Search Results (1,506)

This study focuses on the problem of local instability of sandwich panels, which consist of two thin but relatively stiff facings and a thick but shear-deformable core. Such structures are commonly used in civil engineering and in the aerospace, aviation, and automotive industries. A case is presented in which one of the facings is deep-profiled. Due to typical mechanical or thermal interactions, this facing is subjected to compression. The thick core of the sandwich panel plays a stabilizing role. However, at a specific critical load, local stability is lost, representing a typical form of damage that occurs in sandwich panels. In the case of a deep-profiled facing, the geometry of the facing must also be taken into account, specifically the fact that the bends resulting from profiling constitute a significant limitation to its deformation. In this study, expressions are derived that enable the determination of the critical (wrinkling) stress, taking into account the geometry of the compressed facing bands and various boundary conditions defined along their edges. The energy approach is used to solve the problem. The presented solution to the problem of local instability is illustrated using examples. The obtained results indicate that the use of narrow bands is particularly effective while also allowing for determination of the maximum benefits resulting from deep profiling of the facings. This information is essential when considering changes to the geometry of industrially produced sandwich panels or when optimizing the load-bearing capacity of individual sandwich elements. Full article

Epilepsy affects over 50 million people globally, with accurate seizure type classification directly influencing treatment selection as different seizure types respond to specific antiepileptic medications. Manual electroencephalogram (EEG) interpretation remains time-intensive and requires specialized expertise, creating clinical workflow bottlenecks. This work presents EEG-ARCNet, an attention-residual convolutional network integrating residual connections with channel attention mechanisms to extract discriminative temporal and spectral features from multi-channel EEG recordings. The model combines nine statistical temporal features with five frequency-band power measures through Welch’s spectral decomposition, processed through attention-enhanced convolutional pathways. Evaluated on the Temple University Hospital Seizure Corpus, EEG-ARCNet achieved 99.65% accuracy with 99.59% macro-averaged F1-score across five seizure types (absence, focal non-specific, simple partial, tonic-clonic, and tonic). To validate practical deployment, the model was implemented on Raspberry Pi 4, achieving a 2.06 ms average inference time per 10 s segment with 35.4% CPU utilization and 499.4 MB memory consumption. The combination of high classification accuracy and efficient edge deployment demonstrates technical feasibility for resource-constrained seizure-monitoring applications. Full article

Thermokarst lakes play a crucial role in regulating hydrological, ecological, and biogeochemical processes in permafrost regions. However, due to the limited spatial resolution of earlier satellite imagery, small thermokarst lakes—highly sensitive to climate change and permafrost degradation—have often been overlooked, hindering accurate spatiotemporal analyses. To address this limitation, five water indices—Modified Normalized Difference Water Index (MNDWI), Multi-Band Water Index (MBWI), Automated Water Extraction Index (AWEI_sh and AWEI_nsh), and Red Edge Water Index (RWI)—were employed based on Sentinel-2 imagery from 2021 to extract thermokarst lakes in the Qinghai–Tibet Highway (QTH) region, China. Visual validation indicated that the Red Edge Water Index (RWI) yielded the best performance, with an error of only 10.21%, significantly lower than other indices (e.g., MNDWI: 41.36%; MBWI: 38.80%). Seasonal comparisons revealed that the applicability of different water indices varies, with autumn months (September to October) being the optimal period for lake extraction due to stable and unfrozen surface conditions. Using the RWI, 56 thermokarst lake drainage events were identified in the study area from 2016 to 2025 (as of September 2025), most occurring after 2019—likely associated with climatic factors—and small lakes were found to be more prone to drainage, accompanied by notable surface subsidence in drained regions. These findings are applicable across the Qinghai–Tibet Plateau (QTP) and provide a scientific basis for monitoring thermokarst lakes, delineating accurate lake boundaries, and exploring drainage mechanisms. Full article

