In this paper, the roles of multiple resonances in a tapered bi-modal plasmonic nanoslit metasurface for multi-functional and efficient nonlocal spatial frequency filtering are rigorously studied. The physical mechanisms and design guidelines to engineer wavelength-dependent multiple transfer functions in a single plasmonic metasurface are suggested by analyzing vertical waveguiding and resonance, transversal momentum-matching, and incidence angle and wavelength-dependent light funneling phenomena. In particular, the interplay between multiple resonances enables multi-functional and efficient spatial filtering such as wavelength-dependent low-pass or high-pass filtering in a single nonlocal metasurface. The numerical results obtained from electromagnetic full-field simulations are explained phenomenologically based on the harmonic oscillator model. It is envisioned that the proposed results would provide fruitful insight for developing advanced nonlocal metasurfaces and their applications to analog optical computing and computational imaging.

Digital equalizers typically aim to emulate analog responses, and the finite digital bandwidth leads to a divergence from the analog response near the Nyquist frequency. This paper develops sets of low-pass, band-pass, and high-pass filters that minimize this divergence. The first set is developed using the matched z-transform with numerators designed to control the high-frequency behavior. The second set generalizes the bilinear transform filters to provide a closer match to the analog counterparts at high Q values. A symmetric parametric equalizer is also derived that generalizes previous designs. This equalizer is then modified to better match the analog filter response near the Nyquist frequency.

Accurately modeling the interactions between students and learning content is a central challenge in achieving personalized and adaptive learning in online education. However, existing methods often struggle to simultaneously capture the multi-scale structural dependencies and the rich semantic information embedded in educational materials. To bridge this gap, we propose EduSheaf—a unified framework that integrates large language models (LLMs) with a sheaflet-based signed graph neural network. Specifically, LLMs are employed to extract fine-grained semantic embeddings from multiple-choice questions (MCQs), thereby enriching graph representations with contextual knowledge. A signed graph is then constructed to encode student–MCQ interactions, where correct and incorrect responses are represented as positive and negative edges. On top of this, a novel sheaflet-based signed graph neural network performs multi-frequency learning through low-pass and high-pass filters, enabling the joint modeling of global consensus and local variations, while sheaf structures enforce edge-level consistency. Extensive experiments on multiple real-world educational datasets demonstrate that EduSheaf consistently outperforms state-of-the-art baselines, including both semantic-enhanced and signed graph models, in terms of prediction accuracy and robustness. Ablation studies further reveal the complementary roles of semantic embeddings and multi-frequency graph filters.

Introduction. Currently the most widely used digital signal processing methods are those using specialized digital computers, which utilize analog-to-digital conversion of not only the input signals but also the weighting coefficients of the digital filters. This conversion results in quantization noise in the input signals, which reduces the efficiency of the specialized computer. The operating algorithms of these specialized computers are determined by the discrete transfer functions of the digital filters. The transfer function of a specific digital filter is determined by a set of weighting coefficients, which, like the input signals, are quantized by level and loaded into the computer memory. When quantizing the weighting coefficients, errors arise, the magnitude of which is determined by the quantization step. Weighting coefficient quantization errors inevitably affect the frequency properties of digital filters. Objective. To evaluate the influence of the weighting coefficient quantization step on the frequency characteristics of digital low-pass and high-pass filters, band-pass and notch filters of various orders. Method. The study utilized the bilinear z-transformation of the frequency transfer functions of continuous analog filters. Results. Transfer functions in the z-plane of digital low-pass and high-pass filters, band-pass and notch filters of various orders were obtained. Distortions in the frequency transfer functions of digital filters were calculated as a function of the weighting coefficient quantization step. Conclusions. Quantization of weighting coefficients leads to inevitable distortions in the frequency transfer functions of digital filters, the extent of which is determined by the quantization step size. The influence of the quantization step on the resulting distortions in the frequency transfer functions of digital filters must be taken into account when designing digital computers, specifically when selecting the number of binary digits and the quantization step size of a specialized computer. The results of this work will be of great use to designers of digital signal processing devices.

Dopamine's secret agent: serotonin.

