Search Results (450)

High-spatiotemporal-resolution land surface temperature (LST) data are crucial for analyzing surface energy balance, modeling temperature-related processes, and monitoring thermal environments. However, despite advancements in multi-source fusion and reconstruction techniques, high-frequency LST data remain susceptible to anomalies such as abrupt changes and outliers due to retrieval uncertainties and varying observation conditions. Conventional statistical outlier detection methods risk misidentifying physically plausible rapid weather changes as data errors, introducing systematic biases. To address this, we propose a two-stage anomaly detection framework that follows a “temporal physical pre-screening first, spatial statistical verification later” logic. First, a piecewise empirical model, based on typical diurnal LST variation characteristics, is constructed to identify points violating physical patterns. Subsequently, a spatial consistency test using median absolute deviation (MAD) is introduced to distinguish real weather-driven fluctuations from genuine data anomalies from a spatial synergy perspective. This sequential design effectively reduces the risk of mis-correcting physically reasonable temperature variations. Validated using hourly seamless LST data (2016–2021) and ground observations in the Heihe River Basin, our method outperformed Seasonal-Trend decomposition using Loess (STL), double standardization methods, and robust Holt–Winters. For over 87% of the detected anomalies, the proposed method demonstrated positive improvement rates in RMSE, MAE, R, and R². The overall average improvement rates reached 23.61%, 18.79%, 16.46%, and 61.33%, respectively, indicating robust performance. The results underscore that explicitly incorporating physical constraints enhances the reliability and interpretability of quality control for high-temporal-resolution remote sensing LST data. Full article

There is limited information on the effects of irregular-spaced and clumped planting on forest production and structural diversity. We explored Tagetes patula L. development as a model system to demonstrate stand development under varying planting patterns, and conceptualized in the context of Pseudotsuga menziesii (Mirb.) Franco mesic production forests of the Pacific Northwestern USA. Two variable planting patterns, clumps of 24 plants and of four plants, were compared to square regular patterns in replicate growing boxes. Spatial patterns were compared post-planting and at maturation, along with stand-level metrics and final dry weights, and stand structural statistics were used to compare production and diversity. The clumped spatial structure of 24-plant clumps was maintained to maturity. Groups of four plants maintained clumping at small scales and regular patterns at larger scales. Initial Regular-Square spacing remained at 2 cm at maturity but became indistinguishable from random patterns at larger scales. There was (1) overall greater mean directional index for the Large-Clumped patterns and greater spatial complexity indices for both clumped patterns, (2) greater social class (size) mean mingling index for small clumps, and (3) higher mean dominance index and mean differentiation index and lower crown volume complexity and height-to-diameter ratios for Regular-Square spacing. The structural complexity was accompanied by limited differences in dried weights by plant tissue (total weight, stem, leaf, flower weight) or plant biometric parameters (stem straightness, crown ratio, crown volume, number of leaves and flowers). The results from irregular planted marigold stand development are discussed in the context of increasing forest stand complexity, potentially without compromising productivity. Full article

Urban traffic congestion leads to longer travel times, economic losses, and increased pollution. Recent advances in the Internet of Things (IoT) provide detailed real-time traffic data, yet testing adaptive control strategies directly on live networks remains costly and risky. To address this challenge, we propose an IoT-driven digital twin framework for the design and evaluation of AI-based traffic management systems. The framework is implemented in the Simulation of Urban MObility (SUMO) and uses its Python 3.14.2 API to emulate a dense network of IoT sensors that stream real-time information on vehicle density, queue lengths, and waiting times. This simulated IoT data feeds an AI agent that adapts traffic signal control in real time. The agent is trained with a composite reward function to jointly minimise vehicle waiting times and emissions. Its performance is compared with fixed-time and vehicle-actuated control under varying traffic demand scenarios. Results demonstrate the effectiveness of combining IoT-based simulation with AI control, providing a safe and scalable pathway towards the real-world deployment of intelligent traffic management systems. Full article

