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A crisis of faith: the political discourse of evangelicalism after Trump
Get PDFFrom the moment Donald Trump announced his intention to run as president of the United States, there were some who thought it would inevitably lead to the end of the alliance between white evangelicals and the Republican Party. We now know that this would not happen, and that more evangelicals would vote for Trump than for any Republican candidate previously. The following thesis seeks to examine the reasons for this and the extent to which it might be subject to political intervention and realignment in the future. Previous analyses and explanations for the rise of the religious right tend to replicate an essentialist paradigm with regards to evangelicalism that inadequately accounts for its historical discontinuities and contingencies and can lead to a position of political intractability. Instead, I propose to use a critical approach that derives from the post-Marxist framework of Ernesto Laclau and Chantal Mouffe, the value of which I will proceed to demonstrate by interrogating two key moments in the articulation of evangelical discourse: the formation of the religious right in the 1970s and the response to the crisis that followed Trump’s election
Cooperative Dual-Mode OFDM Index Modulation based Downlink Multi-User NOMA System
Get PDFTo fulfil the demand for high energy and spectral efficiency (SE) while serving multiple users concurrently for next-generation 6G systems, both non-orthogonal multiple access (NOMA) and orthogonal frequency division multiplexing (OFDM) aided index modulation (IM) endorse the requirements. NOMA serves several users concurrently while sharing the frequency and time resources. In addition, the single-mode OFDMIM system improves the SE by broadcasting the additional bits of active subcarrier selection. Meanwhile, dual-mode OFDM-IM relatively enhances the SE via transmitting different constellation sets over a subblock OFDM vector. Furthermore, broadcasting the baseband NOMA symbols over a dual-mode OFDM-IM scheme improves the SE compared to the conventional IM schemes. This paper describes a dual-mode aided cooperative relaying OFDM-IM-based downlink hybrid NOMA system serving multiple users simultaneously. The Monte Carlo simulation results demonstrate that the bit error rate (BER) performance of the proposed system accessed using the maximum likelihood (ML) detector for different modulation schemes is significantly better as compared to the existing cooperative relaying OFDMIM- aided NOMA systems, including single-mode and hybrid systems
Prediction Consistency and Confidence-Based Proxy Domain Construction for Privacy-Preserving in Cross-Subject EEG Classification.
Get PDFDomain adaptation has proven effective for suppressing the inter-subject variability problem in cross-subject EEG classification tasks in which labeled data is available for source subjects while only unlabeled data is provided for target subjects. Existing domain adaptation methods typically reduced the distribution discrepancy between source and target domains by directly utilizing source domain samples or features. To safeguard the privacy of source domain data, we propose to construct a Proxy Domain by simultaneously considering the prediction Consistency and Confidence (PDCC) of locally trained source models on target EEG samples, serving as the substitute to the source domain. The framework commences with the augmentation and alignment of the source domain data to enhance feature generalizability, after which source models are trained independently on each source subject's data in a decentralized manner. Knowledge transfer from source to target domains is achieved exclusively through accessing to the source domain model, enabling the PDCC-based proxy domain construction that encapsulates the source knowledge. Finally, domain adaptation is performed using the proxy domain and target domain. As a result, PDCC eliminates the need to access source domain data while effectively leveraging source knowledge. Experimental results on four benchmark EEG datasets demonstrate that PDCC consistently outperforms eleven existing methods, including several advanced transfer learning and source-free methods. Especially, the effectiveness of the proxy domain is extensively investigated. The source code for reproducing the experimental results is available from https://github.com/SunseaIU/PDCC
Dual-Brain EEG Decoding for Target Detection Via Joint Learning in Shared and Private Spaces
Get PDFHyperscanning enables simultaneous electroencephalography (EEG) recording from multiple individuals, facilitating collaborative brain activity to reduce individual biases and enhance the reliability of decision-making. The decoding of such collaborative paradigm tasks has traditionally relied solely on simple fusion methods based on each individual brain activity, without incorporating cross-brain coupling information. Inspired by social interaction studies on enhanced inter-brain synchrony in collaborative tasks using hyperscanning, we propose a joint learning framework for dual-brain target detection that integrates a shared space construction module and shared feature-guided module. The shared space construction module incorporates brain-to-brain coupling analysis to identify cross-brain synchrony, and further integrates shared and private features through a multi-head fusion mechanism for joint representation learning in shared feature-guided module. Experimental results show an average 10% improvement in balanced accuracy across 12 participant groups compared to traditional single-brain approaches, with some groups achieving up to a 5% gain over state-of-the-art (SOTA) methods. Notably, higher-performing groups exhibit stronger inter-brain coupling and more synchronized target-related responses. These findings advance the development of collaborative brain-computer interface (BCI) systems for more robust and effective target detection
Carlos Cruz-Diez’s Ephemeral Painted Walkways and the Politics of Mobility in Mid-1970s Caracas
Get PDFEnhanced classification of motor imagery EEG signals using spatio-temporal representations
