Investigation of Temperature Sensitive Electrical Properties of Manganese-Zinc Ferrites
Effect of TiO2 Modifier Oxide on a B2O3 Glass System
Synthesis, Structural Characterization and DC Conductivity Study of (PMMA+PEG) Polymer Blend Films
Erbium Rare-Earth Metal Schottky Contact to P-Type Si and its Temperature-Dependent Current-Voltage Characteristics
Study of Moderate Temperature Plasma Nitriding of Inconel 601 Alloy
Study of Moderate Temperature Plasma Nitriding of Inconel 601 Alloy
Exact Solution of an Unsteady Buoyancy Force Effects on MHD Free Convective Boundary Layer Flow of Non-Newtonian Jeffrey Fluid
Enhancement of Mechanical Properties with Nano Polymer Composites
Synthesis and Characterization of SnO2 Nanoparticles
Mapping and Forecasting the Land Surface Temperature in Response to the Land Use and Land Cover Changes using Machine Learning Over the Southernmost Municipal Corporation of Tamilnadu, India
In this decade, global warming and urbanization have become fundamental problems. Numerous locations have experienced a temperature increase that has negatively affected the ecosystem. Land Surface Temperature (LST) is a valuable parameter for studying temperature variation because it is closely correlated with Land Use and Land Cover (LULC). This study combines Machine Learning, Remote Sensing, and Geographic Information System (GIS) techniques to detect the spatial variation of LST and quantify its relationship with LULC. The Nagercoil Municipal Corporation (the Southernmost Municipal Corporation of Tamil Nadu, India) was chosen as the study area to explore the relationship between LST and LULC. From 2014 to 2022, three scenes of Landsat 8 OLI, 9 OLI-2 LULC, and LST data were extracted. Markov Chain Analysis (MCA) is adopted to predict the future LULC and LST of the study. Pearson's correlation method is used in the study to determine the correlation of the LULC and LST. The correlation between LULC and LST is an essential metric for identifying and quantifying higher-temperature areas with urban development. These metrics can be incorporated into advanced UHI detection models and machine learning algorithms for more precise and accurate identification and quantification of Urban Heat Island zones. The proposed urban land use measures and urban land planning should be informed by continuous and detailed Remote Sensing and GIS combined with statistical modeling and analysis of LULC and LST. Possible actions include the conservation of agricultural and vegetated lands and the management of the reclamation of barren lands into croplands toprevent surface impermeability loss and ecosystem fragmentation.
In the study of buoyancy pressure effects on non-Newtonian Jeffrey fluid that go with the drift through unstable heat and mass change in the presence of a temperature gradient heat source and a primary chemical response inside the transferring species, the dimensionless, unstable, coupled, linear perturbation method is used to resolve equations with partial differentials. All relevant flow parameters are given in analyses and graphical displays. The effects of various flow amounts on temperature, concentration, frictional pressure, rate factor, and the cost of mass and heat transmission are quantified and discussed using graphs and tables.
SnO2 represents an n-type semiconductor possessing a substantial wide band gap of 3.6 eV at room temperature, along with excellent optical and electrical characteristics such as distinctive optical transparency, low resistivity, and a high theoretical specific capacity. The hydrothermal method, employing an aqueous solvent as the reaction medium, stands out for its environmental friendliness since it conducts reactions within a closed system. The XRD pattern aligns with the tetragonal (rutile) phase of the SnO2 crystal structure, as validated by the JCPDS data 411445 and 88-0287. Notably, agglomerations in nanoparticles result in irregular sample morphology. This is further evidenced by SEM, where it is distinctly observable that the average particle size has increased.
In recent years, the fields of plasmonics and metamaterials have undergone remarkable advancements, fundamentally altering the landscape of photonics and opening doors to groundbreaking applications across diverse scientific and technological domains. This review paper meticulously presents a survey of the recent strides achieved in plasmonics and metamaterials, focusing on their remarkable capacity for nanoscale manipulation and their pivotal role in propelling the trajectory of next-generation photonics solutions. Through a comprehensive exploration of pivotal advancements and game-changing breakthroughs, coupled with a nuanced analysis of their implications for emerging applications, this review illuminates the profound and transformative potential harbored by these specialized domains. In essence, the paper not only captures the current state of affairs but also offers insights into how these dynamic subfields are poised to shape the future of photonics.
This paper provides a comprehensive review of the emerging field of quantum robotics and its potential applications in space exploration and astrobiology. The current state-of-the-art in quantum robotics is discussed, including key capabilities enabled by the integration of quantum computing and quantum sensing with robotics. Potential uses of quantum robots for autonomous exploration, biomolecule and biomarker detection, chemical and biological simulations, and navigation in extraterrestrial environments are explored. Technical challenges such as dealing with space radiation, thermal regulation, power requirements, reliability, and communication networks are examined. Current and future space missions that could benefit from quantum robots, such as Mars rovers, exoplanet searches, and sample return missions, are discussed through case studies. The future outlook and prospects of quantum robotics in transforming space exploration and astrobiology through improved efficiency, accuracy, and autonomous operations are highlighted.