Preeclampsia is a double-hit vascular disorder centred on the VEGF-HO-1-CSE axis. First, excess placental soluble Flt-1 (sFlt-1) neutralises vascular endothelial growth factor (VEGF) and placental growth factor (PlGF), producing an angiogenic deficit that drives endothelial dysfunction, hypertension, proteinuria and end organ injury. Second, the failure of endogenous vascular brakes, heme oxygenase-1 (HO-1/CO) and cystathionine-γ-lyase (CSE)/hydrogen sulfide (H₂S) removes physiological restraint on anti-angiogenic factor release (sFlt-1; soluble endoglin) and amplifies oxidative–inflammatory stress, lowering the threshold at which VEGF loss precipitates severe disease. We synthesise human, animal and translational data that (i) establish placental sFlt-1 source and release, (ii) demonstrate human mechanistic causality via sFlt-1 removal, (iii) show prospective clinical validation that sFlt-1 rises and free PlGF falls before disease onset, and (iv) identify HO-1 and CSE/H₂S as protective pathways that restrain anti-angiogenic drive. Finally, we summarise preclinical evidence that the orally administered H₂S-donor prodrug MZe786 restores the HO-1/CSE axis, lowers sFlt-1 and soluble endoglin (sEng), and improves maternal haemodynamics and foetal outcomes across complementary pregnancy models, and we outline the role of sFlt-1/PlGF and M-PREG-based triage in clinical decision making. While valuable for short-term triage, current sFlt-1/PlGF-based approaches cannot sub-stratify among positive cases. Framing severe preeclampsia as a double-hit vascular disorder provides a biologically grounded framework that can inform risk stratification strategies like M-PREG^®, a clinical decision support system informed by the double hit framework, and prevention strategies, pairing early risk stratification with mechanism-informed interventions. Full article

Background: Obesity is a recognized risk factor for developing breast cancer (BC), but factors involved remain unclear. We investigated if breast adipose tissue from healthy women, BRCA1/2 mutation carriers and BC patients, can stimulate BC cell line migration and activation. Methods: adipose tissue conditioned medium (ATCM), was prepared from breast adipose tissue from healthy subjects (naïve; group 1 (n = 20)), BRCA1/2 mutation carriers (group 2 (n = 22)) and BC patients (group 3 (n = 38)). ATCM effect on migration of BC cell lines MCF-7, SK-BR-3 and MDA-MB-231 was measured with xCELLigence (ACEA Biosciences, San Diego, CA, USA) cell migration assay. Activation of migration was determined by measuring filopodia activation. Migration and filopodia activation were related to body mass index (BMI) and BC subtypes. Luminex multiplex assay was performed to examine the secretory profile of adipose tissue. Results: ATCM from group 1 induced migration and filopodia activation in MCF-7 and MDA-MB-231, but not in SK-BR-3. ATCM from group 2 induced filopodia activation but no migration. ATCM from group 3 induced less migration in MCF-7 than ATCM from group 1. Higher BMI was associated with increased ATCM-induced activation in MCF-7 (group 1) and MDA-MB-231 (group 2). ATCM from group 1 and 2 showed a metabolic secretory profile, whereas group 3 showed higher pro-angiogenic and inflammatory cytokines. Conclusions: This study shows that breast adipose tissue from healthy women, BRCA1/2 mutation carriers and BC patients, can stimulate BC cell line migration and activation. This effect is related to BC subtype and BMI. These data improve insight in adipose tissue as factor in BC development. Full article

Time series forecasting performance is strongly influenced by the structural properties of the underlying data, yet learning-based models are often applied without sufficient validation of this dependency. This study evaluates a uniformly configured Long Short-Term Memory (LSTM) model on five real-world weekly time series with different levels of periodicity, noise, and volatility. Forecasting is performed in a single-step setting using a fixed sliding window of 12 weeks under a consistent training, validation, and testing framework. Model performance is assessed using mean squared error (MSE) and the coefficient of determination R². The results show that for well-structured series, both the LSTM model and Holt’s exponential smoothing achieve very low MSE values with R² scores close to one, indicating excellent predictive accuracy. For other items, performance varies across methods, with either the LSTM or Holt model providing the best results depending on the data structure. These findings confirm that high forecasting accuracy can be achieved with both advanced and classical methods, and that data characteristics play a more decisive role than model complexity. Full article

