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Autoignition widely exists in combustion devices that feature hot air. The unsteady autoignited flames caused by autoignition are potential hazards to the stable operation of combustion systems, often causing flameout or damage to the combustion devices. In this paper, we present our recent work in understanding the key factors determining the instability of autoignited turbulent jet flames and a novel method to suppress flame instability. Firstly, we studied the flame using a jet-in-hot-coflow burner. The instability behaviors were characterized using a pressure probe and a high-speed camera. When the fuel mole fraction was below a critical value, the amplitude spectrums of the unsteady autoignited flame had low-frequency and high-frequency peaks. The experimental results show that the low-frequency peak is related to the autoignition intermittency and can be eliminated by enhancing chemical reactions. The high-frequency peak is related to the autoignition frequency, and the peak amplitude can be reduced by improving reactants mixing. Considering that the heating rod can enhance the reaction as a heat source and improve mixing as a bluff body, we inserted an electric heating rod into the autoignition spatial region to suppress the flame instability. The suppression of flame instability by heating rod at different temperatures and locations were experimentally studied. The heating rod insertion can significantly reduce low-frequency and high-frequency instability. Moreover, increasing the rod temperature can also effectively reduce the amplitude of high-frequency pressure pulsation. The results show that the electric heating rod is an efficient method to suppress the instability of autoignited flame. [ABSTRACT FROM AUTHOR]

A Cylindrical shell consists of a curved surface that extends around a central axis, with its thickness being relatively small compared to its height and radius. It has wide range applications in pressure vessels and tanks, heat exchangers, aerospace etc. This article analyzes four shell surfaces, the first and third layers of functionally graded material, and the second and fourth layers of isotropic material. The natural frequency is influenced by several thick layers. The shell frequency equation is obtained by usig the Rayleigh-Ritz approach. The strain and curvature-displacement relations are derived from Sander's shell theory. Characteristic beam functions are employed to assess the dependence of axial modal and trigonometric volume fraction law is used to attain the vibration anaylsis. Results are acquired for different thickness-to-radius and length-to-radius ratios under various edge conditions. For simply supported edge circumstances, the natural frequencies are calculated by using MATLAB software. [ABSTRACT FROM AUTHOR]

Complex valued measures of finite total variation are a powerful signal model in many applications. Restricting to the d-dimensional torus, finitely supported measures can be exactly recovered from their trigonometric moments up to some order if this order is large enough. Here, we consider the approximation of general measures, e.g., supported on a curve, by trigonometric polynomials of fixed degree with respect to the 1-Wasserstein distance. We prove sharp lower bounds for their best approximation and (almost) matching upper bounds for effectively computable approximations when the trigonometric moments of the measure are known. A second class of sum of squares polynomials is shown to interpolate the indicator function on the support of the measure and to converge to zero outside. [ABSTRACT FROM AUTHOR]

Hand fatigue commonly occurs in repetitive tasks, such as cutting with scissors, leading to discomfort, reduced productivity, and musculoskeletal disorders. Recent advances in assistive technology have introduced power-assisted scissors to reduce the muscular load. Pinching and grasping mainly involve the adductor pollicis muscles of the hand. Measuring the electromyographic (EMG) activity of these muscles provides valuable insights into the muscular effort required for such tasks. Studies have indicated that power-assisted devices can effectively reduce muscle strain and fatigue. However, research on the effect of power-assisted scissors on adductor pollicis muscle activity is limited. This study examines the differences in adductor pollicis muscle activity using power-assisted scissors versus traditional scissors. The experiment consisted of 20 participants performing a scissor manipulation task with and without power assistance. Frequency analysis of the adductor pollicis muscle EMG data indicated that scissors with power assistance significantly reduced muscle fatigue (95% confidence interval, 10.705 [4.523–19.751], p < 0.01, η2 = 0.01). Understanding the efficacy of power-assisted scissors has significant implications for occupational health—particularly in professions that involve extensive manual cutting, such as tailoring and hairdressing. This study can contribute to the design of ergonomic tools that enhance comfort and reduce musculoskeletal disorder risks. [ABSTRACT FROM AUTHOR]

