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Measurement of coating–substrate interface stiffness can indirectly characterize interface bonding quality. This paper proposes a noval quantitative inversion coating–substrate interface stiffness method based on the multi-resonance frequencies of a constructed ultrasonic echo phase derivative spectrum (UEPDS). The theoretical relationship between UEPDS resonance frequencies and interface stiffness is derived. The detection frequency and high-sensitivity interface stiffness range are optimized based on the sensitivity analyzed. Numerical simulation and experiment are implemented on a 0.48 mm aluminum layer/carbon steel substrate specimen to prove the validity of the proposed ultrasonic method. The simulation results show that the maximal relative error between the inversion and the preset interface stiffnesses is reduced from 23% to 8% compared with the traditional ultrasonic reflection coefficient amplitude spectrum-based (URCAS-based) method. The experiment results indicate that the UEPDS-based inversion interface stiffnesses have the same trend as the nominal contact pressures between the coating–substrate interface. [ABSTRACT FROM AUTHOR]

PURPOSE OF REVIEW: Environmental chemical exposures may disrupt child development, with long-lasting health impacts. To date, U.S. studies of early environmental exposures have been limited in size and diversity, hindering power and generalizability. With harmonized data from over 60,000 participants representing 69 pregnancy cohorts, the National Institutes of Healths Environmental influences on Child Health Outcomes (ECHO) Program is the largest study of U.S. childrens health. Here, we: (1) review ECHO-wide studies of chemical exposures and maternal-child health; and (2) outline opportunities for future research using ECHO data. RECENT FINDINGS: As of early 2024, in addition to over 200 single-cohort (or award) papers on chemical exposures supported by ECHO, ten collaborative multi-cohort papers have been made possible by ECHO data harmonization and new data collection. Multi-cohort papers have examined prenatal exposure to per- and polyfluoroalkyl substances (PFAS), phthalates, phenols and parabens, organophosphate esters (OPEs), metals, melamine and aromatic amines, and emerging contaminants. They have primarily focused on describing patterns of maternal exposure or examining associations with maternal and infant outcomes; fewer studies have examined later child outcomes (e.g., autism) although follow up of enrolled ECHO children continues. The NICHDs Data and Specimen Hub (DASH) database houses extensive ECHO data including over 470,000 chemical assay results and complementary data on priority outcome areas (pre, peri-, and postnatal, airway, obesity, neurodevelopment, and positive health), making it a rich resource for future analyses. ECHOs extensive data repository, including biomarkers of chemical exposures, can be used to advance our understanding of environmental influences on childrens health. Although few published studies have capitalized on these unique harmonized data to date, many analyses are underway with data now widely available.

We have observed that Variational Mode Decomposition (VMD) exhibits instability in the denoising and separation of noisy multitone signals. Specifically, minor changes in factors such as signal-to-noise ratio, frequency spacing, sampling rate, and probability distribution can significantly impact the decomposition results. To address this issue, we have developed the Dual-VMD-correlation algorithm. This algorithm effectively mitigates the impact of beat effects when harmonics of closely spaced frequencies are superimposed, enabling stable denoising of noisy multitone signals and the separation of each individual tone signal. The algorithm holds promise for applications in frequency-modulated continuous wave laser detection. It can address challenges related to denoising laser echo signals interfered with by aerosols and the difficulty in separating backscatter interference from the target reflected signal spectrum. [ABSTRACT FROM AUTHOR]

As one of the most successful platforms of quantum control, trapped ions can be modulated by sequential microwave pulses to realize high-fidelity quantum logic gates, and dephasing noise may lead to invalidation of phase locking. In this work, we utilize the Dirac–Frenkel time-dependent variational approach with Davydov ansatz to simulate spin echo dynamics in the framework of spin-boson model. As the essential modulation parameters, pulse duration and waiting time have been comprehensively investigated to optimize the phase gates by two microwave pulses. We find that, as spin orientation undergoes periodic changes, the phase difference by acting one and two pulses exhibits spontaneous locking following time evolution, which indicates the robustness of the quantum phase gates. Spectrum of the environmental noise that is appropriate for the phase locking is also determined. [ABSTRACT FROM AUTHOR]

In an era of political polarization, growing anti-science sentiment, and pervasive inequities in the social drivers of health, a rising tide of potentially harmful state policy proposals in the United States threaten to undermine the health of the public. In response, our health system's population health and government relations offices partnered with key health advocacy organizations in our state of New Hampshire to offer an interactive virtual learning series aimed at preparing diverse professionals and citizens to effectively advocate for sound health policies. Two hundred forty-seven individuals registered for the six-session series. Our findings indicate that participants experienced increased awareness of the political determinants of health, better understanding of specific legislative proposals in New Hampshire, and enhanced preparedness for advocacy, with many reporting greater active engagement in advocacy. Given its flexible and virtual nature, this innovative learning model could easily be adapted to promote dialogue and advocacy for sound health policy in diverse regional contexts. [ABSTRACT FROM AUTHOR]

As demonstrated in the first part of this study (Part I), wind-shift boundaries routinely form along the west coast of the Pearl River Delta due to the land–sea contrast of a "trumpet" shape coastline in the summer monsoon season. Through multiple numerical simulations, this article (Part II) aims to examine the roles of the trumpet-shaped coastline in the mesovortex genesis during the 1 June 2020 tornadic event. The modeling reproduced two mesovortices that are in close proximity in time and space to the realistic mesovortices. In addition to the modeled mesovortex over the triple point where strong ambient vertical vorticity was located, another mesovortex originated from an enhanced discrete vortex along an airmass boundary via shear instability. On the fine-scale storm morphology, finger-like echoes preceding hook echoes were also reproduced around the triple point. Results from sensitivity experiments suggest that the unique topography plays an essential role in modifying the vorticity budget during the mesovortex formation. While there is a high likelihood of an upcoming storm evolving into a rotating storm over the triple point, the simulation's accuracy is sensitive to the local environmental details and storm dynamics. The strengths of cold pool surges from upstream storms may influence the stretching of low-level vertically oriented vortex and thus the wrap-up of finger-like echoes. These findings suggest that the trumpet-shaped coastline is an important component of mesovortex production during the active monsoon season. It is hoped that this study will increase the situational awareness for forecasters regarding regional non-mesocyclone tornadic environments. [ABSTRACT FROM AUTHOR]

