Coherence Values Research Articles

Correlation Between Culture and Music Used in Advertising

Objective: The objective of this study was to find out if there is a correlation between a country’s culture and the type of music they use in their TV advertisements. Methods: We used the Hofstede model profiles for each of the five countries we selected for this study in order to determine if there is a correlation between each country’s profile (which represents its culture), and the music that was selected in each country’s advertising. Of the six dimensions in the Hofstede model, we chose to focus on three dimensions for the purpose of this study. The three dimensions we used are individualism/collectivism, masculinity, and uncertainty avoidance. We analyzed the music by using Spotify’s Web API tool, which gave us three values: energy, danceability, and valence. We chose those values from Spotify since they are well-suited to be linked to the Hofstede dimensions that we are investigating here. Individualism/collectivism (from Hofstede’s culture model) is associated with danceability (from Spotify’s music tool); masculinity (from Hofstede’s culture model) is associated with energy (from Spotify’s music tool), and uncertainty avoidance (from Hofstede’s culture model) is associated with valence (from Spotify’s music tool). Results: The USA has a medium-high country/song coherence value (321) and a low-medium commercial/song coherence value (492). India has a high country/song coherence value (251) and a low-medium commercial/song coherence value (446). The Netherlands has a low country/song coherence value (548) and a low commercial/song coherence value (512). Japan has a high country/song coherence value (227) and a medium-high commercial/song coherence value (392). Singapore has a medium-high country/song coherence value (342) and a low commercial/song coherence value (529). Conclusion: We have found that there is correlation between a country’s culture and the type of music they use in their TV advertisements in countries with Eastern culture such as India and Japan but not in countries with Western culture such as the US, The Netherlands, and Singapore.

Sentiment Analysis of Google Maps User Reviews on the Play Store Using Support Vector Machine and Latent Dirichlet Allocation Topic Modeling

These days, traveling is made easier by utilizing easily accessible online directions such as Google Maps. Google Maps provides real-time routes by displaying and presenting the closest routes that users can take. However, lately, the routes provided by Google Maps services often get users lost by presenting routes such as forests, narrow roads, and even dead ends. Therefore, this study aims to determine the level of user satisfaction and sentiment into two categories, namely positive and negative, based on reviews on the Google Play Store platform using the Support Vector Machine (SVM) algorithm and topic modeling using Latent Dirichlet Allocation (LDA) to find out the collection of topics that are the main topics of conversation by users regarding Google Maps services. The results of this study show that the SVM algorithm is feasible to use in sentiment analysis classification with an accuracy value of 86%, precision of 93%, recall of 53%, and f1-score of 52%. In addition, topic modeling is applied to generate coherence values for each topic, which shows that the higher the coherence value, the more specific the topic is. The highest coherence value generated in this study was two topic models with a coherence value of 35.15%, but this study took five with a coherence value of 33.39%. The five topic models to be applied in this study are selected because they have a good enough coherence value to identify the main topics and hidden topics in Google Maps user reviews with the Latent Dirichlet Allocation model. The topic model shows five aspects users often discuss: Google Maps route accuracy, system and service errors, navigation application directions, lost time history, and convoluted route provision.

Multi-scale drought variability over West Africa and the associated large-scale circulation patterns

This study addresses the critical research gap in understanding regional drought dynamics in West Africa (WA) using advanced multivariate wavelet coherence (MWC) techniques, revealing critical insights into the complex interplay between large-scale climate indices and regional drought patterns. Utilizing the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI), we examined drought characteristics across WA from 1950 to 2018. Our analysis revealed significant drying trends across all seasons, with the Arid zone experiencing the most severe and prolonged drought (158 months) from August 1981 to September 1994, affecting 79.77% of the area. The entire WA region faced its most extensive drought during this period, impacting over 60% of the study area. MWC analysis demonstrated enhanced drought predictability when combining climate indices, particularly the Southern Oscillation Index with the Atlantic Multidecadal Oscillation, yielding high Average Wavelet Coherence values of 0.52 (SPEI) and 0.53 (scPDSI), with substantial Percentage Area of Significant Coherence of 40.19% and 43.68%, respectively. These findings highlight the crucial role of oceanic-atmospheric interactions in modulating drought conditions across WA. Our results provide valuable insights for improving drought forecasting and management strategies in the region, emphasizing the need for integrated approaches that consider multiple climate indices and their combined effects on drought dynamics.

