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Quartz material irradiated by femtosecond laser has increasingly attracted widespread attention for the micro-fabrication of photonic devices. Mechanism exploration is beneficial for accelerating the digital progress of laser processing. However, the mechanism between femtosecond laser and quartz is complicated and needs further theoretical investigation. This paper established the theoretical model based on the ionization model with the Drude equation to study the space–time evolution of free electron density and its influence on the absorption coefficient, reflectivity, and ablation depth. In addition, we achieved a 10 × 10 micro-holes array with a pore size less than 10 μm, cone angle less than 2° in a 0.25 mm thick quartz on the condition of a laser pulse energy Ep = 3 μJ, scanning velocity v = 0.1 mm/s, and defocusing distance Δf = −0.3 mm via the bottom-up femtosecond laser processing. The work gives a new insight into further understanding the ablation mechanism of transparent materials etching by the femtosecond laser. It provides a practical technical scheme for preparing commercial quartz photonic devices. [ABSTRACT FROM AUTHOR]

We numerically examine the driven transport of an overdamped self-propelled particle through a two-dimensional array of circular obstacles. A detailed analysis of transport quantifiers (mobility and diffusivity) has been performed for two types of channels, channel I and channel II, that respectively correspond to the parallel and diagonal drives with respect to the array axis. Our simulation results show that the signatures of pinning actions and depinning processes in the array of obstacles are manifested through excess diffusion peaks or sudden drops in diffusivity, and abrupt jumps in mobility with varying amplitude of the drive. The underlying depinning mechanisms and the associated threshold driving strength largely depend on the persistent length of self-propulsion. For low driving strength, both diffusivity and mobility are noticeably suppressed by the array of obstacles, irrespective of the self-propulsion parameters and direction of the drive. When self-propulsion length is larger than a channel compartment size, transport quantifiers are insensitive to the rotational relaxation time. Transport with diagonal drives features self-propulsion-dependent negative differential mobility. The amplitude of the negative differential mobility of an active particle is much larger than that of a passive one. The present analysis aims at understanding the driven transport of active species like, bacteria, virus, Janus particle etc. in porous medium. [ABSTRACT FROM AUTHOR]

Inspired by the natural shape-morphing abilities of biological organisms, we introduce a strategy for creating energy-efficient dynamic 3D metasurfaces through spatiotemporal jamming of interleaved assemblies. Our approach, diverging from traditional shape-morphing techniques reliant on continuous energy inputs, utilizes strategically jammed, paper-based interleaved assemblies. By rapidly altering their stiffness at various spatial points and temporal phases during the relaxation of the soft substrate through jamming, we enable the formation of refreshable, intricate 3D shapes with a desirable load-bearing capability. This process, which does not require ongoing energy consumption, ensures energy-efficient and lasting shape displays. Our theoretical model, linking buckling deformation to residual pre-strain, underpins the inverse design process for an array of interleaved assemblies, facilitating the creation of diverse 3D configurations. This metasurface holds notable potential for tactile displays, particularly for the visually impaired, heralding possibilities in visual impaired education, haptic feedback, and virtual/augmented reality applications. This paper introduces a load-bearing 3D dynamic metasurface that alters the stiffness of interleaved assemblies at various spatial points and temporal phases through jamming. This approach does not require continuous energy input and was demonstrated as a tactile display for the visually impaired. [ABSTRACT FROM AUTHOR]

The purpose of the GLocal dataset is to enable research in international development that requires both global scope and local precision. Leveraging modern geospatial analysis tools, we process a diverse array of sources to provide researchers with a growing set of economic, demographic, ecological and socio-political variables for geographic units relevant to public policy. We provide separate data files for different levels of administrative and periodic aggregation, along with ad-hoc files with more detailed information on specific topics. In this data descriptor paper, we discuss both our data processing methodologies and validation pipelines, and provide a short case study to illustrate the research potential of the dataset. We also introduce a simple web app, glocal.streamlit.app, which offers a user-friendly interface for exploring and visualizing the dataset. Given the growing number of public and granular sources of relevance for international development research, we hope to continue adding features and expand the GLocal dataset in the future. [ABSTRACT FROM AUTHOR]

Sepsis stands as a prominent contributor to sickness and death on a global scale. The most current consensus definition characterizes sepsis as a life-threatening organ dysfunction stemming from an imbalanced host response to infection. This definition does not capture the intricate array of immune processes at play in sepsis, marked by simultaneous states of heightened inflammation and immune suppression. This overview delves into the immune-related processes of sepsis, elaborating about mechanisms involved in hyperinflammation and immune suppression. Moreover, we discuss stratification of patients with sepsis based on their immune profiles and how this could impact future sepsis management. [ABSTRACT FROM AUTHOR]

Compared with the traditional two‐step localization methods, the direct position determination (DPD) method is more robust at low signal‐to‐noise ratio. However, to guarantee the optimal location result, the computational complexity of DPD with grid search is too high, especially for moving targets. Therefore, a new DPD method is proposed for moving targets with low computational complexity. First, using a proposed cost function, the position information is obtained from the received array signals directly, which enhances the accuracy of localization. Second, utilizing the method of successional difference the velocity is extracted, which avoid the multi‐dimensional grid search and reduces the computational complexity. Simulation results demonstrate that the proposed method reduces the computational complexity greatly and outperforms the conventional methods. [ABSTRACT FROM AUTHOR]

