7940 research outputs found
Sort by
Evaluation of Flow Resistance and Estimation of Bed Load Transport in Gravel-Bed Channels
No full textA reliable estimation of roughness coefficient and Bed Load transport rates is necessary for the hydraulic analysis of open channels and evaluation of change in conveyance in a specific flow condition. Determining flow resistance and transport parameter in Bed Load transport conditions is difficult because they depend upon the strength of the flow, the complex structures of secondary flow, bed shear stress, and bed particle sizes. For gravel-bed streams, many researchers have analysed flow conditions over movable channel beds and proposed equations for Darcy-Weisbach friction factor (f) and Einstein Bed Load transport parameter (p) for limited geometrical and hydraulic conditions. Movable Bed Load conditions cause many variations of the influencing parameters, which complicate the determination of flow resistance by employing analytical methods. The gravel-bed channel response for a movable Bed Load condition in terms of resistance to the flow is distinct from that of a fixed bed and requires a different technique evaluation. The present study considers the different empirical approaches by various authors to calculate the friction factor and Bed Load transport parameter under movable bed load conditions and propose expressions using advanced computing methods, viz. Artificial Neural Network (ANN) and Genetic Expression Programming (GEP). Various hydraulic and geometric parameters affect flow resistance and transport of Bed Load. A wide range of experimental datasets is used to investigate the effect of these influencing parameters. In the present study, the factors that influence friction factor and Bed Load transport parameter for such a flow condition are the relative submergence depth, bed slope, aspect ratio, Reynolds number, Froude number and shield number. New models have been developed to estimate the roughness coefficient and Bed Load transport rates. The predictability of the proposed model is compared to the various empirical equations in the literature. Unlike the existing models, the proposed models are observed to effectively predict the friction factor and Bed Load transport parameter for many datasets. The evaluated value of the friction factor from different models is used to validate the conveyance capacity of a river. The developed Multi-Gene Genetic Programming (MGGP) model is also observed to reasonably predict discharge in the river, signifying that the model is competent to be applied to field conditions within the specified range of parameter
The Insight of Salmonella Infection and its Impact on Behavioral Plasticity in Caenorhabditis Elegans
No full textSalmonella is one of the intracellular pathogens causing millions of people to succumb to death every year. The two serovars, Salmonella enterica serovar Typhimurium (S. Typhimurium) and Salmonella enterica serovar Typhi (S. Typhi), cause infection in animals and humans, respectively. A free-living soil nematode Caenorhabditis elegans (C. elegans) is used as an established model system for studying host-pathogen interaction. S. Typhimurium is known to cause persistent infection and dauer larva development, but the mechanism behind this phenomenon is not well explained. Dauer is an alternative developmental stage that gives survival benefits under unfavorable and even infection conditions. In pathogenic Escherichia coli K-12 (E. coli K-12), the role of various genes (yjgB, elaA, mutS, fepB, fepC, cyaA, yrfD, srlA, etc.) in inhibiting the C. elegans dauer state has been reported by gene deletion study. In this study, Salmonella genes (cyaA, fepB, and fepC) are targeted, showing maximum homology with that of E. coli K-12, and checked the effect of that gene on C. elegans dauer larvae development. It was observed that absence of the fepB gene from the S. Typhimurium genome, bacteria become less virulent with decreased motility and biofilm-producing ability compared to WT-STM counterparts. Also, the worm exhibited better clearance of the fepB mutant bacterial strain with no damage to the worm’s pharynx. The fepB mutant strain also activated worms' early immune responses and gave worms better survival ability. There are four conserved dauer signaling pathways, i.e., Insulin-like signaling pathway, TGF-β signaling pathway, Guanylyl cyclase pathway, and Steroid hormone pathway found in worms. Here, infection with mutant Salmonella strain altered TGF- β pathway, which led to activate Insulin- like signaling pathway and raised a significantly increased number of dauer larvae in the second generation of worm population compared to WT-STM infection. Dauer formation always requires nuclear translocation of DAF-16, a forkhead transcription factor of the Insulin-like signaling pathway. The activity of DAF-16 plays a major role in determining whether to continue the reproductive growth of animals or develop dauer larvae. Often sensing environmental stress, DAF-16 translocates from cytoplasm to nucleus to activate the dauer signaling cascade. Transgenic animals expressing DAF-16::GFP exhibited more DAF-16::GFP nuclear localization under ΔfepB strain infection conditions than WT-STM. Besides, DAF- 16::GFP localization remained up to F2 generation and strongly implied the negative regulation of fepB gene of S. Typhimurium in modulating dauer larvae development in worms. Next, we wanted to understand how bacteria act as