Student Pharmacists’ Reflections on Personal and Professional Development Following a Series of Longitudinal Faith-Based Courses

ObjectiveThis study aims to explore how integrating Judeo-Christian religious education into the pharmacy curriculum influences students’ spiritual and moral development, professional values, and mindset, preparing them for future holistic health care practice. MethodsA series of Judeo-Christian faith-based courses were integrated into the required Doctor of Pharmacy curriculum with the aim of developing student pharmacists’ moral foundation for future health care roles. Student reflections from these courses were compiled and analyzed using WordStat, combining natural language processing and statistical analysis. Content analysis included word cloud visualization, word co-occurrence, phrase extraction, and topic modeling. Thematic analysis with a deductive coding approach was applied. Descriptive statistics were used to analyze coding frequencies. ResultsWord cluster analysis revealed frequently cited and co-occurring words, including God, patient, care, life, love, ethics, personal, professional, health, and people. The frequently occurring phrases included patient care, professional ethics, personal ethics, and professional developments. The topics with high coherence values included love and compassion, empathetic care, caring health professionals, patient care, and leadership based on biblical principles. The major themes that emerged included spiritual growth and connection with God, the cultivation of virtues and ethical principles in personal development, and integration of faith into professional commitments for these aspiring professionals. ConclusionThe findings suggest that integrating spiritual and religious education into the pharmacy curriculum helps students develop a strong moral foundation, guiding their future practice with compassion and integrity.

Preoperative interhemispheric coherence as a potential predictive marker for seizure outcome after total corpus callosotomy in nonlesional generalized epilepsy: a scalp EEG study.

Corpus callosotomy (CC) is one of the palliative epilepsy surgical procedures available for nonlesional generalized epilepsy, but it is more invasive than other palliative surgical procedures. The main challenge is proper selection of suitable patients for CC. Coherence analysis is a method for evaluating brain connectivity, but the correlation between preoperative coherence and surgical outcomes has not previously been clarified. The authors aimed to evaluate correlations between preoperative interhemispheric coherence and surgical outcome in patients with nonlesional generalized epilepsy. This retrospective study investigated patients with nonlesional generalized epilepsy who underwent total CC. The authors collected data for patients with good seizure outcome (Oguni classification A or B) and bad seizure outcome (Oguni classification D). For coherence analysis, the authors selected a period without interictal discharges. Preoperative interhemispheric coherence values from 8 pairs of symmetrically opposite scalp electrodes were computed across 5 frequency bands. Then, the authors evaluated correlations between coherence and surgical outcomes. Forty patients were included (19 males and 21 females). The mean (range) age at the time of surgery was 5.1 (1-18) years. Seizure outcomes were good in 15 patients and bad in the other 25 patients. Age at onset of epilepsy, duration of epilepsy before surgery, age at time of surgery, and presence of epileptic spasm did not differ significantly between patients with good and bad seizure outcomes (p = 0.36, p = 0.14, p = 0.10, and p = 0.20, respectively). Significant correlations were identified between higher Fp1-Fp2 interhemispheric coherence values in the delta, theta, and alpha frequency bands and bad surgical outcomes (p = 0.0397, p = 0.0322, and p = 0.0476, respectively). The receiver operating characteristic curves of the Fp1-Fp2 coherence values in these frequency bands showed areas under the curve of 67%, 69%, and 67%, respectively. The optimal cutoff values for Fp1-Fp2 interhemispheric coherence to predict surgical outcomes were 55.6 for delta (66.7% sensitivity and 72.0% specificity), 55.9 for theta (60.0% sensitivity and 76.0% specificity), and 50.3 for alpha (53.3% sensitivity and 84.0% specificity). This is the first study to identify potential predictive factors for surgical outcomes based on preoperative interhemispheric coherence in nonlesional generalized epilepsy. Higher coherence between Fp1-Fp2 in the delta, theta, and alpha frequencies correlated with bad seizure outcome after CC.

