Browse Articles

Multifunctional gradations of TPMS architected heat exchanger for enhancements in flow and heat exchange performances

Abstract Heat exchangers (HXs) based on triply periodic minimal surface (TPMS) architectures have recently attracted significant interest due to their continuous and smooth shell structures with extensive surface areas. This study proposes an efficient design methodology for TPMS-based HXs by employing three gradation strategies to enhance their thermofluidic performance: (i) filtering gradation to guide hot and cold fluids through designated inlet and outlet regions with reduced flow resistance; (ii) cell-size gradation to ensure uniform flow distribution by reducing dead zones; and (iii) level-set gradation to maintain a minimum allowable wall thickness under cell-size variations. These multifunctional gradations are realized through adaptive manipulation of the signed distance fields for TPMS formulations. Computational fluid dynamics simulations were performed for various HX designs, identifying a graded design with cell sizes ranging from 6 to 10 mm as optimal for minimizing local flow stagnation. The optimized HX was fabricated via additive manufacturing and validated experimentally. Experimental results revealed a 30% improvement in heat exchange capacity with only a 0.3 kPa increase in pressure drop, resulting in a 28% enhancement in the overall heat exchange performance. These findings demonstrate that the multifunctional gradation approach enables the optimal design of TPMS-based HXs with superior thermofluidic performance and structural integrity.

Correction: The effect of resveratrol, curcumin and quercetin combination on immuno-suppression of tumor microenvironment for breast tumor-bearing mice

Association between timing of surgery and refracture after initial osteoporotic fractures

Spatiotemporal evolution, driving factors, and policy impacts on ecological quality in the li river basin from 1995 to 2021

Optimizing software crowdsourcing requirements design through machine learning

Persistence of hepatitis C virus in peripheral blood mononuclear cells of patients who achieved sustained virological response following treatment with direct-acting antivirals is associated with a distinct pre-existing immune exhaustion status

Abstract Hepatitis C virus (HCV) is a primary hepatotropic pathogen responsible for acute and chronic hepatitis C, however, it can also cause “occult” infection (OCI), defined as the presence of the virus’ genetic material in hepatocytes and/or peripheral blood cells, but not in plasma/serum. Assessment of the sustained virologic response (SVR) after treatment with direct-acting antivirals (DAA) is based exclusively on HCV-RNA testing in plasma/serum, which may preclude the diagnosis of post-treatment OCI. Possible clinical consequences of OCI were described previously, but its occurrence after DAA-based antiviral treatment programs and determinants of the virus persistence are not fully elucidated. The aim of this study was to assess the incidence of post-treatment OCI after successful DAA-based treatment and to identify clinical and immunological factors associated with this phenomenon. In 97 patients treated with DAA, HCV-RNA was tested by RT-PCR in peripheral blood mononuclear cells (PBMC) at baseline (i.e., before the onset of treatment) and at the time of SVR assessment. Before treatment, HCV-RNA was detectable in all patients’ PBMC. All subjects responded to therapy according to the clinical criteria, but 9 (9.3%) patients revealed the HCV-RNA in PBMC at SVR. In most of these cases, post-DAA OCI was related to switch of the dominant infecting genotype. Post-treatment OCI was characterized by significantly lower pre-treatment HCV viral load and lower expression of Tim-3 (T-cell immunoglobulin and mucin domain-containing protein 3) on CD8+ T-cells. Our results imply that post-treatment OCI may be related to lower pretreatment viral load as well as distinct pre-existing immune exhaustion status.

Preliminary clinical study of p-iodo benzoyl moiety-modified PSMA inhibitors and prospective comparison with 68Ga-PSMA-11

Inhibition of levodopa metabolism to dopamine by honokiol short-chain fatty acid derivatives may enhance therapeutic efficacy in Parkinson’s disease

Abstract This study investigates the antimicrobial properties of honokiol (HNK), a naturally occurring polyphenol, when conjugated with short-chain fatty acids (SCFAs) such as butyrate. We examined the effects of HNK-SCFA ester conjugates on Enterococcus faecalis, a gut bacterium that metabolizes levodopa, a drug used to manage Parkinson’s disease symptoms. Our findings indicate that HNK-SCFA-esters (e.g., HNK-acetate, HNK-propionate, HNK-butyrate, and HNK-hexanoate) inhibit E. faecalis growth in a dose-dependent manner, followed by a temporary recovery period during which levodopa remains intact and unmetabolized. Notably, HNK-SCFAs exhibit enhanced cellular permeability and are hydrolyzed within bacterial cells, releasing HNK and SCFAs. These results suggest that HNK-SCFAs may reversibly modulate the gut metabolism of levodopa to dopamine, potentially enhancing its therapeutic efficacy in treating Parkinson’s disease.

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.