Search Results (6,375)

Methodological fusion of materials chemistry, which enables us to create materials, with nanotechnology, which enables us to control nanostructures, could enable us to create advanced functional materials with well controlled nanostructures. Positioned as a post-nanotechnology concept, nanoarchitectonics will enable this purpose. This review paper highlights the broad scope of applications of the new concept of nanoarchitectonics, selecting and discussing recent papers that contain the term ‘nanoarchitectonics’ in their titles. Topics include controls of dopant atoms in solid electrolytes, transforming the framework of carbon materials, single-atom catalysts, nanorobots and microrobots, functional nanoparticles, nanotubular materials, 2D-organic nanosheets and MXene nanosheets, nanosheet assemblies, nitrogen-doped carbon, nanoporous and mesoporous materials, nanozymes, polymeric materials, covalent organic frameworks, vesicle structures from synthetic polymers, chirality- and topology-controlled structures, chiral helices, Langmuir monolayers, LB films, LbL assembly, nanocellulose, DNA, peptides bacterial cell components, biomimetic nanoparticles, lipid membranes of protocells, organization of living cells, and the encapsulation of living cells with exogenous substances. Not limited to these examples selected in this review article, the concept of nanoarchitectonics is applicable to diverse materials systems. Nanoarchitectonics represents a conceptual framework for creating materials at all levels and can be likened to a method for everything in materials science. Developing technology that can universally create materials with unexpected functions could represent the final frontier of materials science. Nanoarchitectonics will play a significant part in achieving this final frontier in materials science. Full article

Feature selection is one of the key stages in the process of creating machine learning models and conducting data analysis. This paper presents the results of research related to the implementation of a new algorithm for feature selection and ranking based on weighted interval aggregations. It took into account interval importance values obtained from dividing the dataset into subsets. The algorithm was highly effective in identifying relevant features. The results of comparative studies with nine known methods of feature importance assessment are presented. Ten synthetic datasets and five real datasets were used for the experiments. The calculations also included tests of the relevance of the results obtained. In most experiments, the IVWFR algorithm proved to be the best, achieving the best classification results after identifying subsets of relevant features. Full article

In marine seismic exploration, ghost waves distort reflection waveforms and narrow the frequency band of seismic records. Traditional deghosting methods are susceptible to practical limitations from sea surface fluctuations and velocity variations. This paper proposes a τ-p domain deghosting method based on the Hybrid Least Squares Residual (HyBR LSMR) algorithm. We first establish a linear forward model in the τ-p domain that describes the relationship between the total wavefield and upgoing wavefield, transforming deghosting into a linear inverse problem. The method then employs the hybrid LSMR algorithm with Tikhonov regularization to address the inherent ill-posedness. A key innovation is the integration of the Generalized Cross Validation (GCV) criterion to adaptively determine regularization parameters and iteration stopping points, effectively avoiding the semi-convergence phenomenon and enhancing solution stability. Applications to both synthetic and field data demonstrate that the proposed method effectively suppresses ghost waves under various acquisition conditions, significantly improves the signal-to-noise ratio and resolution, broadens the effective frequency band, and maintains good computational efficiency, providing a reliable solution for high-precision seismic data processing in complex marine environments. Full article

Cloud contamination, especially thick cloud cover, severely limits the usability of optical remote sensing imagery by obscuring surface information. Due to the strong penetrability of microwave signals, Synthetic Aperture Radar (SAR) has emerged as an effective source for thick cloud removal. While SAR-assisted deep learning methods, such as CNNs and GANs, have made notable progress, the quality of generated imagery still requires improvement. Diffusion models, which offer strong potential for enhancing generation fidelity, could address this limitation but suffer from slow sampling speeds that constrain practical use and underscore the need for greater efficiency. To simultaneously enhance both reconstruction quality and sampling efficiency, this paper proposes a fast-sampling SAR-conditioned consistency model based on consistency distillation, named CM-CR, which adopts a teacher–student architecture to divide the reconstruction process into a rapid coarse prediction stage and a detailed refinement stage, significantly reducing per-scene processing time while maintaining high reconstruction fidelity. Specifically, a SAR-Conditioned Score-Based Diffusion Model (SCSBD) is first developed as the teacher network for learning a SAR-conditioned optical image generation model. Consistency distillation is then used to derive the student network SAR-conditioned consistency model (SCCM), which enables a rapid coarse prediction through single-step sampling. Finally, a Progressive Denoising via Multistep Resampling (PDMSR) strategy is introduced to iteratively refine the single-step output, producing fine-grained reconstructions. Comparative experiments conducted on the widely used cloud removal benchmark dataset SEN12MS-CR demonstrate that the proposed CM-CR method achieves state-of-the-art (SOTA) performance across all image quality metrics. Notably, although its design uses approximately 80 times more parameters compared with a standard Denoising Diffusion Probabilistic Model (DDPM), it delivers up to a 40-fold acceleration at inference. Full article