This study focuses on the acoustic classification of delphinid species at the southern continental slope of Brazil. Recordings were collected between 2013 and 2015 using towed arrays and were processed using a classifier to identify the species in the recordings. Using Raven Pro 1.6 software (Cornell Laboratory of Ornithology, Ithaca, NY), we analyzed whistles for species identification. The random forest algorithm in R facilitates classification analysis based on acoustic parameters, including low, high, delta, center, beginning, and ending frequencies, and duration. Evaluation metrics, such as correct and incorrect classification percentages, global accuracy, balanced accuracy, and p-values, were employed. Receiver operating characteristic curves and area-under-the-curve (AUC) values demonstrated well-fitting models (AUC ≥ 0.7) for species definition. Duration and delta frequency emerged as crucial parameters for classification, as indicated by the decrease in mean accuracy. Multivariate dispersion plots visualized the proximity between acoustic and visual match data and exclusively acoustic encounter (EAE) data. The EAE results classified as Delphinus delphis (n = 6), Stenella frontalis (n = 3), and Stenella longirostris (n = 2) provide valuable insights into the presence of these species between approximately 23° and 34° S in Brazil. This study demonstrates the effectiveness of acousting classification in discriminating delphinids through whistle parameters. [ABSTRACT FROM AUTHOR]

This paper presents a wideband signal (WS) beamforming method based on Inception convolutional neural network (ICNN), named as WS-ICNN algorithm. Firstly, an Inception module is constructed via some convolutional layers with feature maps of different sizes and a pooling layer. It can not only extract different scale information of covariance matrix, but also excavate the spatial correlation information about received wideband signals, so that the inception module can help neural network improve the beamforming output performance. On this basis, an ICNN model is established, which is suitable for wideband beamforming. Then, in order to obtain a good training label for the proposed ICNN model, a taper matrix and a second-order cone programming problem are introduced to calculate a wideband beamforming weight vector label. Based on this label, the training process of the proposed ICNN model is accomplished. Finally, the well-trained ICNN model accepts the input of the covariance matrix, and output the beamforming weight vector. Simulation results demonstrate the performance of the proposed algorithm in the cases of direction-of-arrival estimation error and sensor position error. [ABSTRACT FROM AUTHOR]

Mechanotransduction is an evolutionary conserved and essential physiological process enabling cells to sense and convert mechanical stimuli into intracellular electrochemical events. Transient receptor potential (TRP) channels have been implicated in numerous mechanotransduction processes, including cancer cell migration, bone mineralization, and sensing blood flow. However, limited evidence supports TRP channels acting as primary mechanotransducers – those that directly sense and convert the external stimuli into an intracellular signal. Most evidence is congruent with TRP channels instead acting as molecular amplifiers responding downstream of the activation of a primary mechanotransducer (such as PIEZO1 or PIEZO2), forming mechanosensory modules. Here, we discuss the evidence supporting TRP channels as primary mechanotransducers and alternative models whereby TRP channels together with primary mechanotransducers could contribute to mechanosignalling. Transient receptor potential (TRP) channels are implicated in a wide array of mechanotransduction processes. However, a question remains whether TRP channels directly sense mechanical force, thus acting as primary mechanotransducers. We use several recent examples to demonstrate the difficulty in definitively ascribing mechanosensitivity to TRP channel subfamilies. Ultimately, despite being implicated in an ever-growing list of mechanosignalling events in most cases limited robust or reproducible evidence supports the contention that TRP channels act as primary transducers of mechanical forces. They either (i) possess unique and as yet unspecified structural or local requirements for mechanosensitivity; or (ii) act as mechanoamplifiers responding downstream of the activation of a primary mechanotransducer that could include Ca2+-permeable mechanosensitive (MS) channels or other potentially unidentified mechanosensors. [ABSTRACT FROM AUTHOR]

In recent years, micro-light-emitting diode (micro-LED) displays have attracted much attention due to their high brightness, low power consumption, long lifetime, and fast response. It is considered to have the potential to revolutionize the development direction of next-generation visual display technology. However, the development of micro-LED displays faces numerous issues, primarily due to etching processes for pixel array manufacturing, which cause sidewall damage and decreased photoelectric efficiency. Specifically, these issues are particularly serious when preparing small-sized high-resolution displays. In this work, we effectively overcame the above-mentioned problems by only etching the electron barrier during the preparation process of pixel arrays. The prepared micro-LED display exhibits excellent optoelectronic properties, with the highest brightness and current efficiency reaching 1.66 × 106 nits and 104 cd/A, respectively. The method provides a feasible idea for preparing high-performance micro-LED displays. [ABSTRACT FROM AUTHOR]

In the anti‐interference of broadband communication radio, interference cancellation performance is seriously affected by the uncertainty of the working frequency selection of the communication system and the direction of hostile interference. The closed‐form solution of the interference cancellation weight value and the signal‐to‐interference noise ratio after the cancellation are derived and simplified to clarify the factors affecting interference cancellation quality. Then, the efficient blind area criterion is established for the low computational complexity in view of cancellation quality, which combines the radio communication index and anti‐interference demand index. Based on this criterion, the minimum blind area model is proposed for robust performance. The experimental results in the auxiliary antenna spacing optimisation are consistent with the theoretical analysis, verifying the effectiveness of the proposed model in dealing with uncertain communication frequency and interference direction. It is very promising in communication interference cancellation. [ABSTRACT FROM AUTHOR]

Because of the inhomogeneity and anisotropy of glass fibre reinforced plastics (GFRP), it is always a challenge to detect the real-time dynamic quality during manufacturing and service. In this paper, a virtual loading focusing enhancement technology (VLFET) based on time reversal (TR) theory is proposed to accurately locate the acoustic emission (AE) source in the GFRP. First, the focusing enhancement theoretical model with virtual reverse loading is deduced according to the TR theory. Then, after measuring the optimal detection range and the average acoustic velocity, the sensor array is positioned on the GFRP specimen. The excitation signals applied to simulate the AE source at some positions are produced by using lead breaking, and the detection signals are received by the sensor array. Finally, the signals received by the sensor array are processed according to the theoretical model, and the fluctuation image in the monitoring area is reconstructed with fluctuation amplitude of each pixel to evaluate the AE source position. The experimental results demonstrate that the excitation sources can be accurately evaluated with the VLFET. The location error with the proposed method is less than 3.1%, higher than that from the current AE instrument. The proposed technology can also be applied to the AE evaluation of other anisotropic materials. [ABSTRACT FROM AUTHOR]

