Peer Learning in the Class or on Facebook? _x000D_ A Correlational Experiment on Learning Outcomes

This paper presents two interventions to improve the peer learning practice in an Information System course; namely (1) class-based peer tutoring in small groups and (2) discussions on Facebook group of the course. The article aims at comparing the correlations between the learning outcomes with class-based peer tutoring as well as with Facebook engagement. The findings show that although the learning outcomes are correlated with the both of these two interventions, the students’ engagement on Facebook has a stronger correlation with the learning outcomes. The study also reports the lessons learned in improving students’ engagement on the Facebook group of the course. The results have been discussed in the lens of Theory of Peer Learning and the future avenues of research have been suggested. This study also motivates teaching practitioners in Information Systems to improve peer learning practices by the use of social networking sites in their courses

Boundary control of parabolic PDE using adaptive dynamic programming

In this dissertation, novel adaptive/approximate dynamic programming (ADP) based state and output feedback control methods are presented for distributed parameter systems (DPS) which are expressed as uncertain parabolic partial differential equations (PDEs) in one and two dimensional domains. In the first step, the output feedback control design using an early lumping method is introduced after model reduction. Subsequently controllers were developed in four stages; Unlike current approaches in the literature, state and output feedback approaches were designed without utilizing model reduction for uncertain linear, coupled nonlinear and two-dimensional parabolic PDEs, respectively. In all of these techniques, the infinite horizon cost function was considered and controller design was obtained in a forward-in-time and online manner without solving the algebraic Riccati equation (ARE) or using value and policy iterations techniques. Providing the stability analysis in the original infinite dimensional domain was a major challenge. Using Lyapunov criterion, the ultimate boundedness (UB) result was demonstrated for the regulation of closed-loop system using all the techniques developed herein. Moreover, due to distributed and large scale nature of state space, pure state feedback control design for DPS has proven to be practically obsolete. Therefore, output feedback design using limited point sensors in the domain or at boundaries are introduced. In the final two papers, the developed state feedback ADP control method was extended to regulate multi-dimensional and more complicated nonlinear parabolic PDE dynamics --Abstract, page iv

A Distributed Architecture for Certificate-based Delegation of Business Process Accessibility in Virtual Organizations

In this paper, a distributed architecture has been proposed in order to support an authorization service more precisely in dynamically created Virtual Organizations (VO). In comparison to other existing architectures such as Akenti, VOMS and TAS, our architecture uses certificates on top a of the distributed agent architecture for managing requested resources among the VOs. The most obscure issue in distributed agents is finding the proper node that keeps the particular requested certificates In this paper, Chord’s Finger Table has been improved to add extra search abilities on the ring architecture of Chord. The process of locating keys can be implemented on the top of the improved Chord by associating a key with each data item, and storing the key/data item pair at the node to which the key maps. In this article, a theatrical analysis is presented for simulations, which shows improvement in the number of passed hops to locate keys in the proposed method in comparison of standard chord, so it’s more cost efficient

Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid

The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models

2D:4D Suggests a Role of Prenatal Testosterone in Gender Dysphoria

Gender dysphoria (GD) reflects distress caused by incongruence between one’s experienced gender identity and one’s natal (assigned) gender. Previous studies suggest that high levels of prenatal testosterone (T) in natal females and low levels in natal males might contribute to GD. Here, we investigated if the 2D:4D digit ratio, a biomarker of prenatal T effects, is related to GD. We first report results from a large Iranian sample, comparing 2D:4D in 104 transwomen and 89 transmen against controls of the same natal sex. We found significantly lower (less masculine) 2D:4D in transwomen compared to control men. We then conducted random-effects meta-analyses of relevant studies including our own (k = 6, N = 925 for transwomen and k = 6, N = 757 for transmen). In line with the hypothesized prenatal T effects, transwomen showed significantly feminized 2D:4D (d ≈ 0.24). Conversely, transmen showed masculinized 2D:4D (d ≈ − 0.28); however, large unaccounted heterogeneity across studies emerged, which makes this effect less meaningful. These findings support the idea that high levels of prenatal T in natal females and low levels in natal males play a part in the etiology of GD. As we discuss, this adds to the evidence demonstrating the convergent validity of 2D:4D as a marker of prenatal T effects

Viscous and induced current heating in plasma focus plasmoids

Recently, Abolhasani et al, proposed that the high ion energies observed in plasmoids formed in the plasma focus could be explained by viscous heating. We here elaborate this proposal, demonstrating that during plasmoid formation, ion motion along magnetic field lines can be rapidly converted, at least in part, to thermal energy through viscous diffusion. This effect is strongly enhanced by higher-z ions. We compare the theoretical predictions with the recent observation by Lerner et al, of trapped ion energies of 160 keV. In addition, we propose a second source of heating. The mildly relativistic electron beam emitted by the plasmoid, generates an induced current within the plasmoid comparable to the beam current and confined to approximately the same region. The induced current electrons, with drift velocity vd

Money Attitudes Among Iranians: A Test of Yamauchi and Templer’s Money Attitudes Scale

The factor structure of Yamauchi and Templer’s (1982) attitudes toward money scale was explored in Iran. While some items loaded on the same factors as found in western contexts, some unique factors were also found, reflecting particular cultural and economic impacts on money attitudes in Iran. Both etic and emic factors emerged. Saving was the only factor that emerged with the original scale items found in western cultures. Two of the original Anxiety items loaded onto a single factor, labelled Bargain-Conscious consistent with a small number of previous studies. Some of the Anxiety and Distrust items together loaded on the same factor in this research, as has been found in some existing studies in non-western cultures. Three sub-dimensions of Power were found in this sample, as opposed to one major Power dimension in the original scale, which may reflect specific contextual factors. Further, and contrary to previous findings, no significant correlations were found between any of the scale factors and gender, age, education, job level or salary

An efficient spectral method for solving third-kind Volterra integral equations with non-smooth solutions

This paper is concerned with the numerical solution of the third kind Volterra integral equations with non-smooth solutions based on the recursive approach of the spectral Tau method. To this end, a new set of the fractional version of canonical basis polynomials (called FC-polynomials) is introduced. The approximate polynomial solution (called Tau-solution) is expressed in terms of FC-polynomials. The fractional structure of Tau-solution allows recovering the standard degree of accuracy of spectral methods even in the case of non-smooth solutions. The convergence analysis of the method is studied. The obtained numerical results show the accuracy and efficiency of the method compared to other existing methods

Can fuzzy Multi-Criteria Decision Making improve Strategic planning by balanced scorecard?

Strategic management is momentous for organizational success and competitive advantage in an increasingly turbulent business environment. Balanced scorecard (BSC) is a framework for evaluating strategic management performance which translates strategy into action via various sets of performance measurement indicators. The main objective of this research is to develop a new fuzzy group Multi-Criteria Decision Making (MCDM) model for strategic plans selection process in the BSC. For this to happen, the current study has implemented linguistic extension of MCDM model for robust selection of strategic plans. The new linguistic reasoning for group decision making is able to aggregate subjective evaluation of the decision makers and hence create an opportunity to perform more robust strategic plans, despite of the vagueness and uncertainty of strategic plans selection process. A numerical example demonstrates possibilities for the improvement of BSC through applying the proposed model