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Climatic drivers of floods have been widely used to improve nonstationary flood frequency analysis (FFA). However, the forecast ability of nonstationary FFA with out-of-sample prediction has not been comprehensively evaluated. We use 379 flood records from Brazil to assess the ability of process-informed nonstationary models for out-of-sample FFA using the generalized extreme value (GEV) distribution. Five drivers of floods are used as covariates: annual temperature, El Nino Southern Oscillation, annual rainfall, annual maximum rainfall, and annual maximum soil moisture content. Our results reveal that a nonstationary model is preferable when there is a significant correlation between flood and climate covariates in both the training period and full record. The rainfall-based covariates lead to better out-of-sample nonstationary FFA models. These findings highlight that using climate information as covariates in nonstationary FFA is a promising approach for estimating future floods and, hence, better infrastructure design, risk assessment and disaster preparedness.

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.

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]

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]

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]

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]

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.

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

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 Background Postural tachycardia syndrome (POTS), a subset of orthostatic dysregulation, has been reported to be associated with anxiety. POTS can be classified into two forms based on the degree of tachycardia during orthostasis. Reportedly, POTS with decreased orthostatic heart rate increase is associated with suppressed cardiac parasympathetic activity and increased sympathetic activity in the supine position. In this study, the relationship between the two types of POTS and anxiety was evaluated in terms of autonomic function. Methods Fifty-two patients (23 male, age 10–15 years) who were diagnosed with POTS at the Department of Pediatrics, Osaka Medical and Pharmaceutical University from 2019 to 2021, completed a standing test and were accordingly classified into a Su group, with tachycardia from the supine position and a low heart rate increase on standing, a SI group, with a high heart rate increase during standing. They then completed the State-Trait Anxiety Scale for Children (STAIC) questionnaire. Autonomic function was assessed by frequency analysis (MemCalc method) based on heart rate, blood pressure changes, heart rate and blood pressure variability during the orthostatic test. Results Patients in the Su group had higher trait anxiety and state anxiety, lower cardiac parasympathetic activity (RR-HF) in the supine position, and greater variability in cardiac parasympathetic activity during orthostasis than were found for patients in the SI group. The Su group had a greater decrease in cardiac index on standing than that of the SI group. Conclusions The Su group results may be partly attributed to chronically low venous return. We also found that patients in the Su group had low parasympathetic activity in the supine position, which may interact with the anxiety-prone characteristics of these patients. Therefore, it seems necessary to consider both physical and psychosomatic treatment approaches for patients with POTS.

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.

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.

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.

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.

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.

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.

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]

Copyright of Teras Jurnal: Jurnal Teknik Sipil is the property of Teras Jurnal: Jurnal Teknik Sipil 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.)

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]

This study focuses on investigating the behavior of slider–crank mechanisms with different bearing failures using a vibration analysis technique. The reliability and lifespan of bearings are crucial for such mechanisms, which convert rotary motion to reciprocating motion. Previous research primarily addressed ball-bearing failures, neglecting needle bearings due to their specific applications. To bridge this gap, our experimental setup integrated both roller and ball bearings within a slider–crank mechanism. Vibration data were collected during normal operation, as well as under failure conditions of the ball and roller bearings. By analyzing the vibration signatures during simultaneous multiple failures, we gained insights into the nature of vibrations in the system. Furthermore, a mathematical model based on Hertzian contact was employed to calculate the theoretical frequency of ball bearings; however, due to the variable motion of the needle bearing, a novel mathematical model was proposed to estimate the defective impulse frequency, considering the inter-impact time between two impacts. The experimental results were compared with the healthy crank mechanism setup to draw meaningful conclusions. This research contributes to a comprehensive understanding of bearing failures in slider–crank mechanisms and provides valuable insights for designing reliable and long-lasting systems. [ABSTRACT FROM AUTHOR]

