of body fluid. Water is the basis of all body fluids and the human body is composed of about 60% of water.Nano fluid is a colloidal mixture in which a base fluid (water, oil, ethylene glycol, etc.) is mixed with nanometer-sized particles (metals, carbides, oxides or carbon nano tubes). Fluids constituting two nanometer-sized particles are termed hybrid nano fluids. Nano fluids tend to upgrade and stabilize the thermal properties of the fluid which marked a revolution in the field of fluid dynamics. The description of a system using mathematical concepts and language is known as mathematical model and the process of developing a mathematical model is known as mathematical modelling. Mathematical models finds its use in natural sciences, engineering disciplines and social sciences. A mathematical model helps to explain a system and to study the effects of different components and also to make predictions about its behaviour. The thesis entitled Mathematical Modelling of Biological Variations due to Application of Nano fluids in Body Fluids has been arranged into 12 chapters. Chapter 1 introduces the basic concepts, preliminaries and definitions to the reader. An extensive review of related literature has been presented in Chapter 2. Owing to the practical applications (like biomedical imaging, hyperthermia, pharmaceuticals, biosensors, medical instruments, bio-chromatography, microchip pump, thermostatic, biomedical science, targeted drug delivery, and cancer therapy), nine fluid flow problems are modeled and investigated in this thesis. In Chapter 3, the bio convective stagnation point flow involving carbon nano tubes along a lengthening sheet subject to induced magnetic field and multiple stratification effects is investigated. The dynamics of water conveying single-wall viicarbon nano tubes (SWCNTs) and magnetite nano particles on the bio convective stagnation-point ow along a stretching sheet subject to chemical reaction, viscous dissipation, induced magnetic field, and stratification effects is investigated in Chapter 4. Non-spherical nanoparticles have gained popularity for their ability in changing the thermo physical properties of a nano fluid. Chapter 5 elucidates the significance of multiple slip and nanoparticle shape on stagnation point flow of blood-based silver nano fluid considering chemical reaction, induced magnetic field, thermal radiation, nano particle shape and linear heat source. The numerical study on the stratification effects of bio convective electroencephalographic (EMHD) flow past a stretching sheet using water-based CNT has been presented in Chapter 6. The focal concern of Chapter 7 is to numerically scrutinize the consequences of multiple slip, linear radiation and chemically reactive species on MHD convective Carreau nanoliquid flow over an elongating cylinder. Moreover, statistical scrutiny on the impact of Hartmann number, thermal radiation and thermal slip parameter over heat transfer rate employing Response Surface Methodology (RSM) and sensitivity analysis is also performed. The nanomaterial flow of Chapter 8 has been modeled using the modified Buongiorno nano fluid model. The impact of the stratification constraints and magnetic field are also accounted. Further, the influence of magnetic field parameter, thermal stratification parameter, volume fraction of magnetite nanoparticles, and velocity ratio parameter on the heat transfer rate has been scrutinized statistically using a five-level four-factor response surface optimized model. In Chapter 9, the dynamics of the T iO2 − H2 O nano material over a non linearly stretched surface and modeled using modified Buongiorno model is investigated. Experimentally derived correlations of the thermal conductivity and dynamic viscosity of the nano material are utilized.The hydro-magnetic bio convective flow of a nano material over a lengthening surface is investigated in Chapter 10. Realistic nano material modelling is achieved by incorporating passive control of the nano particles at the boundary. The impact of the Newtonian heating and Stefan blowing constraints are also accounted. The sensitivity of heat transport rate is also computed. Chapter 11 numerically elucidates the dynamics of elector-magneto hydrodynamic flow of blood-gold nano material over a non linearly stretching surface utilizing the Casson model. The impact of second-order hydrodynamic-slip, nano particle radius, first-order thermal-slip, inter-particle spacing and non-uniform heat source are also accounted. Lastly, Chapter 12 presents the concluding remarks of the thesis and proposals for future work.