Search Results (7,635)

Combining an Internet of Things-driven approach with machine learning algorithms holds great promise in discerning pure gases across various applications. Interconnecting gas sensors within a network allows for continuous monitoring and real-time environmental analysis, producing valuable data for machine learning models. Utilizing supervised learning algorithms, like random forests, enables the creation of accurate classification models that can effectively distinguish between different pure gases based on their distinct features, such as spectral signatures or sensor responses. This groundbreaking integration of the Internet of Things and Machine Learning fosters the development of robust, automated gas detection systems, ensuring high accuracy and minimal delay in recognizing pure gases. Consequently, it opens avenues for enhanced safety, efficiency, and environmental sustainability in numerous industrial and commercial scenarios. Full article

The electronic absorption spectral characteristics of cycloimmonium ylids with a zwitterionic structure have been analyzed in forty-three solvents with different hydrogen bonding abilities. The two ylids lack fluorescence emission but are very dynamic in electronic absorption spectra. Using the maximum of the ICT band, the goal was to establish an accurate relationship between the shift of the ICT visible band and the solvent parameters and to estimate two of the descriptors of the first (the) excited state: the dipole moment and the polarizability. Two procedures were involved: the variational method and the relationships of the Abe model. The results indicate that the excited state dipole moment of the two methylids decreases in the absorption process in comparison with the ground state. The introduction of a correction term in the Abe model that neglects the intermolecular H-bonding interactions leads to a more accurate determination of the two descriptors. The strong solvatochromic response of both ylids has been further applied in distinguishing the solvents as a function of their specific parameters. Principal component analysis was applied to five selected properties, including the maximum of the charge transfer band. The results were further applied to discriminate several binary solvent mixtures. Full article

Rice is a globally important food crop, and it is crucial to accurately and conveniently obtain information on rice fields, understand their spatial patterns, and grasp their dynamic changes to address food security challenges. In this study, Chongqing’s Yongchuan District was selected as the research area. By utilizing UAVs (Unmanned Aerial Vehicles) to collect multi-spectral remote sensing data during three seasons, the phenological characteristics of rice fields were analyzed using the NDVI (Normalized Difference Vegetation Index). Based on Sentinel data with a resolution of 10 m, the NDVI difference method was used to extract rice fields between 2019 and 2023. Furthermore, the reasons for changes in rice fields over the five years were also analyzed. First, a simulation model of the rice harvesting period was constructed using data from 32 sampling points through multiple regression analysis. Based on the model, the study area was classified into six categories, and the necessary data for each region were identified. Next, the NDVI values for the pre-harvest and post-harvest periods of rice fields, as well as the differences between them, were calculated for various regions. Additionally, every year, 35 samples of rice fields were chosen from high-resolution images provided by Google. The thresholds for extracting rice fields were determined by statistically analyzing the difference in NDVI values within the sample area. By utilizing these thresholds, rice fields corresponding to six harvesting regions were extracted separately. The rice fields extracted from different regions were merged to obtain the rice fields for the study area from 2019 to 2023, and the accuracy of the extraction results was verified. Then, based on five years of rice fields in the study area, we analyzed them from both temporal and spatial perspectives. In the temporal analysis, a transition matrix of rice field changes and the calculation of the rice fields’ dynamic degree were utilized to examine the temporal changes. The spatial changes were analyzed by incorporating DEM (Digital Elevation Model) data. Finally, a logistic regression model was employed to investigate the causes of both temporal and spatial changes in the rice fields. The study results indicated the following: (1) The simulation model of the rice harvesting period can quickly and accurately determine the best period of remote sensing images needed to extract rice fields. (2) The confusion matrix shows the effectiveness of the NDVI difference method in extracting rice fields. (3) The total area of rice fields in the study area did not change much each year, but there were still significant spatial adjustments. Over the five years, the spatial distribution of gained rice fields was relatively uniform, while the lost rice fields showed obvious regional differences. In combination with the analysis of altitude, it tended to grow in lower areas. (4) The logistic regression analysis revealed that gained rice fields tended to be found in regions with convenient irrigation, flat terrain, lower altitude, and proximity to residential areas. Conversely, lost rice fields were typically located in areas with inconvenient irrigation, long distance from residential areas, low population, and negative topography. Full article