In order to study the influence of the external excitation frequency of the engine on the anti-resonance performance of the camshaft, ANSYS Workbench finite element analysis software was used to establish a parametric model of the camshaft of a diesel engine. The first 8 order vibration frequency and vibration mode of the camshaft were obtained by modal analysis. Based on the stress-strength interference theory of mechanical reliability, according to the relationship between the natural frequency of the structure, the external excitation frequency and the specified threshold value between them, the quasi-failure probability expression of the system based on multiple failure modes can be derived. The frequency reliability evaluation method was used to evaluate the reliability of the camshaft. The analysis results show that the frequency reliability of the camshaft meets the design requirements. Combined with the actual working conditions, the environmental assessment test of the whole machine bench was carried out in the full speed range, and the frequency reliability evaluation theory and optimization design effect were verified. On this basis, a method to avoid the resonance failure of the camshaft was proposed, which can provide support for the study of the anti-resonance performance of the camshaft. [ABSTRACT FROM AUTHOR]

With the construction of the UHV (Ultra High Voltage) AC/DC hybrid power grid and the large-scale access to renewable energy such as wind power, frequency dynamic fluctuation has become a prominent problem affecting the safe and stable operation of large power grids. The expansion of the scale of the power system makes it impossible to use traditional fine modeling to analyze the power system. In order to reduce the calculation scale and storage capacity of power system frequency dynamic simulation, it is necessary to make appropriate equivalent simplification of the external system, so the appropriate dynamic equivalent method is of great significance. This paper mainly studies the equivalent model suitable for frequency dynamic analysis of large power grids. Firstly, the typical models of generator set and load are simplified, and the parameters that have a great influence on frequency in the simplified model are obtained through characteristic analysis. Then, a dynamic aggregation method of generator governor and prime mover parameters and load parameters based on regulation performance weighting (the parameters of the generator or load are weighted and summed according to its regulation ability on the system) is proposed. This method is applied to the simulation example of the East China Power Grid. The simulation proves that the frequency of the East China Power Grid before and after equivalence can be consistent under four different faults, which verifies the effectiveness of the equivalent method proposed in this paper in the frequency dynamic analysis of large power grids. [ABSTRACT FROM AUTHOR]

This study aimed to determine the characteristics of accelerations and angular velocities obtained by an inertial measurement unit (IMU) attached to a cane between older people with and without physical frailty. Community-dwelling older people walked at a comfortable speed using a cane with a built-in IMU. Physical frailty was assessed using exercise-related items extracted from the Kihon Check List. The efficacy of five machine learning models in distinguishing older people with physical frailty was investigated. This study included 48 older people, of which 24 were frail and 24 were not. Compared with the non-frail participants, the older people with physical frailty had a small root mean square value in the vertical and anteroposterior directions and angular velocity in the anteroposterior direction (p < 0.001, r = 0.36; p < 0.001, r = 0.29; p < 0.001, r = 0.30, respectively) and a large mean power frequency value in the vertical direction (p = 0.042, r = 0.18). The decision tree model could most effectively classify physical frailty, with an accuracy, F1 score, and area under the curve of 78.6%, 91.8%, and 0.81, respectively. The characteristics of IMU-attached cane usage by older adults with physical frailty can be utilized to effectively evaluate and determine physical frailty in their usual environments. [ABSTRACT FROM AUTHOR]

