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In the evaluation of magnetic field data, edge enhancement and detection techniques are important treatments for the interpretation of geological structures. In general geological sense, contiguity of deep and shallow magnetic sources leads to weak and intense anomalies that complicates the interpretation to disclose adjacent anomalous sources. Many of the existing filters for edge detection in magnetics mostly have the disadvantage that they require a reduction to pole transformation as the pre-process of the data or they cannot balance weak and intense anomalies and therefore fail in detecting edges of deep and shallow sources simultaneously. This study presents an improved edge detection filter LAS (logistic function of the analytical signal), based on the generalised logistic function configured by the ratio of derivatives of the analytical signal. This novel approach has the capability of reducing the dependence on the direction of the magnetization and also balancing anomalies of sources at different levels of depth. The feasibility of the method is examined on both theoretical and real data cases comparatively with some other methods that utilize the analytical signal in their basis. In comparison, the results demonstrate that the LAS method provides more accurate estimation of edge localization.

The low energy conversion efficiency of electromagnetic acoustic transducers (EMATs) is a critical issue in nondestructive testing applications. To overcome this shortcoming, a butterfly coil EMAT was developed and optimized by numerical simulation based on a 2−D finite element model. First, the effect of the structural parameters of the butterfly coil EMAT was investigated by orthogonal test theory. Then, a modified butterfly coil EMAT was designed that consists of three−square permanent magnets with opposite polarity (TSPM−OP) to enhance the signal amplitude. Finally, the signal amplitude obtained from the three types of EMATs, that is, the traditional EMAT, the EMAT optimized by orthogonal test theory, and the modified EMAT with TSPM−OP, were analyzed and compared. The results show that the signal amplitude achieved by the modified butterfly coil EMAT with TSPM−OP can be increased by 4.97 times compared to the traditional butterfly coil EMAT.

Resting‐state functional magnetic resonance imaging (rs‐fMRI) has been extensively used to study brain aging, but the age effect on the frequency content of the rs‐fMRI signal has scarcely been examined. Moreover, the neuronal implications of such age effects and age–sex interaction remain unclear. In this study, we examined the effects of age and sex on the rs‐fMRI signal frequency using the Leipzig mind–brain–body data set. Over a frequency band of up to 0.3 Hz, we found that the rs‐fMRI fluctuation frequency is higher in the older adults, although the fluctuation amplitude is lower. The rs‐fMRI signal frequency is also higher in men than in women. Both age and sex effects on fMRI frequency vary with the frequency band examined but are not found in the frequency of physiological‐noise components. This higher rs‐fMRI frequency in older adults is not mediated by the electroencephalograph (EEG)‐frequency increase but a likely link between fMRI signal frequency and EEG entropy, which vary with age and sex. Additionally, in different rs‐fMRI frequency bands, the fMRI‐EEG amplitude ratio is higher in young adults. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. Our study demonstrates that Fourier analysis of the fMRI signal can reveal novel information about aging. Furthermore, fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but the signal frequency and complexity, instead of amplitude, may hold their link. This is the first study to investigate the neuronal contribution to age and sex effects in the frequency dimension of the rs‐fMRI signal and may lead to the development of new, frequency‐based rs‐fMRI metrics. fMRI and EEG signals reflect different aspects of age‐ and sex‐related brain differences, but signal frequency and complexity, instead of amplitude, may hold their link.

Abstract On the occasion of the centenary of the discovery of the Lombard effect, we review the literature on noise-dependent regulation of vocal amplitude in humans and other animals. The article addresses the scientific and the biological history of the Lombard effect: first, it sketches the evolution of the study of the Lombard effect, and second it reflects on the biological evolution of the effect itself. By comparing the findings from anurans, birds and mammals, we try to trace back the phylogenetic origins of this basic vocal mechanism for acoustic communication in noise. The current evidence suggests two alternative parsimonious hypotheses: either the Lombard effect is the outcome of a convergent evolution in birds and mammals or it may be a synapomorphy of all amniotes. If the latter is true, then the Lombard effect would have evolved to maintain vocal communication in the presence of noise more than 300 million years ago.

To investigate the effects of slightly acidic electrolyzed water (SAEW) treatment on rice seed germination, rice seeds were soaked with SAEW at different available chlorine concentrations (ACC) of 10, 20, 30, 40, 50, and 60 mg/L. A standard seed germination test was conducted to summarize the influence of SAEW with varying ACC on rice seed germination. Meanwhile, low-field nuclear magnetic resonance (LF-NMR) and magnetic resonance imaging (MRI) technologies were employed to reveal the underlying mechanisms by studying the internal moisture changes, water migration patterns, and water distribution characteristics. The results demonstrated a hormetic effect of SAEW on rice seed germination, characterized by significant promotion at lower ACCs (10–40 mg/L) and inhibition at higher ACCs (50–60 mg/L). The most pronounced promotive effect was observed at an ACC of 30 mg/L. Low ACC SAEW accelerated the water storage rate within the rice seeds, facilitated the conversion and accumulation of free water, thereby providing favorable moisture conditions for seed germination and subsequently promoting rice growth. In contrast, high ACC SAEW damaged the rice cell walls under osmotic stress, leading to a reduced water absorption rate and consequently inhibiting rice growth. This study, starting from the pre-treatment of rice seeds, investigated the entire process, providing theoretical support and data reference for rice production and processing.