Liver fibrosis, a major global health issue, is marked by excessive collagen deposition that impairs liver function. Noninvasive methods for the direct visualization of collagen content are crucial for the early detection and monitoring of fibrosis progression. This study investigates the potential of spectral photoacoustic imaging (sPAI) to monitor collagen development in liver fibrosis. Utilizing a novel data-driven superpixel photoacoustic unmixing (SPAX) framework, we aimed to distinguish collagen presence and evaluate its correlation with fibrosis progression. We employed an established diethylnitrosamine (DEN) model in rats to study liver fibrosis over various time points. Our results revealed a significant correlation between increased collagen photoacoustic signal intensity and advanced fibrosis stages. Collagen abundance maps displayed dynamic changes throughout fibrosis progression. These findings underscore the potential of sPAI for the noninvasive monitoring of collagen dynamics and fibrosis severity assessment. This research advances the development of noninvasive diagnostic tools and personalized management strategies for liver fibrosis. Full article

The objective of pan-sharpening is to effectively fuse high-resolution panchromatic (PAN) images with limited spectral information and low-resolution multispectral (LR-MS) images, thereby generating a fused image with a high spatial resolution and rich spectral information. However, current fusion techniques face significant challenges, including insufficient edge detail, spectral distortion, increased noise, and limited robustness. To address these challenges, we propose a multi-frequency spectral–spatial interaction enhancement network (MFSINet) that comprises the spectral–spatial interactive fusion (SSIF) and multi-frequency feature enhancement (MFFE) subnetworks. The SSIF enhances both spatial and spectral fusion features by optimizing the characteristics of each spectral band through band-aware processing. The MFFE employs a variant of wavelet transform to perform multiresolution analyses on remote sensing scenes, enhancing the spatial resolution, spectral fidelity, and the texture and structural features of the fused images by optimizing directional and spatial properties. Moreover, qualitative analysis and quantitative comparative experiments using the IKONOS and WorldView-2 datasets indicate that this method significantly improves the fidelity and accuracy of the fused images. Full article

In this study, we analyzed the arc magnetic field to assess the stability of the arc welding process, particularly in robotic welding where direct measurement of welding current is challenging, such as under water. The characteristics of the magnetic field were evaluated based on low-frequency fluctuations and the symmetry of the signals. We used double-wire pulsed MIG welding for our experiments, employing Q235 steel with an 8.0 mm thickness as the material. Key parameters included an average voltage of 19.8 V, current of 120 A, and a wire feeding speed of 3.3 m/min. Our spectral analysis revealed significant correlations between welding stability and factors such as the direct current (DC) component and the peak power spectral density (PSD) frequency. To quantify this relationship, we introduced a novel approach using sample entropy and mix sample entropy (MSE) as new evaluation metrics. This method achieved a notable accuracy of 88%, demonstrating its effectiveness in assessing the stability of the robotic welding process. Full article

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10⁻³ V²/Hz to 4.04 × 10⁻⁶ V²/Hz without affecting the recorded AP signal quality in an unshielded experiment environment. Full article

