Safe Autonomous Agent Formation Operations Via Obstacle Collision Avoidance

In this paper, we consider the problem of flocking and shape‐orientation control of multi‐agent systems with inter‐agent and obstacle collision avoidance. We first consider the problem of forcing a set of autonomous agents to form a desired formation shape and orientation while avoiding inter‐agent collision and collision with convex obstacles, and following a trajectory known to only one of the agents, namely the leader of the formation. Then we build upon the solution given to this problem and solve the problem of guaranteeing obstacle collision avoidance by changing the size and the orientation of the formation. Changing the size and the orientation of the formation is helpful when the agents want to go through a narrow passage while the existing size or orientation of the formation does not allow this. We also propose collision avoidance algorithms that temporarily change the shape of the formation to avoid collision with stationary or moving nonconvex obstacles. Simulation results are presented to show the performance of the proposed control laws.     

Estimation of magnitude and time duration of temporary overvoltages using ANN in transmission lines during power system restoration

In most countries, the main step in the process of power system restoration, following a complete/partial blackout, is energization of primary restorative transmission lines. Artificial neural network (ANN) is employed for performing a nonlinear input–output mapping in this work, in order to estimate the temporary overvoltages (TOVs) due to transmission lines energization. In the proposed methodology, Levenberg–Marquardt second order method is used to train the multilayer perceptron. Proposed ANN is trained with equivalent circuit parameters of the network as input parameters, trained ANN has therefore satisfactory generalization capability. Both single and three-phase line energizations are analyzed. The simulated results for 39-bus New England test system, indicate that the proposed technique can estimate the peak values and duration of switching overvoltages with acceptable accuracy.     

Scatter search technique for exam timetabling

At universities where students enjoy flexibility in selecting courses, the Registrar’s office aims to generate an appropriate exam timetable for numerous courses and large number of students. An appropriate, real-world exam timetable should show fairness towards all students, respecting the following constraints: (a) eliminating or minimizing the number of simultaneous exams; (b) minimizing the number of consecutive exams; (c) minimizing the number of students with two or three exams per day (d) eliminating the possibility of more than three exams per day (e) exams should fit in rooms with predefined capacity; and (f) the number of exam periods is limited. These constraints are conflicting, which makes exam timetabling intractable. Hence, solving this problem in realistic time requires the use of heuristic approaches. In this work, we develop an evolutionary heuristic technique based on the scatter search approach for finding good suboptimal solutions for exam timetabling. This approach is based on maintaining and evolving a population of solutions. We evaluate our suggested technique on real-world university data and compare our results with the registrar’s manual timetable in addition to the timetables of other heuristic optimization algorithms. The experimental results show that our adapted scatter search technique generates better timetables than those produced by the registrar, manually, and by other meta-heuristics.     

Data Transformation in Cross-project Defect Prediction

Software metrics rarely follow a normal distribution. Therefore, software metrics are usually transformed prior to building a defect prediction model. To the best of our knowledge, the impact that the transformation has on cross-project defect prediction models has not been thoroughly explored. A cross-project model is built from one project and applied on another project. In this study, we investigate if cross-project defect prediction is affected by applying different transformations (i.e., log and rank transformations, as well as the Box-Cox transformation). The Box-Cox transformation subsumes log and other power transformations (e.g., square root), but has not been studied in the defect prediction literature. We propose an approach, namely Multiple Transformations (MT), to utilize multiple transformations for cross-project defect prediction. We further propose an enhanced approach MT+ to use the parameter of the Box-Cox transformation to determine the most appropriate training project for each target project. Our experiments are conducted upon three publicly available data sets (i.e., AEEEM, ReLink, and PROMISE). Comparing to the random forest model built solely using the log transformation, our MT+ approach improves the F-measure by 7, 59 and 43% for the three data sets, respectively. As a summary, our major contributions are three-fold: 1) conduct an empirical study on the impact that data transformation has on cross-project defect prediction models; 2) propose an approach to utilize the various information retained by applying different transformation methods; and 3) propose an unsupervised approach to select the most appropriate training project for each target project.     

Robust Diversity-based Sine-Cosine Algorithm for Optimizing Hydropower Multi-reservoir Systems

Hydropower energy generation depends on the available water resources. Therefore, planning and operation of the water resource systems are paramount tasks for energy management. Since reservoirs are one of the important components of water resources systems, extracting optimal operating policies for proper management of energy generated from these systems is an imperative step. Optimizing reservoir system operation (ORSO) is a non-linear, large-scale, and non-convex problem with a large number of constraints and decision variables. To solve ORSO problem effectively, a robust diversity-based, sine-cosine algorithm (RDB-SCA) is developed in the present study by introducing several strategies to balance the global exploration and local exploitation ability and to achieve accurate and reliable solutions. An efficient linear operation rule is coupled with the RDB-SCA to maximize the energy generation. The proposed method is then applied to a real-world, multi-reservoir system to extract optimal operational policies and, consequently, maximize the energy production. It is shown that the RDB-SCA is able to generate 24, 14, and 6% more energy than the original SCA, respectively for 2-, 3-, and 4-reservoir systems. The present findings are useful to suggest guidelines for efficient operation of hydropower multi-reservoir systems. This paper is supported by https://imanahmadianfar.com/codes.

