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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]

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.)

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]

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]

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]

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. [ABSTRACT FROM AUTHOR]

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.

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.

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]

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]

The present study examines the advancements of a promising low-toxicity hypergolic propellant combination called HIP_11 for in-space applications, developed as alternative to common hypergolic toxic propellants. The fuel is based on an ionic liquid, storable, stable and simple to handle at ambient conditions, developed at the Institute of Space Propulsion, German Aerospace Centre (DLR). The compound shows hypergolicity behaviour when in contact with Hydrogen Peroxide and is a promising substitution to conventional hypergolic propellants. The present work describes the advancements in the development of HIP_11 through a dedicated experimental investigation of various components, analysing and investigating the performances. The experiments are based on a small modulable thruster that allowed to study the efficiency and stability of combustion of the propellant combination while varying various design parameters of the thruster. Specifically, the effects of different injector designs, as well as variations in combustion chamber shape, characteristic length, and operating pressure have been thoroughly examined and analysed. • Experimental testing of a novel green hypergolic propellant for in-space propulsion. • Investigation of multiple design alternatives for critical components. • Analysis of multiple injector and combustion chamber designs in hot-firing tests. • Evaluation of combustion performance in terms of combustion efficiency and stability. • Enhanced combustion stability noted for short and slender combustion chambers with impinging injectors. [ABSTRACT FROM AUTHOR]

The aim of the article is to observe how countries that have faced armed conflicts in the recent decades are perceived in a corpus of Czech, mostly journalism texts. We focus on eight countries where soldiers of the Czech Republic or the former Czechoslovak Republic have been deployed on the basis of a mandate after 1989 (Afghanistan, Bosnia and Herzegovina, Croatia, Iraq, Kosovo, Kuwait, Mali, and Yugoslavia). The picture of each country is determined via a quantitative, corpus-based approach. First, a frequency analysis is conducted in an attempt to show whether texts tend to mention some conflict-affected countries more frequently than others. Second, a collocation analysis is carried out to identify the stereotypes associated with the studied countries. The results show very different perceptions of the states, ranging from those connected solely to warfare (Iraq, Afghanistan) to those that are nowadays mostly seen as holiday destinations (Croatia). Moreover, some countries do not seem to be paid much attention at all (Mali). The outcome of the research may be of use for journalists, political scholars, and geographers and provides the respective disciplines with a new methodological approach. [ABSTRACT FROM AUTHOR]

With the continuous progress of science and technology, image processing techniques have been used increasingly in recent years. Image processing plays an indispensable role in the fields of computer vision, artificial intelligence, pattern recognition, and related fields. Improvements in basic algorithms and the development of new algorithms have resulted in considerable innovation and progress. This paper is devoted to finding new game applications in a branch of image processing. It introduces an analysis model proposed by the author and discusses the relationship between roughness in the frequency domain and visual image interpretation. By using the concept of roughness, we separated the image features into meaningful information and residual information and analysed the image in the frequency domain. The results were compared with those of traditional image processing methods. The starting point is the visual identification of a feature based on human interpretation. The image information was separated into meaningful features and the residual component to reduce the redundancy of the model. This allowed for a sparse representation of the feature information in the image. By analysing the meaningful features and residual components of an image separately, we established a relationship between the results and the original images. Parameters such as texture, morphology, and the degree of blurring were considered and we developed a parameter called "frequency roughness". The algorithm incorporates the concepts of frequency and roughness and the roughness is determined in the frequency domain. The frequency roughness algorithm successfully separated the rough features in the frequency domain and calculated the residual value in an image. This model provided more accurate image processing results than comparable methods. This paper includes an analysis and game applications of the proposed model for de-blurring, image enhancement, recognition, and other image processing tasks. Some game applications were successful, whereas others require further investigation. [ABSTRACT FROM AUTHOR]