Efficient simulation of object scattering is crucial in virtual and room acoustics, particularly for interactive scenarios where time-varying diffraction must be evaluated repeatedly in real-time. In this context, the rigid sphere is commonly used as a simple geometric representation of more complex three-dimensional objects. Although the analytical solution for rigid sphere scattering is well known, its computation is too involved for interactive audio rendering. In this paper, we propose a physically informed, low-order digital filter approximation of rigid sphere scattering for arbitrary source and receiver positions. For the low-frequency range, the first three terms of the spherical harmonics analytical solution are directly expressed by combined first-order low- and high-pass filters. For high-frequencies, the basic properties of rigid sphere scattering are approximated by modelling the shortest and longest paths reflected or bent around the sphere. Both approximations are combined using a blending function to obtain the wideband result. The magnitude of the low-frequency approximation matches the analytical solution well, yielding mean root mean square errors below 0.5 dB for source and receiver distances greater than twice the sphere radius and about 1.5 dB at smaller distances. For the high-frequency range, the mean error in magnitude is overall larger and is about 2 dB.

Skin Sympathetic Nerve Activity Complexity Predicts Sudden and Non-Sudden Cardiac Mortality in Heart Failure.

Introduction: In chronic stroke, functional MRI (fMRI) is used to map residual motor networks. Standard preprocessing masks the stroke lesion, excluding both the infarct cavity and adjacent T2 hyperintense tissue to correct for tissue displacement. However, this may eliminate regions with abnormal T2 signal that retain metabolic viability, which could support recovery. We implemented a double masking approach that distinguishes the T2 hypointense infarct core from the T2 hyperintense tissue, and evaluated whether this method reveals task-related activity in the hyperintense region that may need further study. Methods: Participants with chronic ischemic stroke underwent wrist-flexion fMRI before a neuromodulation study. Participants performed 8 trials of visually cued wrist flexion followed by rest (TR = 3 s, 100 volumes, 3.0T Siemens scanner). High-resolution T1-weighted and T2-FLAIR images were manually segmented in ITK-SNAP to delineate the cystic infarct and the surrounding hyperintense area. Preprocessing in SPM25 included realignment, co-registration, segmentation, and smoothing with a 6 mm Gaussian kernel. Lesion-aware confound regressors were incorporated into the first-level GLMs, including signal from the infarct core, six head motion parameters, and volumes exceeding 1 mm framewise displacement. Wrist flexion blocks were modeled using the canonical HRF with a high-pass filter of 128 s. Small-volume correction was applied within the T2 hyperintense region. Results: Task-related activation was observed in the T2 hyperintense area in a subset of participants. A significant activation cluster emerged within the T2 hyperintense region during wrist flexion (pFWE=0.005, qFDR=0.025), detectable only when the cystic infarct core was separated from surrounding tissue using the double masking approach. Conclusions: Despite challenges in interpreting BOLD signals within T2 hyperintense regions due to altered neurovascular coupling and T2* signal changes, further investigation is warranted. Double masking preserves potentially viable tissue for analysis and may improve detection of residual network activity. This approach could inform future strategies for stroke rehabilitation.

Organic synaptic devices with environmental stability are important for the development of smart electronic systems. However, there are challenges in preparing air-stable synaptic devices due to the sensitivity of organic semiconductors to moisture and oxygen. Here, poly(3-hexylthiophene)-block-poly(phenyl isocyanide) with pentafluorophenyl ester (P3HT-b-PPI(5F)), which combines both carrier transport and charge trapping functions, was selected to be blended with poly(methyl methacrylate) (PMMA) to prepare synaptic devices. Vertical phase separation was generated after deposition of the blended solution, enabling one-step preparation of the active and encapsulated layers of the device. P3HT-b-PPI(5F) and PMMA are the device active layer and encapsulation layer, respectively. The synaptic device has high stability, with the postsynaptic current remaining above 90% after 2 weeks in air. Basic synaptic behavior was successfully simulated under green light stimulation. The energy consumption of a single synaptic event can be as low as 0.6 fJ after reducing the operating voltage. Further, high-pass filtering and optical decoding of "Morse code" were simulated. In addition, biomimetic visual learning and forgetting behaviors were simulated. This work demonstrates a method for preparing air-stable synaptic devices with potential applications in the field of bionic electronics.

