Search Results (98)

The study investigates the contribution of technology (TECH), quantified by Internet penetration, in influencing tourism performance (TP) among the top ten touristic nations in Europe: France, Spain, Italy, Turkey, the United Kingdom, Germany, Greece, Austria, Portugal, and the Netherlands. Using panel data from 2000–2022, the study includes additional structural controls like environment quality, gross domestic production (GDP) per capita, exchange rate (ER), and safety index (SI). The Method of Moments Quantile Regression (MMQR) is employed to capture heterogeneous effects at different levels of TP, and Driscoll–Kraay standard error (DKSE) correction is employed to make the analysis robust against autocorrelation as well as cross-sectional dependence. Spectral–Granger causality tests are also conducted to check short- and long-run dynamics in the relationships. Empirical results are that TECH and SI are important in TP at all quantiles, but with stronger effects for lower-performing countries. Environmental quality (EQ) and GDP per capita (GDPPC) exert increasing impacts at upper quantiles, suggesting their importance in sustaining high-level tourism economies. ER effects are limited and primarily short-term. The findings highlight the need for integrated digital, environmental, and economic policies to achieve sustainable tourism development. The paper contributes to tourism research by providing a comprehensive, frequency-sensitive, and distributional analysis of macroeconomic determinants of tourism in highly developed European tourist destinations. Full article

Silicon (Si) and silicon carbide (SiC) are second- and third-generation semiconductor materials with excellent properties that are particularly suitable for applications in scenarios such as high temperature, high voltage, and high frequency. Si/SiC wafers face warpage and bending problems during production, which can seriously affect subsequent processing. Fast, accurate, and comprehensive detection of thickness, thickness variation, and flatness (including bow and warpage) of SiC and Si wafers is an industry-recognized challenge. Frequency scanning interferometry (FSI) can synchronize the upper and lower surfaces and thickness information of transparent parallel thin wafers, but it is still affected by multiple interfacial harmonic reflections, reflectivity asymmetry, and phase modulation uncertainty when measuring SiC thin wafers, which leads to thickness calculation errors and face reconstruction deviations. To this end, this paper proposes a high-precision facet reconstruction method for SiC/Si structures, which combines harmonic spectral domain decomposition, refractive index gradient constraints, and partitioning optimization strategy, and introduces interferometric signal “oversampling” and weighted fusion of multiple sets of data to effectively suppress higher-order harmonic interferences, and to enhance the accuracy of phase resolution. The multi-layer iterative optimization model further enhances the measurement accuracy and robustness of the system. The flatness measurement system constructed based on this method can realize the simultaneous acquisition of three-dimensional top and bottom surfaces on 6-inch Si/SiC wafers, and accurately reconstruct the key parameters, such as flatness, warpage, and thickness variation (TTV). A comparison with the Corning Tropel FlatMaster commercial system shows that this method has high consistency and good applicability. Full article

Early detection and continuous monitoring of diseases are critical to improving patient outcomes, treatment adherence, and diagnostic accuracy. Traditional melanoma diagnosis relies primarily on visual assessment and biopsy, with reported accuracies ranging from 50% to 90% and significant inter-observer variability. Among emerging diagnostic technologies, Raman spectroscopy has demonstrated considerable promise for non-invasive disease detection, particularly in early-stage skin cancer identification. A portable, real-time Raman spectroscopy system could significantly enhance diagnostic precision, reduce biopsy reliance, and expedite diagnosis. However, miniaturization of Raman spectrometers for portable use faces significant challenges, including weak signal intensity, fluorescence interference, and inherent trade-offs between spectral resolution and the signal-to-noise ratio. Recent advances in silicon photonics present promising solutions by facilitating efficient light collection, enhancing optical fields via high-index-contrast waveguides, and allowing compact integration of photonic components. This work introduces a numerical analysis of an integrated digital Fourier transform spectrometer implemented on a silicon-nitride (SiN) platform, specifically designed for Raman spectroscopy. The proposed system employs a switch-based digital Fourier transform spectrometer architecture coupled with a single optical power meter for detection. Utilizing a regularized regression method, we successfully reconstructed Raman spectra in the 800 cm⁻¹ to 1800 cm⁻¹ range, covering spectra of both benign and malignant skin lesions. Our results demonstrate the capability of the proposed system to effectively differentiate various skin cancer types, highlighting its feasibility as a non-invasive diagnostic sensor. Full article

