The architectural configuration of the space plays a crucial role in the acoustics of Neonatal Intensive Care Units (NICUs). The design of the environment, the materials used in construction, and the organisation of noise sources within the room significantly influence the noise levels present in these critical areas. For this research, noise levels found in two hospitals, with different architectural configurations and sizes but similar construction materials, have been analyzed. Data were recorded at 1-second intervals over a period of more than 24 hours. The data collected in these hospitals confirm the magnitude of the noise problem in NICUs, highlighting the urgent need to address it effectively to ensure an appropriate and safe environment for the recovery of neonates. Good architectural planning can help mitigate noise, while poor configuration can exacerbate acoustic problems, negatively affecting both patients and healthcare staff. It is crucial to involve architects, acoustic engineers, and healthcare staff in the design and renovation of NICUs to create spaces that not only meet medical needs but also consider the auditory well-being of neonates.

This paper presents a study of different types of parametric signals with application to underwater acoustic communications. In all the signals, the carrier frequency is 200 kHz, which corresponds to the resonance frequency of the transducer under study and different modulations are presented and compared. In this sense, we study modulations with parametric sine sweeps (4 to 40 kHz) that represent binary codes (zeros and ones), getting closer to the application in acoustic communications. The different properties of the transmitting signals in terms of bit rate, directivity, efficiency and power needed are discussed as well.

Accurate power curve modelling is essential to continuously evaluate the performance of a wind turbine (WT). In this work, we characterize the wind power curves using SCADA data acquired at a frequency of 5 minutes in a wind farm (WF) consisting of 5 WTs. Regarding the non-parametric methods, we select artificial neural networks (ANNs) to make curve estimations. Given that, we have the curves provided by the manufacturer of the WTs given by some very precisely measured pair of wind speed and power points. We can evaluate the difference between the manufacturer characterization and the ones estimated with the data provided by the SCADA system. Before the estimation, we propose a method of filtering the anomalies based on the characteristics provided by the manufacturer. We use three-quarters of the available data for curve estimation and one-quarter for the test. One WT suffered a break in the test part, so we can check how the test estimates reflect this problem in its wind-power curve compared to the estimations obtained in the WTs that worked adequately.

Curcumin, found in the rhizome of turmeric, has extensive therapeutic promises via its antioxidant, anti-inflammatory, and antiproliferative properties. Preclinical in vitro and in vivo data have shown curcumin to be an effective treatment for multiple cancers. These effects are drived by curcumin's ability to induce G2/M cell cycle arrest, induction of autophagy, activation of apoptotic pathways, disruption of molecular signaling, inhibition of invasion and metastasis, and by increasing the efficacy of existing chemotherapeutics. Here we focused on the hormetic behaviour of curcumin. Frequently, low doses of toxins and other stressors not only are harmless but also activate an adaptive stress whereas high dose activates acute responses like autophagy and cell death. This phenomenon is referred to as hormesis. Many molecules that cause cell death elicite an initial autophagic step that is a cytoprotective mechanism relying on elimination of dysfunctional structures intracellular, notably by mitophagy. This phenomenon is considered as a primarily protective mechanism against stressors. At higher doses, cells undergo mitochondrial outer membrane permeabilization due to calcium release from the endoplasmic reticulum and die. Herein, we address the complex crosstalk between the induced mitochondrial biogenesis, mitochondrial destabilization accompanied by mitophagy and cell death that can also be at play.

This review investigates links between post-acute sequelae of SARS-CoV-2 infection (PASC), post-infection viral persistence, mitochondrial involvement and aberrant innate immune response and cellular metabolism during SARS-CoV-2 infection. Advancement of proteomic and metabolomic studies now allows deeper investigation of alterations to cellular metabolism, autophagic processes and mitochondrial dysfunction caused by SARS-CoV-2 infection while computational biology and machine learning have advanced methodologies of predicting virus-host gene and protein interactions. Particular focus is given to interaction between viral genes and proteins with mitochondrial function and that of the innate immune system. Finally, the authors hypothesise that viral persistence may be a function of mitochondrial involvement in sequestration of viral genetic material. While further work is necessary to understand the mechanisms definitively, a number of studies now point to resolution of questions regarding the pathogenesis of PASC.

This research is aimed to develop the method for damage degree evaluation in metals and alloys during their deformation and destruction based on acoustic emission (AE) technique. Experimental studies were carried out on tensile specimens of high-strength steel with simultaneous registration of AE signals in tested material. It was shown that the time-dependent AE primary parameters, such as the AE signal maximum amplitudes and AE activity, do not have a clear relationship with the material damage degree. This fact makes it difficult to use standard methods for assessing the damage degree of acoustic signal sources, including defects. It was proposed to use the Kolmogorov-Smirnov criterion to differ the processes of metal plastic deformation and crack propagation during the rupture. The possibility of estimating the material damage degree by high-energy AE hits share was shown. Besides, a particular expression was obtained for evaluating the damage degree for the material under study.

The article presents the results of tests related to failure analysis and finding ways to diagnose used semiconductor elements, among other, in power electronics converter systems on vessels and offshore facilities (drilling and production rigs, wind turbines). Diagnostic relationships were found between the temperature change in the above systems and the signals generated in form of elastic waves of acoustic emission.

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.

In this work, we explore the ability to dynamically group the Wind Turbine (WT) of a Wind Farm (WF) based on the behaviour of some of their Supervisory Control And Data Acquisition (SCADA) signals to detect the turbines that exhibit abnormal behaviour. We centre this study on a small WF of five WTs. We use the fact that the same signals from different turbines in the same WF coherently evolve temporally in a time domain, describing very similar waveforms. In this contribution, we use averaged signals from the SCADA system and omit maximums, minimums and standard deviations, focusing mainly on velocities and other slowly varying signals. For the temporal analysis, sliding windows of different temporal durations and overlapings are explored. The capability to automatically identify WTs whose signals differ from the group’s behaviour can alert and program preventive maintenance operations on such WTs before a major breakdown occurs.

Acoustic emission monitoring (AEM) has emerged as an effective technique for detecting wire-breaks resulting from, e.g., stress corrosion cracking, and its application on prestressed concrete bridges is increasing. The success of this monitoring measure depends crucially on a carefully designed sensor layout. For this the attenuation of elastic waves within the structure’s material is determined ideally in-situ through object-related measurements with a reproducible signal source, typically a rebound-hammer. This assumes that the attenuation coefficients derived from rebound-hammer tests are comparable to those from wire-breaks, thus allowing their results to be directly applied to wire-break detection without further adjustments. This study challenges this assumption by analysing attenuation behaviour through an extensive dataset. Employing time-domain and frequency analysis, the research generates attenuation profiles from laboratory experiments and in-situ measurements across various girders and bridge structures, extracting the slope and standard deviation of the residuals. While generally validating this approach, the findings highlight differences in attenuation behaviour from among wire-break signals and rebound-hammer impulses, whereby the latter potentially underestimates the relevant attenuation of wire-breaks by approximately 20 %. Consequently, a transfer factor is proposed to adjust object-related measurement results for wire-break scenarios, including a variance of 1.0 dB/m to cover a 95 % confidence interval for sample scattering. Moreover, the anisotropic attenuation behaviour across different structures was studied, showing that transverse attenuation consistently exceeds the longitudinal, significantly influenced by structural features such as voids. In prefabricated concrete bridges with in-situ-cast concrete slabs, transverse signal transmission remains unhindered across multiple elements. Finally, the results provide a valuable reference for the design of sensor layouts in bridge monitoring, particularly benefiting scenarios where direct in-situ experiences are lacking.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

A configurable design of a microstrip square spiral RFID tag antenna, for a wide range of microwave frequencies in S- and C-band, is presented. The design is parameterized in dimensions, and hence changing the design frequency (or frequencies) is easy, by changing only an initial value for the spiral geometry. A tag specimen was fabricated using Cu electroplating technique according to the design for frequencies of interest in the areas of 2.4 and 5.8 GHz. The substrate material is a 320 μm high resistivity Si and the bridge dielectric is a 15μm polyimide PI2525. The microfabrication process involves steps of metallic structure pattern transfer techniques with optical UV lithography procedures. The reflection coefficient and gain of the specimen were measured inside an anechoic enclosure using a vector network analyzer (VNA) and a TEM horn test antenna over a frequency range of up to 6 GHz. Simulated and measured results are presented and discussed, exhibiting reasonable agreement.

The paper describes the new method for image compression based on coordinated group signal transformation. The algorithm is a type of difference coding. Coordinated processing significantly simplifies the difference signal conversion scheme using a single group codec for all signals. The described method considers color channels as correlated signals of a multi-channel communication system. The specifics of the processed data required modification of the coordinated group processing algorithm. First, we changed how to calculate the difference signals to prevent data loss. Secondly, the group codec was supplemented with a neural network to improve the quality of reconstructed images. We considered the following types of neural networks: fully connected, recurrent, convolution, and convolution in the Fourier space. Based on the simulation results, it is best to use fully connected neural networks if the goal is to minimize processing delay time. These networks have a response time of 13 ms. On the other hand, if the priority is to improve quality in cases where delays are not critical, then convolution neural networks in the Fourier space should be used. These networks have a response time of 140 ms and are of significant interest.

German noise action plans aim to reduce negative health outcomes from noise exposure and identify quiet areas free of noise pollution. Quiet area identification in German noise action plans is based primarily on noise mapping and spatial analysis and not empirical or qualitative data about acoustic environments, thus leaving a gap in the understanding of the quality of formally recognized quiet areas in noise action plans. This work presents a comparative empirical case study in Dortmund, Germany, with the aim to describe the diurnal dB(A) and biophonic properties of quiet areas versus noise 'hot spots'. Sound observations were collected (n=282,764) in five different natural or recreational land use patch types larger than four acres within 33 proposed quiet areas in Dortmund (n=70) and 23 noise hot spots between April 27^th, 2022 and March 2^nd, 2023. We find that quiet areas are on average more than 20 dB(A) quieter than noise hot spots almost every hour of the day. Forests, managed tree stands, cemeteries, and agriculture diel patterns are dominated by dawn dusk chorus in spring and summer, whereas sport and recreation as well as noise hot spots are dominated by traffic and human noise. A novel composite biophony mapping procedure is presented that finds distinct temporal distribution of biophony in forested and agriculture peri-urban locations positively associated with patch size, distance away from LDEN > 55, proximity to water, and the number of vegetation layers in the plant community. Anthrophony distribution dominates urban land uses in all hours of the day, but expands during day and evening and contracts at night and in dusk hours. The procedures presented here illustrate how qualitative information regarding quiet areas can be integrated into German noise action planning.

