Search Results (738)

This paper focuses on the switching and modulation techniques of terahertz waves, develops ${\mathrm{VO}}_2$ thin-film materials with an SPP structure, and uses terahertz time-domain spectroscopy (THz-TDS) to study the semiconductor–metal phase transition characteristics of ${\mathrm{VO}}_2$ thin films, especially the photoinduced semiconductor–metal phase transition characteristics of silicon-based ${\mathrm{VO}}_2$ thin films. The optical modulation characteristics of silicon-based ${\mathrm{VO}}_2$ thin films to terahertz waves under different light excitation modes, such as continuous light irradiation at different wavelengths and femtosecond pulsed laser irradiation, were analyzed. Combining the optical modulation characteristics of silicon-based ${\mathrm{VO}}_2$ thin films with the filtering characteristics of SPP structures, composite structures of ${\mathrm{VO}}_2$ thin films with metal hole arrays, composite structures of ${\mathrm{VO}}_2$ thin films with metal block arrays, and silicon-based ${\mathrm{VO}}_2$ microstructure arrays were designed. The characteristics of this dual-function device were tested experimentally. The experiment proves that the ${\mathrm{VO}}_2$ film material with an SPP structure has a transmission rate dropping sharply from 32% to 1% under light excitation; the resistivity changes by more than six orders of magnitude, and the modulation effect is remarkable. By applying the SPP structure to the ${\mathrm{VO}}_2$ material, the material can simultaneously possess modulation and filtering functions, enhancing its optical performance in the terahertz band. Full article

In the global energy sector, water-bearing reservoir-typed gas storage accounts for about 30% of underground gas storage (UGS) reservoirs and is vital for natural gas storage, balancing gas consumption, and ensuring energy supply stability. However, when constructing the UGS in the M gas reservoir, selecting suitable areas poses a challenge due to the complicated gas–water distribution in the multi-layered water-bearing gas reservoir with a long production history. To address this issue and enhance energy storage efficiency, this study presents an integrated geomechanical-hydraulic assessment framework for choosing optimal UGS construction horizons in multi-layered water-bearing gas reservoirs. The horizons and sub-layers of the gas reservoir have been quantitatively assessed to filter out the favorable areas, considering both aspects of geological characteristics and production dynamics. Geologically, caprock-sealing capacity was assessed via rock properties, Shale Gouge Ratio (SGR), and transect breakthrough pressure. Dynamically, water invasion characteristics and the water–gas distribution pattern were analyzed. Based on both geological and dynamic assessment results, the favorable layers for UGS construction were selected. Then, a compositional numerical model was established to digitally simulate and validate the feasibility of constructing and operating the M UGS in the target layers. The results indicated the following: (1) The selected area has an SGR greater than 50%, and the caprock has a continuous lateral distribution with a thickness range from 53 to 78 m and a permeability of less than 0.05 mD. Within the operational pressure ranging from 8 MPa to 12.8 MPa, the mechanical properties of the caprock shale had no obvious changes after 1000 fatigue cycles, which demonstrated the good sealing capacity of the caprock. (2) The main water-producing formations were identified, and the sub-layers with inactive edge water and low levels of water intrusion were selected. After the comprehensive analysis, the I-2 and I-6 sub-layer in the M 8 block and M 14 block were selected as the target layers. The numerical simulation results indicated an effective working gas volume of 263 million cubic meters, demonstrating the significant potential of these layers for UGS construction and their positive impact on energy storage capacity and supply stability. Full article