The early and accurate detection of cancer remains a critical challenge in biomedical diagnostics. In this work, we propose and investigate a novel surface plasmon resonance (SPR) biosensor platform based on a multilayer configuration incorporating copper (Cu), silicon nitride (Si₃N₄), and molybdenum disulfide (MoS₂) for the optical detection of various cancer types. Four distinct sensor architectures (Sys₁–Sys₄) were optimized through the systematic tuning of Cu thickness, Si₃N₄ dielectric layer thickness, and the number of MoS₂ monolayers to enhance sensitivity, angular shift, and spectral sharpness. The optimized systems were evaluated using refractive index data corresponding to six cancer types (skin, cervical, blood, adrenal, breast T1, and breast T2), with performance metrics including sensitivity, detection accuracy, quality factor, figure of merit, limit of detection, and comprehensive sensitivity factor. Among the configurations, Sys₃ (BK7–Cu–Si₃N₄–MoS₂) demonstrated the highest sensitivity, reaching 254.64 °/RIU for adrenal cancer, while maintaining a low detection limit and competitive figures of merit. Comparative analysis revealed that the MoS₂-based designs, particularly Sys₃, outperform conventional noble-metal architectures in terms of sensitivity while using earth-abundant, scalable materials. These results confirm the potential of Cu/Si₃N₄/MoS₂-based SPR biosensors as practical and effective tools for label-free cancer diagnosis across multiple malignancy types. Full article

This study presents a numerical investigation of surface plasmon resonance (SPR) sensors based on multilayer configurations incorporating BK7, silver, silicon nitride (Si₃N₄), and black phosphorus (BP). Using the transfer matrix method, the optical performance of four architectures was evaluated under refractive index perturbations consistent with values reported in prior theoretical and experimental studies. The sensor response was characterized through metrics such as angular sensitivity, resonance shift, full width at half maximum, attenuation, and derived figures including detection accuracy and limit of detection. Parametric optimization was performed for the thickness of each functional layer to enhance sensing performance. Among all configurations, those incorporating both Si₃N₄ and BP demonstrated the highest angular sensitivity, reaching up to 394.46°/RIU. These enhancements were accompanied by increased attenuation and spectral broadening, revealing trade-offs in sensor design. The results, based entirely on numerical modeling, provide a comparative framework for guiding SPR sensor optimization under idealized optical conditions. Full article

