Search Results (2,010)

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

Stereotactic radiosurgery (SRS) for multiple brain metastases can be delivered with a single isocenter and non-coplanar arcs, achieving highly conformal dose distributions at the cost of extreme modulation of treatment machine parameters. As a result, SRS plans are at a higher risk of patient-specific quality assurance (PSQA) failure compared to standard treatments. This study aimed to develop a machine-learning (ML) model to predict the PSQA outcome (gamma passing rate, GPR) of SRS plans. Five hundred and ninety-two consecutive patients treated between 2020 and 2024 were selected. GPR analyses were performed using a 3%/1 mm criterion and a 95% action limit for each arc. Fifteen plan complexity metrics were used as input features to predict the GPR of an arc. A stratified and a time-series approach were employed to split the data into training (1555 arcs), validation (389 arcs), and test (486 arcs) sets. The ML model achieved a mean absolute error of 2.6% on the test set, with a 0.83% median residual value (measured/predicted). Lower values of the measured GPR tended to be overestimated. Sensitivity and specificity were 93% and 56%, respectively. ML models for virtual QA of SRS can be integrated into clinical practice, facilitating more efficient PSQA approaches. Full article

Concrete sleepers are essential components of railroad infrastructure, yet their service life has been reduced by one-third due to deterioration caused by internal swelling reactions (ISR), leading a major Brazilian railroad to replace millions of sleepers within a decade. This study investigates the reliability of various non-destructive testing (NDT) techniques to estimate damage levels in concrete sleepers. The methods evaluated include surface hardness testing, stress wave propagation, electromagnetic wave propagation using ground-penetrating radar (GPR), electrical resistivity, and resonant frequency. These techniques were applied to assess sleepers diagnosed as affected by alkali-silica reaction (ASR) and delayed ettringite formation (DEF) at different deterioration degrees. Although findings indicate that most NDT methods are limited and unreliable for quantifying ISR-induced damage, resonant frequency testing combined with energy dissipation analysis provided the highest accuracy across all damage stages and was able to capture microstructural changes before significant expansion occurred. These results support the use of vibration-based screening tools to enhance early detection and guide condition assessment of railroad infrastructure, helping to reduce the premature replacement of ISR-affected concrete sleepers. Full article

Energy raw materials are the basis of the economic system. From this emerges the need to examine in more detail how various uncertainty indices interact with the dynamic of spillover connectedness among energy markets. The TVP-VAR model is used to investigate connectedness among US, European, and Indian oil and gas markets and the S&P carbon allowances Eua index. Following this, the wavelet decomposition technique is used to capture the dynamic correlations between uncertainty indices (GPR and VIX) and connectedness indices. First, the results indicate that energy market spillovers are time-varying and crisis-sensitive. Second, the time–frequency dependence among uncertainty indices and connectedness indices is more marked and can change with the occurrence of unexpected events and geopolitical conflicts. The VIX index shows a positive dependence on total dynamic connectedness in the mid-long-term, while the GPR index has a long-term effect only after 2020. The analysis of the interdependence among the connectedness of each market and the uncertainty indices is more heterogeneous. Political tensions and geopolitical risks are, therefore, causal factors of energy prices. Given their strategic and economic importance, policy makers and investors should establish a risk warning mechanism and try to avoid the transmission of spillovers as much as possible. Full article

Economic stability in emerging market economies is increasingly shaped by the interplay between global financial integration, domestic monetary dynamics, and asset price fluctuations. Yet, early detection of financial market disruptions remains a persistent challenge. This study constructs a Financial Stress Index (FSI) for Pakistan, utilizing monthly data from 2005 to 2024, to capture systemic stress in a globalized context. Using Principal Component Analysis (PCA), the FSI consolidates diverse indicators, including banking sector fragility, exchange market pressure, stock market volatility, money market spread, external debt exposure, and trade finance conditions, into a single, interpretable measure of financial instability. The index is externally validated through comparisons with the U.S. STLFSI4, the Global Economic Policy Uncertainty (EPU) Index, the Geopolitical Risk (GPR) Index, and the OECD Composite Leading Indicator (CLI). The results confirm that Pakistan’s FSI responds meaningfully to both global and domestic shocks. It successfully captures major stress episodes, including the 2008 global financial crisis, the COVID-19 pandemic, and politically driven local disruptions. A key understanding is the index’s ability to distinguish between sudden global contagion and gradually emerging domestic vulnerabilities. Empirical results show that banking sector risk, followed by trade finance constraints and exchange rate volatility, are the leading contributors to systemic stress. Granger causality analysis reveals that financial stress has a significant impact on macroeconomic performance, particularly in terms of GDP growth and trade flows. These findings emphasize the importance of monitoring sector-specific vulnerabilities in an open economy like Pakistan. The FSI offers strong potential as an early warning system to support policy design and strengthen economic resilience. Future modifications may include incorporating real-time market-based metrics indicators to better align the index with global stress patterns. Full article

