Search Results (4,051)

Understanding how brain activity varies across neurological and neurodevelopmental disorders requires tools capable of revealing patterns hidden in complex electroencephalographic (EEG) data. Conditions such as epilepsy, Alzheimer’s disease, dementia, and autism exhibit distinct alterations in neural oscillations and connectivity, which remain difficult to interpret in real time; therefore, this study proposes an interactive interface for intuitive exploration and analysis of disease-specific EEG dynamics. The system integrates classical signal processing techniques and computational modeling to extract spectral features, inter-electrode coherence, and spatial activation patterns, which are visualized through spectrograms, topographic maps, and connectivity graphs that update continuously. In addition, a web-based platform is incorporated to enable clinicians and technicians to store and manage patient information, including diagnosis, severity level, number of recordings, sampling frequency, recording duration, and acquisition dates, supporting structured data organization and longitudinal monitoring. The results demonstrate that the interface captures meaningful differences between disorders, with epileptic patterns showing strong synchronization and burst activity, while neurodegenerative conditions exhibit spectral slowing and reduced connectivity. Overall, the proposed framework provides an effective and accessible tool for EEG visualization, combining interactive analysis with clinical data management to support research, education, and potential clinical applications. Full article

Anchored bored pile walls are widely used to control deformation in deep urban excavations, but their serviceability performance depends on soil stiffness, excavation depth, wall stiffness, anchor configuration, construction staging, groundwater conditions and seismic demand. This study compares three real excavation support projects in contrasting soil groups: soft to hard clay, hard to very hard clay, and dense to very dense gravel. The calculations follow a Eurocode 7-compatible Design Approach 2 framework. Static finite-element analyses, equivalent-static seismic analyses and scaled time-history analyses were compared with in situ inclinometer measurements. The seismic input included site-specific spectral parameters, horizontal acceleration coefficients, Rayleigh damping parameters and 11 scaled PEER ground-motion records. The key design insight is that increasing the number of anchor rows alone cannot compensate for low ground stiffness or limited wall stiffness; soil-structure interaction must be interpreted together with support configuration. The finite-element and measured maximum horizontal displacements were 79.97 and 75.80 mm for the sports hall excavation, 23.22 and 22.70 mm for the residential excavation, and 27.67 and 23.20 mm for the controlling square-project section. The study demonstrates the value of integrating Eurocode-based design checks, dynamic analysis and field monitoring for deep-excavation safety. Full article

Suspended sediment concentration (SSC) is a key indicator of river sediment transport processes and water environmental change. For medium-width rivers, continuous-reach SSC monitoring remains constrained by the spatial discontinuity of station observations and the temporal or consistency limitations of single-source satellite imagery. To improve multi-year SSC characterization in the middle and lower reaches of the Liaohe River, this study integrated Harmonized Landsat and Sentinel-2 (HLS) surface reflectance imagery from 2016 to 2022 with SSC observations from five hydrological stations and developed a random forest retrieval model using multi-band reflectance and sediment-related spectral features. The trained model was applied to valid HLS images to examine SSC spatial distribution, interannual variation, and inter-station reach differences. The model achieved a test-set R² of 0.641, an RMSE of 0.083 kg·m⁻³, and an MAE of 0.067 kg·m⁻³. The median composite of 52 retrieval images showed a lower SSC in the Tieling–Mahushan and Mahushan–Pinganbao reaches and a higher SSC in the Pinganbao–Liaozhong and Liaozhong–Liujianfang reaches. SSC was generally higher in 2016 and 2022 and lower in 2018. These findings indicate that HLS-based retrieval can support continuous-reach SSC monitoring and regional water–sediment dynamic assessment in medium-width rivers, although the accurate quantification of extreme high-SSC events still requires additional in situ samples and higher-frequency observations. Full article