The emergence of large language models (LLMs) and generative artificial intelligence (GenAI) has marked a turning point in health informatics. AI has become a very helpful tool for health informatics applications, with numerous AI applications in health informatics being reported in the last years. The objective of this paper is to synthesize the common concerns and opportunities raised by recent popular reviews on AI and health informatics. The main methodological topics covered in this up-to-date review include traditional AI, GenAI, and LLMs. The literature search was conducted through the popular academic database Scopus, which covers over one hundred million records, including both computer science and healthcare. Among these popular reviews (measured by the number of citations that each one received), clinical decision support, patient care, electronic health records, hospital management, and remote patient monitoring are the most mentioned healthcare topics. Different from the majority of the existing reviews that narrowly cover on one to a few topics in healthcare, our review is designed with the objective to provide a broad coverage, such that practitioners may benefit from comprehensive insights covering the above mentioned five popular topics in AI health informatics applications. Based on an in-depth analysis of these reviews by human experts, the main AI tools used, their main challenges, and some future directions have been identified in our investigation. Patient privacy, cybersecurity, ethics, clinical accountability, engaging health professionals, benchmarks and standardization, as well as lack of explainability are the common concerns identified from the literature covered in this review. Full article

An ultrafast laser system combined with an optical delay line allowed ablation and in-scription at 1 kHz and 1 GHz pulse burst within transparent polyimide films. The two-photon-induced absorption results in clean surface ablation, while inscription results in polymer decomposition, creating carbonised regions within the polymer. Three pulse bursts at 1 GHz increased the observed coupling to the material significantly. Modified regions (with linewidths down to a few microns) were investigated using optical microscopy, white light interferometry, SEM and Raman spectroscopy, supporting the increasing carbon density relative to the pristine polymer. As depth of field was only a few microns at high NA, 3D micro-structuring was achieved. Polymer decomposition produces gaseous products, resulting in internal stress and thus affecting inscription fidelity. An inscribed subsurface electrode with dimensions of 5 mm × 0.3 mm × 3 μm connected to conducting vias had a resistance of R = 10.6 ± 0.2 kΩ, along with resistivity of ρ ~ 0.19 Ω cm; hence, it had DC conductivity, σ ~ 5.3 Scm⁻¹. This conductivity is similar to that of bulk graphite and could well form the basis of future flexible sensors, demonstrating single-step 3D subsurface inscription of carbon or laser-induced graphene structures. Full article

This study examines the sectoral dynamics of natural gas consumption in Colombia by applying additive and multiplicative Holt–Winters exponential smoothing models. The analysis covers the main demand segments (Thermal Generation, Industrial, Residential, Refinery, Compressed Natural Gas for Vehicles (GNVC), Commercial, Petrochemical, and SNT Compressor Stations) using official monthly data from the Colombian Mercantile Exchange for the period April 2020 to July 2025. Model configurations were optimized by minimizing the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE) to identify the most appropriate structure for each sector. The results confirm that natural gas consumption in Colombia does not follow a uniform seasonal pattern. Instead, each segment exhibits distinct dynamics shaped by operational conditions, production schedules, mobility-related behavior, or logistical planning. The Thermal Generation sector was best represented by the multiplicative model, reflecting proportional variability associated with electricity dispatch and system-level operational changes. In contrast, the Industrial, Residential, GNVC, Commercial, and SNT Compressor Stations sectors showed superior performance under the additive model, consistent with relatively stable or constant-magnitude seasonal effects. The Petrochemical and Refinery sectors displayed short-term cyclical behavior, with model accuracy depending on the performance metric prioritized. These findings demonstrate that energy forecasting must incorporate the structural heterogeneity of demand systems rather than treating natural gas consumption as a homogeneous aggregate. Practically, the results provide insights for improving supply planning, contract allocation, and regulatory segmentation. The study also offers a replicable methodological basis for forecasting in emerging economies characterized by diverse consumption profiles. Full article