With the rapid development of big data and artificial intelligence technologies, the era of Industry 4.0 has driven large open-pit mines towards digital and intelligent transformation. This is particularly true in mature mining areas such as the Yanshan Iron Mine, where the depletion of shallow proven reserves and the increasing issues of mixed surrounding rocks with shallow ore bodies make it increasingly important to build intelligent mines and implement green and sustainable development strategies. However, previous mineralization predictions for the Yanshan Iron Mine largely relied on traditional geological data (such as blasting rock powder, borehole profiles, etc.) exploration reports or three-dimensional explicit ore body models, which lacked precision and were insufficient to meet the requirements for intelligent mine construction. Therefore, this study, based on artificial intelligence technology, focuses on geoscience big data mining and quantitative prediction, with the goal of achieving multi-scale, multi-dimensional, and multi-modal precise positioning of the Yanshan Iron Mine and establishing its intelligent mine technology system. The specific research contents and results are as follows: (1) This study collected and organized multi-source geoscience data for the Yanshan Iron Mine, including geological, geophysical, and remote sensing data, such as mine drilling data, centimeter-level drone image data, and high-spectral data of rocks and minerals, establishing a rich mine big data set. (2) SOM clustering analysis was performed on the elemental data of rock and mineral samples, identifying key elements positively correlated with iron as Mg, Al, Si, S, K, Ca, and Mn. TSG was used to interpret shortwave and thermal infrared hyperspectral data of the samples, identifying the main alteration mineral types in the mining area. Combined with spectral and elemental analysis, the universality of alteration features such as chloritization and carbonation, which are closely related to the mineralization process, was further verified. (3) Based on the spectral and elemental grade data of rock and mineral samples, a training model for ore grade–spectrum correlation was constructed using Random Forests, Support Vector Machines, and other algorithms, with the SMOTE algorithm applied to balance positive and negative samples. This model was then applied to centimeter-level drone images, achieving high-precision intelligent identification of magnetite in the mining area. Combined with LiDAR image elevation data, a real-time three-dimensional surface mineral monitoring model for the mining area was built. (4) The Bagged Positive Label Unlabeled Learning (BPUL) method was adopted to integrate five evidence maps—carbonate alteration, chloritization, mixed rockization, fault zones, and magnetic anomalies—to conduct three-dimensional mineralization prediction analysis for the mining area. The locations of key target areas were delineated. The SHAP index and three-dimensional explicit geological models were used to conduct an in-depth analysis of the contributions of different feature variables in the mineralization process of the Yanshan Iron Mine. In conclusion, this study successfully constructed the technical framework for intelligent mine construction at the Yanshan Iron Mine, providing important theoretical and practical support for mineralization prediction and intelligent exploration in the mining area. Full article

The rapid expansion of built-up areas (BUAs) requires effective spatial and temporal monitoring, being a crucial practice for urban land use planning, resource allocation, and environmental studies, and spectral indices (SIs) can provide efficiency and reliability in automating the process of BUAs extraction. This paper explores the use of nine spectral indices and sixteen thresholding methods for the automatic mapping of BUAs using Landsat 8 imagery from a semi-arid climate in Morocco during spring and summer. These indices are the Normalized Difference Built-Up Index (NDBI), the Vis-red-NIR Built-Up Index (VrNIR-BI), the Perpendicular Impervious Surface Index (PISI), the Combinational Biophysical Composition Index (CBCI), the Normalized Built-up Area Index (NBAI), the Built-Up Index (BUI), the Enhanced Normalized Difference Impervious Surfaces Index (ENDISI) and the Built-up Land Features Extraction Index (BLFEI). Results show that BLFEI, SWIRED, and BUI maintain high separability between built-up and each of the other land cover types across both seasons, as evaluated via the Spectral Discrimination Index (SDI). The lowest SDI values for all three indices were observed for bare soil against BUAs, with BLFEI recording 1.21 in the wet season and 1.05 in the dry season, SWIRED yielding 1.22 and 1.08, and BUI showing 1.21 and 1.08, demonstrating their robustness in distinguishing BUAs from other land covers under varying phenological and soil moisture conditions. These indices reached overall accuracies of $93.97\%$, $93.39\%$ and $92.81\%$, respectively, in wet conditions, and $91.57\%$, $89.17\%$ and $89.67\%$, respectively, in dry conditions. The assessment of thresholding methods reveals that the Minimum method resulted in the highest accuracies for these indices in wet conditions, where bimodal medium peaked histograms were observed, whereas the use of Li, Huang, Shanbhag, Otsu, K-means, or IsoData was found to be the most effective under dry conditions, where more peaked histograms were observed. Full article

