Search Results (1,243)

In view of the phenomenon that adulterated lamb with other animal-derived meats in the market could not be quickly identified, this study used visible near-infrared spectroscopy combined with chemometric methods to quickly identify and quantify lamb rolls adulterated with chicken, duck, and pork. The spectra of the visible–near-infrared band (350–1000 nm) and near-infrared band (1000–1700 nm) of 360 lamb samples, which were mixed with chicken, duck, pork, and 10% lamb oil separately in different increasing proportions, were collected. It was found that the qualitative models of heterogeneous meat (adulterated with chicken, duck, and pork) in lamb were constructed by the combination of first derivative and multiplicative scatter correction (MSC); the accuracy of the validation set reached 100%; the meantime accuracy of the cross-validation set reached 100% (pure lamb), 98.3% (adulterated with chicken), 98.7% (adulterated with duck), and 97.3% (adulterated with pork). Furthermore, the correlation coefficient (R²c) of the adulterated chicken, pork, and duck quantitative prediction models reached 0.972 (chicken), 0.981 (pork), and 0.985 (duck). In summary, the use of Vis NIR can identify lamb meat mixed with chicken, duck, and pork and can quantitatively predict the content of adulterated meat. Full article

Free-space optics communication (FSO) can be used as a transmission medium for fiber optic sensing signals to make fiber optic sensing easier to implement; however, interference with the sensing signals caused by the optical turbulence and scattering of airborne particles in the FSO path is a potential problem. This work aims to deep denoise sensed signals from fiber Bragg grating (FBG) sensors based on FSO link transmission using advanced denoising deep learning techniques, such as stacked denoising autoencoders (SDAE). Furthermore, it will demodulate the sensed wavelength of FBGs by applying the deep belief network (DBN) technique. This is the first time the real FBG sensing experiment has utilized the actual noise interference caused by the environmental turbulence from an FSO link rather than adding noise through numerical processing. Consequently, the spectrum of the FBG sensors is clearly modulated by the noise and the issue with peak power variation. This complicates the determination of the center wavelengths of multiple stacked FBG spectra, requiring the use of machine learning techniques to predict these wavelengths. The results indicate that SDAE is efficient in denoising from the FBG spectrum, and DBN is effective in demodulating the central wavelength of the overlapped FBG spectrum. Thus, it is beneficial to implement an FSO link-based FBG sensing system in adverse weather conditions or atmospheric turbulence. Full article

Modern wireless communication systems have undergone a radical change with the introduction of multiple-input multiple-output (MIMO) antennas, which provide increased channel capacity, fast data rates, and secure connections. To achieve real-time requirements, such antenna technology needs to have good gains, wider bandwidths, satisfactory radiation characteristics, and high isolation. This article presents an eight-element CPW-fed antenna for the 5G mid-band. The proposed antenna consists of eight symmetrical, modified circular monopole antennas with a connected CPW-fed ground plane that offers 24 dB isolation over the operating range. The antenna is further investigated in terms of the scattering parameters, and radiation characteristics under both the x and y-axis bending scenarios. The antenna holds a volume of 83 × 129 × 0.1 mm³ and covers a measured impedance bandwidth of 4.5–5.5 GHz (20%) with an average gain of 4 dBi throughout the operating band. MIMO diversity performance of the antenna is performed, and the antenna exhibits good performance suitable for MIMO applications. Furthermore, the channel capacity (CC) is estimated, and the antenna gives a value of 41.8–42.6 bps/Hz within the operating bandwidth, which is very close to an ideal 8 × 8 MIMO system. The antenna shows an excellent match between the simulated and measured findings. Full article