Marine organisms continually adapt their physiology and behaviour to temporal variations in their environment, resulting in diurnal rhythmic behaviour, particularly when foraging. In delphinids, these rhythms can be studied by recording echolocation clicks, which can provide indicators of foraging activity. The foraging rhythms of delphinids and their relationship to temporal parameters are poorly documented and most studies so far have used moored passive acoustic systems. The present study provides, for the first time, information on the activity rhythms of delphinids investigated in relation with temporal variables at a basin scale from a moving platform, in the western and central Mediterranean Sea. We used passive acoustic recordings collected by hydrophones towed along transect lines during the ACCOBAMS Survey Initiative in the summer 2018. We extracted variables that may influence daily and monthly rhythms, including time of day, lunar cycle, lunar illumination and sea state and fitted generalised additive models. The nycthemeral and lunar cycles were the two main factors influencing dolphin activity rhythms. Echolocation activity was predominant at night, with a maximum of 0.026 acoustic events per minute at 21:00/22:00 compared to as few as 0.0007 events per minute at 11:00. These events were also more frequent during the third quarter of the moon; 0.033 acoustic events on day 22 of the lunar cycle as opposed to 0.0008 on day 8 of the lunar cycle, corresponding to the first quarter of the moon. Variations in the echolocation activity of delphinids in the Mediterranean Sea could reflect variation in their foraging effort and be related to prey density, composition, accessibility and catchability within dolphin foraging depth range. These results should also improve interpretation of passive acoustic monitoring data. [ABSTRACT FROM AUTHOR]

In order to analyse the effect of the injection point size of the CBM (Coalbed Methane) well level monitor on the amplitude and frequency of pressure pulsations in the wellhead manifold, numerical simulations and experiments were carried out to investigate the effect of different injection point sizes on the amplitude and frequency of pressure pulsations downstream of the sudden expansion structure. Using compressed air as the fluid and the size of the injection point as the variable, the amplitude and frequency of pressure pulsations at different locations downstream of the sudden expansion structure were tested. The results show that the pressure pulsation amplitude is affected by the size of the injection point, and the larger the injection point is, the larger the pressure pulsation amplitude is; the size of the injection point has less influence on the pressure pulsation frequency downstream of the protruding and expanding structure, and the pressure pulsation frequency at 0.5 m and 1 m downstream of the protruding and expanding structure is in the vicinity of 76 Hz. Therefore, the echo signal processing should be filtered around this frequency to obtain accurate liquid level echo signals, so as to improve the accuracy of liquid level monitoring and realise the efficient development of coalbed methane wells. [ABSTRACT FROM AUTHOR]

Eating disorders (ED) usually involve hospital admission and a high relapse rate, with the return home being a critical moment for patients and their families. After their return home, they often have trouble incorporating the guidelines they have learned into their daily context. ECHOMANTRA intervention program aims to facilitate this transition by offering psychological strategies that involve both patients and their families and carers. Specifically, the ECHO program is aimed at the relatives of these patients. The present study aimed to analyze the efficacy of adding the ECHO program to the usual treatment (TAU) of relatives through a novel format based on individual intervention and with an online format and to examine the acceptability and feasibility of this new format. The study design was multi‐center, randomized, controlled, with a longitudinal design and comparing two parallel groups. A total of 108 family members participated. Results indicated that relatives from both groups, TAU and ECHO + TAU, showed improvements in expressed emotion, accommodation, impact of the ED, emotional well‐being, and caregiver skills. However, effect sizes in the ECHO + TAU group were slightly larger than the TAU group. In addition, the changes were greater in depression and caregiver skills when the ECHO component was added. Most caregivers (81.48%) completed the ECHO and indicated a high level of satisfaction with the program. These results suggest the efficacy and the feasibility of adding the ECHO intervention program to the usual treatment in an individual online format. [ABSTRACT FROM AUTHOR]

Due to suppression jamming by jammers, the signal-to-interference-plus-noise ratio (SINR) during tracking tasks is significantly reduced, thereby decreasing the target detection probability of radar systems. This may result in the interruption of the target track. To address this issue, we propose a multi-static radar power allocation algorithm that enhances the detection and tracking performance of multiple radars in relation to their targets by optimizing power resource allocation. Initially, the echo signal model and measurement model of multi-static radar are formulated, followed by the derivation of the Bayesian Cramér–Rao lower bound (BCRLB). The multi-objective optimization method is utilized to establish the objective function for joint tracking and detection, with dynamic adjustment of the weight coefficient to balance the tracking and detection performance of multiple radars. This ensures the reliability and anti-jamming capability of the multi-static radar system. Simulation results indicate that the proposed algorithm can prevent the interruption of jammer tracking and maintain robust tracking performance. [ABSTRACT FROM AUTHOR]

To improve the signal-to-noise ratio (SNR) of nuclear magnetic resonance (NMR) echo data with SNR below 8 obtained from tight sandstone reservoirs and improve the accuracy of the porosity obtained from T 2 inversion, a novel de-noising method is developed to improve the processing of NMR echo data. The algorithm is based on variational mode decomposition wavelet threshold (VMD-WT). It was found that as the decomposition mode number increases, the center frequency difference between the first mode and the second mode gradually decreases. A method to determine a reasonable mode decomposition number based on the center frequency difference of the modes is proposed. The effectiveness of the algorithm is verified by the application in the numerical simulation experiments and actual NMR echo data, respectively. The results indicated that, compared with wavelet threshold de-noising method, VMD-WT de-noising method is more suitable for the de-noising of NMR echo data with SNR below 8 obtained from tight sandstone reservoirs. [ABSTRACT FROM AUTHOR]

Hereditary transthyretin amyloidosis (ATTRv) is associated with polyneuropathy, cardiomyopathy, or both. The effects of eplontersen on cardiac structure and function were assessed. NEURO-TTRansform was an open-label trial involving 144 adults with ATTRv polyneuropathy (49 patients [34%] with cardiomyopathy) receiving eplontersen throughout and compared with a historical placebo group (n = 60; 30 patients [50%] with cardiomyopathy) from the NEURO-TTR trial at week 65. Treatment effect (eplontersen vs placebo), presented as mean difference (95% confidence interval) was analyzed after adjusting for age, sex, region, baseline value, ATTRv disease stage, previous ATTRv treatment, and V30M transthyretin variant. There were notable differences at baseline between the eplontersen group and historical placebo. In the cardiomyopathy subgroup, 65 weeks of eplontersen treatment was associated with improvement from baseline relative to placebo in left ventricular ejection fraction of 4.3% (95% confidence interval 1.40–21.01; P =.049) and stroke volume 10.64 mL (95% confidence interval 3.99–17.29; P =.002) while the remainder of echocardiographic parameters remained stable. Eplontersen was associated with stable or improved measures of cardiac structure and function vs historical placebo in patients with ATTRv polyneuropathy and cardiomyopathy. Further investigation into eplontersen's effect on transthyretin amyloid cardiomyopathy is being conducted in the CARDIO-TTRansform trial. vATTR, variant transthyretin; LV, left ventricle; E/e′, ratio of early diastolic mitral inflow velocity to early diastolic mitral annulus velocity; NT-proBNP, N-terminal prohormone of brain natriuretic peptide. [Display omitted] [ABSTRACT FROM AUTHOR]

