Estimating the rate constant in second-order kinetics using hard-soft-net analyte signal (HS-NAS) method

Rank deficiency is the major problem associated with the chemometrics modeling of the second-order chemical reactions of the form of A + B → C. In this article, the application of the hard-soft-net analyte signal (HS-NAS), as a newly proposed method in our research group, is described for modeling of second-order reactions. This combined hard-soft method is based on the net analyte signal (NAS) concept, which is defined as a part of total signal that is directly related to the concentration of the component of interest. Therefore, concentration changes versus time can be obtained by calculating NAS for any chemical species involved in the reaction without requiring any pure component spectra or extra runs. The power of the method was verified by applying it to the resolution of simulated data sets containing noises added at different levels. The resolution of the second-order reaction between amoxicillin and 1, 2-naphthoqoinone was also tested as a real chemical system.     

Numerical simulation of tensile strength of upset welded joints with experimental verification

Resistance upset welding (UW) is a widely used process for joining metal parts. In this process current, time and upsetting force are three parameters that affect the quality of welded products. This paper considers numerical simulation and experimental investigation of UW process parameters. The investigated parameters include heating and post-weld heating current and their corresponding duration as well as interference of the part features that form the joint. In this study, evaluation of tensile strength of the welded joint with variation of the process parameters is also reported. For numerical analysis, a two-dimensional axisymmetric model using a coupled electro-thermal finite element method is developed to study the thermal behavior of the welded joints. The results of this numerical simulation are used to determine the status of the weldment and therefore evaluate the quality of the weld at the joint. Both numerical and experimental results suggest an optimum set of welding parameters, i.e. time and electrical current that yields a maximum value for the tensile strength of the joint. Also the effects of post-weld heating time and current on the tensile strength are evaluated and show that these parameters have a remarkable effect on improving tensile strength of the weldment.     

Improved Room Temperature Plasticity of Zr41.2Ti13.8Cu12.5Ni10Be22.5 Bulk Metallic Glass by Channel‐Die Compression

In this work, the effect of channel‐die compression (CDC) on the mechanical behavior of the Zr_41.2Ti_13.8Cu_12.5Ni₁₀Be_22.5 bulk metallic glass is analyzed. The results indicate that CDC can be successfully used as a pre‐deformation process to effectively enhance the room temperature plastic strain ability of metallic glasses. The origin of the improved mechanical properties is most likely due to the creation during CDC of a heterogenous microstructure consisting of hard and soft regions able to hinder the rapid propagation of shear bands.     

Aqueous preparation and evaluation of albumin-chitosan microspheres containing indomethacin

Controlled-release egg albumin-chitosan microspheres containing indomethacin as a model drug were successfully prepared by coacervation method. The proposed method can offer a simple method for microsphere preparation in an aqueous system with the elimination of the use of organic solvents that are usually needed in conventional techniques of microencapsulation. The interaction between negatively charged egg albumin molecules in phosphate buffer, pH 7.2, or sodium hydroxide solution and positively charged chitosan molecules dissolved in diluted acetic acid to form an insoluble precipitate was the principle for the formation of the microspheres. The effects of many process variables, such as amount of formaldehyde as a cross-linking agent, stirring time, final pH of encapsulation medium, initial drug loading, and albumin concentration or albumin-to-chitosan weight ratio, on the properties of the prepared microspheres were investigated. Incorporation efficiencies of the microspheres to the drug were high in most cases and ranged between 63.3 ± 3.6% and 92.39 ± 3.2%, while particle sizes were 435.2 ± 12.6 up to 693.9 ± 34.6 µm for the different tested batches. On the other hand, the values of angles of repose and compressibility indices were in the range of 23.5 ± 0.4 to 32.0 ± 0.7 degrees and 11.1 ± 0.7% to 23.6 ± 0.7% respectively, which indicate overall good free flowing nature of the microspheres of all batches. The maximum required amount of the cross-linking agent was determined to avoid excessive unnecessary chemicals. It was also noticed that excessive time of stirring and excessive initial drug loading are not recommended as it may lead to microspheres of low properties. The pH of the encapsulation media (pH 3.77 up to pH 4.91) significantly affected the properties of the microspheres. As the pH of the encapsulation media was increased, the incorporation efficiency, particle size, and flowability decreased, along with increase of drug release rate, which could be related to incomplete cross linking of the microspheres matrix. It was also observed that high concentration of albumin solution and accordingly the increase of albumin-to-chitosan weight ratio were accompanied with increases in incorporation efficiency and particle size with improved microsphere flowability and slow indomethacin release. Thus, the proposed microspheres showed the ability to control the release of indomethacin, and their properties were highly affected by many process variables that could be controlled to obtain an optimized system.     

