A Dive into the Specific Electric Energy Consumption in Steelworks
The paper describes an application of optimization techniques for the minimization of the specific electrical energy consumption related to the production of steel for a steelworks situated in Italy. The major electrical consumption derives from two internal plants: the Electric Arc Furnace and the Ladle Furnace. This work addresses the problem of understanding the best settings (based on predefined models) to produce a specific steel, which is mainly characterized by its steelgrade and quality, with the minimum energy consumption.
Variable selection through Genetic algorithms for classification purposes
In many real world applications, when coping with classification tasks, the problem of the selection of the variables to be used for the development of any kind of classifier has to be faced. This necessity is normally due to the high number of variables which could be potentially included in the input set combined with the lack of a priori knowledge to support the selection process. In this paper variable selection is achieved by means of the use of GAs through a selection process based on the evaluation of the performance of the possible variable combinations used to train a decision tree. Furthermore the proposed method optimizes some parameters of the employed classifier. Within the proposed method several different approaches have been tested on a real industrial problem. The proposed approaches, which are characterized by different initialization and fitness functions of the GAs, obtain very satisfactory results.
Atomistic insight into novel co-delivery of doxorubicin and paclitaxel using fullerene modified by dimethyl acrylamide trimethyl chitosan: A computational study

 Background: The distribution of drugs could not be controlled in the conventional drug delivery system. This has led to the development of a smart nanoparticle-based drug delivery system, known as smart drug delivery systems. In cancer research, novel biocompatible nanocarriers have received much attention for various ranges of anticancer drugs.In the current study, the effect of a novel co-polymer "dimethyl acrylamide-trimethyl chitosan" was investigated on drug delivery of doxorubicin and paclitaxel utilizing modified fullerene nanocarrier. This study was performed via molecular dynamics simulation based on acidic pH sensitivity of the cancer microenvironment. Furthermore, hydrogen bonds, diffusion coefficient, gyration radius, and drug-carrier interaction energies were investigated here. Interestingly, a simultaneous pH and temperature-sensitive system was proposed for paclitaxel and doxorubicin for a co-polymer. A pH-sensitive and temperature-sensitive copolymer was used based on trimethyl chitosan and dimethyl acrylamide, respectively. In such a dualistic approach, co-polymer makes an excellent option to have two properties in one bio-polymer. Results: The results suggest the dramatic and indisputable role of the co-polymer in the release of doxorubicin and paclitaxel in cancerous tissues, as well as an increased biocompatibility and drug uptake in a neutral state. The validation test was performed by repeated simulations of a similar article. The results are very close to those of the reference paper .Conclusions: Overall, conjugated fullerene and dimethyl acrylamide-trimethyl chitosan (DMAA-TMC) as carriers can be a good proposition for loading, delivery, and release of anti-cancer drugs based on a pH/temperature-sensitive smart drug delivery system.