Background/Objectives: Lower-grade gliomas (LGGs) are a biologically and clinically heterogeneous group of brain tumors, for which molecular stratification plays essential role in diagnosis, prognosis, and therapeutic decision-making. Conventional unimodal classifiers do not necessarily describe cross-layer regulatory dynamics which entail the heterogeneity of glioma. Methods: This paper presents a protein–protein interaction (PPI)-informed hybrid model that combines multi-omics profiles, including RNA expression, DNA methylation, and microRNA expression, with a Graph Attention Network (GAT), Random Forest (RF), and logistic stacking ensemble learning. The proposed model utilizes ElasticNet-based feature selection to obtain the most informative biomarkers across omics layers, and the GAT module learns the biologically significant topological representations in the PPI network. The Synthetic Minority Over-Sampling Technique (SMOTE) was used to mitigate the class imbalance, and the model performance was assessed using a repeated five-fold stratified cross-validation approach using the following performance metrics: accuracy, precision, recall, F1-score, ROC-AUC, and AUPRC. Results: The findings illustrate that a combination of multi-omics data increases subtype classification rates (up to 0.984 ± 0.012) more than single-omics methods, and DNA methylation proves to be the most discriminative modality. In addition, analysis of interpretability using attention revealed the major subtype-specific biomarkers, including UBA2, LRRC41, ANKRD53, and WDR77, that show great biological relevance and could be used as diagnostic and therapeutic tools. Conclusions: The proposed multi-omics based on a biological and explainable framework provides a solid computational approach to molecular stratification and biomarker identification in lower-grade glioma, bridging between predictive power, biological clarification, and clinical benefits. Full article

Tenebrio molitor is a common stored grains pest. The conventional way for its management involves the use of synthetic fumigants. Despite their effectiveness, these can cause environmental damage. The use of essential oils has emerged as an alternative for its management. Therefore, the aim of this study was to assess Lantana camara essential oil (EO) and endo-borneol biological activities against T. molitor. Insecticidal activity and weight gain were evaluated through the impregnated paper method against larvae and adults, while repellency was conducted with a Y-tube olfactometer; L. camara EO showed higher mortality for T. molitor adults (LC₅₀ = 7.2 μL EO L⁻¹ air) than for larvae (LC₅₀ = 13.7 μL EO L⁻¹ air) after 30 d. Furthermore, L. camara EO was found to be repellent for T. molitor adults (RC₅₀ = 0.08 μL EO cm⁻²). Regarding the EO composition, endo-borneol was identified by GC-MS as a major compound with 14.24% abundance. Larvae exhibited higher susceptibility (LC₅₀ = 7.8 μL L⁻¹ air) to endo-borneol than adults (LC₅₀ = 46 μL L⁻¹ air) after 72 h. Notably, endo-borneol demonstrated significantly higher repellent activity (RC₅₀ = 0.03 μL cm⁻²) than L. camara EO (RC₅₀ = 0.08 μL EO cm⁻²). These findings suggest that endo-borneol has potential as a natural source alternative for T. molitor management. Full article

Very-Low Earth Orbit Synthetic Aperture Radar (VLEO SAR) satellites, defined as SAR satellites operating at orbital altitudes 350 km or below, offer distinct technical advantages compared to conventional SAR satellites. Equipped with a high-resolution SAR payload, the Haishao-1 (HS-1) satellite was successfully launched on 4 December 2024. According to publicly available information, the HS-1 satellite represents the world’s first VLEO SAR satellite and has successfully demonstrated 1-m resolution Stripmap mode imaging with continuous azimuth coverage. Through an analysis of the HS-1 satellite’s system parameters and imaging results, this paper comprehensively explores the advantages of VLEO SAR satellites over traditional orbit SAR satellites, particularly in terms of enhanced resolution, reduced payload costs, and improved constellation deployment capabilities. VLEO SAR satellites possess significant advantages, including the potential for higher-resolution imagery and lower-cost payload designs, positioning them for extensive application prospects in fields such as space-based military reconnaissance, natural resource surveying, and natural disaster monitoring. Full article