Abstrak Banjir yang melanda Desa Leuhan Dusun Raja Ampat kabupaten Aceh Barat disebabkan limpasan air hujan yang mengenangi kawasan setempat. Penyelesaian yang tepat untuk mengatasi banjir kota adalah evaluasi kapasitas penampang drainase. Tujuan penelitian ini untuk mengetahui debit banjir rancangan dan kapasitas penampang drainase. Metode penelitian dilakukan dengan analisis hidrologi dan analisis hidrolika. Hujan rencana dan debit rancangan ditentukan dengan analisis hidrologi. Analisis debit banjir rancangan kala ulang dihitung dengan metode rasional. Kapasitas dan dimensi penampang drainase dihitung berdasarkan analisi hidrolika dengan software HEC-RAS 5.0. Hasil penelitian diperoleh hujan rencana dan debit banjir rencana untuk periode ulang sepuluh tahun yaitu, 259,957 mm dan 712,393 m³/s. Hasil analisis menunjukkan sembilan penampang drainase perlu dilakukan evaluasi. Penampang drainase mengalami luapan disebabkaan oleh ketidakamampuan menampung debit banjir pada saat terjadinya hujan. Setelah dilakukan evaluasi dan perlebaran penampang saluran drainase tinggi luapan mengalami penurunan bahkan tidak terjadi luapan di saluran drainase kawasan perumahan Desa Leuhan. Kata kunci: drainase, analisis frekuensi, debit banjir, Hec-Ras 5.0, Log Person III Abstract Flooding in Leuhan Village, Raja Ampat Hamlet, West Aceh District is caused by rainwater runoff that inundates the local area. The right solution to overcome urban flooding is the evaluation of drainage cross-sectional capacity. The purpose of this study was to determine the design flood discharge and drainage cross-sectional capacity. The research method was conducted with hydrological analysis and hydraulics analysis. Rainfall and design discharge were determined by hydrological analysis. The analysis of the design flood discharge of the return period was calculated by the rational method. The capacity and dimensions of the drainage cross section were calculated based on hydrological analysis with HEC-RAS 5.0 software. The results of the study obtained the rainfall plan and the flood discharge plan for the ten-year return period, namely, 259.957 mm and 712.393 m³/s. The analysis results show that nine drainage cross sections need to be evaluated. The drainage cross section experienced overflow due to the inability to accommodate flood discharge during rainfall. After the evaluation and widening of the drainage channel cross section, the overflow height has decreased and there is no overflow in the drainage channel of the Leuhan Village residential area. Keywords: Drainage, frequency analysis, flood discharge, Hec-Ras 5.0, person log lll

INTRODUCTION: Benzodiazepines (BDZs) are commonly used to treat the symptoms of movement disorders; however, deep brain stimulation (DBS) has become a popular treatment for these disorders. Previous studies have investigated the effects of BDZ on cortical activity, no data are currently available on their effects on deep brain regions, nor on these regions responses to DBS. How the BDZ affects the thalamus and basal ganglia in dystonia patients remains unknown. METHODS: DBS recordings were performed in ventral oralis anterior/posterior (VoaVop), ventral intermediate (VIM) and ventral anterior (VA) thalamic subnuclei, as well as globus pallidus interna (GPi) and subthalamic nucleus (STN). Evoked potentials (EP) and frequency domain analysis were performed to determine the BDZ effect on neural activities compared to the control condition (off-BDZ). Three male pediatric patients with dystonia treated with BDZ and undergoing depth electrode evaluation for clinical targeting were recruited for the study. Stimulation was administered at 25 and 55 Hz frequencies and recordings were simultaneously gathered through pairs of externalized stereoelectroencephalography (sEEG) electrodes. EP amplitude and the effect of stimulation on the frequency spectrum of activity were compared at baseline and following clinical administration of BDZ. RESULTS: Frequency analysis showed consistent reductions in activity during BDZ treatment in all studied brain regions for all patients. Evoked potential (EP) analysis showed increased subthalamic nucleus (STN) EP amplitude and decreased ventral intermediate (VIM) and STN EP amplitude during BDZ treatment. INTERPRETATION: BDZs reduce thalamic and basal ganglia activity in multiple regions and alter the efficacy of transmission between these regions. While the mechanism is unknown our results confirm the known widespread effects of this class of medications and identify specific areas within the motor system that are directly affected.