Background: Forward prediction of atrial fibrillation (AF) termination is a challenging technical problem of increasing significance due to rising AF presentations to emergency departments worldwide. The ability to non‐invasively predict which AF episodes will terminate has important implications in terms of clinical decision‐making surrounding treatment and admission, with subsequent impacts on hospital capacity and the economic cost of AF hospitalizations. Methods and Results: MEDLINE, EMCare, CINAHL, CENTRAL, and SCOPUS were searched on 29 July 2023 for articles where an attempt to predict AF termination was made using standard surface ECG recordings. The final review included 35 articles. Signal processing techniques fit into three broad categories including machine learning (n = 14), entropy analysis (n = 12), and time–frequency/frequency analysis (n = 9). Retrospectively processed ECG data was used in all studies with no prospective validation studies. Most studies (n = 33) utilized the same ECG database, which included recordings that either terminated within 1 min or continued for over 1 h. There was no significant difference in accuracy between groups (H(2) = 0.058, p‐value = 0.971). Only one study assessed recordings earlier than several minutes preceding termination, achieving 92% accuracy using the central 10 s of paroxysmal episodes lasting up to 174. Conclusions: No studies attempted to forward predict AF termination in real‐time, representing an opportunity for novel prospective validation studies. Multiple signal processing techniques have proven accurate in predicting AF termination utilizing ECG recordings sourced from a database retrospectively. [ABSTRACT FROM AUTHOR]

In today's era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process such signals in both the time and frequency domains. In particular, signal processing in the frequency domain is primary in industrial practice because dominant components within a specific narrow frequency band are sought. The discrete Fourier transformation (DFT) algorithm is the tool used in practice to find these frequency components. The DFT algorithm provides the full frequency spectrum with a higher number of calculation steps, and its spectrum frequency resolution is low. Therefore, research has focused on finding a method to achieve high-frequency spectrum resolution. An important factor in selecting the technique was that such an algorithm should be implementable on a microprocessor-based system under harsh industrial conditions. Research results showed that the DFT ZOOM method meets these requirements. The frequency zoom has many advantages but requires some modification. It is implemented in high-performance analyzers, but a thorough and detailed description of the respective algorithm is lacking in technical articles and literature. This article mathematically and theoretically describes the modified frequency zoom algorithm in detail. The steps of the frequency zoom, from creating an analytical signal through frequency shifting and decimation to the frequency analysis of the signal, are realized. The algorithm allows for the analysis of a signal with high-frequency resolution in a limited frequency band. A significant modification of DFT ZOOM is that of using the Hilbert transform to create an analytic signal. This resolves the aliasing issue caused by the overlap between fundamental and sideband spectra. Results from processing deterministic and stochastic signals using the modified DFT ZOOM are presented. The presented experimental results contribute to a more detailed frequency analysis of the signal. As part of this scientific research, the issues of frequency zoom were thoroughly addressed, solving the partial problems of this algorithm, both in theory and in the context of signal theory. [ABSTRACT FROM AUTHOR]

Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties. Such signals with time-variant properties may occur at different frequencies and therefore analyzing them in the frequency domain may provide statistical validation for observations already established in the time domain. This necessitates the use of time-variant, frequency-selective signal processing methods as well, which can adeptly quantify the dominant or mean frequencies in the signal. The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise. In this work, we outline a method for spike-based frequency analysis of amperometry traces using FFT that also provides statistical validation of observations on spike characteristics in the time domain. We demonstrate the method by utilizing simulated signals and by subsequently testing it on diverse amperometry datasets generated from different experiments with various chemical stimulations. To our knowledge, this is the first fully automated open-source tool available dedicated to the analysis of spikes extracted from amperometry signals in the frequency domain. [ABSTRACT FROM AUTHOR]

With the increasing demand for sustainable infrastructure maintenance, it has become very important to predict the health condition of the structures in real-time. This study investigates the application of machine learning techniques for assessing the structural health of prototype beam bridges. By employing magnetostrictive sensors, which convert mechanical vibrations into electrical energy, the research aims to perform frequency analysis to predict dominant frequencies in a prototype beam bridge. Data were collected using a Digital Storage Oscilloscope and a Data Acquisition Card, followed by comprehensive feature extraction and dimensionality reduction. Machine learning models, including Random Forest and Deep Neural Networks, were utilised to classify waveform types and predict vibration frequencies. The Random Forest model achieved a classification accuracy of 86.16% and a mean absolute percentage error of 4.33% in frequency prediction, highlighting its superior accuracy and reliability for continuous bridge health monitoring. These results demonstrate the potential to revolutionise modern infrastructure maintenance practices by enabling real-time, automated assessments of structural integrity. [ABSTRACT FROM AUTHOR]