     

Efficient repairing effect of PEG based tri-block copolymer on mechanically damaged PC12 cells and isolated spinal cord

Membrane sealing effects of polymersomes made of tri-block copolymer, PEG-co-FA/SC-co-PEG, (PFSP) were studied on isolated spinal cord strips, PC12 cell lines and artificial bilayer following mechanical impact implemented by aneurism clip, sonication and electric shock, respectively. The homogeneity and size of PFSP, membrane permeability and cell viability were assessed by dynamic light scattering, LDH release and MTT assays. According to the results, the biocompatible, physico-chemical, size, surface charge and amphipathic nature of PFSP polymersome makes it an ideal macromolecule to rapidly reseal damaged membranes of cells in injured spinal cord as well as in culture medium. Compound action potentials recorded from intentionally damaged spinal cord strips incubated with PFSP showed restoration of neural excitability by 82.24 % and conduction velocity by 96.72 % after 5 min that monitored in real time. Thus, they triggered efficient instant and sustained sealing of membrane and reactivation of temporarily inactivated axons. Treatment of ultrasonically damaged PC12 cells by PFSP caused efficient cell membrane repair and led to their increased viability. The optimum effects of PFSP on stabilization and impermeabilizing of the lipid bilayer occurred at the same concentrations applied to the damaged cells and spinal cord fibers and was approved by restoration of membrane conductance and calcein release manifested by NanoDrop technique. The unique physico-chemical characteristics of novel polymersomes introduced here, make them capable to reorganize membrane lipid molecules, reseal the breaches and restore the hydrophobic insulation in spinal cord damaged cells. Thus, they might be considered in the clinical treatment of SCI at early stages.     

Development and Validation of an Artificial Mechanical Skin Model for the Study of Interactions between Skin and Microneedles

Microneedles are small needle‐like structures that are almost invisible to the naked eye. They have an immense potential to serve as a valuable tool in many medical applications, such as painless vaccination. Microneedles work by breaking through the stratum corneum, the outermost barrier layer of the skin, and providing a direct path for drug delivery into the skin. A lot of research has been presented over the past two decades on the applications of microneedles, yet the fundamental mechanism of how they interact, pressure, and penetrate the skin in its native state is worth examining further. As such, a major difficulty with understanding the mechanism of microneedle–skin interaction is the lack of an artificial mechanical human skin model to use as a standardized substrate. In this research news, the development of an artificial mechanical skin model based on a thorough mechanical study of fresh human and porcine skin samples is presented. The artificial mechanical skin model can be used to study the mechanical interactions between microneedles and skin, but not diffusion of molecules across skin. This model can assist in improving the performance of microneedles by enhancing the reproducibility of microneedle depth insertions for optimal drug delivery and biosensing.

     

Feasibility study and performance assessment for the integration of a steam-injected gas turbine and thermal desalination system

This study proposes a systematic approach for retrofitting a steam-injection gas turbine (SIGT) with a multi-effect thermal vapor compression (METVC) desalination system. The retrofitted unit's product cost of the fresh water (RUPC) was used as a performance criterion, which comprises the thermodynamic, economic, and environmental attributes when calculating the total annual cost of the SIGT–METVC system. For the feasibility study of retrofitting the SIGT plant with the METVC desalination system, the effects of two key parameters were analyzed using response surface methodology (RSM) based on a central composite design (CCD): the steam air ratio (SR) and the temperature difference between the effects of the METVC system (?T_METVC) on the fresh water production (Q_freshwater) and the net power generation (W_net) of the SIGT–METVC system. Multi-objective optimization (MOO) which minimizes the modified total annual cost (MTAC) and maximizes the fresh water flow rate was performed to optimize the RUPC of the SIGT–METVC system. The best Pareto optimal solution showed that the SIGT–METVC system with five effects is the best one among the systems with 4–6 effects. This system under optimal operating conditions can save 21.07% and 9.54% of the RUPC, compared to the systems with four and six effects, respectively.     

In vitro evaluation of spray‐dried chitosan microspheres crosslinked with pyromellitic dianhydride for oral colon‐specific delivery of protein drugs

Chitosan microspheres have been prepared using a spray‐drying method, and crosslinked with pyromellitic dianhydride. The chemical structure of the modified chitosan was characterized by FTIR spectroscopy and solid state 13C NMR analysis. The particle size and morphology of the crosslinked chitosan were investigated. These microspheres were evaluated for colon‐specific delivery of bovine serum albumin (BSA) as a model protein drug. The results indicate that the drug was released as follows: 37.1 ± 2.8% after 2 h in SGF, 73.1 ± 4.8% after 8 h (2 h in SGF+ 6 h in SIF), and 80.9 ± 4.1% after 12 h in SCF. The effect of β‐glucosidase on the drug release was also examined. The encapsulation efficiency was decreased from 88.4 ± 3.1% to 62.8 ± 2.9%, with increasing BSA concentration. Loading capacity was significantly increased from 6.3 ± 0.3% to 41.8 ± 4.1% by increasing the initial BSA concentration. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40514.     

Ruthenium(II)–Arene Thiocarboxylates: Identification of a Stable Dimer Selectively Cytotoxic to Invasive Breast Cancer Cells

Ru^II-arene complexes provide a versatile scaffold for novel anticancer drugs. Seven new Ru^II-arene-thiocarboxylato dimers were synthesized and characterized. Three of the complexes ( 2 a , b and 5 ) showed promising antiproliferative activities in MDA-MB-231 (human invasive breast cancer) cells, and were further tested in a panel of fifteen cancerous and noncancerous cell lines. Complex 5 showed moderate but remarkably selective activity in MDA-MB-231 cells (IC₅₀=39±4 μm Ru). Real-time proliferation studies showed that 5 induced apoptosis in MDA-MB-231 cells but had no effect in A549 (human lung cancer, epithelial) cells. By contrast, 2 a and b showed moderate antiproliferative activity, but no apoptosis, in either cell line. Selective cytotoxicity of 5 in aggressive, mesenchymal-like MDA-MB-231 cells over many common epithelial cancer cell lines (including noninvasive breast cancer MCF-7) makes it an attractive lead compound for the development of specifically antimetastatic Ru complexes with low systemic toxicity.