Search Results (3,304)

Staphylococcus aureus is the most common pathogen causing osteomyelitis, a hardly recoverable bone infection that generates significant burden to patients. Osteomyelitis mouse models have long and successfully served to provide phenomenological insights into both pathogenesis and host response. However, direct in situ monitoring of bone microbial pathogenesis and immune response at the cellular level is still conspicuously missing in the published literature. Here, we update a standard pyogenic osteomyelitis in Wistar rat model, in order to investigate bacterial localization and immune response in osteomyelitis of rat tibia upon adding in situ analyses by spectrally resolved Raman spectroscopy. Raman experiments were performed one and five weeks post infections upon increasing the initial dose of bacterial inoculation in rat tibia. Label-free in situ Raman spectroscopy clearly revealed the presence of Staphylococcus aureus through exploiting peculiar signals from characteristic carotenoid staphyloxanthin molecules. Data were collected as a function of both initial bacteria inoculation dose and location along the tibia. Such strong Raman signals, which relate to single and double bonds in the carbon chain backbone of carotenoids, served as efficient bacterial markers even at low levels of infection. We could also detect strong Raman signals from cytochrome c (and its oxidized form) from bone cells in response to infection and inflammatory paths. Although initial inoculation was restricted to a single location close to the medial condyle, bacteria spread along the entire bone down to the medial malleolus, independent of initial infection dose. Raman spectroscopic characterizations comprehensively and quantitatively revealed the metabolic state of bacteria through specific spectroscopic biomarkers linked to the length of staphyloxanthin carbon chain backbone. Moreover, the physiological response of eukaryotic cells could be quantified through monitoring the level of oxidation of mitochondrial cytochrome c, which featured the relative intensity of the 1644 cm⁻¹ signal peculiar to the oxidized molecules with respect to its pyrrole ring-breathing signal at 750 cm⁻¹, according to the previously published literature. In conclusion, we present here a novel Raman spectroscopic approach indexing bacterial concentration and immune response in bone tissue. This new approach enables locating and characterizing in situ bone infections, inflammatory host tissue reactions, and bacterial resistance/adaptation. Full article

This study develops a ship propeller diagnostic system to address the issue of insufficient ship maintenance capacity and enhance operational efficiency. It uses the Remaining Useful Life (RUL) prediction technology to establish a sensing platform for ship propellers to capture vibration signals during ship operations. The Diagnosis and RUL Prediction Model is designed to assess bearing aging status and the RUL of the propeller. The synchronized signal reshaping technology is employed in the Diagnosis and RUL Prediction Model to process the original vibration signals as input to the model. The vibration signals obtained are used to analyze the temporal and spectral energy of propeller bearings. Exponential functions are used to generate the health index as model outputs. Model inputs and outputs are simultaneously input into a Long Short-Term Memory (LSTM) model for training, culminating as the Diagnosis and RUL Prediction Model. Compared to Recurrent Neural Network and Support Vector Regression models used in previous studies, the Diagnosis and RUL Prediction Model developed in this study achieves a Mean Squared Error (MSE) of 0.018 and a Mean Absolute Error (MAE) of 0.039, demonstrating outstanding performance in prediction results and computational efficiency. This study integrates the Diagnosis and RUL Prediction Model, bearing aging experimental data, and real-world vibration measurements to develop the diagnosis and RUL prediction system for ship propellers. Experiments with ship propellers show that when the bearing of the propeller enters the worn stage, this diagnostic system for ship propellers can accurately determine the current status of the bearing and its remaining useful life. This study offers a practical solution to insufficient ship maintenance capacity and contributes to improving the operational efficiency of ships. Full article