Time evolution of the quantum correlation of the hybrid qubit-qutrit system in multilocal and global dephasing under the effect of a classical noises environment is presented. We considered the qutrit in z direction and the qubit x direction. In addition, our system presents two configurations: the different and the common environment and our discussion involves three types of noises: A Static Noise (SN), Random Telegraph Noise (RTN) and Ornstein- Uhlenbeck noise (OU). By using the density matrix approach to determine the evolution of the state of the system, the found that the static noise is fatal for the quantum entanglement, the quantum correlation and for RTN and OU noises the quantum entanglement, the quantum correlation decreases monotonically with a sufficiently long time. Therefore, we can say that, entanglement and the quantum correlation are more robust to the noise precisely those Random telegraph noise and Ornstein-Uhlenbeck noise.

Vibration signal analysis is regarded as a fundamental approach in diagnosing faults in rolling bearings, and recent advancements have shown notable progress in this domain. However, the presence of substantial background noise often results in the masking of these fault signals, posing a significant challenge for researchers. In response, an Adaptive Denoising Autoencoder (ADAE) approach is proposed in this paper. The data representations are learned by the encoder through convolutional layers, while the data reconstruction is performed by the decoder using deconvolutional layers. Both the encoder and decoder incorporate adaptive shrinkage units to simulate denoising functions, effectively removing interfering information while preserving sensitive fault features. Additionally, dropout regularization is applied to sparsify the network and prevent overfitting, thereby enhancing the overall expressive power of the model. To further enhance ADAE's noise resistance, shortcut connections are added. Evaluation using publicly available datasets under scenarios with known and unknown noise demonstrates that ADAE effectively enhances the signal-to-noise ratio in strongly noisy backgrounds, facilitating accurate diagnosis of faults in rolling bearings.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

The production of recombinant proteins in Escherichia coli is often challenged by cytoplasmic expression due to proteolytic degradation and inclusion body formation. Extracellular expression can overcome these problems by simplifying downstream processing and improving protein yields. This study aims to compare the efficiency of two Bacillus subtilis chitosanase signal peptides in mediating extracellular secretion in E. coli. We identified a naturally occurring mutant signal peptide (mCsn2-SP) from B. subtilis CH2 chitosanase (CH2CSN), which is characterized by a deletion of six amino acids in the N-region relative to the signal peptide (Csn1-SP) from B. subtilis CH1 chitosanase (CH1CSN). The CH1CSN and CH2CSN genes were cloned into the pET-11a vector and protein secretion was evaluated in E. coli BL21(DE3) host cells. Expression was induced with 0.1 mM and 1 mM IPTG at 30°C for one and three days. CH2CSN showed higher secretion levels compared to CH1CSN under all experimental conditions, especially with 0.1 mM IPTG induction for 3 days, which resulted in a 2.37-fold increase in secretion. These results suggest that signal peptide deletion variants may have an effect on protein secretion efficiency and help to further understand the function of signal peptides.

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

The occurrence of chaotic instability of oscillations at the output of a single-stage amplifier on a powerful bipolar microwave transistor when a biharmonic signal is amplified is considered. It has been established that at a high level of the input signal at the output of the amplifier, the signal contains a significant chaotic component. Cases were experimentally studied when the frequency distance between the partial frequency components of a biharmonic signal is close to the gain band and when it is much smaller than this band. It has been established that in the first case, the chaos in oscillations is associated with the emergence of a sequence of trains of a forced oscillatory process with arbitrary initial phases and durations. In the second case, the appearance of a chaotic pedestal is associated with the chaos in the passive underexcited mode of the nonlinear circuit formed by the capacitance of the p-n junction and the input matching circuits of the amplifier stage.

Weak signal spectral detection is a serious problem in many applications, especially the target possesses certain frequency band and under low signal-to-noise ratio (SNR). A kind of novel technique based on pre-whitening scale-transformation symmetric bistable parallel stochastic resonance (SR) is proposed to solve the problem. Firstly, the pre-whitening can ensure the even distribution of the receiving signal to fit the requirement of SR processing. Secondly, the scale- transformation can help to utilize the weak signal improvement property especially under low frequency band more effectively. Thirdly, the symmetric bistable parallel SR can try to reduce the variance of the clutter and additive noise fluently. Fourthly, by subtracting the steady state re-sponse of the selected symmetric bistable SR system from the parallel SR output, the weak signal spectral detection objective can be realized successfully. Simulation results based on the real sea clutter radar data guarantee the effectiveness and applicability of the proposed symmetric bistable PSR processing approach.

Plant activators have emerged as promising alternatives to conventional pesticides for crop disease manage-ment due to their unique mode of action. By priming the plant's innate immune system, these compounds can induce systemic acquired resistance against a broad spectrum of pathogens without directly inhibiting their proliferation. Key advantages of plant activators include prolonged defense activity, lower effective dosages, and negligible risk of developing pathogen resistance. Among the various defensive pathways tar-geted, the salicylic acid (SA) signaling cascade has been extensively explored, leading to the successful devel-opment of commercial activators like benzothiadiazole for widespread application in crop protection. While the action sites of many SA-targeting activators have been preliminarily mapped to different steps along the pathway, a comprehensive understanding of their precise mechanisms remains elusive. This review provides a historical perspective on plant activator development and outlines diverse screening strategies employed across multiple levels, from whole-plant bioassays to molecular and transgenic approaches. We expound on the intricate components, biological relevance, and regulatory circuits governing the SA pathway, while criti-cally examining the structural features, bioactivities, and proposed modes of action of classical activators such as benzothiadiazole derivatives, salicylic acid analogs, and other small molecules. Insights from field trials as-sessing their practical applicability are also discussed. Furthermore, we highlight the current status, challenges, and future prospects in the realm of SA-targeting activator development globally, with a focus on recent en-deavors in China. Collectively, this comprehensive review aims to synthesize existing knowledge and provide a roadmap for future research toward developing more potent and mechanistically understood plant activa-tors that fortify crop immunity against disease.

Analysis of non-stationary and nonlinear sound signals obtained from dynamical processes is one of the greatest challenges in signal processing. Turning machine operation is a highly dynamic process persuaded by many events such as dynamical responses, chip formations and operational conditions of machining. Traditional and widely used fast Fourier transformation and spectrogram is not suitable for processing sound signals acquired from dynamical systems as its result have significant deficiencies because of stationary assumptions and having priori basis. The relatively new technique, Discrete Wavelet Transform, which uses Wavelet decomposition and the recently developed technique, Hilbert Huang Transform (HHT) which uses Empirical Mode Decomposition have notably better properties in the analysis of nonlinear and non-stationary sound signals. This paper presents a comparative study on the decomposition of multicomponent sound signals using Empirical Mode Decomposition and Wavelet Decomposition to highlight the suitability found in HHT to analyze tool-emitted sound signals received from turning processes. Apart from the short mathematical and theoretical foundations of the transformations, this paper demonstrates their decomposition strength using an experimental case study of tool flank wear monitoring in turning. It also concludes HHT is more suitable than DWT to analyze tool-emitted sound signals received from turning processes.

The acoustic environment in learning spaces significantly influences learning effectiveness and occupant well-being. Recent studies have shown that a series of architectural factors within physical learning environments can impact students' educational experiences. This research delves into the relationship between architectural characteristics and the perceived acoustic environment among 467 primary students in 19 learning spaces across 9 schools in Chile. The survey comprises a student questionnaire, a checklist evaluating space characteristics, and acoustic measurements of indoor noise levels during occupied conditions. Examined topics include Indoor Environmental Quality (IEQ) comfort perception, noise annoyance, hearing ability, and acoustic preference. Architectural characteristics considered encompass flexible and acoustic space design parameters. The results indicated that noise discomfort was a common issue, especially in traditional learning spaces without acoustic treatment. The type of learning space (traditional or flexible settings) has a greater impact on acoustic perception particularly concerning noise discomfort and acoustic preference. However, poorly acoustic spaces lacking absorbing materials or exhibiting lower spatial density (m2 per student) contribute to increased indoor noise levels affecting noise annoyance caused by children's talking inside. The study emphasizes the importance of considering both architectural design and acoustic conditioning for optimizing the acoustic experience in educational spaces.

In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted, and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x10^4 – 2x10^7 CFU per ml and has a sensitivity of S = 1.655x10^5 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10^-9 grams (g), which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

In contemporary construction, the prevalence of vibration serviceability issues in lightweight and slender structures has become increasingly common, owing to advancements in building materials and construction methods. While these structures often meet the criteria for ultimate limit states, they can still elicit complaints due to excessive vibrations induced by human activity. To address this challenge, the Integral Resonant Control (IRC) technique has emerged as a favored approach for actively damping vibrations in various systems. This study introduces a fresh perspective on implementing a multi-input multi-output (MIMO) IRC scheme for active vibration control (AVC) specifically tailored for pedestrian structures utilizing inertial mass actuators. Building upon a common framework and design methodology outlined in previous research, this work presents a novel application of MIMO IRC for AVC. The designed controller is rigorously tested and implemented on a laboratory floor structure to validate its efficacy.

The partial discharge (PD) event in high voltage insulation releases energy, exerts mechanical pressure, and generates elastic waves. Detecting and locating these PD events through short-duration acoustic pulses is well-established, particularly in gas-insulated systems and oil-insulated transformers. However, its full potential remains untapped in solid insulation systems, where the propagation capability of acoustic pulse and the acoustic reflections pose fundamental challenges to the Acoustic Emission (AE) detection technique. This study investigates the influence of reflections and multiple paths on the propagating acoustic pulse in polymeric insulating materials using a finite element method (FEM) in COMSOL. It was observed that the reflections from the boundary influence the propagating pulse's shape, peak magnitude, and arrival time. An analytical MATLAB model further quantifies the impact of multiple propagation paths on the shape, magnitude, and arrival time of the pulse travelling in a cylinder. Additionally, a Perfect Matched Layer (PML) was implemented in the COMSOL model to eliminate reflections from the boundary, and it revealed that the acoustic pulse magnitude decreases with distance following the inverse square law. In essence, the models aid in measuring how reflections contribute to the observed signals, facilitating the precise identification of the source of the PD event in the tested system.