Trends in intelligent manufacturing research: a keyword co-occurrence network based review

Journal of Intelligent Manufacturing - In recent years, driven by Industry 4.0 wave, academic research has focused on the science, engineering, and enabling technologies for intelligent and cyber...     

Concept learning games

In this paper, we intend to have a game theoretic study on the concept learning problem in a multi-agent system. Concept learning is a very essential and well-studied domain of machine learning when it is studied under the characteristics of a multi-agent system. The most important reasons are the partiality of the environment perception for any agent and also the communication holdbacks, resulting into a deep need for a collaborative protocol in favor of multi-agent transactions. Here we wish to investigate multi-agent concept learning with the help of its components, thoroughly with a game theoretic taste, esp. on the pre-learning processes. Based on two standard notations, we address the non-unanimity of concepts, classification of objects, voting and communicating protocol, and also the learning itself. In such a game of concept learning, we consider a group of agents, communicating and consulting to upgrade their ontologies based on their conceptualizations of the environment. For this purpose, we investigate the problem in two separate and standard distinctions of game theory study, cooperation and competition. Several solution concepts and innovative ideas from the multi-agent realm are used to produce an approach that contains the reasoning process of the agents in this system. Some experimentations come at the end to show the functionality of our approach. These experimentations come distinctly for both cooperative and competitive views.     

A semantic social network-based expert recommender system

This research work presents a framework to build a hybrid expert recommendation system that integrates the characteristics of content-based recommendation algorithms into a social network-based collaborative filtering system. The proposed method aims at improving the accuracy of recommendation prediction by considering the social aspect of experts’ behaviors. For this purpose, content-based profiles of experts are first constructed by crawling online resources. A semantic kernel is built by using the background knowledge derived from Wikipedia repository. The semantic kernel is employed to enrich the experts’ profiles. Experts’ social communities are detected by applying the social network analysis and using factors such as experience, background, knowledge level, and personal preferences. By this way, hidden social relationships can be discovered among individuals. Identifying communities is used for determining a particular member’s value according to the general pattern behavior of the community that the individual belongs to. Representative members of a community are then identified using the eigenvector centrality measure. Finally, a recommendation is made to relate an information item, for which a user is seeking an expert, to the representatives of the most relevant community. Such a semantic social network-based expert recommendation system can provide benefits to both experts and users if one looks at the recommendation from two perspectives. From the user’s perspective, she/he is provided with a group of experts who can help the user with her/his information needs. From the expert’s perspective she/he has been assigned to work on relevant information items that fall under her/his expertise and interests.     

Multi-criteria expertness based cooperative Q-learning

One of the most influential points in cooperative learning is the type of exchanging information. If the content of exchanging information among agents is rich, cooperation gives rise to better results. To extract proper knowledge of agents during the cooperation process, some expertness measures that assign expertness levels to the other agents are used. In this paper, a new method named Multi-Criteria Expertness based cooperative Q-learning (MCE) is proposed that utilizes all of the expertness measures and attempts to enrich the exchanging information more efficiently. In MCE, all expertness measures are considered simultaneously and collective knowledge is equal to the combination of learned knowledge by each of expertness measures. The experimental results confirm outstanding performance of the proposed method on a sample maze world and a hunter-prey problem.     

ETAS: predictive scheduling of functions on worker nodes of Apache OpenWhisk platform

The Journal of Supercomputing - Fast execution of functions is an inevitable challenge in the serverless computing landscape. Inefficient dispatching, fluctuations in invocation rates, burstiness...     

F-LEACH: a fuzzy-based data aggregation scheme for healthcare IoT systems

The Journal of Supercomputing - Internet of Things (IoT) is an emerging paradigm that consists of numerous connected and interrelated devices with embedded sensors, exchanging data with each other...