Search Results (643)

This paper presents the growth and in situ optical characterization of silicon nanowires (Si NWs) on Al₂O₃(0001) substrates that are thermally faceted using the atomic low angle shadowing technique (ATLAS) method. Annealing Al₂O₃ substrates in air before surface faceting was used for the first time, as identified by atomic force microscopy (AFM). Planar Si NW arrays were subsequently deposited and characterized in real-time by reflectance anisotropy spectroscopy (RAS). RAS measurements detected irreversible spectral changes during growth, e.g., red-shift in peak energy for marking amorphous Si NW formation. Blue-shifts in RAS spectra following annealing post-growth at varied temperatures were found to be associated with structural nanowire development. AFM analysis following annealing detected dramatic changes in morphology, e.g., quantifiable differences in NW height and thickness and complete disappearance of nanowire structures at high temperatures. These results confirm the validity of in situ RAS as a monitoring tool for nanowire growth and illustrate Si NW morphology’s sensitivity to thermal processing. Full article

In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS³Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates. Full article

Recently, a link between gravitational tension (GT) and energy density via the Kretschmann scalar (KS) was proposed to construct regular black holes (RBHs) in pure Lovelock (PL) gravity. However, including a negative cosmological constant in PL gravity leads to a curvature singularity. Here, we choose the coupling constants such that the Lovelock equations admit an n-fold degenerate AdS vacuum (LnFDGS), allowing us to construct an RBH with $\Lambda <0$, where the energy density is analogous to the previously mentioned model. To achieve this, we propose alternative definitions for both the KS and GT. We find that, for mass parameter values greater than the extremal value $M_{\min }$, our RBH solution becomes indistinguishable from the AdS vacuum black hole from inside the event horizon out to infinity. At small scales, quantum effects modify the geometry and thermodynamics, removing the singularity. Furthermore, due to the lack of analytical relationships between the event horizon, photon sphere, and shadow in LnFDGS, we propose a numerical method to represent these quantities. Full article

The shadow economy weakens fiscal sustainability, hampers the financing of public goods, and impedes the achievement of sustainable development goals. The informal sector remains a persistent challenge for policymakers, as it distorts competition, reduces transparency, and undermines the effectiveness of economic and fiscal policies. The aim of this article is to identify the key factors determining the size of the shadow economy in European Union countries and to provide policy-relevant insights. The analysis covers data on the share of the informal economy in GDP and macroeconomic variables such as GDP per capita, consumer price index, average wages, household consumption, government expenditure, and unemployment, as well as indicators of digital development in society and the economy (DESI, IDT), the share of cashless transactions in GDP, and information on the implementation of digital tax administration tools and restrictions on cash payments. Five hypotheses (H1–H5) are formulated concerning the effects of income growth, labour market conditions, digitalisation, cashless payments, and tax administration tools on the shadow economy. The research question addresses which factors—macroeconomic conditions, economic and social digitalisation, payment structures, and fiscal innovations in tax administration—play the most significant role in determining the size of the shadow economy in EU countries and whether these mechanisms have broader implications for fiscal sustainability and sustainable development. The empirical strategy is based on multilevel models with countries as clusters, complemented by correlation and comparative analyses. The results indicate that the most significant factor in limiting the size of the shadow economy is the level of GDP per capita and its growth, whereas the impact of card payments appears to be superficial, reflecting overall increases in wealth. Higher wages, household consumption, and digital development as measured by the DESI also play an important role. The implementation of digital solutions in tax administration, such as SAF-T or e-PIT/pre-filled forms, along with restrictions on cash transactions, can serve as complementary measures. The findings suggest that sustainable strategies to reduce the shadow economy should combine long-term economic growth with digitalisation and improved tax administration, which may additionally foster the harmonisation of economic systems and support sustainable development. Full article

One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields. Full article

Multi-modal image segmentation is a key task in various fields such as urban planning, infrastructure monitoring, and environmental analysis. However, it remains challenging due to complex scenes, varying object scales, and the integration of heterogeneous data sources (such as RGB, depth maps, and infrared). To address these challenges, we proposed a novel multi-modal segmentation framework, DyFuseNet, which features dynamic adaptive windows and cross-scale feature fusion capabilities. This framework consists of three key components: (1) Dynamic Window Module (DWM), which uses dynamic partitioning and continuous position bias to adaptively adjust window sizes, thereby improving the representation of irregular and fine-grained objects; (2) Scale Context Attention (SCA), a hierarchical mechanism that associates local details with global semantics in a coarse-to-fine manner, enhancing segmentation accuracy in low-texture or occluded regions; and (3) Hierarchical Adaptive Fusion Architecture (HAFA), which aligns and fuses features from multiple modalities through shallow synchronization and deep channel attention, effectively balancing complementarity and redundancy. Evaluated on benchmark datasets (such as ISPRS Vaihingen and Potsdam), DyFuseNet achieved state-of-the-art performance, with mean Intersection over Union (mIoU) scores of 80.40% and 80.85%, surpassing MFTransNet by 1.91% and 1.77%, respectively. The model also demonstrated strong robustness in challenging scenes (such as building edges and shadowed objects), achieving an average F1 score of 85% while maintaining high efficiency (26.19 GFLOPs, 30.09 FPS), making it suitable for real-time deployment. This work presents a practical, versatile, and computationally efficient solution for multi-modal image analysis, with potential applications beyond remote sensing, including smart monitoring, industrial inspection, and multi-source data fusion tasks. Full article