In seismic data processing, noise not only affects velocity analysis and seismic migration, but also causes potential risks in post-stack processing because of the artifacts. The singular value decomposition (SVD) method based on the time domain and the frequency domain is effective for noise suppression, but it is very sensitive to singular value selection. This paper proposes a method of adaptive SVD denoising in both time and frequency domains (ASTF), with three steps. Firstly, two Hankel matrices are constructed in the time domain and frequency domain, respectively. Secondly, the parameters of the reconstruction matrix are adaptively selected based on the singular value second-order difference spectrum. Finally, the weights of these two matrices are learned through ternary search. Experiments were carried out on synthetic data and field data to prove the effectiveness of ASTF. The results show that this method can effectively suppress noise. Full article

High-quality 3D objects play a crucial role in digital twins, while synthetic data generated from these objects have become essential in deep learning-based computer vision applications. The task of collecting and labeling real defects on industrial object surfaces has many challenges and efforts, while synthetic data generation feasibly replicates huge amounts of labeled data. However, synthetic datasets lack realism in their rendered images. To overcome this issue, this paper introduces a single framework for 3D industrial object reconstruction and synthetic defect generation for digital twin smart factory applications. In detail, NeRF is applied to reconstruct our custom industrial 3D objects through videos collected by a smartphone camera. Several NeRF-based models (i.e., Instant-NGP, Nerfacto, Volinga, and Tensorf) are compared to choose the best outcome for the next step of defect generation on the 3D object surface. To be fairly evaluated, we train four models using the Nerfstudio framework with our three custom datasets of two objects. From the experiment’s results, Instant-NGP and Nerfacto achieve the best outcomes, outperforming all other methods significantly. The exported meshes of 3D objects are refined using Blender before loading into NVIDIA Omniverse Code to generate defects on the surface with the Replicator. To evaluate the object detection performance and to verify the benefits of synthetic defect data, we conducted experiments with YOLO-based models on our synthetic and real-plus-synthetic defects. From the experiment’s results, the synthetic defect data contribute to improving YOLO models’ generalization capability with the highest and lowest accuracy mAP@0.5 enhancement of 18.8 and 1.5 percent on YOLOv6n and YOLOv8s, respectively. Full article

The biological accumulation of microcontaminants and associated antibiotic resistance in food poses significant threats to both human and environmental health. Therefore, it is particularly crucial to design and develop methods of efficient identification and detection. Recently, molecularly imprinted polymers (MIPs) and aptamers (Apts), as novel hybrid recognition elements, have received widespread attention from researchers. Because the dual recognition-based sensors have demonstrated enhanced performance and desirable characteristics, including high sensitivity, strong binding affinity, a low detection limit, and excellent stability under harsh environmental conditions, which are expected to be applied in food safety fields. This paper compares the characteristics of MIP and Apt, highlighting the significant advantages of molecularly imprinted polymer–aptamer (MIP-Apt) dual recognition in selectivity, sensitivity, and stability, which stems from their symmetric integration, akin to an extension of the ‘lock-and-key’ model. It then systematically discusses three synthetic strategies for MIP-Apt hybrid recognition systems and their applications for food safety detection, focusing on analyzing their detection strategies, sensing mechanisms, construction methodologies, performance evaluations, and potential application value. It also offers substantive perspectives on both the prevailing limitations and promising developmental pathways for MIP-Apt hybrid recognition-based sensing platforms. Full article

This paper presents a terahertz (THz) dual-band transmitter for a wideband sparse synthetic bandwidth radar. The transmitter employs an innovative single-path-reuse dual-band architecture. This architecture utilizes a proposed quad-transformer-coupled voltage-controlled oscillator (VCO) as an on-chip local oscillator source. It also incorporates an innovative dual-harmonic generator and a dual-band antenna, which work together within the single signal path to generate both the fundamental frequency and its second harmonic, thereby creating the dual bands required for a sparse synthetic bandwidth radar. Fabricated in a TSMC 40 nm CMOS technology, measurement results show that the transmitter achieves a peak equivalent isotropically radiated power (EIRP) of −7.95 dBm in the low-frequency band (121.34∼126.85 GHz) and −7.86 dBm in the high-frequency band (242.68∼253.7 GHz), validating the proposed architecture’s capability to generate dual-band signals simultaneously. The entire chip occupies a compact area of only 0.54 × 0.62 ${\mathrm{m}\mathrm{m}}^2$ and consumes 136 mW of DC power. Full article

