Hydrodynamic dispersion by electroosmotic flow of viscoelastic fluids within a slit microchannel

The biofluids being manipulated in lab-on-a-chip devices usually contain elastic macromolecules. Accordingly, for an accurate modeling of the relevant flow physics one should invoke viscoelastic constitutive equations. In this paper, attention is paid toward the hydrodynamic dispersion by the fully developed electroosmotic flow of PTT viscoelastic fluids in slit microchannels of low zeta potential. Adopting the Taylor–Aris approach, analytical solutions are derived for late-time solute concentration and effective dispersion coefficient. Finite element-based numerical simulations are also conducted to monitor the broadening of an analyte band from the moment of injection. Both approaches are found to be in a good agreement with an average error of below 8% in the calculated dispersion coefficients. It is observed that, for given zeta potential and electrolyte type, the hydrodynamic dispersion is severely pronounced by increasing the level of elasticity in the fluid. The variations are reversed and less pronounced when the analysis is made for a fixed mean flow rate. Moreover, the effective dispersivity grows by thickening the EDL when it is sufficiently thin, whereas the opposite is true for thick EDLs. Finally, an inspection of the average concentration reveals the formation of tails for thick EDLs that may reduce the resolution in sensing applications.     

Mealiness Detection in Agricultural Crops: Destructive and Nondestructive Tests: A Review

Mealiness is known as an important internal quality attribute of fruits/vegetables, which has significant influence on consumer purchasing decisions. Mealiness has been a topic of research interest over the past several decades. A number of destructive and nondestructive techniques are introduced for mealiness detection. Nondestructive methods are more interesting because they are rapid, noninvasive, and suitable for real‐time purposes. In this review, the concept of mealiness is presented for potato, apple, and peach, followed by an in‐depth discussion about applications of destructive and nondestructive techniques developed for mealiness detection. The results suggest the potential of electromagnetic‐based techniques for nondestructive mealiness evaluation. Further investigations are in progress to find more appropriate nondestructive techniques as well as cost and performance.     

AVLR-EBP: A Variable Step Size Approach to Speed-up the Convergence of Error Back-Propagation Algorithm

A critical issue of Neural Network based large-scale data mining algorithms is how to speed up their learning algorithm. This problem is particularly challenging for Error Back-Propagation (EBP) algorithm in Multi-Layered Perceptron (MLP) Neural Networks due to their significant applications in many scientific and engineering problems. In this paper, we propose an Adaptive Variable Learning Rate EBP algorithm to attack the challenging problem of reducing the convergence time in an EBP algorithm, aiming to have a high-speed convergence in comparison with standard EBP algorithm. The idea is inspired from adaptive filtering, which leaded us into two semi-similar methods of calculating the learning rate. Mathematical analysis of AVLR-EBP algorithm confirms its convergence property. The AVLR-EBP algorithm is utilized for data classification applications. Simulation results on many well-known data sets shall demonstrate that this algorithm reaches to a considerable reduction in convergence time in comparison to the standard EBP algorithm. The proposed algorithm, in classifying the IRIS, Wine, Breast Cancer, Semeion and SPECT Heart datasets shows a reduction of the learning epochs relative to the standard EBP algorithm.     

Common understanding in a multi-agent system using ontology-guided learning

Traditionally, communication among agents has been established based on the group commitment to a common ontology which is unfortunately often too strong or unrealistic. In the real world of communicating agents, it is preferred to enable agents to exchange information while they keep their own individual ontology. While this assumption makes agents represent their knowledge more independently and give them more flexibility, it also adds to the complexity of communication. We believe that agents can overcome this complexity by using their learning capability. The agents can learn any concept they do not know but want to communicate about with other agents in the multi-agent system where they work in. Our goal in this paper is to present a general method for agents using ontologies to teach each other concepts to improve their communication, and therefore cooperation abilities. In our method, a particular agent that understands a concept only ambiguously intends to learn it by receiving positive and negative examples for that concept from the other agents. Then, utilizing one of the known concept learning methods, the agent learns the concept in question. In case of conflicts in the received set of examples, the learning agent asks other agents again to get involved in the learning process by taking votes. While this method allows agents not to share common ontologies, it enables agents to establish common grounds on the concepts known only by some of them if these common grounds are needed during cooperation. In fact, the learned concepts by an agent are compromised among the views of other agents the method improves the autonomy of agents using them significantly.     

Room Temperature Exchange Bias in Multifunctional Sb<Subscript>2</Subscript>FeO<Subscript>6</Subscript> Multiferroic

Sb₂FeO₆ was prepared and its crystal structure was examined using x-ray diffraction (XRD). From the latter, it was found that the former adopted the tetragonal structure with space group P42/mnm. Room temperature hysteresis loop (M- H) measurements clarified the evidence of exchange bias. Semiconducting-like behavior was predominant where the correlated barrier hopping is the principal mechanism. The gained results pushed us to classify the Sb₂FeO₆ as a type-I multiferroic material.     