(1) Background: Vibrotactile stimulation has been studied for tremor, but there is little evidence for Essential Tremor (ET). (2) Methods: This research employed a dataset from a previous study, with data collected from 18 individuals subjected to four vibratory stimuli. To characterise tremor changes before, during, and after stimuli, time and frequency domain features were estimated from the signals. Correlation and regression analyses verified the relationship between features and clinical tremor scores. (3) Results: Individuals responded differently to vibrotactile stimulation. The 250 Hz stimulus was the only one that reduced tremor amplitude after stimulation. Compared to the baseline, the 250 Hz and random frequency stimulation reduced tremor peak power. The clinical scores and amplitude-based features were highly correlated, yielding accurate regression models (mean squared error of 0.09). (4) Conclusions: The stimulation frequency of 250 Hz has the greatest potential to reduce tremors in ET. The accurate regression model and high correlation between estimated features and clinical scales suggest that prediction models can automatically evaluate and control stimulus-induced tremor. A limitation of this research is the relatively reduced sample size. [ABSTRACT FROM AUTHOR]

This study presents a novel approach to monitor and predict surface roughness and tool wear in the turning process, which is crucial for anticipating tool failures, reducing replacement costs, and optimizing production efficiency. The study analyzes vibration signals collected during the turning process of a stainless-steel workpiece with a carbide insert until the tool wear threshold (VB = 300 µm) is reached. Firstly, the vibration signature associated with the machine and the noise were isolated using the Fourier transform (FFT). Then, the optimal frequency band is selected to extract maximum valuable information using the estimated power spectral density (PSD) through the Welch method. The correlation between the vibration signals and surface roughness is then analyzed by calculating the average root mean square (RMS) acceleration of all the obtained PSD curves. Finally, a mathematical prediction model is extracted using a simple linear regression equation between GRMS and surface roughness. The results show a good agreement between the predicted data and the experimental values. The coefficients MSE, RMSE, and MAE have low values of 0.025, 0.1581, and 0.1174, respectively, confirming the accuracy of the proposed model. [ABSTRACT FROM AUTHOR]

In drilling small holes with diameters of 1 mm or less, minute clogging and twining of chips or the adhesion of the workpiece material can become factors in causing breakages of the drill bit; moreover, it can be difficult to identify the machining state. Acoustic emission (AE) sensing is a nondestructive inspection technique that measures the elastic-stress waves that are generated when a material is deformed and fractured. AE sensing permits highly sensitive measurements to be made without changing the rigidity of the experimental system, unlike force sensing of cutting resistance, etc. In the present study, attempts were made to identify the machining state and tool wear, and to predict abnormalities in small-diameter drilling by using the change in the frequency of AE signal waveforms arising from deformation and fracture. It was shown that it is possible to predict the breakage of the drill bit by detecting high-frequency AE signals at about 1 MHz, caused by the adhesion of the workpiece material. In addition, a correlation map of the AE frequency spectrum for identifying the machining state in a drilling operation is suggested. [ABSTRACT FROM AUTHOR]

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Background: Postural tachycardia syndrome (POTS), a subset of orthostatic dysregulation, has been reported to be associated with anxiety. POTS can be classified into two forms based on the degree of tachycardia during orthostasis. Reportedly, POTS with decreased orthostatic heart rate increase is associated with suppressed cardiac parasympathetic activity and increased sympathetic activity in the supine position. In this study, the relationship between the two types of POTS and anxiety was evaluated in terms of autonomic function. Methods: Fifty-two patients (23 male, age 10–15 years) who were diagnosed with POTS at the Department of Pediatrics, Osaka Medical and Pharmaceutical University from 2019 to 2021, completed a standing test and were accordingly classified into a Su group, with tachycardia from the supine position and a low heart rate increase on standing, a SI group, with a high heart rate increase during standing. They then completed the State-Trait Anxiety Scale for Children (STAIC) questionnaire. Autonomic function was assessed by frequency analysis (MemCalc method) based on heart rate, blood pressure changes, heart rate and blood pressure variability during the orthostatic test. Results: Patients in the Su group had higher trait anxiety and state anxiety, lower cardiac parasympathetic activity (RR-HF) in the supine position, and greater variability in cardiac parasympathetic activity during orthostasis than were found for patients in the SI group. The Su group had a greater decrease in cardiac index on standing than that of the SI group. Conclusions: The Su group results may be partly attributed to chronically low venous return. We also found that patients in the Su group had low parasympathetic activity in the supine position, which may interact with the anxiety-prone characteristics of these patients. Therefore, it seems necessary to consider both physical and psychosomatic treatment approaches for patients with POTS. [ABSTRACT FROM AUTHOR]

