Connectome Specific Harmonic Wave Analysis of Disordered Brain States

Background: Connectome Harmonics analysis is a novel neuroimaging framework that defines brain states as neural spatial patterns associated with different frequencies emerging within a brain. Frequencies corresponding to specific brain states, or connectome-specific harmonic waves (CSHWs), are estimated to be the building blocks of brain activity, linking cortical oscillations, functional connectivity, and structural connectivity. Using this framework, studies will examine CSHWs of patients to catalog and analyze the spatiotemporal neural dynamics of patients with disordered brain states. Methods: By using MRI (or fMRI) and DTI data extracted from MRI scans of patients, cortical surface anatomy and the underlying neural tracts can be tracked respectively and combined to generate a patient’s connectome. Once the connectome is generated, it is converted into its graphical form where Eigen decompositions of the Laplacian operator are graphed. Application of this function to the connectome’s graph results in a spectrum of harmonic brain modes corresponding to a patient’s brain’s natural resonant frequencies (eigenvalues). Results: The CSHW framework has already been used to examine brains in a variety of ways. Previous research findings show that neocortical organization and development may be shaped by the harmonic modes corresponding to the brain’s functional connectivity, Patients given classical psychedelics (LSD, Psilocybin, and DMT) display an expanded repertoire of brain states, and neuroplasticity may be underpinned by neurons shifting into metastable states that are modulated according to a brain’s CSHWs. Discussion: Common neuroimaging methods like CT scans and PET scans are important in extracting information about regional activation during tasks but fail to contextualize or explain the interconnectedness of brain activity. CSHW analysis utilizes multiple imaging techniques and mathematical functions to derive an alphabet of brain states that can be used to describe our subjective states, from mental disorders to flow states and everyday emotions. Clinical trials here at the UTRGV institute of neuroscience will apply CSHW analysis to patients suffering from bipolar disorder, depression, and alcohol withdrawal syndrome. This research will not only allow us to examine and catalog the spatiotemporal dynamics of these disorders but potentially map out treatment plans tailored to each patient’s connectome harmonics

Investigating a theoretical framework for e-learning technology acceptance

E-learning has gained recognition and fame in delivering and distributing educational resources, and the same has become possible with the occurrence of Internet and Web technologies. The research seeks to determine the factors that influence students' acceptance of E-learning and to find out the way these factors determine the students' intention to employ E-learning. A theoretical framework was developed based on the technology acceptance model (TAM). To obtain information from the 270 university students who utilized the E-learning system, a questionnaire was formulated. The results revealed that “social influence, perceived enjoyment, self-efficacy, perceived usefulness, and perceived ease of use” are the strongest and most important predictors in the intention of and students towards E-learning systems. The outcomes offer practical implications for practitioners, lawmakers, and developers in effective E-learning systems implementation to improve ongoing interests and activities of university students in a virtual E-learning atmosphere, valuable recommendations for E-learning practices are given by the research findings, and these may turn out to be as guidelines for the efficient design of E-learning systems

A systematic review on sequence-to-sequence learning with neural network and its models

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications

Multi-band Superconductivity in the Chevrel Phases SnMo6S8 and PbMo6S8

Sub-Kelvin scanning tunnelling spectroscopy in the Chevrel Phases SnMo6S8 and PbMo6S8 reveals two distinct superconducting gaps with Delta_1 = 3 meV, Delta_2 ~ 1.0 meV and Delta_1 = 3.1 meV, Delta_2 ~ 1.4 meV respectively. The gap distribution is strongly anisotropic, with Delta_2 predominantly seen when scanning across unit-cell steps on the (001) sample surface. The spectra are well-fitted by an anisotropic two-band BCS s-wave gap function. Our spectroscopic data are confirmed by electronic heat capacity measurements which also provide evidence for a twin-gap scenario.Comment: 5 pages, 4 figure

A systematic review of text classification research based on deep learning models in Arabic language

Classifying or categorizing texts is the process by which documents are classified into groups by subject, title, author, etc. This paper undertakes a systematic review of the latest research in the field of the classification of Arabic texts. Several machine learning techniques can be used for text classification, but we have focused only on the recent trend of neural network algorithms. In this paper, the concept of classifying texts and classification processes are reviewed. Deep learning techniques in classification and its type are discussed in this paper as well. Neural networks of various types, namely, RNN, CNN, FFNN, and LSTM, are identified as the subject of study. Through systematic study, 12 research papers related to the field of the classification of Arabic texts using neural networks are obtained: for each paper the methodology for each type of neural network and the accuracy ration for each type is determined. The evaluation criteria used in the algorithms of different neural network types and how they play a large role in the highly accurate classification of Arabic texts are discussed. Our results provide some findings regarding how deep learning models can be used to improve text classification research in Arabic language

Technology acceptance drivers for AR smart glasses in the middle east : a quantitative study

This study aims to establish Middle East users' perspectives on the major factors that impact their decision to adopt Augmented Reality AR smart glasses (ARSG). Thus, an online questionnaire was designed and sent directly to the respondents, and 584 valid data points were collected from individuals living in the Middle East. The data were analyzed using Pearson correlations and Exploratory Factor Analysis (EFA) techniques using SPSS. Eleven hypotheses were tested using Multiple Regression analysis, where seven independent variables out of eleven were confirmed to have a significant impact on the perceived adoption of ARSG. The results indicate that four of the independent variables including Pre-Market Knowledge, Image, Own privacy and Technology innovativeness show the significant impact on ARSG adoption at the 1% significant level. In addition, the results indicate that three of the social and technological factors include Perceived Ease of use, Perceived usefulness and Other's privacy show the significant effect on ARSG adoption at the 5% significant level. Among the 7 social and technological factors, the results suggest that technology innovation expresses the strongest effect on ARSG adoption with the highest coefficient value of 0.413 (b = 0.413, t = 12.881, ρ < 0.01). Moreover, user intention is significantly impacted by gender and place of living but not by education or age. The research also provides pre-market insights on users' personal types that represent who will most likely adopt the new smart glasses and that differentiate them based on their priorities. To the best of our knowledge, this is among the first works to investigate technology acceptance drivers of AR smart glasses in the Middle East