Artificial neural network‐based modeling of snow properties using field data and hyperspectral imagery

This study attempts to model snow wetness and snow density of Himalayan snow cover using a combination of Hyperspectral image processing and Artificial Neural Network (ANN). Initially, a total of 300 spectral signature measurements, synchronized with snow wetness and snow density, were collected in the field. The spectral reflectance of snow was then modeled as a function of snow properties using ANN. Four snow wetness and three snow density models were developed. A strong correlation was observed in near‐infrared and shortwave‐infrared region. The correlation analysis of ANN modeled snow density and snow wetness showed a strong linear relationship with field‐based data values ranging from 0.87–0.90 and 0.88–0.91, respectively. Our results indicate that an Artificial Intelligence (AI) approach, using a combination of Hyperspectral image processing and ANN, can be efficiently used to predict snow properties (wetness and density) in the Himalayan region. Recommendations for resource managers

• Snow properties, such as snow wetness and snow density are mainly investigated through field‐based survey but rugged terrains, difficult weather conditions, and logistics management issues establish remote sensing as an efficient alternative to monitor snow properties, especially in the mountain environment.
• Although Hyperspectral remote sensing is a powerful tool to conduct the quantitative analysis of the physical properties of snow, only a few studies have used hyperspectral data for the estimation of snow density and wetness in the Himalayan region. This could be because of the lack of synchronized snow properties data with field‐based spectral acquisitions.
• In combination with Hyperspectral image processing, Artificial Neural Network (ANN) can be a useful tool for effective snow modeling because of its ability to capture and represent complex input‐output relationships.
• Further research into understanding the applicability of neural networks to determine snow properties is required to obtain results from large snow cover areas of the Himalayan region.

     

Surface Functionalization of Magnetite Nanoparticles with Multipotent Antioxidant as Potential Magnetic Nanoantioxidants and Antimicrobial Agents

Functionalized magnetite nanoparticles (Fe₃O₄) were prepared using the coprecipitation method followed by functionalization with a multipotent antioxidant (MPAO). The MPAO was synthesized and analyzed using FTIR and NMR techniques. In this study, the functionalized nanoparticles (IONP@AO) were produced and evaluated using the FTIR, XRD, Raman, HRTEM, FESEM, VSM, and EDX techniques. The average determined particle size of IONP@AO was 10 nanometers. In addition, it demonstrated superparamagnetic properties. The magnitude of saturation magnetization value attained was 45 emu g⁻¹. Virtual screenings of the MPAO’s potential bioactivities and safety profile were performed using PASS analysis and ADMET studies before the synthesis step. For the DPPH test, IONP@AO was found to have a four-fold greater ability to scavenge free radicals than unfunctional IONP. The antimicrobial properties of IONP@AO were also demonstrated against a variety of bacteria and fungi. The interaction of developed nanoantioxiants with biomolecules makes it a broad-spectrum candidate in biomedicine and nanomedicine.     

Biotic Cathode of Graphite Fibre Brush for Improved Application in Microbial Fuel Cells

The biocathode in a microbial fuel cell (MFC) system is a promising and a cheap alternative method to improve cathode reaction performance. This study aims to identify the effect of the electrode combination between non-chemical modified stainless steel (SS) and graphite fibre brush (GFB) for constructing bio-electrodes in an MFC. In this study, the MFC had two chambers, separated by a cation exchange membrane, and underwent a total of four different treatments with different electrode arrangements (anodeǁcathode)—SSǁSS (control), GFBǁSS, GFBǁGFB and SSǁGFB. Both electrodes were heat-treated to improve surface oxidation. On the 20th day of the operation, the GFBǁGFB arrangement generated the highest power density, up to 3.03 W/m³ (177 A/m³), followed by the SSǁGFB (0.0106 W/m³, 0.412 A/m³), the GFBǁSS (0.0283 W/m³, 17.1 A/m³), and the SSǁSS arrangements (0.0069 W/m⁻³, 1.64 A/m³). The GFBǁGFB had the lowest internal resistance (0.2 kΩ), corresponding to the highest power output. The other electrode arrangements, SSǁGFB, GFBǁSS, and SSǁSS, showed very high internal resistance (82 kΩ, 2.1 kΩ and 18 kΩ, respectively) due to the low proton and electron movement activity in the MFC systems. The results show that GFB materials can be used as anode and cathode in a fully biotic MFC system.     

