Actual Record Research Articles

Cardiac Auscultation: A Practical Method for Instruction and Learning

In response to the worldwide deficiency in the use of standard acoustic stethoscopes for the analysis of cardiac disorders, we have developed a cost-free platform for learning and teaching of cardiac auscultation by combining graphic images with actual patient recordings.

Attention-based hybrid network for structural nonlinear response prediction under long-period earthquake

To develop structures that can respond to severe seismic events, models and tools are essential for quickly and precisely assessing and controlling the structure's performance. Long-period ground motions may pose potentially significant hazards to the safety of high-rise buildings located in basins or alluvial plains. However, traditional regression-based methods and pure data-driven machine learning methods are insufficient to address this problem of response prediction, which is characterized by non-stationarity, nonlinearity, and scarcity of samples. To tackle this issue, we propose a novel semi-explicit physics-informed deep learning method. The method incorporates the physical information into the model, thereby achieving better prediction accuracy. Through numerical and experimental validations, including hysteresis experimental data, the actual seismic response record of a six-story hotel, as well as data from single-degree-of-freedom structures across various periods and multi-story buildings in Los Angeles, Seattle, and Boston, we verified the proposed method significantly outperforms traditional regression-based methods and data-driven machine learning methods in terms of accuracy, efficiency, and robustness.

The Problems of Management of Informal Sector Activities: A Case Study of Ikwo Local Government, Ebonyi State, Nigeria

There have been growing concerns about the persistent rise in the size of the informal sector given the troubles and difficulties it presents the government in keeping an actual record of all economic activities in the state. This study investigated the reason behind this continuous rise and how this continuous rise has affected the management of informal activities in Ikwo Local Government. Adopting the Binary Logistics Regression (BLR) model, the study analysed a cross sectional dataset obtained from 200 informal sector operators. Based on the result of the empirical findings, the study discovered that government policies was a major factor that pushed individuals into the informal sector and that the persistent rise in the size informal sector was a strong factor that have made managing the sector difficult for the government. Therefore, the study established that if the government can reduce its tax rates, cut down a bit on the bureaucratic protocols in the formal sector, many informal operators will be encouraged to operate in the formal sector and thus contribute maximally to the overall economy of the state.

Multi‐view synergistic enhanced fault recording data for transmission line fault classification

AbstractFault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method.

Wide-area voltage stability assessment using loading margin sensitivity for increasing linear load levels and wind power penetration

Increase in integration of fluctuating and unpredictable renewable energy to the power grid, raise the complexity of real-time assessment of voltage stability. A precise measurement-dependent real-time wide area loading-margin-sensitivity study is a novel solution to this problem. This is a challenging task for a wind farm integrated WAMS-based large power system with GPS-aided PMU technology. The study here shows the computation of a real-time sensitivity index while increasing static load levels linearly. The index will be utilized for identifying the weakest buses for load shedding and provides situational awareness to the grid operator for load voltage control to avoid any power outages. Moreover, it assists in determining the fittest bus for load adjustment during scheduled maintenance and the auxiliary bus for instant load increment decisions based on demand. This study also investigates the effects of increasing DFIG-based wind energy penetration to determine the best wind-power penetration level. The proposed analysis was found to be accurate for voltage assessment on the WSCC three-machine nine-bus network and the New England sixteen-machine sixty-eight bus large power networks. An experimental study, to validate the results, is performed using a three-phase industrial category load on the SEL-based hardware test-bed using actual PMU recordings.

Validity of Acoustic Measures Obtained Using Various Recording Methods Including Smartphones With and Without Headset Microphones.

The goal of this study was to assess various recording methods, including combinations of high- versus low-cost microphones, recording interfaces, and smartphones in terms of their ability to produce commonly used time- and spectral-based voice measurements. Twenty-four vowel samples representing a diversity of voice quality deviations and severities from a wide age range of male and female speakers were played via a head-and-thorax model and recorded using a high-cost, research standard GRAS 40AF (GRAS Sound & Vibration) microphone and amplification system. Additional recordings were made using various combinations of headset microphones (AKG C555 L [AKG Acoustics GmbH], Shure SM35-XLR [Shure Incorporated], AVID AE-36 [AVID Products, Inc.]) and audio interfaces (Focusrite Scarlett 2i2 [Focusrite Audio Engineering Ltd.] and PC, Focusrite and smartphone, smartphone via a TRRS adapter), as well as smartphones direct (Apple iPhone 13 Pro, Google Pixel 6) using their built-in microphones. The effect of background noise from four different room conditions was also evaluated. Vowel samples were analyzed for measures of fundamental frequency, perturbation, cepstral peak prominence, and spectral tilt (low vs. high spectral ratio). Results show that a wide variety of recording methods, including smartphones with and without a low-cost headset microphone, can effectively track the wide range of acoustic characteristics in a diverse set of typical and disordered voice samples. Although significant differences in acoustic measures of voice may be observed, the presence of extremely strong correlations (rs > .90) with the recording standard implies a strong linear relationship between the results of different methods that may be used to predict and adjust any observed differences in measurement results. Because handheld smartphone distance and positioning may be highly variable when used in actual clinical recording situations, smartphone + a low-cost headset microphone is recommended as an affordable recording method that controls mouth-to-microphone distance and positioning and allows both hands to be available for manipulation of the smartphone device.

