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The present study aims at the integration of the “oxalic conversion” route into “green chemistry” for the synthesis of copper oxide nanoparticles (CuO-NPs) with controllable structural, morphological, and magnetic properties. Two oxalate-containing precursors (H2C2O4.2H2O and (NH4)2C2O4.H2O) and different volume ratios of a mixed water/glycerol solvent were tested. First, the copper oxalates were synthesized and then subjected to thermal decomposition in air at 400 °C to produce the CuO powders. The purity of the samples was confirmed by X-ray powder diffraction (XRPD), and the crystallite sizes were calculated using the Scherrer method. The transmission electron microscopy (TEM) images revealed oval-shaped CuO-NPs, and the scanning electron microscopy (SEM) showed that morphological features of copper oxalate precursors and their corresponding oxides were affected by the glycerol (V/V) ratio as well as the type of C2O42− starting material. The magnetic properties of CuO-NPs were determined by measuring the temperature-dependent magnetization and the hysteresis curves at 5 and 300 K. The obtained results indicate the simultaneous coexistence of dominant antiferromagnetic and weak ferromagnetic behavior.

Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.

Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of research. Dynamic load balancing is feasible in distributed computing systems since it is a significant key to maintaining stability of heterogeneous distributed computing systems (HDCS). The challenge of load balancing is an objective function of optimization with exponential complexity of solutions. The problem of dynamic load balancing raises with the scale of the HDCS and it is hard to tackle effectively. The solution to this unsolvable issue is being explored under a particular algorithm paradigm. A new codification strategy, namely hybrid nearest-neighbor ant colony optimization (ACO-NN), which, based on the metaheuristic ant colony optimization (ACO) and an approximate nearest-neighbor (NN) approaches, has been developed to establish a dynamic load balancing algorithm for distributed systems. Several experiments have been conducted to explore the efficiency of this stochastic iterative load balancing algorithm; it is tested with task and nodes accessibility and proved to be effective with diverse performance metrics.

This paper investigates the characteristics of web server response delay in order to understand and analyze the optimisation techniques of reducing latency. The analysis of the latency behavior for multi-process Apache HTTP server with different thread count and various workloads, was made. It was indicated, that the insufficient number of threads used by the server handling the concurrent requests of clients, is responsible for increasing latency under various loads. The problem can be solved by using a modified web server configuration allowing to reduce the response time.