Oblivious Transfer (OT) extension, in particular, the construction of Ishai, Kilian, Nissim, and Petrank (CRYPTO 2003) requires a hash function H that is correlation-robust(CR). All practical instantiations model H as a random oracle or an ideal cipher, leaving CR with no quantifiable reduction to a structural property of the deployed hash. It is natural to ask whether the most nonlinear balanced Boolean functions available on an odd number of variables, the near-bent functions of the Maiorana–McFarland (MM) class, furnish an algebraic, standard-model CR candidate. We prove that they do not, and we identify precisely why. First, we keep a correct spectral fact: a balanced $H:{\{0,1\}}^n\to \{0,1\}$ is $\epsilon$-CR if and only if ${\max }_{\Delta \ne 0}\left|{\mathcal{A}}_f(\Delta )\right|\le 4\epsilon ·2^n$, reducing CR to an autocorrelation bound. Against this criterion we establish three obstructions: (i) The MM-doubling family ${\mathcal{NB}}_k$ on $n=2k+1$ variables has autocorrelation supported only on the directions $(a,0,1)$, where it equals $2^{k+1}{W}_a$ with ${\sum }_{a\ne 0}{W}_a^2=2^{2k}$; hence $\epsilon \ge \frac{1}{4}{(2^k-1)}^{-1/2}$, a factor $\ge 2^{k/2}$ above the value one would need, and an exhaustive search over all balanced members for $k\le 2$ returns the maximal $\epsilon =\frac{1}{4}$ in every case. (ii) Near-bentness controls the Walsh maximum (nonlinearity), not autocorrelation: every near-bent function satisfies ${\sum }_{\Delta \ne 0}{\mathcal{A}}_f{(\Delta )}^2=2^{2n}$, so ${\max }_{\Delta \ne 0}\left|{\mathcal{A}}_f(\Delta )\right|\ge 2^n{(2^n-1)}^{-1/2}$ and no near-bent function is even approximately CR. (iii) A deterministic $\mathbf{H}:{\{0,1\}}^{\kappa }\to {\{0,1\}}^{\mathcal{\ell }}$ admits the support bound $\mathbf{SD}\left((\mathbf{H}\left(x\right),\mathbf{H}(x\oplus \Delta )),(U_{\mathcal{\ell }},U_{\mathcal{\ell }})\right)\ge 1-2^{\kappa -2\mathcal{\ell }}$, so statistical multi-output CR is impossible for $\mathcal{\ell }>\kappa /2$ and in particular at the IKNP regime $\mathcal{\ell }\approx \kappa$. Together, these results close the near-bent route to standard-model CR and clarify which design objective (low absolute indicator, not high nonlinearity) and which parameter regime ($\mathcal{\ell }\le \kappa /2$) a viable algebraic candidate would have to target. Full article

Melon (Cucumis melo L.) is a globally significant horticultural crop, characterized by high nutritional value and substantial commercial status. However, frequent outbreaks of powdery mildew severely threaten its yield and fruit quality. Current early detection methods primarily focus on detached leaf assays, which often lack sufficient model generalization. This study proposes a temporal 3D multispectral point cloud reconstruction method for melon plants by integrating multispectral imaging with 3D reconstruction technology. An Artificial Neural Network (ANN) model for 3D spatial light field distribution was developed based on a hemispherical white reference to achieve precise reflectance calibration of the multispectral point clouds. Post-calibration, the coefficient of variation (CV) for the spectral reflectance of the hemispherical reference in 3D space was reduced to less than 2.4%. On this basis, an early classification model for melon powdery mildew was constructed using Partial Least Squares Discriminant Analysis (PLS-DA) based on the mean reflectance spectra of individual plant point clouds. The results demonstrate that the average recognition accuracy reaches 85.94% from 4 days post-inoculation onwards, enabling disease early warning three days in advance. This research provides critical theoretical support and technical reference for the non-destructive early monitoring and precision smart plant protection of crops in facility agriculture. Full article

Multibody dynamics (MBD) simulations involving hundreds to thousands of bodies give rise to large-scale, sparse, and structurally indefinite linear systems. Traditional direct solvers incur prohibitive memory and computational costs, while iterative methods suffer from slow convergence due to severe ill-conditioning. This paper proposes HPI-MBD, a hybrid preconditioned iterative framework. It combines an algebraic multigrid (AMG) for global error smoothing with a block Jacobi preconditioner tailored to the kinematic constraint graph. The framework exploits graph topology to construct a block-diagonal Schur complement approximation, incorporates Tikhonov regularisation for redundant constraints, and maintains $\mathcal{O}\left(n\right)$ work per iteration, where n is the number of degrees of freedom. A rigorous spectral analysis supports the problem-size independent convergence of the Minimal Residual (MINRES) solver. Evaluated on five benchmark systems with ${10}^4$ to ${10}^6$ degrees of freedom, the HPI-MBD achieves speedups up to $12.7\times$ and memory reductions up to 68% against MA57, with comparable gains against PARDISO. All solutions maintain relative residuals below ${10}^{-6}$. Comparisons against ILU(0)-preconditioned Generalised Minimal Residual (GMRES), Finite Element Tearing and Interconnecting method (FETI-1), and a block-Jacobi-only variant confirm the essential role of AMG. The framework exhibits near-linear scalability and strong parallel efficiency on up to 32 processors, along with robust performance under redundant constraints and varying time step sizes. These results position HPI-MBD as a scalable, memory-efficient alternative for real-time simulation in virtual prototyping, robotics, and biomechanics. Full article

