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.

     

Medicinal Potential of Isoflavonoids: Polyphenols That May Cure Diabetes

In recent years, there is emerging evidence that isoflavonoids, either dietary or obtained from traditional medicinal plants, could play an important role as a supplementary drug in the management of type 2 diabetes mellitus (T2DM) due to their reported pronounced biological effects in relation to multiple metabolic factors associated with diabetes. Hence, in this regard, we have comprehensively reviewed the potential biological effects of isoflavonoids, particularly biochanin A, genistein, daidzein, glycitein, and formononetin on metabolic disorders and long-term complications induced by T2DM in order to understand whether they can be future candidates as a safe antidiabetic agent. Based on in-depth in vitro and in vivo studies evaluations, isoflavonoids have been found to activate gene expression through the stimulation of peroxisome proliferator-activated receptors (PPARs) (α, γ), modulate carbohydrate metabolism, regulate hyperglycemia, induce dyslipidemia, lessen insulin resistance, and modify adipocyte differentiation and tissue metabolism. Moreover, these natural compounds have also been found to attenuate oxidative stress through the oxidative signaling process and inflammatory mechanism. Hence, isoflavonoids have been envisioned to be able to prevent and slow down the progression of long-term diabetes complications including cardiovascular disease, nephropathy, neuropathy, and retinopathy. Further thoroughgoing investigations in human clinical studies are strongly recommended to obtain the optimum and specific dose and regimen required for supplementation with isoflavonoids and derivatives in diabetic patients.     

Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO-LiPF6-EC-CNT)

In this study, the ionic conductivity of a nanocomposite polymer electrolyte system (PEO-LiPF₆-EC-CNT), which has been produced using solution cast technique, is obtained using artificial neural networks approach. Several results have been recorded from experiments in preparation for the training and testing of the network. In the experiments, polyethylene oxide (PEO), lithium hexafluorophosphate (LiPF₆), ethylene carbonate (EC) and carbon nanotubes (CNT) are mixed at various ratios to obtain the highest ionic conductivity. The effects of chemical composition and temperature on the ionic conductivity of the polymer electrolyte system are investigated. Electrical tests reveal that the ionic conductivity of the polymer electrolyte system varies with different chemical compositions and temperatures. In neural networks training, different chemical compositions and temperatures are used as inputs and the ionic conductivities of the resultant polymer electrolytes are used as outputs. The experimental data is used to check the system’s accuracy following the training process. The neural network is found to be successful for the prediction of ionic conductivity of nanocomposite polymer electrolyte system.     

Synthesis and Coordination Behavior of a New Hybrid Bidentate Ligand with Phosphine and Silylene Donors

This work describes the synthesis and coordination behavior of a new mixed-donor ligand PhC(NtBu)₂SiC₆H₄PPh₂ ( 1 ) containing both silylene and phosphine donor sites. Ligand 1 was synthesized from a reaction of ortho-lithiated diphenylphosphinobenzene (LiC₆H₄PPh₂) with chlorosilylene (PhC(NtBu)₂SiCl). Treatment of 1 with Se and GeCl₂ resulted in Si^IV compounds 2 and 3 by selective oxidation of the silylene donor. This strong σ-donor ligand induces dissociation of CuCl and PhBCl₂ leading to formation of ionic complexes 4 and 5 respectively. The reaction of 1 with ZnCl₂ and AlCl₃ resulted in the formation of chelate complexes 5 and 7 , respectively, while treatment with EtAlCl₂ and GaCl₃ forms monodentate complexes 8 and 9 . X-ray analysis of 4 showed that the copper is in the spiro center of the two five-membered rings. Moreover, the copper(I)chloride has not been oxidized but dissociates to Cu⁺ and [CuCl₂]⁻. All the compounds are well characterized by mass spectrometry, elemental analysis, NMR spectroscopy, and single-crystal X-ray diffraction studies.     

Temperature dependence of turn-on time delay of semiconductor laser diode: Theoretical analysis

Temperature dependence of the turn-on time delay (t_on) of uncooled semiconductor laser diodes biased below and above threshold is analyzed in presence of data pattern effect. We show that even when the laser is biased at or slightly above threshold, the increase in temperature of operation will lead to increase in the threshold carrier (N_th) and consequently the laser diode will be biased below the threshold again and a significant value of t_on will be produced. Thus, knowledge about a value of dc-bias current required to achieve zero t_on within wide range of temperature degrees is important when considering uncooled laser diode in high-speed optical communication systems. The temperature dependence of t_on is calculated according to the temperature dependence of N_th and Auger recombination coefficient (C) and not by the well-know exponentional relationship of threshold current with temperature. The temperature dependence of N_th is calculated according to the temperature dependence of laser cavity parameters. Advanced analytical model is derived in term of carrier density, recombination coefficients and the injection current (I_inj). The validity of proposed model is confirmed by a numerical method. In addition, approximated models are included where under specified assumptions the proposed model reduces to the well-known approximate models of t_on. According to our typical values and at a specified value of modulation current, the dc-bias one (I_ib) should be increased from I_ib = I_th to I_ib ≈ 1.25 and 1.5I_th in order to achieve approximately zero t_on when the temperature increases from 25°C to 55°C and 85°C, respectively.     