In this paper, we introduce new formulas for calculating the restoring and repulsive magnetic forces between two non-coaxial circular loops with parallel axes. These formulas are derived from a modified version of Grover’s formula for mutual inductance between the coils in question. Utilizing these formulas, we compute the restoring and repulsive magnetic forces between two non-coaxial thick coils of rectangular cross-sections with parallel axes. In these calculations, we apply the filament method and conduct investigations to determine the optimal number of subdivisions for the coils in terms of computational time and accuracy. The method presented in this paper is also applicable to all conventional non-coaxial coils, such as disks, solenoids, and non-conventional coils like Bitter coils, all with parallel axes. This paper emphasizes the accuracy and computational efficiency of the calculations. Furthermore, the new method is validated according to several previously established methods

Pipelines extend thousands of kilometers to transport and distribute oil and gas. Given the challenges often faced with corrosion, fatigue, and other issues on steel pipes, the demand for glass fiber reinforced plastics (GFRP) pipes is increasing in the oil and gas gathering and transmission systems. However, the medium transported through these pipelines contains multiple acid gases such as CO2 and H2S, as well as ions including Cl-、Ca2+、Mg2+、SO42-、CO32- and HCO3-. These substances can cause a series of problems, such as aging, debonding, delamination, and fracture. In this study, a series of aging damage experiments were conducted on V-shaped GFRP pipes with depths of 2mm and 5mm. The aging and failure of GFRP were studied under the combined effects of external force and acidic solution using acoustic emission (AE) techniques. It was found that the acidic aging solution promoted matrix damage, fiber/matrix desorption, and delamination damage in GFRP pipes over a short period. However, the overall aging effect was relatively weak. Based on the experimental data, the SSA-LSSVM algorithm was proposed and applied to the damage pattern recognition of GFRP. An average recognition rate of up to 90% was achieved, indicating that this method is highly suitable for analyzing AE signals related to GFRP damage.

Article explores participant specific study of muscle inherehit dynamics hidden inside the diognostic electromyography (D-EMG) signals during load pull exercises, aiming to uncover underlying patterns and nonlinear behaviors. Employing a multi-step analytical framework, the study first evaluates signal stationarity through Quantile-Quantile (Q-Q) plot and surrogate data analyses, revealing insights into signal stability. Subsequent use of phase space reconstruction techniques exposes intrinsic dynamics within the EMG signals, visualized through Poincar´e maps, elucidating attractor geometry and system dynamics. Histogram analysis further dissects the Poincar´e maps, offering insights into signal behavior relative to physiological processes. Additionally, the study quantifies the largest Lyapunov exponent from the Poincar´e maps, providing a measure of dynamical complexity within the muscle EMG signals during load pull exercises. By integrating these methods, this research sheds light on the nuanced interplay between EMG signals and muscle activity during dynamic exercises, offering valuable insights for rehabilitation strategies.

Remote Photoplethysmography (rPPG) has emerged as a non-intrusive and promising physiological sensing capability in HCI research, gradually extending its applications in health-monitoring and clinical care contexts. With advanced machine learning models, recent datasets collected in real-world conditions have gradually enhanced the performance of rPPG methods in recovering heart-rate and heart-rate variability metrics. However, the signal quality of reference ground-truth PPG data in existing datasets is by and large neglected, while poor quality references negatively influence models. Here, this work introduces a new imaging blood volume pulse (iBVP) dataset of synchronized RGB and thermal infrared videos with PPG ground-truth signals from the ear and its high resolution signal quality labels, for the first time. Participants perform rhythmic breathing, head-movement, and stress-inducing tasks, which help reflect real-world variations in psycho-physiological states. This work conducts dense (per sample) signal quality assessment to discard noisy segments of ground-truth and corresponding video frames. We further present a novel end-to-end machine learning framework, iBVPNet that features an efficient and effective spatio-temporal feature aggregation for reliable estimation of BVP signals. Finally, this work examines the feasibility of extracting BVP signals from thermal video frames, which is underexplored. The iBVP dataset and source codes are publicly available for research use.

This study aims at an acoustic design of the classical concert hall and evaluation of the acoustic performance. In terms of three acoustic parameters (i.e., reverberation time (RT), clarity (C80), and lateral fraction (LF)), this study performed acoustic simulation modeling and site measurement with the K Art Hall located in South Korea as a case study. First, in order to meet the acoustic performance of the K Art Hall (target RT: 1.4~1.7 seconds, target C80: -2dB or more +2dB or less, and target LF: 10~35%), the finish materials and shape of the room as an interior acoustic design were determined. Second, the average values of the RT, C80, and LF using the acoustic simulation modeling were estimated at 1.4 second, 1.2~1.6 dB, and 29%, respectively. Third, the average values of the RT, C80, and LF through site measuring were measured at 1.5~1.64 second, 0.07~1.31dB, and 22.22~31.37%, respectively. Thus, the results of both the acoustic simulation modeling and site measuring were analyzed so as to satisfy the target acoustic performance. The results of this study will help the decision-makers (i.e., owner, construction managers, etc.) to plan the classical concert hall in terms of the RT, C80, and LF.

This paper addresses the analysis for the nonlinear vibration response of a rectangular tube with a flexible end and non-rigid acoustic boundaries. This is a further work of the linear structural acoustic problem in a well-known acoustic book. In fact, the acoustic boundaries of an enclosed space sometimes are non-rigid and the structural vibration responses are nonlinear. These two points are the focuses of this paper. The multi-level residue harmonic balance method is applied to this nonlinear structural acoustic problem. The results obtained from the multi-level residue harmonic balance method and numerical method are generally in good agreement. The effects of excitation magnitude, tube length, and phase shift parameter etc. are examined.

Metamaterials, man-made composites scaled smaller than the wavelength, have demonstrated a huge potential in their applications in acoustics, opening up for sub--wavelength acoustic absorbers, acoustic invisibility, perfect acoustic mirrors and acoustic lenses for hyper focusing, acoustic illusions and enabling new degrees of freedom in the control of the acoustic field. The zero, or even negative, refractive sound index of metamaterials offers possibilities in control of the acoustic pattern and sound at sub--wavelength scales. Despite the tremendous growth of the research on acoustic metamaterials during the last decade, the potential of metamaterial-based technologies in aeronautics is still not fully explored and its utilization is still in its infancy. Thus the principal concepts mentioned above could very well provide means to develop devices that would allow the mitigation of the impact of the civil aviation noise on the community. This paper gives a review of the state of the art of the most relevant works on acoustic metamaterials, analyzing them against their potential applicability in aeronautics, and in this process identifying possible implementation areas and interesting metabehaviors. It also identifies some technical challenges and possible future directions for research with the goal of unveiling the potential of metamaterials technologies in aeronautics.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

In this paper, the time-resolved model of photoacoustic signal for samples with a complex inner structure is derived including local non-equilibrium of structural elements with multiple degrees of freedom, i.e. structural entropy of the system. The local non-equilibrium is taken into account through the fractional operator. By analyzing the model for two types of time-dependent excitation, a very short pulse and a very long pulse, it is shown that the rates of non-equilibrium relaxations in complex samples can be measured by applying the derived model and time-domain measurements. Limitations of the model and further directions of its development are discussed.

Chymotrypsin is an important proteolytic enzyme in human digestion system that cleaves milk proteins, through hydrolysis reaction, which makes it interesting subject to study activity of milk proteases. In this work, we compared detection of chymotrypsin by spectrophotometric, dynamic light scattering (DLS) and quartz crystal microbalance (QCM) methods and determined the limit of chymotrypsin detection (LOD), 0.15 ± 0.01 nM for spectrophotometric, 0.67 ± 0.05 nM for DLS and 1.40 ± 0.30 nM for QCM methods, respectively. We discuss peculiarities and give perspective for implementation of detection methods and note that while the optical detection methods are simple to implement, the QCM method is more robust for sample preparation. We give an overview on methods and instruments for detection of chymotrypsin and other milk proteases.

The RV144 trial represents the only vaccine trial to demonstrate any protective effect against HIV-1 infection. While the reason(s) for this protection are still being evaluated, it serves as justification for widespread efforts aimed at developing new, more effective HIV-1 vaccines. Advances in our knowledge of HIV-1 immunogens and host antibody responses to these immunogens are crucial to informing vaccine design. While the envelope (Env) protein is the only viral protein present on the surface of virions, it exists in a complex trimeric conformation and is decorated with an array of variable N-linked glycans, making it an important but difficult target for vaccine design. Thus far, efforts to elicit a protective humoral immune response using structural mimics of native Env trimers have been unsuccessful. Notably, the aforementioned N-linked glycans serve as a component of many of the epitopes crucial for the induction of potentially protective broadly neutralizing antibodies (bnAbs). Thus, a greater understanding of Env structural determinants, most critically Env glycosylation, will no doubt be of importance in generating effective immunogens. Recent studies have identified the HIV-1 Env signal peptide (SP) as an important contributor to Env glycosylation. Further investigation into the mechanisms by which the SP directs glycosylation will be important, both in the context of understanding HIV-1 biology and in order to inform HIV-1 vaccine design.

With the widespread use of new equipment such as distributed photovoltaics, distributed energy storage, electric vehicles, and distributed wind power, the control of low-voltage distribution networks (LVDNs) has become increasingly complex. Acquiring the most recent topological structure is essential for conducting accurate analysis and real-time control of LVDNs. The signal-injection-based topology identification algorithm is favored for its speed and efficiency. This study proposes a new signal-injection-based topology identification algorithm aimed at solving the issues of reduced completeness and correctness of identification caused by missing feature signal records (FSRs). By analyzing the correlations between FSRs of different positions, the algorithm employs vertical and horizontal completion methods to effectively address missing data, combined with inclusion detection algorithms, to identify the topology of LVDNs. Based on the study of actual LVDN data, the results indicate that the algorithm not only significantly improves the completeness and correctness of topology identification for LVDNs but can also increase the number of devices in operation by evaluating the status of topological equipment, further enhancing the topology identification performance.

Digital filtering is a fundamental technique in digital signal processing, which operates on a digital sequence without any information on how the sequence was generated. This paper proposes a methodology for designing the equivalent of digital filtering for neuromorphic samples, which are a low power alternative to conventional digital samples. In the literature, filtering using neuromorphic samples is done by filtering the reconstructed analog signal, which is required to belong to a predefined input space. We show that this requirement is not necessary, and introduce a new method for computing the neuromorphic samples of the filter output directly from the input samples, backed by theoretical guarantees. We show numerically we can achieve a similar accuracy compared to the conventional method. However, given that we bypass the analog signal reconstruction step, our results show significantly reduced computation time for the proposed method and good performance even when signal recovery is not possible.