In this study, Fe₂O₃–TiO₂ nanocomposites with different TiO₂ contents (1–50%) were synthesized via a solvothermal method using pre-formed α-Fe₂O₃ nanoparticles as cores. We systematically evaluated the influence of TiO₂ loading on the nanocomposites’ structural, morphological, optical, and photocatalytic properties. X-ray diffraction revealed the coexistence of hematite and anatase phases, with an increase in TiO₂ content inducing reduced crystallite size, enhanced dislocation density, and microstrain, indicating interfacial lattice distortion. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) showed a uniform elemental distribution at low TiO₂ contents, evolving into irregular agglomerates at higher loadings. Fourier-transform infrared (FTIR) spectra indicated the suppression of Fe–O vibrations and the appearance of hydroxyl-related bands with TiO₂ enrichment. Diffuse reflectance spectroscopy (DRS) analysis confirmed the simultaneous presence of hematite (~2.0 eV) and anatase (3.2–3.35 eV) absorption edges, with a slight blue shift in the TiO₂ band gap at higher concentrations. Photocatalytic activity, assessed using methylene blue degradation under xenon lamp irradiation, demonstrated a strong dependence on the TiO₂ fraction. The composite containing 33% TiO₂ achieved the best performance, with 98% dye removal and a pseudo-first-order rate constant of 0.045 min⁻¹, outperforming both pure hematite and commercial P25 TiO₂. These results highlight that intermediate TiO₂ content (~33%) provides an optimal balance between structural integrity and photocatalytic efficiency, making Fe₂O₃–TiO₂ heterostructures promising candidates for water purification under simulated solar irradiation. Full article

The transition from Fifth Generation (5G) New Radio (NR) systems to Beyond 5G (B5G) and Sixth Generation (6G) networks requires innovative architectures capable of supporting ultra-high data rates, sub-millisecond latency, and massive connection densities in dense urban environments. This paper proposes a comprehensive design methodology for a mini-cluster architecture operating in sub-THz (0.1–0.3 THz) and THz (0.3–3 THz) frequency bands. The proposed framework aims to enhance existing 5G infrastructure while enabling B5G/6G capabilities, with a particular focus on hotspot coverage and mission-critical applications in dense urban environments. The architecture integrates mini Base Stations (mBS), Distributed Edge Computing Units (DECUs), and Intelligent Reflecting Surfaces (IRS) for coverage enhancement and blockage mitigation. Detailed link budget analysis, coverage and capacity planning, and propagation modeling tailored to complex urban morphologies are performed for representative case study cities, Quito and Guayaquil (Ecuador). Simulation results demonstrate up to 100 Gbps peak data rates, sub 100 μs latency, and tenfold energy efficiency gains over conventional 5G deployments. Additionally, the proposed framework highlights the growing importance of THz communications in the 5G evolution towards B5G and 6G systems, where ultra-dense, low-latency, and energy-efficient mini-cluster deployments play a key role in enabling next-generation connectivity for critical and immersive services. Beyond the studied cities, the proposed framework can be generalized to other metropolitan areas facing similar propagation and capacity challenges, providing a scalable pathway for early-stage sub-THz/THz deployments in B5G/6G networks. Full article

The rapid advancement of micro/nano-electromechanical systems (MEMS/NEMS) and precision manufacturing has fundamentally challenged traditional friction theories at the nanoscale. Classical continuum models fail to capture energy dissipation mechanisms at the atomic level, which are influenced by interfacial phenomena such as electron transfer, charge redistribution, and energy level realignment. Density functional theory (DFT), renowned for its accurate description of ground-state properties in many-electron systems, has emerged as a key tool for uncovering quantized friction mechanisms. By quantifying potential energy surface (PES) fluctuations, the evolution of interfacial charge density, and dynamic electronic band structures, DFT establishes a universal correlation between frictional dissipation and electronic behavior, transcending the limitations of conventional models in explaining stick–slip motion, superlubricity, and non-Amonton effects. Research breakthroughs in the application of DFT include characterizing frictional chemical potentials, designing heterojunction-based superlubricity, elucidating strain/load modulation mechanisms, and resolving electronic energy dissipation pathways. However, these advances remain scattered across interdisciplinary studies. This article systematically summarizes methodological innovations and cutting-edge applications of DFT in computational tribology, with the aim of constructing a unified framework for carrying out the “electronic structure–energy dissipation–frictional response” predictions. It provides a state of the art of using DFT to help design high-performance lubricants and actively control interfacial friction. Full article

