Search Results (4,343)

In order to improve the inversion accuracy of electrical resistivity tomography (ERT) and overcome the limitations of traditional linear methods, this paper proposes an improved shuffled frog leaping algorithm (SFLA). First, an equilibrium grouping strategy is designed to balance the contribution weight of each subgroup to the global optimal solution, suppressing the local optimum traps caused by the dominance of high-quality groups. Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. Additionally, the root mean square error is reduced by 57%. In the engineering validation at the Baota Mountain mining area in Jurong, the improved SFLA inversion clearly reveals the undulating bedrock morphology. At a measuring point 55 m along the profile, the bedrock depth is 14.05 m (ZK3 verification value 12.0 m, error 17%), and at 96 m, the depth is 6.9 m (ZK2 verification value 6.7 m, error 3.0%). The characteristic of deeper bedrock to the south and shallower to the north is highly consistent with the terrain and drilling data (RMSE = 1.053). This algorithm provides reliable technical support for precise detection of complex geological structures using ERT. Full article

This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

Background/Objectives: The incorporation of bioactive molecules into mesoporous carriers is a promising strategy to improve stability, solubility, and therapeutic efficacy. In this study, we report for the first time the encapsulation of the synthetic chalcone 4-Cl into KIT-6 mesoporous silica and evaluate its cytotoxicity, toxicological profile, and pharmacological activities (antinociceptive, anti-inflammatory, and anxiolytic) using an in vivo zebrafish (Danio rerio) model. Methods: Zebrafish were orally dosed with 4-Cl, 4-Cl/KIT-6, or KIT-6 (4, 20, 40 mg/kg) and mortality was recorded for 96 h. For analgesia, zebrafish pretreated with 4-Cl, 4-Cl/KIT-6, KIT-6, or morphine received a tail stimulus (0.1% formalin). Locomotor activity (quadrant crossings) was monitored for 30 min to assess analgesia (neurogenic: 0–5 min; inflammatory: 15–30 min). For inflammation, abdominal edema and weight gain were assessed 4 h after intraperitoneal carrageenan (1.5%). Zebrafish (n = 6/group) received 4-Cl, 4-Cl/KIT-6, or KIT-6 (4, 20, 40 mg/kg, p.o.). Controls received ibuprofen (100 mg/kg, p.o.) or 3% DMSO. Weight was measured hourly for 4 h post-carrageenan (difference between baseline and hourly weights). Results: Physicochemical characterizations confirmed successful encapsulation without compromising the ordered structure of KIT-6, as evidenced by a significant reduction in surface area and pore volume, indicating efficient drug incorporation. In vivo assays demonstrated that the 4-Cl/KIT-6 formulation maintained the pharmacological activities of the free chalcone, reduced toxicity, and, notably, revealed a significant anxiolytic effect for the first time. Conclusions: These findings highlight KIT-6 as a promising platform for chalcone delivery systems and provide a solid basis for future preclinical investigations. Full article

Non-destructive evaluation of reinforced concrete structures is essential for effective maintenance and safety assessments. This study explores the combined use of active microwave thermography and deep learning to detect and localize steel reinforcement within concrete elements. Numerical simulations were developed to model the thermal response of reinforced concrete subjected to microwave excitation, generating synthetic thermal images representing the surface temperature patterns of reinforced concrete, influenced by subsurface steel reinforcement. These images served as training data for a deep neural network designed to identify and localize rebar positions based on thermal patterns. The model was trained exclusively on simulation data and subsequently validated using experimental measurements obtained from large-format concrete slabs incorporating a structured layout of embedded steel reinforcement bars. Surface temperature distributions obtained through infrared imaging were compared with model predictions to evaluate detection accuracy. The results demonstrate that the proposed method can successfully identify the presence and approximate location of internal reinforcement without damaging the concrete surface. This approach introduces a new pathway for contactless, automated inspection using a combination of physical modeling and data-driven analysis. While the current work focuses on rebar detection and localization, the methodology lays the foundation for broader applications in non-destructive testing of concrete infrastructure. Full article