Background and Clinical Significance: The pathology of the round ligament (RL) is rare and often remains in the shadow of common surgical emergencies. The preoperative diagnosis is challenging, leaving the surgeon perplexed as to whether and when to operate. The presented case deserves attention due to the difficult decision to operate based solely on the clinical picture, despite negative imaging diagnostic results. Case presentation: A 76-year-old woman was admitted to the Emergency Department with 6 h complaints of epigastric pain, nausea, and vomiting. She was afebrile with stable vital signs. The abdomen was slightly tender in the epigastrium, without rebound tenderness or guarding. The following blood variables were beyond the normal range: WBC—13.5 × 109/L; total bilirubin 26 mmol/L; amylase—594 U/L; CRP 11.4 mg/L; ASAT—158 U/L; and ALAT—95 U/L. The ultrasound (US) and multislice computed tomography (MSCT) of the abdomen were normal. A working diagnosis of acute pancreatitis was established, and intravenous infusions were initiated. The next day, the patient became hemodynamically unstable with blood pressure 80/60 mm Hg, heart rate 130/min, chills and fever of 39.5 °C, and oliguria. There was remarkable guarding and rebound tenderness in the epigastrium. The blood analysis revealed the following: WBC—9.9 × 109/L; total bilirubin—76 µmol/L; direct bilirubin—52 µmol/L; amylase—214 U/L; CRP 245 mg/L; ASAT—161 U/L; ALAT—132 U/L; GGT—272 U/L; urea—15.7 mmol/L; and creatinine—2.77 mg/dL. She was taken to the operating room for exploration, which revealed local peritonitis and phlegmon of the RL. Resection of the RL was performed. The microbiological analysis showed Klebsiella varicola. The patient had an uneventful recovery and was discharged on the 5th postoperative day. In the next months, the patients had several readmissions due to mild cholestasis and pancreatitis. The magnetic resonance demonstrated a duodenal diverticulum adjacent to the papilla, located near the junction of the common bile and pancreatic duct. This clinical manifestation and the location of the diverticulum were suggestive of Lemmel’s syndrome, but a papillary dysfunction attributed to the diverticulum or food stasis cannot be excluded. Conclusion: To our knowledge, we report the first association between RL gangrene and Lemmel’s syndrome. We speculate that duodenal diverticulitis with lymphatic spread of the infection or transient bacteriemia in the bile with bacterial translocation due to papillary dysfunction, as well as cholestasis resulting from the diverticulum, could be plausible and unreported causes of the RL infection. The preoperative diagnosis of RL gangrene is challenging because it resembles the most common emergency conditions in the upper abdomen. The present case warrants attention due to the difficult decision to operate based solely on the clinical picture, despite negative imaging results. A high index of suspicion should be maintained in a case of unexplained septic shock and epigastric tenderness, even in negative imaging findings. MSCT, however, is a valuable tool to avert unnecessary operations in conditions that must be managed conservatively, such as acute pancreatitis. Full article

Agrivoltaics (APV) mitigates land-use competition between photovoltaic installations and agricultural activities, thereby supporting multifaceted policy objectives in energy transition and sustainability. The availability of organic residuals from agrifood practices may also open the way to their energy valorization. This paper examines a small-scale farm in the Basilicata Region, southern Italy, to investigate the potential installation of an APV plant or a combined APV and bioenergy system to meet the electrical needs of the existing processing machinery. A dynamic numerical analysis is performed over an annual cycle to properly size the storage system under three distinct APV configurations. The panel shadowing effects on the underlying crops are quantified by evaluating the reduction in incident solar irradiance during daylight and the consequent agricultural yield differentials over the life period of each crop. The integration of APV and a biomass-powered cogenerator is then considered to explore the possible off-grid farm operation. In the sole APV case, the single-axis tracking configuration achieves the highest performance, with 45.83% self-consumption, a land equivalent ratio (LER) of 1.7, and a payback period of 2.77 years. For APV and bioenergy, integration with a 20 kW cogeneration unit achieves over 99% grid independence by utilizing a 97.57 kWh storage system. The CO₂ emission reduction is 49.6% for APV alone and 100% with biomass integration. Full article