The analysis of hydroclimate extremes is gaining more attention due to the devastating effects of intense floods, droughts, etc. This study aims to analyze the stationary (S) and non-stationary (NS) behavior of the annual maximum temperatures (AMT) for two different climatic zones of Iran including the arid and excessively humid provinces of Kerman and West Azerbaijan, respectively. The research datasets included maximum temperature (from CRU TS) and soil moisture (from ERA5) on a monthly time scale (spanning 1901–2019 and 1979–2019). Trend, homogeneity, and stationarity tests were applied to define the basic characterization of the AMTs. The frequency analyses of the AMTs were carried out using generalized extreme value (GEV) under two assumptions of S-GEV and NS-GEV. Moreover, the fitted distribution parameters were estimated using a maximum likelihood estimator. In addition to time-varying NS-GEV investigations, the soil moisture during summer (SM-June, July, and August) was also employed as the covariate to quantify the relationship between drought and AMTs in these climatic zones. The research findings revealed that the Akaike information criterion in S-GEV and NS-GEV estimations decreased from 309 to 223 and 329 to 254 for arid and excessively humid climatic zones, respectively. Therefore, the NS-GEV frequency analyses has increasing effects on return levels of the AMTs than the S-GEV. In the following, the spatial NS-GEV investigations in all 12 and 15 stations of both provinces, showed that NS-GEV with SM as a covariate has better performance in excessively humid climatic zones. [ABSTRACT FROM AUTHOR]

The paper shows that mechanical vibrations occur in a wide frequency range in the hydraulic systems operating in the real world. Hydraulic valves are also exposed to these vibrations. The paper gives examples of vibration sources and suggests that the influence of vibrations on hydraulic valves could be reduced. Particular attention was paid to the vibrating proportional distributor. The amplitude-frequency spectrum of pressure pulsation in a hydraulic system with a vibrating proportional distributor was analysed. During the tests, the frequency of external mechanical vibrations acting on the proportional distributor and their direction was changed. [ABSTRACT FROM AUTHOR]

Due to the scarcity of established rain gauge stations, obtaining continuous time series daily rainfall observation data is a major challenge. The availability of these data is very essential to conduct hydrological studies and predicting flood events. Nowadays, several satellites could be used to provide such data. This study aims to investigate the appropriate remote sensing (RS) rainfall product and use its data to develop frequency analysis and intensity–duration–frequency curve (IDF curve) at 5-, 10-, 25-, 50-, 100-, and 200-year return periods in the study area. The evaluation of the PERSIANN family products (PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now) with NASA-POWER datasets compared to the available annual maximum of daily observation rainfall in Suez Rain Gauge (SRG) station as a reference was conducted. The results show that the PERSIANN-CDR product is the appropriate satellite-based rainfall dataset product based on the outcomes of the comparison criteria (RMSE, Bias, CC, and R2 error measures) of rainfall characteristics analysis and Chi-squared test of distributions. The frequency analysis results confirmed that the values of about 30.3–38.6 and 40–53.3 mm/day corresponding to the biggest flood reported in 1965 and 2020 in SRG station and PERSIANN-CDR product have a return period of 50–100 years, classifying it as an extreme event in Wadi Ghoweiba. These findings would considerably benefit decision-makers in estimating flood risks and planning the appropriate structure protections in the Wadi Ghoweiba Watershed. [ABSTRACT FROM AUTHOR]