When multi-dynamic target UAVs escape, the uncertainty of the formation method and the external environment causes difficulties in rounding them up, so suitable solutions are needed to improve the roundup success rate. However, traditional methods can generally only enable the encirclement of a single target, and when the target is scattered and escaping, this will lead to encirclement failure due to the inability to sufficiently allocate UAVs for encirclement. Therefore, in this paper, a real-time roundup and dynamic allocation algorithm for multiple dynamic targets is proposed. A real-time dynamic obstacle avoidance model is established for the roundup problem, drawing on the artificial potential field function. For the escape problem of the rounding process, an optimal rounding allocation strategy is established by drawing on the linear matching method. The algorithm in this paper simulates the UAV in different obstacle environments to round up dynamic targets with different escape methods. The results show that the algorithm is able to achieve the rounding up of multiple dynamic targets in a UAV and obstacle scenario with random initial positions, and the task UAV, which is able to avoid obstacles, can be used in other algorithms for real-time rounding up and dynamic allocation. The results show that the algorithm is able to achieve the rounding up of multi-dynamic targets in scenarios with a random number of UAVs and obstacles with random locations. It results in a 50% increase in the rounding efficiency and a 10-fold improvement in the formation success rate. And the mission UAV is able to avoid obstacles, which can be used in other algorithms for real-time roundup and dynamic allocation. Full article

This study aims to address the gap in understanding of the impact of the sample quantity, traceability range, and shelf life on the accuracy of mung bean origin traceability models based on near-infrared spectroscopy. Mung beans from Baicheng City, Jilin Province, Dorbod Mongol Autonomous, Tailai County, Heilongjiang Province, and Sishui County, Shandong Province, China, were used. Through near-infrared spectral acquisition (12,000–4000 cm⁻¹) and preprocessing (Standardization, Savitzky–Golay, Standard Normal Variate, and Multiplicative Scatter Correction) of the mung bean samples, the total cumulative variance contribution rate of the first three principal components was determined to be 98.16% by using principal component analysis, and the overall discriminatory correctness of its four origins combined with the K-nearest neighbor method was 98.67%. We further investigated how varying sample quantities, traceability ranges, and shelf lives influenced the discrimination accuracy. Our results indicated a 4% increase in the overall correct discrimination rate. Specifically, larger traceability ranges (Tailai-Sishui) improved the accuracy by over 2%, and multiple shelf lives (90–180–270–360 d) enhanced the accuracy by 7.85%. These findings underscore the critical role of sample quantity and diversity in traceability studies, suggesting that broader traceability ranges and comprehensive sample collections across different shelf lives can significantly improve the accuracy of origin discrimination models. Full article

In the present investigation, we have devised a hyperspectral imaging (HSI) apparatus to assess the chemical characteristics and freshness of the yellow croaker (Larimichthys polyactis) throughout its storage period. This system operates within the shortwave infrared spectrum, specifically ranging from 900 to 1700 nm. A variety of spectral pre-processing techniques, including standard normal variate (SNV), multiple scatter correction, and Savitzky–Golay (SG) derivatives, were employed to augment the predictive accuracy of total volatile basic nitrogen (TVB-N)—which serves as a critical freshness parameter. Among the assessed methodologies, SG-1 pre-processing demonstrated superior predictive accuracy (R_p² = 0.8166). Furthermore, this investigation visualized freshness indicators as concentration images to elucidate the spatial distribution of TVB-N across the samples. These results indicate that HSI, in conjunction with chemometric analysis, constitutes an efficacious instrument for the surveillance of quality and safety in yellow croakers during its storage phase. Moreover, this methodology guarantees the freshness and safety of seafood products within the aquatic food sector. Full article

