Description:
This paper examines how speed shapes internet culture. To do so, it analyses ‘memetic moments’ on Twitter, short-lived and rapidly circulated memes that quickly reach saturation. The paper examines two ‘memetic moments’ on Twitter in 2018 and 2019 to assess how they develop over time. Each case study comprises a week’s worth of relevant tweets that were analysed for temporal patterns. We analyse these ‘memetic moments’ through Lefebvre’s (2004) work on rhythmanalysis, arguing that the temporal patterns of memes on Twitter can be understood through his concepts of repetition, presence and dialogue. While seemingly trivial, memetic moments underscore the didactic relationship between social media and news media while also providing a way to approach complex social issues.
Description:
This paper examines how speed shapes internet culture. To do so, it analyses ‘memetic moments’ on Twitter, short-lived and rapidly circulated memes that quickly reach saturation. The paper examines two ‘memetic moments’ on Twitter in 2018 and 2019 to assess how they develop over time. Each case study comprises a week’s worth of relevant tweets that were analysed for temporal patterns. We analyse these ‘memetic moments’ through Lefebvre’s (2004) work on rhythmanalysis, arguing that the temporal patterns of memes on Twitter can be understood through his concepts of repetition, presence and dialogue. While seemingly trivial, memetic moments underscore the didactic relationship between social media and news media while also providing a way to approach complex social issues.
Description:
Individual differences such as social anxiety and extraversion have been shown to influence education outcomes. However, there has been limited investigation of the relationship between individual differences and attitudes towards online and offline learning. This study aimed to investigate for the first time how social anxiety and extraversion influence student attitudes to online and offline learning, specifically in relation to tertiary level practical activities. Based on the social compensation hypothesis, it was predicted that students with higher levels of extraversion and lower levels of social anxiety would report more favourable attitudes to face-to-face learning activities. It was further predicted that less extraverted and more socially anxious students would have more favourable attitudes to online learning activities. Undergraduate students (N = 322, 67% female) completed the HEXACO-60 personality inventory, the Mini Social Phobia Inventory, and measures of attitudes towards online and offline activities. Two hierarchical multiple regressions were conducted. The first revealed that neither extraversion nor social anxiety contributed significantly to preference for online practical activities. The second regression revealed that greater emotionality, greater extraversion, greater conscientiousness, and lower levels of social anxiety were associated with more favourable attitudes towards face-to-face practical activities. In contrast to predictions, extraversion and social anxiety did not significantly contribute to attitudes to online learning activities. However, in line with predictions, greater extraversion and lower levels of social anxiety were associated with more favourable attitudes towards face-to-face practical activities. These findings indicate that online learning activities have limited compensatory effects for students who experience social discomfort, and that the social compensation hypothesis may apply within an educational framework, but in unexpected ways. •Applies the social compensation hypothesis of Internet use as a theoretical paradigm.•Examines role of social anxiety (SA) and extraversion in online and offline learning.•Extraversion and SA not associated with attitudes to online activities.•Extraversion and low SA positively predict face-to-face activity attitudes.•Online learning has limited compensatory effects for socially anxious students.
Description:
The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes as well as the available computing resources. However, this limits the network's ability to learn from small retinal structures in OCT volumes. In this paper, our goal is to improve the performance by providing guidance to DL model during training in order to learn from finer ocular structures in 3D OCT volumes. Therefore, we propose an end-to-end attention guided 3D DL model for glaucoma detection and estimating visual function from retinal structures. The model consists of three pathways with the same network architecture but different inputs. One input is the original 3D-OCT cube and the other two are computed during training guided by the 3D gradient class activation heatmaps. Each pathway outputs the class-label and the whole model is trained concurrently to minimize the sum of losses from three pathways. The final output is obtained by fusing the predictions of the three pathways. Also, to explore the robustness and generalizability of the proposed model, we apply the model on a classification task for glaucoma detection as well as a regression task to estimate visual field index (VFI) (a value between 0 and 100). A 5-fold cross-validation with a total of 3782 and 10,370 OCT scans is used to train and evaluate the classification and regression models, respectively. The glaucoma detection model achieved an area under the curve (AUC) of 93.8% compared with 86.8% for a baseline model without the attention-guided component. The model also outperformed six different feature based machine learning approaches that use scanner computed measurements for training. Further, we also assessed the contribution of different retinal layers that are relevant to glaucoma. The VFI estimation model achieved a Pearson correlation and median absolute error of 0.75 and 3.6%, respectively, for a test set of size 3100 cubes.
