Prediction of Long-Term Treatment Response to Selective Serotonin Reuptake Inhibitors (SSRIs) Using Scalp and Source Loudness Dependence of Auditory Evoked Potentials (LDAEP) Analysis in Patients with Major Depressive Disorder

Background: Animal and clinical studies have demonstrated that the loudness dependence of auditory evoked potentials (LDAEP) is inversely related to central serotonergic activity, with a high LDAEP reflecting weak serotonergic neurotransmission and vice versa, though the findings in humans have been less consistent. In addition, a high pretreatment LDAEP appears to predict a favorable response to antidepressant treatments that augment the actions of serotonin. The aim of this study was to test whether the baseline LDAEP is correlated with response to long-term maintenance treatment in patients with major depressive disorder (MDD). Methods: Scalp N1, P2 and N1/P2 LDAEP and standardized low resolution brain electromagnetic tomography-localized N1, P2, and N1/P2 LDAEP were evaluated in 41 MDD patients before and after they received antidepressant treatment (escitalopram (n = 32, 10.0 ± 4.0 mg/day), sertraline (n = 7, 78.6 ± 26.7 mg/day), and paroxetine controlled-release formulation (n = 2, 18.8 ± 8.8 mg/day)) for more than 12 weeks. A treatment response was defined as a reduction in the Beck Depression Inventory (BDI) score of >50% between baseline and follow-up. Results: The responders had higher baseline scalp P2 and N1/P2 LDAEP than nonresponders (p = 0.017; p = 0.036). In addition, changes in total BDI score between baseline and follow-up were larger in subjects with a high baseline N1/P2 LDAEP than those with a low baseline N1/P2 LDAEP (p = 0.009). There were significantly more responders in the high-LDAEP group than in the low-LDAEP group (p = 0.041). Conclusions: The findings of this study reveal that a high baseline LDAEP is associated with a clinical response to long-term antidepressant treatment.     

Modelling the propagation of social response during a disease outbreak

Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviours from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response. We couple the disease spread and panic spread processes and model them through local interactions between agents. The social contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analysing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City and 2003 severe acute respiratory syndrome and 2009 H1N1 outbreaks in Hong Kong, accurately predicting population-level behaviour. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.     

Using learning methodologies and resources in the development of critical thinking competency: An exploratory study in a virtual learning environment

This study aims to design and implement new learning methodologies and resources that seek to advance the development and assessment of one of the fundamental students’ competencies from any Business Administration Degree, such as critical thinking, that is, this is an exploratory study on computing for human learning, specifically, learning of key competencies for business. We are going to use audiovisual case methodology through the use of short film clips, usually real stories, to help students understand the practical implications of theoretical concepts explained in the classroom. A theoretical model test uses data from 32 business students from the Open University of Catalonia. The first results show positive attitudes toward a new technological resource ‘audio-visual cases’. They indicate that the use of this tool can improve the comprehension of a problem and its origins and, at the same time, stimulate learning. In addition, this tool helps to develop critical thinking competency. This study offers important contributions to an e-learning environment and their applicability to the workplace, since it is the first type of research about the impact of audiovisual cases in the acquisition of critical thinking competency. Furthermore, this methodology promotes collaborative learning.     

Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic

Sustainability has been considered as a growing concern in supply chain network design (SCND) and in the order allocation problem (OAP). Accordingly, there still exists a gap in the quantitative modeling of sustainable SCND that consists of OAP. In this article, we cover this gap through simultaneously considering the sustainable OAP in the sustainable SCND as a strategic decision. The proposed supply chain network is composed of five echelons including suppliers classified in different classes, plants, distribution centers that dispatch products via two different ways, direct shipment, and cross-docks, to satisfy stochastic demand received from a set of retailers. The problem has been mathematically formulated as a multi-objective optimization model that aims at minimizing the total costs and environmental effect of integrating SCND and OAP, simultaneously. To tackle the addressed problem, a novel multi-objective hybrid approach called MOHEV with two strategies for its best particle selection procedure (BPSP), minimum distance, and crowding distance is proposed. MOHEV is constructed through hybridization of two multi-objective algorithms, namely the adapted multi-objective electromagnetism mechanism algorithm (AMOEMA) and adapted multi-objective variable neighborhood search (AMOVNS). According to achieved results, MOHEV achieves better solutions compared with the others, and also crowding distance method for BPSP outperforms minimum distance. Finally, a case study for an automobile industry is used to demonstrate the applicability of the approach.     

A prediction-based dynamic file assignment strategy for parallel file systems

Nowadays, the rapid development of the internet calls for a high performance file system, and a lot of efforts have already been devoted to the issue of assigning nonpartitioned files in a parallel file system with the aim of pursuing a prompt response to requests. Yet most of the existing strategies still fail to bring about an optimal performance on system mean response time metrics, and new strategies which can achieve better performance in terms of mean response time become indispensable for parallel file systems. This paper, while addressing the issue of assigning nonpartitioned files in parallel file systems where the file accesses exhibit Poisson arrival rates and fixed service times, presents an on-line file assignment strategy, named prediction-based dynamic file assignment (PDFA), to minimize the mean response time among disks under different workload conditions, and a comparison of the PDFA with the well-known file assignment algorithms, such as HP and SOR. Comprehensive experimental results show that PDFA is able to improve the performance consistently in terms of mean response time among all algorithms for comparison.     

