Solving the stochastic support vector regression with probabilistic constraints by a high-performance neural network model

This paper offers a recurrent neural network to support vector machine (SVM) learning in stochastic support vector regression with probabilistic constraints. The SVM is first converted into an equivalent quadratic programming (QP) formulation in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees obtaining the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by three illustrative examples.

     

Leaching of a zinc ore and concentrate using the Geocoat™ technology

In this study, the Geocoat™ technology was used for the extraction of zinc from a mineral concentrate obtained from the Kooshk mine (Yazd, Iran) by a culture dominated by the mesophilic bacterium Acidithiobacillusferrooxidans in a packed column bioreactor. A low grade sphalerite ore was used as support for the concentrate coating. During the 100 days of leaching pH, Fe³⁺, Fe^total, microbial population density and zinc extraction were measured. The final zinc extraction from concentrate and low grade support was 97% and 78%, respectively, and it was found that leaching from the support does not proceed significantly before leaching from the coating is completed.     

Evaluation of TBM performance prediction models and sensitivity analysis of input parameters

Bulletin of Engineering Geology and the Environment - In recent years, anumber of models and formulas have been proposed to estimate the penetration rate of hard rock tunnel boring machines (TBMs)....     

Modeling dynamic viscosity of n-alkanes using LSSVM technique

One of the important thermophysical properties is viscosity which expresses the resistance of fluid to flow. The least squares support vector machine (LSSVM) algorithm is proposed as a novel method for prediction of dynamic viscosity of different normal alkanes in a wide range of pressure and temperature. As this study is purely computational, 228 experimental data points were gathered from literature for training and validation of the model. The outcomes of the LSSVM algorithm were compared with the actual data with acceptable average absolute relative deviation and the coefficient of determination (R²) of 1.014 and 0.9968, respectively. The comparisons showed that the predicting model has the potential of prediction of n-alkane dynamic viscosity in terms of pressure, temperature, and carbon number of n-alkane, so this strategy can be used as a simple tool for predicting the behavior of reservoir fluids.     

The effects of food essential oils on cardiovascular diseases: A review

Essential oils (EO) are complex secondary metabolites, which are produced by aromatic plants and identified by their powerful odors. Present studies on EO and their isolated ingredients have drawn the attention of researchers to screen these natural products and evaluate their effect on the cardiovascular system. Some EO, and their active ingredients, have been reported to improve the cardiovascular system significantly by affecting vaso-relaxation, and decreasing the heart rate and exert a hypotension activity. Several mechanisms have been proposed for the role of EO and their main active components in promoting the health of the cardiovascular system. The objective of this review is to highlight the current state of knowledge on the functional role of EO extracted from plants for reducing the risk of cardiovascular diseases and their mechanisms of action. Research on EO has the potential to identify new bioactive compounds and formulate new functional products for the treatment of cardiovascular diseases such as arterial hypertension, angina pectoris, heart failure, and myocardial infarction.     

OSPM: A design methodology for open strategic planning

This study employs a design science perspective to propose a methodology for open strategic planning (OSP). Habermas’ discourse theory and Bryson’s strategy change cycle are used as informing kernel theories. A methodology is proposed to satisfy the requirements retrieved from the kernel theories. The proposed methodology contains modules for a planning system and a planning process. Design principles are explained through a blueprint of the system and process. The proposed methodology is applied and evaluated in two cases. Contributions to the literature involve extending the literature on OSP to an applicable methodology with guidelines on how to implement open strategy.     

Dynamic modeling and CPG-based trajectory generation for a masticatory rehab robot

Human mastication is a complex and rhythmic biomechanical process which is regulated by a brain stem central pattern generator (CPG). Masticatory patterns, frequency and amplitude of mastication are different from person to person and significantly depend on food properties. The central nervous system controls the activity of muscles to produce smooth transitions between different movements. Therefore, to rehab human mandibular system, there is a real need to use the concept of CPG for development of a new methodology in jaw exercises and to help jaw movements recovery. This paper proposes a novel method for real-time trajectory generation of a mastication rehab robot. The proposed method combines several methods and concepts including kinematics, dynamics, trajectory generation and CPG. The purpose of this article is to provide a methodology to enable physiotherapists to perform the human jaw rehabilitation. In this paper, the robotic setup includes two Gough–Stewart platforms. The first platform is used as the rehab robot, while the second one is used to model the human jaw system. Once the modeling is completed, the second robot will be replaced by an actual patient for the selected physiotherapy. Gibbs–Appell’s formulation is used to obtain the dynamics equations of the rehab robot. Then, a method based on the Fourier series is employed to tune parameters of the CPG. It is shown that changes in leg lengths, due to the online changes of the mastication parameters, occur in a smooth and continuous manner. The key feature of the proposed method, when applied to human mastication, is its ability to adapt to the environment and change the chewing pattern in real-time parameters, such as amplitudes as well as jaw movements velocity during mastication.     

The Role of Welding Parameters on Achieving a Combination of High Strength and Ductility in Gas Tungsten Arc Welding of Cu–7Al–3Fe Aluminum Bronze

The weldability of aluminum bronze alloys lends it to fabrication of items such as pipes and pressure vessels. Thus, obtaining high strength along with good ductility is necessary. Gas tungsten arc welding of Cu–7Al–3Fe aluminum bronze alloy was performed by variation of some parameters such as shielding gas flow rate, preheat and post weld heat treatment. Optical and scanning electron microscopy along with EDX analysis was applied for different samples. Microhardness measurements and tensile tests were performed to see the effect of these parameters on ultimate tensile strength and ductility of samples.