Seafloor geodetic observation technology is a pivotal method for capturing plate boundary earthquakes and related phenomena. Currently, this task is performed by Global Navigation Satellite System-Acoustic ranging (GNSS-A), a technique that integrates GNSS observations with acoustic ranging. This method involves measuring the round-trip travel time of an acoustic signal between a surface platform and a preinstalled seafloor station, which is then converted into a distance for positioning. While the horizontal positioning accuracy can reach the centimeter level, the vertical accuracy tends to be loser. Recent research has revealed that equipment-dependent distortion of sound waves causes problems for accurate travel time identification, leading to a decrease in vertical component accuracy. In this paper, we conducted an analysis of distortion tendencies using water tank test data and quantitatively identified differences in distortion magnitude related to instruments, angle dependence, and directional dependence attributed to the sea surface platform. We proposed a new identification algorithm, Acoustic Ambiguity Reduction (AAR) method, using a catalog of distortions for each device, and improved the observational capabilities of the vertical component. [ABSTRACT FROM AUTHOR]

Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial-(EC) (n = 136) and cervical (CC) (n = 132) cancer. 1.5 T and 3 T, T1-weighted MRI 2 min post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patients were clustered into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication were analyzed using Kruskal–Wallis tests, Kaplan–Meier plots, log-rank test, random survival forests and LASSO Cox regression with time-dependent area under curve (tdAUC) (α = 0.05). A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score-versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forests; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75; LASSO Cox; z-score: mean tdAUC = 0.64/0.76; LRM: mean tdAUC = 0.76/0.75). Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were similarly associated with DSS in EC and CC patients. [ABSTRACT FROM AUTHOR]

Echo state network (ESN) has widely attracted many researchers due to its training process without backpropagation. However, it is hard for single ESN to fit those complex and polytrophic situations. Under this case, a novel multiscale deep ESN (MDESN) is developed in this study, which integrates the deep learning, parallel structure and multiscale reservoir state matrix mode, respectively. The deep framework is responsible for collecting multivariate dimensional reservoir states; Upgradation of various reservoir states could efficiently dig hidden characteristics for decoding. The parallel structure could be trained simultaneously to decrease time consumption through the multiple thread method. According to the multivariate high dimensional mapping method, MDESN could obtain more robust generalization information compared to the state-of-the-art models. MDESN is evaluated in two chaos prediction benchmarks (Lorenz and Mackey-Glass) and real solar irradiance predictions. Various prediction horizons including one-step-ahead and multi-step-ahead prediction are designed and conducted respectively to verify the effectiveness and robustness of MDESN. Furthermore, RMSE, MAE, MAPE, and R are adopted to evaluate our proposed model. The statistical results demonstrate that MDESN has the best-performing adaptability and robustness compared to classical ESN, deep ESN (DESN), and other state-of-the-art models. [ABSTRACT FROM AUTHOR]

Accurately extracting and segmenting boundaries between targets and backgrounds is difficult in Synthetic Aperture Radar (SAR) images because of the many scattering mechanisms that produce echo signals. These complications combined with the ubiquitous speckle noise present in SAR images cause traditional segmentation algorithms to frequently produce unacceptable results. In order to tackle these problems, we provide a unique segmentation strategy that makes use of a minimum error thresholding technique based on the theory of the Rayleigh distribution. Our technique builds a local translation Rayleigh model by utilizing the stationary wavelet domain, which successfully suppresses speckle noise and generates SAR images that are easier to segment. Next, we utilize a two-dimensional entropy thresholding method to segment images. We present a variable code length genetic technique that incorporates the optimization of segmentation category numbers encoded in the chromosomes into the fitness function in order to further improve segmentation performance. Furthermore, our method yields minimum segmentation error pixels and excellent segmentation efficiency since the segmentation thresholds it finds is accurate. By addressing major issues with speckle noise and segmentation precision, this enhanced approach provides notable improvements in the precise and effective segmentation of SAR images. [ABSTRACT FROM AUTHOR]

Background: This study presents a 4-year (January 2019–April 2023) X-band radar network-based bow echo observation over Great Bandung Indonesia. This study provides insight into the temporal and spatial variability of bow echo distribution and presents the atmospheric condition associated with the bow echo events. Temporal analysis is categorical into monthly, seasonally, and diurnal. The analysis was performed using X-band radar network and reanalysis data (ERA5). Result: At least 26 bow echoes were identified across the Bandung basin from X-band radar network during the study period. From this total number of bow echoes, the observation of initiation modes is primarily generated from a weakly organized cell, with few coming from the squall line. The bow echo mostly evolved from noon until afternoon. The rainy season (December–January–February) and transition season (March–April–May) is the most frequent period of bow echo occurrence, with March being the most active month. Nevertheless, this study also found bow echo occurrence in the dry season (June–July–August). For the spatial analysis, the studied area is divided into two regions representing the eastern and western part of Bandung basin. The eastern region recorded the most intense occurrences with 14 events. The movement of bow echo in this region covered a shorter distance (average distance only 4.56 km), with all initiation modes occurring inside the region. The atmospheric condition within this region has less moisture flux, with higher CAPE and slightly higher surface temperature. Meanwhile, in the western region have different characteristics with higher moisture flux, a slight effect of CAPE and CINH, with longer distance and zonal movement direction of bow echo displacement. Conclusion: These conditions indicate that local convection is the dominant mode of bow echo initiation mode in the eastern region of Bandung basin. Meanwhile, the monsoon effect influences the bow echo initiation mode in the western region. Given that the observed 4-year bow echo has different characteristics from previous studies of bow echo in mid latitudes, developing different criteria for bow echo detection in the tropics is crucial. [ABSTRACT FROM AUTHOR]

Azimuth multi-channel synthetic aperture radar (SAR) has always been an important technical means to achieve high-resolution wide-swath (HRWS) SAR imaging. However, in the space-borne azimuth multi-channel SAR system, random phase noise will be produced during the operation of each channel receiver. The phase noise of each channel is superimposed on the SAR echo signal of the corresponding channel, which will cause the phase imbalance between the channels and lead to the generation of false targets. In view of the above problems, this paper proposes a random phase noise compensation method for space-borne azimuth multi-channel SAR. This method performs feature decomposition by calculating the covariance matrix of the echo signal and converts the random phase noise estimation into the optimal solution of the cost function. Considering that the phase noise in the receiver has frequency-dependent and time-varying characteristics, this method calculates the phase noise estimation value corresponding to each range-frequency point in the range direction and obtains the phase noise estimation value by expectation in the azimuth direction. The proposed random phase noise compensation method can suppress false targets well and make the radar present a well-focused SAR image. Finally, the usefulness of the suggested method is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

Echo state networks (ESNs) belong to the class of recurrent neural networks and have demonstrated robust performance in time series prediction tasks. In this study, we investigate the capability of different ESN architectures to capture spatial relationships in images without transforming them into temporal sequences. We begin with three pre-existing ESN-based architectures and enhance their design by incorporating multiple output layers, customising them for a classification task. Our investigation involves an examination of the behaviour of these modified networks, coupled with a comprehensive performance comparison against the baseline vanilla ESN architecture. Our experiments on the MNIST data set reveal that a network with multiple independent reservoirs working in parallel outperforms other ESN-based architectures for this task, achieving a classification accuracy of 98.43%. This improvement on the classical ESN architecture is accompanied by reduced training times. While the accuracy of ESN-based architectures lags behind that of convolutional neural network-based architectures, the significantly lower training times of ESNs with multiple reservoirs operating in parallel make them a compelling choice for learning spatial relationships in scenarios prioritising energy efficiency and rapid training. This multi-reservoir ESN architecture overcomes standard ESN limitations regarding memory requirements and training times for large networks, providing more accurate predictions than other ESN-based models. These findings contribute to a deeper understanding of the potential of ESNs as a tool for image classification. [ABSTRACT FROM AUTHOR]