Straw incorporation (SI) is a key strategy for promoting sustainable agriculture. It aims to mitigate environmental pollution caused by straw burning and enhances soil organic matter content, which increases crop yields. Consequently, the accurate and efficient monitoring of SI is crucial for promoting sustainable agricultural practices and effective management. In this study, we employed the Google Earth Engine (GEE) to analyze time-series Sentinel-2 data with the Mann–Kendall (MK) algorithm. This approach enabled the extraction and spatial distribution retrieval of SI regions in a representative household mode area in Northeast China. Among the eight tillage indices analyzed, the simple tillage index (STI) exhibited the highest inversion accuracy, with an overall accuracy (OA) of 0.85. Additionally, the bare soil index (BSI) achieved an overall accuracy of 0.84. In contrast, the OA of the remaining indices ranged from 0.28 to 0.47, which were significantly lower than those of the STI and BSI. This difference indicated the limited performance of the other indices in retrieving SI. The high accuracy of the STI is primarily attributed to its reliance on the bands B11 and B12, thereby avoiding potential interference from other spectral bands. The geostatistical analysis of the SI distribution revealed that the SI rate in the household mode area was 36.10% in 2022 in the household mode area. Regions A, B, C, and D exhibited SI rates of 34.76%, 33.05%, 57.88%, and 22.08%, respectively, with SI mainly concentrated in the eastern area of Gongzhuling City. Furthermore, the study investigated the potential impacts of household farming practices and national policies on the outcomes of SI implementation. Regarding state subsidies, the potential returns from SI per hectare of cropland in the study area varied from RMB −65 to 589. This variation indicates the importance of higher subsidies in motivating farmers to adopt SI practices. Sentinel-2 satellite imagery and the MK test were used to effectively monitor SI practices across a large area. Future studies will aim to integrate deep learning techniques to improve retrieval accuracy. Overall, this research presents a novel perspective and approach for monitoring SI practices and provides theoretical insights and data support to promote sustainable agriculture. Full article

This paper investigates the wavelength-dependent sensitivity of a ridge waveguide based on a silicon nitride (Si₃N₄) platform, combining numerical analysis and experimental validation. In the first part, the modal characteristics of a Si₃N₄ ridge waveguide are analyzed in detail, focusing on the effective refractive index (n_eff), evanescent field ratio (EFR), and propagation losses (α_prop). These parameters are critical for understanding the interplay of guided light with the surrounding medium and optimizing waveguide design for sensing applications. In the second part, the wavelength-dependent sensitivity of a racetrack ring resonator (RTRR) based on the Si₃N₄ waveguide is experimentally demonstrated. The results demonstrate a clear increase in the sensitivity of the RTRR, rising from 116.3 nm/RIU to 143.3 nm/RIU as the wavelength shifts from 1520 nm to 1600 nm. This trend provides valuable insights into the device’s enhanced performance at longer wavelengths, underscoring its potential for applications requiring high sensitivity in this spectral range. Full article

The objective of this article is to create soil burn severity maps to serve as field support for erosion tasks after forest fire occurrence in Spain (2017–2022). The Analytical Spectral Device (ASD) FieldSpec and the CIMEL CE-312 radiometers (optical and thermal, respectively) were used as input data to establish relationships between soil burn severity and reflectance or emissivity, respectively. Spectral indices related to popular forest fire studies and soil assessment were calculated by Sentinel-2 convolved reflectance. All the spectral indices that achieve the separability index algorithm (SI) were validated using specificity, sensitivity, accuracy (ACC), balanced accuracy (BACC), F1-score (F1), and Cohen’s kappa index (k), with 503 field plots. The results displayed the highest overall accuracy results using the Iron Oxide ratio (IOR) index: ACC = 0.71, BACC = 0.76, F1 = 0.63 and k = 0.50, respectively. In addition, IOR was the only spectral index with an acceptable k value (k = 0.50). It is demonstrated that, together with NIR and SWIR spectral bands, the use of blue spectral band reduces atmospheric interferences and improves the accuracy of soil burn severity mapping. The maps obtained in this study could be highly valuable to forest agents for soil erosion restoration tasks. Full article