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms—Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks. [ABSTRACT FROM AUTHOR]

Statistical descriptions of extreme met-ocean conditions are essential for the safe and reliable design and operation of structures in marine environments. The significant wave height ( H S ) is one of the most essential wave parameters for coastal and offshore structural design. Recent studies have reported that a time-varying component exists globally in the H S . Therefore, the non-stationary behavior of an annual maximum series of H S is important for various ocean engineering applications. This study aims to analyze the frequency of H S over the northern Indian Ocean by modeling the non-stationarity in the H S series using a non-stationary Generalized Extreme Value (GEV) distribution. The hourly maximum H S data (with a spatial resolution of 0.5° longitude × 0.5° latitude) collected from the global atmospheric reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ECMWF) is used for the study. To model the annual maximum series of H S using a non-stationary GEV distribution, two physical covariates (El-Ni n ~ o Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) and time covariates are introduced into the location and scale parameters of the GEV distribution. The return levels of various frequencies of H S are estimated under non-stationary conditions. From the results, average increases of 13.46%, 13.66%, 13.85%, and 14.02% are observed over the study area for the 25-year, 50-year, 100-year, and 200-year return periods, respectively. A maximum percentage decrease of 33.3% and a percentage increase of 167% are observed in the return levels of various return periods. The changes in the non-stationary return levels over time highlight the importance of modeling the non-stationarity in H S . [ABSTRACT FROM AUTHOR]

Destination Image can be considered as both, a theoretical and practical tool, to better understand how a destination is perceived in the minds of potential visitors. Given the im- pressive growth of digital sources of tourism-related data in the last decades, methods that exploit this information have been designed to explore this construct. Due to its capacity to emulate human intelligence and its ability to uncover hidden patterns, Artificial Intelligence has captured the attention of the academic and business sectors, for this reason, several ap- proaches from tourism research take advantage of such techniques. However, to date, there is neither sufficient information about what specific methods are being employed nor an eval- uation of their usefulness for the task. In this work, we identify the main techniques, as well as the representations, measurements, and results derived from the computational science perspective related to destination image in tourism studies. As a result, two taxonomies emerged: one related to the group of methods and techniques, and the other pertaining to the results obtained through these particular methodological designs. From our analysis, we found that electronic information is gaining strength as a primary information source, how- ever, our results showed that surveys are still on the top. On the other hand, the preferred techniques for information analysis are based on word frequencies but with a growing trend in the use of neural networks and deep learning techniques. [ABSTRACT FROM AUTHOR]

Situational awareness detection and characterization of mental states have a vital role in medicine and many other fields. An electroencephalogram (EEG) is one of the most effective tools for identifying and analyzing cognitive stress. Yet, the measurement, interpretation, and classification of EEG sensors is a challenging task. This study introduces a novel machine learning-based approach to assist in evaluating situational awareness detection using EEG signals and spiking neural networks (SNNs) based on a unique spike continuous-time neuron (SCTN). The implemented biologically inspired SNN architecture is used for effective EEG feature extraction by applying time–frequency analysis techniques and allows adept detection and analysis of the various frequency components embedded in the different EEG sub-bands. The EEG signal undergoes encoding into spikes and is then fed into an SNN model which is well suited to the serial sequence order of the EEG data. We utilize the SCTN-based resonator for EEG feature extraction in the frequency domain which demonstrates high correlation with the classical FFT features. A new SCTN-based 2D neural network is introduced for efficient EEG feature mapping, aiming to achieve a spatial representation of each EEG sub-band. To validate and evaluate the performance of the proposed approach, a common, publicly available EEG dataset is used. The experimental results show that by using the extracted EEG frequencies features and the SCTN-based SNN classifier, the mental state can be accurately classified with an average accuracy of 96.8% for the common EEG dataset. Our proposed method outperforms existing machine learning-based methods and demonstrates the advantages of using SNNs for situational awareness detection and mental state classifications. [ABSTRACT FROM AUTHOR]

