New product launch decisions with robust optimization

We consider a problem where a company must decide the order in which to launch new products within a given time horizon and budget constraints, and where the parameters of the adoption rate of these new products are subject to uncertainty. This uncertainty can bring significant change to the optimal launch sequence. We present a robust optimization approach that incorporates such uncertainty on the Bass diffusion model for new products as well as on the price response function of partners that collaborate with the company in order to bring its products to market. The decision-maker optimizes his worst-case profit over an uncertainty set where nature chooses the time periods in which (integer) units of the budgets of uncertainty are used for worst impact. This leads to uncertainty sets with binary variables. We show that a conservative approximation of the robust problem can nonetheless be reformulated as a mixed integer linear programming problem, is therefore of the same structure as the deterministic problem and can be solved in a tractable manner. Finally, we illustrate our approach on numerical experiments. Our model also incorporates contracts with potential commercialization partners. The key output of our work is a sequence of product launch times that protects the decision-maker against parameter uncertainty for the adoption rates of the new products and the response of potential partners to partnership offers.     

The variational iteration method for solitary patterns solutions of gBBM equation

We consider solitary patterns solutions of generalized Benjamin–Bona–Mahony equations (shortly gBBM). The variational iteration method (shortly VIM) is applied for the numerical solution subject to appropriate initial condition. The numerical solutions of our model equation are calculated in the form of convergence power series with easily computable components. The VIM performs extremely well in terms of accuracy, efficiently, simplicity, stability and reliability.     

Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance

Multivariate adaptive regression splines (MARS) has become a popular data mining (DM) tool due to its flexible model building strategy for high dimensional data. Compared to well-known others, it performs better in many areas such as finance, informatics, technology and science. Many studies have been conducted on improving its performance. For this purpose, an alternative backward stepwise algorithm is proposed through Conic-MARS (CMARS) method which uses a penalized residual sum of squares for MARS as a Tikhonov regularization problem. Additionally, by modifying the forward step of MARS via mapping approach, a time efficient procedure has been introduced by S-FMARS. Inspiring from the advantages of MARS, CMARS and S-FMARS, two hybrid methods are proposed in this study, aiming to produce time efficient DM tools without degrading their performances especially for large datasets. The resulting methods, called SMARS and SCMARS, are tested in terms of several performance criteria such as accuracy, complexity, stability and robustness via simulated and real life datasets. As a DM application, the hybrid methods are also applied to an important field of finance for predicting interest rates offered by a Turkish bank to its customers. The results show that the proposed hybrid methods, being the most time efficient with competing performances, can be considered as powerful choices particularly for large datasets.     

in vitro Selection of Aptamer for Imidacloprid Recognition as Model Analyte and Construction of a Water Analysis Platform

Pesticide use in agriculture is one of the threats to water safety. Therefore, detection of pesticide residues is crucial for human health. Compared to conventional chromatographic methods, aptasensors are promising tools for fast, cheap and sensitive detection of environmental contaminants. To the best of our knowledge, such an aptasensor has not been reported for imidacloprid (Imi) which is one of the most widely used pesticides. In order to meet this demand, we initially selected two novel aptamers designated as ‘Apta‐1’ and ‘Apta‐2’ by graphene oxide‐SELEX (GO‐SELEX) method. Then, these aptamers were used to fabricate the gold electrode‐based aptasensor platforms and characterized by using electrochemical methods such as cyclic voltammetry, and electrochemical impedance spectroscopy as well as X‐Ray photoelectron spectroscopy. It was found that the limit of detection value of Apta‐1 based sensor for the Imi was found better than Apta‐2 based system, although linear ranges were similar. Based on that finding, Apta‐1 based system was further tested against possible interference molecules. The proposed platform was successfully used for detection of very low concentrations of Imi in the range of ng/mL. Thus, it eliminates the need for sample pre‐treatment and enables a practical analysis in real wastewater samples.     

High-performance thin-layer chromatographic determination of trigonelline content in various extracts and different varieties of some commercial coffees available in the Saudi Arabian market

JPC – Journal of Planar Chromatography – Modern TLC - The proposed research study was undertaken to establish a simple, rapid and sensitive high-performance thin-layer chromatography...     

Restructuring forward step of MARS algorithm using a new knot selection procedure based on a mapping approach

In high dimensional data modeling, Multivariate Adaptive Regression Splines (MARS) is a popular nonparametric regression technique used to define the nonlinear relationship between a response variable and the predictors with the help of splines. MARS uses piecewise linear functions for local fit and apply an adaptive procedure to select the number and location of breaking points (called knots). The function estimation is basically generated via a two-stepwise procedure: forward selection and backward elimination. In the first step, a large number of local fits is obtained by selecting large number of knots via a lack-of-fit criteria; and in the latter one, the least contributing local fits or knots are removed. In conventional adaptive spline procedure, knots are selected from a set of all distinct data points that makes the forward selection procedure computationally expensive and leads to high local variance. To avoid this drawback, it is possible to restrict the knot points to a subset of data points. In this context, a new method is proposed for knot selection which bases on a mapping approach like self organizing maps. By this method, less but more representative data points are become eligible to be used as knots for function estimation in forward step of MARS. The proposed method is applied to many simulated and real datasets, and the results show that it proposes a time efficient forward step for the knot selection and model estimation without degrading the model accuracy and prediction performance.     