Background/Objectives: Fasting-induced elevation of blood ketone body levels suppresses allergic reactions; however, the underlying mechanism remains unclear. This study investigated whether elevated ketone body levels affect allergic contact dermatitis (ACD) and explored nutritional interventions that effectively increase β-hydroxybutyrate (BHB) levels. Additionally, we examined the role of GPR109A, a receptor for β-hydroxybutyrate (BHB), in ketone body-induced allergy suppression through ingestion of a ketogenic substrate. Methods: To evaluate the effects of ketone body precursors, medium-chain triglyceride (MCT) oil or 1,3-butanediol (BD) was administered as a single oral dose (2 g/kg body weight) under fed conditions. Blood BHB concentrations were measured at the time of euthanasia. ACD was induced using 2,4-dinitrofluorobenzene (DNFB), and its severity was assessed by measuring ear swelling and mast cell (MC) degranulation. To determine whether GPR109A mediates ketone body-induced allergy suppression, mepenzolate bromide (MPN), a GPR109A antagonist, was subcutaneously administered before BD treatment. Results: Both MCT oil and BD significantly increased the blood BHB levels. Elevated BHB concentrations were accompanied by reduced ear swelling and MC degranulation in DNFB-treated mice. The anti-allergic effects of BD were abolished by MPN administration, indicating that these effects were mediated by GPR109A activation. Conclusions: Nutritional supplementation with ketogenic substrates, such as MCT oil and BD, may serve as a dietary intervention for ACD by elevating blood BHB levels. GPR109A activation appears to be involved in ketone body-induced allergy suppression, suggesting a mechanistic link between ketone metabolism and immunomodulation. Full article

Accurate soil moisture estimation is critical for precision agriculture and water resource management, yet traditional sampling methods are time-consuming, destructive, and provide limited spatial coverage. Ground Penetrating Radar (GPR) offers a promising non-destructive alternative, but optimal machine learning approaches for GPR-based soil moisture prediction remain unclear. This study presents a comparative analysis of regression tree and boosted tree algorithms for predicting soil moisture content from Ground Penetrating Radar (GPR) histogram features across 21 sites in Eastern Thailand. Soil moisture content was measured at multiple depths (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m) using samples collected during Standard Penetration Test procedures. Feature extraction was performed using 16-bin histograms from processed GPR radargrams. A single regression tree achieved a cross-validation RMSE of 5.082 and an R² of 0.761, demonstrating superior training accuracy and interpretability. In contrast, the boosted tree ensemble achieved significantly better generalization performance, with a cross-validation RMSE of 4.7915 and an R² of 0.708, representing a 5.7% improvement in predictive performance. Feature importance analysis revealed that specific histogram bins effectively captured moisture-related variations in GPR signal amplitude distributions. A comparative evaluation demonstrates that while single regression trees offer superior interpretability for research applications, boosted tree ensembles provide enhanced predictive performance that is essential for operational deployment in precision agriculture and hydrological monitoring systems. Full article

The evolving legal landscape has increased marijuana accessibility across the United States, including for medical use to manage clinical symptoms among people with HIV. The effects of marijuana use remain understudied in youth with HIV (YWH), who face lifelong antiretroviral therapy (ART) and an elevated risk of developing comorbidities. This study applied a multi-modal approach, including plasma biomarker analysis, peripheral blood cell phenotyping, and transcriptome profiling, to examine the effects of recreational marijuana alone, tobacco alone, or marijuana combined with tobacco in virally suppressed YWH (≤50 RNA copies/mL) on ART compared to youth without HIV and YWH who used no substance. Marijuana use alone was associated with elevated IL-10 levels and normalization of pro-inflammatory genes and pathways, suggesting an immunomodulatory effect. Conversely, tobacco use alone or combined with marijuana was linked to increased IL-1β levels and heightened pro-inflammatory responses, including upregulation of genes involved in inflammasome activation. This study is the first to demonstrate GPR15 upregulation and potential marijuana-associated epigenetic modulation in HIV-suppressed youth. The findings identify potential markers for early detection of inflammation-related comorbidities in YWH, particularly among those exposed to tobacco and underscore the need for targeted profiling to guide personalized monitoring and early substance use intervention strategies for YWH. Full article