SYNTHESIS AND CHARACTERIzATION OF TRIPHENYLANTIMONY (V) (O-ALKYL,O-CYCLOALKYL AND O-ARYLTRITHIOPHOSPHATES)

Triphenylantimony (V) (O-alkyl,O-cycloalkyl and O-aryltrithiophosphates) of the type Ph ₃ Sb[S ₂ (S)P(OR)] (R = Me, Et, Pr ⁿ , Pr ⁱ , Bu ⁿ , Bu ^s , Bu ⁱ , Am ⁱ , Ph and C.h. = cyclohexyl) have been synthesized for the first time by the reaction of triphenylantimony (V) dibromide with potassium trithiophosphates in 1:1 molar ratio in methanol. These new compounds have been characterized by elemental analysis, molecular weight determinations, and spectroscopic (IR,¹³C and ³¹P NMR) studies. On the basis of these data trigonal bipyramidal geometry has been proposed for these compounds.     

A Grafting Study on Partially Dehydrochlorinated Poly(vinyl chloride) by Atom Transfer Radical Polymerization

HCl elimination in low ratio was first carried out from poly(vinyl chloride) to increase allylic chlorines. Partially dehydrochlorinated poly(vinyl chloride), having a macroinitiator effect, was grafted with tert‐butyl methacrylate via atom transfer radical polymerization in the presence of CuBr/2,2′‐bipyridine at 64°C in tetrahydrofuran. Original poly(vinyl chloride) was also grafted with tert‐butyl methacrylate under the same conditions to compare with that of partially dehydrochlorinated poly(vinyl chloride). The graft copolymers were characterized by elemental analysis, FTIR, ¹H and ¹³C‐NMR, differential scanning calorimetry, and gel permeation chromatography (GPC). Thermal stabilities of the graft copolymers were investigated by thermogravimetric analysis as compared with those of the macroinitiators.     

Sensitive voltammetric determination of paracetamol by poly (4-vinylpyridine)/multiwalled carbon nanotubes modified glassy carbon electrode

A novel glassy carbon electrode (GCE) modified with a composite film of poly (4-vinylpyridine) (P4VP) and multiwalled carbon nanotubes (P4VP/MWCNT GCE) was used for the voltammetric determination of paracetamol (PCT). This novel electrode displayed a combined effect of P4VP and MWCNT on the electro-oxidation of PCT in a solution of phosphate buffer at pH 7. Hence, conducting properties of P4VP along with the remarkable physical properties of MWCNTs might have combined effects in enhancing the kinetics of PCT oxidation. The P4VP/MWCNT GCE has also demonstrated excellent electrochemical activity toward PCT oxidation compared to that with bare GCE and MWCNT GCE. The anodic peak currents of PCT on the P4VP/MWCNT GCE were about 300 fold higher than that of the non-modified electrodes. By applying differential pulse voltammetry technique under optimized experimental conditions, a good linear ratio of oxidation peak currents and concentrations of PCT over the range of 0.02–450 μM with a limit of detection of 1.69 nM were achieved. This novel electrode was stable for more than 60 days and reproducible responses were obtained at 99% of the initial current of PCT without any influence of physiologically common interferences such as ascorbic acid and uric acid. The application of this electrode to determine PCT in tablets and urine samples was proposed.     

A simple microextraction and preconcentration approach based on a mixed matrix membrane

A simple adsorption/desorption procedure using a mixed matrix membrane (MMM) as extraction medium is demonstrated as a new miniaturized sample pretreatment and preconcentration technique. Reversed-phase particles namely polymeric bonded octadecyl (C₁₈) was incorporated through dispersion in a cellulose triacetate (CTA) polymer matrix to form a C₁₈-MMM. Non-steroidal anti-inflammatory drugs (NSAIDs) namely diclofenac, mefenamic acid and ibuprofen present in the environmental water samples were selected as targeted model analytes. The extraction setup is simple by dipping a small piece of C₁₈-MMM (7 mm × 7 mm) in a stirred 10 mL sample solution for analyte adsorption process. The entrapped analyte within the membrane was then desorbed into 100 μL of methanol by ultrasonication prior to high performance liquid chromatography (HPLC) analysis. Each membrane was discarded after single use to avoid any analyte carry-over effect. Several important parameters, such as effect of sample pH, salting-out effect, sample volume, extraction time, desorption solvent and desorption time were comprehensively optimized. The C₁₈-MMM demonstrated high affinity for NSAIDs spiked in tap and river water with relative recoveries ranging from 92 to 100% and good reproducibility with relative standard deviations between 1.1 and 5.5% (n = 9). The overall results obtained were found comparable against conventional solid phase extraction (SPE) using cartridge packed with identical C₁₈ adsorbent.