Soil salinity is considered one of the biggest constraints to crop production, particularly in arid and semi-arid regions affected by recurrent and long periods of drought, where high salinity levels severely impact plant stress and consequently agricultural production. Climate change accelerates soil salinization, driven by factors such as soil conditions, land use/land cover changes, and water deficits, over extensive spatial and temporal scales. Continuous monitoring of areas at risk of salinization plays a critical role in supporting effective land management and enhancing agricultural production. For these purposes, this work aims to propose a spatiotemporal method for monitoring soil salinization using spectral indices derived from Earth observation data. The proposed approach was tested in the Zaghouan Region in northeastern Tunisia, a region where soils are characterized by alarming levels of salinization. To address this concern, remote sensing techniques were applied for the analysis of satellite imagery generated from Landsat 5, Landsat 8, and Landsat 9 missions. A comprehensive field survey complemented this approach, involving the collection of 229 geo-referenced soil samples. These samples were representative of distinct soil salinity classes, including non-saline, slightly saline, moderately saline, strongly saline, and very strongly saline soils. Soil salinity modeling using Landsat-8 OLI data revealed that the SI-5 index provided the most accurate predictions, with an R² of 0.67 and an RMSE of 0.12 dS/m. By 2023, 42.3% of the study area was classified as strongly or very strongly saline, indicating a significant increase in salinity over time. This rise in salinity corresponds to notable land use and land cover (LULC) changes, as 55.9% of the study area experienced LULC shifts between 2000 and 2023. A decline in vegetation cover coincided with increasing salinity, showing an inverse relationship between these factors. Additionally, the results highlight the complex interplay among these variables demonstrating that soil salinity levels are significantly impacted by climate change indicators, with a negative correlation between precipitation and salinity (r = −0.85, p < 0.001). Recognizing the interconnections between soil salinity, LULC changes, and climate variables is essential for developing comprehensive strategies, such as targeted irrigation practices and land suitability assessments. Earth observation and remote sensing play a critical role in enabling more sustainable and effective soil management in response to both human activities and climate-induced changes. Full article

A nondestructive approach for accurate crop yield prediction at the field scale is vital for precision agriculture. Considerable progress has been made in the use of the spectral index (SI) derived from unmanned aerial vehicle (UAV) hyperspectral images to predict crop yields before harvest. However, few studies have explored the most sensitive wavelengths and SIs for crop yield prediction, especially for different nitrogen fertilization levels and soil types. This study aimed to investigate the appropriate wavelengths and their combinations to explore the ability of new SIs derived from UAV hyperspectral images to predict yields during the growing season of spring maize. In this study, the hyperspectral canopy reflectance measurement method, a field-based high-throughput method, was evaluated in three field experiments (Wang-Jia-Qiao (WJQ), San-Ke-Shu (SKS), and Fu-Jia-Jie (FJJ)) since 2009 with different soil types (alluvial soil, black soil, and aeolian sandy soil) and various nitrogen (N) fertilization levels (0, 168, 240, 270, and 312 kg/ha) in Lishu County, Northeast China. The measurements of canopy spectral reflectance and maize yield were conducted at critical growth stages of spring maize, including the jointing, silking, and maturity stages, in 2019 and 2020. The best wavelengths and new SIs, including the difference spectral index, ratio spectral index, and normalized difference spectral index forms, were obtained from the contour maps constructed by the coefficient of determination (R²) from the linear regression models between the yield and all possible SIs screened from the 450 to 950 nm wavelengths. The new SIs and eight selected published SIs were subsequently used to predict maize yield via linear regression models. The results showed that (1) the most sensitive wavelengths were 640–714 nm at WJQ, 450–650 nm and 750–950 nm at SKS, and 450–700 nm and 750–950 nm at FJJ; (2) the new SIs established here were different across the three experimental fields, and their performance in maize yield prediction was generally better than that of the published SIs; and (3) the new SIs presented different responses to various N fertilization levels. This study demonstrates the potential of exploring new spectral characteristics from remote sensing technology for predicting the field-scale crop yield in spring maize cropping systems before harvest. Full article