Ship-radiated noise (SRN) recognition is vital for underwater acoustics, with applications in both military and civilian fields. Traditional manual recognition by sonar operators is inefficient and error-prone, motivating the development of automated recognition systems. However, most existing deep learning approaches demand high computational resources, limiting their deployment on resource-constrained edge devices. To overcome this challenge, we propose LW-MS-LFTFNet, a lightweight model informed by time-frequency analysis of SRN that highlights the critical role of low-frequency components. The network integrates a multi-scale depthwise separable convolutional backbone with CBAM attention for efficient spectral representation, along with two LSTM-based modules to capture temporal dependencies in low-frequency bands. Experiments on the DeepShip dataset show that LW-MS-LFTFNet achieves 75.04% accuracy with only 0.85 M parameters, 0.38 GMACs, and 3.27 MB of storage, outperforming representative lightweight architectures. Ablation studies further confirm that low-frequency temporal modules contribute complementary gains, improving accuracy by 2.64% with minimal overhead. Guided by domain-specific priors derived from time-frequency pattern analysis, LW-MS-LFTFNet achieves efficient and accurate SRN recognition with strong potential for edge deployment. Full article

Low-temperature superconductivity has been known since 1957 to be described by BCS theory for effective single-band metals controlled by the density of states at the Fermi level, very far from band edges, the electron–phonon coupling constant l, and the energy of the boson in the pairing interaction w₀, but BCS has failed to predict high-temperature superconductivity in different materials above about 23 K. High-temperature superconductivity above 35 K, since 1986, has been a matter of materials science, where manipulating the lattice complexity of high-temperature superconducting ceramic oxides (HTSCs) has driven materials scientists to grow new HTSC quantum materials up to 138 K in HgBa₂Ca₂Cu₃O₈ (Hg1223) at ambient pressure and near room temperature in pressurized hydrides. This perspective covers the major results of materials scientists over the last 39 years in terms of investigating the role of lattice inhomogeneity detected in these new quantum complex materials. We highlight the nanoscale heterogeneity in these complex materials and elucidate their special role played in the physics of HTSCs. Especially, it is highlighted that the geometry of lattice and charge complex heterogeneity at the nanoscale is essential and intrinsic in the mechanism of rising quantum coherence at high temperatures. Full article

This study investigates jet plume deflection in underexpanded supersonic rectangular nozzles with aft-decks. To determine the underlying mechanism, 117 two-dimensional, Reynolds-averaged Navier–Stokes simulations were performed across a nozzle pressure ratio (NPR) range of $1.9\le \mathrm{NPR}\le 5.0$ and aft-deck length ($L_{\mathrm{aft}}/D_h$) range of $1.36\le L_{\mathrm{aft}}/D_h\le 3.37$. For each simulation, the first shock reflection $S_1$, the wall-pressure field, the vertical force $F_y$, and the presence of any separation bubble were recorded to characterize the relationships among $\mathrm{NPR}$, $L_{\mathrm{aft}}$, and $\theta$. Accordingly, a cause-and-effect path was delineated as $(\mathrm{NPR},L_{\mathrm{aft}})\to S_1\to F_y\to \theta$. A weighted regression captured 96% of the variance in the deflection angle and revealed that shifts in shock position set the wall-pressure imbalance. The imbalance fixes the vertical force and the force ultimately rotates the jet plume. Downward deflection arises when the shock reflects near the deck edge, whereas upstream reflection initiates a shock–boundary-layer interaction that forms a separation bubble and drives the jet plume upward. Between these extremes, a narrow operating band allows either outcome, explaining the divergent trends reported in prior work. The quantitative model assumes steady, two-dimensional flow and the regression prioritises illuminating the underlying physics over exact prediction of $\theta$. Nevertheless, under these assumptions, the analysis establishes a physics-based framework that reconciles earlier observations and offers a basis for understanding how nozzle pressure ratio and aft-deck length govern jet plume deflection. Full article