The echo signal of ultrasonic internal inspection is highly sensitive to the incident deflection angle between the probe and the measured surface of the pipeline, which is the key factor affecting the accuracy of ultrasonic inspection. In practical engineering, the echo signal of ultrasonic inspection is easy to be interfered by the external environment, which makes it difficult to identify the deflection angle. The data of defect echo signal under different circumferential declination were collected by the pipeline ultrasonic internal inspection system, and the characteristics of signal variation in time domain under different circumferential declination were analysed. The results show that the existence of circumferential declination reduced the absolute value of the overall amplitude of ultrasonic echo signal. A signal processing method based on amplitude reduction is proposed to reduce the sampling random error. Comparative analysis shows that the circumferential declination can be identified based on amplitude reduction method, and the accuracy of ultrasonic recognition can be improved. By extracting the characteristic value of waveform, the empirical formula is obtained. Within 2°, the maximum deviation is 0.137°. The research results have certain guiding significance for the identification of circumferential declination in practical engineering. [ABSTRACT FROM AUTHOR]

Intended for practical use in the Anglican liturgy but even more deeply invested in their physical status as a book, Smart's 1765 Psalms of David invert the private, ecstatically spiritualized stylizations of Jubilate Agno. Smart's strange and compulsive rendering of Psalm CXIX explores the limits of the earlier poem's engagements with the echoic potential of the phoneme, confining the sound that it exalts to the material and specifically typographical space thereby shown to generate it. I argue that this technique echoes the sonic architecture of Horace Walpole's exactly contemporary Castle of Otranto , linking Smart's Psalm CXIX to the acousmatic pretenses that became a signature of gothic literary form. [ABSTRACT FROM AUTHOR]

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced. [ABSTRACT FROM AUTHOR]

The squall line is a type of convective system that is characterized by storm cells arranged in a line or band pattern and is usually associated with disastrous weather. The identification and tracking of squall lines thus play important roles in early warning systems for meteorological disasters. Here, a clustering-based identification and tracking algorithm for squall lines is presented based on weather radar data. A clustering analysis is designed to distinguish the strong echo area and estimate the feature value, including the reflectivity value, length, width, area, endpoints, central axes, and centroid. The linearly arranged clusters are merged to improve the identification of squall line development. The three-dimensional structure and movement tracking of the squall line are obtained using the centroid and velocity of the squall lines identified in each layer. The results demonstrate that the method can effectively identify and track one or more squall lines across the radar surveillance area. The results also show that the recognition accuracy rate for the single scan elevation of this method is 95.06 %, and the false-positive rate is 3.17 %. This method improves the accuracy of squall line identification in the development stage of squall lines and still works efficiently even when high interference contamination occurs. [ABSTRACT FROM AUTHOR]

Accurate air temperature measurements are essential in eddy covariance systems, not only for determining sensible heat flux but also for applying density effect corrections (DECs) to water vapor and CO2 fluxes. However, the influence of wind-induced vibrations of mounting structures on temperature fluctuations remains a subject of investigation. This study examines 30 min average temperature variances and fluxes using eddy covariance systems, combining Campbell Scientific sonic anemometers with closely co-located fine-wire thermocouples alongside LI-COR CO2 – H2O gas analyzers at multiple heights above a sagebrush ecosystem. The variances of sonic temperature after humidity corrections (Ts) and sensible heat fluxes derived from Ts are underestimated (e.g., by approximately 5 % for temperature variances and 4 % for sensible heat fluxes at 40.2 m , respectively) as compared with those measured by a fine-wire thermocouple (Tc). Spectral analysis illustrates that these underestimated variances and fluxes are caused by the lower energy levels in the Ts spectra than the Tc spectra in the low-frequency range (natural frequency < 0.02 Hz). These underestimated Ts spectra in the low-frequency range become more pronounced with increasing wind speeds, especially when wind speed exceeds 10 ms-1. Moreover, the underestimated temperature variances and fluxes cause overestimated water vapor and CO2 fluxes through DEC. Our analysis suggests that these underestimations when using Ts are likely due to wind-induced vibrations affecting the tower and mounting arms, altering the time of flight of ultrasonic signals along three sonic measurement paths. This study underscores the importance of further investigations to develop corrections for these errors. [ABSTRACT FROM AUTHOR]

Infrared (IR) small target detection in sky scenes is crucial for aerospace, border security, and atmospheric monitoring. Most current works are typically designed for generalized IR scenes, which may not be optimal for the specific scenario of sky backgrounds, particularly for detecting small and dim targets at long ranges. In these scenarios, the presence of heavy clouds usually causes significant false alarms due to factors such as strong edges, streaks, large undulations, and isolated floating clouds. To address these challenges, we propose an infrared dim and small target detection algorithm based on morphological filtering with dual-structure elements. First, we design directional dual-structure element morphological filters, which enhance the grayscale difference between the target and the background in various directions, thus highlighting the region of interest. The grayscale difference is then normalized in each direction to mitigate the interference of false alarms in complex cloud backgrounds. Second, we employ a dynamic scale awareness strategy, effectively preventing the loss of small targets near cloud edges. We enhance the target features by multiplying and fusing the local response values in all directions, which is followed by threshold segmentation to achieve target detection results. Experimental results demonstrate that our method achieves strong detection performance across various complex cloud backgrounds. Notably, it outperforms other state-of-the-art methods in detecting targets with a low signal-to-clutter ratio (MSCR ≤ 2). Furthermore, the algorithm does not rely on specific parameter settings and is suitable for parallel processing in real-time systems. [ABSTRACT FROM AUTHOR]

The integrated detection and jamming system employs integrated signals devoid of typical radar signal characteristics for detection and jamming. This allows for the sharing of resources such as waveform, frequency, time, and aperture, significantly enhancing the overall utilization rate of system resources. However, to achieve effective interference, the integrated waveform must overlap with the adversary radar signal within the frequency band. Consequently, the detection echoes are susceptible to the strong co-frequency direct wave generated by the adversary signals. This paper proposes a co-frequency direct wave interference suppression algorithm based on 2D generalized smoothed- l 0 norm sparse recovery. The algorithm exploits a joint dictionary comprising our integrated signals and adversary signals, along with the sparsity of 2D range-Doppler maps. The direct solution of the sparse decomposition optimization problem, formulated for the entire echo matrix, enhances the target detection performance for integrated signals even in the presence of robust co-frequency direct wave interference. Furthermore, the proposed method achieves robustness to interference of varying intensities through the adaptive updating and adjustment of relevant parameters. The effectiveness of the proposed method is validated through simulation and experimental results. [ABSTRACT FROM AUTHOR]