The work clarified the empirical laser measured with optical coherent quadrate amplitude modulation receiver for upgrading fiber optic systems. Peak signal amplitude and output power spectrum are measured after light receivers under the control of extinction modulator ratio, radio frequency switching voltage and switching bias voltage. The better the fiber optic system performance can be achieved with the higher values of extinction ratio, both switching radio frequency and bias voltages based LiNb modulators.

In this work, analytic signal amplitude (ASA) inversion of total field magnetic anomalies has been achieved by differential evolution (DE) which is a population-based evolutionary metaheuristic algorithm. Using an elitist strategy, the applicability and effectiveness of the proposed inversion algorithm have been evaluated through the anomalies due to both hypothetical model bodies and real isolated geological structures. Some parameter tuning studies relying mainly on choosing the optimum control parameters of the algorithm have also been performed to enhance the performance of the proposed metaheuristic. Since ASAs of magnetic anomalies are independent of both ambient field direction and the direction of magnetization of the causative sources in a two-dimensional (2D) case, inversions of synthetic noise-free and noisy single model anomalies have produced satisfactory solutions showing the practical applicability of the algorithm. Moreover, hypothetical studies using multiple model bodies have clearly showed that the DE algorithm is able to cope with complicated anomalies and some interferences from neighbouring sources. The proposed algorithm has then been used to invert small- (120 m) and large-scale (40 km) magnetic profile anomalies of an iron deposit (Kesikköprü-Bala, Turkey) and a deep-seated magnetized structure (Sea of Marmara, Turkey), respectively to determine depths, geometries and exact origins of the source bodies. Inversion studies have yielded geologically reasonable solutions which are also in good accordance with the results of normalized full gradient and Euler deconvolution techniques. Thus, we propose the use of DE not only for the amplitude inversion of 2D analytical signals of magnetic profile anomalies having induced or remanent magnetization effects but also the low-dimensional data inversions in geophysics.

Microwave drying is usually combined with vacuum environment in conjunction with hot air flow to draw the moisture rapidly. The moisture content of the vegetables undergoing drying is hard to measure online. This research designed a microwave vacuum drying (MVD)-low-field nuclear magnetic resonance (NMR) smart device and investigated the feasibility of NMR method for online measurement of state of moisture during MVD. The relation between the signal amplitude ( A 2 ) and the true moisture content ( M 1 ) of six kinds of vegetables (mushroom, carrot, potato, lotus, edamame, vegetable corn) was fitted to estimate if NMR can measure the M 1 of vegetables directly. Results showed that A 2 and M 1 of different fresh vegetables had no single empirical mathematical model to fit. However, for each kind of these vegetables, the A 2 and corresponding M 1 in different MVD stages showed a significant linear relationship. The predicted moisture content ( M 2 ) of mushroom: M 2  = 5.25351 × 10 −4 A 2  − 0.34042, R  = 0.996; carrot: M 2  = 5.78756 × 10 −4 A 2  − 0.14108, R  = 0.998; potato: M 2  = 3.10019 × 10 −4 A 2  − 0.10612, R  = 0.991; lotus: M 2  = 2.32415 × 10 −4 A 2  − 0.01573, R  = 0.998; edamame: M 2  = 3.13310 × 10 −4 A 2  − 0.4198, R  = 0.996; vegetable corn: M 2  = 1.69461 × 10 −4 A 2  − 0.09063, R  = 0.995. The linear models between M 2 and A 2 were able to estimate the end point ( M 1  < 8%) of MVD with a high accuracy ( P  > 0.950).

This study investigates the effect of local structural defects on the acoustic emission (AE) response during composite failure under the von Mises criterion. A fiber bundle model (FBM) is used to simulate failure under transverse shear while varying the initiation time of fracture, which corresponds to changes in defect size or heterogeneity. The results show that increasing the failure initiation time leads to a decrease in AE signal amplitude and a simultaneous increase in its duration, reflecting a slower energy release during fracture. These relationships were confirmed experimentally on fine-grained composite specimens. The obtained findings demonstrate that amplitude–time AE parameters can serve as sensitive indicators of defectiveness and local heterogeneity in composite materials, offering potential for improved nondestructive evaluation and structural health monitoring.

This article describes the characterization of facial and ocular gestures using the electroencephalogram (EEG) method connected with an EMOTIV EPOC+ Brainwear® device. This characterization is developed by the storage of raw data (unprocessed data) acquired by the device. The experiment was applied to nine subjects, considering that EEG explores neurophysiologically with high levels of statistical confidence the bioelectric activity in the brain in the condition of resting state such as wakeups or dreaming states. In contrast to non-resting states, the registered data showed a random and distinct activation of hyperpnea and intermittent luminous stimulus. Despite the reduced number of samples in the experiment, the results showed that the level of confidence was greater than 75%. The data was characterized and processed by a support vector machine (SVM).