Abstract Nanofluidic devices have been widely utilized to simulate the electronic functionalities recently, since their unique ion transport behaviors, such as nonlinear ion transport, selectivity and so on. However, the correlation of the ion transport behavior and the transition among various nanofluidic capacitive and inductive hysteresis still remains unclear, which impedes the development of nanofluidic systems. Here, we report a concentration-dependent transition between capacitive and inductive hysteresis in gold-nanoparticle-stacked nanochannels. Quantitative analysis reveals this transition is governed by the interionic distance relative to the Bjerrum length, establishing a universal mechanism for ion transport modulation. Notably, our system enables unidirectional plasticity (both facilitation and depression) by simply altering the ionic species, demonstrating programmable plasticity without structural reconfiguration. Additionally, a high-pass filter (HPF) circuit with tunable cut-off frequency is implemented through two identical nanofluidic devices. These findings establish a new paradigm for multifunctional nanofluidic devices and provide a rational foundation for the design of aqueous-phase neuromorphic computing circuits.

Continuous monitoring of physiological signals is inevitably disrupted by motion artifacts and ambient mechanical noise. Signal processing is typically required to extract genuine physiological signals from motion artifacts, yet the signals can be distorted and classified incompletely. Previously, we presented a noise-selective damper based on gelatin hydrogel and chitosan, however, the hydrogel is unstable due to dehydration. In addition, various types of mechanical filters, such as high-pass, low-pass, and band-pass filters, are needed as alternatives to signal processing. Here, we present viscoelastic polyborodimethylsiloxane (PBDMS) based mechanical pass filters, which maintain stable damping properties for over three months. Dynamic bonding from hydrogen bonds and B─O bonds enables energy dissipation through chain rearrangement and entanglement. The damping behaviors can be tuned by adjusting its molecular weight. As molecular weight increases, the reconfiguration and re-bonding of these chains slow down, resulting in a longer relaxation time. This molecular-weight-dependent relaxation behavior allows precise control over the transition frequency. Furthermore, by parallelly assembling materials with distinct phase transition characteristics, not only high-pass, but also low-pass and band-pass mechanical filtering is achieved. Using PBDMS-based wearable bioelectronics, we successfully separate more than two concurrent mechanical signals without any additional signal processing.

Traditional fusion methods for integrating multi-source gravity data rely on predefined mathematical models that inadequately capture complex nonlinear relationships, particularly at wavelengths shorter than 10 km. We developed a convolutional neural network incorporating differential marine geodetic data (DMGD-CNN) to enhance marine gravity anomaly recovery from HY-2A satellite altimetry. The DMGD-CNN framework encodes spatial gradient information by computing differences between target points and their surrounding neighborhoods, enabling the model to explicitly capture local gravity field variations. This approach transforms absolute parameter values into spatial gradient representations, functioning as a spatial high-pass filter that enhances local gradient information critical for short-wavelength gravity signal recovery while reducing the influence of long-wavelength components. Through systematic ablation studies with eight parameter configurations, we demonstrate that incorporating first- and second-order seabed topography derivatives significantly enhances model performance, reducing the root mean square error (RMSE) from 2.26 mGal to 0.93 mGal, with further reduction to 0.85 mGal achieved by the differential learning strategy. Comprehensive benchmarking against international gravity models (SIO V32.1, DTU17, and SDUST2022) demonstrates that DMGD-CNN achieves 2–10% accuracy improvement over direct CNN predictions in complex topographic regions. Power spectral density analysis reveals enhanced predictive capabilities at wavelengths below 10 km for the direct CNN approach, with DMGD-CNN achieving further precision enhancement at wavelengths below 5 km. Cross-validation with independent shipborne surveys confirms the method’s robustness, showing 47–63% RMSE reduction in shallow water regions (<2000 m depth) compared to HY-2A altimeter-derived results. These findings demonstrate that deep learning with differential marine geodetic features substantially improves marine gravity field modeling accuracy, particularly for capturing fine-scale gravitational features in challenging environments.

