Browse Items (16488 total)
Sort by:
-
Reinforcement Q Learning for Terrain-Energy-Aware Lunar Rover Navigation
Effective lunar navigation is difficult in rough terrain and scarce energy resources. Classical path-planning has difficulty with terrain adaptation and energy optimization. This work introduces a Reinforcement Learning (RL)-based solution for energy-optimal lunar rover navigation based on NavCam data from Chandrayaan-3. A Q-learning framework translates terrain characteristics - elevation, slope, and hazards - into a reward scheme, balancing safe travel, minimal energy consumption, and mission effectiveness. The RL agent learns to respond to varying conditions, punishing dangerous regions such as craters and slopes. Simulations on lunar grids demonstrate better energy efficiency and accuracy than traditional approaches. This research pushes autonomous planetary exploration forward, optimizing rover navigation with actual mission imagery for future lunar missions. 2025 IEEE. -
From maximum force to the field equations of general relativity and implications
There are at least two ways to deduce Einstein's field equations from the principle of maximum force c4/4G or from the equivalent principle of maximum power c5/4G. Tests in gravitational wave astronomy, cosmology, and numerical gravitation confirm the two principles. Apparent paradoxes about the limits can all be resolved. Several related bounds arise. The limits illuminate the beauty, consistency and simplicity of general relativity from an unusual perspective. 2022 World Scientific Publishing Company. -
Physics of Gravitational Waves: Sources and Detection Methods
[No abstract available] -
Performing Arts Teaching Pedagogies and Models Evolved During COVID-19
As academicians and teachers at global institutions were scrambling to handle challenges in the wake of COVID-19, online tools such as Zoom, Webex, and Teams along with course management systems like Moodle and Blackboard were adept in meeting the effective synchronous and asynchronous teaching-learning processes in schools in different parts of the world. Meanwhile, the performing arts discipline coped with the situation through some innovative performance projects and pedagogies. This chapter explores those innovative and hybrid pedagogies introduced and experimented by different professors at the Department of Performing Arts, Music and Theatre at Christ University and related institutions in Bangalore, India. Several faculty members are interviewed to find out the innovative pedagogies and strategies they have designed and implemented, along with their plans to use those pedagogic models in the post-pandemic scenario. These new insights and models would contribute to the body of knowledge, especially to teaching-learning processes in the performing arts discipline. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors. -
Crisis management, destination recovery and sustainability: Tourism at a crossroads
The COVID-19 pandemic brought travel to a halt and the global tourism industry has been one of the sectors hit hardest during the pandemic. This book looks at how the tourism industry can enhance its resilience and prepare for future crises more effectively. The book provides insights into the economic, social, geopolitical and environmental implications of the COVID-19 pandemic on the tourism and hospitality industries and the responses in diverse international contexts. It highlights key concepts and includes cases with real-life applications. The book also discusses future research directions in a post-pandemic scenario. This book will be an invaluable resource for practitioners in the areas of tourism and crisis management and for readers to compare and contrast tourism destination recovery and crisis management practices through different research methodologies and settings. 2023 selection and editorial matter, James Kennell, Priyakrushna Mohanty, Anukrati Sharma and Azizul Hassan. All rights reserved. -
Introduction: Tourism at a crossroads
[No abstract available] -
Bridging Travel and Learning: Can Ecotourism Be a Resource for Learning Sustainability?