As screen sizes are becoming larger and larger, which are beyond human’s physical limitation for direct touching interaction, remote controls are inevitable. However, as techniques applied so far, inertial gyroscopes are susceptible to position inaccuracies, and gesture recognition is limited by cameras’ focus depth and viewing angle. Grating antenna light trapping technology will be an ideal candidate providing the ghost point problem for multipoint inputs can be solved. Therefore, we propose a differential amplitude modulation scheme for grating antenna based multi beam optical touch, which can recognize different incidence points. Where, the amplitude of the incident beams was coded with different pulse width at first. Then, after been trapped by the grating antenna and been transferred into currents by the lined detector arrays, the incident points of the individual beams were recognized and differentiated. The scheme was successfully verified on an 18-inch screen, where two-point optical touch with position accuracy error under 3mm and response time less than 7ms under modulation frequency of 10kHz on both incident beams are obtained. This work paves a practical way for remote multi-point touch, which can make digital mouse in consistent to the users pointing direction obeying natural three-point one ling aiming rule instantaneously.

Spray technology is widely used in various industries, including medicine, food production, mechanical engineering, and nanopowder manufacturing. Achieving high dispersion and a narrow particle size distribution is crucial for many applications. Ultrasonic spraying is commonly used to achieve this. On other hand, hydraulic nozzles provide high atomization performance. Combining these two technologies promises to offer significant benefits, but the complex processes that occur simultaneously in such a device require careful study. This work proposes a fundamental design for an acoustic-hydraulic nozzle and investigates the physical processes when a liquid is sprayed using this nozzle, both theoretically and experimentally. The study identifies the critical modes of spraying and confirms that the simultaneous use of ultrasound and hydraulic pressure can produce a fine spray (droplet size less than 50 μm) with high productivity (5-10 ml/s). This approach has significant potential for modern industries and technologies.

The early elongation events in transcription or translational control in the N-terminal of genes are assuming critical roles in their heterologous expression. The secretory expression of the alkaline pectin lyase APL in B. subtilis were studied to explore the effects of the N-terminal 5-7 amino acids sequences of different signal peptides on gene transcription and protein secretion in this study. The first 5 amino acid sequences of the N-terminal of the signal peptide LipA were identified to play an important role in promoting the expression of APL for the first time. Meanwhile, it also revealed that the signal peptide LipA could lead to higher secretory expression than other signal peptides not because of its sequence structure associated with secretion is stronger but because the N-terminal sequences showed greater advantages in gene transcription. At this basis, the recombinant strain structed in this work showed a new record for the highest extracellular yields (12 295 U/ml) of APL in B. subtilis which was 1.9 times higher than that expressed in the recombinant Escherichia coli strain we had reported before. The novel theories revealed in this study would assuming significance roles for improving expression of foreign proteins in or out of the cells.

Discussing quantum theory foundations, von Neumann noted that the measurement process should not be regarded in terms of a temporal evolution. A reason for his claim is insurmountability of the gap between reversible and irreversible processes. The time operator formalism that goes beyond such a gap is adequate framework for elaboration of the measurement problem. It considers signals to be stochastic processes, whether they correspond to variables or distribution densities. Signal processing like that utilizes statistical properties to perform its tasks, which is the definition of statistical signal processing. A hierarchy of the measurement process is indicated by crossing between states and devices, which implies an evolution in the temporal domain. The concept has generalized to an open system by the use of duality in frame theory.

The problem of event detection in general noisy signals arises in many applications; usually, either a functional form for the event is available, or a previous annotated sample with instances of the event that can be used to train a classification algorithm. There are situations, however, where neither functional forms nor annotated samples are available; then it is necessary to apply other strategies to separate and characterize events. In this work, we analyze 15 minute-long samples of an acoustic signal, and are interested in separating sections, or segments, of the signal which are likely to contain significative events. For that, we apply a sequential algorithm with the only assumption that an event alters the energy of the signal. The algorithm is entirely based on Bayesian methods.

Manipulation of radiation pattern control is challenging, especially at low frequencies. In this paper, we demonstrate that acoustic metamaterials as an effective array of quadrupoles can remarkably improve the directionality of acoustic radiation at low frequencies, compared with previous metamaterials as monopole and dipole structures. The directivity of the acoustic radiation can be adjusted by changing the characteristic parameter and the symmetry of the structure, which provide a flexible method of adjustable radiation di-rections. The directionality can be further improved by constructing linear array of structure. Our work opens an approach of acoustic radiation control via quadrupolar metamaterials.

With the advancement of technology, signal modulation types are becoming increasingly diverse and complex. The phenomenon of signal time-frequency overlap during transmission poses significant challenges for the classification and recognition of mixed signals, including poor recognition capabilities and low generality. This paper presents a recognition model for fine-grained analysis of mixed signal characteristics, proposing a Geometry Coordinate attention mechanism and introducing a low-rank bilinear pooling module to more effectively extract signal features for classification. The model employs a residual neural network as its backbone architecture and utilizes the Geometry Coordinate attention mechanism for time-frequency weighted analysis based on information geometry theory. This analysis targets multiple-scale features within the architecture, producing time-frequency weighted features of the signal. These weighted features are further analyzed through a low-rank bilinear pooling module, combined with the backbone features, to achieve fine-grained feature fusion. This results in a fused feature vector for mixed signal classification. Experiments were conducted on a simulated dataset comprising 39,600 mixed signal time-frequency plots. The model was benchmarked against a baseline using a residual neural network. The experimental outcomes demonstrated an improvement of 9% in the exact match ratio and 5% in the Hamming score. These results indicate that the proposed model significantly enhances the recognition capability and generalizability of mixed signal classification.

This study explores the dependence of drone acoustic detection systems performance on distance, using learning machines with different complexities, from simple linear discriminants, to deep neural networks, such as YAMNet, which exploits transfer learning. Other machine learning systems studied are the multilayer perceptron, support vector machines and random forest. These methods have been evaluated with a meticulously selected database that includes a wide variety of drone and interference sounds, previously processed by array signal processing and affected by ambient noise. Two strategies for training learning machines are considered. In the first one, they are trained with unattenuated signals, trying to preserve the information generated by the sound sources, but they are tested considering the attenuation with distance, achieving effective detection at distances up to 200 metres with some methods, especially linear discriminant. In all cases, the existence of interferences that hinder detection is considered. In the second strategy, the systems are trained and tested with attenuated signals as a function of distance. The effective detection range increases up to 300 metres for most methods and up to 500 metres for the YAMNet based detector. In addition, this second approach raises the possibility of developing specialised detectors for each distance range, thus significantly extending the effective detection range. These results underline the promising potential of acoustic detection of drones at various distances, encouraging further exploration in this research area.

Practically important properties of real liquids, including seawater, are their nonlinear properties, which include a nonlinear acoustic parameter, as well as cavitation strength – a rupture of the continuity of a liquid at high intensities in an acoustic wave. The manifestation of nonlinear effects is greatly facilitated by the presence of various nuclei in the liquid – gas bubbles, foreign particles, and other inclusions of various origins. For practical applications, it is important to study the properties of real liquids with inclusions in them. Due to the difficulties of measuring cavitation strength by acoustic methods, other methods are being sought. It is proposed to use an optoacoustic method associated with the use of laser radiation that causes optical breakdown - optical cavitation, that accompanied by a strong sound generation effect. The connection between optical breakdown thresholds and acoustic cavitation thresholds has been established, which can later be used to identify cavitation thresholds at high static pressures (at great depths in the sea), where the use of acoustic emitters is extremely difficult. The paper studies the bubble size distribution function in seawater, combined with studies of the acoustic nonlinearity parameter and cavitation strength at various depths. The interrelation of these characteristics for seawater is shown.

The unique porous crystal structure of zeolite offers various important utilizations, it is one of the considerations in selecting zeolite at this study as component of composite for restraining noise. It so happens, previous experiments show that banana stem has porous structure, fibrous, high flexibility and can be applied as material for many various products including as component of acoustic material. The combination of both is alleged that it has capability in absorbing noise. This paper presents an investigation on the composite that it was synthesized of Activated Zeolite and Banana Stem Fibre in various weight for determining its sound absorption coefficient alpha (a). Activating natural zeolite was conducted by using 6M HCl in order for enlarging zeolite pores. The sound absorption coefficient was measured in the frequency range between 125 Hz up to 6000 Hz. The results show that the different weight of banana stem fibre as component of the synthesized composite affects the value of alpha and shifts the frequency area.

In this paper, we develop a tractable mathematical model and emulation framework for communicating information through water using acoustic signals. Water is considered as one of the most complex mediums to model owing to its vastness and variety in characteristics depending on the scenario, what kind of water-body (lakes, rivers, tanks, sea etc.) and geographical location of the water-body is considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water) and Milne’s oscillator technique to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are used to characterize the travelling acoustic signal. Signal strength, phase shift and time-delay generated from the mathematical model are fed to a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator uses wide sense stationary uncorrelated scattering (WSSUS) assumption and finite sum-of-sinusoids (SOS) with uniformly distributed phase to generate the channel samples. Using the emulator platform it is possible to generate amplitude variation profile, Doppler shift and spread experienced by any travelling signal through different underwater communication scenarios. Such an emulator platform can be used to simulate different communication scenarios, underwater network topologies, data to train different learning models and predict performance of different modulation, multiplexing, error correction and multi-access techniques for underwater acoustic communications (UWAC) systems.

Microwave photonic (MWP) signal processors, which process microwave signals based on photonic technologies, bring advantages intrinsic to photonics such as low loss, large processing bandwidth, and strong immunity to electromagnetic interference. Optical microcombs can offer a large number of wavelength channels and compact device footprints, which make them powerful multi-wavelength sources for MWP signal processors to realize a variety of processing functions. In this paper, we experimentally demonstrate the capability of microcomb-based MWP signal processors to handle diverse input signal waveforms. In addition, we quantify the processing accuracy for different input signal waveforms, including Gaussian, triangle, parabolic, super Gaussian, and nearly square waveforms. Finally, we analyze the factors contributing to the difference in the processing accuracy among the different input waveforms, and our theoretical analysis well elucidates the experimental results. These results provide a guidance for microcomb-based MWP signal processors when processing microwave signals of various waveforms.

Diagnostics and decomposition of atmospheric disturbances in a planar flow are considered and applied to numerical modeling results with the direct possibility to use in atmosphere monitoring especially in such strong events which follow magnetic storms. The study examines a situation in which the stationary equilibrium temperature of a gas may depend on a vertical coordinate, that seriously complicates the problem solution. The relations connecting perturbations for acoustic and entropy modes are analytically established and led to the solvable diagnostic equations. These perturbation structures, found as the equation solutions specify acoustic and entropy modes in an arbitrary stratified gas under the condition of stability. These time-independent diagnostic relations link gas perturbation variables of the acoustic and the entropy modes. Hence, they provide the ability to decompose the total vector of perturbations into acoustic and non-acoustic (entropy) parts uniquely at any instant within the all accessible heights range. As a prospective model, we consider the diagnostics at the height interval [120;180] km, where the equilibrium temperature of a gas depends linearly on the vertical coordinate. For such a heights range it is possible to proceed with analytical expressions for pressure and entropy perturbations of gas variables. Individual profiles of acoustic and entropy parts for some data, obtained by numerical experiment, are illustrated by the plots for the pure numerical data against ones obtained by the model. The total energy of a flow is determined for both approaches and its height profiles are compared.