As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. However, its block-based coding often introduces compression artifacts. While CNN-based methods effectively reduce these artifacts, maintaining robust performance across varying quantization parameters (QPs) remains challenging. Recent QP-adaptive designs like QA-Filter show promise but are still limited. This paper proposes DRIFT, a QP-adaptive in-loop filtering network for VVC. DRIFT combines a lightweight frequency fusion CNN (LFFCNN) for local enhancement and a Swin Transformer-based global skip connection for capturing long-range dependencies. LFFCNN leverages octave convolution and introduces a novel residual block (FFRB) that integrates multiscale extraction, QP adaptivity, frequency fusion, and spatial-channel attention. A QP estimator (QPE) is further introduced to mitigate double enhancement in inter-coded frames. Experimental results demonstrate that DRIFT achieves BD rate reductions of 6.56% (intra) and 4.83% (inter), with an up to 10.90% gain on the BasketballDrill sequence. Additionally, LFFCNN reduces the model size by 32% while slightly improving the coding performance over QA-Filter. Full article

Air purification is a key process aimed at removing harmful impurities and providing a safe and comfortable environment for human life and work. This study presents the results of an investigation into the composition, textural, and sorption properties of a multichannel carbon filtering material developed for air purification from biological (infectious) contaminants. The filtering block has a cylindrical shape and is manufactured by extrusion of a plastic composition based on carbonized rice husk with the addition of binding agents, followed by staged thermal treatment (calcination, activation, and demineralization). The filter’s effectiveness is based on the inactivation of pathogenic microorganisms as the air passes through the porous surface of the sorbent, which is modified with broad-spectrum antiseptic agents (active against bacteria, bacilli, fungi, and protozoa). X-ray diffraction analysis revealed the presence of amorphous carbon in a tubostratic structure, with a predominance of sp- and sp²-hybridized carbon atoms not incorporated into regular graphene lattices. IR spectroscopy demonstrated the presence of reactive functional groups characteristic of the developed porous structure of the material, which is capable of selective sorption of antiseptic molecules. SEM surface analysis revealed an amorphous texture with a loose structure and elements in the form of spherical semi-ring formations formed by overlapping carbon plates. An experimental setup was also developed using cylindrical multichannel carbon blocks with a diameter of 48 mm, a length of 120 mm, and 100–120 longitudinal channels with a cross-section of 1 mm². The obtained results confirm the potential of the proposed material for use in air purification and disinfection systems under conditions of elevated biological risk. Full article

Ascon is a family of lightweight cryptographic algorithms designed for Authenticated Encryption with Associated Data (AEAD), hashing, and Extendable Output Functions (XOFs) in resource-constrained environments. While the AEAD variants of Ascon provide confidentiality and authenticity, they do not inherently detect replayed messages. This work presents an FPGA implementation of Ascon-128, the primary AEAD variant, on a Xilinx Artix-7 device with integrated replay detection. A 128-bit Linear Feedback Shift Register (LFSR) is used to generate a unique sequential nonce per encryption, enabling high-speed, stateless nonce generation with minimal logic complexity. At the decryption end, replay detection is performed by hashing the received nonce using Ascon-XOF128 and verifying its freshness via a Bloom Filter stored in on-chip Block RAM (BRAM). Leveraging the flexibility of Ascon-XOF128 to generate variable length outputs, our design derives all ten Bloom Filter indices from a single 256-bit XOF output using the same permutation core as the AEAD data path, thereby eliminating the need for additional hashing logic. The Bloom Filter ensures zero false negatives, and our configuration achieves a low False Positive Rate (FPR) of 0.77% theoretically and 0.17% empirically after testing 100,000 nonces, consistent with analytical models. Replay detection is fully overlapped with decryption and introduces no additional delay for messages of 64 bytes or more when using the optimized two Rounds Per Clock Cycle (RPCC) permutation core operating at 100 MHz. This architecture extends Ascon with hardware-based replay protection, offering a lightweight and scalable security solution for practical IoT deployments. Full article