Wind‐speed measurement is crucial in designing wind‐resistant buildings and controlling the wind environment in urban areas. However, the current methods of measuring wind speed, such as anemometers and Doppler lidar, are associated with high costs. This study investigated an alternative approach to measuring wind speed using the eolian sound emitted from cylindrical objects in urban settings, such as electrical cables. The proposed method relies on a relationship between the frequency of Karman vortex generation, which gives rise to eolian sound, and wind speed, considering the diameter of a cylindrical object. This study addressed 2 research questions: (1) the possibility of capturing and identifying the eolian sound emitted from a cylindrical object using a generic recording device and whether the peak frequency of the recorded sound aligns with the theoretically predicted frequency; (2) the feasibility of estimating the direction of the eolian sound source to identify the object responsible for the sound emission. The authors conducted a series of experiments involving artificially generated eolian sound in an outdoor environment and analyzed the recorded sound signals. The analysis results were encouraging, confirming positive responses to both research questions. These results indicate the potential for a novel method capable of estimating wind speed. [ABSTRACT FROM AUTHOR]

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.

This study aimed to present a dynamic simulation of hydrological drought using Vensim and the constant threshold level method in the Dorud-Borujerd basin in the 1991-2019 period. First, the threshold levels were estimated, and then the drought duration and volume were quantified. Next, the frequency of the highest duration and volume values was analyzed to determine the return periods. While confirming the superiority of the systems thinking approach, the results showed that the highest drought duration figures were 229 and 123 days, respectively, at the 70 and 90% threshold levels of the flow duration curve in the Bitun station. Tire station recorded 21.320 and 38.47 MCM for 70% and 90% threshold levels, respectively, which were the largest figures for drought volume. The frequency analysis of these figures obtained from dynamic simulation showed that the drought duration and volume will have a return period of 25 and 8 years and 4 and 14 years, respectively, at 70% and 90% threshold levels. Moreover, 2012 and 2015 were very dry years in terms of both duration and volume. Therefore, drought episodes have been occurring in the Dorud-Borujerd basin for many years and will continue with more intensity in the future.

Based on the Hodgkin–Huxley theory, this paper establishes several nonlinear system models, analyzes the models’ stability, and studies the conditions for repetitive discharge of neuronal membrane potential. Our dynamic analysis showed that the main channel currents (the fast transient sodium current, the potassium delayed rectifier current, and the fixed leak current) of a neuron determine its dynamic properties and that the GHK formula will greatly widen the stimulation current range of the repetitive discharge condition compared with the Nernst equation. The model including the change in ion concentration will lead to spreading depression (SD)-like depolarization, and the inclusion of a Na-K pump will weaken the current stimulation effect by decreasing the extracellular K accumulation. The results indicate that the Hodgkin–Huxley model is suitable for describing the response to initial stimuli, but due to changes in ion concentration, it is not suitable for describing the response to long-term stimuli.

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.

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, R2: 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.

Purpose. Natural language text processing techniques are used to solve a wide range of tasks. One of the most difficult tasks when working with natural language texts for different languages is to find certain indicators for further determining its authorship. The problem is still relevant due to the lack of a unified tool or method for working with texts in different languages. Working with texts in Ukrainian requires taking into account its peculiarities of word and sentence construction to obtain the best result. The main purpose of this article is to analyze the existing methods of text processing, their features and effectiveness in working with texts of different languages. Methodology. Natural language text processing methods are systematized by type and format, according to the tools and approaches used. For each method, its features, effectiveness, scope, and limitations are considered. The means of system analysis were used to form the final characterization of the method, taking into account its purpose and capabilities. Findings. The study of methods has revealed the following ones used for the intellectual analysis of texts in different languages, their scope, effectiveness in working with different languages, strengths and weaknesses. This will make it possible to choose an effective toolkit for working with Ukrainian texts. It has been established that intelligent text processing is a complex task that requires an individual approach to each language to take into account its peculiarities and obtain the best result. Originality. The basis for choosing an effective method for working with Ukrainian-language texts is formed, the existing methods of intellectual text processing, their application features, capabilities and efficiency in working with texts of different languages are analyzed and systematized. Practical value. The work allowed us to identify the most promising, effective and appropriate methods of intellectual analysis of natural language texts in order to use them for processing Ukrainian-language texts in the future.