food signals modulating the worm’s chemosensory system to mediate such behavioral plasticity in its second generation. C. elegans possess a well- developed chemosensory system with 302 neurons, including gustatory neurons that sense water-soluble environmental cues and olfactory neurons that sense volatile, attractive, or repellent compounds from the environment. Worms’ chemosensory neurons play an important role in navigating food search, avoiding harmful compounds, larval development, and even male mating. These neurons are widely located in the head or tail region of the worm body and grouped into sense organs, i.e., amphid, phasmid, inner labial, and outer labial organs. Sensing environmental stress often induces worms to enter the dauer state to maintain cellular homeostasis. Here we initially evaluated the olfactory preference of C. elegans toward the pathogenic WT-STM and its mutant strain along with regular E. coli OP50 food and found worms’ initial approach over 1-2 hours was dominated by WT-STM but not for fepB mutant Salmonella strain. Besides, exposing longer time duration under infection conditions showed a less aversive response of worms against the fepB mutant Salmonella strain. Still, it exhibited a better associative learning response against the fepB mutant strain than the WT-STM counterpart. With these altered behavioral responses, we next looked into mRNA expression of certain genes playing an important role in worms’ olfaction. We found upregulation of odr- 3 and ceh-36 genes expression along with the secondary signal transduction genes, i.e. tax- 2/tax-4 and daf-11 gene located upstream of tax-2/tax-4, a cyclic nucleotide-gated channel at 24 hours of ΔfepB post-infection. However, using mutant C. elegans strains showing a defect in worms’ olfactory neuron mediated chemosensation implied the involvement of the ceh-36 gene, which encodes the CEH-36 transcription factor required for terminal differentiation of AWC neuron, for sensing fepB mutant bacterial strain. Further, exposing AWC ablated C. elegans strain to the infection strains in a time-dependent manner strongly indicated AWC neuron's participation in sensing ΔfepB strain. Further, to understand the involvement of AWC neuron in worms’ behavioral plasticity, we exposed mutant worms and AWC ablated worms and observed AWC neuron playing an important role in modulating worms' behavioral plasticity against mutant Salmonella strain. Overall, our study deciphering the relationship between chemosensory neurons and bacteria emitted signals that alter worms’ behavioral plasticity and can help us understand the complex phenomenon of host-pathogen interaction benefiting pathogen in host dissemination
Design and Development of Biomimetic Hydrogel Scaffold for Bone Tissue Engineering
No full textThe use of injectable hydrogels is currently restricted by the challenge of achieving fast gelation, good mechanical strength, and cytocompatibility. Perhaps the greatest challenge is the need for biomaterials to satisfy the bone's high compressive properties, a prerequisite for in vivo functioning. One of the drawbacks of biopolymer-based scaffolds is that under aqueous conditions, their mechanical characteristics reduce significantly. We developed an injectable, thermosensitive polyelectrolyte complex-based hydrogel to mimic the native extracellular matrix using a biomimetic approach for the repair and regeneration of defective tissue. We synthesized self-assembled structures of polyelectrolyte complexes (PECs) of polyanionic sodium alginate with the polycationic chitosan at room temperature. The PECs prepared at different pH values exhibited two distinct morphologies. The chitosan-alginate PECs self-assembled into the fibrous structure in a low pH range of pH 3 to 7. The PECs obtained at high pH series around pH 8 and above resulted in the formation of colloidal nanoparticles in the range of 120±9 nm to 46±17 nm. Furthermore, the practicability of developing a chitosan-based thermogelling solution using hydroxyapatite and polyelectrolyte complex (PEC) self-assembled fibers was evaluated. The effect of βGP concentration on gelation time was studied by varying the concentration of βGP added to the chitosan solution. Various combinations were tested to create a suitable hydrogel environment for cell encapsulation, growth, and proliferation at physiological pH and temperature. We investigated interfacial bonding between PEC fibers with βGP, NaHCO3, and HAp. The combination of hydroxyapatite and polymer self-assembly techniques improved the efficiency of injectable hydrogels which are helpful in minimally invasive applications. Later we fabricated nanocomposite scaffolds using hydrothermal treatment of polyelectrolyte complex (PEC) of chitosan and polygalacturonic acid. PEC fibers with Hap 5% by weight are obtained as 3D porous scaffolds by freeze-drying. FTIR analysis revealed the ability of the thermal treatment to set the interaction of HAp with polymeric PEC fibers. FESEM analysis suggests the influence of hydrothermal gelation on pore arrangement and unique molecular organization. Mechanical tests revealed that thermal heating exhibits a beneficial effect on PEC fibers and the molecular structuration of PEC-HAp, improving their stiffness and compressive strength. Hence, the hydrothermal treatment proved an effective crosslinker-free gelation with improved mechanical strength and nanofibrous structure. Later, we developed a novel process to fabricate