Analog switching with high pass filtering in a Pt/h-BN/Pt memristor using double layered h-BN

ABSTRACT The capacitive behavior and structural characterization of xenotime located in the Atotonilco el Grande Formation, Hidalgo, Mexico, were investigated. The study area is associated with an extensional rift environment known as the Molango Rift, where tectonic and volcanic processes facilitated the concentration of strategic minerals such as xenotime.Xenotime was characterized using X-ray diffraction (XRD), scanning electron microscopy with energy-dispersive spectroscopy (SEM–EDS), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). The identified crystalline phases include xenotime-(Y) (PDF 96-101-1144) and xenotime-(Yb) (PDF 96-152-9051). The xenotime particles exhibit subhedral morphologies, along with mineral phases such as zirconium pyrophosphate (ZrP2O7, PDF 96-153-4358) and pretulite (ScPO4, PDF 96-900-1950), which may be remnants of diffusive metasomatic processes that promoted isomorphic substitution of rare earth elements within a phosphorus- and zirconium-rich matrix. Electrical tests demonstrated that xenotime behaves as a high-pass filter (HPF), with efficient signal response above 100 kHz, suggesting its potential use in high-frequency electronic devices. Thermal characterization revealed high dielectric constants (~500) and two ferroelectric transitions (at 70 °C and 300 °C), indicative of a Class I dielectric material with Class II ferroelectric stability. These findings position xenotime as a promising material for the technology sector, particularly in the development of high-stability capacitors, energy storage devices, and solid-state rectifiers. Furthermore, the study underscores the relevance of rift systems as favorable geological environments for the concentration of strategic critical elements in Mexico, encouraging new exploration strategies for high-tech mineral resources. Key Words: Rift, Rare Earth Elements (REEs), Xenotime mineralization, Metasomatic, Thermal and electrical characterization.

In this article, the variational generalized Nash equilibrium (vGNE) seeking problem for general monotonic game with multiple coupling constraints involving dynamical players is explored. Specifically, a distributed vGNE-seeking neural network (vGSNN) with a feedback controller is designed based on high-pass filter, which efficiently transforms players' high-order dynamics into equivalent second-order ones. To further relax the requirement on parameter predesign, we propose a controller that uses adaptive weights to replace the traditional fixed gains, which realizes the full distribution of the vGSNN. Furthermore, to enhance the robustness of the vGSNN against disturbances, a novel sliding-mode controller is incorporated to ensure finite-time disturbance rejection while maintaining the full distribution of the vGSNN. Finally, an uncrewed aerial vehicle (UAV) swarm game is put forward to verify the effectiveness of the vGSNNs.

Viscoelasticity explains fast adaptation in the auditory amplifiers of mammals.

Accurate low-frequency response in digital electrocardiogram (ECG) systems is critical for preserving ST-segment morphology and ensuring diagnostic fidelity in ischemia detection. This study validates the low-frequency performance of SydäntekTM, a wearable 12-lead ECG platform, using impulse-based protocols aligned with IEC 60601-2-25 standards. Synthetic 3 mV square wave-forms with 100 ms isoelectric segments were injected to simulate baseline drift and assess high-pass filter behavior near the 0.67 Hz cutoff. Forward–reverse finite impulse response (FIR) convolution and filtfilt-based processing ensured zero-phase distortion and amplitude preservation. Hardware calibration employed WhaleTeq modules generating programmable impulses and sinusoidal drifts, confirming analog-to-digital fidelity across physiologic bradycardia thresholds. Real-world validation was conducted using 79 annotated records from the European ST-T Database from Physionet, encompassing both ST elevation and depression events. The SydäntekTM system consistently preserved ST-segment morphology across heart rate bins, maintaining a bias of less than 5 µV at J+40, J+60, and J+80 ms intervals. Among these, J-point anchoring at J+60 ms demonstrated the strongest correlation with reference annotations in the ST-T Database. Of note, J+60 aligns with published literature identifying it as a sensitive and specific marker for subendocardial ischemia. Artifact suppression in lead V2 was achieved using adaptive Wiener filtering, which effectively minimized motion-induced distortion while preserving low-amplitude ST features critical for diagnostic fidelity. Digitization fidelity was confirmed through direct waveform analysis, demonstrating consistent temporal precision and preservation of ST-segment morphology. The platform adheres to key performance criteria outlined in IEC 60601-2-25, -2-27, and -2-47, supporting its suitability for deployment in resting, monitoring, and ambulatory ECG contexts. These findings position SydäntekTM as a clinically capable solution for low-frequency ECG acquisition, with relevance to infarct detection, pericarditis, and myocardial disease screening. The methodology provides a reproducible framework for waveform validation, morphology-aware signal processing, and alignment with regulatory expectations in next-generation cardiac diagnostics.