Development of a Wind–Wave Coherence Function Based on Numerical Studies

The synchronization and intensity of fluctuating wind speeds and wave surfaces in wind–wave joint propagation processes are affected by the coherence of the marine ambient factors of fluctuating wind and random waves. This coherence further affects the precise calculations of wind–wave joint actions on marine structures. Therefore, a wind–wave joint propagation numerical flume was established based on the numerical simulation of random waves and fluctuating wind fields. A series of numerical simulations of wind–wave joint propagations were carried out. Based on the numerical results, the influences and influence laws of factors such as wind speed position height, significant wave height and wave spectrum peak frequency on the wind–wave coherence values were studied. According to the influence characteristics of these factors, a function of wind–wave coherence values for random wind–wave joint propagation was calculated. The coherence function takes frequency as the variable, while parameters include significant wave height, wind speed position height and wave spectrum peak frequency. Through a series of numerical simulation results, data fitting was used to calculate the parameter coefficients of the coherence function. The established random wind–wave coherence function can be described using the wind–wave joint fields of marine structures and the computational analyses of structural wind–wave joint actions.

Chemistry and relevant thermophysical properties of Pb84.3Li15.7 liquid solutions: Updated view and identification of new and coherent values

The liquid breeding blanket concept of a fusion reactor is based on the employment of the eutectic Lead-Lithium solution (LLE), where Lithium, enriched in 6Li, has the purpose of regenerating Tritium by reacting with neutrons, while Lead enhances the process acting as neutrons multiplier. Anyway, despite the huge interest on this binary system, LLE is not characterized yet by a fully consolidated description of its basic thermophysical properties and, in many cases, the spread of values reported so far in literature remains still significant. The purpose of this paper is hence to check again the many data available in literature, with a special attention to the ones more recently published, trying to explain and solve their inhomogeneity. Taking into account the general features of the Pb-Li interaction and particularly the not ideal behaviour of the Pb-Li liquid solutions, the following thermophysical properties of the LLE are dealt in detail: specific heat; density and volumetric thermal expansion coefficient; thermal conductivity and diffusivity; electrical resistivity; Sieverts' constant of Hydrogen. Based on the deep analysis of all the reported experimentation and through the adoption of several comparison criteria, the most trustable correlation is sought for each of the above properties; additionally, when a robust correlation couldn't be retrieved, a new, optimized, one has been proposed, capable also to foresee correctly the effect of small composition variations and to assure the internal coherence among linked properties.Specific heat and density values resulted at the end accurately described and not needing for additional experimentation; thermal properties and electrical resistivity can be evaluated with decent confidence, even if their uncertainty could be somehow reduced by future investigations; for Sieverts' constant it has not possible yet to identify a unique, trustable correlation, anyway the range of values correctly assumed up to 900K has been significantly restricted.

Approaches to improve preprocessing for Latent Dirichlet Allocation topic modeling

As a part of natural language processing (NLP), the intent of topic modeling is to identify topics in textual corpora with limited human input. Current topic modeling techniques, like Latent Dirichlet Allocation (LDA), are limited in the pre-processing steps and currently require human judgement, increasing analysis time and opportunities for error. The purpose of this research is to allay some of those limitations by introducing new approaches to improve coherence without adding computational complexity and provide an objective method for determining the number of topics within a corpus. First, we identify a requirement for a more robust stop words list and introduce a new dimensionality-reduction heuristic that exploits the number of words within a document to infer importance to word choice. Second, we develop an eigenvalue technique to determine the number of topics within a corpus. Third, we combine all of these techniques into the Zimm Approach, which produces higher quality results than LDA in determining the number of topics within a corpus. The Zimm Approach, when tested against various subsets of the 20newsgroup dataset, produced the correct number of topics in 7 of 9 subsets vs. 0 of 9 using highest coherence value produced by LDA.