The escalating global emphasis on sustainability, coupled with stringent regulatory frameworks, has spurred the quest for environmentally viable alternatives to petroleum-derived materials. Within this context, the adhesives industry has been actively seeking renewable options and eco-friendly synthesis pathways. This study introduces geraniol, a monoterpenoid alcohol, in its unmodified form, as a key component in the production of waterborne pressure-sensitive adhesives (PSAs) based on acrylic latex through emulsion polymerization. Multiple formulations were developed at varying reaction times. The adhesives underwent comprehensive chemical characterization employing techniques such as Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), Nuclear Magnetic Resonance (NMR), Gel Permeation Chromatography (GPC), and dynamic light scattering (DLS). The viscosities of the formulations were measured between 4000 and 5000 cP. Adhesion tests showed peel strength values of 0.52 N/mm on cardboard and 0.32 N/mm on painted steel for the geraniol-based formulations. The results demonstrate the potential for geraniol-based PSAs to offer a sustainable alternative to petroleum-derived adhesives, with promising thermal and adhesive properties. Full article

The Chinese mitten crab (Eriocheir sinensis) is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions lead to considerable price differences, fueling the prevalence of counterfeit crabs in the market. Currently, there are no rapid detection methods to verify the origin of Chinese mitten crabs, making it crucial to develop fast and accurate detection techniques to protect consumer rights. This study focused on Chinese mitten crabs from different regions, specifically Hongze Lake, Tuo Lake, and Weishan Lake, by collecting near-infrared (NIR) diffuse reflectance spectral data from both the abdomen and carapace regions of the crabs. To eliminate noise from the spectral data, pretreatment was performed using Savitzky–Golay (SG) smoothing, Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Key wavelengths reflecting the origin of Chinese mitten crabs were selected using Competitive Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Uninformative Variable Elimination (UVE) algorithms. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Back Propagation Neural Network (BP) models were developed for rapid detection of crab origin. The results demonstrated that MSC provided the best preprocessing performance for NIR spectral data from both the abdomen and back of the crabs. For abdomen data, the SVM model developed using feature wavelengths selected by the CARS algorithm after MSC preprocessing achieved the highest accuracy (Acc) of 90.00%, with precision (P), recall (R), and F1-score for crabs from Weishan Lake at 89.29%, 86.21%, and 87.72%, respectively; for crabs from Tuo Lake at 86.96%, 95.24%, and 90.91%; and for crabs from Hongze Lake at 90.00%, 93.10%, and 91.53%. For carapace data, the SVM model based on wavelengths selected by the BOSS algorithm after MSC pretreatment achieved the best performance, with an Acc of 87.50%, and P, R, and F1 for crabs from Weishan Lake at 77.14%, 93.10%, and 84.38%; for Tuo Lake crabs at 100%, 90.47%, and 95.00%; and for Hongze Lake crabs at 92.31%, 80.00%, and 85.71%. In conclusion, NIR spectroscopy can effectively detect the origin of Chinese mitten crabs, providing technical support for developing rapid detection instruments and thereby safeguarding consumer rights. Full article

Previous studies have demonstrated that the surface curvature of cylindrical magnetic nonawires can induce fascinating dynamic magnetization properties. It was recently proposed that ferromagnetic nanotubes can be utilized as skyrmion guides, enabling the avoidance of the annihilation of skyrmions in the lateral boundaries as in flat thin-film strips. In this work, we demonstrate via micromagnetic simulation that multiple skyrmions can be stabilized in a cross-section of a ferromagnetic nanotube with interfacial Dzyaloshinskii–Moriya interaction (iDMI). When uniformly arranged, these skyrmions together can perform as a crystal lattice for spin waves (SWs) propagating in the nanotube. Our simulations show that the skyrmion lattice can contribute a chiral effect to the SW passing through, namely a circular polarization of the SW. The handedness of the polarization is found to be determined by the polarity of the skyrmions. A physical explanation of the observed effect is provided based on the exchange of angular momentum between SWs and skyrmions during the scattering process. Our results display more possibilities to exploit magnetic nanotubes as SW and skyrmion guide in the development of novel spintronic devices. Full article