thermosensitive hydrogels mimicking the ECM of the native tissue with a homogeneous, interconnected porous fibrous structure for tissue engineering applications without using organic solvents. Moreover, the hydrogel formed at physiological temperature and pH exhibits potential for cell encapsulation and injectability for tissue engineering applications. It contained self-assembled fibrous PEC with HAp-gelatin interspersed with chitosan matrix. The introduction of hydrothermal treatment resulted in the enhanced overall stiffness of the hydrogels. The hydrogels' fibrous and interconnected porous structure offered ample space for the embedded cells, which enhanced the direct interaction with the hydrogel matrix. Thus, the increase in ALP activity and collagen production confirmed the differentiation of preosteoblasts cultured in the hydrogels. This indicates that these hydrogels are promising biomaterial for the repair and regeneration of target bone tissue (any shape) and clinical application. These hydrogels hold great potential to accommodate MSCs for bone repair and regeneration purposes, which warrants future in- vivo studies
Response of Atmospheric Thermodynamics to Pre-monsoon Season Thunderstorms over Eastern and North-eastern India
No full textThunderstorm forecasting is necessary to safeguard society from severe damage over different parts of the globe. Forecasting these events is more important in tropical countries like India. Eastern and north-eastern India encounters severe thunderstorms and associated casualties during the pre-monsoon season (March-May). My doctoral research analyses temporal and spatial variation thermodynamic indices and the changing climate scenario impact with respect to pre-monsoon thunderstorms over eastern (Odisha, Jharkhand, and West Bengal) and north-eastern (Assam and Tripura) India. The thermodynamic indices employed for this work are the Lifted Index (LI), K Index (KI), Severe Weather Threat Index (SWEAT), Total Totals Index (TTI), Vertical Totals Index (VTI), Cross Totals Index (CTI), Showalter Index (SHOW), Humidity Index (HI), Boyden Index (BI), Convective INhibition energy (CIN) and Convective Available Potential Energy (CAPE). Thermodynamic indices and their threshold values have been calculated at Kolkata, Bhubaneswar, Ranchi, Agartala and Guwahati stations using the radiosonde data for the past thirty years (1987-2016). The thunderstorm occurrence information has been obtained from the India Meteorological Department (IMD), Pune. The latent and conditional instability indices showed a change in threshold values over these sites. The climatological atlas of various thermodynamic indices was developed using hourly (00 UTC and 12 UTC) ERA-5 re-analysis data to differentiate Thunderstorm and Non-Thunderstorm days (TD and NTD) throughout the study over eastern and north-eastern India. The study also employed Mann-Kendall Trend analysis (MKT) over the spatial domain to find the trends in the spatial variability of thermodynamic indices during TD and NTD. As the Chhota Nagpur Plateau (CNP) and Shillong Plateau (SP) have been identified to provide triggering for thunderstorm development over eastern India and parts of north-eastern India, the study also evaluated the role of CNP and SP in the development and propagation of thunderstorms. Sensitivity experiments are performed (with increasing and decreasing topography of CNP and SP) using state-of-the-art mesoscale model Advanced Weather Research and Forecasting (WRF-ARW) over the eastern and north-eastern India region. The results showed that the characteristics of several thermodynamic indices change substantially with the changes (increases/decreases) in the CNP’s and SP’s topography from its actual topography
Quantitative Analysis of Gait Disorder using Machine Learning Techniques
No full textClinical gait analysis has a significant role during the health diagnostics as well as the rehabilitation process. In a conventional clinical gait setup, patients are assessed by clinicians by observing the gait features or with questionnaires. Such qualitative approaches make the gait assessment process subjective to the understanding of the clinician. The gait features also vary from one gait cycle to another, especially for highly quasi-periodic gait patterns observed in subjects with gait disorders. This thesis aims to automate the different aspects of a reliable gait assessment system for patients with neurological and musculoskeletal disorders by minimizing human intervention using machine learning techniques. Segmenting a gait signal into strides is required to derive gait features. The existing fixed-length stride segmentation methods consider single periodicity for signals which is not effective to process quasi-periodic signals. Therefore, a varying length stride extraction algorithm using a template of a stride and measuring the similarity based on statistical tests is proposed to address this issue. A visualization-based assistive tool is devised which embeds different gait stride extraction methods along with the proposed statistical test-based stride extraction method. The users can auto-annotate as well as update the start and end of each stride along with the events in the stride. Advanced machine learning techniques are explored to automate the process of gait abnormality detection. In literature, the automated feature learning techniques are applied to the time-series signal that have only temporal information. The