Prediction of Human Reaching Pose Sequences in Human–Robot Collaboration

Abstract In human–robot collaboration, robots and humans must work together in shared, overlapping, workspaces to accomplish tasks. If human and robot motion can be coordinated, then collisions between robot and human can seamlessly be avoided without requiring either of them to stop work. A key part of this coordination is anticipating humans’ future motion so robot motion can be adapted proactively. In this work, a generative neural network predicts a multi-step sequence of human poses for tabletop reaching motions. The multi-step sequence is mapped to a time-series based on a human speed versus motion distance model. The input to the network is the human’s reaching target relative to current pelvis location combined with current human pose. A dataset was generated of human motions to reach various positions on or above the table in front of the human starting from a wide variety of initial human poses. After training the network, experiments showed that the predicted sequences generated by this method matched the actual recordings of human motion within an L2 joint error of 7.6 cm and L2 link roll–pitch–yaw error of 0.301 rad on average. This method predicts motion for an entire reach motion without suffering from the exponential propagation of prediction error that limits the horizon of prior works.

SENTI Aid: Sentiment Analysis on Personal Relations and Aiding Mental Health

This research examined the ability of a novel mobile application designed to provide proactive mental health support by analyzing the user’s conversations and recommends interventions accordingly. Employing sentiment analysis of the user's recorded discussions with designated social contacts (parents, siblings, partner), the application identifies indicators of potential issues in mental health. A personalized chatbot then interacts with the user, offering feedback based on the sentiment analysis and engages in positive conversation to uplift user’s mood. Additionally, the system monitors the user's application activities and chatbot interaction patterns, detecting atypical behaviors for further feedback or prompting emergency alerts to pre-defined contacts. The research employed a two-phased approach: an initial pilot study with simulated data to refine the sentiment analysis and chatbot algorithms, followed by a validation study with a limited user group, utilizing actual conversation recordings. Analysis of the pilot data showed promising accuracy in identifying negative sentiments, while the validation study demonstrated a significant improvement in positive engagement and self- reported well-being among participants. Overall, the findings suggest that this multi-faceted approach using sentiment analysis and conversational AI holds potential for early detection and proactive intervention in mental health issues, justifying further investigation and refinement for broader implementation.

Temporal interference stimulation targets deep primate brain

Temporal interference (TI) stimulation, a novel non-invasive stimulation strategy, has recently been shown to modulate neural activity in deep brain regions of living mice. Yet, it is uncertain if this method is applicable to larger brains and whether the electric field produced under traditional safety currents can penetrate deep regions as observed in mice. Despite recent model-based simulation studies offering positive evidence at both macro- and micro-scale levels, the absence of electrophysiological data from actual brains hinders comprehensive understanding and potential application of TI. This study aims to directly measure the spatiotemporal properties of the interfered electric field in the rhesus monkey brain and to validate the effects of TI on the human brain. Two monkeys were involved in the measurement, with implantation of several stereo-electroencephalography (SEEG) depth electrodes. TI stimulation was applied to anesthetized monkeys using two pairs of surface electrodes at differing stimulation parameters. Model-based simulations were also conducted and subsequently compared with actual recordings. Additionally, TI stimulation was administered to patients with motor disorders to validate its effects on motor symptoms.Through the integration of computational electric field simulation with empirical measurements, it was determined that the temporally interfering electric fields in the deep central regions are capable of attaining a magnitude sufficient to induce a subthreshold modulation effect on neural signals. Additionally, an improvement in movement disorders was observed as a result of TI stimulation. This study is the first to systematically measure the TI electric field in living non-human primates, offering empirical evidence that TI holds promise as a more focal and precise method for modulating neural activities in deep regions of a large brain. This advancement paves the way for future applications of TI in treating neuropsychiatric disorders.

