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.     

Synthesis,spectroscopic, DFT,biological studies and molecular docking of oxovanadium (IV), copper (II) and iron (III) complexes of a new hydrazone derived from heterocyclic hydrazide

A new Schiff base hydrazone (Z)‐2‐(2‐aminothiazol‐4‐yl)‐N′‐(2‐hydroxy‐3‐methoxybenzylidene) acetohydrazide (H₂L) and its chelates [VO (HL)₂]·5H₂O, [Cu (HL)Cl(H₂O)]·2H₂O and [Fe(L)Cl(H₂O)₂]·3H₂O have been isolated and characterized using different physico‐chemical methods, for example infrared (IR), electron paramagnetic resonance (EPR), thermogravimetric analysis and DTG in the solid state, and ¹H‐NMR, ¹³C‐NMR and UV in solution. Magnetic and UV–visible measurements proposed that the coordination environments are square pyramidal, tetrahedral and octahedral geometries for oxovanadium (IV), Cu (II) and Fe (III), respectively. The ligand acts as mono‐negative NO towards oxovanadium (IV) and Cu (II) ions, and bi‐negative ONO for Fe (III) ion. The geometries of the ligand and its complexes were performed using Gaussian 9 program with density functional theory. The EPR spectral data of oxovanadium (IV) and Cu (II) chelates confirmed the mentioned geometries. The molecular modeling was done, and illustrated bond lengths, bond angles, molecular electrostatic potential, Mulliken atomic charges and chemical reactivity for the inspected compounds. Theoretical IR and ¹H‐NMR of the free ligand were calculated. Furthermore, thermodynamic and kinetic parameters for thermal decomposition steps were studied. Docking study of H₂L was applied against the proteins of both bacterial strains Staphylococcus aureus and Escherichia coli, as well as the protein of xanthine oxidase as antioxidant agent by Schrödinger suite program utilizing XP glide protocol. Furthermore, antimicrobial, antioxidant and DNA‐binding activities of the compounds have been carried out.     

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.

     

Comparative analysis of refrigerant performance between LPG and R134a under subtropical climate

Journal of Thermal Analysis and Calorimetry - In this investigation, a series of experiments were conducted to explore the effects of liquefied petroleum gas (LPG) mixture of 60% propane and 40%...     

Divalent manganese,cobalt, copper and cadmium complexes of (Z)‐N‐benzoyl‐N′‐(1H‐1,2,4‐triazol‐3‐yl)carbamimidothioic acid: Preparation,characterization, computational and biological studies

In this work, (Z)‐N‐benzoyl‐N′‐(1H‐1,2,4‐triazol‐3‐yl)carbamimidothioic acid and its Mn(II), Co(II), Cu(II) and Cd(II) complexes were introduced for the first time. This carbonyl thiourea ligand was prepared by the reaction of 1H‐1,2,4‐triazol‐3‐amine with benzoyl isothiocyanate. The structural elucidation of these compounds was performed using elemental analysis and spectral and magnetic measurements. Octahedral structures of all complexes, except Cd(II) complex with a tetrahedral geometry, were confirmed by applying DFT structural optimization. The thermal decomposition behaviour of metal complexes of carbonyl thiourea ligand is discussed. The calculation of kinetic parameters for prepared complexes (E_a, A, ΔH*, ΔS* and ΔG*) of all thermal degradation stages has been evaluated using two comparable approaches. Antimicrobial and ABTS‐antioxidant studies indicated potent activity of Cd(II) complex compared with the other investigated compounds. The cytotoxic activity of the prepared compounds was investigated in vitro. The results indicated potent activity of Mn(II) complex against both HePG2 (liver carcinoma) and MCF‐7 (breast carcinoma) cancer cells.     

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.