Synthesis and evaluation of novel 2-[(1,2,4-triazol-3-yl)thio]acetamide derivatives as potential serum paraoxonase-1 (PON1) activators

Coronary artery disease and low-density lipoprotein (LDL) levels in the blood have long been known to be associated with peripheral vascular diseases. Paraoxonase-1 (PON1) enzyme is related to serum levels of high-density lipoprotein (HDL) and protects LDL from oxidation which may result in development of microvascular disease in diabetes. The enzyme has a major role in the prevention of atherosclerosis besides antioxidant properties. Additionally, PON1 is important in the detoxification of organophosphate insecticides from the body. In this study, we aimed to synthesize highly active new compounds which can be a drug candidate and evaluate their effects on PON1 activity. Nine novel triazole compounds bearing thioacetamide moiety (5a-i) were synthesized and their in vitro PON1 activity was investigated. The PON1 enzyme was purified from human serum using ammonium sulfate precipitation method. Also, it was further purified using Sepharose 4B-L-tyrosine- 1-naphthylamine affinity chromatography. Among the synthesized triazole compounds, 5b, 5c, 5f and 5h have been determined to increase PON1 activity, remarkably. Compounds 5b, 5c, 5f and 5h bearing 5-nitrothiazole, benzothiazole, 6-ethoxybenzothiazole and 6-florobenzothiazole moieties could be considered to proceed in vivo investigations which is a further stage for a drug candidate. © 2017, Marmara University. All rights reserved

A Critical Review of Multi-hole Drilling Path Optimization

Hole drilling is one of the major basic operations in part manufacturing. It follows without surprise then that the optimization of this process is of great importance when trying to minimize the total financial and environmental cost of part manufacturing. In multi-hole drilling, 70 % of the total process time is spent in tool movement and tool switching. Therefore, toolpath optimization in particular has attracted significant attention in cost minimization. This paper critically reviews research publications on drilling path optimization. In particular, this review focuses on three aspects; problem modeling, objective functions, and optimization algorithms. We conclude that most papers being published on hole drilling are simply basic Traveling Salesman Problems (TSP) for which extremely powerful heuristics exist and for which source code is readily available. Therefore, it is remarkable that many researchers continue developing “novel” metaheuristics for hole drilling without properly situating those approaches in the larger TSP literature. Consequently, more challenging hole drilling applications that are modeled by the Precedence Constrained TSP or hole drilling with sequence dependent drilling times do not much research focus. Sadly, these many low quality hole drilling research publications drown out the occasional high quality papers that describe specific problematic problem constraints or objective functions. It is our hope through this review paper that researchers’ efforts can be refocused on these problem aspects in order to minimize production costs in the general sense

A simulated annealing algorithm based solution method for a green vehicle routing problem with fuel consumption

This chapter presents a new G-VRP model that aims to reduce the fuel consumption of the vehicle’s gas tank. The fuel consumption of a vehicle is related to total vehicle weight through route and thus, this changes the CO2 levels as a result of the changes of total weight and distance for any arc {i, j} in the route. To minimize CO2 levels, a simulated annealing-based algorithm is proposed. About the experiments, firstly, we applied small-VRP problem set for defining the proposed algorithm and then, the Christofides et al. (Combinatorial optimization. Wiley, 1979) small/medium scale C1–C14 datasets are used with proposed G-VRP model and a convex composition solution with two objective functions. The proposed methods are compared with statistical analysis techniques to explain the statistical significance of solutions. The procedures are also tested using additional examples previously analyzed in the literature. The result has shown good solutions for minimizing the emitted CO2 levels

A simulated annealing algorithm based solution method for a green vehicle routing problem with fuel consumption

This chapter presents a new G-VRP model that aims to reduce the fuel consumption of the vehicle’s gas tank. The fuel consumption of a vehicle is related to total vehicle weight through route and thus, this changes the CO2 levels as a result of the changes of total weight and distance for any arc {i, j} in the route. To minimize CO2 levels, a simulated annealing-based algorithm is proposed. About the experiments, firstly, we applied small-VRP problem set for defining the proposed algorithm and then, the Christofides et al. (Combinatorial optimization. Wiley, 1979) small/medium scale C1–C14 datasets are used with proposed G-VRP model and a convex composition solution with two objective functions. The proposed methods are compared with statistical analysis techniques to explain the statistical significance of solutions. The procedures are also tested using additional examples previously analyzed in the literature. The result has shown good solutions for minimizing the emitted CO2 levels. © Springer Nature Switzerland AG 2019