Current challenges in collecting and analyzing subway vibration data include the absence of standardized data collection methods, limitations in data analysis techniques, and an unclear understanding of the effects of geological conditions on vibrations. This study investigated vertical vibrations of tunnel walls and the ground above tunnels under different geological conditions of soft soil and rock strata at horizontal distances of 0, 15 m, and 30 m from the tunnel center line during train passages. The collected data underwent Fourier transformation and 1/3 octave processing to extract spectral characteristics and analyze transmission losses across different frequency bands. Our findings revealed two vibration peaks in the transmission process for both soft soil and rock formation geology. Specifically, high-frequency vibrations in soft soil experienced greater attenuation when transmitted from the tunnel wall to the ground at 0 m, while low-frequency vibrations in rock formations showed greater attenuation. We also observed a vibration amplification phenomenon at 15 m under soft soil geology conditions. Although low-frequency vibrations below 12.5 Hz showed slight attenuation within a 30 m test distance under both geological conditions, vibrations above 40 Hz experienced significant attenuation. These results offer valuable insights for reducing vibrations in subway superstructures and planning subway lines under diverse geological conditions. Furthermore, this study serves not only as a basis for mitigating vibrations in metro spans and designing metro lines in various geological contexts but also establishes a scientific foundation for future research. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study is to analyze the consistency, variability, and potential standardization of terminology used to describe architectural variables (AVs) and health outcomes in evidence-based design (EBD) studies. Background: In EBD research, consistent terminology is crucial for studying the effects of AVs on health outcomes. However, there is a possibility that diverse terms have been used by researchers, which could lead to potential confusion and inconsistencies. Methods: Three recent large systematic reviews were used as a source of publications, and 105 were extracted. The analysis aimed to extract a list of the terms used to refer to the unique concepts of AVs and health outcomes, with a specific focus on people with dementia. Each term's frequency was calculated, and statistical tests, including the χ2 and the post hoc test, were employed to compare their distributions. Results: The study identified representative terms for AVs and health outcomes, revealing the variability in terminology usage within EBD field for dementia-friendly design. The comparative analysis of the identified terms highlighted patterns of frequency and distribution, shedding light on potential areas for standardization. Conclusions: The findings emphasize the need for standardized terminologies in EBD to improve communication, collaboration, and knowledge synthesis. Standardization of terminology can facilitate research comparability, enhance the generalizability of findings by creating a common language across studies and practitioners, and support the development of EBD guidelines. The study contributes to the ongoing discourse on standardizing terminologies in the field and provides insights into strategies for achieving consensus among researchers, practitioners, and stakeholders in health environmental research. [ABSTRACT FROM AUTHOR]

The classical JPEG compression is a rich source of cues for forensic image analysis. However, this compression standard will in the near future be complemented by a new, highly efficient learning-based compression standard called JPEG AI. JPEG AI is fundamentally different from classical JPEG. Hence, its forensic traces can also be expected to be fundamentally different. We argue that there is a pressing need for image forensics research to investigate these traces. In this work, we characterize forensic compression traces of different AI compression algorithms. Our analysis investigates AI compression artifacts in frequency domain and in spatial domain. Both domains exhibit similar artifacts that likely stem from upsampling operations of the decoders. Additionally, we report for one AI codec another artifact in homogeneous regions. We also investigate the artifact detectability in several scenarios including unseen AI compression traces and postprocessing. Here, frequency and autocorrelation features are better on additive noise and classical JPEG post-compression, while RGB features perform better on blurred and downsampled images. [Display omitted] • Characterization of forensic cues of different AI-based image codecs. • Frequency domain and autocorrelation exhibit periodic artifacts. • Artifacts stem likely from upsampling operations and homogeneous regions. • Experiments show that these artifacts are well detectable with deep neural networks. • Further experiments show that detection generalization of artifacts is challenging. [ABSTRACT FROM AUTHOR]

The intersection of bibliographic control and information behavior is explored in 237 issues of CCQ literature published from 1980–2023 using a combination of expanded searches within three prominent journal databases and frequency analysis of the terms "user", "seek", and "behavior" in the approximately 2,391 published items. The database searches, while inconsistent, produced a set of 43 articles (approx. 2%). Frequency and mention rates analysis using Voyant provided unique insight into both high and low occurrences of the terms. Cross-referencing both data sets produced a set of 37 volumes/issues with two prominent clusters between the years of 2009 and 2017. [ABSTRACT FROM AUTHOR]