Medical signal processing requires noise and interference‐free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two‐level transpose Vedic multiplier. The proposed architecture identifies the zero‐noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero‐noise segregation. This is performed independently for the zero‐ and nonzero‐noise intervals. Finally, the finite impulse response is estimated as the sum of zero‐ and nonzero‐noise inputs at any finite classification. [ABSTRACT FROM AUTHOR]

Automatic micro-defect detection is crucial for promoting efficiency in the production lines of patterned OLED panels. Recently, deep learning algorithms have emerged as promising solutions for micro-defect detection. However, in real-world industrial scenarios, the scarcity of training data or annotations results in a drop in performance. A multi-stage few-shot micro-defect detection approach is proposed for patterned OLED panels to deal with this problem. Firstly, we introduce a converter from defective to defect-free images based on our redesigned Vector Quantized-Variational AutoEncoder (VQ-VAE), aiming to inpaint defects with normal textures. Next, we exploit a region-growing method with automatic seed points to obtain the defect's segmentation and geometric parameters in each image block. Reliable seed points are provided by structural similarity index maps between defective sub-blocks and reconstructed reference. Finally, a multi-scale Siamese neural network is proposed to identify the category of extracted defects. With our proposed approach, detection and classification results of defects can be obtained successively. Our experimental results on samples at different array processes demonstrate the superb adaptability of VQ-VAE, with a defect detection rate ranging from 90.0% to 96.0%. Additionally, compared with existing classification models, our multi-scale Siamese neural network exhibits an impressive 98.6% classification accuracy for a long-tailed defect dataset without overfitting. In summary, the proposed approach shows great potential for practical micro-defect detection in industrial scenarios with limited training data. [ABSTRACT FROM AUTHOR]

The article will focus on vector autoregressive‐moving average (VARMA) models with time‐dependent coefficients (td) to represent general nonstationary time series, not necessarily Gaussian. The coefficients depend on time, possibly on the length of the series n, hence the name tdVARMA (n) for the models, but not necessarily on the rescaled time t/n. As a consequence of the dependency on n of the model, we need to consider array processes instead of stochastic processes. Under appropriate assumptions, it is shown that a Gaussian quasi‐maximum likelihood estimator is consistent in probability and asymptotically normal. The theoretical results are illustrated using three examples of bivariate processes, the first two with marginal heteroscedasticity. The first example is a tdVAR (n)(1) process while the second example is a tdVMA (n)(1) process. In these two cases, the finite‐sample behavior is checked via a Monte Carlo simulation study. The results are compatible with the asymptotic properties even for small n. A third example shows the application of the tdVARMA (n) models for a real time series. [ABSTRACT FROM AUTHOR]

The molecular mechanisms driving the onset and metastasis of prostate cancer remain poorly understood. Actin, under the control of actin-binding proteins (ABPs), plays a crucial role in shaping the cellular cytoskeleton, which in turn supports the morphological alterations in normal cells, as well as the invasive spread of tumor cells. Previous research indicates that ABPs of various types serve distinct functions, and any disruptions in their activities could predispose individuals to prostate cancer. These ABPs are intricately implicated in the initiation and advancement of prostate cancer through a complex array of intracellular processes, such as severing, linking, nucleating, inducing branching, assembling, facilitating actin filament elongation, terminating elongation, and promoting actin molecule aggregation. As such, this review synthesizes existing literature on several ABPs linked to prostate cancer, including cofilin, filamin A, and fascin, with the aim of shedding light on the molecular mechanisms through which ABPs influence prostate cancer development and identifying potential therapeutic targets. Ultimately, this comprehensive examination seeks to contribute to the understanding and management of prostate diseases. [ABSTRACT FROM AUTHOR]

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time‐consuming data transmission to an external processor, and high energy consumption in multi‐stage data processing. In this study, a gas sensing system using on‐chip annealing for fast and power‐efficient gas detection is proposed. By utilizing a micro‐heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET‐type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on‐chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability. [ABSTRACT FROM AUTHOR]

Xinjiang Qitai is constructing a 110-m fully steerable radio telescope (QiTai radio Telescope [QTT]) equipped with a phased array feeds (PAF) receiver, which will install the focal plane operating from 0.7 to 1.8 GHz. In this article, we introduce a PAF receiver model for beamforming, and the numerical model of the internal and external noise for this PAF receiver is provided using electromagnetic field simulation software. The linear constraint minimum variance (LCMV) algorithm is used to simulate the interference mitigation. The interference mitigation rate is from 0.581 to 0.921, and the signal restoration rate is from 0.998 to 1.512 within the error range of an interference arrival angle of 10°. This conclusion can be used in the signal correction of the PAF receiver for interference mitigation. [ABSTRACT FROM AUTHOR]

In this paper, we propose an online adaptation method for Fourier series-based acoustic transfer function (FS-ATF) models for robot audition systems using microphone array signal processing. The ATF represents the characteristics of signal propagation from a sound source to a microphone, which is essential for sound source localization (SSL) and sound source separation (SSS). For practical applications in dynamically changing real-world environments, ATF models must satisfy two criteria: (1) they must be adaptable to changes in the acoustic environment; and (2) they must be lightweight to be suitable for resource-constrained systems, such as robots with limited memory and computational capacity. The proposed method addresses these challenges using Fourier series expansions for interpolation, which reduces the memory footprint of the ATF model and facilitates online adaptation to acoustic environmental changes. The experimental results demonstrate that the proposed online adaptation method both improves the SSL and SSS performance while reducing the size of the ATF model, which represents a significant improvement over existing online ATF adaptation methods. [ABSTRACT FROM AUTHOR]