Synthetic nanopores were recently demonstrated with memristive and nonlinear voltage-current behaviors, akin to ion channels in a cell membrane. Such ionic devices are considered a promising candidate for the development of brain-inspired neuromorphic computing techniques. In this work, we show the composite behavior of nanopore-array large memristors, formed with different membrane materials, pore sizes, electrolytes, and device arrangements. Anodic aluminum oxide (AAO) membranes with 5 nm and 20 nm diameter pores and track-etched polycarbonate (PCTE) membranes with 10 nm diameter pores are tested and shown to demonstrate memristive and nonlinear behaviors with approximately 10⁷–10¹⁰ pores in parallel when electrolyte concentration across the membranes is asymmetric. Ion diffusion through the large number of channels induces time-dependent electrolyte asymmetry that drives the system through different memristive states. The behaviors of series composite memristors with different configurations are also presented. In addition to helping understand fluidic devices and circuits for neuromorphic computing, the results also shed light on the development of field-assisted ion-selection-membrane filtration techniques as well as the investigations of large neurons and giant synapses. Further work is needed to de-embed parasitic components of the measurement setup to obtain intrinsic large memristor properties. Full article

Bisphenols (BPs), particularly bisphenol A (BPA) and its structural analogues, are synthetic compounds widely used in plastics and industrial materials. These substances are also recognised as endocrine-disrupting chemicals (EDCs) due to their ability to interfere with hormonal systems, which has significant implications for aquatic organisms. This review summarises the occurrence, environmental distribution, and toxicity of BPs in fish, with a focus on estrogenic, androgenic, thyroid, and glucocorticoid disruptions. Studies consistently show that exposure to BPs leads to altered gene expression, developmental abnormalities, impaired reproduction, and disrupted hormonal signalling in various fish species. Although BPA alternatives like bisphenol S, bisphenol F, or bisphenol AF were introduced as safer options, emerging evidence suggests they may pose equal or greater risks. Regulatory measures are evolving, particularly within the European Union, but legislation remains limited for many bisphenol analogues. This review emphasises the need for comprehensive environmental monitoring, stricter regulatory frameworks, and the development of genuinely safer alternatives to minimise the ecological and health impacts of BPs in aquatic systems. Full article

Magnetic nanoparticles (MNPs), especially iron oxide (Fe₃O₄), display distinctive superparamagnetic characteristics and elevated surface-area-to-volume ratios, facilitating improved physicochemical interactions with solutes and pollutants. These characteristics make MNPs strong contenders for use in water treatment applications. This research investigates the application of iron oxide MNPs synthesized via co-precipitation as innovative draw solutes in forward osmosis (FO) for treating synthetic produced water (SPW). The FO membrane underwent surface modification with sulfobetaine methacrylate (SBMA), a zwitterionic polymer, to increase hydrophilicity, minimize fouling, and elevate water flux. The SBMA functional groups aid in electrostatic repulsion of organic and inorganic contaminants, simultaneously encouraging robust hydration layers that improve water permeability. This adjustment is vital for sustaining consistent flux performance while functioning with MNP-based draw solutions. Material analysis through thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR) verified the MNPs’ thermal stability, consistent morphology, and modified surface chemistry. The FO experiments showed a distinct relationship between MNP concentration and osmotic efficiency. At an MNP dosage of 10 g/L, the peak real-time flux was observed at around 3.5–4.0 L/m²·h. After magnetic regeneration, 7.8 g of retrieved MNPs generated a steady flow of ~2.8 L/m²·h, whereas a subsequent regeneration (4.06 g) resulted in ~1.5 L/m²·h, demonstrating partial preservation of osmotic driving capability. Post-FO draw solutions, after filtration, exhibited total dissolved solids (TDS) measurements that varied from 2.5 mg/L (0 g/L MNP) to 227.1 mg/L (10 g/L MNP), further validating the effective dispersion and solute contribution of MNPs. The TDS of regenerated MNP solutions stayed similar to that of their fresh versions, indicating minimal loss of solute activity during the recycling process. The combined synergistic application of SBMA-modified FO membranes and regenerable MNP draw solutes showcases an effective and sustainable method for treating produced water, providing excellent water recovery, consistent operational stability, and opportunities for cyclic reuse. Full article