Background/Objectives: After fermentation, yogurt is often supplemented with probiotics, yet sweetened with added sugars that can negatively impact cardiometabolic health. Honey provides rare sugars, oligosaccharides and phenolics that may promote gut and cardiometabolic health. We aimed to determine the impact of yogurt sweetened with commercial clover blossom honey on pro-inflammatory Th17 cytokines and microbial-derived metabolites in healthy postmenopausal women. Methods: In a randomized controlled crossover dietary intervention trial, postmenopausal women (45–65 years of age) consumed two 150 g servings of yogurt for breakfast for 4 weeks, with each serving sweetened with a tablespoon of clover blossom honey or an isocaloric amount of sugar. Blood samples were collected for the measurement of plasma lipids, bile acids (BA) and Th17 cytokines, along with fecal short-chain fatty acids (SCFA). The primary outcome was plasma interleukin (IL)-23. Results: Neither dietary intervention significantly changed IL-23, plasma lipids, fecal SCFA or plasma BA. Compared to sugar-sweetened yogurt, IL-33 was significantly lower after 4 weeks of honey-sweetened yogurt intake. Conclusions: In a healthy population of postmenopausal women, the daily intake for 4 weeks of honey-sweetened yogurt did not significantly impact our primary outcome of IL-23. Instead, lower plasma levels of IL-33 were observed with honey compared to sugar-sweetened yogurt intake. The impact of the intervention on this cytokine was independent of changes in fecal SCFA and plasma BA. Confirmatory studies, in a larger population with levels of honey intake within dietary recommendations for added sugar, are warranted. Full article

Background/Objectives: Functional limitations are common among older cancer survivors and tend to increase with age and survivorship duration. Physical activity (PA) associates with better functional outcomes, but little is known about how these associations vary as time passes post-diagnosis. This study examined how years since diagnosis, three types of physical activity, and their interactions associate with functional limitations in older cancer survivors. Methods: Data drawn from the 2021 National Health Interview Survey (NHIS), representing adults aged 55+ and with a prior cancer diagnosis (n = 9356; mean age = 72.17 ± 8.5 years), were studied. A four-item self-reported difficulty index (i.e., washing/dressing, walking one block, climbing stairs, and picking up/opening objects) was summed to measure functional limitations. PA was assessed using the items aligned with the United States PA Guidelines. Hierarchical regression was used to evaluate associations between functional limitations and years since diagnosis, vigorous physical activity, moderate physical activity, and strength training. Interaction effects of years since diagnosis and each activity type were also examined. Covariates were age, sex, BMI, and educational attainment. Results: Elapsed time since cancer diagnosis positively associated with functional limitations in interaction with physical behaviors, while moderate physical activity and strength training negatively associated with functional limitations. Interactions of years since diagnosis and both moderate physical activity and strength training revealed smaller increases in functional limitations. No interaction effects were observed for vigorous physical activity. Conclusions: Among older cancer survivors, the association between survivorship duration and functional limitations differs by engagement in moderate and resistance-based physical activity. These findings support the clinical importance of promoting sustainable, non-vigorous physical activity in long-term survivorship care. Full article

Accurate passenger traffic forecasting is vital for strategic planning in Thailand’s aviation industry. This study forecasts the monthly total number of passengers at Suvarnabhumi (BKK), Don Mueang (DMK), Chiang Mai (CNX), and Phuket (HKT) airports using data from 2017 to 2024. The dataset was partitioned into training (January 2017–December 2023) and testing (January–December 2024) sets. Six methods were compared: Single Exponential Smoothing, Holt’s, Holt’s with Events Adjustment, Holt–Winters Multiplicative, TBATS model, and Box–Jenkins. Performance was evaluated using Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The results indicate that the optimal forecasting method varies by airport characteristics. Holt’s Method with Events Adjustment, which incorporates major disruptions such as the COVID-19 pandemic, produced the most accurate forecasts for BKK and DMK by effectively capturing external shocks. In contrast, the Holt–Winters Multiplicative method performed best for CNX and HKT, reflecting strong seasonal patterns typically driven by tourism activities in these destinations. Full article