Providing higher precision Direction of Arrival (DOA) estimation has become a hot topic in the field of array signal processing for parameter estimation in recent years. However, when the physical aperture of the actual array is small, its aperture limitation means that even with super-resolution estimation algorithms, the achievable estimation precision is limited. This paper takes a novel approach by constructing an optimization algorithm using the covariance fitting criterion based on the array output's covariance matrix to fit and obtain the covariance matrix of a large aperture virtual array, thereby providing high-precision angular resolution through virtual aperture expansion. The covariance fitting expansion analysis and discussion are unfolded for both uniform linear arrays (ULAs) and sparse linear arrays (SLAs) under four different scenarios. Theoretical analysis and simulation experiments demonstrate that these methods can enhance the effective performance of angle estimation, especially in low signal-to-noise ratios (SNRs) and at small angular intervals by fitting virtual extended aperture data. [ABSTRACT FROM AUTHOR]

Simple Summary: We show how the relationship between the sub-network entropy of malignant up-regulated genes in twelve different types of cancer, spanning the entire spectrum of 5-year overall survival rates, can serve as a benchmark for optimizing RNA-seq workflows. Assessing the Shannon entropy of sub-networks formed by malignant up-regulated genes by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by evaluating nine normalization methods on paired samples of TCGA RNA-seq, we found that the pipeline incorporating TPM normalization coupled with log2 fold change yielded the best correlation coefficient between cancer aggressiveness and tumor entropy. RNA-seq faces persistent challenges due to the ongoing, expanding array of data processing workflows, none of which have yet achieved standardization to date. It is imperative to determine which method most effectively preserves biological facts. Here, we used Shannon entropy as a tool for depicting the biological status of a system. Thus, we assessed the measurement of Shannon entropy by several RNA-seq workflow approaches, such as DESeq2 and edgeR, but also by combining nine normalization methods with log2 fold change on paired samples of TCGA RNA-seq representing datasets of 515 patients and spanning 12 different cancer types with 5-year overall survival rates ranging from 20% to 98%. Our analysis revealed that TPM, RLE, and TMM normalization, coupled with a threshold of log2 fold change ≥1, for identifying differentially expressed genes, yielded the best results. We propose that Shannon entropy can serve as an objective metric for refining the optimization of RNA-seq workflows and mRNA sequencing technologies. [ABSTRACT FROM AUTHOR]

Localizing sources in the near-field is one of the emerging challenges for array signal processing, which has received a great deal of attention in recent years. The development of accurate localization algorithms requires the definition of a reliable model of the received signal that takes into account all wavefront characteristics, such as angle, range, and polarization, as well as electromagnetic effects, such as mutual coupling between antennas and the amplitude and phase behaviour of electromagnetic wavefronts. A system model that considers the electromagnetic-informed wave behaviour effects, independent of the type of receiver antennas, array structure, degree of correlation of sources signals and other electromagnetic effects, is considered an " exact model " in the literature. However, due to the mathematical complexity of this modeling approach, simplifications using several approximations are conventionally used. For instance, the phase of the exact model is approximated using the Fresnel approximation, while the magnitude of the exact model is simplified by assuming equal distances between the source and all elements in the array. In this work, we evaluate the accuracy of a localization algorithm, the multiple signal classification (MUSIC), using the exact and approximated models in the near-field region. Through a series of simulations, we demonstrate that the localization algorithm designed based on the electromagnetic-informed exact model outperforms the one designed using the approximated model. We also show that considering electromagnetic factors in the system model through the exact model results in a 13% improvement in the direction of arrival (DOA) root mean square error (RMSE) and a 57.7% improvement in range RMSE at signal-to-noise ratio (SNR) of 15 dB. [ABSTRACT FROM AUTHOR]

The dependence of seismic wave speeds on propagation or polarization direction, called seismic anisotropy, is a relatively direct indicator of mantle deformation and flow. Mantle seismic anisotropy is often inferred from measurements of shear-wave splitting. A number of standard techniques to measure shear-wave splitting have been applied globally; for example, *KS splitting is often used to measure upper mantle anisotropy. In order to obtain robust constraints on anisotropic geometry, it is necessary to sample seismic anisotropy from different directions, ideally using different seismic phases with different incidence angles. However, many standard analysis techniques can only be applied for certain epicentral distances and source–receiver geometries. To search for new ways to detect mantle anisotropy, instead of focusing on the sensitivity of individual phases, we investigate the wavefield as a whole: we apply a 'wavefield differencing' approach to (systematically) understand what parts of the seismic wavefield are most affected by splitting due to seismic anisotropy in the mantle. We analyze differences between synthetic global wavefields calculated for isotropic and anisotropic input models, incorporating seismic anisotropy at different depths. Our results confirm that the seismic phases that are commonly used in splitting techniques are indeed strongly influenced by mantle anisotropy. However, we also identify less commonly used phases whose waveforms reflect the effects of anisotropy. For example, PS is strongly affected by splitting due to seismic anisotropy in the upper mantle. We show that PS can be used to fill in gaps in global coverage in shear-wave splitting data sets (for example, beneath ocean basins). We find that PcS is also a promising phase, and present a proof-of-concept example of PcS splitting analysis across the contiguous United States using an array processing approach. Because PcS is recorded at much shorter distances than *KS phases, PcS splitting can therefore fill in gaps in backazimuthal coverage. Our wavefield differencing results further hint at additional potential novel methods to detect and characterize splitting due to mantle seismic anisotropy. [ABSTRACT FROM AUTHOR]

Beamforming is an effective way of resolving target direction and anti‐jamming in short wave (SW) radar systems. In conventional beamforming (CBF) at a certain frequency, to get high resolution, the array aperture should be increased, and this is often not allowed in practical applications. A new narrow beam forming (NBF) method for beam sharpening based on the generalised oblique projection (GOP) filter with a flexible parameter is proposed. This method uses a GOP filter bank to form deep nulls in the undesired azimuth range on the pattern and utilises the logic product process to synthesise the GOP filters' outputs and thus obtains a narrow beam. Compared to traditional beamforming methods, the result of NBF has the characteristics of narrower beam width and bigger side lobe suppression ratio (SLSR). Especially, a narrower beam can be obtained in the case of a small array aperture, which is valuable for practical applications. Experimental results of the range‐Doppler spectrum of short wave radar show that this narrow beam forming method can achieve super resolution of targets within a wide beam and greatly suppress clutter. Therefore, NBF can improve the azimuth resolution and achieve interference suppression in a conventional beam. [ABSTRACT FROM AUTHOR]

