Search Results (3,846)

Optical and passive remote sensing-based estimation of aboveground biomass (AGB) using forest structural stratification has shown improvements over global models. This study investigated whether stratification by human-mediated disturbances improves prediction accuracy. Disturbance variables included proximity to mines, roads, and settlements, evaluated across three regimes: the full Atewa landscape (“FSR”), the Atewa Range Forest Reserve (“FR”), and the surrounding disturbed area (“SR”). Predictor selection for regimes was performed using recursive feature elimination with cross-validation, applied to random forest (RF) and support vector machine (SVM) algorithms. AGB was then estimated using local, global, and retuned global models, and the results were compared using the coefficient of determination (r²) and root mean square error (RMSE). The global RF model achieved the best performance (r² = 0.54; RMSE = 57.71 Mg/ha), likely due to structured heterogeneity captured across combined regimes. The “SR” models, however, performed poorly, indicating that excessive unstructured heterogeneity introduces noise and redundancy that weaken predictions. The low performance of the “FR” regime was attributed to spectral saturation and limited variance in observed AGB. Although disturbance factors added minimal bias, heteroscedasticity was evident in the “SR” and “FSR” regimes. Overall, this study indicates that disturbance-based stratification may not necessarily improve AGB estimation accurately compared to global models. However, it highlights the value of disturbance information for AGB modeling in heterogeneous forest landscapes. Full article

Quantum reinforcement learning (QRL) is often evaluated under idealized, noiseless assumptions, yet realistic quantum devices inevitably introduce noise that can severely degrade performance. This paper improves the robustness of quantum deep Q-learning (QDQN) by redesigning the variational quantum circuit (VQC) used in its value-function approximator. Motivated by recent advances in quantum convolutional neural networks (QCNNs), we construct four QCNN-inspired VQC variants (Models A–D) by combining representative QCNN two-qubit building blocks with an explicit fully connected (all-to-all) layer. Using a 10-fold evaluation protocol at a fixed noise level p = 0.005, Model D achieves the best robustness, reducing the mean number of episodes required to reach a target reward from 1981 (baseline) to 1243. Under a stricter success criterion, Model D also doubles the empirically observed noise-tolerance boundary from 0.002 to 0.004. These results indicate that carefully chosen QCNN-style circuit components and connectivity can significantly improve the noise robustness of QDQN-like QRL agents. Full article

The N,N′-Bis(salicylidene)-1,3-propanediamine Schiff base (Salpn) was synthesized, characterized, and assessed as a corrosion inhibitor for low-carbon steel (LCS) in a 0.5 mol L⁻¹ HCl solution. The study included chemical, electrochemical, and quantum mechanical methods to provide a comprehensive assessment. Experimental results revealed that the inhibition efficiency (IE) of Salpn increased with concentration, reaching a maximum of 69.1% at 300 ppm and 298 K, while a slight decrease to 64.3% was observed as the temperature increased. Tafel plot identified Salpn as a mixed-type inhibitor, while electrochemical impedance spectroscopy (EIS) revealed that the double layer capacitance decreased while the charge-transfer resistance increased as the concentration of Salpn increased. The thermodynamic study revealed that the adsorption of Salpn on the LCS surface follows the Langmuir isotherm model. The calculated standard free energy of adsorption (ΔG^°_ads) values ranged from −27.53 to −30.17 kJ mol⁻¹, confirming that the inhibition process occurs via a mixed mechanism involving both physisorption and chemisorption. The presence of a protective film on the LCS surface was suggested by SEM observations, while EDX analysis showed an increase in C, O, and N signals, providing further indication of the inhibitor’s integration into the surface layer. Density functional tight-binding (DFTB+) calculations supported the high inhibitory performance by showing a low hardness value (0.091 eV). The compound’s high global softness (σ = 10.989 eV⁻¹) suggested that it is an effective corrosion inhibitor. The Monte Carlo (MC) simulations demonstrated a strong interaction with a highly negative adsorption energy of −654.145 kJ mol⁻¹. These findings collectively validate Salpn as an effective and strongly adsorbing corrosion inhibitor. Full article

Background: Differentiating Ménière’s disease (MD) from vestibular migraine (VM) remains difficult because current diagnostic frameworks are predominantly clinical and incorporate pure-tone thresholds, risking incorporation bias. We asked whether speech discrimination scores (SDS) alone can separate MD from VM at the patient level and whether adding a prespecified vestibular marker, the caloric–vHIT dissociation, pattern A (abnormal calorics with normal horizontal vHIT), improves performance. Methods: In a retrospective cohort (2015–2018) including definite MD (n = 60) and definite VM (n = 40) by Bárány/ICHD criteria, we trained patient-level logistic regression models with 5-fold out-of-fold validation and in-fold preprocessing. To avoid incorporation bias, PTA was excluded from all models. Predefined feature sets were as follows: (1) SDS-only (bilateral SDS), (2) CalHiT-A-only (Yes/No; canal paresis ≥22% with horizontal-canal vHIT gain ≥0.80 in either ear), and (3) SDS+CalHiT-A. Discrimination was assessed by ROC–AUC with bootstrap 95% CIs; calibration and decision-curve analysis (DCA) are reported. An exploratory model encoded SDS as “affected/healthy.” Results: The SDS-only model achieved AUC 0.866 (95% CI 0.787–0.937). CalHiT-A-only yielded AUC 0.674 (0.561–0.778). Adding CalHiT-A to SDS did not improve discrimination (SDS+CalHiT-A AUC 0.844 [0.760–0.913]). The exploratory “affected/healthy” SDS encoding underperformed (AUC 0.801 [0.706–0.882]). CalHiT-A was significantly more prevalent in MD than in VM (56.7% [34/60] vs. 17.5% [7/40]; Fisher’s exact p = 1.49 × 10⁻⁴). Calibration favored SDS-only, and DCA showed the highest net benefit for SDS-only across thresholds p = 0.05–0.40. Conclusions: Bilateral SDS alone provides robust, well-calibrated discrimination between MD and VM and outperforms CalHiT-A and the affected/healthy SDS encoding. In this cohort, vestibular test dissociation did not add diagnostic value beyond SDS at the patient level, supporting SDS-centered diagnostic workflows while reserving CalHiT-A for adjudication and phenotyping rather than primary classification. Full article