effect of representing signal data using wavelet decomposition on a 1-dimensional Convolutional Neural Network is examined for classifying gait patterns of Cerebral Palsy, a neurological disorder. Gait data of healthy individuals and cerebral palsy children are collected using inertial sensors. The results demonstrate improvement in the performance when compared with state-of the-art methods. An investigation is done to analyze the effect of varying levels of wavelet decomposition on the performance of the model. It reveals that the proposed method reaches the highest accuracy and lowest loss value at decomposition level 2. A capsule neural network is trained on a small-scale dataset by automatically extracting features in order to distinguish the gait pattern of children with Autism Spectrum Disorder (ASD) from healthy population. ASD is a neurological condition that affects the natural movement of an individual. Existing researches report the minimal difference between walking overground data of healthy and ASD children. Gait data is acquired from healthy and ASD children to investigate for overground walking, ascending, and descending the stairs. The precision-recall curves of the experimental evaluation reveal that the proposed capsule network is more skilled than the state-of-the-art automated feature learning methods, across varying thresholds. The performance of the system is further enhanced using parameter transfer learning which is useful when only a small-scale dataset is available. The experimental study shows that gait data of descending the stairs can be more effective than overground walking for ASD gait analysis. Patients with musculoskeletal injuries are required to exhibit specific tests for clinicians to monitor the recovery progress during the rehabilitation period. A long short term memory cell-based auto-encoder (LSTM-AE) model is implemented for recovery assessment with GaitRec Ground Reaction Force (GRF) dataset. The reconstruction losses generated for the test GRF signals are assessed to indicate the recovery status of the patient. The result analysis suggests that the reconstruction loss of the LSTM-AE model gradually reduces towards the ending phase of recovery. The promising performances of the proposed approaches indicate the potential solutions to the limitations of the existing gait assessment approaches for emerging applications in the healthcare domain
Study on Various Stochastic Comparisons of Order Statistics Arising from General Families of Distributions
No full textThis thesis addresses the study of various stochastic comparisons of order statistics. The main objective of the thesis is to develop several ordering results between two order statistics according to the usual stochastic order, hazard rate order, reversed hazard rate order, dispersive order, star order, Lorenz order, likelihood ratio order and the ageing faster order. Several sufficient conditions have been established to obtain the comparison results between order statistics, when the random variables are taken from general models, namely, the exponentiated location-scale, MPHRS, MPRHRS and multiple-outlier scale models. These studies comprise two batches of independent or interdependent exponentiated location-scale distributed (MPHRS and MPRHRS distributed, multiple-outlier scale distributed) heterogeneous random variables, where the interdependent observations in sample are modeled by (i) the Archimedean (survival) copula and (ii) the generalized survival copula. Furthermore, several numerical examples/counterexamples are provided to illustrate the effectiveness of the established theoretical results
Impact of Creativity and Innovation Drives of Employees on Organisational Sustainability
No full textThe Indian automobile industry provides a unique platform to carry out this research. It is the largest and most competitive in the world. Over the past decade, it has undergone several changes by adopting innovative technology and creative process management to keep up with the competitive market and the growing demand. The industry contributes significantly to the Indian economy as it is closely associated with other sectors. The automobile industry in the car manufacturing sector is an assembly-based industry where various components are manufactured by different units located in India and abroad. In this sense, the automobile industry has a global impact. The need to adopt a robust, sustainable trajectory of innovation has created urgency in the automotive industry, especially in developing lower-emission vehicles like driverless, electric and fuel-cell-driven cars, which have presented a significant challenge to the car manufacturers. Thus, there is a need for creative employees and continuous organisational innovation to attain a competitive edge over other companies. Seminal works in developed nations have advocated that organisational sustainability is mainly influenced by organisational innovation and employee creativity through various drivers impacting it. However, the past studies emphasise that drivers of employee creativity, and drivers of organisational innovation enhances employee creativity and organisational innovation, which are scarce in India. Thus, this research examined the impact of drivers of employee creativity, drivers of organisational innovation on employee creativity and organisational innovation for attaining organisational sustainability in the Indian automobile industry. Specifically, the