Description:
Research is being conducted on the use of transcranial direct current stimulation (tDCS) for therapeutic effects, and also on the mechanisms through which such therapeutic effects are mediated. A bottleneck in the progress of the research has been the large size of the existing tDCS systems which prevents subjects from performing their daily activities. To help research into the principles, mechanisms, and benefits of tDCS, reduction of size and weight, improvement in simplicity and user friendliness, portability, and programmability of tDCS systems are vital. This paper presents a design for a low-cost, light-weight, programmable, and portable tDCS device. The device is head-mountable and can be concealed in a hat and worn on the head by the subject while receiving the stimulation. The strength of the direct current stimulation can be selected through a simple user interface. The device is constructed and its performance evaluated through bench and in vivo tests. The tests validated the operation of the device in inducing neuromodulatory changes in primary motor cortex, M1, through measuring excitability of dominant M1 of resting right first dorsal interosseus muscle by transcranial magnetic stimulation induced motor evoked potentials. It was observed that the tDCS device induced comparable neuromodulatory effects in M1 as the existing bulky tDCS systems.
Description:
•A decoupled extended power flow method is proposed for islanded microgrids.•Partial derivatives of scheduled power injections are included in the Jacobean matrix.•The resistive nature of low-voltage microgrids is used to decouple the algorithm.•The droop control features of distributed generations are formulated in detail.•Different networks are investigated to analyze the performance of the method. Recently, microgrid (MG) power flow (PF) studies have gained a lot of attention due to the emergence of autonomous MGs which feature distributed generations (DGs). There are some inherent limitations in the islanded operation mode of MGs which cannot be addressed by conventional PF methods. In this regard, recent studies have proposed some updates to the conventional approaches by inclusion of network frequency as a variable in their modeling and omission of the slack bus from the grid. These considerations specifically in the Newton-Raphson (NR) method change the Jacobean matrix (JM) formulations. This paper concentrates on improving the previous NR methods for MGs which include droop controlled DGs. For this purpose, the partial derivatives of both calculated and scheduled powers are considered in the Taylor series expansion of bus power injections. Thus, the convergence and accuracy of the PF method are enhanced by adding these derivatives in modeling of generations, loads and losses. Moreover, these extended PF equations are decoupled by reformulating the JM based on the consideration that the lines in MGs are mostly resistive, which results in simplified JM calculations and improved convergence speed. The effectiveness of the proposed decoupled extended NR (DENR) method for MG PF analysis is illustrated in several case studies including 6-bus and 38-bus networks. Moreover, two convergence enhancement methods are also incorporated into the proposed approaches and their merits are investigated.
Description:
This paper employs detectability ideas to decide uniform global asymptotic stability (UGAS) of the trivial solution for a class of switched nonlinear time-varying systems when the trivial solution is uniformly globally stable. Using the notion of limiting behaviors of the state, output, and switching signals, the concept of a limiting zeroing-output solution is introduced. This leads to a definition of weak zero-state detectability (WZSD) that can be used to check UGAS, (uniformly for a set of switched signals). En route to establish this, a number of new stability results are derived. For example, under appropriate conditions, it is feasible to decide UGAS even when the switching signal does not satisfy an averaged dwell-time condition. It is also shown that WZSD of the original switched system can be verified by detectability conditions of much simpler auxiliary systems. Moreover, UGAS can be guaranteed without requiring that in each allowable system (without switching), the trivial solution is attractive. The effectiveness of the proposed concept is illustrated by a few examples including a switched semi-quasi-Z-source inverter.
Description:
The growth of databases in the healthcare domain opens multiple doors for machine learning and artificial intelligence technology. Many medical devices are available in the medical field however, medical errors remain a severe challenge. Different algorithms are developed to identify and solve medical errors, such as detecting anomalous readings, anomalous health conditions of a patient, etc. However, they fail to answer why those entries are considered an anomaly. This research gap leads to an outlying aspect mining problem. The problem of outlying aspect mining aims to discover the set of features (a.k.a subspace) in which the given data point is dramatically different than others. In this paper, we present a framework that detects anomalies in healthcare data and then provides an explanation of anomalies. This paper aims to effectively and efficiently detect anomalies and explain why they are considered anomalies by detecting outlying aspects. First, we re-introduced four anomaly detection techniques and outlying aspect mining algorithms. Then, we evaluate the performance of anomaly detection techniques and choose the best anomaly detection algorithm. Later, we detect the top k anomaly as a query and detect their outlying aspect. Lastly, we evaluate their performance on 16 real-world healthcare datasets. The experimental results show that the latest isolation-based outlying aspect mining measure, SiNNE, has outstanding performance on this task and has promising results.
Description:
Thermochemical ablation (TCA) is a thermal ablation therapy that utilises heat released from acid-base neutralisation reaction to destroy tumours. This procedure is a promising low-cost solution to existing thermal ablation treatments such as radiofrequency ablation (RFA) and microwave ablation (MWA). Studies have demonstrated that TCA can produce thermal damage that is on par with RFA and MWA when employed properly. Nevertheless, TCA remains a concept that is tested only in a few animal trials due to the risks involved as the result of uncontrolled infusion and incomplete acid-base reaction. In this study, a computational framework that simulates the thermochemical process of TCA is developed. The proposed framework consists of three physics, namely chemical flow, neutralisation reaction and heat transfer. An important parameter in the TCA framework is the neutralisation reaction rate constant, which has values in the order of 108 m3/(mol