Objectives: The central nervous system plays a fundamental role in chronic pain; however, its behavior in this condition remains unclear, especially when associated with interventions such as kinesiotaping (KT). This study aimed to analyze the effects of KT on the somatosensory cortex of women with chronic low back pain. Methods: This case series involved 15 women with chronic low back pain. Participants underwent a progressive-tension KT protocol for 8 weeks, and electroencephalogram recordings were performed in two positions, namely sitting and standing while load bearing (10% of body weight), in the first and eighth weeks. The following instruments were employed: Oswestry lumbar disability index, fear avoidance beliefs questionnaire, and the numerical pain intensity scale. Results: All participants showed significant pain improvement and a reduction in Oswestry disability index scores from moderate to minimal. Additionally, activity in the alpha band within the somatosensory cortex and insula (central region—represented by the electrode Cz) decreased. This was confirmed by reduced power spectral density, indicating diminished cortical activity in these regions. Conclusions: KT positively affects women with chronic low back pain, providing pain reduction and improved functional capacity, as indicated by the fear avoidance beliefs questionnaire and numerical pain intensity scale. Moreover, KT reduces cortical activity in the somatosensory cortex, which is related to the progression of painful sensations, described above after the intervention. Full article

Distributed Acoustic Sensing (DAS) is an emerging technology in the maritime domain, enabling the use of existing fiber optic cables to detect acoustic signals in the marine environment. In this study, we present an automated signal detection and classification framework for DAS data that supports near-real-time processing. Using data from the SHEFA-2 cable between the Faroe and Shetland Islands, we develop a method to identify acoustic signals and generate both labeled and unlabeled datasets based on their spectral characteristics. Principal component analysis (PCA) is used to explore separability in the labeled data, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is applied to classify unlabeled data. Experimental validation using clustering metrics shows that with the full dataset, we can achieve a Davies–Bouldin Index of 0.828, a Silhouette Score of 0.124, and a Calinski–Harabasz Index of 189.8. The clustering quality degrades significantly when more than 20% of the labeled data is excluded, highlighting the importance of maintaining sufficient labeled samples for robust classification. Our results demonstrate the potential to distinguish between signal sources such as ships, vehicles, earthquakes, and possible cable damage, offering valuable insights for maritime monitoring and security. Full article

This study evaluates the capabilities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Hyperion remote sensing sensors for mapping ophiolitic sequences and identifying manganese mineralization in the Bela Ophiolite region, located along the axial fold–thrust belt northwest of Karachi, Pakistan. The study area comprises tholeiitic basalts, gabbros, mafic and ultramafic rocks, and sedimentary formations where manganese occurrences are associated with jasperitic chert and shale. To delineate lithological units and Mn mineralization, advanced image processing techniques were applied, including band ratio (BR), Principal Component Analysis (PCA), and Spectral Angle Mapper (SAM) on visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Using these methods, gabbros, basalts, and mafic-ultramafic rocks were effectively mapped, and previously unrecognized basaltic outcrops and gabbroic outcrops were also discovered. The ENVI Spectral Hourglass Wizard was used to analyze the hyperspectral data, integrating the Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and N-Dimensional Visualizer to extract the spectra of end-members associated with Mn-bearing host rocks. In addition, the Hyperspectral Material Identification (HMI) tool was tested to recognize Mn minerals. The remote sensing results were validated by petrographic analysis and ground-truth data, confirming the effectiveness of these techniques in ophiolite mapping and mineral exploration. This study shows that ASTER band combinations (3-6-7, 3-7-9) and band ratios (1/4, 4/9, 9/1 and 3/4, 4/9, 9/1) provide optimal results for lithological discrimination. The results show that remote sensing-based image processing is a powerful tool for mapping ophiolites on a regional scale and can help geologists identify potential mineralization zones in ophiolitic sequences. Full article

The Fourier decomposition technique has notable advantages in filtering vibration acceleration signals and enhances the feasibility of frequency-domain mode decomposition. To improve the accuracy of mode extraction, this paper proposed a novel Fourier decomposition technique based on spectral clustering. The methodology comprises three key steps. First, spectral clustering is performed using feature vectors derived from the spectrum envelope, specifically the frequency and amplitude of its maximum value, along with the average amplitude of local spectral peaks. Subsequently, the spectrum is adaptively segmented based on clustering feedback to determine spectral segmentation boundaries. Followed by this, a filter bank is constructed via Fourier decomposition for signal reconstruction. Finally, a harmonic correlation index is computed for all decomposed components to identify fault-sensitive modes exhibiting the highest diagnostic relevance. These selected modes are subsequently subjected to demodulation for fault diagnosis. The effectiveness of the proposed method is validated through both simulated signals and experimental datasets, demonstrating its improved ability to capture critical fault information. Full article