Novel HPTLC-densitometric method for concurrent quantification of linalool and thymol in essential oils

Monoterpenes, linalool and thymol have been widely used in not only the treatments of several diseases but also food flavouring, dental, perfumery, and toilet products. Plants of the several Lamiaceae family are good sources of linalool and thymol. A rapid, simple, and precise high-performance thin layer chromatography (HPTLC) method was developed for the concurrent estimation of linalool and thymol, and this method was orchestrated using the essential oils extracted from two Lamiaceae plants leaves i.e. O. basilicum and T. vulgaris. Two standards were separated using pre-coated thin-layer chromatography glass plates. The developed HPTLC method was validated by following ICH guidelines (linearity; limit of detection, LOD; limit of quantitation; specificity; accuracy; precision; and robustness). The calibration curves of both the compounds were linear (100–700 ng/spot), with a correlation coefficient (r²) of >999. The developed HPTLC method was effectively applied to the concurrent detection and quantification of linalool and thymol in commercial essential oils.     

Solubilization of Trans-Resveratrol in Some Mono-Solvents and Various Propylene Glycol + Water Mixtures

This research deals with the determination of solubility, Hansen solubility parameters, dissolution properties, enthalpy–entropy compensation, and computational modeling of a naturally-derived bioactive compound trans-resveratrol (TRV) in water, methanol, ethanol, n-propanol, n-butanol, propylene glycol (PG), and various PG + water mixtures. The solubility of TRV in six different mono-solvents and various PG + water mixtures was determined at 298.2–318.2 K and 0.1 MPa. The measured experimental solubility values of TRV were regressed using six different computational/theoretical models, including van’t Hoff, Apelblat, Buchowski–Ksiazczak λh, Yalkowsly–Roseman, Jouyban–Acree, and van’t Hoff–Jouyban–Acree models, with average uncertainties of less than 3.0%. The maxima of TRV solubility in mole fraction was obtained in neat PG (2.62 × 10⁻²) at 318.2 K. However, the minima of TRV solubility in the mole fraction was recorded in neat water (3.12 × 10⁻⁶) at 298.2 K. Thermodynamic calculation of TRV dissolution properties suggested an endothermic and entropy-driven dissolution of TRV in all studied mono-solvents and various PG + water mixtures. Solvation behavior evaluation indicated an enthalpy-driven mechanism as the main mechanism for TRV solvation. Based on these data and observations, PG has been chosen as the best mono-solvent for TRV solubilization.     

Carduus edelbergii Rech. f. Mediated Fabrication of Gold Nanoparticles; Characterization and Evaluation of Antimicrobial,Antioxidant and Antidiabetic Potency of the Synthesized AuNPs

Background: Due to the high expense, less effectiveness and more side effects of available synthetic medicine, the researchers and communities are focusing on phyto-based natural bioactive compounds, which are considered safer for the treatment of syndromes and chronic diseases. Aim: The current project was aimed to determine the phytochemicals constituents available in the aerial parts of methanol extract of Carduus edelbergii via GC-MS, fabrication of AuNPs mediated with the mentioned extract; characterization and evaluation of antimicrobial, antioxidant and antidiabetic potency of the synthesized AuNPs. Methods: Confirmation of green synthesis of AuNPs, functional groups responsible for the reduction in Au⁺, size and crystallinity, morphology and quantity of gold (Au) were carried out by Ultraviolet-Visible (UV-Vis) spectroscopy, Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD) and dispersive X-ray (EDX), respectively, whereas in vitro antioxidant characteristics were assessed by DPPH and ABTS assays. Wistar albino rats were used to test the anti-diabetic properties of the methanol extract and AuNPs. Results: GC-MS revealed that the diluted methanol extract of Carduus edelbergii consists of about 19 chemical constituents. Among the identified compounds, the 13-Docosenoic acid, methyl ester, (Z)—has the highest concentration (38.16%), followed by 9-Octadecenoic acid, methyl ester, (E)—(15.72%) and n-Hexadecanoic acid (15.07%). Methanol extract and its fabricated nanoparticles showed significant antioxidant and antimicrobial activities. In vivo antidiabetic study revealed a noteworthy (p < 0.05) decline in body weight and HDL and elevated concentration of blood glucose, bilirubin, creatinine, urea, triglyceride, VLDL, LDL, ALP, ALT and AST in diabetic control. The said changes were recovered significantly (p < 0.05) by treatment of diabetic rats with methanol extract (150 and 300 mg/Kg BW) and AuNPs of Carduus edelbergii (5 and 10 mg/Kg BW). Conclusion: The green synthesized AuNPs exhibit significant antioxidant, antimicrobial and antidiabetic characteristics.     

Sensor Platform with a Custom‐tailored Aptamer for Diagnosis of Synthetic Cannabinoids

Synthetic cannabinoids (SCs) are the large group of abused drugs and detection of them is still a challenge. Hence, new methods for analysis of SCs are being investigated. We aimed to develop a novel system for selective analysis of SCs. First, various custom‐tailored aptamers against the target SCs were selected through GO‐SELEX process. Toggling between different SC analytes during successive rounds of selection was performed to generate cross‐reactive aptamers. Then, the amino‐capped aptamers were synthesized and easily attached to the cysteamine‐covered gold electrodes. Analytical parameters and selectivity of the aptasensors were compared by using electrochemical techniques. After comparison of the analytical features and selectivity towards target analytes, one of the aptamers designated as Apta‐1 was chosen for further measurements. The aptasensor was tested by using differential pulse voltammetry technique against JWH‐018 (5‐pentanoic acid), selected as a model for SCs. The linearity and limit of detection were determined as 0.01–1.0 ng/mL and 0.036 ng/mL. Finally, sample application in synthetic urine samples was successfully performed with standard addition method, as confirmed by LC‐QTOF/MS. JWH‐018 (4‐hydroxypentyl), JWH‐073 (3‐hydroxybutyl), JWH‐250 (5‐hidroxypentyl) and HU‐210 were used to test the selectivity of the aptasensor and the system was shown to recognize all these SCs. Also other illegal drugs did not significantly interfere with the signal responses.