Polymer residues can be reused in civil construction by partially replacing mineral aggregates in concrete, thereby reducing the extraction of natural resources. This study aimed to evaluate the use of powdered poly (ethylene terephthalate) (PET) and polyethylene (PE) residues, accumulated in shaving-mill filters during the extrusion of multilayer films used in food packaging, in the production of sealing masonry blocks. The PET/PE residues were characterized by Fourier Transform Infrared Spectroscopy (FTIR), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). Cylindrical specimens were produced in which part of the sand, by volume, was replaced with 10, 20, 30, 40 and 50% polymer residue. The cylindrical specimens were evaluated for specific mass, water absorption and axial and diametral compressive strengths. The 10% content provided the highest compressive strength. This formulation was selected for the manufacture of concrete blocks, which were evaluated and compared with the specifications of ABNT NBR 6136:2014. The concrete blocks showed potential for applications without structural function and were classified as Class C. The results, in line with previous investigations on the incorporation of plastic waste in concrete, underscore the promising application potential of this strategy. Full article

Conventional filters for air filtration typically feature compact nonwoven structures, which not only lead to high pressure drop, significant energy consumption, and a decay in filtration efficacy, but are also uncleanable, resulting in substantial pollution upon disposal. In this study, filters with high-voltage electrostatic loading capability were developed with a dopamine binding layer to facilitate the establishment of an Ag conductive layer on the surface of ultraloose woven structure fabrics (pore size: 73.7 μm). The high-voltage-loaded woven structure filtration (VLWF) system was constructed with a negative-ion zone, a high-voltage filtration zone, and a grounded filter. The morphological, chemical, and electrical properties of the filters and the filtration performance of the VLWF system were evaluated. The single-pass filtration efficiencies for PM_2.5 and E. coli were 67.4% and 97.0%, respectively. Notably, the pressure drop was reduced to 6.2 Pa, and the quality factor reached 0.1810 Pa⁻¹ with no detectable ozone release. After three cycles of ultrasonic cleaning, approximately 58.4% of filtration efficiency was maintained without any increase in air resistance. The removal of PM_2.5 and microorganisms by this system was not solely reliant on blocking and electrostatic attraction but may also involve induced repulsion and biostructure inactivation. By integrating the ultraloose woven structure with high-voltage assistance, this VLWF system effectively balanced the requirements for high filtration efficacy and low air resistance. More importantly, this VLWF system provided a cleanable filter model that reduced the pollution associated with conventional disposable filters and lowered costs for customers. Full article

Laser-Directed Energy Deposition (LDED) has recently been widely used for 3D-printing metal components and repairing high-value parts. One key performance indicator of the LDED process is represented by melt pool stability and spatter behavior. In this research study, an off-axis vision monitoring system is employed to characterize spatter formation based on different anomalies in the process. This study utilizes a 1 kW fiber laser-based LDED system equipped with a monochrome high-dynamic-range (HDR) vision camera and an SP700 Near-IR/UV Block visible bandpass filter positioned at various locations. To extract meaningful features from the original images, a novel image processing algorithm is developed to quantify spatter counts, orientation, area, and distance from the melt pool under harsh conditions. Additionally, this study analyzes the average number of spatters for different laser power settings, revealing a strong positive correlation. Validation experiments confirm over 93% detection accuracy, underscoring the robustness of the image processing pipeline. Furthermore, spatter detection is employed to assess the impact of spatter formation on deposition continuity. This research study provides a method for detecting spatters, correlating them with LDED process parameters, and predicting deposit quality. Full article

This paper presents MS-MTSA, a multi-scale multi-type self-attention network designed to enhance AV1-compressed video through targeted post-filtering. The objective is to address two persistent artifact issues observed in our previous MTSA model: visible seams at patch boundaries and grid-like distortions from upsampling. To this end, MS-MTSA introduces two key architectural enhancements. First, multi-scale block-wise self-attention applies sequential attention over 16 × 16 and 12 × 12 blocks to better capture local context and improve spatial continuity. Second, refined patch-wise self-attention includes a lightweight convolutional refinement layer after upsampling to suppress structured artifacts in flat regions. These targeted modifications significantly improve both perceptual and quantitative quality. The proposed network achieves BD-rate reductions of 12.44% for Y, 21.70% for Cb, and 19.90% for Cr compared to the AV1 anchor. Visual evaluations confirm improved texture fidelity and reduced seam artifacts, demonstrating the effectiveness of combining multi-scale attention and structural refinement for artifact suppression in compressed video. Full article