A multidisciplinary investigation was conducted in southwestern Sicily, near the seismically active Belice Valley, based on the analysis of morphostructural features. These were observed as open fractures between 2014 and 2017; they were subsequently filled anthropogenically and then reactivated during a seismic swarm in 2019. We generated a seismic event distribution map to analyze the location, magnitude, and depth of earthquakes. This analysis, combined with multitemporal satellite imagery, allowed us to investigate the spatial and temporal relationship between seismic activity and fracture evolution. To investigate the spatial variation in thickness of the superficial cover and to assess the depth to the underlying bedrock or stiffer substratum, 45 Horizontal-to-Vertical Spectral Ratio (HVSR) ambient noise measurements were conducted. This method, which analyzes the resonance frequency of the ground, produced maps of the amplitude, frequency, and vulnerability index of the ground (K_g). By inverting the HVSR curves, constrained by Multichannel Analysis of Surface Waves (MASW) results, a subsurface model was created aimed at supporting the structural interpretation by highlighting variations in sediment thickness potentially associated with fault-controlled subsidence or deformation zones. The surface investigation revealed depressed elliptical deformation zones, where mainly sands outcrop. Grain-size and morphoscopic analyses of sediment samples helped understand the processes generating these shapes and predict future surface deformation. These elliptical shapes recall the liquefaction process. To investigate the potential presence of subsurface fluids that could have contributed to this process, Electrical Resistivity Tomography (ERT) was performed. The combination of the maps revealed a correlation between seismic activity and surface deformation, and the fractures observed were interpreted as inherited tectonic and/or geomorphological structures. Full article

This study proposes a novel non-destructive approach to assessing and predicting the quality of stored date fruits using a composite quality index (Qi) modeled via visible–near-infrared (VIS–NIR) spectroscopy and artificial neural networks (ANNs). Two leading cultivars, Sukkary and Khlass, were stored for 12 months using three temperature regimes (25 °C, 5 °C, and −18 °C) and five types of packaging. The samples were grouped into six moisture content categories (4.36–36.70% d.b.), and key physicochemical traits, namely moisture, pH, hardness, total soluble solids (TSSs), density, color, and microbial load, were used to construct a normalized, dimensionless Qi. Spectral data (410–990 nm) were preprocessed using second-derivative transformation and modeled using partial least squares regression (PLSR) and the ANNs. The ANNs outperformed PLSR, achieving the correlation coefficient (R²) values of up to 0.944 (Sukkary) and 0.927 (Khlass), with corresponding root mean square error of prediction (RMSEP) values of 0.042 and 0.049, and the relative error of prediction (REP < 5%). The best quality retention was observed in the dates stored at −18 °C in pressed semi-rigid plastic containers (PSSPCs), with minimal microbial growth and superior sensory scores. The second-order Qi model showed a significantly better fit (p < 0.05, AIC-reduced) over that of linear alternatives, capturing the nonlinear degradation patterns during storage. The proposed system enables real-time, non-invasive quality monitoring and could support automated decision-making in postharvest management, packaging selection, and shelf-life prediction. Full article

Soybean cyst nematode (SCN) is a pathogen with serious impacts on soybean yields, yet traditional field-based assessment is labor-intensive and often ineffective for early interventions, and the existing spectral vegetation indices (VIs) also lack the ability to accurately detect SCN infested plants. This study aimed to develop an improved detection method using hyperspectral data. A greenhouse-based experiment was designed to collect 100 hyperspectral datasets from 20 soybean plants inoculated with four SCN egg levels (0–10,000) from the 68th to 97th day after planting. Based on spectral similarity and inoculation levels, three stress classes were defined as proxies for actual plant stress: healthy (0 egg), moderate (1000 and 5000 eggs), and severe (10,000 eggs). These classifications are based on predefined inoculation thresholds and spectral trends, which may not fully align with direct physiological stress measurements due to inherent variability in individual plant responses. Through analysis of variance (ANOVA), principal component analysis (PCA), feature selection, and classification comparison, a new spectral VI, called SCNVI, was proposed using bands 338 nm and 665 nm. The SCNVI coupled with eXtreme Gradient Boosting (XGBoost) achieved an accurate classification of 70% for three classes and outperformed the 12 traditional VIs. These findings suggest that integrating the SCNVI and XGBoost algorithm provides the potential for improving the detection of SCN infestation, though further validation in field environments is required to confirm its practical applicability. Full article

