The impact of the conflict on solving distributed constraint satisfaction problems

Distributed Constraint Satisfaction Problems (DCSPs) involve a vast number of AI andMulti-Agent problems. Many important efforts have been recen accomplished for solving these kinds of problems using both backtracking-based and mediation-based methods. One of the most successful mediation based algorithms in this field is Asynchronous Partial Overlay (APO) algorithm. By choosing some agents as mediators, APO tries to centralize portions of the distributed problem, and then each mediator tries to solve its centralized sub-problem. This work continues until the whole problem is solved. This paper presents a new strategy to select mediators. The main idea behind this strategy is that the number of mediators conflicts (violated constraints) impacts directly on its performance. Experimental results show that choosing the mediators with the most number of conflicts not only leads to considerable decrease in APO complexity, but also it can decrease the complexity of the other extensions of the APO such as IAPO algorithm. MaxCAPO and MaxCIAPO are two new expansions of APO which introduce this idea and are presented in this article. The results of using this mediator selection strategy show a rapid and desirable improvement over various parameters in comparison with APO and IAP

Optimized Deep Feature Selection for Pneumonia Detection: A Novel RegNet and XOR-Based PSO Approach

Pneumonia remains a significant cause of child mortality, particularly in developing countries where resources and expertise are limited. The automated detection of Pneumonia can greatly assist in addressing this challenge. In this research, an XOR based Particle Swarm Optimization (PSO) is proposed to select deep features from the second last layer of a RegNet model, aiming to improve the accuracy of the CNN model on Pneumonia detection. The proposed XOR PSO algorithm offers simplicity by incorporating just one hyperparameter for initialization, and each iteration requires minimal computation time. Moreover, it achieves a balance between exploration and exploitation, leading to convergence on a suitable solution. By extracting 163 features, an impressive accuracy level of 98% was attained which demonstrates comparable accuracy to previous PSO-based methods. The source code of the proposed method is available in the GitHub repository

Evaluation of Fertility Rate in the Couples after Uterine Septum Resection

Introduction: Uterine septum is one of the most common congenital abnormalities in women that leads to numerous gynecological problems and adverse obstetrics outcomes. This study aimed to evaluate the effects of Hysteroscopic Resection on pregnancy outcomes in women undergone the surgery.Methods: In this quasi-experimental study, 90 women were included from April 2016 to June 2018 from patients attending to Rasoul Akram hospital of Tehran. The inclusion criteria included: the age lower than 35 years old, primary infertility, idiopathic recurrent spontaneous abortion, BMI between 19 and 30, and having informed consen. Septum was resected by scissor upward and lateral. After 10 months of follow-up in average, we assessed rate of live births, abortions, birth weight and presentation.Results: 82 individuals were assessed for occurrence of conception (response rate=91%).  The mean age of patients was 30.01 ± 6.76 years and the mean BMI was 26.25 ± 4.88. Out of 82 patients, 36 patients were pregnant, of whom 16 (44.4%) had abortions. 5 (14.9%) of the pregnancies ended with preterm birth, and 6 (17%) ended with stillbirth.Conclusion: The present study showed that the infertile patients with uterine septum and with no other causes of infertility were more likely to be pregnant compared to other patients with idiopathic infertility. Our study showed that post-operation fertility following Hysteroscopic Resection was lower than that in previous reports. According to the findings of this study, scissors may be safe, effective and cost-effective method for removing uterine septa.

Distributed Target Engagement in Large-scale Mobile Sensor Networks

Sensor networks comprise an emerging field of study that is expected to touch many aspects of our life. Research in this area was originally motivated by military applications. Afterward sensor networks have demonstrated tremendous promise in many other applications such as infrastructure security, environment and habitat monitoring, industrial sensing, traffic control, and surveillance applications. One key challenge in large-scale sensor networks is the efficient use of the network's resources to collect information about objects in a given Volume of Interest (VOI). Multi-sensor Multi-target tracking in surveillance applications is an example where the success of the network to track targets in a given volume of interest, efficiently and effectively, hinges significantly on the network's ability to allocate the right set of sensors to the right set of targets so as to achieve optimal performance. This task can be even more complicated if the surveillance application is such that the sensors and targets are expected to be mobile. To ensure timely tracking of targets in a given volume of interest, the surveillance sensor network needs to maintain engagement with all targets in this volume. Thus the network must be able to perform the following real-time tasks: 1) sensor-to-target allocation; 2) target tracking; 3) sensor mobility control and coordination. In this research I propose a combination of the Semi-Flocking algorithm, as a multi-target motion control and coordination approach, and a hierarchical Distributed Constraint Optimization Problem (DCOP) modelling algorithm, as an allocation approach, to tackle target engagement problem in large-scale mobile multi-target multi-sensor surveillance systems. Sensor-to-target allocation is an NP-hard problem. Thus, for sensor networks to succeed in such application, an efficient approach that can tackle this NP-hard problem in real-time is disparately needed. This research work proposes a novel approach to tackle this issue by modelling the problem as a Hierarchical DCOP. Although DCOPs has been proven to be both general and efficient they tend to be computationally expensive, and often intractable for large-scale problems. To address this challenge, this research proposes to divide the sensor-to-target allocation problem into smaller sub-DCOPs with shared constraints, eliminating significant computational and communication costs. Furthermore, a non-binary variable modelling is presented to reduce the number of inter-agent constraints. Target tracking and sensor mobility control and coordination are the other main challenges in these networks. Biologically inspired approaches have recently gained significant attention as a tool to address this issue. These approaches are exemplified by the two well-known algorithms, namely, the Flocking algorithm and the Anti-Flocking algorithm. Generally speaking, although these two biologically inspired algorithms have demonstrated promising performance, they expose deficiencies when it comes to their ability to maintain simultaneous reliable dynamic area coverage and target coverage. To address this challenge, Semi-Flocking, a biologically inspired algorithm that benefits from key characteristics of both the Flocking and Anti-Flocking algorithms, is proposed. The Semi-Flocking algorithm approaches the problem by assigning a small flock of sensors to each target, while at the same time leaving some sensors free to explore the environment. Also, this thesis presents an extension of the Semi-Flocking in which it is combined with a constrained clustering approach to provide better coverage over maneuverable targets. To have a reliable target tracking, another extension of Semi-Flocking algorithm is presented which is a coupled distributed estimation and motion control algorithm. In this extension the Semi-Flocking algorithm is employed for the purpose of a multi-target motion control, and Kalman-Consensus Filter (KCF) for the purpose of motion estimation. Finally, this research will show that the proposed Hierarchical DCOP algorithm can be elegantly combined with the Semi-Flocking algorithm and its extensions to create a coupled control and allocation approach. Several experimental analysis conducted in this research illustrate how the operation of the proposed algorithms outperforms other approaches in terms of incurred computational and communication costs, area coverage, target coverage for both linear and maneuverable targets, target detection time, number of undetected targets and target coverage in noise conditions sensor network. Also it is illustrated that this algorithmic combination can successfully engage multiple sensors to multiple mobile targets such that the number of uncovered targets is minimized and the sensors' mean utilization factor sensor surveillance systems.is maximized