Passive radar detection emerges as a pivotal method for environmental perception and target de-tection within radar applications. Leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an increasing selection of passive radar signal sources, and the emerging 5G has the characteristics of high frequency band, high bandwidth, and a large number of base stations, which make it have significant advantages in passive radar. Therefore, this paper introduces a passive radar target detection method based on 5G signals and designs a rotating target speed measurement experiment. In the experiment, this paper validated the method of de-tecting rotating targets using 5G signals and evaluated the measurement accuracy, providing a research foundation for passive radar target detection using 5G signals and detecting rotating targets such as drone rotors.

The cell’s capacity to integrate and respond to spatial information is a crucial feature of morphogenesis and development. The Planar Cell Polarity (PCP) pathway is a signaling mechanism, widely conserved across metazoans, providing spatial orientation along the plane of an epithelium in morphogenic processes ranging from insect wing patterning to mammalian cochleae. Although the core genes involved in the PCP pathway have been molecularly identified in the 1990s, the PCP signaling mechanism remains controversial. In this article I discuss the main players and previous models of PCP signaling reported in the literature, and propose a new model. According to it PCP is established through an homophobic signal by transmembrane protein Frizzled (Fz): 1) a Fz signal in one cell repeals Fz itself in the adjacent cell, thereby generating symmetry breaking; 2) the instructive PCP signal is conveyed through Fz interaction with atypical cadherin Flamingo (Fmi). More broadly, homophobic signaling may represent a novel mechanism for cell-cell signaling of spatial information through modulation of cell adhesion rather than canonical ligand-receptor binding.

The cornea is the optical window to the brain. Its optical and structural properties are responsible for optical transparency and vision. The shape, elasticity, rigidity or stiffness are given by its biomechanical properties, whose stability results in ocular integrity and intraocular pressure dynamics. Here, we report in vivo observation of structural changes and biomechanical alterations of the human cornea induced by acoustic wave pressure within the frequency range of 50-350 Hz and 90 dB of sound pressure level. Central corneal thickness (CCT) and eccentricity (e2) were measured using Scheimpflug imaging and biomechanical properties [corneal hysteresis (CH) and intraocular pressure (IOP] were assessed with air-puff tonometry in 6 young healthy subjects. At the specific 150 Hz acoustic frequency, the variation in CCT and e2 were of 0.058 and 7.33 µm, respectively. Biomechanical alterations were also observed in both IOP (decrease of 3.60 mmHg) and CH that showed an increase of 0.40 mmHg.

Different types of resonators are used to create acoustic metameterials and metasurfaces. Recent studies focused on the use of multiple resonators of dipole, quadrupole, octupole and even hexadecapole types. This paper considers the theory of an acoustic metasurface, which is a flat surface with a periodic arrangement of multiple resonators. The sound field reflected by the metasurface is determined. If the distance between the resonators is less than a half the wavelength of the incident plane wave, the far field can be described by a reflection coefficient that depends on the angle of incidence. This allows to characterize the acoustic properties of the metasurface by a homogenized boundary condition, which is a high order tangential impedance boundary condition. The tangential impedance depending on the multiple order of the resonators is introduced. In addition, we analyze sound absorption properties of these metasurfaces, which are a critical factor in determining their performance. The paper presents a theoretical model that accounts for the multiple orders of resonators and their impact on sound absorption. The maximum absorption coefficient for a diffuse sound field, as well as the optimal value for the homogenized impedance, are calculated for arbitrary multipole orders.

Abstract: Passive acoustic monitoring (PAM) is a non-invasive technique to supervise the wildlife. Acoustic surveillance is preferable in some situation such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example, to identify species based on audio recordings. But some care should be taken to evaluate the capability of a system. We define PAM-filters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise and recording devices in both training and test sets. A random division of a database present accuracies much higher than accuracies obtained with protocols generated with PAM-filter. Although we use the animal vocalizations, in our method we convert the audio into spectrogram images, after that, we describe the images using the texture. Those are well-known techniques for audio classification, and they have already been used for species classification. Also, we perform statistical tests to demonstrate the significant difference between accuracies generated with and without PAM-filters with several well-known classifiers. The configuration of our experimental protocols and the database were made available online.

We propose an asynchronous acoustic chirp slope keying to map bit sequences on single or multiple bands without preamble or error correction coding on the physical layer. Details of the implementation are disclosed and discussed, the performance verified on laboratory scale in a pool measurement, as well as simulated for a channel containing Rayleigh fading and Additive White Gaussian Noise. For time-bandwidth products of 50 in single band mode, a raw data rate of 100~bit/s is simulated to achieve bit error rates below 0.001 for signal-to-noise ratios above -6~dB. In dual-band mode and a data rate of 200~bit/s, this bit error level was achieved for signal-to-noise ratios above 0~dB for time-bandwidth product of 25. The packet error rates follow this behavior with an offset of 1~dB.

(1) Background: In nervous system information is conveyed by a sequence of action potentials, called spikes-trains. As MacKay and McCulloch proposed, spike-trains can be represented as bits sequences coming from Information Sources. Previously, we studied relations between Information Transmission Rates (ITR) carried out by the spikes, their correlations, and frequencies. Here, we concentrate on the problem of how spikes fluctuations affect ITR. (2) Methods: The Information Theory Method developed by Shannon is applied. Information Sources are modeled as stationary stochastic processes. We assume such sources as two states Markov processes. As a spike-trains' fluctuation measure, we consider the Standard Deviation sigma, which, in fact, measures average fluctuation of spikes around the average spike frequency. (3) Results: We found that character of ITR and signal fluctuations relation strongly depends on the parameter s which is a sum of transitions probabilities from no spike state to spike state and vice versa. It turned out that for smaller s (s<1) the quotient ITR\sigma has a maximum and can tend to zero depending on transition probabilities. While for s large enough (1<s) the ITR\sigma is separated from 0 for each s. Similar behavior was observed also when we replaced Shannon entropy terms in Markov entropy formula by their approximation with polynomials. We also show that the ITR quotient by Variance behaves in a completely different way. (4) Conclusions: Our results show that for large transition parameter s the Information Transmission Rate by sigma will never decrease to zero. Specifically, for 1<s<1.7 the ITR will be always, independently on transition probabilities which form this s, above the level of fluctuations, i.e. in this case we have sigma<ITR. Thus, we conclude that in a more noisy environment, to get appropriate reliability and efficiency of transmission, Information Sources with higher tendency of transition from the state no spike to spike state and vice versa should be applied.

TGF-β signaling transduces immunosuppressive biochemical and mechanical signals in the tumor microenvironment. In addition to canonical SMAD signaling, TGF-β can promote tumor growth and survival by inhibiting proinflammatory signaling and extracellular matrix remodeling. In this article, we will review how TAK1 activation lies at the intersection of proinflammatory signaling by immune receptors and anti-inflammatory signaling by TGF-β receptors. Additionally, we will discuss the role of TGF-β in the mechanobiology of cancer. Understanding how TGF-β dampens proinflammatory responses and induces pro-survival mechanical signals throughout cancer development will be critical in designing therapeutics which inhibit tumor progression while bolstering the immune response.

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. Two main approaches are considered: (i) long-range correlation analysis using both the Hurst (H) and the Detrended Fluctuation Analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data collected from two application examples: a glass fiber reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis is also performed to reference the study of the other methods. Results indicate that the proposed methods yield a reliable indication of failure in the studied structures.

This paper addresses the problem of assessing and optimizing acoustic positioning system for underwater target localization with range measurements only. We present a new three-dimensional assessment model to assess the optimal geometric beacon formation whether meet user needs. For the sake of mathematical tractability, it is assumed that the measurements of the range between the target and beacons are corrupted with white Gaussian noise with variance is distance-dependent. Then by adopting dilution of precision (DOP) parameters in the assessment model, the relationship between DOP parameters and positioning accuracy is derived. In addition, the optimal geometric beacon formation that will yield the best performance is achieved by minimizing the values of geometric dilution of precision (GDOP) on condition that the position of target is known and fixed. Next, in order to make sure whether the estimate positioning accuracy over interesting region satisfy the precision needed by the users, geometric positioning accuracy (GPA), horizonal positioning accuracy (HPA) and vertical positioning accuracy (VPA) are utilized to assess the optimal geometric beacon formation. Simulation examples are designed to illustrate the exactness of the conclusion. Unlike other work which only use GDOP to optimize the formation and cannot assess the performance of the specified dimensions, this new three-dimensional assessment model can assess the optimal geometric beacon formation in each dimension for any point in three-dimensional space, which can provide users with guidance advices to optimize performance of every specified dimension.

This comprehensive review delves into the multifaceted aspects of ERK signaling and the intricate mechanisms underlying distinct cellular fates. ERK1 and ERK2 (ERK) govern proliferation, transformation, epithelial-mesenchymal transition, differentiation, senescence, or cell death, con-tingent upon activation strength, duration, and context. The biochemical mechanisms underlying these outcomes are inadequately understood, shaped by signaling feedback and the spatial localization of ERK activation. Generally, ERK activation aligns with the Goldilocks principle in cell fate determination. Excessive or insufficient ERK activity inhibits growth, whereas moderated activation supports proliferation and survival. Unraveling the intricacies of how the degree of ERK activation dictates cell fate requires deciphering mechanisms encompassing protein stability, transcription factors downstream of ERK, and the chromatin landscape.

Separation and detection of cells and particles in a suspension are essential for various applications, including biomedical investigations and clinical diagnostics. Microfluidics realizes the miniaturization of analytical devices by controlling the motion of a small volume of fluids in microchannels and microchambers. Accordingly, microfluidic devices have been widely used in particle/ cell manipulation processes. Different microfluidic methods for particle separation include dielectrophoretic, magnetic, optical, acoustic, hydrodynamic, and chemical techniques. Dielectrophoresis (DEP) is a method for manipulating polarizable particles’ trajectories in non-uniform electric fields using unique dielectric characteristics. It provides several advantages for dealing with neutral bioparticles owing to its sensitivity, selectivity, and noninvasive nature. This review provides a detailed study on the signal-based DEP methods that use the applied signal parameters, including frequency, amplitude, phase, and shape for cell/particle separation and manipulation. Rather than employing complex channels or time-consuming fabrication procedures, these methods realize sorting and detecting the cells/particles by modifying the signal parameters while using a simple device. In addition, these methods can significantly impact clinical diagnostics by making low-cost and rapid separation possible.