Compressed sensing (CS) enables signal reconstruction from fewer measurements when the signal is sparse in a transform domain. However, executing ${\ell }_1$-regularized recovery on MCU-class hardware is challenging due to limited compute resources and the cost of repeated forward and adjoint operator evaluations. This paper presents a band-limited proximal variant of FISTA that enforces known spectral support during thresholding, restricting the effective optimization domain without changing the measurement model. We implement a complete CS reconstruction pipeline on an STM32F407 (Cortex-M4) using CMSIS-DSP FFT/IFFT kernels and evaluate it using ECG waveforms acquired through an AD8232 front end as benchmark signals. With $M=340$ measurements (33% of uniform sampling), the embedded implementation achieves a PRDN of 24.38%, closely matching MATLAB references (CVX: 22.64%, FISTA: 22.39%) under identical hyperparameters. Cycle-accurate profiling shows that FFT/IFFT-based forward/adjoint operators dominate the per-iteration runtime. Under a 60 Hz band-limited setting, the required iterations are reduced from 30 to 16 with an acceptable PRDN, demonstrating a practical trade-off between reconstruction accuracy and computational cost on MCU-class devices. Full article

Purpose: To analyze choroidal thickness (CT) and choroidal volume (CV) using ultra-wide-field (UWF) spectral-domain optical coherence tomography (SD-OCT) in patients with central serous chorioretinopathy (CSC) and to assess their associations with disease duration and best-corrected visual acuity (BCVA). Methods: This prospective case–controlled study included 50 eyes of 41 CSC patients and 56 eyes of 32 healthy controls matched for age and sex. CT was measured at 24 points using the REVO 130 UWF SD-OCT device with a wide-field adapter, covering a 21 × 21 mm retinal area across central, mid-peripheral (4 mm), and peripheral (8 mm) zones. CV was estimated using a quadratic nonlinear model. ROC curve analysis and univariate logistic regression were applied to evaluate discriminative capacity and odds ratios (OR) for CT and CV. Results: CT was significantly higher in CSC eyes at all 24 measurement points (all p < 0.0001). Mean subfoveal CT was 472.6 µm vs. 344.8 µm in controls (+37%), with greater relative increases at mid-peripheral (+46%) and peripheral (+44%) zones. Mean CV was 61.47 (±11.37) mm³ vs. 42.29 (±10.02) (+45%; p < 0.0001). CV showed a higher OR for CSC occurrence than central CT (OR = 2.88; 95% CI: 1.53–5.42 vs. OR = 1.04; 95% CI: 1.02–1.07). Significant discriminative CT points (AUC > 0.60) clustered at the 2/8, 4/10, and 6/12 clock meridians. Both CT and CV correlated positively with disease duration (Spearman rho 0.35–0.41; p ≤ 0.0004) but not with BCVA. Conclusions: UWF SD-OCT confirms diffuse pachychoroid thickening in CSC extending to the periphery. CV is a sensitive biomarker in association with CSC status. Peripheral CT and CV correlate with disease duration, supporting the link between higher volumetric choroidal values and longer disease course. Integration of these parameters may improve CSC diagnosis and prognostic evaluation. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

The southeastern flank of Mt. Etna is affected by the presence of active faults capable of adapting to deformation through both seismic slip and aseismic creep, posing challenges for seismic microzonation and for land-use planning. Structural surveys in the urban area of San Gregorio di Catania revealed a ~1 km long, N–S trending secondary fracture zone with an extensional component, inducing progressive damage to buildings and infrastructure. To characterize this scarcely visible structure, passive seismic single-station surveys processed with Horizontal-to-Vertical Spectral Ratio (HVSR) tecnique were integrated with Multichannel Analysis of Surface Waves (MASW). The HVSR data enabled the mapping of the spatial distribution of resonance frequencies, tracking an anomalous trend in the seismic bedrock geometry and depth directly correlatable with the presence of the secondary fracture zone. Directional analyses exhibit systematic preferential orientations of resonance peaks near the fracture corridor, confirming a rigorous structural control and a tectonic origin for the recorded anomalies. Furthermore, reconstructed 2D impedance contrast sections show distinct discontinuities and a local westward dislocation of the main seismo-stratigraphic interface across the deformation zone. The lack of correlated instrumental seismicity supports the interpretation that the displacement is primary accommodated via aseismic fault creep. Methodologically, these findings demonstrate that the passive seismic method provides a highly effective, non-invasive approach for identifying hard-to-detect tectonic structures that remain unobliterated by dense urbanization. Ultimately, these results offer critical, actionable constraints for seismic microzonation and urban land-use setback zoning. Full article