Background: mRNA LNP technology is now being widely applied as a highly effective vaccine platform. Antigen multimerization is a well-established approach to enhance the antibody titers and protective efficacy of several protein subunit vaccines. However, this approach has been less explored for mRNA LNP vaccines. Methods: Here, within the context of mRNA LNP vaccination, we used mStrawberry (mSb) as a model antigen to conduct a comprehensive, head-to-head comparison of the ability of the foldon (3-mer), IMX313 (7-mer), and ferritin (24-mer) multimerization domains to enhance immunogenicity in mice. Results: We compared multimerized antigen to monomeric secreted antigen and monomeric surface-displayed antigen and observed that the IMX313 domain efficiently multimerized mSb protein and significantly enhanced anti-mSb antibody titers, whereas the foldon and ferritin domains failed to multimerize or improve antibody levels. Conclusions: Our results extend the observation of improved immunogenicity from antigen multimerization to mRNA LNP vaccines and indicate that the 7-mer forming IMX313 multimerization domain may be an ideal candidate for multimer formation in the context of mRNA LNP vaccination. Future studies are needed to evaluate the multimerization of pathogen-derived antigens, in the mRNA LNP format, for the enhancement of neutralization and protective efficacy. Full article

Complications are frequent in cancer patients and contribute to adverse outcomes and higher healthcare costs, underscoring the need for earlier identification and prediction. This study evaluated the feasibility of using passively generated smartphone sensor data to explore early-warning signals of complications and symptom worsening during cancer treatment. A total of 108 patients were continuously monitored using accelerometer, GPS, and screen on/off data collected through the LAIMA application, while symptoms of depression, fatigue, and nausea were assessed every two weeks and complications were confirmed during clinic visits or emergency presentations. Smartphone data streams were aggregated into variables describing activity and sociability patterns. Machine learning models, including Decision Tree, Extreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine, were used for complication prediction, and time-series models such as Autoregressive Integrated Moving Average, Holt–Winters, TBATS, Long Short-Term Memory neural network, and General Regression Neural Network were applied to identify early behavioral changes preceding symptom reports. In this exploratory analysis, the ensemble model demonstrated high sensitivity (89%) for identifying complication events. Smartphone-derived behavioral indicators enabled earlier detection of depression, fatigue, and vomiting by about nine days in a subset of patients. These findings demonstrate the feasibility of passive smartphone sensor data as exploratory early-warning signals, warranting validation in larger cohorts. Full article

We study one of the world’s largest cattle markets by revisiting and extending previous work on the forecasting of Brazil’s Boi Gordo Index (BGI). Using an updated daily dataset (July 2006–September 2025, inflation-adjusted), we evaluate classical and machine learning (ML) approaches for price prediction. Methods include Exponential Smoothing (Simple, Holt, and Holt–Winters), ARMA/ARIMA/SARIMA, GARMA variants, GARCH, Theta, Prophet, and XGBoost; models are compared under a strictly chronological 90/10 holdout (~476 test days) using RMSE, MAE, and MSE, with the AIC guiding within-family selection. Results show that, for the full out-of-sample window, GARMA delivers the best overall accuracy, with ARMA and Holt–Winters close behind, while Prophet and XGBoost perform comparatively worse in this volatile setting. Performance is horizon-dependent: in the first 180 test days, prior to the late-2024 level shift, Holt attains the lowest RMSE/MSE, and XGBoost achieves the lowest MAE. No method anticipates the October–November 2024 exogenous jump and subsequent correction, highlighting the difficulty of structural breaks and the need for timely re-specification. We conclude that GARMA is a robust default for long, turbulent windows, whereas smoothing and ML methods can be competitive on shorter horizons. These findings inform risk measurement and risk mitigation strategies in Brazil’s cattle futures market. Full article