Terahertz synthetic aperture radar (SAR) can achieve high-resolution imaging of the target area through a large bandwidth, while squint imaging can flexibly detect the target area by adjusting the beam direction. However, the two-dimensional coupling effect intensifies under squint conditions, making it challenging for traditional frequency domain algorithms for high-resolution imaging. This paper analyzes the Doppler variations and proposes a two-dimensional decoupling algorithm for squint SAR imaging in the terahertz band. The proposed algorithm decouples in the time domain and combines the improved frequency scaling operation with the azimuthal nonlinear frequency scaling operation to obtain the focused SAR image. Compared to the Range Doppler algorithm and nonlinear frequency scaling algorithm, the simulation and experimental results verified the effectiveness of the proposed algorithm, which demonstrates the application potential for squint SAR imaging in the terahertz band. Full article

Outlier detection is a critical task in the intelligent operation and maintenance (O&M) of transportation equipment, as it helps ensure the safety and reliability of systems like high-speed trains, aircraft, and intelligent vehicles. Nearest neighbor-based detectors generally offer good interpretability, but often struggle with complex data scenarios involving diverse data distributions and various types of outliers, including local, global, and cluster-based outliers. Moreover, these methods typically rely on predefined contamination, which is a critical parameter that directly determines detection accuracy and can significantly impact system reliability in O&M environments. In this paper, we propose a novel chain-based theory for outlier detection with the aim to provide an interpretable and transparent solution for fault detection. We introduce two methods based on this theory: Cascaded Chain Outlier Detection (CCOD) and Parallel Chain Outlier Detection (PCOD). Both methods identify outliers through sudden increases in chaining distances, with CCOD being more sensitive to local data distributions, while PCOD offers higher computational efficiency. Experimental results on synthetic and real-world datasets demonstrate the superior performance of our methods compared to existing state-of-the-art techniques, with average improvements of 11.3% for CCOD and 14.5% for PCOD. Full article

Domain adaptation in image semantic segmentation has attracted more and more attention from computer vision and machine learning researchers. While the high cost of manual annotation is an unavoidable bottleneck in the semantic segmentation task, it is a high-quality solution to adopt pixel-level annotation from synthetic data, which provides additional support for deep learning training. Numerous studies have attempted to comprehensively investigate deep domain adaptation, but there is less focus on the sub-direction of the semantic segmentation task. This paper is devoted to this new topic in transfer learning. First, we describe the terminology and background concepts in this field. Next, the main datasets and evaluation metrics are introduced. Then, we classify the current research methods and introduce their contributions. Moreover, the quantitative results of the methods involved are compared, and the results are discussed. Finally, we suggest future research directions for this field. We believe researchers who are interested in this field will find this work to be an effective reference. Full article

Cationic polyelectrolytes, characterized by positively charged functional groups, play an essential role in industries ranging from food solutions, water treatment, medical, cosmetic, textiles and agriculture due to their electrostatic interactions, biocompatibility, and functional versatility. This paper critically examines the transition from petroleum-based synthetic polymers such as poly(diallyldimethylammonium chloride) and cationic polyacrylamides to sustainable natural alternatives derived from agri-forestry resources like starch derivatives and cellulose. Through a cradle-to-gate life cycle assessment, we highlight the superior renewability, biodegradability, and lower carbon footprint of bio-based polycations, despite challenges in agricultural sourcing and processing. This study examines cationization processes by comparing the environmental limitations of traditional chemical methods, such as significant waste production and limited scalability, with those of second-generation reactive extrusion (REX), which enables solvent-free and rapid modification. REX also allows for adjustable degrees of substitution and ensures uniform charge distribution, thereby enhancing overall functional performance. Groundbreaking research and optimization achieved through the integration of artificial intelligence and machine learning for parameter regulation and targeted mechanical energy management underscore REX’s strengths in precision engineering. By methodically addressing current limitations and articulating future advancements, this work advances sustainable innovation that contributes to a circular economy in materials science. Full article