Hyperspectral imagery (HSI), with its rich spectral information across continuous wavelength bands, has become indispensable for fine-grained land cover classification in remote sensing applications. Although some existing deep neural networks have exploited the rich spectral information contained in HSIs for land cover classification by designing some adaptive learning modules, these modules were usually designed as additional submodules rather than basic structural units for building backbones, and they failed to adaptively model the spectral correlations between adjacent spectral bands and nonadjacent bands from a local and global perspective. To address these issues, a new adaptive spectral-correlation learning neural network (ASLNN) is proposed for HSI classification. Taking advantage of the group convolutional and ConvLSTM3D layers, a new adaptive spectral correlation learning block (ASBlock) is designed as a basic network unit to construct the backbone of a spatial–spectral feature extraction model for learning the spectral information, extracting the spectral-enhanced deep spatial–spectral features. Then, a 3D Gabor filter is utilized to extract heterogeneous spatial–spectral features, and a simple but effective gated asymmetric fusion block (GAFBlock) is further built to align and integrate these two heterogeneous features, thereby achieving competitive classification performance for HSIs. Experimental results from four common hyperspectral data sets validate the effectiveness of the proposed method. Specifically, when 10, 10, 10 and 25 samples from each class are selected for training, ASLNN achieves the highest overall accuracy (OA) of 81.12%, 85.88%, 80.62%, and 97.97% on the four data sets, outperforming other methods with increases of more than 1.70%, 3.21%, 3.78%, and 2.70% in OA, respectively. Full article

To facilitate intracellular growth and replication, the virulent human malaria parasite P. falciparum remodels its host erythrocyte by exporting many proteins into the host cell cytosol. Along with a few other exported proteins, the parasite CLAG3 protein is then inserted in the host erythrocyte membrane, exposing a small variant loop to host plasma and contributing to essential nutrient acquisition via the plasmodial surface anion channel (PSAC). To explore trafficking mechanisms and develop therapies that block host cell remodeling, we have now used a split NanoLuc reporter and performed a high-throughput screen for inhibitors of parasite CLAG3 trafficking and insertion at the host membrane. We screened ~52,000 small molecules and uncovered 65 chemically diverse hits. Hits that inhibit the NanoLuc reporter without blocking protein export were filtered out by a secondary screen whose signal does not depend on protein export. Because chemicals that interfere with parasite maturation were found to compromise CLAG3 export indirectly, a third screen using a NanoLuc reporter-tagged intracellular protein was used to evaluate nonspecific toxicity. Although our relatively small chemical screen did not identify bona fide inhibitors of CLAG3 host membrane insertion, these studies establish a framework for larger screens to identify novel export inhibitors. Such novel inhibitors will provide important insights into how Plasmodia remodel their host cells and may seed the development of therapies that block the export and membrane insertion of proteins needed for intracellular parasite survival. Full article

Efficient and realistic large-scale scene modeling is an important application of low-altitude remote sensing. Although the emerging 3DGS technology offers a simple process and realistic results, its high computational resource demands hinder direct application in large-scale 3D scene reconstruction. To address this, this paper proposes a novel grid-based scene-segmentation technique for the process of reconstruction. Sparse point clouds, acting as an indirect input for 3DGS, are first processed by Z-Score and a percentile-based filter to prepare the pure scene for segmentation. Then, through grid creation, grid partitioning, and grid merging, rational and widely applicable sub-grids and sub-scenes are formed for training. This is followed by integrating Hierarchy-GS’s LOD strategy. This method achieves better large-scale reconstruction effects within limited computational resources. Experiments on multiple datasets show that this method matches others in single-block reconstruction and excels in complete scene reconstruction, achieving superior results in PSNR, LPIPS, SSIM, and visualization quality. Full article