Assessing Cloud Transient Impacts of Solar and Battery Energy Systems on Grid Inertial Responses

To assess the dynamic impact of intermittency of rapidly increasing solar photovoltaic generation on the grid, this article presents the modeling and integration of the components that need to be considered, including the solar photovoltaic plant, battery energy storage system, grid-tied interface, and associated control systems. The complexity and accuracy of these models are suitable for evaluating the transient impact on bulk power systems. Of particular interest is the grid inertial response in such situations as different penetration levels of solar generation and fast cloud transient induced solar generation decrease coupled with outages that recurrently occur in the grid, e.g., a generator trip. The impact of such events on the grid frequency responses is investigated using a simplified simulation approach to account for the locational or spatial irradiance variation patterns and cloud movements. Responsive battery energy storage systems are recognized as an effective means to improve the inertial response.     

Sensitivity Analysis on Ladder Network Equivalent Circuit Parameters of Power Transformer

In this article, novel methods and ideas are introduced, which altogether lead to an accurate model of power transformer windings. First, by the determination of non-dominant (hidden) resonances from frequency-response tests, a ladder model is proposed. Next, it is improved by assigning different values for similar elements of each section of the model. The parameters are obtained by minimizing the error function via a genetic algorithm. Sensitivity analysis is then applied to the obtained model to achieve further examinations and tests. Measurements have been driven from the windings of a 20/0.4-kV, 1600-kVA transformer. Modeling, methodologies, and sensitivity analysis in this article can be very useful for future research aiming to find internal faults of the transformer with the frequency response analysis.     

Impact of Co doping on physical,structural, microstructural and magnetic features of MgZn nanoferrites for high frequency applications

Cobalt (Co) doped MgZn spinel nanoferrites with composition Mg_0.5Zn_0.5Co_x Fe_2-xO₄ at x = 0.0, 0.10, 0.20, 0.30, 0.40, 0.50 were prepared using sol-gel auto ignition method. The characterizations techniques such as FESEM, FTIR, XRD and VSM were used to determine the morphology, force constants, phase, structure and magnetic features of the samples. Lattice parameters, FWHM, d-spacing, crystallite size, micro strains and volume were investigated using high score plus software. Materials analysis using diffraction (MAUD) software was also used to study the Rietveld refinement properties of the Co doped MgZn ferrites. Physical properties such as porosity, X-ray and bulk density were also determined. Force constants of their respective absorption bands were calculated from FTIR of the Co doped MgZn nanoferrites. Single phase structure with cubic phase were observed for MgZn and Co doped MgZn at x = 0.0 whereas second phase was observed at higher Co concentrations respectively. FESEM show regular shape of the particles at low Co concentrations whereas agglomerations were observed at higher Co concentrations respectively. The magnetic properties of the Co doped MgZn ferrites were also investigated from VSM study. Magnetic remanence, coercivity, initial permeability, saturation magnetization, Bohr magneton and anisotropy constant were determined from VSM analysis. The coercivity, saturation magnetization, remanence, anisotropy constant and initial permeability were enhanced with the doping of ‘Co’ in MgZn nanoferrites. Response of the Co doped MgZn nanoferrites at high frequency regime was also evaluated. It can be seen that the response from all the Co doped MgZn nanoferrites was 2.84 GHz–5.96 GHz respectively and suggested the use of these nanoferrites for the operation of nanodevices in the X-band high frequency regime.     

考虑流固耦合的管道机器人冲击环焊缝过程动力学建模与分析

以管道机器人（Pipeline inspection gauge，PIG）为载体的内检测技术是保障油气管道安全运输的重要手段。针对管内高压流体作用下，管道机器人在冲击管内环焊缝过程中产生的动力学行为突变问题。建立了管道周向受限空间中基于Kelvin弹簧阻尼的管道机器人密封盘等效动力学模型，结合管道机器人本体建立了多体系管道机器人动力学模型；详细推导了管道机器人轴向振动微分方程，以及管内流体的流动方程；并使用Matlab/Simulink与Adams进行流固耦合仿真，作为重要的工艺参数之一，研究了管道机器人速度改变时，其在冲击环焊缝过程中的动力学响应情况。结果表明：所建立的密封盘及管道机器人动力学模型能够很好地表征密封盘在管道轴向、径向以及周向的力学特性；运行速度越快，管道机器人通过环焊缝引起的轴向振动越剧烈，冲击振动越明显；而垂向和俯仰振动现象随运动速度增大而显著减弱。