This study explores the role of ecotourism as an educational tool for catering to experiential learning and sustainability awareness. Using a qualitative imaginary narrative method, two ecotourism scenarios are constructed based on insights from two expert environmentalists and existing research. The constructed scenarios were exclusive to undergraduate students from the life sciences and humanities. Using framework analysis, the obtained data were mapped to the SDG framework to understand students' engagement with nature- based learning and sustainability- focused activities, highlighting their emotional connections to nature and behavioural shifts toward sustainability. The framework analysis mapped the scenarios to relevant SDGs, analysing how they cater to ESD. The findings align with SDG 4 (Quality Education), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), emphasising ecotourism's potential in education. This research contributes to discussions on integrating sustainability learning into educational frameworks. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Facial Recognition Model Using Custom Designed Deep Learning Architecture
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Nae Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Interacting Dark Energy and Its Implications for Unified Dark Sector
Alternative dark energy models were proposed to address the limitation of the standard concordance model. Though different phenomenological considerations of such models are widely studied, scenarios where they interact with each other remain unexplored. In this context, we study interacting dark energy scenarios (IDEs), incorporating alternative dark energy models. The three models that are considered in this study are time-varying ?, Generalized Chaplygin Gas (GCG), and K-essence. Each model includes an interaction rate ? to quantify energy density transfer between dark energy and matter. Among them, GCG coupled with an interaction term shows promising agreement with the observed TT power spectrum, particularly for ?<70, when ? falls within a specific range. The K-essence model (??0.1) is more sensitive to ? due to its non-canonical kinetic term, while GCG (??1.02) and the time-varying ? (??0.01) models are less sensitive, as they involve different parameterizations. We then derive a general condition when the non-canonical scalar field ? (with a kinetic term Xn) interacts with GCG. This has not been investigated in general form before. We find that current observational constraints on IDEs suggest a unified scalar field with a balanced regime, where it mimics quintessence behavior at n<1 and phantom behavior at n>1. We outline a strong need to consider alternative explanations and fewer parameter dependencies while addressing potential interactions in the dark sector. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Conformal Invariance and Phase Transitions: Implications for Stable Black Hole Horizons?
Abstract: The behavior of black hole horizons under extreme conditionssuch as near collapse or phase transitionsremains less understood, particularly in the context of soft hair and Aretakis instabilities. We show that the breakdown of conformal symmetry during the balding phase induces a topological reorganization of the horizon, leading to divergent entropy corrections and emergent pressure terms. These corrections exhibit universal scaling laws, analogous to quantum phase transitions in condensed matter systems, with extremal limits functioning as quantum critical points. Interestingly, by employing quasi-equilibrium boundary conditions, one could stabilize horizon dynamics without explicitly introducing ad hoc higher-order corrections, further limiting the universal applicability of conformal invariance in black hole physics. Pleiades Publishing, Ltd. 2025. -
QuintessenceChameleon transitions in anisotropic Kiselev model of neutron stars
We investigate a chameleon scalar field dynamically interacting with a Kiselev-type metric, where the static anisotropic fluid part of the metric is replaced by a density-dependent scalar field nonminimally coupled to curvature. This construction enables a transition from screened behavior in high-density regions where the scalar acquires an effective mass m???1?2 to unscreened quintessence dynamics at large scales, characterized by a critical screening radius rcrit?m??1. By solving the modified TolmanOppenheimerVolkoff equations under spherical symmetry, we show that radial scalar gradients (?r?) induce pressure anisotropies (?p?r?1) in neutron star envelopes, while deviations from general relativity (GR) are suppressed deep in the core (r -
Parametric effect on machining characteristics of laser machined Al7075TiB2 in-situ composite
The effect of laser parameters on the machining characteristics of an Al7075 based in-situ metal matrix composite reinforced with Titanium diboride(TiB2) is investigated. The cutting speed (at 10001200 m/hr), stand-off distance (SOD) (0.30.5 mm), and gas pressure (0.50.7 bar) were studied. Scanning electron microscopy (SEM) was used to validate the machining behaviour of in-situ composites. Surface roughness and dimensional error decrease as the SOD increases up to 0.4 mm, but both increases as the SOD increases to 0.5 mm. whereas the volumetric material removal rate (VMRR) increases up to 0.4 mm SOD and then decreases as SOD increases (0.5 mm). Surface roughness, VMRR, and dimensional error were all found to increase with laser speed. Surface roughness and dimensional error increase as gas pressure increase up to 0.5 bar, then decreases. The VMRR, on the other hand, increased continuously as the assist gas pressure increased. Copyright 2021 Inderscience Enterprises Ltd. -