Bluetooth devices have been widely used for pedestrian positioning and navigation in complex indoor scenes. Bluetooth beacons are scattered throughout the entire indoor walkable area containing stairwells, and pedestrian positioning can be obtained by the received Bluetooth packets. However, the positioning performance is sharply deteriorated by the multipath effects originating from indoor clutter and walls. In this work, an ultra-wideband (UWB)-assisted Bluetooth acquisition of signal strength value method is proposed for the construction of a Bluetooth fingerprint library, and a multi-frame fusion particle filtering approach is proposed for indoor pedestrian localization for online matching. First, a polynomial regression model is developed to fit the relationship between signal strength and location. Then, particle filtering is utilized to continuously update the hypothetical location and combine the data from multiple frames before and after to attenuate the interference generated by the multipath. Finally, the position corresponding to the maximum likelihood probability of the multi-frame signal is used to obtain a more accurate position estimation with an average error as low as 70 cm. Full article

Recycling citrus peel waste offers several significant contributions to sustainability, transforming what would otherwise be discarded into valuable resources. In this study, the colour of sausages stored for 16 days, with varying amounts of orange extract added to the modified casing solution, was evaluated using response surface methodology (RSM) and a hyperspectral imaging system within the spectral range of 350–1100 nm for the first time. To enhance model performance, spectral pre-treatments such as normalisation, first derivative, standard normal variate (SNV), second derivative, and multiplicative scatter correction (MSC) were applied. Both raw and pre-treated spectral data, along with colour attributes, were fitted to a partial least squares regression model. The RSM results indicated that the highest R² value, 80.61%, was achieved for the b* (yellowness) parameter using a second-order polynomial model. The interactive effects of soy oil and orange extracts on b* were found to be significant (p < 0.05), and the square effects of soy oil on b* were significant at the 1% level. The identified key wavelengths for colour parameters can simplify the model, making it more suitable for practical industrial applications. Full article

The effect of sintering temperature on the structural, magnetic, and dielectric properties of NiCr₂O₄ ceramics was investigated. A powder X-ray analysis indicates that the prepared nanocrystallites effectively inhibit the cooperative Jahn–Teller distortion, thereby stabilizing the high-temperature cubic phase structure with space group Fd-3m. Multiple transitions are confirmed by temperature-dependent magnetization M(T) data. Moreover, the magnetization value decreases and the Curie temperature increases with a decrease in the crystallite size. The low-temperature-dependent real permittivity (ε′-T) for a NiCr₂O₄ crystallite size of 78 nm exhibits a broad maximum at 40 K that is independent of frequency. This establishes a correlation between electric ordering and the underlying magnetic structure. The temperature dependency of the dielectric constant at fixed frequencies for both NiCr₂O₄ crystallite sizes rises with temperature for a certain range of frequencies. A significant improvement is evident: the dielectric constant (ε’) at room temperature reaches approximately 5738 for the sample with 28 nm crystallites, while the 78 nm crystallite sample shows a noticeable drop to ε’~174. The frequency-dependent conductivity curves for both types of NiCr₂O₄ nanocrystallites have different conductivity values. The lower-crystallite-size sample demonstrates higher conductivity values than the 78 nm crystallite size one. This observation is attributed to the decrease in crystallite size, which increases the number of grain boundaries and, consequently, scatters a higher number of charge carriers. Full article