EEG-CogNet: A deep learning framework for cognitive state assessment using EEG brain connectivity

The assessment of cognitive states such as workload, attention, and fatigue is crucial in cognitive science and human performance fields due to its significant impact on work efficiency and decision-making. This paper introduces a deep learning framework for cognitive state assessment using Electroencephalogram (EEG) brain connectivity. The framework was evaluated using EEG data from 26 participants through three computer-based tasks: the Dual N-Back Task, Visual Search Task, and Continuous Performance Task, each designed to induce varying levels of cognitive workload, attention, and fatigue, respectively. A comprehensive dataset was meticulously compiled, including physiological, behavioral, and subjective data to ensure robust analyses. The EEG data underwent rigorous pre-processing and feature extraction processes, focusing on brain connectivity metrics such as Coherence and Phase-Locking Value across multiple frequency bands and employing three neural network architectures: Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), and Convolutional Long Short-Term Memory networks (ConvLSTM) for classification. The proposed work achieved classification accuracies of 96.53% for workload, 98.40% for attention, and 97.86% for fatigue in the combined frequency bands. The study’s findings underscore the potential of EEG-based methods for non-invasive cognitive state monitoring. By addressing existing limitations such as small sample sizes and task-specific models, this research enhances the generalizability and applicability of EEG-based cognitive assessments. The implications of these advancements are significant for fields requiring continuous cognitive monitoring, such as defense, healthcare, and high-risk operational environments.

Polysomnographic features prior to dream enactment behaviors in isolated rapid eye movement sleep behavior disorder

ObjectiveThis study aimed to identify electroencephalogram correlates of dream enactment behaviors (DEBs) and elucidate their cortical dynamics in patients with isolated/idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD). MethodsThis cross-sectional study included 15 patients with iRBD. Two REM sleep periods in routine polysomnography were compared: the 60 s preceding the DEBs (“pre-representative behavior” [preR]), and the 60 s with the least submental electromyogram activity (“background” [BG]). Six EEG frequency bands and electrooculogram were analyzed; power spectra, coherence and phase-locking values in four 15-s periods were examined to assess trends. These indices were also compared between preR and BG. ResultsCompared with BG, significantly higher delta power in the F3 channel and gamma power in the F4 and O2 channels were observed during preR. For functional connectivity, the widespread beta-band connectivity was significantly increased during preR than BG. ConclusionBefore notable REM sleep behaviors, uneven distributed higher EEG spectral power in both very low and high frequencies, and increased wide-range beta band functional connectivity, were observed over 60 s, suggesting cortical correlates to subsequent DEBs. SignificanceThis study may shed light on the pathological mechanisms underlies RBD through the routine vPSG analysis, leading to detection of DEBs.

Seismoacoustic Wavefield at Popocatépetl Volcano, Mexico, Captured by a Temporary Broadband Network from 2021 to 2022

Abstract Popocatépetl is a highly active stratovolcano in central Mexico with recurrent activity of Vulcanian-type explosions and frequent degassing. The proximity of Popocatépetl volcano to Mexico City, one of the most populated cities in the world, demands continuous monitoring to achieve an adequate volcano risk assessment. We present an overview of the first high-dynamic-range and high-broadband (0.01–200 Hz; 400 Hz sampling rate) seismoacoustic network (PoPiNet), which we operated around Popocatépetl volcano from August 2021 to May 2022. Here, we show preliminary results of the explosions recorded in September 2021. We deployed five seismoacoustic stations within 4–25 km horizontal distance (range) from the vent. We identify infrasonic waveforms associated with tremor and explosions, with pressures ranging from 16 to 134 Pa and dominant frequencies between 0.2 and 5.0 Hz. The frequency content of the recorded signals at the closest stations to the volcano spans the sub-bass (20–60 Hz) and bass (60–250 Hz) ranges. The associated seismic signals of moderate explosions exhibit air-to-ground coupled waves with maximum coherence values at frequencies up to 5 and 25 Hz for the farthest and closest stations to the volcano, respectively. Conversely, we observe infrasound signal amplitudes from relatively small explosions reaching maximum pressures of 10 Pa that do not couple into the ground, even at the closest stations. These infrasound signals are associated with type-I long-period events as reported in previous investigations. The waveform consistency suggests repetitive and nondestructive sources beneath the volcano.