Osteoarthritis and rheumatoid arthritis are common cartilage and joint diseases that globally affect more than 200 and 20 million people, respectively. Several transcription factors have been implicated in the onset and progression of osteoarthritis, including Runx2, C/EBPβ, HIF2α, Sox4, and Sox11. IL-1β also leads to osteoarthritis through NF-ĸB, IκBζ, and Zn²⁺-ZIP8-MTF1 axis. IL-1, IL-6, and TNFα play a major pathological role in rheumatoid arthritis through NF-ĸB and JAK/STAT pathways. Indeed, inhibitory reagents for IL-1, IL-6, and TNFα provide clinical benefits for rheumatoid arthritis patients. Several growth factors, such as BMP, FGF, PTHrP, and Indian hedgehog, play roles regulating chondrocyte proliferation and differentiation. Disruption and excess of these signaling cause genetic disorders in cartilage and skeletal tissues. FOP, an autosomal genetic disorder characterized by ectopic ossification, is induced by mutant ACVR1. mTOR inhibitors were found to prevent ectopic ossification by ACVR1 mutations. ACH and related diseases are autosomal genetic diseases, which manifest severe dwarfism. CNP is currently the most promising therapy for ACH. In these ways, investigation of cartilage and chondrocyte diseases at molecular and cellular levels sheds light on the development of effective therapies. Thus, identification of signaling pathways and transcription factors implicated in these diseases is important.

Underwater acoustic spiral sources are able to generate spiral acoustic fields where the phase depends on the bearing angle. It allows to estimate the bearing angle relatively to a receiver by subtracting the phases of a spiral and a circular wavefront, and finds application in the bearing angle estimate with a single hydrophone, e.g., for unmanned underwater vehicles localization. This paper presents a new prototype for a spiral acoustic source, which is able to generate the required wavefronts, and is composed of four monopoles in the same piezoceramic cylinder. This paper reports the prototyping and the acoustic tests performed in a water tank where the spiral source was characterized in terms o Transmitting Voltage Response, phase, and horizontal and vertical directivity patterns. A receiving calibration method for the spiral source is proposed and shows a maximum angle error of 1 degree when the calibration and the operation are carried under the same conditions and a mean angle error up to 6 degrees for frequencies above 25 kHz when the same conditions are not fulfilled.

A microwave-photonics method has been developed for measuring distributed acoustic signals. This method uses microwave-modulated low coherence light as a probe to interrogate distributed in-fiber interferometers, which are used to measure acoustic-induced strain. By sweeping the microwave frequency at a constant rate, the acoustic signals are encoded into the complex microwave spectrum. The microwave spectrum is transformed into the joint time-frequency domain and further processed to obtain the distributed acoustic signals. The method is first evaluated using an intrinsic Fabry Perot interferometer (IFPI). Acoustic signals of frequency up to 15.6 kHz were detected. The method was further demonstrated using an array of in-fiber weak reflectors and an external Michelson interferometer. Two piezo-ceramic cylinders (PCCs) driven at frequencies of 1700 Hz and 3430 Hz were used as acoustic sources. The experiment results show that the sensing system can locate multiple acoustic sources. The system resolves 20 nε when the spatial resolution is 5 cm. The recovered acoustic signals match the excitation signals in frequency, amplitude, and phase, indicating an excellent potential for distributed acoustic sensing (DAS).

Electro-optic modulator (EOM) is one of the key devices of high-speed optical fiber communication system and ultra-wideband microwave photonic system. Silicon-organic hybrid (SOH) integration platform combines the advantages of silicon photonics and organic materials, providing a high electro-optic effect and compact structure for photonic integrated devices. In this paper we present a SOH integrated EOM with comprehensive investigation of EOM structure design, silicon waveguide fabrication with Slot structure, on-chip poling of organic electro-optic material, and characterization of EO modulation response. The SOH integrated EOM is measured with 3dB-bandwidth of over 50 GHz and half-wave voltage length product of 0.26 V·cm. Furthermore, we demonstrate a microwave photonics phase shifter by using the fabricated SOH integrated dual parallel Mach-Zehnder modulator. The phase shift range of 410 degree is completed from 8 GHz to 26 GHz with a power consumption of less than 38 mW.

This paper addresses the problem of low accuracy of conventional channel estimation and signal detection in complex fields like mines; and the problem of replacing the deep receiver at the receiver side with a deep neural network requires a large amount of data for training and slow training time. Combining the receiver side with traditional communication knowledge, a channel estimation and signal detection method based on the joint ReEsNet and SDRNet under the mine is proposed. The detailed step is to use ReEsNet network for interpolation of conventional channel estimation. Then, on the basis of ReEsNet, the output data is channel equalized and pilots removed, and the previously processed data is input to SDRNet to perform signal recovery. In the complex mine environment, the MSE values are 2e-2 for LS, 6e-3 for MMSE, and 8.44e-5 for ReEsNet at the 1500th sample data for interpolation compared to the conventional channel estimation. the MSE of ReEsNet is the smallest and is closer to the real channel estimation. Compared with the proposed joint channel estimation and signal detection methods, fewer trainable parameters reduce 97% of the parameters compared to CCRNet and 84% of the parameters compared to CNNNet; their detection results, however, are not very different.

In this contribution a new configuration of a DC-DC Boost and Buck-Boost converters is proposed to track sinusoidal signals exactly, with zero tracking error. The stage is conformed by two DC-DC converters connected to the same load, this is, they are not connected in cascade. To track exactly the sinusoidal reference the control scheme is base on the indirect control algorithm and the harmonic balance method. The proposed algorithm considers a second control input with the end of cancel out the second harmonic, this second harmonic is produced by the non linearity of the model and must be cancelled since there must be a single frequency in the output. The result is such that the converters can track sinusoidal signals with exactly zero error, some numerical results are presented to corroborate the proposal.

Epilepsy is a neurological disorder that affects approximately 1% of the world's population. To diagnose and estimate the occurrence of epilepsy, the analysis of recorded brain activity is performed by a neurologist, which is not only time-consuming and tedious but also occasionally accompanied by human error. Therefore, in recent decades, researchers have aimed to unravel an approach for designing and building an automated method for diagnosing and estimating the occurrence of epilepsy. Accordingly, the present study proposed two new-fangled ways based on brain signals and a convolutional neural network (CNN). Moreover, this research implements a CNN with a sequential three-layer structure. Numerous experiments were performed, and the accuracy of estimating epilepsy using the developed methods was achieved at 95% without feedback and 97% with feedback. The proposed methods were proven to be more accurate than the previous techniques and can be employed as a physician's assistant once entering the field of operation.

An area has been designated for demonstrating the utility of marine hydrokinetic turbines in Minas Passage, Bay of Fundy. Marine renewable energy may be useful for the transition from carbon-based energy sources but there is concern for the safety of fish that might encounter turbines. Acoustic receivers that detect signals from acoustically tagged fish that pass through the tidal demonstration area and detection efficiency of tag signals might be used to estimate the likelihood of fish encountering marine hydrokinetic turbines. The method requires that tagged fish passing through the development area will be reliably detected by a receiver array. The present research tests the reliability with which passing tags are detected by suspending tags beneath GPS-tracked drifters. Drifters carrying high residency Innovasea tags that transmitted every 2 seconds were usually detected by the receiver array even in fast currents during spring tides but pulse position modulation tags were inadequate. Sometimes very few high residency tag signals were detected when fast tidal currents swept a drifter through the receiver array, so increasing the transmission interval degrades performance at the tidal energy development area. High residency tags suspended close to the sea surface were slightly less likely to be detected if they passed by during calm conditions. Previously measured detection efficiencies were found to slightly overestimate the chances of a high residency tag carried by a drifter being detected as it passed by a receiver. This works elucidates the effectiveness with which acoustically tagged fish are detected in fast, highly turbulent tidal currents and informs the application of detection efficiency measurements to calculate the probability that fish encounter a marine hydrokinetic turbine.

This work presents a methodology for the generation and classification of phase resolved partial discharges (PRPD) patterns based on the use of a printed UHF monopole antenna and signal con-ditioning circuit to reduce hardware requirements. For this purpose, envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric disks with internal cavities in an oil cell, potential transformer, and tip-tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as reference. After acquisition of conditioned UHF signals, digital signal filtering threshold technique was used, and peaks of partial discharges envelopes pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector ma-chine and decision tree. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.

Nowadays, the most advanced micromachined microphones on the market are based on a capacitive coupling principle. Capacitive micro-electro-mechanical systems-based (MEMS) microphones resemble their millimetric counterparts, both in function and in performances. The most advanced MEMS microphones reached a competitive level compared to commonly used measuring microphones in most of the key performance parameters except the acoustic overload point (AOP). In an effort to find a solution for measurement of high-level acoustic fields, microphones with piezoelectric coupling principle have been proposed. Such novel microphones exploit the piezoelectric effect of a thin layer of aluminum nitride, which is incorporated in their diaphragm structure. In these microphones fabricated with micromachining technology, no fixed electrode is necessary, in contrast to capacitive microphones. This specificity significantly simplifies both the design and the fabrication and opens the door for a new improvement of the acoustic overload point as well as harsh environmental applications. Several variations of piezoelectric structures together with an idea leading to electrically controlled sensitivity of MEMS piezoelectric microphone are discussed in the paper.

The purpose of this work is to study the mechanism of running-in during the friction and to determine the informative parameters characterizing the degree of its completion. During the friction, contact interaction of rough surfaces causes various wave phenomena covering a wide range of frequencies, the subsequent frequency analysis can provide information about the sizes of wave sources and thereby clarify the mechanism of interaction between surface roughness. The using of the wavelet transform for processing of the signals of audible acoustic emission made it possible to determine the beginning and the end of the change in the frequency ranges of the interaction of roughness. The code developed by the authors was used to analyze the acoustic emission signals by using wavelet energy and entropy criteria. The mother wavelet was chosen by carefully evaluating the effectiveness of 54 preliminary candidates for the mother wavelet from 7 wavelet families, ac-cording to three criteria: 1) maximum wavelet energy; 2) Shannon entropy minimum; 3) maximum energy-to-Shannon entropy ratio.