Objectives: Hepatocellular carcinoma (HCC) remains a major indication for liver transplantation (LT), but accurate pretransplant risk stratification is critical to improve long-term outcomes. Traditional morphometric criteria such as tumor size and number are limited in predicting recurrence and survival. The HALP (hemoglobin, albumin, lymphocyte, platelet), gamma-glutamyl transpeptidase to platelet ratio (GPR), and FIB-4 indices are emerging systemic inflammatory and nutritional biomarkers that may provide additional prognostic value in HCC patients undergoing LT. Materials and Methods: This retrospective, two-center cohort study included 200 patients who underwent LT for HCC between 2012 and 2023. Preoperative HALP, GPR, and FIB-4 scores were calculated, and their associations with overall survival (OS) and recurrence-free survival (RFS) were assessed using ROC analyses and Cox proportional hazard models. Cut-off values were determined for each biomarker, and survival outcomes were analyzed using Kaplan–Meier methods. Results: A low HALP score (≤0.39) was independently associated with reduced OS but not with RFS. Conversely, low GPR (≤0.45) and FIB-4 (≤3.1) values were significantly associated with both poor OS and higher recurrence risk. Tumor size, number of lesions, and microvascular invasion also independently predicted poor outcomes. Multivariate analysis confirmed HALP, GPR, and FIB-4 as significant preoperative predictors of prognosis in this population. Conclusions: HALP, GPR, and FIB-4 are readily available, cost-effective indices that provide significant prognostic information in HCC patients undergoing LT. Their integration with morphometric criteria may improve pretransplant risk stratification and support individualized clinical decision-making. Full article

Localizing ground-penetrating radar (LGPR) benefits from deep subsurface coupling, ensuring robustness against surface variations and adverse weather. While LGPR is widely recognized as the complement of existing vehicle localization methods, its reliance on prior maps introduces significant challenges. Channel misalignment during traversal positioning and time-dimension distortion caused by non-uniform platform motion degrade matching accuracy. Furthermore, rain and snow conditions induce subsurface water-content variations that distort ground-penetrating radar (GPR) echoes, further complicating the localization process. To address these issues, we propose a weather-resilient adaptive spatio-temporal mask alignment algorithm for LGPR. The method employs adaptive alignment and dynamic time warping (DTW) strategies to sequentially resolve channel and time-dimension misalignments in GPR sequences, followed by calibration of GPR query sequences. Moreover, a multi-level discrete wavelet transform (MDWT) module enhances low-frequency GPR features while adaptive alignment along the channel dimension refines the signals and significantly improves localization accuracy under rain or snow. Additionally, a local matching DTW algorithm is introduced to perform robust temporal image-sequence alignment. Extensive experiments were conducted on both public LGPR datasets: GROUNDED and self-collected data covering five challenging scenarios. The results demonstrate superior localization accuracy and robustness compared to existing methods. Full article

The impact of burdock tea (BT) made from burdock (Arctium lappa) roots in normal individuals and animal models remains largely unknown, particularly on lung protection. This study examined responses of oxidative stress, inflammation, and the microbiota within the cecum and the lung to BT treatment in healthy Wistar rats. A middle-dose BT reduced the Chao1 and Shannon indices, and both low and middle doses induced structural alterations in the cecal microbiota. Additionally, low doses increased the abundances of Phascolarctobacterium, Alloprevotella, Desulfovibrio, and the NK4A214 group. In the lung, middle and high doses increased Corynebacterium, with high doses also boosting Megasphaera and Lactobacillus. Functionally, low doses downregulated the biosynthesis of antibiotics in the cecal microbiota, while middle doses reduced the Epstein–Barr virus and Escherichia coli pathogenic infection pathways; additionally, middle and high doses modulated chromosomal proteins and bile acid biosynthesis in the pulmonary microbiota. BT treatment enhanced the content of short-chain fatty acids (SCFAs), upregulated the expression of GPR43, and suppressed NLRP3 expression in both the colon and lung tissues, while concurrently promoting the expression of ZO-1 and Occludin. Furthermore, serum levels of IL-1β and IL-6, as well as tissue levels of MDA, were significantly reduced. Notably, propionate exhibited an inverse correlation with MDA, IL-6, and NLRP3, while showing a positive correlation with ZO-1. Similarly, acetate was negatively correlated with MDA and NLRP3 and positively correlated with ZO-1. Overall, BT exhibits a nontoxic profile and may protect lung tissue through its antioxidant nature and gut–lung axis mediated by SCFAs. Full article