Machine learning based urban land cover classification using PolInSAR data: a study with ALOS-2 and RADARSAT-2 datasets

A substantial variation in the land cover dynamics has been observed as a consequence of increasing urban expansion. Polarimetric synthetic aperture radar (PolSAR) data is widely being used for land cover studies in urban areas due to its all-weather, day-and-night imaging capabilities. However, in densely built-up areas, challenge arises with buildings having large Azimuth Orientation Angles (AOAs). These buildings are often misclassified as vegetation due to the depolarization of radar signal causing volumetric scattering response from the structures. This study addresses this issue by proposing an approach that integrates polarimetric information with interferometric SAR (InSAR) coherence to improve the differentiation between urban structures and vegetated areas, enhancing the accuracy of urban land-cover classification. Vegetated areas exhibit lower temporal coherence due to changes in the orientation of its leaves and branches caused by wind, seasonal variations, growth phenology, and other factors. In contrast, urban structures, being relatively stable targets, maintain high temporal coherence values. In present research various decomposition and scattering parameters were evaluated, along with PolInSAR coherence derived from L-band (ALOS-2) and C-band (RADARSAT-2), using two machine learning algorithms namely, Random Forest (RF) and Convolutional Neural Network (CNN). The C-band RADARSAT-2 data, particularly with six-component decomposition parameters, performed better, achieving an overall accuracy as 85.85% using RF algorithm. To further improve classification results, optical datasets from Landsat constellation were fused with SAR parameters using Gram-Schmidt fusion technique. This fusion led to significant improvements, achieving an overall accuracy of 94.50% and kappa statistics of 0.92, when CNN algorithm was applied to the fused optical and C-band RADARSAT-2 dataset. These results demonstrate the effectiveness of combining PolInSAR and optical data for more accurate urban land-cover classification, particularly in complex urban environments.

Phase coherence-A time-localized approach to studying interactions.

Coherence measures the similarity of progression of phases between oscillations or waves. When applied to multi-scale, nonstationary dynamics with time-varying amplitudes and frequencies, high values of coherence provide a useful indication of interactions, which might otherwise go unnoticed. However, the choice of analyzing coherence based on phases and amplitudes (amplitude-weighted phase coherence) vs only phases (phase coherence) has long been seen as arbitrary. Here, we review the concept of coherence and focus on time-localized methods of analysis, considering both phase coherence and amplitude-weighted phase coherence. We discuss the importance of using time-localized analysis and illustrate the methods and their practicalities on both numerically modeled and real time-series. The results show that phase coherence is more robust than amplitude-weighted phase coherence to both noise perturbations and movement artifacts. The results also have wider implications for the analysis of real data and the interpretation of physical systems.

Comparison between NDVI and InSAR data coherence in deformation analysis caused by the 2022 Pasaman earthquake