Acoustic focusing of particle flow in microfluidics has been shown to be an efficient tool for particle separation for various chemical and biomedical applications. The mechanism behind the method is the selective effect of the acoustic radiation force on distinct particles. In this way, they can be selectively focused and separated. The technique can also be applied under stationary conditions, i.e., in the absence of fluid flows. In this study, the manipulation of self-propelled particles, such as Janus particles, in an acoustofluidic setup is investigated. In experiments with self-propelled Janus particles and passive beads, we explore the interplay between self-propulsion and the acoustic radiation force. Our results demonstrate unusual and potentially useful effects such as selective trapping, escape and assisted escape in binary mixtures of active and passive particles. We also analyze various aspects related to the behavior of Janus particles in acoustic traps in the presence and absence of flows.

Passive marine listening, involving the acoustic monitoring of underwater environments, has become increasingly vital for scientific research, environmental monitoring, and defense applications. However, the success of such applications critically depends on the ability to extract meaningful information from the often noisy and dynamic underwater acoustic environment. In this context, Mel-frequency cepstral coefficients (MFCCs) have emerged as an essential tool for feature extraction, capturing the spectral characteristics of marine sounds and making them ideal for species identification and sound event detection. This research presents an innovative approach to enhance the robustness of MFCC-based passive marine listening through adaptive noise reduction techniques. The proposed approach utilizes dynamic spectral subtraction to counter underwater noise, resulting in enhanced signal-to-noise ratios for desired sounds. This adaptive system adjusts to dynamic underwater noise, allowing it to concentrate on target sounds and suppress interference effectively. The experimentation further validates the effectiveness of the proposed approach, with results reaching 99% on the full dataset of 570 mammals and vessels, demonstrating the effectiveness of SVM and Random Forests in the classification of underwater audio.

Quantitative evaluation of the musical timbre and its variations is important for the analysis of audio recordings and computer-aided music composition. Using the FFT acoustic descriptors and their representation in an abstract timbral space, variations of a sample of monophonic sounds of chordophones (violin, cello) and aerophones (trumpet, transverse flute, and clarinet) sounds are analyzed. It is concluded that the FFT acoustic descriptors allow us to distinguish the timbral variations of the musical dynamics, including crescendo and vibrato. Furthermore, using the Random Forest algorithm, it is shown that the FFT-Acoustic provides a statistically significant classification to distinguish musical instruments, family of instruments, and dynamics. We observed a better behavior for the FFT-Acoustic descriptors when classifying pitch compared to some timbral features of Librosa.

The timely diagnosis of defects at their incipient stage of formation is crucial to extend the life-cycle of technical appliances. This is the case of mechanical-related stress, either due to long aging degradation processes (e.g., corrosion) or in-operation forces (e.g., impact events), which might provoke detrimental damages, such as cracks, disbonding or delaminations, most commonly followed by the release of acoustic energy. The localization of these sources can be successfully fulfilled via adoption of Acoustic Emission (AE)-based inspection techniques through the computation of the Time of Arrival (ToA), namely the time at which the induced mechanical wave released at the occurrence of the acoustic event arrives to the acquisition unit. However, the accurate estimation of the ToA may be hampered by poor Signal-to-Noise ratios (SNRs). In these conditions, standard statistical methods typically fail. In this work, two alternative Deep Learning methods are proposed for ToA retrieval, namely a Dilated Convolutional Neural Network (DilCNN) and a Capsule Neural Network for ToA (CapsToA). These methods have the additional benefit of being portable on resource-constrained microprocessors. Their performance has been extensively studied on both synthetic and experimental data, focusing on the problem of ToA identification for the case of a metallic plate. Results show that the two novel methods can achieve localization errors which are up to 70% more precise than those yielded by conventional strategies, even when the SNR is severely compromised (i.e., down to 2 dB). Moreover, DilCNN and CapsNet have been implemented in a tiny machine learning environment and then deployed on microcontroller units, showing a negligible loss of performance with respect to offline realizations.

Tinnitus is a heterogeneous auditory disorder without a pharmacological cure but with several palliative treatments. One of the most applied treatments for tinnitus relief combines counselling with sound therapy. The efficiency of this sound therapy depends on the type of sound stimulus and the exposition time. The fundamentals of a customized sound therapy that stimulates the auditory system with a continuous or sequential sound which compensates for the subject hearing loss are described here. A sample of 137 tinnitus subjects are treated with this sound therapy, named Enriched Acoustic Environment. 90% of these patients achieved a clinically relevant and statistically significant tinnitus-related distress relief quantified as a mean drop of their Tinnitus Handicap Inventory score of 24.3 points. These subjects exposed with sequential stimuli (31%) achieved greater distress relief (5.1 points more in their Tinnitus Handicap Inventory score) than those stimulated with continuous sound (69%). Therefore, this sound constitutes an optimized stimulus for tinnitus treatment.

We propose a new statistical analysis of the Acoustic Emissions (AE) produced in a series of triaxial deformation experiments leading to fractures and failure of two different rocks, namely, Darley Dale Sandstone (DDS) and AG Granite (AG). By means of q-statistical formalism, we are able to characterize the pre-failure processes in both types of rocks. In particular, we study AE inter-event time and AE inter-event distance distributions. Both of them can be reproduced with q-exponential curves, showing universal features which are observed here for the first time and could be important in order to understand more in detail rock fractures dynamics.

Fiber distributed optical fiber acoustic sensor (DAS) is generally used in distributed long-distance acoustic/vibration measurement. We found that DAS with excellent performance of low self-noise and anti-fading in combination with the plastic structure in daily life as an acoustic transducer can achieve high-sensitivity acoustic measurement at a single point rather than designing a state-of-the-art acoustic transducer or using special enhanced scattering fiber. The simple acoustic transducer we proposed for DAS can reach the sensitivity level of -106.5dB re. 1rad/μPa at a sensing range of 5.1 km, which can meet many demands on the industrial site.

We discuss two methods to detect the presence and location of a person in a small-scale room and compare the performances. The first method is Direct Intersection, which determines a coordinate point based on the intersection of spheroids defined by observed distances of high-intensity reverberations. The second method, Sonogram analysis, overlays all channel’s room impulse responses to generate an intensity map for the observed environment. We demonstrate that the former method has lower computation complexity and higher accuracy for small numbers of channels, while the latter performs more robustly.

In the past two decades, acoustic metamaterials have garnered much attention owing to their unique functional characteristics, which is difficult to be found in naturally available materials. The acoustic metamaterials have demonstrated to exhibit excellent acoustical characteristics that paved a new pathway for researchers to develop effective solutions for a wide variety of multifunctional applications such as low-frequency sound attenuation, sound wave manipulation, energy harvesting, acoustic focusing, acoustic cloaking, biomedical acoustics, and topological acoustics. This review provides an update on the acoustic metamaterials' recent progress for simultaneous sound attenuation and air ventilation performances. Several variants of acoustic metamaterials, such as locally resonant structures, space-coiling, holey and labyrinthine metamaterials, and Fano resonant materials, are discussed briefly. Finally, the current challenges and future outlook in this emerging field is discussed as well.

The acoustic emission (AE) technique was applied to monitor the pitting corrosion of carbon steel in NaHCO₃ + NaCl solutions. The open circuit potential (OCP) measurement and the corrosion morphology in-situ capturing using optical microscope were conducted during AE monitoring. The corrosion micromorphology was characterized with scanning electron microscope (SEM). The propagation behavior and AE features of natural pitting on carbon steel were investigated. After the performing of signal processing including pre-treatment, shape preserving interpolation and denoising for raw AE waveforms, three types of AE signals can be classified in the correlation diagrams of new waveform parameters. Finally, a 2D pattern recognition method was established to calculate the similarity of different continuous AE graphics, which is quite effective to distinguish the localized corrosion from uniform corrosion.

Alzheimer's Disease (AD) is a neurodegenerative disease that is common in the elderly. This paper introduces the overview of Alzheimer's disease and the application of relevant signal processing methods in its detection. Signal processing is a technique that converts raw data into meaningful information and is widely used in the medical field. This paper lists common signal processing techniques, including Fourier transform, time-frequency analysis and statistical signal processing, and discusses their applications in the detection of Alzheimer's disease. Fourier transform can convert time domain signals into frequency domain representations, providing an effective tool for the study of EEG in Alzheimer's disease. Time-frequency analysis can perform a combined time and frequency analysis of the signal to help detect the signal characteristics of Alzheimer's disease. Statistical signal processing methods can be used to identify the features of Alzheimer's disease by building mathematical models. Finally, the challenges of Alzheimer's disease detection are discussed, including signal noise, diversity, and insufficient data volume. Through in-depth research and development of signal processing methods, it is expected to improve the accuracy and efficiency of early detection of Alzheimer's disease.

This paper demonstrates an on-off keying (OOK) super-regenerative quenching transmitter operating in 402- 405MHz MICs band applications. To reduce power consumption, the transmitter is controlled by a novel digital quenching signal controller that generates a digital control signal to start transmitter operation when a baseband signal is input to the transmitter. The digital signal controller consists of an envelope detector, a comparator, and a quench timer designed using a state machine to synchronize the operation between the digital controller and the input baseband signal. The transmitter consists of a Colpitts oscillator operating in double operating frequency followed by a frequency divider by 2, this configuration reduces system area and improves phase noise and signal spectrum. The proposed transmitter is implemented using UMC 130nm CMOS technology, and a 1.2V supply. Simulation shows that the proposed transmitter can meet MICS band mask specifications with data rates up to 1Mbps and total power dissipation of 537uW.

The great Greek philosopher Aristotle (384–322 BCE) is almost unanimously acclaimed as the founder of zoology. There is a consensus that he was interested in attributes of animals, but whether or not he tried to develop a zoological taxonomy remains controversial. Fürst von Lieven and Humar compiled a data matrix and showed, through a parsimony analysis published in 2008, that these data produced a hierarchy that matched several taxa recognized by Aristotle. However, their analysis leaves some questions unanswered because random data can sometimes yield fairly resolved trees. In this study, we update the scores of many cells and add four new characters to the data matrix (147 taxa scored for 161 characters) and quote passages from Aristotle’s Historia animalium to justify these changes. We confirm the presence of a phylogenetic signal in these data through a test using skewness in length distribution of a million random trees, which shows that many of the characters discussed by Aristotle were systematically relevant. Our parsimony analyses on the updated matrix recover far more trees than reported by Fürst von Lieven and Humar, but their consensus includes many taxa that Aristotle recognized and apparently named for the first time, such as selachē (selachians) and dithyra (Bivalvia). This study suggests that even though taxonomy was clearly not Aristotle’s chief interest in Historia animalium, it was probably among his secondary interests. These results may pave the way for further taxonomic studies in Aristotle’s zoological writings in general. Despite being almost peripheral to Aristotle’s writings, his taxonomic contributions are clearly major achievements.

