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Simultaneous influence of nanoPSS and photonic crystal on light extraction in AlGaN 304nm UVB LEDs
Abstract The external-quantum efficiency (EQE) of AlGaN-based ultraviolet-B light-emitting diodes (UVB LEDs) has achieved a world record value of 9.6% on wafers but suffers from a low light extraction efficiency (LEE) of < 15%, notably lower than that of the LEE of InGaN blue LEDs (> 89%). This study employed the finite-difference time-domain (FDTD) method to explore how micro-patterned c-plane Sapphire substrates (microPSS) or nano-patterned c-plane Sapphire substrates (nanoPSSs) and reflecting photonic crystals (R-PhCs) influence light scattering in flip-chipped AlGaN-based UVB LEDs, with or without an Al-side reflector. First, various microPSS and nanoPSS shapes (Pillar-like and Hole-like) were analysed by the FDTD to optimise the pitch (a), diameter (d), height (h), and diffraction order (m) under Bragg’s condition. The nanoPSS were found most effective for UVB LEDs at an emission peak of 304 nm with cylindrical Hole-like nanoPSS (m = 10, d = 596 nm, a = 746 nm, h = 500 nm, R/a = 0.38), (R is the radius of the holes of the nanoPSS or PhC) improving LEE enhancement to the maximum possible value of approximately 18%. Next, an Al-side reflector was introduced to evaluate the combined impact of optimised nanoPSS and R-PhC (Hole-like) on theoretical light extraction. Parameters (m = 3; h = 150 nm; R/a = 0.40) applied in p-GaN or p-AlGaN contact layers boosted light extraction to approximately 148% or 150% (with an Al-side reflector) and approximately 120% (without an Al-side reflector), marking significant theoretical and experimental advancements in AlGaN UVB LED efficiency.
Grouting failure mechanism and new pre-grouting reinforcement technology of narrow coal pillar in fully-mechanized caving gob-side entry
Burden of ischemic stroke attributable to high low-density lipoprotein cholesterol in China from the global burden of disease study 2021
Boosting fluorescence efficiency via filling technique prepared photonic crystal composites
Abstract Au-doped photonic crystals offer considerable potential for boosting optical signals, however, precisely controlling the distance between luminescent particles and Au nanoparticles (NPs) faces severe challenges. We proposed a “filling” technique to prepare porous Au-doped inverse-opal PC (IOPC) with encapsulated Au NPs uniformly dispersing in insulating silica. The effective separation between Au NPs and infiltrated luminescent quantum dots successfully addresses the issue of fluorescence quenching, enhancing the photoluminescence intensity by 106-fold. Additionally, the double-layer IOPC-OPC composite, integrating an Au-doped IOPC and an opal photonic crystal (OPC) completely reflecting excitation or emission light, significantly improves the fluorescence intensity to 242-fold, far superior to the published counterparts. This synergy of localized surface plasmon resonance, high density of state, and photonic band gap in the IOPC-OPC composite offers an effective and low-loss approach for the precise modulation and amplification of photoluminescence. This strategy is crucial for the development of next-generation optical devices with improved sensitivity and stability.
Research on the electro-magnetic-thermal–mechanical characteristics of shearer cables
Data driven assessment of built environment impacts on urban health across United States cities
Quantum-enhanced intelligent system for personalized adaptive radiotherapy dose estimation
Abstract This research introduces a novel quantum-enhanced intelligent system tailored for personalized adaptive radiotherapy dose estimation. The system efficiently models radiation transport and predicts patient-specific dose distributions by integrating quantum algorithms, deep learning, and Monte Carlo simulations. Quantum-enhanced Monte Carlo simulations, employing algorithms such as Harrow-Hassidim-Lloyd (HHL) and Variational Quantum Eigensolver (VQE), achieve computational speedups of 8–15 times compared to classical methods while maintaining high accuracy. The deep learning architecture leverages convolutional and recurrent neural networks to capture complex anatomical and dosimetric patterns. Validation on simulated datasets demonstrates a 50–70% reduction in mean absolute error and 2–3% improvements in gamma index metrics compared to conventional approaches. Dose-volume histogram analysis further highlights enhanced Dice coefficients and reduced Hausdorff distances. These advancements underscore the potential for precise, efficient, and clinically relevant dose estimations, paving the way for improved outcomes in personalized adaptive radiotherapy.
Spatial distribution and population structure of the invasive Anopheles stephensi in Kenya from 2022 to 2024
Mathematical mechanistic model representing the cancer immunity cycle under radiation effects
Dysregulation of cell migration by matrix metalloproteinases in geleophysic dysplasia
Estimation of mass loss under wear test of nanoclay-epoxy nanocomposite using response surface methodology and artificial neural networks
Abstract In this work, the wear behavior of nanoclay-epoxy nanocomposites is studied through Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) as predictive models. This study aims to measure mass loss under wear conditions by studying critical parameters like nanoclay wt%, load, speed, time, and water soaking time. Experimental runs are planned based on the Box-Behnken design of RSM to create a regression model, which is then validated by ANOVA analysis. An ANN model is also trained and tested to improve predictive accuracy, performing better than RSM. The results show that wear resistance is greatly enhanced by increasing nanoclay content, which minimizes material loss. Water absorption adversely affects wear performance, resulting in enhanced mass loss caused by plasticization and swelling. The ANN model is more accurate in prediction than RSM, with minimal variation from experimental data. Scanning Electron Microscopy (SEM) analysis gives insights into wear mechanisms. The research demonstrates the efficiency of combining statistical and machine-learning methods for optimizing wear-resistant polymer nanocomposites.