Current lake and reservoir water body extraction algorithms are confronted with two critical challenges: (1) design dependency on specific geographical features, leading to constrained cross-regional adaptability (e.g., the JRC Global Water Body Dataset achieves ~90% overall accuracy globally, while the ESA WorldCover 2020 reaches ~92% for water body classification, both showing degraded performance in complex karst terrains); (2) information loss due to cloud occlusion, compromising dynamic monitoring accuracy. To address these limitations, this study presents a multi-feature fusion and multi-level hierarchical extraction algorithm for lake and reservoir water bodies, leveraging the Google Earth Engine (GEE) cloud platform and Sentinel-2 multispectral imagery in the karst landscape of Bijie City. The proposed method integrates the Automated Water Extraction Index (AWEIsh) and Modified Normalized Difference Water Index (MNDWI) for initial water body extraction, followed by a comprehensive fusion of multi-source data—including Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Red-Edge Index (NDREI), Sentinel-2 B8/B9 spectral bands, and Digital Elevation Model (DEM). This strategy hierarchically mitigates vegetation shadows, topographic shadows, and artificial feature non-water targets. A temporal flood frequency algorithm is employed to restore cloud-occluded water bodies, complemented by morphological filtering to exclude non-target water features (e.g., rivers and canals). Experimental validation using high-resolution reference data demonstrates that the algorithm achieves an overall extraction accuracy exceeding 96% in Bijie City, effectively suppressing dark object interference (e.g., false positives due to topographic and anthropogenic features) while preserving water body boundary integrity. Compared with single-index methods (e.g., MNDWI), this method reduces false positive rates caused by building shadows and terrain shadows by 15–20%, and improves the IoU (Intersection over Union) by 6–13% in typical karst sub-regions. This research provides a universal technical framework for large-scale dynamic monitoring of lakes and reservoirs, particularly addressing the challenges of regional adaptability and cloud compositing in karst environments. Full article

This study evaluated the integration of non-invasive remote sensing and colorimetry to classify the maturity stages of Coffea arabica fruits across four varieties: Caturra Amarillo, Excelencia, Milenio, and Típica. Multispectral signatures were captured using a Parrot Sequoia camera at wavelengths of 550 nm, 660 nm, 735 nm, and 790 nm, while colorimetric parameters L*, a*, and b* were measured with a high-precision colorimeter. We conducted multivariate analyses, including Principal Component Analysis (PCA) and multiple linear regression (MLR), to identify color patterns and develop predictors for fruit maturity. Spectral curve analysis revealed consistent changes related to ripening: a decrease in reflectance in the green band (550 nm), a progressive increase in the red band (660 nm), and relative stability in the RedEdge and near-infrared regions (735–790 nm). Colorimetric analysis confirmed systematic trends, indicating that the a* component (green to red) was the most reliable indicator of ripeness. Additionally, L* (lightness) decreased with maturity, and the b* component (yellowness to blue) showed varying importance depending on the variety. PCA accounted for over 98% of the variability across all varieties, demonstrating that these three parameters effectively characterize maturity. MLR models exhibited strong predictive performance, with adjusted R² values ranging between 0.789 and 0.877. Excelencia achieved the highest predictive accuracy, while Milenio demonstrated the lowest, highlighting varietal differences in pigmentation dynamics. These findings show that combining multispectral imaging, colorimetry, and statistical modeling offers a non-destructive, accessible, and cost-effective method for objectively classifying coffee maturity. Integrating this approach into computer vision or remote sensing systems could enhance harvest planning, reduce variability in specialty coffee lots, and improve competitiveness by ensuring greater consistency in cup quality. Full article