Effect of concentration and particle size on nonlinearity of Au nano-fluid prepared by γ (Co) radiation

Nonlinear properties of Au nano-fluid prepared by γ-radiation method at different concentrations were investigated. Measurements of nonlinear refractive index and nonlinear absorption coefficient were carried out using a single beam Z-scan technique. A green CW laser beam operated at 532 nm was used as excitation source. The Au nano-fluid shows a good third order nonlinear response. The sign of the nonlinear refractive index is found to be negative and the magnitude is in the order of 10⁻⁷ cm²/W. This nonlinear effect increases as the concentration increases from 3.119 × 10⁻⁴ to 2.354 × 10⁻³ M which correspond to particle sizes of 4.0-30.5 nm, respectively. A good linear relationship was obtained between nonlinear refractive index and concentration. However the relationship between nonlinear refractive index and particle size was nonlinear behavior.     

Dynamic magnetic properties of the kinetic cylindrical Ising nanotube

The nature (time variation) of response magnetizations m(wt) of the kinetic cylindrical Ising nanotube in the presence of a periodically varying external magnetic field h(wt) is studied by employing the effective-field theory (EFT) with correlations as well as the Glauber-type stochastic dynamics. We have determined the time variations of m(wt) and h(wt) for various temperatures, and investigated the dynamic magnetic hysteresis behavior. Temperature dependence of the dynamic magnetizations, hysteresis loop areas and correlations are investigated in order to characterize the nature (first- or second-order) of the dynamic transitions as well as to obtain the dynamic phase transition temperatures. We also present the dynamic phase diagrams in the three different planes and compare the results of the equilibrium and nonequilibrium states. The phase diagrams exhibit dynamic tricritical, isolated critical, multicritical and triple points. The results are in good agreement with some experimental and theoretical results.     

Mechanical and Thermal Properties Enhancement of Poly(Lactic Acid)/Halloysite Nanocomposites by Maleic-Anhydride Functionalized Rubber

Poly(lactic acid) (PLA)/halloysite composites were prepared using melt compounding followed by compression molding. Maleic anhydride grafted styrene-ethylene/butylene-styrene (SEBS-g-MAH) was used to toughen the PLA composites. The mechanical properties of the PLA composites were studied through tensile, flexural, and impact tests. The thermal properties were characterized by using differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The fracture surfaces of the composites were assessed by using field emission scanning electron microscopy (FESEM). The impact strength and thermal properties of the PLA/halloysite composites were increased by addition of SEBS-g-MAH.     

Krasnoselski-Mann type iterative method for hierarchical fixed point problem and split mixed equilibrium problem

In this paper, we suggest and analyze a Krasnoselski-Mann type iterative method to approximate a common element of solution sets of a hierarchical fixed point problem for nonexpansive mappings and a split mixed equilibrium problem. We prove that sequences generated by the proposed iterative method converge weakly to a common element of solution sets of these problems. Further, we derive some consequences from our main result. Furthermore, we extend the considered iterative method to a split monotone variational inclusion problem and deduce some consequences. Finally, we give a numerical example to justify the main result. The method and results presented in this paper generalize and unify the corresponding known results in this area.     

Spatially Homogeneous Bianchi Type V Cosmological Model in the Scale-Covariant Theory of Gravitation

We discuss spatially homogeneous and anisotropic Bianchi type-V spacetime filled with a perfect fluid in the framework of the seale-covariant theory of gravitation proposed by Canuto et al. By applying the law of variation for Hubble＇s parameter, exact solutions of the field equations are obtained, which correspond to the model of the universe having a big-bang type singularity at the initial time t = 0. The cosmological model, evolving from the initial singularity, expands with power-law expansion and gives essentially an empty space for a large time. The physical and dynamical properties of the model are also discussed.