Space targets move in orbit at a very high speed, so in order to obtain high-quality imaging, high-speed motion compensation (HSMC) and translational motion compensation (TMC) are required. HSMC and TMC are usually adjacent, and the residual error of HSMC will reduce the accuracy of TMC. At the same time, under the condition of low signal-to-noise ratio (SNR), the accuracy of HSMC and TMC will also decrease, which brings challenges to high-quality ISAR imaging. Therefore, this paper proposes a joint ISAR motion compensation algorithm based on entropy minimization under low-SNR conditions. Firstly, the motion of the space target is analyzed, and the echo signal model is obtained. Then, the motion of the space target is modeled as a high-order polynomial, and a parameterized joint compensation model of high-speed motion and translational motion is established. Finally, taking the image entropy after joint motion compensation as the objective function, the red-tailed hawk–Nelder–Mead (RTH-NM) algorithm is used to estimate the target motion parameters, and the joint compensation is carried out. The experimental results of simulation data and real data verify the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a bolt preload monitoring system, including the system architecture and algorithms. We show how Finite Element Method (FEM) simulations aided the design and how we processed signals to achieve experimental validation. The preload is measured using a Piezoelectric Micromachined Ultrasonic Transducer (PMUT) in pulse-echo mode, by detecting the Change in Time-of-Flight (CTOF) of the acoustic wave generated by the PMUT, between no-load and load conditions. We performed FEM simulations to analyze the wave propagation inside the bolt and understand the effect of different configurations and parameters, such as transducer bandwidth, transducer position (head/tip), presence or absence of threads, as well as the frequency of the acoustic waves. In order to couple the PMUT to the bolt, a novel assembly process involving the deposition of an elastomeric acoustic impedance matching layer was developed. We achieved, for the first time with PMUTs, an experimental measure of bolt preload from the CTOF, with a good signal-to-noise ratio. Due to its low cost and small size, this system has great potential for use in the field for continuous monitoring throughout the operative life of the bolt. [ABSTRACT FROM AUTHOR]

This article gives the overview of the Japan Aerospace Exploration Agency (JAXA) level 2 (L2) Standard and Research algorithms and products by Japanese science teams for EarthCARE Clouds, Aerosols and Radiation Explorer (EarthCARE), which is a JAXA and the European Space Agency (ESA) joint satellite mission. First three single sensor algorithms for 94GHz cloud profiling radar (CPR)-only, 355nm-atmospheric lidar with high spectral resolution function (ATLID)-only, and multi-spectral imager (MSI)-only retrievals, and their products were briefly reviewed. CPR-echo algorithms provide radar reflectivity factor, Doppler velocity, normalized radar cross section and path integral attenuation. CPR-only, CPR-ATLID synergy and CPR-ALTID-MSI synergy algorithms for standard cloud products provide cloud detection, cloud particle type and cloud microphysics, and the research products further provide Doppler velocity, terminal velocity and vertical air motion inside clouds. ATLID standalone algorithms produce aerosol, cloud and clear sky classification products as well as total aerosol extinction and extinction and number concentration of each aerosol types. ATLID-MSI synergy algorithms are developed to retrieve effective radius for each aerosol species in addition to the ATLID-only products. MSI algorithms retrieve cloud effective radius, ice and water content and cloud top pressure. Four sensor algorithms are prepared to produce shortwave and longwave radiative fluxes at the top of atmosphere, those at the surface and also heating rate profiles by using the outputs from CPR, ATLID and their synergy algorithms. The shortwave and longwave fluxes from the four sensor algorithms will then be compared with broad band radiation (BBR) to examine the consistency of the JAXA L2 retrievals. The algorithms are developed and evaluated by using observational data from satellites and ground-based instruments, and simulation data from the Japanese global cloud-resolving model, the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with Joint simulator. As for space-borne data, existing space-borne satellites data such as CloudSat, CALIPSO, MODIS and CERES datasets are intensively used. For ground-based observations, High-sensitivity Ground-based Super Polarimetric Ice-crystal Detection and Explication Radar (HG-SPIDER) with a minimum sensitivity of -40 dBZ at 15 km and over -60 dBZ at 1 km, Electronic Scanning SPIDER (ES-SPIDER), 355 nm high spectral resolution lidar, multiple-field-of-view multiple scattering lidar and Doppler lidars are installed at EarthCARE super-site in Koganei, Tokyo and offers unique opportunities to evaluate and analyse EarthCARE data. [ABSTRACT FROM AUTHOR]

The in situ acoustic measurement of seafloor sediment is an important technical means to obtain the acoustic parameters of seafloor. The time‐of‐flight method is commonly used to calculate the sound speed in seafloor sediment. Accurate identification of signal feature points is essential for determining travel time or travel time difference of acoustic signals. However, the precise identification of feature points, such as the take‐off point of the first wave of a sound wave signal, is challenging. The conventional manual identification method is inefficient and prone to errors. The development of a feature point auto‐identification method is imperative for accurately calculating the travel time of acoustic signals. In this study, we employed the cross‐correlation method, the level threshold method and the short window‐long window energy ratio method to extract the acoustic travel time differences and calculate the sound speeds in seawater and in seafloor sediment. We then analysed the effectiveness of these calculated results. The sound speeds in seawater obtained through the aforementioned methods were compared with the sound speeds measured using a sound velocity profiler. The comparison revealed that these processing methods exhibit a high level of accuracy. The sound speed results in sediments show that the programme‐based auto‐identification methods significantly reduce the standard deviation compared to the manual identification method. This study successfully assessed the processing accuracy of different methods and expanded the processing methods for in situ acoustic signals of seafloor sediments. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) is a leading cause of infant mortality, accounting for more deaths than any other type of malformation. The incidence of CHD varies between 4-10/1000 live births in India.About 1 in every 4 babies born with a heart defect has a critical congenital heart defect (Critical CHD, also known as critical congenital heart disease).In Asymptomatic new borns measurement of oxygen saturations using pulse oximeter on the Right hand and foot was carried out after 24hrs of birth. Saturations above 95% was regarded as having negative screen. Out of total 400 neonates were screened and 7 cases of hypoxemia were identified. They were subjected to ECHO and4 had diagnosed to have critical congenital heart disease (TGA, TAPVC, DORV) and two cases had ASD and one was normal. Negative screen did not report back with any symptoms. [ABSTRACT FROM AUTHOR]