Background/Objectives: X-ray computed tomography (XCT) is a powerful tool for volumetric imaging, where three-dimensional (3D) images are generated from a large number of individual X-ray projection images. However, collecting the required number of low-noise projection images is time-consuming, limiting its applicability to scenarios requiring high temporal resolution, such as the study of dynamic processes. Inspired by stereo vision, we previously developed stereo X-ray imaging methods that operate with only two X-ray projections, enabling the 3D reconstruction of point and line fiducial markers at significantly faster temporal resolutions. Methods: Building on our prior work, this paper demonstrates the use of stereo X-ray techniques for 3D reconstruction of sharp object corners, eliminating the need for internal fiducial markers. This is particularly relevant for deformation measurement of manufactured components under load. Additionally, we explore model training using synthetic data when annotated real data is unavailable. Results: We show that the proposed method can reliably reconstruct sharp corners in 3D using only two X-ray projections. The results confirm the method’s applicability to real-world stereo X-ray images without relying on annotated real training datasets. Conclusions: Our approach enables stereo X-ray 3D reconstruction using synthetic training data that mimics key characteristics of real data, thereby expanding the method’s applicability in scenarios with limited training resources. Full article

The present investigation highlights the importance of evaluating the painting process on a composite material, namely the Kevlar validation process. Kevlar, a synthetic fabric, is well known for its remarkable strength and durability. Kevlar is used in the construction of spaceships and airplanes because it is lightweight and five times stronger than steel. This paper will present the methods for measuring paint layer thickness in accordance with EN ISO 2808:2019, confirming that organic coatings have fully cured, and coating thickness will be measured using magnetic currents. This study will also address the topic of determining liquid resistance. The protocols for manufacturing the Kevlar specimen are in accordance with ISO 2812-2:2018 using the water immersion method and structural testing. The investigation also demonstrates the progress of the framing test following immersion in Airbus PTP metal test tubes. Full article

Ammonia (NH₃) emissions affect the environment, climate and human health and originate mainly from agricultural sources like synthetic nitrogen fertilizers. Accurate and replicable measurements of NH₃ emissions are crucial for research, inventories and evaluation of mitigation measures. There exist specific application limitations of NH₃ emission measurement techniques and a high variability in method performance between studies, in particular from small plots. Therefore, the aim of this study was the assessment of measurement methods for ammonia emissions from replicated small plots. Methods were evaluated in 18 trials on six sites in Germany (2021–2022). Urea was applied to winter wheat as an emission source. Two small-plot methods were employed: inverse dispersion modelling (IDM) with atmospheric concentrations obtained from Alpha samplers and the dynamic chamber Dräger tube method (DTM). Cumulative NH₃ losses assessed by each method were compared to the results of the integrated horizontal flux (IHF) method using Alpha samplers (Alpha IHF) as a micrometeorological reference method applied in parallel large-plot trials. For validation, Alpha IHF was also compared to IHF/ZINST with Leuning passive samplers. Cumulative NH₃ emissions assessed using Alpha IHF and DTM showed good agreement, with a relative root mean square error (rRMSE) of 11%. Cumulative emissions assessed by Leuning IHF/ZINST deviated from Alpha IHF, with an rRMSE of 21%. For low-wind-speed and high-temperature conditions, NH₃ losses detected with Alpha IDM had to be corrected to give acceptable agreement (rRMSE 20%, MBE +2 kg N ha⁻¹). The study shows that quantification of NH₃ emissions from small plots is feasible. Since DTM is constrained to specific conditions, we recommend Alpha IDM, but the approach needs further development. Full article

The Online Droplet Inspection system plays a vital role in closed-loop control for OLED inkjet printing. However, generating realistic synthetic droplet images for reliable restoration and precise measurement of droplet parameters remains challenging due to the complex, multi-factor degradation inherent to microscale droplet imaging. To address this, we propose a physics-informed degradation model, Diffraction–Gaussian–Motion–Noise (DGMN), that integrates Fraunhofer diffraction, defocus blur, motion blur, and adaptive noise to replicate real-world degradation in droplet images. To optimize the multi-parameter configuration of DGMN, we introduce the MISABO (Multi-strategy Improved Subtraction-Average-Based Optimizer), which incorporates Sobol sequence initialization for search diversity, lens opposition-based learning (LensOBL) for enhanced accuracy, and dimension learning-based hunting (DLH) for balanced global–local optimization. Benchmark function evaluations demonstrate that MISABO achieves superior convergence speed and accuracy. When applied to generate synthetic droplet images based on real droplet images captured from a self-developed OLED inkjet printer, the proposed MISABO-optimized DGMN framework significantly improves realism, enhancing synthesis quality by 37.7% over traditional manually configured models. This work lays a solid foundation for generating high-quality synthetic data to support droplet image restoration and downstream inkjet printing processes. Full article