Smart antenna and digital antenna systems are considered as a crucial technology for current and future communication systems supporting 5G/6G and nonterrestrial‐networks communications. Based on the information of the received signal/s, smart antenna systems toward the radiation pattern to the desired direction/s. This technique is known as adaptive beamforming. The theories and the applications of adaptive beamforming are taught in engineering educational courses. This paper proposes a set of real‐time interactive experiments for adaptive beamforming algorithms in antenna array processing subject. The proposed experiments can be performed with the computer‐aided experimental studies tool prepared in the MATLAB® environment. The users can easily make comparisons and can perform trade‐offs between different types of adaptive beamforming algorithms, scenarios, and antenna architecture. This is used as a teaching tool for a graduate course on adaptive beamforming algorithms. [ABSTRACT FROM AUTHOR]

In array signal processing, Estimating the quantity of signal sources represents a crucial area of investigation. In this paper, a comprehensive introduction and analysis of the estimation methods for determining the number of signal sources are presented, including the background and significance, and the significance of precise estimation of the quantity of signal sources. The influence of factors such as signal-to-noise ratio (SNR), noise background, and number of snapshots on the estimation algorithm is discussed in detail. At the same time, common array models are introduced. Then, different signal source number estimation algorithms are analyzed in detail, and their respective advantages and applicable conditions are pointed out. Finally, the performance of each algorithm in different situations is evaluated by comparing the performance of the algorithms under different SNRs, snapshot numbers, and array elements. The experimental results show that with the increase of the SNR and the number of array elements, the correct estimation probability of the algorithm also increases correspondingly, which provides a reliable experimental basis and performance evaluation for the estimation. [ABSTRACT FROM AUTHOR]

Introduction: In the field of industrial manufacturing, accurate inspection of mechanical components such as gears and bearings is of Paramount importance. However, the traditional mechanical testing methods are often disturbed by human factors, which not only affects the stability of the test results, but also leads to low efficiency and large error. In order to solve these problems, this research focuses on developing a new edge detection model. Methods: A novel edge detection model based on field-programmable gate array image processing technology was used in this study. The model uses adaptive threshold multi-directional edge detection technology to identify the edge features of mechanical gears and bearings, aiming at improving the precision of detection. Results and Discussion: After performance verification, the running time of the model was controlled within 11 s, and the detection error was limited to less than 9%. Compared with the control group and the experimental group, their performance was superior. Further analysis data show that the detection accuracy of this model is as high as 0.9004, its internal resource utilization rate is 88%, and the detection rate is as high as 91%, which are better than the comparison model. Conclusion: The proposed test model not only significantly improves the efficiency and accuracy of the test, but also fully meets the requirements of the test. This new edge detection model has potential application value in industrial manufacturing field, and provides a new solution for industrial manufacturing quality inspection. [ABSTRACT FROM AUTHOR]

In array signal processing, the mutual coupling among physical sensors can inevitably affect the estimation of the direction of arrival (DOA). Despite the fact that multiple-input and multiple-output (MIMO) radar can provide greater degrees of freedom (DOFs), the influence of mutual coupling is largely overlooked in many current MIMO radar designs. To tackle this issue, we propose the utilization of a generalized nested array (GNA) in transmitter array and we introduce an expansion factor into the nested array in the receiver array. Thereby, a novel GNA-MIMO radar is put forward. The proposed MIMO radar offers O (N 4) consecutive DOFs with N sensors and avoids the adverse effects of high mutual coupling caused by closely located sensors. Furthermore, we derive the closed-form expressions for the position of physical sensors and the attainable consecutive DOFs of the proposed MIMO radar. Through simulation experiments, we demonstrate the superior accuracy of the proposed MIMO configuration in DOA estimation and angle resolution under the condition of mutual coupling effect. [ABSTRACT FROM AUTHOR]

The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots. [ABSTRACT FROM AUTHOR]

New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H2, CO, CH4, and CO2. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression—Ordinary Least Square "MLR-OLS" and Multi Linear Regression—Partial Least Square "MLR-PLS") and non-linear (Artificial Neural Network "ANN") models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor's response to CH4 is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity. [ABSTRACT FROM AUTHOR]

The self-trajectory determination is an effective method to continuously track the target's motion position. However, the traditional methods are relied on auxiliary parameters, which cause the problems of information loss and error accumulation. In order to handle these problems, we propose a direct self-trajectory determination algorithm based on evolutionary particle filter (EPF) for unmanned aerial vehicle (UAV) mounted with an antenna array. Firstly, the array sensing data are eigenvalue decomposed to obtain the observation function and the state transition function is constructed with the process control parameters. Then, particles are distributed randomly around the position of UAV and their weighted values are estimated using the likelihood function derived from the observation function. The resampling algorithm is adopted to select particles with larger values and the position of UAV is determined from these reserved particles. To overcome the decrease in particle diversity, the reserved particles get more dense after mutation and the new particle group for next moment is obtained with the state transition function. In this way, the self-trajectory is iteratively refined with EPF. Finally, the simulation test and the practical experiment based on UAV are conducted to verify that the proposed algorithm is more accurate and more stable when tracking real-time positions of UAV. [ABSTRACT FROM AUTHOR]