Random noise degrades the quality and reduces the interpretability of Ground Penetrating Radar (GPR) data. The Block Matching Three Dimension (BM3D) algorithm is effective at suppressing Gaussian noise, but ineffective at handling salt-and-pepper noise. On the other hand, the Improved Adaptive Median (IAM) filter is suitable for eliminating salt-and-pepper noise, but performs poorly against Gaussian noise. In this paper, we introduce and implement JBI, a joint denoising algorithm that integrates both BM3D and improved adaptive median filtering, exploiting the advantages of both algorithms to effectively remove both Gaussian and salt-and-pepper noise from GPR data. Applying the proposed joint filter to both synthetic and real field GPR data, infested with various proportions of different noise types, shows that the proposed joint denoising algorithm yields significantly better results than these two filters when used separately, and better than other commonly used denoising filters. Full article

Deepfake technology uses artificial intelligence to create highly realistic but fake audio, video, or images, often making it difficult to distinguish from real content. Due to its potential use for misinformation, fraud, and identity theft, deepfake technology has gained a bad reputation in the digital world. Recently, many works have reported on the detection of deepfake videos/images. However, few studies have concentrated on developing robust deepfake voice detection systems. Among most existing studies in this field, a deepfake voice detection system commonly requires a large amount of training data and a robust backbone to detect real and logistic attack audio. For acoustic feature extractions, Mel-frequency Filter Bank (MFB)-based approaches are more suitable for extracting speech signals than applying the raw spectrum as input. Recurrent Neural Networks (RNNs) have been successfully applied to Natural Language Processing (NLP), but these backbones suffer from gradient vanishing or explosion while processing long-term sequences. In addition, the cross-dataset evaluation of most deepfake voice recognition systems has weak performance, leading to a system robustness issue. To address these issues, we propose an acoustic feature-fusion method to combine Mel-spectrum and pitch representation based on cross-attention mechanisms. Then, we combine a Transformer encoder with a convolutional neural network block to extract global and local features as a front end. Finally, we connect the back end with one linear layer for classification. We summarized several deepfake voice detectors’ performances on the silence-segment processed ASVspoof 2019 dataset. Our proposed method can achieve an Equal Error Rate (EER) of 26.41%, while most of the existing methods result in EER higher than 30%. We also tested our proposed method on the ASVspoof 2021 dataset, and found that it can achieve an EER as low as 28.52%, while the EER values for existing methods are all higher than 28.9%. Full article

We present the performance and application of a commercial off-the-shelf Si PIN diode (Hamamatsu S14605) as a charged particle detector in a compact ion beam system (IBS) capable of generating D–D and p–B fusion charged particles. This detector is inexpensive, widely available, and operates in photoconductive mode under a reverse bias voltage of 12 V, supplied by an A23 battery. A charge-sensitive preamplifier (CSP) is mounted on the backside of the detector’s four-layer PCB and powered by two ±3 V lithium batteries (A123). Both the detector and CSP are housed together on the vacuum side of the IBS, facing the fusion target. The system employs a CF-2.75-flanged DB-9 connector feedthrough to supply the signal, bias voltage, and rail voltages. To mitigate the high sensitivity of the detector to optical light, a thin aluminum foil assembly is used to block optical emissions from the ion beam and target. Charged particles generate step responses at the preamplifier output, with pulse rise times in the order of 0.2 to 0.3 µs. These signals are recorded using a custom-built data acquisition unit, which features an optical fiber data link to ensure the electrical isolation of the detector electronics. Subsequent digital signal processing is employed to optimally shape the pulses using a CR-RCⁿ filter to produce Gaussian-shaped signals, enabling the accurate extraction of energy information. Performance results indicate that the detector’s baseline RMS ripple noise can be as low as 0.24 mV. Under actual laboratory conditions, the estimated signal-to-noise ratios (S/N) for charged particles from D–D fusion—protons, tritons, and helions—are approximately 225, 75, and 41, respectively. Full article