The development of new encapsulating coatings for flexible solar cells (SCs) can help address the complex problem of the short lifespan of these devices, as well as optimize the technological process of their production. In this study, new laminate-type protective composite coatings were prepared using a silicon oxynitride thin-film matrix obtained by curing the pre-ceramic polymer perhydropolysilazane (PHPS) through two low-temperature methods: (i) thermal annealing at 180 °C and (ii) exposure to UV radiation at wavelengths of 185 and 254 nm. Single-walled carbon nanotubes (SWCNTs) were used as fillers via dry transfer, facilitating their horizontal orientation within the matrix. The optical, adhesive, and structural properties of the matrix films and SiO_xN_y/SWCNT composite coatings, along with their long-term stability, were studied using Fourier transform infrared spectroscopy (FTIR), UV-Vis spectroscopy, HR-SEM, spectral ellipsometry, and a progressive-load scratch test. In this work, the optical constants of PHPS-derived films were systematically studied for the first time. An antireflection effect was observed in the composites revealing their two-component nature associated with (i) the refractive index of the SiO_xN_y matrix film and (ii) the embedding of a SWCNT filler into the SiO_xN_y matrix. The curing method of PHPS was shown to significantly affect the resulting properties of the films. In addition to being used as protective multifunctional coatings for SCs, both SiO_xN_y/SWCNT composites and SiO_xN_y matrix films also function as broadband optical antireflective coatings. Furthermore, due to the very low friction coefficients observed in the mechanical tests, they show potential as scratch resistant coatings for mechanical applications. Full article

Detailed analysis covered the optical and structural properties of Eu³⁺-doped NaY₉Si₆O₂₆ oxyapatite phosphors, which were obtained via hydrothermal synthesis. X-ray diffraction patterns of NaY₉Si₆O₂₆:xEu³⁺ (x = 0, 1, 5, 7, 10 mol% Eu³⁺) samples proved a single-phase hexagonal structure (P63/m (176) space group). Differential thermal analysis showed an exothermic peak at 995 °C attributed to the amorphous to crystalline transformation of NaY₉Si₆O₂₆. Electron microscopy showed agglomerates composed of round-shaped nanoparticles ~53 nm in size. Room temperature photoluminescent emission spectra consisted of emission bands in the visible spectral region corresponding to ⁵D₀ → ⁷F_J (J = 0, 1, 2, 3, 4) f-f transitions of Eu³⁺. Lifetime measurements showed that the Eu³⁺ concentration had no substantial effect on the rather long ⁵D₀-level lifetime. The Eu³⁺ energy levels in the structure were determined using room-temperature excitation/emission spectra. Using the ⁷F₁ manifold, the Nv-crystal field strength parameter was calculated to be 1442.65 cm⁻¹. Structural, electronic, and optical properties were calculated to determine the band gap value, density of states, and index of refraction. The calculated direct band gap value was 4.665 eV (local density approximation) and 3.765 eV (general gradient approximation). Finally, the complete Judd–Ofelt analysis performed on all samples confirmed the experimental findings. Full article

The Programmable Hyperspectral Seawater Scanner (PHySS) represents a significant breakthrough in monitoring harmful algal blooms (HABs), specifically targeting the “Florida red tide” caused by Karenia brevis. By utilizing a Fourth-Derivative Spectral Similarity Index (SI), this study establishes a strong positive correlation between the SI and phytoplankton counts, underscoring the PHySS’s potential for early detection and effective management of HABs. Our findings suggest that the PHySS could act as a predictive tool, offering crucial lead time to mitigate the ecological and economic repercussions of blooms. However, this study also identifies certain limitations of the PHySS technology, such as its inability to differentiate among various phytoplankton species without additional physical verification of cell counts. This limitation highlights the need for a multifaceted approach to HAB management. Our research suggests that adopting a multi-modal monitoring strategy could lead to more sophisticated and effective methods for combating HABs, fostering an optimistic outlook for future advancements in this area. Full article