This study presents a compact controlled urban geophysics test site developed at the University of Malta to evaluate the response of complementary near-surface sensing methods under known shallow subsurface conditions. The experimental setup is designed to investigate buried target detection and the response to a simulated shallow leak, used here as a controlled water-release experiment in a shallow carbonate setting characterized by thin, laterally variable soil cover and anthropogenic disturbance. A preliminary passive seismic survey based on the horizontal-to-vertical spectral ratio (HVSR) method was used to compare candidate sectors and select the most suitable area for installation. The test site includes a buried iron plate and a perforated PVC pipe, the latter used to release water under controlled shallow conditions. Ground-penetrating radar (GPR), smartphone magnetometry, electrical resistivity tomography (ERT), and UAV-based thermal imaging were applied to assess target detectability and leak-related surface–subsurface responses. Results show that GPR provides the clearest response for static target detection, while smartphone magnetometry identifies the buried ferrous target under favourable conditions. For the simulated leak experiment, ERT provides the most robust subsurface evidence of moisture redistribution after water injection. UAV thermal imaging captures a complementary surface thermal response influenced by both moisture dynamics and local surface disturbance. The results show that a compact controlled test site can support the comparison of professional and low-cost sensing methods for shallow target detection and simulated leak assessment. In this configuration, the controlled water-release experiment provides a practical basis for evaluating leak-related surface–subsurface responses under known shallow conditions. The proposed setup has implications for methodological assessment, training, and near-surface environmental monitoring in heterogeneous urban settings. Full article

Rapid and non-destructive detection of nitrogen content in greenhouse cucumber leaves is essential for precision fertilization, yet traditional chemical methods are destructive and time-consuming, and existing spectral technologies suffer from high cost and poor field adaptability. This study aims to propose a high-precision detection scheme for cucumber leaf nitrogen content based on a lightweight model, suitable for complex scenarios. A total of 698 cucumber leaf images covering three growth stages were collected to build a segmentation dataset. Four categories and eight types of deep learning segmentation models were optimized and compared, and the optimal one was selected to extract leaf regions. Nine color features were extracted and combined with Kjeldahl-measured nitrogen content to construct and optimize three machine learning models, forming a deep learning segmentation–color feature extraction–machine learning prediction process. The results showed that YOLOv11n achieved the best segmentation accuracy, with an IoU of 0.9212 and AP of 0.9998 for high-resolution images. The optimized XGBoost had the highest prediction accuracy, with an MAE of 0.469, MSE of 0.461, and RMSE of 0.679, which are 10.15%, 8.71%, and 4.36% lower than Support Vector Regression with Radial Basis Function kernel (SVR_RBF) respectively, and its predicted nitrogen content aligned well with true values. The proposed scheme integrating YOLOv11n and XGBoost offers a lightweight technical solution for nitrogen nutrition diagnosis and precise fertilization of greenhouse cucumbers. Full article