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract A Cylindrical shell consists of a curved surface that extends around a central axis, with its thickness being relatively small compared to its height and radius. It has wide range applications in pressure vessels and tanks, heat exchangers, aerospace etc. This article analyzes four shell surfaces, the first and third layers of functionally graded material, and the second and fourth layers of isotropic material. The natural frequency is influenced by several thick layers. The shell frequency equation is obtained by usig the Rayleigh-Ritz approach. The strain and curvature-displacement relations are derived from Sander’s shell theory. Characteristic beam functions are employed to assess the dependence of axial modal and trigonometric volume fraction law is used to attain the vibration anaylsis. Results are acquired for different thickness-to-radius and length-to-radius ratios under various edge conditions. For simply supported edge circumstances, the natural frequencies are calculated by using MATLAB software.

Abstract Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties. Such signals with time-variant properties may occur at different frequencies and therefore analyzing them in the frequency domain may provide statistical validation for observations already established in the time domain. This necessitates the use of time-variant, frequency-selective signal processing methods as well, which can adeptly quantify the dominant or mean frequencies in the signal. The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise. In this work, we outline a method for spike-based frequency analysis of amperometry traces using FFT that also provides statistical validation of observations on spike characteristics in the time domain. We demonstrate the method by utilizing simulated signals and by subsequently testing it on diverse amperometry datasets generated from different experiments with various chemical stimulations. To our knowledge, this is the first fully automated open-source tool available dedicated to the analysis of spikes extracted from amperometry signals in the frequency domain.

In recent years, deep learning technology has made significant progress in the field of target detection and recognition in synthetic aperture radar (SAR) images, yet its application still faces complex challenges. Despite deep neural network models achieving recognition rates exceeding 99% under standard operating conditions on the moving and stationary target acquisition and recognition dataset, the unique imaging mechanisms of SAR, its background dependency, variations in imaging parameters, and diversity in preprocessing lead to highly variable image statistical characteristics, thereby affecting the performance of deep learning models. Dataset bias, particularly the bias induced by different SAR image quantization methods, is one of the key factors impacting the generalization capability of models. This article delves into the impact of quantization methods on neural network performance and analyzes strategies to overcome dataset bias to enhance the stability and generalization capability of SAR image recognition. Experimental results indicate that models trained with adaptive quantization can learn more general features; linear quantization exhibits poor generalization when not enhanced, but this can be improved through data augmentation. Furthermore, pretraining and data augmentation techniques significantly enhance the classification performance of models under different quantization strategies, providing scientific evidence for optimizing SAR imaging system design and constructing reasonable datasets.

Plant growth monitoring techniques are of great interest to agricultural engineering. The interaction between root and soil water is one important plant response to environmental variations. This paper aims to develop a new method to estimate plant biological response using root-soil water interaction. It provides a case study on moisture transfer at the boundary area of a soil water retention zone (SWRZ). We produced a SWRZ around growing roots of a cultivated tomato plant in homogenous dried soil using water-saving drip irrigation. The irrigation was designed to supply moisture only in the root zone to meet the minimum need of plant growth. High-resolution soil moisture sensors were used to detect moisture transfer at the boundary area of the SWRZ. We applied frequency analysis to the acquired vibration spectrum using filtering and Fast Fourier Transform (FFT) in order to investigate the frequency content at each sensor location. Distinct frequencies of moisture vibration were identified at the boundary area of the SWRZ which indicated water transfer to the roots caused by plant water absorption. A mechanical vibration model was proposed to describe this phenomenon. The pinpoint irrigation to the root zone in the water-saving cultivation method enabled a well-structured spherical root system to form via hydrotropism. This enabled a simple method to analyze moisture transfer based on a mechanical vibration model. The results suggest a new method to estimate plant biological response by studying root-soil water interaction.

