Fabricating Janus membranes via physicochemical selective chemical vapor deposition

Membranes with asymmetric wettability-Janus membranes-have recently received considerable attention for a variety of critical applications. Here, we report on a simple approach to introduce asymmetric wettability into hydrophilic porous domains. Our approach is based on the physicochemical-selective deposition of polytetrafluoroethylene (PTFE) on hydrophilic polymeric substrates. To achieve selective deposition of PTFE, we inhibit the polymerization reaction within the porous domain. We prefill the substrates with glycerol, containing a known amount of free radical inhibitor, and utilize initiated chemical vapor deposition (iCVD) for the polymerization of PTFE. We show that the glycerol/inhibitor mixture hinders the deposition of PTFE within the membrane pores. As a result, the surface of the substrates remains open and porous. The fabricated Janus membranes show stable wetting-resistant properties, evaluated through sessile drop contact angle measurements and direct contact membrane distillation (DCMD).     

Optimizing environmental flow regime by integrating river and reservoir ecosystems

Water Resources Management - The present study develops a novel form of optimization framework to assess environmental flow in the reservoirs in which upstream and downstream river ecosystems and...     

Combination of multiple diverse classifiers using belief functions for handling data with imperfect labels

This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster-Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes based on the level of ambiguity concerning its class label. Multilayer perceptron neural network is employed to learn the characteristics of the data with new labels and for a given test pattern its outputs are considered as basic belief assignment. Experiments with artificial and real data demonstrate that taking into account the ambiguity in labels of the learning data can provide better classification results than single and ensemble classifiers that solve the classification problem using data with initial imperfect labels.     

Continuous size-based focusing and bifurcating microparticle streams using a negative dielectrophoretic system

Dielectrophoresis (DEP) is an electrokinetic phenomenon which is used for manipulating micro- and nanoparticles in micron-sized devices with high sensitivity. In recent years, electrode-based DEP by patterning narrow oblique electrodes in microchannels has been used for particle manipulation. In this theoretic study, a microchannel with triangular electrodes is presented and a detailed comparison with oblique electrodes is made. For each shape, the behavior of particles is compared for three different configurations of applied voltages. Electric field, resultant DEP force, and particle trajectories for configurations are computed by means of Rayan native code. The separation efficiency of the two systems is assessed and compared afterward. The results demonstrate higher lateral DEP force, responsible for particle separation, distributed wider across the channel width for triangular shape electrodes in comparison with the oblique ones. The proposed electrode shape also shows the ability of particle separation by attracting negative DEP particles to or propelling them from the flow centerline, according to the configuration of applied voltages. A major deficiency of the oblique electrodes, which is the streamwise variation of the lateral DEP force direction near the electrodes, is also eliminated in the proposed electrode shape. In addition, with a proper voltages configuration, the triangular electrodes require lower voltages for particle focusing in comparison with the oblique ones.     

Achieving dynamic adaptation via management and interpretation of runtime models

In this article, we present a generic model-centric approach for realizing fine-grained dynamic adaptation in software systems by managing and interpreting graph-based models of software at runtime. We implemented this approach as the Graph-based Runtime Adaptation Framework (GRAF), which is particularly tailored to facilitate and simplify the process of evolving and adapting current software towards runtime adaptivity. As a proof of concept, we present case study results that show how to achieve runtime adaptivity with GRAF and sketch the framework's capabilities for facilitating the evolution of real-world applications towards self-adaptive software. The case studies also provide some details of the GRAF implementation and examine the usability and performance of the approach.     

A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system

In the past several years, many studies have been carried out on cellular manufacturing based on a two-dimensional machine–part incidence matrix. Since workers have important role in doing jobs on machines, assignment of workers to cells becomes a crucial factor for fully utilization of cellular manufacturing systems. In this paper, an attempt is made to solve cell formation problem and minimize the number of voids and exceptional elements in a three dimensional (cubic) machine–part–worker incidence matrix. The proposed mathematical model captures the capability of workers in doing different jobs. To demonstrate the effectiveness of the proposed model, the solution of some test problems is compared with the literature method.