The present paper deals with the combined application of near-surface geophysical techniques in a sustainable agriculture project. Their application is focused on the identification of any subsurface water in the context of sustainable water management for the selected living hub, located in the semi-arid area of Agios Georgios-Mandra Attiki. The objective of the multidisciplinary geophysical study was to determine the depth of the bedrock and the thickness of the post-Alpine deposits. In addition, the subsurface karstification and the possible aquifer presence were examined. For that reason, the following techniques were implemented: Electrical Resistivity Tomography, Seismic Refraction Tomography, Ground-Penetrating Radar, and Very-Low Frequency electromagnetic technique. The study was also supported by drone LiDAR usage. The investigation revealed several hydrogeological characteristics of the area. The thickness of the post-Alpine sediments is almost 3 m. However, no shallow aquiferous systems have been developed in this formation, as indicated by their relatively high resistivity values (100–1000 Ohm.m). Furthermore, the alpine bedrock exhibits extensive karstification, facilitated by the development of fracture zones. The absence of an underlying impermeable layer prevented the development of aquiferous zones, at least up to a depth of 100 m. Full article

Intelligent prediction of rock integrity is essential for tunneling construction. Ground-Penetrating Radar (GPR), a high-resolution detection technique, is usually used for rock integrity prediction. However, the geological conditions of the detection workface are rarely considered when utilizing the GPR to forecast rock integrity. In this paper, a multi-model intelligent prediction method for tunnel rock integrity based on geological differences of GPR exploration workfaces is proposed. Firstly, the structural features are extracted from the GPR detection data through matrix calculations. A statistic is proposed to judge the abnormal data, and filtering rules are formulated to eliminate abnormal data. Then, considering the difference of geological conditions of the GPR exploration workface, multi-models are established with different degrees of fragmentation of the exploration workface. Finally, the validity of the multi-model prediction method is proved by practical engineering verification. Full article

This study investigates the Holocene evolution of the Laraquete-Carampangue strandplain on the tectonically active coast of south-central Chile using ground penetrating radar and light detection and ranging data. The Laraquete-Carampangue strandplain, on the tectonically active coast of south-central Chile, is a rare accretionary feature in a region dominated by rocky shorelines and limited sediment supply. The light detection and ranging data-derived digital elevation model reveals a complex geomorphology comprising 52 beach ridges, aeolian dunes, and fluvial paleochannels, while ground penetrating radar radargrams uncover marine and aeolian facies influenced by past seismic and climatic events. We interpret these units in the frame of past seismic and climatic events. Our geomorphological and stratigraphic findings suggest that the strandplain progradation was driven by relative sea-level changes associated with Holocene seismic cycles and climate change. We propose that the transition from drier to humid conditions in the late Holocene triggered the onset of dune formation at the end of the Little Ice Age. This integrated approach highlights the interplay of tectonic and climatic forcings in shaping coastal landforms, offering insights into their long-term response to environmental change. Full article

Ground-Penetrating Radar (GPR) is a non-destructive detection technique widely employed for identifying underground targets. Despite its utility, conventional approaches suffer from limitations, including poor adaptability to multi-scale targets and suboptimal localization accuracy. To overcome these challenges, we propose a lightweight deep learning framework, the Dual Attentive YOLOv11 (You Only Look Once, version 11) Keypoint Detector (DAYKD), designed for robust underground target detection and precise localization. Building upon the YOLOv11 architecture, our method introduces two key innovations to enhance performance: (1) a dual-task learning framework that synergizes bounding box detection with keypoint regression to refine localization precision, and (2) a novel Convolution and Attention Fusion Module (CAFM) coupled with a Feature Refinement Network (FRFN) to enhance multi-scale feature representation. Extensive ablation studies demonstrate that DAYKD achieves a precision of 93.7% and an mAP50 of 94.7% in object detection tasks, surpassing the baseline model by about 13% in F1-score, a balanced metric that combines precision and recall to evaluate overall model performance, underscoring its superior performance. These findings confirm that DAYKD delivers exceptional recognition accuracy and robustness, offering a promising solution for high-precision underground target localization. Full article