In the last decade, unprecedented progress in the development of neural networks influenced dozens of different industries, among which are signal processing for the electroencephalography process (EEG). Electroencephalography, even though it appeared in the first half of the 20th century, to this day didn’t change the physical principles of operation. But the signal processing technique due to the use of neural networks progressed significantly in this area. Evidence for this can serve that for the past 5 years more than 1000 publications on the topic of using machine learning have been published in popular libraries. Many different models of neural networks complicate the process of understanding the real situation in this area. In this manuscript, we provided the most comprehensive overview of research where were used neural networks for EEG signal processing.

This paper develops a two-layer optimization approach that provides energy-optimal control for vehicles and traffic signal controllers. The optimizer in the first layer computes the traffic signal timings to minimize the total energy consumption levels of approaching vehicles from upstream traffic. The traffic signal optimization can be easily implemented in real-time signal controllers, and it overcomes the issues in the traditional Webster’s method of overestimating the cycle length when the traffic volume-to-capacity ratio exceeds 50 percent. The second layer optimizer is the vehicle speed controller, which calculates the optimal vehicle brake and throttle levels to minimize the energy consumption of individual vehicles. The A-star dynamic programming is used to solve the formulated optimization problem in the second layer to expedite the computation speed so that the optimal vehicle trajectories can be computed in real-time and can be easily implemented in simulation software for testing. The proposed integrated controller is first tested on an isolated signalized intersection, and then an arterial network with multiple intersections to investigate the performance of the proposed controller under various traffic demand levels. The test results demonstrate that the proposed integrated controller can greatly improve energy efficiency with fuel savings up to 17.7%, at the same time enhancing traffic mobility by up to 47.18% reduction in traffic delay and up to 24.84% reduction in vehicle stops.

The Cosmic Theory of Structure (CTS) model presents a comprehensive framework that integrates gravitational waves, neutrinos, dark matter, and quantum field interactions. This model aims to provide a unified understanding of the universe's formation and evolution. The CTS model has undergone rigorous validation using the latest datasets, demonstrating its robustness and predictive power. Key updates include detailed mathematical formulations, refined theoretical frameworks, and enhanced descriptions of fundamental cosmic phenomena. This manuscript presents the final validated version of the CTS model, highlighting its alignment with observational data and its implications for future research in cosmology and quantum physics.

In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverters-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative to enhance the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state space model and eigenvalues analysis is presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small- signal stability of the entire system. And, the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. Case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations.

Frequency combs (FCs)—spectra containing equidistant coherent peaks—have enabled researchers and engineers to measure the frequencies of complex signals with high precision thereby revolutionising the areas of sensing, metrology and communications and also benefiting the fundamental science. Although mostly optical FCs have found widespread applications thus far, in general FCs can be generated using waves other than light. Here, we review and summarise recent achievements in the emergent field of acoustic frequency combs (AFCs) including phononic FCs and relevant acousto-optical, Brillouin light scattering and Faraday wave-based techniques that have enabled the development of phonon lasers, quantum computers and advanced vibration sensors. In particular, our discussion is centred around potential applications of AFCs in precision measurements in various physical, chemical and biological systems in conditions, where using light, and hence optical FCs, faces technical and fundamental limitations, which is, for example, the case in underwater distance measurements and biomedical imaging applications. This review article will also be of interest to readers seeking a discussion of specific theoretical aspects of different classes of AFCs. To that end, we support the mainstream discussion by the results of our original analysis and numerical simulations that can be used to design the spectra of AFCs generated using oscillations of gas bubbles in liquids, vibrations of liquid drops and plasmonic enhancement of Brillouin light scattering in metal nanostructures. We also discuss the application of non-toxic room-temperature liquid-metal alloys in the field of AFC generation.

Acoustic energy mapping provides the functionality to obtain characteristics of acoustic sources, such as: presence, localization, type and trajectory of sound sources. Several beamforming-based techniques can be used for this purpose, however, they rely on the difference of arrival times of the signal at each capture node (or microphone), so it is of major importance to have synchronized multi-channel recordings. A Wireless Acoustic Sensor Network (WASN) can be very practical to install when used for mapping the acoustic energy of a given acoustic environment. However, they are known for having low synchronization between the recordings from each node. The objective of this paper is to characterize the impact of current popular synchronization methodologies as part of the WASN to capture reliable data to be used for acoustic energy mapping. The two evaluated synchronization protocols are: Network Time Protocol (NTP) y Precision Time Protocol (PTP). Additionally, three different audio capture methodologies were proposed for the WASN to capture the acoustic signal: two of them, recording the data locally and one sending the data through a local wireless network. As a real-life evaluation scenario, a WASN was built using nodes conformed by a Raspberry Pi 4B+ with a single MEMS microphone. Experimental results demonstrate that the most reliable methodology is using the PTP synchronization protocol and audio recording locally.

Cells receive external stimuli to incur structural and functional damages. On scanning acoustic microscopy (SAM), speed-of-sound (SOS), attenuation-of-sound (AOS), and thickness values are plotted on the screen to create cellular images, which are related to stiffness, viscosity, and cell size, respectively. The obtained digital data compared using statistical analysis. We aimed to investigate the effects of anticancer drugs, acidic fluids, and heat effects on the cells by using SAM. Anticancer drug cisplatin induced cancer cell apoptosis/necrosis and regeneration in culture, causing elevated SOS, reduced AOS, and thickness. During a more prolonged incubation, the SAM values fluctuated differently between the cisplatin-treated and untreated cells. The tannic and acetic acid and microwave stimuli induced SOS and AOS elevations. These stimuli altered the cell size, number, differentiation, viscosity, and stiffness, which corresponded well to the fluctuation of the SOS and AOS values after incubation. Different anticancer drugs interacted with cancer cells to induce the characteristic alterations of the SAM values. These structural and mechanical alterations induced in cells was difficult to observe on light microscopy. Cellular damages were statistically compared between different stimuli and time-lapse cellular changes were observed using a SAM analysis. SAM is a useful modality to evaluate cellular damage.

We propose a probabilistic graphical model for discriminative substrate characterization, to support geological and biological habitat mapping in aquatic environments. The model, called a fully connected conditional random field (CRF), is demonstrated using multispectral and monospectral acoustic backscatter from heterogeneous seafloors in Patricia Bay, British Columbia, and Bedford Basin, Nova Scotia. Unlike previously proposed discriminative machine learning algorithms, the CRF model considers both the relative backscatter magnitudes of different substrates and their relative proximities. The model therefore combines the statistical flexibility of a machine learning algorithm with an inherently spatial treatment of the substrate. The CRF model predicts substrates such that nearby locations with similar backscattering characteristics are likely to be in the same substrate class. The degree of proximity and allowable backscatter similarity are controlled by parameters that are learned from the data. CRF model results were evaluated against a popular generative model known as a Gaussian Mixture model that doesn't include spatial dependencies, only covariance between substrate backscattering response over different frequencies. Both models are used in conjunction with sparse bed observations/samples in a supervised classification. A detailed accuracy assessment, including a leave-one-out cross-validation analysis, was performed using both models. Using multispectral backscatter, the GMM model trained on 50% of the bed observations resulted in a 75% and 89% average accuracies in Patricia Bay and Bedford Basin, respectively. The same metrics for the CRF model were 78% and 95%. Further, the CRF model resulted in a 91% mean cross-validation accuracy across four substrate classes at Patricia Bay, and a 99.5% mean accuracy across three substrate classes at Bedford Basin, which suggest that the CRF model generalizes extremely well to new data. This analysis also showed that the CRF model was much less sensitive to the specific number and locations of bed observations than the generative model, owing to its ability to incorporate spatial autocorrelation in substrates. The CRF approach therefore may prove to be a powerful `spatially aware' alternative to other discriminative classifiers.

The increasing focus on the development of positioning techniques reflects the growing interest in applications and services based on indoor positioning. Many applications necessitate precise indoor positioning or tracking of individuals and assets, leading to rapid growth in products based on these technologies in certain market sectors. Ultrasonic systems have already proven effective in achieving the desired positioning accuracy and refresh rates. The typical signal used in ultrasonic positioning systems for estimating the range between the target and reference points, specifically the linear chirp, can undergo shape aberration due to the effects of acoustic diffraction when the aperture exceeds a certain limit. The extent of the aberration is influenced by the shape and size of the transducer, as well as the angle at which the transducer is observed by the receiver. This aberration also impacts the shape of the cross-correlation, causing it to lose its characteristic easily detectable single global peak. In such instances, cross-correlation techniques yield results with a significantly higher error than anticipated. In this study, an alternative technique to global peak detection is proposed, leveraging the inherent symmetry observed in the shape of the aberrated cross-correlation. The numerical simulations, performed using the academic acoustic simulation software Field II, conducted using a typical ultrasonic chirp and ultrasonic emitter, compare the classical and the proposed range techniques in a standard office room. The results demonstrate that the proposed technique enables accurate range estimation even in the presence of severe cross-correlation shape aberrations. This allows the use of emitting transducers with a much larger aperture than that allowed by the classical cross-correlation technique. Consequently, it becomes possible to have greater acoustic power available, leading to improved Signal-to-Noise Ratio (SNR).

Orthogonal Frequency Division Multiplexing (OFDM) is a technique widely used in today's wireless communication systems due to its ability to combat the effects of multi-path in the signal. However, one of the main limitations of the use of OFDM is its high Peak-to-Average Power Ratio (PAPR), which reduces the efficiency of the OFDM system. The effects of PAPR can produce both out-of-band and in-band radiation, which degrades the signal by increasing the bit error rate (BER), this occurs in both baseband and bandpass sginals. In this document the effect of the PAPR in a OFDM passband signal is analyzed considering the implementation of a High Power Amplifier (HPA) and the Simple Amplitude Predistortion-Orthogonal Pilot Sequences (OPS-SAP) scheme to reduce the PAPR.

Directional Modulation (DM) technique, as an emerging promising approach to secure wireless communications, has been attracting increasing attention for its unique characteristic in the past decade. The baseband signal is synthesized at the antenna level for maintaining the standard constellation format along the prescribed direction(s) while simultaneously distorting the signal constellations in other undesired directions. In this paper, we present a novel DM signal synthesis method based on phased array using a hybrid genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, which takes advantages of both GA and PSO algorithm. Then, the bit error rate of the proposed method is analyzed and simulated. Simulation results are presented to verify that the proposed DM signal have a narrower information beam-width. Therefore, it can offer a better physical layer security (PLS) performance compared with the conventional directional modulation signal.