Experimental investigation of tribocorrosion
This chapter discusses various techniques available for evaluation of tribocorrosion behavior of industrial components, their applications, and limitations. Numerous influential factors of tribocorrosion, their mechanisms, and their characteristics have been discussed at length. Further, a case study of tribocorrosion behavior of aluminum-based in situ metal matrix composites have been deliberated comprehensively. 2021 Elsevier Inc. All rights reserved. -
Friction and wear behaviour of copper reinforced acrylonitrile butadiene styrene based polymer composite developed by fused deposition modelling process
This paper focuses on the development of copper filled Acrylonitrile Butadiene Styrene (ABS) composites by fused deposition modelling (FDM) and to characterize its friction and wear behaviour. Twin screw extrusion technique was employed to extract copper-ABS composite filament. Three different materials were tested, i.e. pure ABS, ABS+2.5wt% Cu and ABS+5wt% Cu. Friction and wear characteristics of pure ABS and copper filled ABS composites were tested under various loads and sliding velocities. Addition of Copper powder has significantly improved the friction and wear properties of the developed composites. Further, it is also observed that friction and wear behaviour increased with increase in copper content in ABS. Worn out surfaces were subjected to scanning electron microscopy studies to analyse and identify the possible wear mechanisms involved. Faculty of Mechanical Engineering, Belgrade. -
Microstructural evolution and damping response in ARB-processed ZK60 alloy
This study investigates the effect of microstructural evolution on the damping behaviour of ZK60 magnesium alloy processed via Accumulative Roll Bonding (ARB). ARB was employed at 300C for up to four cycles, significantly refining the grain structure and altering dislocation and precipitation behaviour. Comprehensive microstructural analysis revealed the formation of fine equiaxed grains (?6.7 m), dissolution of coarse precipitates, and increased dislocation density. TEM and Selected Area Diffraction (SAD) patterns confirmed dynamic recrystallisation and uniform grain orientation, while XRD patterns exhibited peak broadening and intensity changes, indicating crystallite refinement and texture evolution. Damping results showed substantial improvements in the ARB-processed alloy, particularly at low-to-mid frequencies, with up to 21% higher damping capacity than the base alloy. This enhancement is attributed to increased grain boundary sliding, dislocation interactions, and refined precipitatematrix interfaces. Both alloys exhibited similar damping responses at higher frequencies, suggesting saturation of energy dissipation mechanisms. 2025 Canadian Institute of Mining, Metallurgy and Petroleum. -
Influence of shot peening on microstructure and electrochemical behavior of al-Cu alloy; [Influence du grenaillage sur la microstructure et le comportement.]
The current study explores the effects of shot peening on the microstructural evolution and corrosion behavior of precipitation-hardened Al-Cu alloy. Microstructural analysis revealed significant grain size reduction from 100 m to 6.5 m, and this process improves grain boundary density, reduces precipitate size from 2 m to 0.5 m, and ensures a uniform elemental distribution, effectively mitigating galvanic corrosion risks. Energy-dispersive spectroscopy (EDS) confirms the homogenisation of alloying elements. At the same time, X-ray diffraction (XRD) analysis highlights crystallographic modifications, including refined crystallite size, redistributed secondary phases, and compressive residual stresses, contributing to enhanced strength and fatigue resistance. Corrosion behavior studies reveal a dramatic reduction in weight loss from 0.09 mg to 0.03 mg and a corrosion rate decrease from 3.5 10?3 mpy to 1.5 10?3 mpy, along with a noble shift in corrosion potential from ?1150 mV to ?775 mV. As observed in optical microstructures, grain refinement, residual stress introduction, and uniform surface characteristics lead to a significant shift from severe to mild corrosion attacks. Impedance analysis demonstrated improved corrosion resistance in the shot-peened alloy, evidenced by higher charge transfer resistance (Rct) and lower double-layer capacitance (Cdl). 2025 Canadian Institute of Mining, Metallurgy and Petroleum. -
Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches
This study evaluates advanced machine learning (ML) and deep learning (DL) models for predicting the tensile shear and bending strength of friction stir spot welding joints involving Al 5754 and Al 6111 alloys. ML techniques include Linear Regression, Decision Tree, Random Forest (RF), K-Nearest Neighbors, Support Vector Regression, and XGBoost, while DL models comprise Recurrent Neural Network (RNN) and Backpropagation Neural Network (BPNN). The models were assessed for discrepancies between experimental and predicted results, with the best-performing model identified using R-squared (R2), Root-Mean-Square Error, Mean Square Error, and Mean Absolute Error. The data preprocessing phase included feature scaling and an 85:15 train-test split. Key input process parameters included spindle speed, dwell time, plunge depth, and tool pin profile. The results demonstrate that XGBoost yielded the highest predictive accuracy, achieving an R2 score of 99.99% for both tensile shear and bending strength, while RF offered a strong balance between accuracy and robustness. Other ML models struggled with the datasets complexity, resulting in lower performance. Among DL approaches, the BPNN outperformed the RNN, achieving approximately 99.8% accuracy by effectively capturing complex data patterns. ASM International 2025. -