Groundwater resources are receiving a lot of attention, especially in arid and semi-arid areas as a result of the growing demands for water brought on by urbanization, population growth, and agricultural development. Finding the most significant contributing parameters such as lineament density, frequency, and junction nodes that reveal the groundwater potential is the foundation of this investigation. To determine suitable sites for drilling wells, analysis of linear structures is necessary to detect and exploit groundwater sustainably. This study highlights the importance of geospatial systems including remote sensing and geographic information system technologies to effectively explore and manage groundwater resources. To identify linear structures in the study area, different image enhancement techniques are applied such as band composite (BC) and high pass filtering (HPF). These methods work well and they are suitable for identifying these structures. The results show that the area contains a large number of short and long fractures, most of which have an orientation from northwest to southeast, and that it is possible to explore groundwater in areas with a high density of areas with intersections in linear structures. The area, where groundwater occurrence is most promising for sustainable use of groundwater, has been identified within the region. Using remote sensing (RS) data and geographic information systems (GIS), a thematic map of each parameter is created. The final groundwater potential zones of the studied region are created by combining these input layers using the GIS Raster Calculate Module. Different groundwater prospective potential zones are depicted on the final output map: very high (49 km2), high (261 km2), and moderate (1041 km2).

The Great Sumatran Fault (GSF), with a length of approximately 1900 km, has been the cause of significant earthquakes on the island of Sumatra. This fault is divided into several segments, such as Seulimum and Aceh at the tip of Sumatra, which have produced many earthquakes. The Tripa segment in the northern part of Aceh is predicted to cause earthquakes of Mw> 7 due to seismic activity that has not been released significantly. Gravity satellite is one of the fastest and cheapest methods for mapping regional fault structures and rock lithology below the surface. This study aims to visualize the Tripa fault using Topex gravity with a resolution of 1.8 km/px, which is integrated with seismicity data from USGS and BMKG as an initial model for depth in density inversion. Residual anomalies processed with high pass filtering can clearly show the Tripa segment's existence, characterized by a high Bouguer value of 30 - 50 mGal in the NW-SE direction on the East side. In addition, the results of derivative techniques, such as vertical and horizontal, can also clarify the structure of the Tripa fault, especially in the northern part of the research area; the use of this filter can also respond to local faults characterized by high and low Bouguer values ​​in the same direction as the Tripa Fault. Furthermore, the tilt derivative can describe the total structure of the Tripa Fault from the West to the East, which is characterized by high derivative values> 1 rad/m. 2D density model from Smoothness constrained and Occam inversion with RMS 0.5% can show the geometry of the Tripa segment and other local faults located at a depth of 12 km below the surface, thus providing an overview of the effectiveness of the Topex gravity application for the study of the Tripa fault in Southeast Aceh which can be used as an initial survey of faults in other areas.

Pneumonia is a major global health challenge, particularly affecting children under five, and remains a leading cause of mortality due to diagnostic delays and variability in clinical interpretation. This study investigates the application of classical machine learning (ML) classifiers—K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM)—to enhance pneumonia detection using pediatric chest X-ray images. A dataset of 5,856 chest X-rays was preprocessed using spatial (Gaussian Blur, Histogram Equalization) and frequency-domain (Discrete Cosine Transform, High-Pass Filtering) techniques. Feature extraction was performed using a hybrid approach combining handcrafted methods (GLCM, HOG, Wavelets) and deep features via a pretrained ResNet-50. Among the models evaluated, SVM with Gaussian Blur achieved the highest accuracy (96.75%) and F1-score (0.97), followed by RF with Gaussian Blur (95.32% accuracy, F1-score 0.95). In contrast, DCT preprocessing consistently underperformed across all models. Statistical analyses, including ANOVA (p = 0.0003) and Tukey’s HSD, confirmed that model-preprocessing interactions significantly influenced diagnostic performance. While DenseNet121, a deep learning baseline, achieved slightly higher accuracy (93.1%), classical models demonstrated superior computational efficiency and interpretability, making them better suited for deployment in resource-constrained settings. This research highlights the importance of image preprocessing and feature engineering in ML-based pneumonia diagnostics, offering promising solutions for improving early detection and clinical decision-making in low-resource environments.