study evaluates percptions of Indian automobile manufacturing units' employees to build a logical relationship among the study variables. A survey was conducted among the employees of the Indian automobile industry across India. A structured questionnaire was distributed to elicit the opinion of employees on the study variables. The responses obtained were analysed using SPSS 20 and AMOS 20. The findings reveal that employee creativity and organisational innovation positively impact organisational sustainability. It is also found that innovative work behaviour mediates the relationship between employee creativity and organisational innovation with the moderating role of rewards and recognition. So, organisational innovation has positive impact on organisational sustainability with the mediating effect of organisational performance and moderating influences of the facilitating environment. Thus, this research provides a holistic framework that may serve as a blueprint for the Indian automobile industry to attain organisational sustainability
Development and Characterization of Epoxy Based Hybrid Composites Reinforced with Hair Fibers
No full textThe research reported in this thesis essentially has four broad parts. One part is about exploring the possibility of composite making with human hair fiber as the reinforcing element in epoxy. The fabrication details of such a composite and its hybridization with secondary fillers have been discussed. The physical, mechanical and microstructural characteristics of these composites are presented. The following part describes the development of theoretical correlations for the prediction of effective thermal conductivity (keff) of polymer composites reinforced with short fibers and hybrid composites filled with any particulate filler along with the fiber. The third part has provided the experimental and analytical details on the dry sliding wear response of epoxy based composites reinforced with short hair fiber (SHF) and hybrid composites filled with glass microspheres in different proportions. The last part has reported on the thermal, acoustic and dielectric characteristics of the composites that includes an assessment of the effective thermal conductivities using the proposed correlations and their experimental validation. This research shows that successful fabrication of epoxy composites reinforced with short hair fiber and hybrid composites incorporated with various secondary fillers like solid glass microspheres (SGM), boron nitride (BN) and aluminum oxide (Al2O3) with solution casting technique is possible. The density and porosity of these composites are greatly influenced by the type and content of filler materials. Tensile, flexural and compressive strength of the composites can also be largely modified with the incorporation of short human hair fiber. It is further found that the wear performance of the epoxy+SHF composites can be improved significantly with the addition of solid glass microspheres and a parametric appraisal of the wear process can be made using Taguchi method. Using response surface methodology, correlations are established between the independent process parameters and output response. Further, an artificial neural networks (ANN) based model is used to predict the specific wear rates of epoxy+SHF and epoxy+SHF+SGM composites for different SHF contents and sliding velocities. It is found that reinforcement of short hair fiber improves the thermal insulation capability of the composite which can be further improved with the addition of SGM as the secondary filler. On the other hand, conductive fillers like BN and Al2O3 enhance the thermal conduction behavior of composite. An increment in the glass transition temperature (Tg) and drop in the coefficient of thermal expansion (CTE) of the composite have been observed with the reinforcement of hair fiber. Similarly, incorporation of the secondary fillers also causes enhancement in the Tg and drop in CTE of the hybrid composites. The reinforcement of hair fiber also improves the acoustic insulation ability of composite with operating frequency. It is further observed experimentally that for all such composites, hair fiber reinforcement causes a drop in dielectric constant value. Thus this work opens up a new avenue for the value added utilization of a bio waste like human hair as a reinforcing element in polymer based composites with promising application potential
Robust Control Schemes for a Doubly Fed Induction Generator based Wind Energy Conversion System
No full textDue to many advantages, such as variable speed operation, low noise, high torque, and ease of maintenance, the Doubly Fed Induction Generator (DFIG) is extensively employed in a Wind Energy Conversion System (WECS). To control a DFIG-based WECS, active power extraction from the wind must be regulated while reactive power must be kept at zero in order to ensure unity power factor operation. A number of control algorithms for controlling the active and reactive power of WECS have been proposed in the past. A WECS is encountered with several parametric uncertainties and external disturbances. Thus, it is essential to tackle the impact of parametric uncertainties and disturbances in the WECS characteristics by suitable design and implementation of appropriate robust control algorithms to achieve good performance. Controlling the active and reactive power of WECS has been the subject of a lot of research. Parametric uncertainties, on