Background: Drusen and drusenoid deposits are a hallmark of age-related macular degeneration (AMD). Nowadays, a multimodal retinal imaging approach enables the detection of these deposits. However, quantitative data on subretinal drusenoid deposits (SDDs) are still missing. Here, we compare the capability of en-face drusen and SDD area detection in eyes with non-exudative AMD using conventional imaging modalities versus Retro mode imaging. We also quantitatively assess the topographic distribution of drusen and SDDs. Methods: In total, 120 eyes of 90 subjects (mean age ± standard deviation = 74.6 ± 8.6 years) were included. Coherent en-face drusen and SDD areas were measured via near-infrared reflectance, green (G-) and blue (B-) fundus autofluorescence (AF), and Retro mode imaging. Drusen phenotypes were classified by correlating en-face drusen areas using structural high-resolution spectral domain optical coherence tomography. The topographic distribution of drusen was analyzed according to a modified ETDRS (Early Treatment of Diabetic Retinopathy Study) grid. Intraclass correlation coefficient (ICC) analysis was applied to determine the inter-reader agreement in the SDD en-face area assessment. Results: The largest coherent en-face drusen area was found using Retro mode imaging with a mean area of 105.2 ± 45.9 mm² (deviated left mode (DL)) and 105.4 ± 45.5 mm² (deviated right mode (DR)). The smallest en-face drusen areas were determined by GAF (50.9 ± 42.6 mm²) and BAF imaging (49.1 ± 42.9 mm²) (p < 0.001). The inter-reader agreement for SDD en-face areas ranged from 0.93 (DR) to 0.70 (BAF). The topographic analysis revealed the highest number of SDDs in the superior peripheral retina, whereas sub-retinal pigment epithelium drusen were mostly found in the perifoveal retina. Retro mode imaging further enabled the detection of the earliest SDD stages. Conclusions: Retro mode imaging allows for a detailed detection of drusen phenotypes. While hundreds/thousands of SDDs can be present in one eye, the impact of SDD number or volume on AMD progression still needs to be evaluated. However, this new imaging modality can add important knowledge on drusen development and the pathophysiology of AMD. Full article

Rapid and reliable identification of mineral species is a challenging but crucial task with promising application prospects in mineralogy, metallurgy, and geology. Spectroscopic techniques such as laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy (RS) efficiently capture the elemental composition and structural information of minerals, making them a potential tool for in situ and real-time analysis of minerals. This study introduces an integrated LIBS-RS system and the fusion of LIBS and RS spectra coupled with machine learning to classify six different types of natural mineral. In order to visualize the separability of different mineral species clearly, the spectral data were projected into low-dimensional space through t-distributed stochastic neighbor embedding (t-SNE). Additionally, the Fisher score (FS) was used to identify important variables that contribute to the data classification, and the corresponding chemical elements and molecular bonds were then interpreted. The between-minerals difference in the feature spectral intensity of LIBS and RS variables could also be observed. After the minerals spectra were pre-processed, the relationship between spectral intensity and the mineral category was modeled using machine learning methods, including partial least squares–discriminant analysis (PLS-DA) and kernel extreme learning machine (K-ELM). The results show that K-ELM and PLS-DA based on the fusion LIBS-RS data achieved the highest accuracy of 98.4%. These findings demonstrate the feasibility of the integrated LIBS-RS system combined with machine learning for the fast and reliable classification of minerals. Full article

Eggs are a valuable source of nutrients, but they represent a food safety risk due to the presence of microbes. In this work, three types of egg liquids (albumen, yolk and whole egg) previously contaminated with E. coli were treated with ultrasound (US) and a combination of ultrasound and low (55 °C) temperature (US+H). The US treatment parameters were 20 and 40 kHz and 180 and 300 W power and a 30, 45 or 60 min treatment time. The ultrasonic treatment alone resulted in a reduction in the microbial count of less than 1 log CFU, while the US+H treatment resulted in a reduction in CFU counts to below detectable levels in all three egg liquids. Heat treatment and ultrasound treatment had a synergistic effect on E. coli reduction. For all measurements, except for the whole egg samples treated with US, the 20 kHz treated samples showed a significantly (>90% probability level) lower bactericidal effect than the 40 kHz treated samples. PCA and aquaphotometric analysis of NIR spectra showed significant differences between the heat-treated groups’ (H and US+H) and the non-heat-treated groups’ (US and control) NIR spectra. LDA results show that heat-treated groups are distinguishable from non-heat-treated groups (for albumen 91% and for egg yolk and whole egg 100%). Full article