This work investigates a comprehensive temperature-dependent photoluminescence (PL) study (7–300 K) of β-Ga₂O₃ single crystals under 250 nm excitation. The emission consists of three competing bands at ~3.55 eV (J₁), ~3.37 eV (J₂), and ~3.07 eV (J₃), exhibiting a redshift, band broadening, and a crossover near ~140 K with increasing temperature. The novelty of this study lies in the first quantitative investigation of the temperature-dependent photoluminescence of undoped β-Ga₂O₃ single crystals, revealing activation, trap-release, and phonon-coupling parameters that define the competition between STE (Self-trapped exciton)- and DAP-related emission channels. A two-channel Arrhenius analysis of global thermal quenching at E_max (at maximum PL), J₁, and J₂ reveals a common shallow barrier (E₁ = 7–12 meV) alongside deeper, band-specific barriers (E₂ = 27 meV for J₁ and 125 meV for J₂). The J₃ band shows non-monotonic intensity (dip–peak–quench) reproduced by a trap-assisted generation model with a release energy E_rel = 50 meV. Linewidth analysis yields effective phonon energies (E_ph ≈ 40–46 meV), indicating strong electron–phonon coupling and a transition to multi-phonon broadening at higher temperatures. These results establish a coherent picture of thermally driven redistribution from near-edge STE-like states to deeper defect centers and provide quantitative targets (activation and phonon energies) for defect engineering in β-Ga₂O₃-based optoelectronic and scintillation materials. Full article

Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha⁻¹. Red and red-edge bands saturated at around 80 Mg·ha⁻¹, while certain spectral indices delayed the threshold to 100–150 Mg·ha⁻¹. Topographic features maintained stable contributions below 300 Mg·ha⁻¹, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring. Full article

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of soybean plants to various water regimes via hyperspectral reflectance (350–2500 nm) and machine learning (ML) models. The plants were subjected to eleven distinct water regimes, ranging from 100% to 0% field capacity, over 14 days. Seventeen key physiological parameters, including chlorophyll, carotenoids, flavonoids, proline, stress markers and water content, and hyperspectral data were measured to capture changes induced by water deficit. Principal component analysis (PCA) revealed significant spectral differences between the water treatments, with the first two principal components explaining 88% of the variance. Hyperspectral indices and reflectance patterns in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) regions are linked to specific stress markers, such as pigment degradation and osmotic adjustment. Machine learning classifiers, including random forest and gradient boosting, achieved over 95% accuracy in predicting drought-induced stress. Notably, a minimal set of 12 spectral bands (including red-edge and SWIR features) was used to predict both stress levels and biochemical changes with comparable accuracy to traditional laboratory assays. These findings demonstrate that spectroscopy by hyperspectral sensors, when combined with ML techniques, provides a nondestructive, field-deployable solution for early drought detection and precision irrigation in soybean cultivation. Full article

This study explores the use of mid-infrared (MIR) free-electron laser (FEL) irradiation as a tool for tailoring the surface properties of electrochemically synthesized TiO₂—graphene quantum dots (QDs). The QDs, prepared in colloidal form via a cost-effective electrochemical method in a KCl—citric acid medium, were exposed to MIR wavelengths (5.76, 8.02, and 9.10 µm) at the Kyoto University FEL facility. Post-irradiation measurements revealed a pronounced inversion of zeta potential by 40–50 mV and approximately 10% reduction in hydrodynamic size, indicating double-layer contraction and ionic redistribution at the QD—solvent interface. Photoluminescence spectra showed enhanced emission for GQDs and TiO₂/GQD composites, while Tauc analysis revealed modest bandgap blue shifts (0.04–0.08 eV), both consistent with trap-state passivation and sharper band edges. TEM confirmed intact crystalline structures, verifying that FEL-induced modifications were confined to surface chemistry rather than bulk lattice damage. Taken together, these results demonstrate that MIR FEL irradiation provides a resonance-driven, non-contact method to reorganize ions, suppress defect states, and improve the optoelectronic quality of QDs. This approach offers a scalable post-synthetic pathway for enhancing electron transport layers in perovskite solar cells and highlights the broader potential of photonic infrastructure for advanced nanomaterial processing and interface engineering in optoelectronic and energy applications. Full article