A new pricing constrained single-product inventory-production model in perishable food for maximizing the total profit

In this paper, a new constrained single-product pricing and inventory model is presented. The goal of the proposed model is to decide on the prices as well as on the inventory and production decisions in order to maximize the total profit. The developed model is a nonlinear programming model which is solved by using hybrid genetic algorithm (HGA) and simulated annealing. Comparison of results obtained from the two algorithms shows that HGA is better. To enhance the performance of our algorithms, we apply the Taguchi experimental design method to tune their parameters. Finally, some recommendations for future developments are presented.     

Intelligent Sound-Based Early Fault Detection System for Vehicles

An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage to differentiate between faulty and healthy cars. We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts. We used the time domain, frequency domain, and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly. We used abnormal car data to classify it into fifteen other classical vehicle problems. We experimented with various signal processing techniques and presented the comparison results. In the detection and further problem classification, random forest showed the highest results of 97% and 92% with time-frequency features.     

Robust estimation of surface properties and interpolation of shadow/specularity components

The Polynomial Texture Map framework (PTM) extends the simple model of image formation from the Lambertian variant of Photometric Stereo (PST) to more general reflectances and to more complex-shaped surfaces. It forms an alternative method for apprehending object color, albedo, and surface normals. Here we consider solving such a model in a robust version, not to date attempted for PTM, with the upshot that both shadows and specularities are identified automatically without the need for any thresholds. Instead of the linear model used in few-source PST for Lambertian surfaces, PTM adopts a higher degree polynomial model. PTM has two aims: interpolation of images for new lighting directions, and recovery of surface properties. Here we show that a robust approach is a good deal more accurate in recovering surface properties. For new-lighting interpolation, we demonstrate that a simple radial basis function interpolation can accurately interpolate specularities as well as attached and cast shadows even with a medium-sized image set, with no need for reflectance sharing across pixels or extremely large numbers of interpolation coefficients.     

A novel approach to accelerate the convergence speed of a stochastic multi-agent system using recurrent neural nets

One problem in the design of multi-agent systems is the difficulty of predicting the occurrences that one agent might face, also to recognize and to predict their optimum behavior in these situations. Therefore, one of the most important characteristic of the agent is their ability during adoption, to learn, and correct their behavior. With consideration of the continuously changing environment, the back and forth learning of the agents, the inability to see the agent’s action first hand, and their chosen strategies, learning in a multi-agent environment can be very complex. On the one hand, with recognition to the current learning models that are used in deterministic environment that behaves linearly, which contain weaknesses; therefore, the current learning models are unproductive in complex environments that the actions of agents are stochastic. Therefore, it is necessary for the creation of learning models that are effective in stochastic environments. Purpose of this research is the creation of such a learning model. For this reason, the Hopfield and Boltzmann learning algorithms are used. In order to demonstrate the performance of their algorithms, first, an unlearned multi-agent model is created. During the interactions of the agents, they try to increase their knowledge to reach a specific value. The predicated index is the number of changed states needed to reach the convergence. Then, the learned multi-agent model is created with the Hopfield learning algorithm, and in the end, the learned multi-agent model is created with the Boltzmann learning algorithm. After analyzing the obtained figures, a conclusion can be made that when learning impose to multi-agent environment the average number of changed states needed to reach the convergence decreased and the use of Boltzmann learning algorithm decreased the average number of changed states even further in comparison with Hopfield learning algorithm due to the increase in the number of choices in each situation. Therefore, it is possible to say that the multi-agent systems behave stochastically, the more closer they behave to their true character, the speed of reaching the global solution increases.     

Balancing and Trajectory Tracking of Two-Wheeled Mobile Robot Using Backstepping Sliding Mode Control: Design and Experiments

The key attributes of Two Wheeled Balancing Mobile Robots (TWBMRs) are nonholonomic constraints and inherent instability. This paper deals with the problem of balancing and trajectory tracking of TWBMR using backstepping Sliding Mode Controller (SMC). First, the mathematical representation of TWBMR is derived using Lagrangian method by incorporating the dynamics of DC motors. Then, a decoupling approach is applied for simplifying the dynamic equations. The backstepping SMC technique is finally adopted to achieve the balancing and trajectory tracking of the TWBMR, whereas both model uncertainties and exogenous disturbance are taken into account in the controller design methodology. In order to determine the velocity, the trajectory tracking is achieved by the kinematic control, which is a common backstepping controller. For the velocity convergence of TWBMR to the generated desired value, two SMCs are designed, in which the motors voltage are directly controlled as the control laws. Simplicity in practical implementation and control law, ability to overcome uncertainties and appropriate performance are the main advantages of the proposed controller. The effectiveness of the proposed controller is verified through simulation and experimental results.     

Human-Recombinant-Elastin-Based Bioinks for 3D Bioprinting of Vascularized Soft Tissues

Bioprinting has emerged as an advanced method for fabricating complex 3D tissues. Despite the tremendous potential of 3D bioprinting, there are several drawbacks of current bioinks and printing methodologies that limit  the ability to print elastic and highly vascularized tissues. In particular, fabrication of complex biomimetic structure that are entirely based on 3D bioprinting is still challenging primarily due to the lack of suitable bioinks with high printability, biocompatibility, biomimicry, and proper mechanical properties. To address these shortcomings, in this work the use of recombinant human tropoelastin as a highly biocompatible and elastic bioink for 3D printing of complex soft tissues is demonstrated. As proof of the concept, vascularized cardiac constructs are bioprinted and their functions are assessed in vitro and in vivo. The printed constructs demonstrate endothelium barrier function and spontaneous beating of cardiac muscle cells, which are important functions of cardiac tissue in vivo. Furthermore, the printed construct elicits minimal inflammatory responses, and is shown to be efficiently biodegraded in vivo when implanted subcutaneously in rats. Taken together, these results demonstrate the potential of the elastic bioink for printing 3D functional cardiac tissues, which can eventually be used for cardiac tissue replacement.     

Physical,antimicrobial and antibiofilm properties of Zataria multiflora Boiss essential oil nanoemulsion

It was evaluated physical, antibacterial and antibiofilm properties of Zataria multiflora Boiss essential oil (ZEO) and its nanoemulsion. Long‐term stability of nanoemulsion prepared by emulsion phase inversion was satisfying based on low narrow size distribution (polydispersity index ?0.2) and low droplet size (200 nm) over 21 days of storage. Nanoemulsion showed lower minimum inhibitory concentration (MIC) on Listeria monocytogenes (2500 µg mL^?1) than Salmonella Typhimurium (5000 µg mL^?1). Killing kinetics study revealed that nanoemulsion was more effective in inhibiting the growth of bacteria in milk than culture media. Both bacteriostatic and bactericidal effects were observed depending on the type of bacteria, nanoemulsion concentration and the time of exposure. Nanoemulsion at 4×MIC concentration reduced 64% and 75% of one‐day‐old biofilm of L. monocytogenes and S. Typhimurium, respectively. In conclusion, nanoemulsion revealed antimicrobial activity, but converting the ZEO to nanoemulsion did not improve its antibacterial activity; however, antibiofilm properties were enhanced.