To support dual-carbon objectives and enhance the accuracy of rooftop distributed photovoltaic (PV) planning, this study proposes a multidimensional coupled evaluation framework that integrates an improved rooftop segmentation network (CESW-TransUNet), a residual-fusion ensemble, and physics-based shading and performance simulations, thereby correcting the bias of conventional 2-D area–based methods. First, CESW-TransUNet, equipped with convolution-enhanced modules, achieves robust multi-scale rooftop extraction and reaches an IoU of 78.50% on the INRIA benchmark, representing a 2.27 percentage point improvement over TransUNet. Second, the proposed residual fusion strategy adaptively integrates multiple models, including DeepLabV3+ and PSPNet, further improving the IoU to 79.85%. Finally, by coupling Ecotect-based shadow analysis with PVsyst performance modeling, the framework systematically quantifies dynamic inter-building shading, rooftop equipment occupancy, and installation suitability. A case study demonstrates that the method reduces the systematic overestimation of annual generation by 27.7% compared with traditional 2-D assessments. The framework thereby offers a quantitative, end-to-end decision tool for urban rooftop PV planning, enabling more reliable evaluation of generation and carbon-mitigation potential. Full article

This study has compiled the first impact-crater dataset for Mercury with diameters greater than 400 m by a multimodal deep-learning pipeline. We present an enhanced deep learning framework for large-scale planetary crater detection, extending the YOLOLens architecture through the integration of multimodal inputs: optical imagery, digital terrain models (DTMs), and hillshade derivatives. By incorporating morphometric data, the model achieves robust detection of impact craters that are often imperceptible in optical imagery alone, especially in regions affected by low contrast, degraded rims, or shadow-dominated illumination. The resulting catalogs LU6M371TGT for the Moon and ME6M300TGT for Mercury constitute the most comprehensive automated crater inventories to date, demonstrating the effectiveness of multimodal learning and cross-planet transfer. This work highlights the critical role of terrain information in planetary object detection and establishes a scalable, high-throughput pipeline for planetary surface analysis using modern deep learning tools. To validate the pipeline, we compare its predictions against the manually annotated catalogs for the Moon, Mercury, and several regional inventories, observing close agreement across the full diameter spectrum, revealing a high level of confidence in our approach. This work presents a spatial density analysis, comparing the spatial density maps of small and large craters highlighting the uneven distribution of crater sizes across Mercury. We explore the prevalence of kilometer-scale (1–5 km range) impact craters, demonstrating that these dominate the crater population in certain regions of Mercury’s surface. Full article

The Central Yunnan Water Diversion Project (CYWDP) is currently under construction and represents China’s most extensive and geologically challenging water transfer infrastructure, facing significant geohazard risks induced by intensive engineering activities, posing severe threats to its entire lifecycle safety. Therefore, monitoring and spatiotemporal evolution analysis of surface deformation along the CYWDP is critically important. This study presents the first integrated analysis of geometric distortions and multi-dimensional spatiotemporal deformation characteristics along the CYWDP, utilizing both ascending and descending orbit data from Sentinel-1. First, by integrating the Layover-Shadow Mask (LSM) model and R-Index method, we identified geometric distortion types in SAR imagery and evaluated their suitability for deformation monitoring. Subsequently, SBAS-InSAR technology was employed to derive line-of-sight (LOS) deformation information from 124 images (ascending) and 90 images (descending) acquisitions (2022–2024), enabling the identification of significant deformation zones and analyzing their spatial distribution characteristics. Finally, two-dimensional (2D) deformation fields were obtained through the joint inversion of ascending and descending orbit data in typical deformation zones. The results reveal that geometric distortions in Sentinel-1 imagery along the CYWDP are dominated by foreshortening effects, accounting for 35.3% of the study area in the ascending-orbit data and 37.9% in the descending-orbit data. A total of 10 significant deformation-prone areas were detected, and the most pronounced subsidence, amounting to −164 mm/y, was observed in the northern Jinning District (Luoci-Qujiang section), showing expansion trends toward water conveyance infrastructure. This study reveals surface deformation’s multi-dimensional spatiotemporal evolution patterns along the CYWDP. The findings support geohazard mitigation and provide a methodological reference for safety monitoring of major water conservancy projects in complex geological environments. Full article