We estimated convex-structured covariance/correlation matrices by minimizing the entropy loss corresponding to the given matrix. We first considered the estimation of the Weighted sum of known Rank-one matrices with unknownWeights (W-Rank1-W) structural covariance matrices, which appeared commonly in array signal processing tasks, e.g., direction-of-arrival (DOA) estimation. The associated minimization problem is convex and can be solved using the primal-dual interior-point algorithm. However, the objective functions (the entropy loss function) can be bounded above by a sequence of separable functions--we proposed a novel estimation algorithm based on this property under the Majorization-Minimization (MM) algorithmic framework. The proposed MM algorithm exhibited very low computational complexity in each iteration, and its convergence was demonstrated theoretically. Subsequently, we focused on the estimation of Toeplitz autocorrelation matrices, which appeared frequently in time-series analysis. In particular, we considered cases in which the autocorrelation coefficient decreased as the time lag increased. We transformed the Toeplitz structure into aW-Rank1-W structure via special variable substitution, and proposed anMMalgorithm similar to that for the W-Rank1-W covariance estimation. However, each MM iteration involved a second-order cone programming SOCP problem that must be resolved. Our numerical experiments demonstrated the high computational efficiency and satisfactory estimation accuracy of the proposedMMalgorithms in DOA and autocorrelation matrix estimation. [ABSTRACT FROM AUTHOR]

One of the new areas of cryptography considered-homomorphic cryptography. The article presents the main areas of application of homomorphic encryption. An analysis of existing developments in the field of homomorphic encryption carried out. The analysis showed that existing library implementations only allow processing bits or arrays of bits and do not support division and subtraction operations. However, to solve applied problems, support for performing integer operations are necessary. Because of the analysis, the need to implement the homomorphic division and subtraction operations identified, as well as the relevance of developing our own implementation of a homomorphic encryption library over integers. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the areas of application of homomorphic encryption. A homomorphic division and subtraction methods proposed that allows the division operation performed on homomorphically encrypted data. An architecture for a library of fully homomorphic operations on integers is proposed. The library supports basic homomorphic operations on integers, as well as homomorphic division method. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library. [ABSTRACT FROM AUTHOR]

Two‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy. [ABSTRACT FROM AUTHOR]

Brain–computer interface (BCI) technology is currently a cutting-edge exploratory problem in the field of human–computer interaction. However, in experiments involving the implantation of electrodes into brain tissue, particularly high-speed or array implants, existing technologies find it challenging to observe the damage in real time. Considering the difficulties in obtaining biological brain tissue and the challenges associated with real-time observation of damage during the implantation process, we have prepared a transparent agarose gel that closely mimics the mechanical properties of biological brain tissue for use in electrode implantation experiments. Subsequently, we developed an experimental setup for synchronized observation of the electrode implantation process, utilizing the Digital Gradient Sensing (DGS) method. In the single electrode implantation experiments, with the increase in implantation speed, the implantation load increases progressively, and the tissue damage region around the electrode tip gradually diminishes. In the array electrode implantation experiments, compared to a single electrode, the degree of tissue indentation is more severe due to the coupling effect between adjacent electrodes. As the array spacing increases, the coupling effect gradually diminishes. The experimental results indicate that appropriately increasing the velocity and array spacing of the electrodes can enhance the likelihood of successful implantation. The research findings of this article provide valuable guidance for the damage assessment and selection of implantation parameters during the process of electrode implantation into real brain tissue. [ABSTRACT FROM AUTHOR]

Shaped cathode electrochemical machining is often used to process array microgrooves on difficult-to-machine materials because of its great flexibility. Unlike the single-groove structure, the current density distribution varies greatly during machining of array microgrooves, which is mainly dependent on the sheet cathode array offset (SCAO). This study establishes and validates a mathematical model based on SCAO for the stray-current density, U, and ton. Then the end gap and side gap are modeled. Next, through simulation, the current density distribution at various SCAO is examined. It has been discovered that when the SCAO grows, the current density distribution becomes more uniform, and the stray-current density becomes milder. The array microgrooves having high quality and high density can be fabricated at a SCAO of 1.4 mm. Additionally, the experiments show that quality and efficiency can be ensured under the optimal parameters. [ABSTRACT FROM AUTHOR]

Sparse arrays are widely used to achieve full array performance with fewer elements to reduce the cost of array and beamforming computation. Sparse array synthesis methods such as Bayesian compressed sensing (BCS) yield small element numbers; however, they are limited by the tradeoff between complexity and accuracy. Herein, a novel sparse array synthesis method with two-stage progressive BCS and an undirected graph-based element spacing constraint is proposed. The two-stage progressive BCS includes fast on-grid sparsification and accurate off-grid global re-estimation. First, the multitask BCS is solved using a relevance vector machine to efficiently select elements from candidate positions. Subsequently, a convex surrogate cost function is applied to the global re-estimation of the element weights to increase the beam pattern matching accuracy of the sparse array. Global optimization can improve the array performance. In addition, to satisfy the spacing constraint, a weighted merging method based on an undirected graph is proposed and inserted between the two stages to merge elements that are too close, which ensures the processability of the array. Simulations and experiments involving a variety of arrays were conducted to confirm the advantages of the method with regard to array sparsity, sidelobe suppression, beam pattern matching accuracy, and array processability. The proposed method achieved accurate and effective sparse array synthesis and outperformed existing methods. [ABSTRACT FROM AUTHOR]

The ADP-ribosylation factors (ARFs) and ARF-like (ARL) GTPases serve as essential molecular switches governing a wide array of cellular processes. In this study, we used proximity-dependent biotin identification (BioID) to comprehensively map the interactome of 28 out of 29 ARF and ARL proteins in two cellular models. Through this approach, we identified ~3000 high-confidence proximal interactors, enabling us to assign subcellular localizations to the family members. Notably, we uncovered previously undefined localizations for ARL4D and ARL10. Clustering analyses further exposed the distinctiveness of the interactors identified with these two GTPases. We also reveal that the expression of the understudied member ARL14 is confined to the stomach and intestines. We identified phospholipase D1 (PLD1) and the ESCPE-1 complex, more precisely, SNX1, as proximity interactors. Functional assays demonstrated that ARL14 can activate PLD1 in cellulo and is involved in cargo trafficking via the ESCPE-1 complex. Overall, the BioID data generated in this study provide a valuable resource for dissecting the complexities of ARF and ARL spatial organization and signaling. [ABSTRACT FROM AUTHOR]