In this article, the frequency characteristics of the forces and torques in the various cycloidal gearbox designs were investigated. The aim of the article is the search for frequency patterns that could be used in the formulation of a fault diagnosis methodology. Numerical analysis was performed in the cycloidal gearbox without defects as well as in cycloidal gearboxes with lobe defects or with removed lobes. The results of the numerical analysis were obtained in the multibody dynamics model of the cycloidal gearbox, implemented in Fortran and using the 2nd-order Runge-Kutta method for the integration of the motion equations. The used model is planar and uses Hunt and Crossley’s nonlinear contact modelling algorithm, which was modified using the Heaviside function and backlash to fit cycloidal gearbox model convergence demands. In the analysis of fault diagnosis methods, the coherence function and Morris minimum-bandwidth wavelets were used. It is difficult to find a unique pattern in the results to use in the fault diagnosis because of the random characteristics of the torques at the input and output shafts. Based on obtained results, a promising, low-vibration cycloidal gearbox design with removed 7 lobes of the single wheel was studied using the FFT algorithm.

【Objective】 Statistical characteristics and frequency of runoff is crucial for water resources planning and water resource project construction, especially when runoff in watersheds shows significant variation due to environmental changes. Taking upper reach of the Heihe River basin as an example, this paper examines the impact of runoff variation on statistical characteristics and frequency of the runoff analysis. 【Method】 Annual runoff and annual maximum flooding flow measured from the reach were used in our study. Two types of analysis windows, the fixed-point variable-width window (Type A window) and the variable-point fixed-width window (Type B window), were used to extract the runoff sequences (window sequences) for analysis. The P-Ш type curves were utilized to analyze the statistical characteristics of the window series, from which we calculated the runoff design values. The trends and discontinuity in the window series were identified using the linear trend analysis, MMK trend test, and heuristic segmentation method. 【Result】 ① The estimated annual runoff parameters (Ex) showed a significant increasing trend and abrupt changes in both Type A and Type B window analyses. The coefficient of variation (Cv) showed a significant increasing trend in Type A window analysis and abrupt changes in Type B window analysis. The coefficient of skewness (Cs) showed abrupt changes in Type B window analysis. Design values for different frequencies of the annual runoff showed a significant increasing trend and abrupt changes in both types of windows. ② The estimated parameters in the annual maximum showed abrupt changes in Type A window analysis, while the Ex showed abrupt changes in Type B window analysis. Cs showed a significant increasing trend and abrupt changes in Type B window analysis. Design values for different frequencies of the annual maximum flooding did not identify any significant increasing trend in Type A window analysis but showed sudden changes. In Type B window analysis, there was a significant increasing trend and abrupt changes. 【Conclusion】 Analyzing runoff processes should use long sequences to capture the whole spectrum of the runoff pattern. Using long runoff series can mitigate the influence of extreme runoffs on statistical characteristics and frequency of runoff analysis.

The paper presents the results of static and dynamic experimental tests conducted on historical heritage structures and sculptures. In recent years, there was an increasing need to address the behaviour of these types of structures due to several reasons. Diagnosing the actual condition of a historical structure involves not only identifying the cause of a detected defect, but also determining its impact and assessing whether the structure can continue to perform safely. This article utilises unconventional loading methods to generate measurable mechanical responses. In one case, a lifting procedure is employed to study strains in a composite structure, while in another example, the mass and movement of people are used as a form of loading. Proof load tests conducted on a monumental sculpture allowed for the determination of load distribution among its heterogeneous structural components, namely a bronze shell and an iron reinforcing frame. Furthermore, the static and dynamic loading of a ceiling supported by a masonry vault demonstrated its ability to withstand anticipated loads resulting from unconventional use as a temporary exhibition space.