the other hand, have a substantial impact on the performance of active and reactive power regulation in a WECS. As a result, developing appropriate controllers for managing the active and reactive power of the WECS in the presence of parametric uncertainties and disturbances is regarded as a challenging control problem. The purpose of this dissertation is to develop robust control algorithms for a DFIG-based grid-connected WECS that can control both active and reactive power in the presence of parametric uncertainties and disturbances. As the active and reactive power of a DFIG are dependent, it becomes necessary to design suitable controller such as that the active and reactive power can be regulated separately by decoupling the active and reactive power control loops. The thesis starts with the development of a Proportional-Integral (PI) controller and Sliding Mode Controller (SMC) to control the active and reactive power of DFIG-based WECS that are delivered to the grid. The performance of PI and SMC controllers are evaluated under nominal conditions. An Autoregressive Moving Average with Exogenous (ARMAX) model is developed for DFIG-based WECS. This ARMAX model of the WECS is used to design a Model Predictive Control (MPC). By using the input-output values from previous sampling instants over a time horizon, the MPC predicts the system’s future output. The computing time, on the other hand, is a substantial barrier to MPC implementation. As a result, a variety of approaches have been used to lessen MPC’s computational load. To lessen the time complexity of the MPC problem, usually optimal solutions are adopted. A Linear Matrix Inequality (LMI) approach is used to reduce the computation time caused by the MPC. The optimization problem is solved using the LMI. PI is an excellent choice in the majority of industrial applications among all classical and current control methods. Because WECS has uncertainties due to intermittency in the wind speed various suitable feedback control mechanisms are necessary to address these concerns. An Extended State Observer (ESO) successfully estimates the unknown dynamics and disturbance. The rotor resistance and mutual inductance of the DFIG are modified to evaluate the robustness of the proposed MADRC. Peak overshoot and settling duration of the active power response are studied as a function of the aforementioned parameters. In the face of parametric uncertainty, the developed controller is found effective in managing the active and reactive power of DFIG, as well as rejecting external disturbance in desired value tracking. The proposed MADRC successfully handles parametric variation for set point tracking of active and reactive power of WECS, according to simulation and experimentation results. To improve the tracking accuracy of a DFIG-based WECS an online optimization approach based on wavelet neural networks for parameter adjustment of Active Disturbance Rejection Control (ADRC)
Statistical Inference Based on Progressive type-II Censored Samples from Lifetime Distributions
No full textThe problems of estimation and prediction for statistical models based on progressively type-II censored sample play a crucial role in various areas of research such as reliability theory, survival analysis and statistics. In this thesis, statistical inferences for five distributions are considered under progressively type-II censored sample. For generalized Rayleigh, gamma-mixed Rayleigh, log-logistic and generalized Fréchet distributions, the estimators have been obtained for the unknown parameters, reliability and hazard rate functions. For the case of exponentiated Gumbel type-II distribution, the estimands are proposed for the model parameters. Various estimates are proposed in this thesis. The maximum likelihood estimates are obtained. These are not of closed-form. Thus, Newton-Raphson method, expectation-maximization and stochastic expectation-maximization algorithms are used to compute the maximum likelihood estimates. Sufficient conditions for the existence and uniqueness of the maximum likelihood estimates are obtained for the exponentiated Gumbel type-II distribution. For each problem, Bayes estimates are derived with respect to various symmetric and asymmetric loss functions. The priors are considered as independent gamma distributions for the purpose of Bayesian estimation. It is observed that the Bayes estimates are not of closed-form. So, approximation techniques such as Lindley's method, importance sampling method and Metropolis-Hastings algorithm are employed. The approximate confidence intervals are constructed using the normal approximation of the maximum likelihood estimates, normal approximation of the log-transformed maximum likelihood estimates and two bootstrap procedures. Highest posterior density credible intervals are introduced. In addition, the problem of Bayesian prediction and interval estimation is studied for gamma-mixed Rayleigh and generalized Fréchet distributions. In this purpose, one- and two-sample prediction problems are studied. To observe the performance of the proposed estimates, a detailed simulation is conducted using R software. The performance of the maximum likelihood and Bayes estimates is observed based on the average values and mean squared errors. The interval estimates are compared with respect to the average lengths and coverage probabilities. For every problem, real datasets are considered and analysed for illustrative purposes