Get PDFDeep learning has shown promising results in motor imagery brain-computer interfaces. However, most existing methods fail to account for the topological relationships between electrodes and the nonlinear features of electroencephalogram (EEG) signals. To address this, we propose a model combining Gramian Angular Fields (GAF) and Phase-Locking Value (PLV) with a parallel convolutional neural network (CNN). GAF captures time-domain nonlinear features, while PLV represents spatial features based on electrode topology. Comparative experiments between the end-to-end parallel CNN model and the model with spatiotemporal feature representation demonstrate that considering both time-domain correlations and electrode topology significantly enhances model performance. Furthermore, when separately evaluating the temporal and spatial features of EEG signals, the results confirm that jointly considering spatiotemporal features leads to a substantial improvement. On the Physionet dataset, our model achieves an accuracy of 99.73% in binary classification tasks and 83.37% in four-class classification tasks, showing improvement over the comparison algorithms used in the paper
Benchmark Arabic news posts and analyzes Arabic sentiment through RMuBERT and SSL with AMCFFL technique
Get PDFSentiment analysis aims to extract emotions from textual data; sentiment analysis and text recognition are two of the most common tasks associated with natural language processing. Emergent technologies have been developed and employed in various fields, including marketing, health care, and policy making. However, with the growth of social media platforms and the flow of data, especially in the Arabic language, substantial difficulties have emerged that call for the creation of new frameworks to address problems, such as the lack of datasets related to news platforms, the complicated formation of the Arabic language, and complications with classifying, and system challenges, whether in machine learning, deep learning, or online analysis tools. This paper provides a new framework that helps address ASA challenges and work on various tasks based on the state-of-the-art ASA. First, it presents a new collection named (ANP5) from Arabic news posts from several Arabic platforms, then uses SSL with AMCFFL technique to analyze the Arabic sentiment and generate a second dataset (ANPS2). Next, applied ML classifiers, RF and SVM, do the best among the other classifiers, with an accuracy of 82.00%; however, the measurement distributions for each class are different (Experiment 1). Following that, DL models, BIGRU, CNN-LSTM, LSTM, and CNN, had accuracies of 88.10%, 89.30%, 89.85%, and 90.10% (Experiment 2). Experiments 1 and 2 represent the initial benchmark classification as the first baseline. Afterward, a new RMuBERT Model was developed and compared with four transformers on the two datasets: ANPS2 accuracy (90.87%) and ANP5 (90.33%). RMuBERT performed better than the baselines (Experiment 3). Further testing of RMuBERT on various Arabic corpora with different classes, lengths, and sizes: ArSarcasm (3C), STD (2C), AJGT (2C), and AAQ (2C), revealed accuracies of 77.76%, 91.79%, 94.07%, and 93.48%, respectively. Still, RMuBERT performed better than the baselines (Experiment 4). Finally, on the largest Arabic sentiment corpora with six million Arabic tweets, the performance is up to (91.12%); RMuBERT works efficiently with less training time (Experiment 5)
Molecular Mechanisms and Treatment Strategies of ALK-Positive Lung Cancer: A Beginner's Guide for Patients, Their Families and Carers.
Get PDFThis review has been written with the intention of explaining to the patients with ALK-positive lung cancer, and to their families, friends, carers and medical teams, in simple terms, the fundamentals, and the current state of knowledge of this particular type of cancer. The review begins with basic facts about lung anatomy and lung cancer, then explains general principles of how cell proliferation is regulated at the molecular level. The coverage of the molecular events underlying the development of ALK-positive lung cancer and principles of targeted therapies then follows. The review concludes with an analysis of various therapeutic approaches to treat ALK-positive lung cancer. The Supporting Information section contains additional advanced information illustrating specific points of interest
What Are You Thinking When You Coach? An Exploration of Tennis Coaches’ Cognitions Using Stimulated Recall
Get PDFTo date, much sports coaching research has tended to focus on components such as decision-making, knowledge structures, and mental models. However, limited attention has been given to the “types of thinking” coaches engage in when coaching athletes in situ. This gap in the research literature is problematic for both coaches and coach developers in improving coaching practice. Therefore, we explored tennis coaches’ cognitive processes (i.e., the what) and their type (i.e., the how). Using stimulated recall interviews (a knowledge elicitation method based on watching video recordings of one’s own practice), data were collected from four expert tennis coaches within coaching sessions (n = 8). In addition, (n = 4) background interviews were conducted to help make sense of the data and inform the stimulated recall interview questions. A Reflexive Thematic Analysis revealed a framework of nine higher psychological functions (noticing, questioning, confirming, risk evaluation, forecasting, contextualizing, normalizing, problem-solving, and deductive reasoning) in which coaches engage during coaching practice. These findings have potential theoretical and practical applications in sports coaching practice and coach development
The STEM Conundrum: Sex Differences in Intraindividual Academic Strengths and the Gender Equality Paradox Across Academic Achievement Levels
Get PDFGirls typically perform better in reading than in mathematics or science, whereas boys typically perform better in mathematics or science than in reading. We assessed these sex differences in intraindividual academic strengths using data from 1.6 million adolescents across 82 countries and regions for three waves (2012, 2015, and 2018) of the Programme for International Student Assessment (PISA) among high (95th percentile), average (> 5th to < 95th percentile), and low (5th percentile) achievers. Girls’ intraindividual strength in reading and boys’ strength in mathematics or science were stable across countries, waves, and achievement levels. For countries in which boys had larger advantages in mathematics or science as an intraindividual strength, girls had an even larger advantage in reading. In line with a gender equality paradox, the magnitude of the sex differences in reading and science as intraindividual strengths increased with increases in national gender equality at each PISA achievement level. Interaction models suggest that the paradox arises because, as national gender equality increases, the sex with an overall advantage improves on their intraindividual strength, while the sex with an overall disadvantage shows a decline. The results have implications for understanding sex disparities in science, technology, engineering, and mathematics (STEM) fields