A gadolinium-based magnetic ionic liquid for supramolecular dispersive liquid–liquid microextraction followed by HPLC/UV for the determination of favipiravir in human plasma

Favipiravir is a potential antiviral medication that has been recently licensed for Covid-19 treatment. In this work, a gadolinium-based magnetic ionic liquid was prepared and used as an extractant in dispersive liquid–liquid microextraction (DLLME) of favipiravir in human plasma. The high enriching ability of DLLME allowed the determination of favipiravir in real samples using HPLC/UV with sufficient sensitivity. The effects of several variables on extraction efficiency were investigated, including type of extractant, amount of extractant, type of disperser and disperser volume. The maximum enrichment was attained using 50 mg of the Gd-magnetic ionic liquid (MIL) and 150 μl of tetrahydrofuran. The Gd-based MIL could form a supramolecular assembly in the presence of tetrahydrofuran, which enhanced the extraction efficiency of favipiravir. The developed method was validated according to US Food and Drug Administration bioanalytical method validation guidelines. The coefficient of determination was 0.9999, for a linear concentration range of 25 to 1.0 × 10⁵ ng/ml. The percentage recovery (accuracy) varied from 99.83 to 104.2%, with RSD values (precision) ranging from 4.07 to 11.84%. The total extraction time was about 12 min and the HPLC analysis time was 5 min. The method was simple, selective and sensitive for the determination of favipiravir in real human plasma.     

One-pot Synthesis,Crystal Structures and Antimicrobial Activities of Two New 1,4-Disubstituted 1,2,3-Triazole-4-Carboxylates

Two new 1,4-disubstituted 1,2,3-triazoles-4-carboxylates were synthesized via click reaction. Compound 1a was synthesized by the interaction of 6-nitro-tetrazolo[1.5-a]-pyridine with ethyl propynoate at room temperature in the presence of Cu(OAc)2 as a catalyst and THF as solvent. Compound 1b was also synthesized by the same manner except that tert-butyl propionate, instead of ethyl propynoate, was used. The compounds were characterized by IR, 1H-NMR, 13C-NMR and single-crystal X-ray diffraction analysis. Compound 1a(C10H9N5O4) crystallizes in the triclinic system, space group P1 with a = 5.0894(9), b = 8.9834(13), c = 13.089(2) ?, α= 83.041(7), β= 80.256(7), γ=87.296(8)°, V = 585.24(16)?3, Z = 2, Mr = 263.22, crystal size(mm) = 0.37 × 0.20 ×0.18,(I 2σ(I)) = 8557, 2493, 1229, Rint = 0.057. Compound 1b(C12H13N5O4) crystallizes in the monoclinic system, space group P21/c with a = 6.8854(5), b = 21.783(2), c = 9.3986(8) ?,β = 93.239(4)°, V = 1407.4(2)?3, Z = 4, Mr = 291.27, crystal size(mm) = 0.38 × 0.22 × 0.20,(I 2σ(I)) = 11842, 3172, 1866, Rint = 0.047. Antimicrobial assay results showed that the title compounds display excellent activities to different bacterial and fungal strains.     

Analytic hierarchy process for multi-sensor data fusion based on belief function theory

Multi-sensor data fusion is an evolving technology whereby data from multiple sensor inputs are processed and combined. The data derived from multiple sensors can, however, be uncertain, imperfect, and conflicting. The present study is undertaken to help contribute to the continuous search for viable approaches to overcome the problems associated with data conflict and imperfection. Sensor readings, represented by belief functions, have to be fused according to their corresponding weights. Previous studies have often estimated the weights of sensor readings based on a single criterion. Mono-criteria approaches for the assessment of sensor reading weights are, however, often unreliable and inadequate for the reflection of reality. Accordingly, this work opts for the use of a multi-criteria decision aid. A modified Analytical Hierarchy Process (AHP) that incorporates several criteria is proposed to determine the weights of a sensor reading set. The approach relies on the automation of pairwise comparisons to eliminate subjectivity and reduce inconsistency. It assesses the weight of each sensor reading, and fuses the weighed readings obtained using a modified average combination rule. The efficiency of this approach is evaluated in a target recognition context. Several tests, sensitivity analysis, and comparisons with other approaches available in the literature are described.     

Path integral quantization corresponding to the deformed Heisenberg algebra

In this paper, the deformation of the Heisenberg algebra, consistent with both the generalized uncertainty principle and doubly special relativity, has been analyzed. It has been observed that, though this algebra can give rise to fractional derivative terms in the corresponding quantum mechanical Hamiltonian, a formal meaning can be given to them by using the theory of harmonic extensions of function. Depending on this argument, the expression of the propagator of the path integral corresponding to the deformed Heisenberg algebra, has been obtained. In particular, the consistent expression of the one dimensional free particle propagator has been evaluated explicitly. With this propagator in hand, it has been shown that, even in free particle case, normal generalized uncertainty principle and doubly special relativity show very much different result.     

Sampling with a String

Kramer's sampling theorem forms a bridge between the Whittaker-Shannon-Kotel'nikov sampling theorem and boundary-value problems. It has been shown that sampling expansions associated with Sturm-Liouville boundary-value problems are Lagrange-type sampling series, i.e., Lagrange series with infinitely many terms converging to entire functions. String theory as developed by Feller, Kac, and Krein, is a generalization of the Sturm-Liouville theory. We investigate sampling series associated with strings and compare them with those associated with Sturm-Liouville problems. We show that unlike sampling series associated with Sturm-Liouville problems, those associated with strings include not only Lagrange-type sampling series, but also Lagrange polynomial interpolation.     

On Quantum Team Games

Recently Liu and Simaan (2004) convex static multi-team classical games have been introduced. Here they are generalized to both nonconvex, dynamic and quantum games. Puu's incomplete information dynamical systems are modified and applied to Cournot team game. The replicator dynamics of the quantum prisoner's dilemma game is also studied.     

Synthèse de nouveaux dérivés tétrahydroquinoléines et quinoléines via la réaction d'aza-Diels-Alder suivie d'aromatisation

The Aza-Diels-Alder reaction of aldimines with isoeugenol is described. New 1,2,3,4-tetrahydroquinoline and quinoline structures are reported. The stereo- and regioselectivities of the [4+2] cycloaddition are discussed.     

The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study

The sample average approximation (SAA) method is an approach for solving stochastic optimization problems by using Monte Carlo simulation. In this technique the expected objective function of the stochastic problem is approximated by a sample average estimate derived from a random sample. The resulting sample average approximating problem is then solved by deterministic optimization techniques. The process is repeated with different samples to obtain candidate solutions along with statistical estimates of their optimality gaps.We present a detailed computational study of the application of the SAA method to solve three classes of stochastic routing problems. These stochastic problems involve an extremely large number of scenarios and first-stage integer variables. For each of the three problem classes, we use decomposition and branch-and-cut to solve the approximating problem within the SAA scheme. Our computational results indicate that the proposed method is successful in solving problems with up to 2¹⁶⁹⁴ scenarios to within an estimated 1.0% of optimality. Furthermore, a surprising observation is that the number of optimality cuts required to solve the approximating problem to optimality does not significantly increase with the size of the sample. Therefore, the observed computation times needed to find optimal solutions to the approximating problems grow only linearly with the sample size. As a result, we are able to find provably near-optimal solutions to these difficult stochastic programs using only a moderate amount of computation time.