Regression and ANN models for estimating minimum value of machining performance

Surface roughness is one of the most common performance measurements in machining process and an effective parameter in representing the quality of machined surface. The minimization of the machining performance measurement such as surface roughness (R_a) must be formulated in the standard mathematical model. To predict the minimum R_a value, the process of modeling is taken in this study. The developed model deals with real experimental data of the R_a in the end milling machining process. Two modeling approaches, regression and Artificial Neural Network (ANN), are applied to predict the minimum R_a value. The results show that regression and ANN models have reduced the minimum R_a value of real experimental data by about 1.57% and 1.05%, respectively.     

Design,synthesis, and biological evaluation of indolylidinepyrazolones as potential anti-bacterial agents

A series of indolylidinepyrazolones were synthesized using a simple, green, and effective route and evaluated as anti-bacterial agents. The compounds were further studied via structure-guided docking study. One of the compounds exhibiting H-bonding interactions with conserved residue Arg144 turned out to be the most potent compound of the series. The minimum inhibitory concentration values ranged from 50 to 25 μg/mL against Staphylococcus aureus in their anti microbial evaluation.     

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.     

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 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.