Dietary composition and overlap between cattle and endangered mountain gazelle (Gazella gazella)
Abstract Israel’s Mediterranean biogeographical region is characterized by high habitat diversity and stark seasonal changes in forage composition, availability and quality. Managers of protected areas in this region advocate livestock ranching to mitigate fire risk and enhance conservation merits. However, competition between livestock and endangered, native ungulates in these areas might impair their functioning as refugia. We used fecal DNA metabarcoding to study the diets of native mountain gazelles (Gazella gazella) and domestic cattle (Bos taurus), in two nature reserves with distinct vegetation types (shrubland vs grassland), and during different seasons. Dietary overlap was ubiquitously low, and seasonal changes in the diets of both ungulates translated into differences in their dietary overlap, with the highest overlap found in grassland during winter. This generally low overlap may be attributed to the extreme differences in their body size or may also result from long-lasting sympatry of gazelles and cattle – first wild and later domesticated—shaping a robust dietary separation. Yet, since cattle biomass is typically much higher than gazelles’, a low dietary overlap in key food items of gazelles may result in their depletion which might negatively affect gazelles, especially during the fawning season and drought years. Our results highlight the need to cover diverse conditions when studying herbivore dietary composition and overlap.
EIF4A3 enhances the viability, invasion and osteogenic differentiation of BMSCs via the USP53/SMAD5 pathway
Nationwide association between ambient ozone and sudden cardiac arrests in South Korea
Machine learning method based on radiomics help differentiate posterior pituitary tumors from pituitary neuroendocrine tumors and craniopharyngioma
Abstract Posterior pituitary tumors (PPTs) are rare neoplasms, but easily misdiagnosed as pituitary neuroendocrine tumor (PitNET) and craniopharyngioma. This study aimed to differentiate PPTs from PitNET and craniopharyngioma using a machine learning method based on radiomics. The cohort used for training and testing contained 33 PPTs and 99 non-posterior pituitary tumors (NPPTs). The validation cohort consisted of prospectively included patients (9 PPTs and 33 NPPTs). Radiomics features based on T1-weighted images and contrast-enhanced (CE) T1-weighted images were extracted, or both. Data of training and testing cohort were input to a nested 10-fold to build models, which were independently validated in the validation cohort. A least absolute shrinkage and selection operator (LASSO) was used for dimensionality reduction and random forest was used as classifier. Predictive models were successfully established, and models based on CE features had the best performance with an accuracy of 0.786, precision of 0.929, specificity of 0.778, sensitivity of 0.788, and area under the curve of 0.818 in validation. Nine features selected by more than 75% of the models based on CE features were identified as the most predictive features. We established a group of machine learning models to noninvasively differentiate PPTs from NPPTs before surgery, which may improve the surgical plan of PPTs to better complete resection of the tumors and protection of important structures around the tumors.
Author Correction: Impact of behavioral and psychological symptoms of Alzheimer’s disease on caregiver outcomes
Correction: Analyzing cardiovascular disease hospitalization risks due to cold and heat waves in Dezful
Impacts of soil physical and mechanical behaviors under different tillage depths for agrotechnical operation in Bukito, Sidama, Ethiopia
An on-chip deformability checker demonstrates that the severity of iron deficiency is associated with increased deformability of red blood cells
Abstract The deformability of red blood cells (RBCs) is essential for peripheral circulation and RBC survival and is reportedly altered in several diseases. However, its detail in iron-deficiency (ID) anemia remains poorly understood. We investigated the association between ID and RBC deformability in 120 participants classified into four groups according to their ferritin and hemoglobin levels: non-ID/non-anemia (n = 61), ID/non-anemia (n = 32), ID/anemia (n = 15), and non-ID/anemia (n = 12). In the analysis using the established on-chip deformability checker, the normalized transit velocity of RBCs through the peripheral-vessel constriction models was significantly higher in the ID/anemia group than in the other groups. The RBC deformability index (RDI), derived from the normalized transit velocity and degree of RBC deformation, was highest in the ID/anemia group, followed by the ID/non-anemia group. The RDI was negatively correlated with log10 ferritin levels (r = -0.66, p < 0.01), even after adjusting for hemoglobin levels. Lower log10 ferritin levels correlated with thinner, more oval-shaped RBCs and lower internal viscosity. These RBC characteristics were significantly associated with a higher RDI. These results suggest that, among several known determinants of RBC deformability, RBC morphology and internal viscosity are altered in ID, resulting in higher deformability.