Nonorthogonal multiple access (NOMA) superposes several user signals for simultaneous transmission, considerably ameliorating spectral efficiency. To exploit efficiently radio frequency energy sources surrounding NOMA transmitters for improved energy efficiency, NOMA networks further employ energy harvesting, which practically possesses nonlinear characteristic. In addition, multiple antennas can be deployed at users to transfer-and-harvest energy efficiently and to transmit/receive information reliably with antenna array processing. As such, this paper evaluates numerically the throughput and the outage probability of NOMA networks accounting for practical factors such as nonlinear energy harvesting, all multi-antenna users, and imperfect channel information. Illustrative results expose that nonlinear energy harvesting and channel information imperfection considerably degrade system performance, which is governed flexibly by multiple parameters. Notably, NOMA networks can prevent a total outage and achieve the best performance with a proper parameter selection. Moreover, their performance is considerably ameliorated with accreting the number of antennas. [ABSTRACT FROM AUTHOR]

This article proposes a novel design for an in-memory computing SRAM, the DAM SRAM CORE, which integrates storage and computational functionality within a unified 11T SRAM cell and enables the performance of large-scale parallel Multiply–Accumulate (MAC) operations within the SRAM array. This design not only improves the area efficiency of the individual cells but also realizes a compact layout. A key highlight of this design is its employment of a dynamic aXNOR-based computation mode, which significantly reduces the consumption of both dynamic and static power during the computational process within the array. Additionally, the design innovatively incorporates a self-stabilizing voltage gradient quantization circuit, which enhances the computational accuracy of the overall system. The 64 × 64 bit DAM SRAM CORE in-memory computing core was fabricated using the 55 nm CMOS logic process and validated via simulations. The experimental results show that this core can deliver 5-bit output results with 1-bit input feature data and 1-bit weight data, while maintaining a static power consumption of 0.48 mW/mm2 and a computational power consumption of 11.367 mW/mm2. This showcases its excellent low-power characteristics. Furthermore, the core achieves a data throughput of 109.75 GOPS and exhibits an impressive energy efficiency of 21.95 TOPS/W, which robustly validate the effectiveness and advanced nature of the proposed in-memory computing core design. [ABSTRACT FROM AUTHOR]

Covalent organic frameworks (COFs) with reversible redox units and a conjugated molecular skeleton possess novel photoelectrochemical properties. In this work, tris(4-aminophenyl)amine and 4,4-biphenyldiformaldehyde were chosen to synthesize TABP-COF electrochromic film with triphenylamine (TPA) active unit by a solvothermal method in a Teflon-lined reactor. The growth process and reaction time optimization of the TABP-COF were studied. TABP-COF shows a self-assembly growth process from an array of nanosheets to nanospheres. Owing to the redox characteristics of TPA, the TABP-COF film exhibits reversible electrochromic behavior. With a solvothermal reaction time of 72 h, the TABP-COF-3 film exhibits the best electrochromic properties, including contrast of 0.483 and response time of 7.3 s/13.6 s. Considering the abundance of redox-active units that can be chosen, COF materials with different redox units and color changes can be designed for a variety of applications in the field of electrochromic materials. [ABSTRACT FROM AUTHOR]

Wideband digital phased array radar offers the advantages of high range resolution, which improves the recognition ability for multiple targets, group targets, and high-speed targets. Traditional wideband phased arrays use true time delay to compensate for aperture fill time; however, the cost increases significantly. In this paper, a wideband elemental digital array architecture based on the stretch processing method is proposed. By utilizing the time-domain and frequency-domain translation equivalence of the LFM (Linear Frequency Modulation) signal waveform, the equivalent aperture fill time is compensated for through frequency shift and phase shift after stretch processing. Compared to traditional wideband digital arrays, this method can dramatically reduce the required sampling rate and lower the requirements on antenna hardware, thereby reducing the manufacturing cost of system. A comprehensive analysis of the signal processing process and stretch processing method is provided. And an antenna array prototype is developed to verify the T/R channel compensation and wideband beamforming. Measured results show that the antenna is capable of ±60° scanning in azimuth plane and ±40° scanning in elevation plane, with a bandwidth of 500 MHz in S-band. The results demonstrate excellent wideband beam performance and accurate lobe scanning, which confirms the validity of the proposed wideband architecture for stretch processing, frequency shift, and phase shift. This method can be widely applied to the low-cost design and wideband performance improvement of wideband digital array radar. [ABSTRACT FROM AUTHOR]

The Ruscheweyh derivative operator is used in this paper to introduce and investigate interesting general subclasses of the function class Σ m of m-fold symmetric bi-univalent analytic functions. Estimates of the initial Taylor-Maclaurin coefficients | a m + 1 | and | a 2 m + 1 | are obtained for functions of the subclasses introduced in this study, and the consequences of the results are discussed. Additionally, the Fekete-Szegö inequalities for these classes are investigated. The results presented could generalize and improve some recent and earlier works. In some cases, our estimates are better than the existing coefficient bounds. Furthermore, within the engineering domain, the utilization of the Ruscheweyh derivative operator can encompass a broad spectrum of engineering applications, including the robotic manipulation control, optimizing optical systems, antenna array signal processing, image compression, and control system filter design. It emphasizes the potential for innovative solutions that can significantly enhance the reliability and effectiveness of engineering applications. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to theorizing and empirically investigating a phenomenon variously described by sociologists as internalized oppression or symbolic violence. Located at the intersection of internal worlds and external reality, the intrapsychic and the interpersonal and social, this object of inquiry—here termed self-negation—is crucial to many forms of societal domination. The paper explores its inner workings, analytically disaggregating it into an array of psychosocial processes drawn from the psychoanalytic theory of the defenses. Much of the work's originality consists in showing how these processes operate across multiple systems of domination and drive many and varied outward manifestations of the phenomenon. [ABSTRACT FROM AUTHOR]