Abstract One of the destructive earthquakes occurred in Pasaman Barat on February 25, 2022, with a magnitude of 6.1 Mw that caused 27 deaths, 19,221 people evacuated, and an estimated 6,627 houses destroyed. Currently, InSAR is one of the fastest methods to analyze the deformation impacted by an earthquake from processing Sentinel-1 Single Look Complex images. Analyzing the deformation of the 2022 Pasaman Earthquake using InSAR could be challenging because the area consists of high vegetation. It can cause decorrelation due to time difference which increases the possibility of the low coherence value of this data. Excluding the low-coherence result is essential for a better interpretation. This study will analyze the correlation between NDVI and interferogram coherence and eliminate low-coherence data for the displacement deformation map. The NDVI value was calculated using band 4 (Red) and band 5 (NIR) from Landsat 8 images. The InSAR was processed using SNAP, starting from interferogram formation until the conversion phase into displacements, and the map visualization was designed using QGIS. This application of the InSAR method (ascending mode) from Sentinel-1 SAR images can show the deformation, but the coherence level is relatively low. Deformations around the earthquake are shown as uplift and subsidence near the epicenter of the 2022 Pasaman Earthquakes. The high value of vegetation density in the study area causes low coherence between images, so the fringes that identify deformation due to the 2022 Pasaman Earthquake were not seen significantly for the entire study area.

Analysis Of The Audio-Magnetotelluric (AMT) Data Quality Using The Coherence Parameter at Malabar Mountains

Noise recorded during measurements can diminish the quality of AMT data. This can lead to decreased penetration depth and unreliable 1D inversion models. This study aimed to reduce noise and improve data quality by analyzing coherence, curve trend, and skewness. Coherence analysis was performed using robust processing and XPR editing, yielding an average coherence value of 77.5%, an improvement of 10.8% from the raw data. Noise can also alter data dimensionality; hence, skewness analysis was used to determine the effect of distortion on the impedance tensor. Results showed that the 3D dimensionality effect at 6 stations was significantly reduced following coherence and curve trend analyses.

Local coherence by thermalized intra-system coupling

Quantum superposition of energy eigenstates can appear autonomously in a single quantum two-level system coupled to a low-temperature thermal bath, if such coupling has a proper composite nature. We propose here a principally different and more feasible approach employing engineered interactions between two-level systems being thermalized into a global Gibbs state by weakly coupled thermal bath at temperature T. Therefore, in such case quantum coherence appears by a different mechanism, whereas the system-bath coupling does not have to be engineered. We demonstrate such autonomous coherence generation reaching maximum values of coherence. Moreover, it can be alternatively built up by using weaker but collective interaction with several two-level systems. This approach surpasses the coherence generated by the engineered system-bath coupling for comparable interaction strengths and directly reduces phase estimation error in quantum sensing. This represents a necessary step towards the autonomous quantum sensing.

Topic Modeling for Scientific Articles: Exploring Optimal Hyperparameter Tuning in BERT

Topic modeling has emerged as a successful approach to uncovering topics from textual data. Various topic modeling techniques have been introduced, ranging from traditional algorithms to those based on neural networks. In this research, we explore advanced topic modeling techniques, including BERT-based approaches, to enhance the analysis of scientific articles. We first investigate a widely used Latent Dirichlet Allocation (LDA) model and then explore the capabilities of BERT, to automatically uncover latent topics within scientific papers. The goal of this study is to identify the optimal hyperparameter setting for BERT-based topic modeling of scientific articles. We conduct experiments across several scenarios involving combinations of word embedding, dimension reduction, and clustering methods. The results were analyzed based on the coherence values, average execution time, number of topics generated, visualization through the inter-topic distance map, and the top-N-words of each topic. Our findings suggest that combination of RoBERTa for word embedding, PCA for dimension reduction, and K-Means for clustering yields superior results among the tested scenarios. Further evaluation of BERT-based topic modeling is necessary to validate these findings and explore its applications in various academic and industrial contexts. The implications of these advanced techniques could significantly streamline the process of staying updated with scientific literature, potentially revolutionizing research methodologies across disciplines.