Study on physio-chemical properties of plasma polymerization in C2H2/N2 plasma and their impact on COL X

Nitrogen-containing plasma polymerization is of considerable interest for tissue engineering due to their properties on cell adhesion and mesenchymal stem cells (MSCs) response. In this study, low-pressure RF plasma of acetylene and nitrogen was used to deposit nitrogen-containing plasma polymerized coatings on several substrates. Deposition kinetics and surface characteristics of coatings were investigated in terms of RF power and gas flow ratio. OES was used to monitor the plasma process and investigate the relation between the film structure and plasma species. Presence of several bonds and low concentration of amine functional groups were determined using FTIR and Colorimetric methods. Contact angle goniometry results indicated about 30% increase in surface hydrophilicity. Stability of coatings in air and two different liquid environments was examined by repeating surface free energy measurements. Deposited films exhibited acceptable stability during the storage duration. Surface roughness measured by AFM was found to decrease with growing concentration of nitrogen. The deposition rate increased with increasing RF power and decreased with growing concentration of nitrogen. Zeta potential measurements of coatings revealed the negative potential on the surface of the thin films. Temporary suppression of collagen X in the presence of plasma coatings was confirmed by RT-PCR results

Comparing the effectiveness of music therapy and alpha-theta neuro-feedback training on anxiety and depression among patients with chronic irritable bowel syndrome

BACKGROUND: Non-pharmaceutical interventions are a promising area of research in psychiatry. Traditional treatment of Irritable Bowel Syndrome (IBS) lacked notable efficacy. The aim of this study was to examine the effectiveness of music therapy (MT) and alpha-theta neurofeedback training (NFT) on anxiety and depression symptoms among patients with IBS.METHODS: Patients with IBS, based on ROME III criteria, and high level of anxiety or depression symptoms were randomly assigned into three groups: (A) music, (B) alpha-theta training, and (C) control. In intervention groups, participants received ten 30-minute sessions of either music or alpha-theta NFT. The Hospital Anxiety and Depression Scale (HADS) was administered for all patients before and after the training period. Thirty-three patients completed the study. Data were analyzed using analysis of covariance (ANCOVA) to compare changes in HADS scores among the three study groups.RESULTS: There was a significant main effect of HADS scores (F1,18 = 17.79, P < 0.001) in the responses of MT group. Significant decreases were observed in HADS scores from pre-intervention to post-intervention tests in MT group comparing to control group. The MT accounted for 49 percent of variance in HADS scores. There was also a significant main effect of HADS scores (F1,20 = 17.79, P < 0.010) in the responses of NFT group. HADS scores from pre-intervention to post-intervention tests in alpha-theta NFT group comparing to control group showed significant decreases, too. In addition, MT and alpha-theta NFT did not show any significant difference in somatic symptoms scores between pretest and posttest among patients with IBS.CONCLUSION: This study showed that MT and alpha-theta NFT significantly alleviated anxiety and depression level among patients with IBS

The Effect of Teaching Collocations on Enhancing Iranian EFL Learners’ Reading Comprehension

Collocations might be described as the words that are located or found together in predictable patterns in speech and writing. This quasi-experimental study was designed to examine the effects of collocation instruction on enhancing Iranian EFL learners’ reading comprehension. For this purpose, 70 students were chosen from Safir English institute at intermediate level.Their level of English proficiency was determined on the basis of their scores on Nelson proficiency test. Two intact classes were randomly selected as the experimental group and two other classes were selected as the control group for the purpose of current study. Results of paired-sample t-test indicated that the students in the experimental group outperformed the control group in reading comprehension.In fact, teaching collocations could play a significant role in enhancing EFL learners’ reading comprehension