Photogrammetry-based 3D reconstruction of the shape of fast-moving objects from image sequences presents a complex yet increasingly important challenge. The 3D reconstruction of a large number of fast-moving objects may, for instance, be of high importance in the study of dynamic phenomena such as impact experiments and explosions. In this context, analyzing the 3D shape, size, and motion trajectory of the resulting fragments provides valuable insights into the underlying physical processes, including energy dissipation and material failure. High-speed cameras are typically employed to capture the motion of the resulting fragments. The high cost, the complexity of synchronizing multiple units, and lab conditions often limit the number of high-speed cameras that can be practically deployed in experimental setups. In some cases, only a single high-speed camera will be available or can be used. Challenges such as overlapping fragments, shadows, and dust often complicate tracking and degrade reconstruction quality. These challenges highlight the need for advanced 3D reconstruction techniques capable of handling incomplete, noisy, and occluded data to enable accurate analysis under such extreme conditions. In this paper, we use a combination of photogrammetry, computer vision, and artificial intelligence techniques in order to improve feature detection of moving objects and to enable more robust trajectory and 3D shape reconstruction in complex, real-world scenarios. The focus of this paper is on achieving accurate 3D shape estimation and motion tracking of dynamic objects generated by impact loading using stereo- or monoscopic high-speed cameras. Depending on the object’s rotational behavior and the number of available cameras, two methods are presented, both enabling the successful 3D reconstruction of fragment shapes and motion. Full article

Face recognition under uneven illumination conditions presents significant challenges, as asymmetric shadows often obscure facial features while overexposed regions lose critical texture details. To address this problem, a novel symmetry-aware illumination enhancement method named face shadow detection network (FSDN) is proposed, which features a nested U-Net architecture combined with Gaussian convolution. This method enables precise illumination intensity maps for the given face images through higher-order quadratic enhancement curves, effectively extending the low-light dynamic range while preserving essential facial symmetry. Comprehensive evaluations on the Extended Yale B and CMU-PIE datasets demonstrate the superiority of the proposed FSDN over conventional approaches, achieving structural similarity (SSIM) indices of 0.48 and 0.59, respectively, along with remarkably low face recognition error rates of 1.3% and 0.2%, respectively. The key innovation of this work lies in its simultaneous optimization of illumination uniformity and facial symmetry preservation, thereby significantly improving face analysis reliability under challenging lighting conditions. Full article

We perform large-eddy simulations (LESs) with realistic radiation, including topographic shading, and an advanced land surface model to investigate drainage flow dynamics in an idealized compound-slope mountain geometry. This allows an analysis not only of fully developed profiles in steady state—the subject of existing analytical solutions—but also of transient two- and three-dimensional dynamics. The evening onset of downslope flow is related to the duration of shadow front propagation along the eastern slopes, for which an analytic form is derived. We demonstrate that the flow response to this radiation pattern is mediated by the thermal inertia of the land through sensitivity to soil moisture. Onset timing differences on opposite sides of the peak are explained by convective structures that persist after sunset over the western slopes when topographic shading is considered. Although these preceding convective systems, as well as the presence of neighboring terrain, inhibit the initial development of drainage flows, the LES develops an approximately steady-state, fully developed flow over the finite slopes and finite nocturnal period. This allows a comparison to analytical models restricted to such cases. New analytical solutions based on surface heat flux boundary conditions, which can be estimated by the coupled land surface model, suggest the need for improved representation of the eddy diffusivity for analytical models of drainage flows. Full article

Research in western countries concludes that children and adolescents are exposed to multiple forms of online risks and harms. However, in the context of Saudi Arabia, research in online safety education is lagging. Currently, online safety education is generic and not research informed. Hence, this exploratory study seeks to generate a qualitative understanding of online risks and harms experienced by Saudi children, adolescents, and parents as well as online safety strategies. Using a semi-structured interview, this study explores the views of 15 children (12–15 years) and 10 parents. Interpretative Phenomenological Analysis (IPA) yielded four key themes: Negotiating the Promise and Peril of the internet, Living with the Shadows of the Online World, Psychological, and Physical Health Consequences, and Navigating Safety in a Digital Landscape of Uncertainty. While the benefits are clear (e.g., education and socialisation), children and parents have shared worries about cyberbullying, aggression, and exploitation. Internet addiction and isolation are notable consequences along with vision impairment and obesity. Children’s online safety practices are reactive, e.g., blocking and deleting risky content/behaviour, while parents share their struggles in monitoring children online. Online safety education, or the lack of it, is to blame. Children’s and parents’ limited awareness of online risks and poor online safety practices need to improve in Saudi Schools and households; there is an urgent need for further research and adequate implementation of systematic online safety education. Full article