Back ground of the study: Pulmonary thromboembolism (PTE) is an occlusion of pulmonary arterial circulation by a blood clot formed elsewhere, usually in the deep veins of the leg; less than 5% of venous thrombosis occurs in other sites. It is a major health concern with high mortality and morbidity. The aim of this Research was to study clinical profile, management and outcome of Pulmonary embolism. Methods: Prospective assessment of clinical features, radiology and outcome of patients presenting with symptomatic PE over one year period, between 01/06/2018 to 31/05/2019. PESI (pulmonary embolism severity index score), predisposing factors, symptoms, clinical feature at the time of admission, ECG features, echocardiogram, treatment received and the outcome were reviewed. Results: The study has 53 participants. The mean age was 49.15±16.08 years. The most prevalent clinical symptoms were tachycardia (79.25%) and hypotension (33.96%).The study population had a mean PESI score of 96.72 ± 31.95. The most prevalent ECG results were sinus tachycardia (79.25%) and s1q3t3 pattern (52.83%). Most patients had right atrial and right ventricular dilatation (73.58%) and 2D echo showed pulmonary arterial hypertension (73.58%).CT pulmonary angiograms showed lobar thrombus distribution in 67.92% of patients. Tenecteplase thrombolysis (69.81%), traditional heparin infusion for 3-5 days, or acitrom anticoagulation were the most common, followed by streptokinase (9.43%) and reteplase (1.89%).10 (18.87%) individuals received just anticoagulation. In this trial, most patients in the anticoagulation-only group got standard heparin for 3-5 days before acitrom (80%).The most common anticoagulants at discharge were acitrom (75.47%), rivaroxaban (13.20%), dabigatran (3.77%), apixaban (1.88%), and warfarin (1.88%). At discharge, the mean acitrom dose was 2.39 ± 0.9 mg. Most patients (96.23%) were released in stable clinical condition, with a 3.77% mortality rate. Conclusion: Pulmonary embolism is a prevalent condition that can be readily diagnosed if it is clinically suspected. Early detection and proactive treatment are crucial for a successful outcome. [ABSTRACT FROM AUTHOR]

Background- Primary Hyperparathyroidism (PHPT) has been associated with increased cardiovascular morbidity and early death. Data on the extent and clinical significance of cardiovascular abnormalities in primary hyperparathyroidism (PHPT) are conflicting. The main objective of this study was to evaluate the cardiac function in patients with PHPT without other known risk factors and to analyze the effect of parathyroidectomy. Methodology- In a Prospective Observational Cohort design, sixteen patients with Primary Hyperparathyroidism underwent Two-dimensional echocardiography, Electrocardiography, serum N-terminal pro-brain natriuretic peptide (NT-proBNP, a myocardial damage marker), before and 3 months after successful parathyroidectomy. Result- We observed significant improvement in Left ventricular ejection fraction (p = 0.010) in patients undergoing parathyroid surgery after 3 months postoperatively. There is significant reduction in LVMI (p=0.044) & posterior wall thickness (p=0.007), which are indicators of left ventricular hypertrophy after 3 months of surgery. Conclusion- Patients with PHPT who are symptomatic exhibit significant cardiovascular dysfunction, which improves three months following successful parathyroidectomy. As a part of screening, we should consider routine cardiovascular evaluation in all symptomatic patients of PHPT. Symptomatic PHPT patients should undergo early curative surgery for the reversal of the cardiovascular dysfunction and in asymptomatic PHPT patients also early surgery is recommended for the prevention of end organ damage. [ABSTRACT FROM AUTHOR]

We conduct simulations of Spin Echo Small Angle Neutron Scattering (SESANS) by employing Monte Carlo methods to a setup using four magnetic Wollaston prisms. Our primary focus involves the validation of these models, encompassing monochromatic scenarios across various neutron wavelengths to ascertain the reliability of the simulations. Subsequently, we extend this validation to encompass simulations in time-of-flight mode. Our model consistently and precisely predicts the scattering patterns emanating from dilute spheres in both monochromatic and time-of-flight modes. Notably, it also accurately reproduces the intricate encoding associated with scattering occurring between the third and fourth magnetic Wollaston prism, which provides us with another approach to increase the solid angle coverage of a SESANS instrument. This validation process conclusively demonstrates the efficacy of our simulation methods. Importantly, it paves the way for simulating more intricate and realistic instrumental configurations, broadening the horizons for future research endeavours. [ABSTRACT FROM AUTHOR]

Using the electronic spin of nitrogen-vacancy (NV) centers in diamond is a promising approach to realizing high-precision temperature sensors; furthermore, pulsed optically detected magnetic resonance (pulsed-ODMR) is one way to measure the temperature using these NV centers. However, pulsed-ODMR techniques such as D-Ramsey, thermal echo, or thermal Carr–Purcell–Meiboom–Gill sequences require careful calibration and strict time synchronization to control the microwave (MW) pulses, which complicates their applicability. Continuous-wave ODMR (CW-ODMR) is a more advantageous way to measure temperature with NV centers because it can be implemented simply by continuous application of a green laser and MW radiation. However, CW-ODMR has lower sensitivity than pulsed-ODMR. Therefore, it is important to improve the temperature sensitivity of CW-ODMR techniques. Herein, we thus propose and demonstrate a method for measuring temperature using CW-ODMR with a quantum spin state dressed by a radio-frequency (RF) field under a transverse magnetic field. The use of an RF field is expected to suppress the inhomogeneous broadening resulting from strain and/or electric-field variations. The experimental results confirm that the linewidth is decreased in the proposed scheme when compared to the conventional scheme. In addition, we measured the temperature sensitivity to be about 50.4 ± 3.5 mK / Hz , and this is approximately eight times better than that of the conventional scheme. [ABSTRACT FROM AUTHOR]

Abstract Background Organizations implement innovations to disrupt the status quo and create value. Within sectors such as healthcare, innovations need to navigate large scale system and organizational factors to succeed. This research explores the implementation of a global innovation– Project ECHO®. Project ECHO® is a validated virtual communities of practice model organizational teams implement to build workforce capacity and capability. Project ECHO® has experienced broad global adoption, particularly within the healthcare sector, and is experiencing growth across other sectors. This study sought to examine the state of implementation success for Project ECHO® globally, to understand how these implementations compare across geographic and sectoral contexts, and understand what enablers/barriers exist for organizational teams implementing the innovation. Methods An empirical study was conducted to collect data on 54 Project ECHO® implementation success indicators across an international sample. An online survey questionnaire was developed and distributed to all Project ECHO® hub organizations globally to collect data. Data was analyzed using descriptive statistics. Results The 54 implementation success indicators measured in this survey revealed that the adoption of Project ECHO® across 13 organizations varied on a case-by-case basis, with a strong rate of adoption within the healthcare sector. Implementation teams from these organizations successfully implemented Project ECHO® within 12–18 months after completing Immersion partner launch training and operated 51 ECHO® Networks at the time of data collection. Implementation teams which liaised more regularly with ECHO® Superhub mentors often went on to launch a higher number of ECHO® Networks that were sustained over the longer term. This suggests that these implementation teams better aligned and consolidated their Project ECHO® pilots as new innovations within the local context and strategic organizational priorities. Access to research and evaluation capability, and a more automated digital client relationship management system were key limitations to showcasing implementation success outcomes experienced by the majority of implementation teams. Conclusions These findings make a valuable contribution to address a knowledge gap regarding how a global sample of organizations adopting Project ECHO® measured and reported their implementation successes. Key successes included pre-launch experimentation and expansion, Superhub mentorship, stakeholder engagement, and alignment to strategic priorities.