Leveraging Topic Modelling to Analyze Biomedical Research Trends from the PubMed Database Using LDA Method

Biomedical research has become an essential entity in human life. However, finding trends related to research topics in the health sector contained in the repository is a challenging matter. In this study, we implemented topic modelling to analyze biomedical research trends using the LDA method. Topic modelling was carried out using data from 7000 articles from PubMed, which were processed with text processing such as lowercase, punctuation removal, tokenization, stop-word removal, and lemmatization. For topic modelling, the LDA with corpus conditions varied to 75% and 100% for validation. Alpha and beta parameters are also set with variations between 0.01, 0.31, 0.61, 0.91, symmetry, and asymmetry when the number of the corpus is changed. When the number of the corpus is 75%, the optimal number of topics is 7, with a coherence value of 0.52. Whereas when the number of the corpus is 100%, the optimal number of topics is 10 with a coherence value of 0.51. In addition, based on the results of article topic modelling, several topics are trending, including disease diagnosis, patient care, and genetic or cell research. Based on the classification of biomedical topics into seven categories, the optimal accuracy, precision, and recall values using the Random Forest algorithm were obtained, namely 85.57%, 87.36%, and 87.58%. The results of this study suggest that topic modelling using the LDA can be used to identify trends in biomedical research with high accuracy. This information can help stakeholders make informed decisions about the direction of future research.

Interpersonal Motor Coordination in Children with Autism and the Establishment of Machine Learning Models to Objectively Classify Children with Autism and Typical Development

BackgroundThe global prevalence of autism spectrum disorder (ASD) is around 1%. Yet the current diagnosis of ASD mainly depends on clinicians' experience and caregivers' report, which are subjective, time consuming, and labor demanding. An objective and efficient way to diagnose ASD is urgently needed. The objective of this study was to quantify an omnipresent yet least studied behavioral characteristic in children with ASD – interpersonal motor coordination (IMC), and to investigate the feasibility of using IMC related features to identify ASD by implementing machine learning algorithms. MethodsTwenty children with ASD and twenty-three children with typical development (TD) were filmed in a conversation with an interviewer. Motion energy analysis was implemented to obtain the movement time series, and cross wavelet analysis (CWA) quantified the level of IMC at different movement frequencies. Machine learning algorithms were utilized to examine whether these two groups of children could be accurately classified using features of IMC. ResultsStatistical analysis revealed reduced IMC in the ASD group at relatively high movement frequencies. The establishment of machine learning (ML) models showed that the maximum classification accuracy was 85.37% (specificity = 95.24%, sensitivity = 75.00%) using five original coherence values computed with CWA. In addition, the classification accuracy could be improved to 92.68% (specificity = 95.24%, sensitivity = 90.00%) with three novel features created by taking the sum of statistically significant features. ConclusionsChildren with ASD demonstrated an atypical profile of IMC, and IMC could be used to objectively classify children with ASD and TD. In addition, our analyses showed that creating novel features based on statistically significant features could help improve classification performance. It is proposed that such economic, contactless, and calibration-free approach to data collection might well serve both ASD research and practice, particularly early objective identification. However, this study could be improved with respect to larger sample size with balanced gender ratio and different severity.

Lake Baikal Microseisms Based on Regional Seismic Network Data

Seismic ambient noise in the regional seismic network in the central part of the Baikal rift is studied. The probabilistic approach is used to thoroughly investigate the pattern of diurnal variations in microseisms and to analyze amplitude level and frequency content of spatial anomalies and temporal changes (seasonal and annual). Based on the 2020–2021 data, a regional probabilistic model of the microseismic noise is built in a wide range of periods. The study of microseisms in the frequency band of about 1 Hz revealed a seasonal anomaly against the level of the global minimum in the microseismic noise power spectrum. The anomaly is observed from May to December at seismic stations surrounding Lake Baikal except for the northern part of the lake. The direction of the back azimuth in the frequency range of about 1 Hz indicates the location of the lake, suggesting that these signals can be identified as lake microseisms. The high coherence values suggest a linear relationship between the wind speed and the occurrence of lake microseisms. The detailed analysis of the spectral and polarization parameters of seismic ambient noise revealed two types of lake microseisms with frequencies of 0.4–0.7 and 0.7–1.5 Hz. The first frequency interval is likely to correspond to single-frequency lake microseisms, while the second interval contains the frequency ranges of dual-frequency microseisms.