To accurately assess the seismic damage of high-rise structures under long-period ground motions (LPGMs), a 36-story SRC frame-RC core tube high-rise structure was designed. Twelve groups of LPGMs and twelve groups of ordinary ground motions (OGMs) were selected and bidirectionally input into the structure. The spectral acceleration S_90c considering the effect of higher-order modes was adopted as the intensity measure (IM) of ground motions, and the maximum inter-story drift angle θ_max under bidirectional ground motions was taken as the engineering demand parameter (EDP). Structural Incremental Dynamic Analysis (IDA) was conducted, the structural vulnerability was investigated, and seismic vulnerability curves, damage state probability curves, vulnerability index curves, as well as the probabilities of exceeding performance levels and vulnerability index of the structure during 8-degree frequent, design, and rare earthquakes were obtained, respectively. The results indicate that structural damage is significantly aggravated under LPGMs, and the exceeding probabilities for all performance levels are greater than those under OGMs, failing to meet the seismic fortification target specified in the code. When encountering an 8-degree frequent earthquake, the structure is in a moderate or severe damage state under LPGMs and is basically intact or in a slight damage state under OGMs. When encountering an 8-degree design earthquake, the structure is in a severe damage or near-collapse state under LPGMs and is in a moderate damage state under OGMs. When encountering an 8-degree rare earthquake, the structure is in a near-collapse state under LPGMs and in a severe damage state under OGMs. Full article

Silicon carbide (SiC) has become the material of choice for precision optical systems due to its exceptional optical characteristics. However, conventional anti-reflection strategies for SiC components predominantly utilize deposited thin-film coatings, which are frequently compromised by insufficient environmental robustness and long-term stability concerns. To overcome these limitations, direct nanostructuring of SiC substrates has emerged as a promising alternative solution. This work introduces an innovative graded-index microcone array design fabricated on SiC substrates, achieving superior broadband anti-reflection performance. Our two-step fabrication methodology comprises plasma-induced formation of tunable nanofiber etch masks through controlled argon bombardment parameters, followed by precision reactive ion etching (RIE) for microcone array formation. By systematically varying plasma exposure duration, we demonstrate precise control over nanofiber mask morphology, which in turn enables the fabrication of height-optimized SiC microcone arrays. The resulting structures exhibit exceptional optical performance, achieving an ultra-low average reflectivity of 2.25% across the spectral range of 2.5–8 μm. This breakthrough fabrication technique not only extends the available toolbox for SiC micro/nanofabrication but also provides a robust platform for next-generation optical applications. Unlike conventional thin-film approaches, our nanostructuring method preserves the intrinsic mechanical and environmental durability of the SiC substrate while delivering a favorable optical performance. Full article

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

Maize (Zea mays L.) is a key global cereal crop with significant relevance to food security. Maize yield prediction is challenged by cultivar diversity and varying management practices. This preliminary study was conducted at Youyi Farm, Heilongjiang Province, China. Three maize cultivars (Songyu 438, Dika 1220, Dika 2188), two fertilization rates (700 and 800 kg·ha⁻¹), and three planting densities (70,000, 75,000, and 80,000 plants·ha⁻¹) were evaluated across 18 distinct cropping treatments. During the V6 (Vegetative 6-leaf stage), VT (Tasseling stage), R3 (Milk stage), and R6 (Physiological maturity) growth stages of maize, multi-temporal canopy spectral images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor. In situ measurements of key agronomic traits, including plant height (PH), stem diameter (SD), leaf area index (LAI), and relative chlorophyll content (SPAD), were conducted. The optimal vegetation indices (VIs) and agronomic traits were selected for developing a maize yield prediction model using the random forest (RF) algorithm. Results showed the following: (1) Vegetation indices derived from the red-edge band, particularly the normalized difference red-edge index (NDRE), exhibited a strong correlation with maize yield (R = 0.664), especially during the tasseling to milk ripening stage; (2) The integration of LAI and SPAD with NDRE improved model performance, achieving an R² of 0.69—an increase of 23.2% compared to models based solely on VIs; (3) Incorporating SPAD values from middle-canopy leaves during the milk ripening stage further enhanced prediction accuracy (R² = 0.74, RMSE = 0.88 t·ha⁻¹), highlighting the value of vertical-scale physiological parameters in yield modeling. This study not only furnishes critical technical support for the application of UAV-based remote sensing in precision agriculture at the field-plot scale, but also charts a clear direction for the synergistic optimization of multi-dimensional agronomic traits and spectral features. Full article