Experimental Investigation and Predictive Modelling of Tribological Behaviour in Fly Ash Reinforced Polymer Composites Fabricated via Stereolithography
Tribological evaluation of fly ash-reinforced UV-curable resin composites produced through stereolithography additive manufacturing formed the central focus of this study. Controlled fly ash additions extending up to 2% by weight were examined, and all tests ran under dry sliding conditions. Neat resin was designated as the experimental reference throughout. Even with the modest addition of 0.5% fly ash, wear came down by roughly 9.8%, which was small but directionally significant. Moving to 1% fly ash pushed wear reduction to 16.4%. At 1.5%, the reduction advanced further to 21.3% with all evaluations performed under identical testing conditions. The 2% fly ash specimen produced the highest wear reduction of 25.8% among all compositions examined. Frictional behavior followed a comparable declining trend across filler levels. At 0.5% fly ash COF fell 7.6% below the neat resin value, and at 1% that reduction grew to 12.9%. The 1.5% addition brought friction reduction to 17.4%. Total COF reduction at 2% fly ash reached 20.3%, which confirmed a steady and consistent frictional improvement with progressive filler incorporation. Linear regression and artificial neural networks and XGBoost-based gradient boosting were the three predictive frameworks developed alongside experimental work. All three tracked closely with measured tribological outcomes. Prediction accuracies reached up to 95.4% with lower error values documented across every model. The 2% fly ash formulation stood out as the most effective composition tested. It delivered the strongest combined improvements in wear resistance and friction stability while remaining fully compatible with the operational demands of additive manufacturing. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Flexural performance of FDM-fabricated PLA composites reinforced with short carbon fiber
Adding short carbon fiber reinforcement to thermoplastic matrices can improve the mechanical performance of additively manufactured polymer-based composites. This work examines the flexural behavior of Fused Deposition Modeling (FDM)-produced polylactic acid (PLA)-based composites enhanced by short carbon fibers at 3 wt% and 6 wt%. For comparison, unreinforced PLA specimens were also fabricated under identical processing conditions. All the samples were tested for flexural strength using a three-point bend test, following the ASTM standards for polymer composites. The results showed a clear improvement in strength as the reinforcement content increased. The PLA composite with the 3% short carbon fiber reinforcement showed a noticeable increase in load-bearing ability, while the 6% reinforced composite had an impressive 88% higher flexural strength than the plain PLA. To examine how the materials failed, SEM has been utilized to assess the samples' fractured surfaces. 2026, Gruppo Italiano Frattura. All rights reserved. -
Bioinformatics Research Challenges and Opportunities in Machine Learning
This research work has studied about the utilization of machine learning algorithms in bioinformatics. The primary purpose of studying this is to understand bioinformatics and different machine algorithms which are used to analyze the biological data present with us. This research study discusses about different machine learning approaches like supervised, unsupervised, and reinforcement which play an essential role in understanding and analyzing biological data. Machine learning is helping us to solve a wide range of bioinformatics problems by describing a wide range of genomics sequences and analyzing vast amounts of genomic data. One of the biggest real-world problems is that machine learning is helping us to identify cancer with a given gene expression, which is done using a support vector machine. In addition, this study discusses about the classification of molecular data, which will help find out minor diseases. With the advancement of machine learning in healthcare and other related applications, data collection becomes a tedious process. This article also focuses on some of the research problems in machine learning domain. The uses of machine learning algorithms in bioinformatics have been extensively studied. These objectives will help to understand bioinformatics and different machine algorithms that are used to analyze the biological data. This research study presents different machine learning approaches like supervised, unsupervised, and reinforcement, which play an important role in understanding and analyzing biological data. Machine learning helps to solve a wide range of bioinformatics related challenges by describing a wide range of genomics sequences and analyzing huge amounts of genomic data. One of the biggest real-time challenges is that the machine learning is helping to identify cancer with a given gene expression, and this is done by using a support vector machine. Finally, this research study has discussed about the classification of molecular data, which will be helpful in finding out minor diseases. 2022 IEEE.