Abstract Passive non-destructive evaluation tools such as acoustic emission (AE) testing and acousto-ultrasonics (AU) approach present a complex problem in damage localisation in complex and nonhomogeneous geometries. A novel AU-guided AE frequency interpretation approach is proposed in this research work which aims at overcoming this limitation. For the experimental evaluation, the damage sources from a geometrically complex clear dental aligners are tested under cyclic compression load and their origins are evaluated. Despite the rapid worldwide diffusion of the clear aligners, their mechanical behaviour is poorly investigated. In this work, the frequency characteristics of the artificially simulated stress wave, generated from different dental positions of the clear aligners, are studied using the AU approach. These frequency characteristics are then used to analyse the AE signals generated by these aligners when subjected to cyclic compressive loading. In addition, the time domain characteristics of the AE signals are studied using their Time of Arrival (ToA). The Akaike Information Criterion (AIC) is used to estimate the ToA. These frequency and time domain characteristics of the AE signals are used to estimate the local damage origin in the clear dental aligners. This will help in identifying localised damage sources during the usage period of the aligners. Experimental results revealed significant damages in the left maxillary premolar and right maxillary third molar of the aligners.

Abstract The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability.

In sports, virtual spaces are sometimes utilized to enhance performance or user experience. In this study, we conducted a frequency analysis, semantic network analysis, and topic modeling using 134 abstracts obtained through keyword searches focusing on “sport(s)” in combination with “metaverse”, “augmented reality”, “virtual reality”, “lifelogging”, and “mixed reality”. First, the top 20 words were extracted through frequency analysis, and then each type of extracted word was retained to select seven words. The analysis revealed the emergence of key themes such as “user(s)”, “game(s)”, “technolog(y/ies)”, “experience(d)”, “physical”, “training”, and “video”, with variations in intensity depending on the type of metaverse. Second, the relationships between the words were reconfirmed using semantic networks based on the seven selected words. Finally, topic modeling analysis was conducted to uncover themes specific to each type of metaverse. We also found that “performance/scoring” was a prominent word across all types of metaverses. This suggests that in addition to providing enjoyment through sports, there is a high possibility that all users (both general users and athletes) utilize the metaverse to achieve positive outcomes and success. The importance of “performance/scoring” in sports may seem obvious; however, it also provides significant insights for practitioners when combined with metaverse-related keywords. Ultimately, this study has managerial implications for enhancing the performance of specialized users in the sports industry.

Passive non-destructive evaluation tools such as acoustic emission (AE) testing and acousto-ultrasonics (AU) approach present a complex problem in damage localisation in complex and nonhomogeneous geometries. A novel AU-guided AE frequency interpretation approach is proposed in this research work which aims at overcoming this limitation. For the experimental evaluation, the damage sources from a geometrically complex clear dental aligners are tested under cyclic compression load and their origins are evaluated. Despite the rapid worldwide diffusion of the clear aligners, their mechanical behaviour is poorly investigated. In this work, the frequency characteristics of the artificially simulated stress wave, generated from different dental positions of the clear aligners, are studied using the AU approach. These frequency characteristics are then used to analyse the AE signals generated by these aligners when subjected to cyclic compressive loading. In addition, the time domain characteristics of the AE signals are studied using their Time of Arrival (ToA). The Akaike Information Criterion (AIC) is used to estimate the ToA. These frequency and time domain characteristics of the AE signals are used to estimate the local damage origin in the clear dental aligners. This will help in identifying localised damage sources during the usage period of the aligners. Experimental results revealed significant damages in the left maxillary premolar and right maxillary third molar of the aligners. [ABSTRACT FROM AUTHOR]