Background: Patients with chronic obstructive pulmonary disease (COPD) have an increased risk of cardiovascular involvement, which is among the leading causes of morbidity and mortality worldwide. Echocardiography (ECHO) could be a reliable, non-invasive tool for predicting the risk of cardiovascular modalities in patients with COPD. Combining the ECHO parameters with highly selective cardiac troponin could predict the severity and outcome of patients with COPD. Methods: This prospective observational study was conducted at a tertiary care hospital in South India. All patients who met the criteria were included. Patients with other concomitant chronic lung diseases were excluded. An echocardiographic examination was performed, and blood samples for hs-Tnt were taken on admission for patients admitted with COPD. Categorical variables were analyzed using Pearson’s Chi-square test, and the T-test was used to compare the means. One-way analysis of variance (ANOVA) followed by the Bonferroni multiple comparison tests was done to compare different echo parameters concerning COPD severity. Results: The mean tricuspid annulus plane systolic excursion (TAPSE) and right ventricle (RV) fraction area change (FAC) values were lower with the increase in the disease severity (P < 0.001). There was a significant increase in the mean systolic pressures in the right atrium and ventricle in patients with severe COPD (P < 0.001). The mean hs-TnT values were significantly higher in patients with severe COPD (18.86 ± 18.12) and correlated well with the increase in the severity of the disease (P < 0.001). Changes in the echo parameters, such as mean TAPSE and RV FAC values, negatively correlated with COPD severity. There was an increase in systolic pressure in both atria and ventricles with the progression of COPD. Troponin helped predict mortality during hospitalization. Conclusion: Comprehensive echocardiographic parameters, such as TAPSE and RV FAC, help assess the disease’s severity, predict mortality, and evaluate whether the proper ventricular function is reliable. Troponin is a valuable adjunct that is an independent and strong predictor of overall mortality in patients with COPD.

Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, and they are as hazardous and fatal as many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations and the complex criteria employed to define the phenomenon. This study proposes a physically based definition of derechos that contains the key features of derechos described in the literature and allows their automated objective identification using either observations or model simulations. The automated detection is composed of three algorithms: the Flexible Object Tracker algorithm to track mesoscale convective systems (MCSs), a semantic segmentation convolutional neural network to identify bow echoes, and a comprehensive algorithm to classify MCSs as derechos or non-derecho events. Using the new approach, we develop a novel high-resolution (4 km and hourly) observational dataset of derechos over the United States east of the Rocky Mountains from 2004 to 2021. The dataset is analyzed to document the derecho climatology in the United States. Many more derechos (increased by ~50–400 %) are identified in the dataset (~31 events per year) than in previous estimations (~6–21 events per year), but the spatial distribution and seasonal variation patterns resemble earlier studies with a peak occurrence in the Great Plains and Midwest during the warm season. In addition, around 20 % of damaging gust (≥ 25.93 m s-1) reports are produced by derechos during the dataset period over the United States east of the Rocky Mountains. The dataset is available at https://doi.org/10.5281/zenodo.10884046 (Li et al., 2024). [ABSTRACT FROM AUTHOR]

Active laser imaging utilizes time-of-flight and echo intensity measurements to generate distance and intensity images of targets. However, scattering caused by cloud and fog particles, leads to imaging quality deterioration. In this study, we introduce a novel approach for improving imaging clarity in these environments. We employed a matched filtering method that leverages the distinction between signal and noise in the time domain to preliminarily extract the signal from one- dimensional photon-counting echo data. We further denoised the data by utilizing the Long Short-Term Memory (LSTM) neural network in extracting features from extended time-series data. The proposed method displayed notable improvement in the signal-to-noise ratio (SNR), from 7.227 dB to 31.35 dB, following an analysis of experimental data collected under cloud and fog conditions. Furthermore, processing positively affected the quality of the distance image with an increase in the structural similarity (SSIM) index from 0.7883 to 0.9070. Additionally, the point-cloud images were successfully restored. These findings suggest that the integration of matched filtering and the LSTM algorithm effectively enhances beam imaging quality in the presence of cloud and fog scattering. This method has potential application in various fields, including navigation, remote sensing, and other areas susceptible to complex environmental conditions. [ABSTRACT FROM AUTHOR]

Millions of ionograms are acquired annually to monitor the ionosphere. The accumulated data contain untapped information from a range of locations, multiple solar cycles, and various geomagnetic conditions. In this study, we propose the application of deep convolutional neural networks to automatically classify and scale high-latitude ionograms. A supervised approach is implemented and the networks are trained and tested using manually analyzed oblique ionograms acquired at a receiver station located in Skibotn, Norway. The classification routine categorizes the observations based on the presence or absence of E- and F-region traces, while the scaling procedure automatically defines the E- and F-region virtual distances and maximum plasma frequencies. Overall, we conclude that deep convolutional neural networks are suitable for automatic processing of ionograms, even under auroral conditions. The networks achieve an average classification accuracy of 93% ± 4% for the E-region and 86% ± 7% for the F-region. In addition, the networks obtain scientifically useful scaling parameters with median absolute deviation values of 118 kHz ± 27 kHz for the E-region maximum frequency and 105 kHz ± 37 kHz for the Fregion maximum O-mode frequency. Predictions of the virtual distance for the E- and F-region yield median distance deviation values of 6.1 km ± 1.7 km and 8.3 km ± 2.3 km, respectively. The developed networks may facilitate EISCAT 3D and other instruments in Fennoscandia by automatic cataloging and scaling of salient ionospheric features. This data can be used to study both long-term ionospheric trends and more transient ionospheric features, such as traveling ionospheric disturbances. [ABSTRACT FROM AUTHOR]

In practical radar applications, the diversity of wideband transmission signals presents a significant challenge for radar target recognition systems. Traditional electromagnetic (EM) simulation methods often require the point-by-point sampling of target echo signals under specific radiation sources, and this process must be repeated for different transmission signals. This results in extensive EM computations and low computational efficiency. Additionally, the physical attribute information of the target is often obscured within the simulated echoes, complicating feature extraction. To address these issues, we propose a novel method for the efficient and rapid modeling and analysis of complex target time-domain echoes based on a forward scattering center model. This method constructs an impulse response model of the target under unit impulse signal excitation, representing the inherent scattering properties of the target and remaining unaffected by changes in the radiation source. Subsequently, this model is convolved with arbitrary wideband input signals to quickly reconstruct the wideband echo signals of the target. Numerical examples validate the accuracy and effectiveness of the proposed model. This method offers several advantages: firstly, it provides high computational efficiency by quickly convolving with the input signals, thereby avoiding the extensive EM calculations required by traditional methods due to changes in the radiation source. Secondly, it achieves significant data compression; the proposed echo model only requires the model parameters to be calculated at the central frequency point, enabling the rapid reconstruction of target echo data across a wideband range and effectively reducing storage requirements. Finally, it presents clear and complete physical attributes by employing a "cause-to-effect" forward-modeling approach, where the model parameters fully represent the inherent physical properties of the target. This effectively addresses the issue of physical interpretability in optical region radar target recognition. [ABSTRACT FROM AUTHOR]