Transferring the concept of chemical‐driven responses into artificial intelligence technology holds the key to mimicking olfactory for neuromorphic computing of chemical recognition. Currently, artificial olfactory systems are designed based on chemical sensor arrays. Time‐dependent responses of the sensor arrays are processed by artificial neural networks for recognition. However, the sensors generate instantly volatile responses, and algorithms for the processing of the time‐dependent responses have not been involved. The recognition accuracy and speed are severely impeded. A sensor array can only achieve an accuracy of 90% after at least 5 training epochs. Herein an artificial olfactory chemical‐resistant synapse consisting of 3D hierarchical WO3@WO3 nanofibers are demonstrated. The nanofibers exhibit persistent resistance responses through chemical exposures due to the strong chemisorption of water molecules. Typical synaptic behaviors including paired‐pulse facilitation, long‐term −1 short‐term memory, and learning experience have been achieved. Next, a recurrent neural network that is committed to processing the time‐dependent data is used to identify gas‐phase chemicals of 3‐hydroxy‐2‐butanone, triethylamine, and trimethylamine. Training‐free gas recognition has been realized by a WO3@WO3 nanofiber synapse only, in which the accuracy is above 90% at the first epoch. The results have great potential to satisfy stringent performance requirements on artificial perception systems. [ABSTRACT FROM AUTHOR]

In this paper a comparative study, restricted to one-dimensional stationary case, between several Direction of Arrival (DOA) estimation algorithms of narrowband signals is presented. The informative signals are corrupted by an Additive White Gaussian Noise (AWGN), to show the performance of each method by applying directly the algorithms without pre-processing techniques such as forward-backward averaging or spatial smoothing. [ABSTRACT FROM AUTHOR]

This paper mainly introduces the implementation method of a high PPI transparent Micro‐LED display. By optimizing the pixel layout, proposing an innovative substrate array structure and process method, and introducing a high brightness uniformity driving scheme, a 8.07" high PPI transparent Micro‐LED display is produced while maintaining high PPI, while also considering high transmittance and high brightness, which proposes a new technology carrier for Micro‐LED transparent display applications. [ABSTRACT FROM AUTHOR]

Bit-serial neural network accelerators address the growing need for compact and energy-efficient deep learning tools. Traditional neural network accelerators, while effective, often grapple with issues of size, power consumption, and versatility in handling a variety of computational tasks. To counter these challenges, this paper introduces an approach that hinges on the integration of bit-serial processing with advanced dataflow techniques and architectural optimizations. Central to this approach is a column-buffering (CB) dataflow, which significantly reduces access and movement requirements for the input feature map (IFM), thereby enhancing efficiency. Moreover, a simplified quantization process effectively eliminates biases, streamlining the overall computation process. Furthermore, this paper presents a meticulously designed LeNet-5 accelerator leveraging a convolutional layer processing element array (CL PEA) architecture incorporating an improved bit-serial multiply–accumulate unit (MAC). Empirically, our work demonstrates superior performance in terms of frequency, chip area, and power consumption compared to current state-of-the-art ASIC designs. Specifically, our design utilizes fewer hardware resources to implement a complete accelerator, achieving a high performance of 7.87 GOPS on a Xilinx Kintex-7 FPGA with a brief processing time of 284.13 μs. The results affirm that our design is exceptionally suited for applications requiring compact, low-power, and real-time solutions. [ABSTRACT FROM AUTHOR]

Modern applications need to flexibly adjust the processing process according to the different environments and real-time processing, thus putting forward higher requirements for the reconfiguration performance of coarse-grained reconfigurable architecture (CGRA). Traditional dynamic reconfiguration requires constant configuration of the processor by the host computer. Still, this reconfiguration method severely restricts switching different tasks, making it difficult to achieve dynamic real-time reconfiguration and limiting performance improvement. This paper presents a real-time monitoring-based self-reconfiguration mechanism (RMSRM) based on the implementation of CGRA. This mechanism improves the reconfiguration performance by monitoring the execution process of the array in real-time through a programmable controller on the basis of the homogeneous processing element (PE) array and dynamic scheduling of PE array resources according to different application requirements. The proposed RMSRM is capable of feeding array execution state information to the programmable controller within 1 clock cycle, switching configurations between different tasks within 10 clock cycles, and shutting down the unnecessary PE/PEs according to the current configuration. To verify the correctness and efficiency, we model hardware with synthesizable RTL coding and implement it on FPGA and a chip. The experimental results show that RMSRM can effectively reduce the reconfiguration overhead compared with traditional reconfiguration methods. The volume of bits in the configuration file has decreased by an average of 39.37%, and the reconfiguration time has decreased by an average of 43.57%. Based on the SMIC 55nm process, the operating frequency can reach 320 MHz. Meanwhile, the resource consumption is 80506 logic gates, the chip area is 12 mm 2 , and the energy efficiency is 312 GOPS/W. [ABSTRACT FROM AUTHOR]

In mixed-field source localization, the physical properties of a sensor array, such as the degrees of freedom (DOFs), aperture, and coupling leakage, directly affect the accuracy of estimating the direction of arrival (DOA). Compared to conventional symmetric uniform linear arrays, symmetric non-uniform linear arrays (SNLAs) have a greater advantage in mixed-field source localization due to their larger aperture and higher DOF. However, current SNLAs require improvements in their physical properties through modifications to the array structure in order to achieve more accurate source localization estimates. Therefore, this study proposes a symmetric double-supplemented nested array (SDSNA), which translates nested subarrays based on symmetric nested arrays to increase the aperture and inserts two symmetric supplemented subarrays to fill the holes created by the translation. This method results in longer consecutive difference coarray lags and larger apertures. The SDSNA is compared to existing advanced SNLAs in terms of their physical properties and DOA estimation. The results show that, with the same number of sensors, the SDSNA has a higher DOF, a larger aperture, and smaller coupling, indicating the advantages of the SDSNA in terms of its physical properties. Under the same experimental conditions, the SDSNA has a lower root-mean-square error of source location, thus indicating better performance in terms of both DOA and distance estimation. [ABSTRACT FROM AUTHOR]