Flood hazard assessment is essential for climate-change management planning. River flow frequency and size affect basin flood regime. This study evaluates the flood hazard of delta region of a large Indian river basin, the Mahanadi River basin, for possible future flood scenarios. Based on flood severity and frequency, basin flood regime change is examined. Four different flood scenarios are considered such as reference scenario (1955–2001), present scenario (1981–2011) and two possible future scenarios, by modifying the peak flood series with percentage change approach. Flood hazard was assessed based on the results of hydrodynamic modelling. Based on flood estimations at head of delta region, flood intensity is increasing in delta region of Mahanadi basin. Floods of higher return period (61 years) in the reference scenario happens to be a lower return period flood (11 years) in possible future scenario. In recent years, high floods have maximum water level profiles equal to or higher than 5- and 10-year return period floods of hypothetical future flood scenarios. Flood inundation evaluation under multiple land-uses shows that economically significant land-uses including farmland, built-up land, and aquaculture are more vulnerable to future floods. In the Mahanadi delta, 'high' and 'very high' flood depth coverage increases and 'low' depth diminishes. Inundation area for 'very high' flood depth is increasing, indicating agricultural vulnerability in the delta region. [ABSTRACT FROM AUTHOR]

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability. [ABSTRACT FROM AUTHOR]

In recent years, the new wave of change that came with the Fourth Industrial Revolution has been rapidly progressing. This wave has become a challenge that no one can resist, and individuals and companies are facing the reality that they have to accept it. Artificial intelligence (AI) and big data analytics are two of the most critical technologies in the Fourth Industrial Revolution. By utilizing R, a big data analysis tool, you can provide analysis results using appropriate charting functions through cleaned text data. The data used in this study is the information of online courses conducted in Korea from March 2015 to October 2023 (about 9 years) by the KOREA Data Industry Association. The first keyword mentioned in the final analysis was Database (147), the second was Expert (131), and the third was Big data (100). Based on the analysis results, practical implications and limitations of the study are discussed. [ABSTRACT FROM AUTHOR]

The manuscript presents the applicability of the Gumbel distribution in the frequency analysis of extreme events in hydrology. The advantages and disadvantages of using the distribution are highlighted, as well as recommendations regarding its proper use. A literature review was also carried out regarding the methods for estimating the parameters of the Gumbel distribution in hydrology. Thus, for the verification of the methods, case studies are presented regarding the determination of the maximum annual flows and precipitations using nine methods for estimating the distribution parameters. The influence of the variability of the observed data lengths on the estimation of the statistical indicators, the estimation of the parameters, and the quantiles corresponding to the field of small exceedance probabilities (p < 1%) is also highlighted. In each case, the results are analyzed compared to those obtained with the Generalized Extreme Value distribution, the four-parameter Burr distribution, and the five-parameter Wakeby distribution estimated using the L-moments method. The results of the case studies highlight and reaffirm the statistical, mathematical, and hydrological recommendations regarding the avoidance of applying the Gumbel distribution in flood frequency analysis and its use with reservations in the case of maximum precipitation analysis, especially when the statistical indicators of the analyzed data are not close to the characteristic ones and unique to the distribution. [ABSTRACT FROM AUTHOR]

This paper presents a research attempt towards frequency-based crack effect study in laminated composite beams (LCBs) through combined computational and experimental work. The focused objective of the undertaken research is to quantify the crack effects on the frequency behavior of LCBs. Towards achieving the objective, carbon/epoxy LCBs are the subject materials under consideration and to achieve the natural frequencies for the LCBs under free vibration, a combination of numerical simulation and experimental tests are conducted. ABAQUS, a powerful finite element (FE) simulation software is employed to develop precise numerical models representing the LCB's mechanical behavior and response to crack propagation. The free vibration experiments on LCB samples are conducted through a Fast Fourier Transform (FFT) analyzer under controlled crack scenarios aligning with the numerical simulation model. Through the frequency analysis, critical crack depths and positions that significantly influence the dynamic impact are identified. The study also explores the correlation between the computational and experimental results, enhancing the confidence in simulation models. The results from the study can aid in the early detection of damage and the optimization of repair strategies to ensure the structural reliability and safety of LCBs or alike composite components. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method to estimate rainfall intensity by analyzing the sound of raindrops. An innovative device for collecting acoustic data was designed, capable of blocking ambient noise in rainy environments. The device was deployed in real rainfall conditions during both the monsoon season and non-monsoon season to record raindrop sounds. The collected raindrop sounds were divided into 1 s, 10 s, and 1 min intervals, and the performance of rainfall intensity estimation for each segment length was compared. First, the rainfall occurrence was determined based on four extracted frequency domain features (average of dB, frequency-weighted average of dB, standard deviation of dB, and highest frequency), followed by a quantitative estimation of the rainfall intensity for the periods in which rainfall occurred. The results indicated that the best estimation performance was achieved when using 10 s segments, corresponding to the following metrics: accuracy: 0.909, false alarm ratio: 0.099, critical success index: 0.753, precision: 0.901, recall: 0.821, and F1 score: 0.859 for rainfall occurrence classification; and root mean square error: 1.675 mm/h, R 2 : 0.798, and mean absolute error: 0.493 mm/h for quantitative rainfall intensity estimation. The proposed small and lightweight device is convenient to install and manage and is remarkably cost-effective compared with traditional rainfall observation equipment. Additionally, this compact rainfall acoustic collection device can facilitate the collection of detailed rainfall information over vast areas. [ABSTRACT FROM AUTHOR]