In order to investigate the variations of cloud and aerosol vertical profiles over the Tibetan Plateau (TP) in winter, we performed ground-based lidar observations in Lhasa, a city on the TP, from November 2021 to January 2022. The profiles of extinction coefficient, depolarization ratio, and signal-to-noise ratio (SNR) were retrieved using the atmospheric echo signals collected by the lidar. Clouds were identified by the range-correction echo signals and classified into water clouds, mixed clouds, horizontally oriented ice crystal clouds (HOICC), and ice clouds by the depolarization ratio and the hourly temperature from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5). The clouds mainly appeared at a height of 3~5 km from 14:00–22:00 Beijing Time throughout the field campaign. The height and frequency (~30%) for cloud appearance were significantly lower than that reported in previous studies in summer. The cloud categories were dominated by mixed clouds and ice clouds during the observation period. The proportions of ice clouds gradually increased with increasing heights. After eliminating profiles influenced by clouds, the aerosol extinction coefficient and depolarization ratio were obtained, and the atmospheric boundary layer height (ABLH) was calculated. The aerosol extinction coefficient decreased with increasing height in the ABLH, and there were no obvious changes for the aerosol extinction coefficient above the ABL. The aerosol extinction coefficients near the Earth's surface presented two peaks, appearing in the morning and evening, respectively. The high aerosols at the surface in the morning continually spread upward for 4–5 h and finally reached an altitude of 1 km with the development of ABLH. In addition, the depolarization ratio of aerosols decreased slowly with increasing altitudes. There was no obvious diurnal variation for depolarization ratios, indicating partly that the source of aerosols did not change significantly. These results are beneficial in understanding the evolution of cloud and aerosol vertical profiles over the TP. [ABSTRACT FROM AUTHOR]

High-velocity reentry objects suffer from plasma sheath during reentry through the atmosphere, which affects the propagation characteristics of radar signals. The existing research mainly focuses on the time-frequency characteristics of radar signals, neglecting the polarization within the geomagnetic environment. In this article, the distortion of polarization characteristics for L-band fully polarimetric radar is analyzed, and the influence of the geomagnetic field is evaluated. Based on the Appleton–Hartree formula, the refractive index of the plasma sheath considering the geomagnetic field is derived and analyzed. The error model for the polarization deflection (PD) of radar waves is then established based on the phase screen model. The magnetized plasma sheath causes the deflection of the polarization plane for the radar signal, leading to distortion in the polarization characteristics and the attenuation of the echo amplitude. Considering the typical parameters of the plasma sheath, the influences of the electron density, collision frequency, the geomagnetic field and the radar frequency are analyzed quantitatively. Specifically, the PD anomaly phenomenon is analyzed and the corresponding analytical result of radar frequency is also derived. The relationship between the geomagnetic field and the PD, as well as the attenuation, is considered to be approximately linear. The absorption attenuation is primarily influenced by collision frequency and is immune to the geomagnetic field. In addition, the increasing electron density expands them, whereas the radar frequency and the collision frequency have the opposite effect. Simulations with real SAR data from ALOS-2 demonstrate the distortions resulting from the magnetized plasma sheath on the radar echoes in an L-band fully polarimetric radar system. [ABSTRACT FROM AUTHOR]

Reverberation mapping is the leading technique used to measure direct black hole masses outside of the local Universe. Additionally, reverberation measurements calibrate secondary mass-scaling relations used to estimate single-epoch virial black hole masses. The Australian Dark Energy Survey (OzDES) conducted one of the first multi-object reverberation mapping surveys, monitoring 735 AGN up to z ∼ 4, over 6 years. The limited temporal coverage of the OzDES data has hindered recovery of individual measurements for some classes of sources, particularly those with shorter reverberation lags or lags that fall within campaign season gaps. To alleviate this limitation, we perform a stacking analysis of the cross-correlation functions of sources with similar intrinsic properties to recover average composite reverberation lags. This analysis leads to the recovery of average lags in each redshift-luminosity bin across our sample. We present the average lags recovered for the Hβ, Mg  ii , and C  iv samples, as well as multiline measurements for redshift bins where two lines are accessible. The stacking analysis is consistent with the Radius–Luminosity relations for each line. Our results for the Hβ sample demonstrate that stacking has the potential to improve upon constraints on the R – L relation, which have been derived only from individual source measurements until now. [ABSTRACT FROM AUTHOR]

Phonon dispersion in crystals determines many important material properties, but its measurement usually requires large-scale facilities and is limited to bulk samples. Here, we demonstrate the measurement of full phonon dispersion along the stacking direction in nanoscale systems by using picosecond acoustics. A heterostructure sample was prepared consisting of layers of hexagonal boron nitride (hBN) sandwiching a thin layer of black phosphorus (BP), within which a strain pulse was generated by photoexcitation and observed with an optical probe in the BP layer. The strain pulse traverses to the few nanometer thick hBN layers, where it propagates to the edge and echoes back, like acoustic waves in Newton's cradle. The echoes returning to the BP layer provide information on the frequency-dependent time-of-flight and group velocity dispersion of the sample system. The microscopic origin of the photoinduced strain pulse generation and its propagation is revealed from first principles. Phonon frequency combs observed in the Fourier transform spectrum confirm the strain wave round trips and demonstrate the feasibility of determining group velocity dispersion through photoacoustics. [ABSTRACT FROM AUTHOR]

This paper considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement: High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths. [ABSTRACT FROM AUTHOR]

Electromagnetic ultrasonic detection technology utilizes the electromagnetic coupling method to generate and receive ultrasonic waves without a couplant, which is suitable for rapid detection. However, the detection can be affected by the spatial distribution of the acoustic field and the polarization direction of the shear wave, which can result in suboptimal detection performance. The acoustic field directivity of the shear wave generated by the butterfly coil electromagnetic acoustic transducer was measured using the transmission method. The data indicate that the acoustic pressure amplitude of the shear wave is maximized along the axis of the acoustic field, thereby meeting the requirements of synthetic aperture focusing technique imaging. We used the reflection method to detect the through-hole defects and investigated the effect of shear wave polarization direction. By comparing the experimental data and imaging results, it can be concluded that higher echo amplitudes are obtained when the polarization direction of the shear wave is perpendicular to the axis of the through-hole defects. Based on the explosive reflection model, the frequency domain phase shift migration (PSM) method converts the time-domain signal to the frequency domain for processing and uses a phase-shift factor for layer-by-layer imaging. We used the PSM method to process the experimental data, which not only produced high-resolution images but also had a high computational speed. [ABSTRACT FROM AUTHOR]