Reproducing burrows in modelled sedimentary strata

Studying bioturbated sedimentary strata is crucial; however, sampling these strata poses notable challenges. Modelling these strata has emerged as a promising solution to bridge this gap. This study introduces a workflow to model burrows utilizing the multipoint statistics (MPS) method. A key step in MPS modelling is the use of training images, and this study describes a process to create them using CT scans of rock samples contain burrows. These scans give a 3D visual representation of burrows in actual rock record. The process involves selecting suitable rock samples, CT scanning them, importing and processing the scans in Petrel™, and then transforming the scan data into training images which can be used for MPS modelling. The MPS models allow for precise replication of burrows, variations in their size and percentage, and modeling properties like porosity and permeability. This enables a more detailed analysis, paving the way for further advancements in understanding and simulating the geological implications of burrows. To guarantee reproducibility, this study has precisely documented the workflow with video guidance and provided the necessary data. This comprehensive documentation aims to encourage the broader adoption of MPS modelling for bioturbated strata, setting the stage for further advancements in the field.

Reliability of Self-Reported Road Crash and Violation Data of Professional Drivers: The Case of Qatar

Having accurate road traffic crashes (RTCs) and violations data is essential for establishing more realistic crash and casualty reduction programs. Although crash data commonly suffer from issues of accuracy and comprehensiveness, these factors are often disregarded in crash analyses. This research aims to evaluate the trustworthiness of self-reported RTCs and violations data in relation to actual record data among professional taxi and bus drivers in the state of Qatar. The analysis will account for divergences across socio-demographic features and driver types. A statistical analysis i.e. Wilcoxon signed-rank test was used on the collected data (one year self-reported and two years actual data) of 566 participants, comprising 361 taxi drivers and 206 bus drivers who were employed by Karwa driving school. The results revealed significant differences between self-reported RTCs and traffic violation data and actual records. The accuracy of self-reporting also varies depending on the type of driver and across various socio-demographic categories. The variations observed in the data have significant inferences for the overall reliability of the data and their influence on crash analysis.

Understanding Effects of Evolutionary Arias Intensity on Nonlinear Behavior of Hysteretic Systems and Liquefiable Grounds

ABSTRACT The effects of evolutionary Arias intensity are thoroughly investigated based on ground motion simulation and nonlinear dynamic response. A new methodology is developed to generate a series of spectral-equivalent synthetic motions with different energy accumulation paths using wavelet packets. Systematic studies are performed on dynamic responses of hysteretic systems and liquefiable grounds using the simulated motions and actual recordings. An attempt is made to develop the relationship between liquefaction-induced deformation and shaking intensity rate which is used to quantify the rate of Arias intensity accumulation. The results indicate the need of considering evolutionary Arias intensity in nonlinear dynamic analysis.

Functional connectivity via total correlation: Analytical results in visual areas

Recent studies invoke the superiority of the multivariate Total Correlation concept over the conventional pairwise measures of functional connectivity in biological networks. Those seminal works certainly show that empirical measures of Total Correlation lead to connectivity patterns that differ from what is obtained using the most popular measure, linear correlation, or its higher order and nonlinear alternative Mutual Information. However, they do not provide analytical results that explain the differences beyond the obvious multivariate versus bivariate definitions. Moreover, the accuracy of the empirical estimators could not be addressed directly because no controlled scenario with known analytical result was provided either. This point is critical because empirical estimation of information theory measures is always challenging.As opposed to previous empirical approaches, in this work we present analytical results to prove the advantages of Total Correlation over Mutual Information to describe the functional connectivity. In particular, we do it in neural networks for early vision (retina–LGN–cortex) which are realistic but simple enough to get analytical results. The presented analytical setting is also useful to check empirical estimates of Total Correlation. Therefore, once certain estimate can be trusted, one can explore the behavior with natural signals where the analytical results (that assume Gaussian signals), may not be valid. In this regard, as applications (a) we explore the effect of connectivity and feedback in the analytical retina–LGN–cortex network with natural images, and (b) we assess the functional connectivity in visual areas V1–V2–V3–V4 from actual fMRI recordings.