This paper focuses on the modeling and analysis of a four-groove passenger car tire, size 235/55R19, using finite element analysis. The Mooney–Rivlin material model is employed to define the hyperelastic behavior of the tire rubber compounds for all solid elements. The tire rim is modeled as a rigid body using aluminum alloy material, and the beads are modeled as beam elements using steel material. The tire model is validated in both static and dynamic domains through several simulations and is compared to published measured data. The tire is validated using footprint and vertical stiffness tests in the static domain. In the static footprint test, a steady-state vertical load is applied, and the tire–road contact area is computed. In the vertical stiffness test, a ramp vertical load is applied, and the tire's vertical displacement is measured to calculate the tire's vertical stiffness. In the dynamic domain, the tire is validated using drum-cleat and cornering tests. In the drum-cleat test, a drum with a 2.5 m diameter and a cleat with a 15 mm radius is used to excite the tire structure and obtain the frequency of the vertical and longitudinal first modes of vibration, that is, by applying the fast Fourier transformation (FFT) of the vertical and longitudinal reaction forces at the tire center. In addition to this test, the tire model is pre-steered on a flat surface with a two-degree slip angle and subjected to a steady state linear speed of 10 km/h to predict the cornering force and compute the cornering stiffness. In addition, the effect of tire longitudinal speed on the rolling resistance coefficient is then predicted at zero slip angle using the ISO 28580 rolling resistance test. The findings of this research work provide insights into passenger car tire–road interaction analysis and will be further used to perform tire rubber compound material model sensitivity analysis. [ABSTRACT FROM AUTHOR]

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In the presented study, the search for the acting force in stressor acoustic signal and common everyday acoustic signal is presented. As stressors the signals of acoustic siren of air raid alert and other dangers in the different counties were used, and as everyday signals the sound of motorbikes passing by observers were used. In total five different signals of alert sirens were used. Numerical values presented in research were obtained via frequency analysis with Hann’s window and later – via spectrogram survey. This survey allowed us to find the presence of a steady frequency components in the observed signals, and, most importantly, the presence of rises and falls in said components. These changes in frequency had their speed of change calculated for sirens and motorbikes. For the rise of frequency mean speed in the siren group was calculated as 164 Hz/second, fall was 80 Hz/second. For the motorbike, the speed of frequency rise had a mean value calculated as 166 Hz/second and fall of frequency was estimated as 67 Hz/second. Possible sources for said effect in motorbike signals are Doppler effect and rise of RPM during acceleration. During the statistical analysis via implementation of the non-parametric method due to the character of data distribution in the studied group the lack of statistically meaningful differences between speeds of frequency rise in frequency components of the signals was found. Said rise is presumed to be the acting factor in stress from everyday sounds.

This research examines the perceptions of Twitter users regarding the prevalent topics within Work-Life Balance communication before and after the COVID-19 pandemic. The pressing questions surrounding current labour market drivers are addressed, particularly regarding the ongoing Fourth Industrial Revolution and the COVID-19 pandemic's impact on communicated themes, particularly in the Human Resource Management field, where Work-Life Balance has emerged as a key concept. Social media platforms like Twitter are pivotal in fostering discussions on Work-Life Balance in society. Over the past decade, Twitter has evolved into a significant research platform researchers utilise in more than ten thousand research articles. The online discourse on Twitter raises awareness of the importance of balancing work and personal life. The COVID-19 pandemic has unveiled new facets of Work-Life Balance, with social media as a key platform for discussing these issues. This research uses Social Media Analysis based on the Hashtag Research framework. A total of 1,768,628 tweets from 499,574 users were examined, and frequency, topic, and sentiment analysis were conducted. Pre-pandemic, the most communicated Work-Life Balance topics were performance and time management, while recruitment and employee development were identified post-pandemic. Pre-pandemic, the highest proportion of negative sentiment was time management and mental health prevention, shifting to time, employee development, and mental health prevention post-pandemic. Despite the limitations of our research, a proposed redefinition of the concept is also presented, including a design for an integrated Work-Life Balance model based on topics communicated by Twitter users. Given the need for a more robust approach to redefining the concept and developing an integrative Work-Life Balance model, the article provides fresh insights for future research.

This study investigates the incidence of heat wave and cold wave condition during 2000 – 2022 in the Bathinda district of South-Western region of Punjab. Notable spikes in heat wave (HW) activity were observed in 2002 and 2022 with 29 and 27 days respectively. Similarly, for severe heat waves (SHW), 2010 and 2022 witnessed the highest frequencies recording 16 and 18 days respectively. Conversely, cold wave (CW) events peaked in 2005 and 2008 with 10 and 11 days respectively. Notably, 2008 also observed the highest frequency of severe cold wave (SCW) days with 15 days. However, results revealed decline in cold wave days towards the latter years, while severe cold wave days also exhibited decreasing frequencies like 2015 and 2016 recorded zero CW and SCW days. One key finding highlights a substantial correlation between land surface temperature (LST) and maximum air temperature during heat wave periods (R2 = 0.83), indicating LST's efficacy as an indicator for monitoring temperature trends during heat wave events.