Nanosecond pulsed electric fields (nsPEFs) have recently shown promise as a cancer therapy. Although nsPEF‐induced apoptotic responses have been observed, the stress on individual cell components has not been quantified. Therefore, the authors built a cell equivalent circuit, including mitochondria, to quantitatively calculate the voltage, electric field, and electrical energy applied to the cell components in the frequency domain. Additionally, pulse width and electrical energy changed in mitochondrial membrane potential over time. The results indicated that the change of pulsed voltage switched the cell stimulation pathway, resulting in different mitochondrial membrane potentials over time. The frequency response analysis confirmed that the potential on mitochondrial membranes increased under shorter pulse width conditions. This suggested that the frequency analysis provided in this paper is useful for relating the site of cellular stimulation to induce physiological effects. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

We have developed a novel device for automatic sensing, luring, and imaging insects that use substrate-borne vibrational signals for identifying and locating mating partners. The device is capable of measuring the activity patterns of these insects in a local area. It is intended to be used for monitoring pest insects; the current version of the device focuses on the treehopper species Stictocephala basalis (Walker, Hemiptera: Membracidae: Smiliinae) that may serve as a vector for Grapevine Red Blotch Disease. The device detects male treehoppers by sensing their mating calls using a piezoelectric contact microphone attached to a host plant, and lures them towards an imaging area by playing a prerecorded female mating call using a vibration exciter. This work is significant because previous efforts towards agricultural pest monitoring through biotremology methods has achieved only limited practical application. The trap has successfully detected and recorded wild treehopper mating calls and activity patterns, and it provides a pathway towards targeted, non-toxic pest control of various insect species that use vibrational communication. The system may be adapted to physically trap insects or alter damaging behavior in various cropping systems.

ABSTRACT Background Forward prediction of atrial fibrillation (AF) termination is a challenging technical problem of increasing significance due to rising AF presentations to emergency departments worldwide. The ability to non‐invasively predict which AF episodes will terminate has important implications in terms of clinical decision‐making surrounding treatment and admission, with subsequent impacts on hospital capacity and the economic cost of AF hospitalizations. Methods and Results MEDLINE, EMCare, CINAHL, CENTRAL, and SCOPUS were searched on 29 July 2023 for articles where an attempt to predict AF termination was made using standard surface ECG recordings. The final review included 35 articles. Signal processing techniques fit into three broad categories including machine learning (n = 14), entropy analysis (n = 12), and time–frequency/frequency analysis (n = 9). Retrospectively processed ECG data was used in all studies with no prospective validation studies. Most studies (n = 33) utilized the same ECG database, which included recordings that either terminated within 1 min or continued for over 1 h. There was no significant difference in accuracy between groups (H(2) = 0.058, p‐value = 0.971). Only one study assessed recordings earlier than several minutes preceding termination, achieving 92% accuracy using the central 10 s of paroxysmal episodes lasting up to 174. Conclusions No studies attempted to forward predict AF termination in real‐time, representing an opportunity for novel prospective validation studies. Multiple signal processing techniques have proven accurate in predicting AF termination utilizing ECG recordings sourced from a database retrospectively.