Background: Multiple sclerosis (MS) is characterized by white matter demyelinating plaques, which can be classified as active, chronic active, or chronic inactive based on the extent of demyelination, cellularity, and inflammation. Microglia and macrophages play a central role in these processes. This study aimed to investigate the morphological characteristics of HLA-DR-immunopositive cells in these plaques to improve our understanding of the roles of these cells in MS plaques. Methods: This study is a retrospective post-mortem histopathological study. We analyzed 90 plaques from 6 MS cases. Of the plaques studied, 77 were grouped into three categories: 28 active, 34 chronic active, and 15 chronic inactive. Additionally, five vacuolated white matter lesions, two axonal degeneration lesions, and six lesions with mixed histological features were included. Six control cases were also examined to assess HLA-DR-immunopositive cell expression across various age groups. The cells were classified based on their morphology into two types: round cells without processes (macrophages) and cells with varying processes and shapes (ramified microglia). Results: Both macrophages and ramified microglia were present in all lesion types, with a focus on identifying the predominant cell type. Of the 28 active plaques, macrophages were the primary cell type in 25 plaques, while ramified microglia predominated in 3. In the center of 49 chronic plaques, scattered ramified microglia were observed in 46, with three plaques showing a predominance of macrophages. Among the 34 chronic active lesions, ramified microglia were the main cell type in the periphery of 32 plaques, with the remaining two predominantly exhibiting macrophages. Conclusions: The predominance of macrophages in active lesions and the presence of scattered ramified microglia in the center of chronic plaques are consistent with the phagocytic role of macrophages. Meanwhile, the prevalence of ramified microglia at the periphery of chronic active lesions suggests a potential protective function in maintaining lesion stability. Full article

Microplastics (MPLs) and nanoplastics (NPLs) are smaller particles derived from larger plastic material, polymerization, or refuse. In context to environmental health, they are separated into the industrially-created “primary” category or the degradation derivative “secondary” category where the particles exhibit different physiochemical characteristics that attenuate their toxicities. However, some particle types are more well documented in terms of their fate in the environment and potential toxicological effects (secondary) versus their industrial fabrication and chemical characterization (primary). Fourier Transform Infrared Spectroscopy (FTIR/µ-FTIR), Raman/µ-Raman, Proton Nuclear Magnetic Resonance (H-NMR), Curie Point-Gas Chromatography-Mass Spectrometry (CP-gc-MS), Induced Coupled Plasma-Mass Spectrometry (ICP-MS), Nanoparticle Tracking Analysis (NTA), Field Flow Fractionation-Multiple Angle Light Scattering (FFF-MALS), Differential Scanning Calorimetry (DSC), Thermogravimetry (TGA), Differential Mobility Particle [Sizing] (DMPS), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Scanning Transmission X-ray Microspectroscopy (STXM) are reviewed as part of a suite of characterization methods for physiochemical ascertainment and distinguishment. In addition, Optical-Photothermal Infrared Microspectroscopy (O-PTIR), Z-Stack Confocal Microscopy, Mueller Matrix Polarimetry, and Digital Holography (DH) are touched upon as a suite of cutting-edge modes of characterization. Organizations, like the water treatment or waste management industry, and those in groups that bring awareness to this issue, which are in direct contact with the hydrosphere, can utilize these techniques in order to sense and remediate this plastic polymer pollution. The primary goal of this review paper is to highlight the extent of plastic pollution in the environment as well as introduce its effect on the biodiversity of the planet while underscoring current characterization techniques in this field of research. The secondary goal involves illustrating current and theoretical avenues in which future research needs to address and optimize MPL/NPL remediation, utilizing nanotechnology, before this sleeping giant of a problem awakens. Full article

In Microwave Radar Coincidence Imaging (MRCI), the imaging region is typically discretized into a fine grid. In other words, it assumes that the equivalent scatterers of the target are precisely located at the centers of these pre-discretized grids. However, this approach usually encounters the off-grid problem, which can significantly degrade the imaging performance. In this paper, to establish a criterion for grid quantization, the performance of the MRCI system related to the grid size and the distribution of imaging points is investigated. First, the discretization of the imaging scene is regarded as a random sampling problem, and the off-grid imaging model for MRCI is established. Then, the probability distribution function (PDF) of the imaging amplitude for a single point target is analyzed, and the mean first-order imaging error (MFE) for multiple point targets is derived based on the Basic Correlation Algorithm (BCA). Finally, the relationship between the grid quantization of the imaging area and the performance of the MRCI system is analyzed, providing a theoretical guidance for enhancing the performance of MRCI. The validity of the analyses is verified through simulation experiments. Full article