Remote Sensing with UAVs for Flood Modeling: A Validation with Actual Flood Records

The use of unmanned aerial vehicles (UAVs) is steadily increasing due to their capacity to capture terrain elevation data with remarkable precision and cost-effectiveness. Nonetheless, their application for estimating water surface elevations and submerged terrain, such as channel bathymetry, remains constrained. Consequently, the development of a digital terrain model that relies on UAV data during low-water periods assumes a more extensive dry channel surface area, thus alleviating the information gap regarding submerged terrain. The objective of this brief report is to validate a hydraulic model for flood calculation. To this end, a 1D steady-state hydrological model of the Ñuble River based on a UAV survey in the low-water period of 2016 was constructed in HEC-RAS v.5.0.3 and compared to water surface elevation observations of the flood on 24 June 2023. The model tends to overestimate the flood, but the errors are considered tolerable for flood calculation (on average, a 10.6% depth error was obtained for a 30-year return period flood); therefore, the hydraulic model derived from remote sensing seems to be an effective alternative for the construction of hydraulic models for flood studies.

Towards a yardstick of spatial impression: Psychophysical experiments using virtual piano stimuli

A rich “spatial impression” is an important factor in the product value of musical instruments. However, it also represents a development challenge since there is no established method to measure and control it. We introduce a novel yardstick for spatial impression, changing tacit knowledge into a measurable property. We focused on the difference in spatial impression between digital pianos and grand pianos, and conducted psychophysical experiments using piano sounds to identify the features that contribute to grand pianos’ spatial impression. We used multiple virtual stimuli which are created by varying physical parameters and conducted the experiment in a virtual environment including head-tracking from a VR headset. This allowed us to find a trend in the spatial impression and identify a physical parameter that correlates to the perceptual results of the experiment. This parameter, “phase entropy,” denotes the time complexity of the interaural phase difference. The correlation coefficient between the psychophysical experiment results based on virtual stimuli and the phase entropy was 0.8752. In addition to the virtual stimuli, we confirmed the correlation between perceptual score and phase entropy in an experiment using actual piano recordings; the correlation coefficient in this case was 0.6500.

Unconventional criticality, scaling breakdown, and diverse universality classes in the Wilson-Cowan model of neural dynamics.

The Wilson-Cowan model constitutes a paradigmatic approach to understanding the collective dynamics of networks of excitatory and inhibitory units. It has been profusely used in the literature to analyze the possible phases of neural networks at a mean-field level, e.g., assuming large fully connected networks. Moreover, its stochastic counterpart allows one to study fluctuation-induced phenomena, such as avalanches. Here we revisit the stochastic Wilson-Cowan model paying special attention to the possible phase transitions between quiescent and active phases. We unveil eight possible types of such transitions, including continuous ones with scaling behavior belonging to known universality classes-such as directed percolation and tricritical directed percolation-as well as six distinct ones. In particular, we show that under some special circumstances, at a so-called "Hopf tricritical directed percolation" transition, rather unconventional behavior is observed, including the emergence of scaling breakdown. Other transitions are discontinuous and show different types of anomalies in scaling and/or exhibit mixed features of continuous and discontinuous transitions. These results broaden our knowledge of the possible types of critical behavior in networks of excitatory and inhibitory units and are, thus, of relevance to understanding avalanche dynamics in actual neuronal recordings. From a more general perspective, these results help extend the theory of nonequilibrium phase transitions into quiescent or absorbing states.

Evaluation of Seismic Performance of Masonry Stone Pagoda: Dynamic Centrifuge Test and Numerical Simulation Analysis

This study provides a comparison of results from dynamic centrifuge tests and a finite element analysis model to confirm the behavior of a representative Korean stone pagoda cultural heritage structure against earthquakes. The indirect stiffness or physical state of the structure was estimated by measuring the natural frequency of the masonry stone pagoda. The actual natural frequency of the stone pagoda was measured to be 4.5 Hz. It was determined to have a lower natural frequency than other stone pagodas of similar size. For the experiment, a dynamic centrifugal separator test was performed on a 1/15 model. Data from an actual earthquake record, the Ofunato earthquake, were used, with various maximum acceleration levels ranging from 0.069 g to 0.424 g. In order to confirm the accuracy of the finite element analysis model, the measured natural frequency of the masonry stone pagoda was compared with the modal analysis of the finite element model. When the data obtained from the experiment and the PGA (peak ground acceleration) of the FEM analysis with a complex masonry stone pagoda using a validated model result were compared, it was found to have a low average error rate of 13%. This shows that structural behavior can be predicted for similar masonry stone pagoda structures using finite element method analysis in the future.