Multiple cluster validity indices (CVIs) have been introduced for diverse applications. In practice, clusters exhibit varying shapes, sizes, densities, and closely spaced centers, which are typically unknown beforehand. It is desirable to develop a versatile CVI that performs well in general settings rather than being tailored to specific ones. Drawing inspiration from distance based on local density, where it is observed that cluster centers feature higher densities than their neighbors and are relatively distant from higher-density points, this paper introduces a novel CVI. This CVI employs a modified distance, adjusted for local density, to measure cluster compactness, replacing the traditional Euclidean distance with the minimum distance to a higher-density point. This adjustment accounts for cluster shapes and densities. The experimental results highlight the proposed index’s dual capability: it not only outperforms conventional methods by a remarkable margin of 32 percentage points in controlled synthetic environments but also maintains a 23+ percentage-point accuracy lead in real-world data regimes characterized by noise and heterogeneity. This consistency validates its generalizability across data modalities. Full article

With advancements in radar sensors, communications, and computer technologies, alongside an increasing number of ground observation tasks, Synthetic Aperture Radar (SAR) remote sensing is transitioning from being theory and technology-driven to being application-demand-driven. Since the late 1960s, Interferometric Synthetic Aperture Radar (InSAR) theories and techniques have continued to develop. They have been applied significantly in various fields, such as in the generation of global topography maps, monitoring of ground deformation, marine observations, and disaster reduction efforts. This article classifies InSAR into repeated-pass interference and single-pass interference. Repeated-pass interference mainly includes D-InSAR, PS-InSAR and SBAS-InSAR. Single-pass interference mainly includes CT-InSAR and AT-InSAR. Recently, China has made significant progress in the field of SAR satellite development, successfully launching several satellites equipped with interferometric measurement capabilities. These advancements have driven the evolution of spaceborne InSAR systems from single-frequency to multi-frequency, from low Earth orbit to higher orbits, and from single-platform to multi-platform configurations. These advancements have supported high precision and high-temporal-resolution land observation, and promoted the broader application of InSAR technology in disaster early warning, ecological monitoring, and infrastructure safety. Full article

With the growing number and diversity of network attacks, traditional security measures such as firewalls and data encryption are no longer sufficient to ensure robust network protection. As a result, intrusion detection systems (IDSs) have become a vital component in defending against evolving cyber threats. Although many modern IDS solutions employ machine learning techniques, they often suffer from low detection rates and depend heavily on manual feature engineering. Furthermore, most IDS models are designed to identify only a limited set of attack types, which restricts their effectiveness in practical scenarios where a network may be exposed to a wide array of threats. To overcome these limitations, we propose a novel approach to IDSs by implementing a combined dataset framework based on an enhanced hybrid principal component analysis–Transformer (PCA–Transformer) model, capable of detecting 21 unique classes, comprising 1 benign class and 20 distinct attack types across multiple datasets. The proposed architecture incorporates enhanced preprocessing and feature engineering, followed by the vertical concatenation of the CSE-CIC-IDS2018 and CICIDS2017 datasets. In this design, the PCA component is responsible for feature extraction and dimensionality reduction, while the Transformer component handles the classification task. Class imbalance was addressed using class weights, adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN). Experimental results show that the model achieves 99.80% accuracy for binary classification and 99.28% for multi-class classification on the combined dataset (CSE-CIC-IDS2018 and CICIDS2017), 99.66% accuracy for binary classification and 99.59% for multi-class classification on the CSE-CIC-IDS2018 dataset, 99.75% accuracy for binary classification and 99.51% for multi-class classification on the CICIDS2017 dataset, and 99.98% accuracy for binary classification and 98.01% for multi-class classification on the NF-BoT-IoT-v2 dataset, significantly outperforming existing approaches by distinguishing a wide range of classes, including benign and various attack types, within a unified detection framework. Full article