Motivated by important applications to the analysis of complex noise-induced phenomena, we consider a problem of the constructive description of randomly forced equilibria for nonlinear systems with multiplicative noise. Using the apparatus of the first approximation systems, we construct an approximation of mean square deviations that explicitly takes into account the presence of multiplicative noises, depending on the current system state. A spectral criterion of existence and exponential stability of the stationary second moments for the solution of the first approximation system is presented. For mean square deviation, we derive an expansion in powers of the small parameter of noise intensity. Based on this theory, we derive a new, more accurate approximation of mean square deviations in a general nonlinear system with multiplicative noises. This approximation is compared with the widely used approximation based on the stochastic sensitivity technique. The general mathematical results are illustrated with examples of the model of climate dynamics and the van der Pol oscillator with hard excitement. Full article

Accurate measurement of track irregularity and the corresponding spectrum is essential for evaluating the performance of transportation systems. Chord measuring methods can achieve fine accuracy but are limited by waveform distortion and a restricted range of recoverable wavelength. To address this, this work explores the effectiveness of integrating inclination data in chord-based measurement to obtain a higher precision and more reliable spectrum. Firstly, the theoretical principles and mathematics of the proposed method are described. We demonstrate that by utilizing inclinometer sensors, the measuring reference can be maintained throughout the measurement, therefore obtaining an authentic waveform of track irregularity. Adaptive technics are employed to examine and extract cumulative components in the measured signal, which also benefits the accuracy of spectral estimation. Error analysis is then conducted by simulated sampling. Furthermore, a case study of field measurement and numerical simulation via multi-body dynamics for a monorail system is presented. The results verify the accuracy and robustness of the proposed method, showing that it provides a broader range of recoverable wavelength, minimum parametric interference, and advantages of signal authenticity. The simulation results prove the significant effects of track irregularity on the dynamic response of the monorail system, hence revealing the value of the presented methods and results. Full article

Recent advancements in cognitive neuroscience, particularly in electroencephalogram (EEG) signal processing, image generation, and brain–computer interfaces (BCIs), have opened up new avenues for research. This study introduces a novel framework, Bridging Artificial Intelligence and Neurological Signals (BRAINS), which leverages the power of artificial intelligence (AI) to extract meaningful information from EEG signals and generate images. The BRAINS framework addresses the limitations of traditional EEG analysis techniques, which struggle with nonstationary signals, spectral estimation, and noise sensitivity. Instead, BRAINS employs Long Short-Term Memory (LSTM) networks and contrastive learning, which effectively handle time-series EEG data and recognize intrinsic connections and patterns. The study utilizes the MNIST dataset of handwritten digits as stimuli in EEG experiments, allowing for diverse yet controlled stimuli. The data collected are then processed through an LSTM-based network, employing contrastive learning and extracting complex features from EEG data. These features are fed into an image generator model, producing images as close to the original stimuli as possible. This study demonstrates the potential of integrating AI and EEG technology, offering promising implications for the future of brain–computer interfaces. Full article

Aquasols produced by exploding copper wires represent complex systems in which identifying individual colloidal components poses challenges due to broad and multimodal size distributions and varying shares among oxidation states. To evaluate the reproducibility of copper wire explosion, the size distribution of metallic and oxidized colloidal components within the 10–300 nm diameter range was assessed. Classification of each individual particle into bins according to size and chemical composition was accomplished by reconstructing the recorded optical extinction spectra of three sols produced under identical conditions as the weighted sum of the extinction spectra of individual copper and copper-oxide particles, computed using Mie theory. Our spectrophotometry-based component analysis revealed differences in particle number concentrations of the mainly oxidized nanoparticles, corresponding to deviations observed in the ultraviolet portion of the extinction spectra. Notable uniformity was observed, however, in the number of metallic fine particles, consistent with agreement in spectral features in the visible range. Regarding mass concentration, practically no differences were observed among the three samples, with nano-to-fine ratios of copper particles agreeing within 0.45%. Despite the complex processes during explosion leading to limited reproducibility in the ratio of different copper oxidation states, very good reproducibility (54.2 ± 0.7%) was found when comparing the total copper content of the samples to the mass of the exploded copper wire. Full article

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes. Full article