In vitro bactericidal effect of ultrasonically sol–gel-coated novel CuO/TiO<Subscript>2</Subscript>/PEG/cotton nanocomposite for wound care

Protection against bacterial contamination remains a demand for healthcare textiles such as wound dressings to reduce or eliminate hospital-acquired infections related to antibiotic-resistant bacteria. We report herein a simple and straightforward in situ approach to deposit copper oxide and titanium oxide nanoparticles onto cotton fabric using a sonochemical-mediated sol–gel method. Modification of the cotton surface was achieved by incorporation of citric acid (CA) and polyethylene glycol (PEG) to improve the attachment of the nanoparticles and reduce the attachment of bacteria to the cotton surface, respectively. The resultant cotton fabric was used against Escherichia coli as a Gram-negative bacterium and Staphylococcus aureus as a Gram-positive bacterium in dark condition as an in vitro model for treatment of bacterial wound infection. The effects of different treatment parameters including duration and frequency of ultrasonic irradiation, surface modification with PEG and/or CA, and cotton chemical composition with different metal oxide molar ratios on the antibacterial activity of the treated cotton fabric were studied. All treated cotton fabrics showed antibacterial activity, with higher efficiency for those coated with CuO or CuO/TiO₂ (1:1 molar ratio) among the single metal oxide and composite-modified cotton fibers, respectively. Our results show that such functionalized cotton fibers could actively fight the spread of bacterial infections by preventing bacterial adhesion, enabling more efficient bonding, and ultrasonically promoting generation of nanoparticles and their strong adhesion to the fabric surface.     

An improved fireworks algorithm for discrete sizing optimization of steel skeletal structures

The fireworks algorithm (FWA), a newly introduced metaheuristic algorithm, has exhibited remarkable performance in a variety of optimization problems. However, FWA still has some shortcomings that need to be addressed. In the present study, an improved fireworks algorithm (IFWA) is proposed to mitigate the drawbacks of the original algorithm. In the IFWA, the possibility of interaction among different solutions during the optimization process is provided. Moreover, a strategy is considered to decrease the computational effort of the algorithm. The IFWA is used to deal with the discrete structural optimization problems of steel trusses and frames. The results demonstrate the efficiency of the proposed IFWA compared with other well-known metaheuristics in the literature, in terms of the optimum solutions and the convergence rate.     

A large-scale study of architectural evolution in open-source software systems

From its very inception, the study of software architecture has recognized architectural decay as a regularly occurring phenomenon in long-lived systems. Architectural decay is caused by repeated, sometimes careless changes to a system during its lifespan. Despite decay’s prevalence, there is a relative dearth of empirical data regarding the nature of architectural changes that may lead to decay, and of developers’ understanding of those changes. In this paper, we take a step toward addressing that scarcity by introducing an architecture recovery framework, ARCADE, for conducting large-scale replicable empirical studies of architectural change across different versions of a software system. ARCADE includes two novel architectural change metrics, which are the key to enabling large-scale empirical studies of architectural change. We utilize ARCADE to conduct an empirical study of changes found in software architectures spanning several hundred versions of 23 open-source systems. Our study reveals several new findings regarding the frequency of architectural changes in software systems, the common points of departure in a system’s architecture during the system’s maintenance and evolution, the difference between system-level and component-level architectural change, and the suitability of a system’s implementation-level structure as a proxy for its architecture.     

Anomaly Detection and Reliability Analysis of Groundwater by Crude Monte Carlo and Importance Sampling Approaches

In this paper, Crude Monte Carlo method and importance sampling are utilized to determine the reliability of long-term changes in groundwater level. Furthermore, different data analysis methods are used to determine the abnormal patterns and to investigate the cause of spatial variations of failure probability. For this purpose, three methods including robust covariance, one-class SVM, and Isolation Forest are applied to define the decision function. In the preliminary detection of the outliers, DFFITS and COOK measures are used to confirm the existence of abnormal plains in a two-dimensional space. The validity of prediction results is verified through the developed method of uncertain monitoring by selecting the most significant outlier points. In addition, the abnormal pattern detection methods are compared using non-random pattern discovery decision functions. The reliability analysis of groundwater is conducted during the two periods from 1994 to 2007 and 2008 to 2021. In the second period, parts of the eastern part of the northwest, central parts of the desert of Iran, and areas from west-southwest and east-south-east to other regions exposed to a lower probability of passing through the critical conditions. In contrast, the outcomes confirm the occurrence of drought with probability more than 80% for most of the plains. Eventually, the importance sampling method showed the closest relation in the correct distribution of the decision function. In contrast, due to the cluster shape and density of the outliers, the upper part of the decision function was determined with high certainty in the discovery of abnormal plains.