Rapid Thermal Processing of Kesterite Thin Films

Multinary chalcogenides with Kesterite structure Cu2ZnSn(S,Se)4 (CZTSSe) are a prospective material base for the enhancement of the photovoltaics industry with abundant and environmentally friendly constituents and appropriate electro-physical properties for building highly efficient devices at a low cost with a short energy pay-back time. The actual record efficiency of 13.6%, which was reached recently, is far below the current isostructural chalcopyrite’s solar cells efficiency of near 24%. The main problems for future improvements are the defects in and stability of the Kesterite absorber itself and recombination losses at interfaces at the buffer and back contacts. Here, we present an investigation into the rapid thermal annealing (RTA) of as-electrodeposited thin films of Cu2ZnSnS4 (CZTS). The treatment was carried out in a cold wall tubular reactor in dynamic conditions with variations in the temperature, speed and time of the specific elements of the process. The effect of annealing was investigated by X-ray diffractometry, Raman scattering and Scanning Electron Microscopy (SEM). The phase composition of the films depending on treatment conditions was analyzed, showing that, in a slow, prolonged, high-temperature process, the low-temperature binaries react completely and only Kesterite and ZnS are left. In addition, structural investigations by XRD have shown a gradual decrease in crystallite sizes when the temperature level and duration of the high-temperature segment increases, and respectively increase in the strain due to the formation of the phases in non-equilibrium conditions. However, when the speed of dynamic segments in the process decreases, both the crystallite size and strain of the Kesterite non-monotonically decrease. The grain sizes of Kesterite, presented by SEM investigations, have been shown to increase when the temperature and the duration increase, while the speed decreases, except at higher temperatures of near 750 °C. The set of experiments, following a scrupulous analysis of Raman data, were shown to have the potential to elucidate a way to ensure the fine manipulation of the substitutional Cu/Zn defects in the structure of CZTS thin films, considering the dependences of the ratios of Q = I287/I303 and Q′ = I338/(I366 + I374) on the process variables. Qualitatively, it can be concluded that increases in the speed, duration and temperature of RTA lead to increases in the order of the structure, whereas, at higher temperatures of near 750 °C, these factors decrease.

Conditionally Simulated Spatial Non-Stationary Earthquake Accelerograms Using Wavelet Packets

ABSTRACT For regional hazard analysis or engineering applications, it is necessary to simulate a group of correlated ground motions containing information from given recordings. A new method is presented to conditionally generate ground motions at unobserved points using actual recordings in this localized region. This method is based on an extension of the traditional conditional simulation using a wavelet packet. Utilizing the advantages of wavelet-packet transformation, the time and frequency characteristics of ground motions are effectively identified. Besides by decomposing the signal into non-overlapping sub-frequency bands, it is possible to conditionally simulating stationary, space-time, and Gaussian random fields. These simulated motions are consistent with the actual recordings in terms of temporal variations in the frequency domain and its design spectrum, and their coherence functions satisfy the target coherence values. Two numerical examples have been provided to illustrate the capability of the proposed method: the simulation of ensembles of earthquake accelerograms using actual recordings and blind tests in SMART-1 array.

A Computational Fluid Dynamics Modelling Approach for the Numerical Verification of the Bioclimatic Design of a Public Urban Area in Greece

Recent recordings of urban overheating reveal a dramatic increase in cities’ population exposure to heatwaves. Heatwaves’ implications are escalated in urban environments due to the intensification of the Urban Heat Island (UHI) effect. To combat the overheating and UHI adverse effects, novel urban rehabilitation actions are needed based on reliable predictions of appropriate Key Performance Indicators (KPIs) (such as pedestrian-level air temperature and thermal comfort) for alternative design scenarios. The objective of the current study is to present the application of a previously developed Computational Fluid Dynamics (CFD) model for the calculation of urban microclimatic conditions for the assessment of the UHI and thermal-comfort conditions in a central urban area in the city of Heraklion in Crete, Greece. Empirical validation of the model is performed through comparisons with monitored microclimate data (i.e., air temperature, relative humidity, wind speed) and actual sensation vote recordings found in another research study. In compliance with the latter, the validation campaign is conducted for a typical hot summer day in July 2009 from 10:00 to 16:00. The model is then used to assess the UHI effects for both the existing urban configuration and a given suggested environmental upgrade of the space. Simulations of the existing situation reveal that the squares located in the studied area already stand for efficient resistances to urban overheating, and heat vulnerabilities are detected mainly in non-shaded traffic and pedestrian roads perimetric to the squares. Based on the CFD simulations, the suggested environmental upgrading plan exhibits a reduction in the peak mean air temperature of 0.46 °C, and thermal comfort is improved by at least 5% (based on SET) throughout the studied area in summer. At the same time, simulations of winter conditions suggest that there are limited potential pedestrian thermal-sensation and building heating penalties under the considered renovation scenario.