Driven by both natural and anthropogenic factors, farmland abandonment and recultivation constitute complex and widespread global phenomena that impact the ecological environment and society. In the Inner Mongolia Yellow River Basin (IMYRB), a critical tension lies between agricultural production and ecological conservation, characterized by dynamic bidirectional transitions that hold significant implications for the harmony of human–nature relations and the advancement of ecological civilization. With the development of remote sensing, it has become possible to rapidly and accurately extract farmland changes and monitor its vegetation restoration status. However, mapping abandoned farmland presents significant challenges due to its scattered and heterogeneous distribution across diverse landscapes. Furthermore, subjectivity in questionnaire-based data collection compromises the precision of farmland abandonment monitoring. This study aims to extract crop phenological metrics, map farmland abandonment, and recultivation dynamics in the IMYRB and assess post-transition vegetation changes. We used Landsat time-series data to detect the land-use changes and vegetation responses in the IMYRB. The Farmland Abandonment and Recultivation Extraction Index (FAREI) was developed using crop phenology spectral features. Key crop-specific phenological indicators, including sprout, peak, and wilting stages, were extracted from annual MODIS NDVI data for 2020. Based on these key nodes, the Landsat data from 1999 to 2022 was employed to map farmland abandonment and recultivation. Vegetation recovery trajectories were further analyzed by the Mann–Kendall test and the Theil–Sen estimator. The results showed rewarding accuracy for farmland conversion mapping, with overall precision exceeding 79%. Driven by ecological restoration programs, rural labor migration, and soil salinization, two distinct phases of farmland abandonment were identified, 87.9 kha during 2002–2004 and 105.14 kha during 2016–2019, representing an approximate 19.6% increase. Additionally, the post-2016 surge in farmland recultivation was primarily linked to national food security policies and localized soil amelioration initiatives. Vegetation restoration trends indicate significant greening over the past two decades, with particularly significant increases observed between 2011 and 2022. In the future, more attention should be paid to the trade-off between ecological protection and food security. Overall, this study developed a novel method for monitoring farmland dynamics, offering critical insights to inform adaptive ecosystem management and advance ecological conservation and sustainable development in ecologically fragile semi-arid regions. Full article

Urbanization is a dominant force reshaping human settlements, driving socio-economic development while also causing significant environmental challenges. With over 56% of the world’s population now residing in urban areas—a figure expected to rise to two-thirds by 2050—land use changes are accelerating rapidly. The conversion of natural landscapes into impervious surfaces such as concrete and asphalt intensifies the Urban Heat Island (UHI) effect, raises urban temperatures, and strains local ecosystems. This study investigates land use and landscape changes in Ceredigion County, UK, utilizing remote sensing and GIS techniques to analyze urbanization impacts over two decades (2003–2023). Results indicate significant urban expansion of approximately 122 km², predominantly at the expense of agricultural and forested areas, leading to vegetation loss and changes in water availability. County-wide mean land surface temperature (LST) increased from 21.4 °C in 2003 to 23.65 °C in 2023, with urban areas recording higher values around 27.1 °C, reflecting a strong UHI effect. Spectral indices (NDVI, NDWI, NDBI, and NDBaI) reveal that urban sprawl adversely affects vegetation health, water resources, and land surfaces. The Urban Thermal Field Variance Index (UTFVI) further highlights areas experiencing thermal discomfort. Additionally, machine learning models, including Linear Regression and Random Forest, were employed to forecast future LST trends, projecting urban LST values to potentially reach approximately 27.4 °C by 2030. These findings underscore the urgent need for sustainable urban planning, reforestation, and climate adaptation strategies to mitigate the environmental impacts of rapid urban growth and ensure the resilience of both human and ecological systems. Full article