Floodplain wetlands are globally important ecosystems, yet altered hydrological regimes increasingly disrupt the balance between woody and non-woody vegetation. In Australia’s regulated Murray–Darling Basin, it remains unclear whether woody plant encroachment represents a persistent shift toward terrestrialisation or a dynamic process that can be periodically reversed by flooding. This study quantified long-term patterns of woody-vegetation encroachment and retreat across 32,000 ha of mapped wetlands in the mid-Murrumbidgee River floodplain from 1988 to 2023, and assessed how hydrological variability and floodplain connectivity mediate these dynamics. Using open, analysis-ready Earth observation data from Digital Earth Australia (DEA) within the Open Data Cube (ODC) framework, we combined DEA Land Cover for transition mapping, Water Observations for hydrological masking, Landsat surface reflectance for Enhanced Vegetation Index (EVI)-based spectral plausibility testing, and the Wetlands Insight Tool for qualitative temporal context. Woody-vegetation dynamics were strongly non-linear and closely linked to alternating drought and flood phases. During the Millennium Drought (2001–2009), mapped woody-cover decline exceeded 50% of wetland area in some sub-regions, whereas the post-drought recovery interval (2008–2013) produced encroachment exceeding 40% in the most affected areas. Across the full 35-year record, mean encroachment rates ranged from 85 to 250 ha yr⁻¹ among sub-regions, summing to approximately 865 ha yr⁻¹ of woody expansion across the floodplain, while retreat rates were lower overall (approximately 634 ha yr⁻¹), resulting in a net expansion of woody cover. Local hydrological connectivity strongly mediated these responses: infrequently inundated wetlands showed persistent terrestrialisation, whereas more frequently inundated, better-connected wetlands experienced periodic flood-driven retreat. Landsat-derived EVI broadly supported the mapped transitions, indicating general consistency with canopy greening and canopy decline, supporting the ecological plausibility of the detected changes. This open DEA–ODC workflow provides a transparent, transferable framework for operational wetland monitoring and demonstrates that maintaining natural flood frequency, duration, and connectivity is essential for sustaining the resilience of regulated floodplain systems. Full article

Stevia (Stevia rebaudiana Bertoni) is an important source of natural sweeteners. Since its commercial value depends on steviol glycosides, quality assessment primarily involves quantifying these compounds in leaves and shoots. While chromatography is the standard analytical method, it is labor-intensive and time-consuming; it involves multiple processing steps that may cumulatively introduce errors and remains relatively expensive. Although chromatography remains the most accurate method, this exploratory study evaluates the potential of using spectroscopy as an auxiliary method for the approximate assessment of steviol glycoside content. Leaf reflectance spectroscopy could be a simpler and more cost-effective approach. However, relationships between leaf reflectance and steviol glycoside content are indirect and mediated by physiological processes. To account for these indirect dependencies, cumulative UVB exposure was included as an additional feature because it influences both leaf optical properties and plant metabolic processes. A low-cost spectrometer was utilized as the measuring instrument. The study was conducted over a period of three months on 77 S. rebaudiana clones, divided into four groups based on their level of UVB irradiance (control without irradiation, 400, 600, and 800 μW m⁻²). Based on the collected data, linear and polynomial regression, Random Forest, XGBoost, PLSR, and ElasticNetCV models were trained. Cumulative UVB exposure was found to be the most important feature. Of the spectral features, the most informative for assessing the content of steviol glycosides were spectral indicators in the far-red and near-infrared (NIR) ranges. Our results indicate a detectable relationship, with Random Forest being the best-performing model and achieving a moderate predictive performance (R² = 0.66). Despite their limited predictive performance, the models demonstrate that leaf reflectance spectra combined with cumulative UVB exposure contain information related to steviol glycoside content. These findings support further investigation of remote sensing approaches for crop quality assessment. Full article

This study demonstrates single-channel fiber Bragg grating (FBG) sensing for relative vibration-state monitoring of a motor–support system under angle-dependent boundary conditions. A packaged FBG accelerometer-type sensing unit was mounted on the motor–support structure, and the reflected Bragg wavelength was recorded as a one-dimensional optical vibration response. Because the sensor was installed away from the rotating shaft, the measured wavelength fluctuation was interpreted as a coupled vibration-sensitive response of the motor, fixture, sensor package, bonding condition, and changing boundary state, rather than as a calibrated shaft speed or absolute acceleration signal. Adaptive variational mode decomposition (AVMD) was applied to track the time-varying narrowband spectral-response trajectory of the Bragg-wavelength signal. In parallel, raw wavelength windows were supplied to LSTM, 1D-CNN, and CNN–LSTM autoencoders to evaluate waveform departures from learned nominal fixed-angle behavior. The fixed-angle results showed stable but distinguishable optical vibration responses under different boundary states, whereas the dynamic angle-transition records produced local trajectory changes and alarm-candidate intervals. Baseline and autoencoder comparisons further clarified the trade-off between transition coverage and false-alarm tendency. The RMS threshold baseline was more sensitive to transition-related amplitude changes but produced more false alarms, whereas the CNN–LSTM autoencoder provided the most selective response among the tested autoencoder branches. The results are interpreted as task-specific evidence for relative vibration-state transition monitoring rather than as general motor fault diagnosis. Overall, the framework demonstrates a compact FBG-based route for relative vibration-state transition monitoring when speed references, dense sensor layouts, and labeled fault data are unavailable. Full article