Bipolar Polymer Membranes (BPMs) enable the creation of large, stable pH gradients by driving water dissociation (WD) at the cation/anion junction under reverse bias, a process central to electrodialysis, CO₂ capture, and emerging acid–alkaline water electrolysis. Yet despite decades of study, the mechanism by which intense interfacial electric fields accelerate WD remains debated and is often modeled with ad hoc assumptions. In this study, we present a power dissipation model in which minority ions from water autoprotolysis act as carriers that continuously dissipate field-supplied power in the hydrated nanometric junction. This dissipative input increases the local probability of heterolytic O–H bond cleavage and analytically leads to a quadratic dependence of the dissociation rate constant on the field. Without adjustable parameters, the model reproduces the required orders of magnitude for the enhancement ratio k_d(E)/k_d(0), where k_d(E) is the field-enhanced water dissociation rate constant and k_d(0) is its zero-field value across typical BPM fields, and yields a quadratic current–voltage junction law. A proof-of-principle measurement on a commercial Fumasep^® FBM from FUMATECH BWT, GmbH located at Bietigheim-Bissingen (Baden-Württemberg), Germany, purchased from Fuel Cell Store located at, Bryan, Texas, USA, confirms the quadratic current–voltage trend, supporting a power dissipation field-driven WD and providing a concise, falsifiable baseline for future studies. Full article

Crop yield estimation, particularly early-season yield prediction, is highly important for global food security and disaster mitigation. In this study, we utilized deep learning models combined with remote sensing data to develop in-season crop yield estimation models, enabling immediate yield prediction. We employed a convolutional neural network (CNN) for spatial feature extraction and a long short-term memory network (LSTM) for temporal patterns, complemented by Gaussian process regression (GP) that introduced geographical coordinates. Three groups of in-season yield prediction experiments were designed, utilizing four-phase, two-phase, and single-phase data, respectively. The results indicated that under the two-phase training scheme, the LSTM_GP model achieved the highest performance in the sixth period, with an $R^2$ value of 0.61 and a root mean square error ($RMSE$) value of 983.38 kg/ha. When trained on single-phase data at the twelfth phase (approximately mid-to-late July), the LSTM_GP model also performed best, attaining an $R^2$ value of 0.62 and an $RMSE$ value of 969.06 kg/ha. The single-phase prediction model outperformed time-series models in yield prediction accuracy. The periods from mid-to-late July to early-to-mid August represent critical crop growth stages were essential for accurate yield prediction. From our research, we found that adding GP can improve the prediction accuracy, especially for LSTM. Moreover, the proposed single-phase prediction model realized reliable crop yield prediction as well as the silking to early grain-filling stage (mid-to-late July), providing a critical lead time of approximately 2–2.5 months before harvest to support pre-harvest agricultural decision-making. Full article

Accurate prediction of progression to Alzheimer’s disease (AD) is crucial for early intervention and personalized patient management. In this study, we developed a robust, data-driven survival analysis pipeline to model time-to-progression from cognitively normal (CN) and mild cognitive impairment (MCI) at baseline to AD, integrating cognitive, clinical, MRI and PET neuroimaging biomarkers, and biospecimen features from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The ADNI cohort can be regarded as a multi-center platform for multimodal data integration that jointly captures cognitive performance, MRI/PET imaging-sensor biomarkers, and biofluid biosensing assays within a unified prognostic framework. Accordingly, our pipeline is designed to be robust to cross-site and cross-instrument variability through harmonized preprocessing and quality-check aware integration of heterogeneous multimodal data. Indeed, we employed eXtreme Gradient Boosting (XGBoost) for predicting survival data, which allows for the native handling of missing values that are frequently observed in real-world clinical datasets. Our results confirm that strong predictive performance can be achieved using a minimal set of features, obtaining a concordance index (C-index) of 0.92 using 13 features and 0.90 using only 4 features. These findings underscore the importance of multi-domain feature integration, transparent feature selection, and the inclusion of underexplored biomarkers such as lipid metabolites for prognostic modeling. Full article

Conventional centralized drainage systems exacerbate urban flooding, pollution, and water stress. Low-impact development (LID) is a decentralized alternative; however, its multifunctional benefits, which go beyond the control of stormwater, are often undervalued in planning. This study fills this gap by developing an integrated benefit valuation framework to systematically quantify and estimate the economic value of the co-benefits of five widely implemented LID facilities (vegetated swale, green roof, in-filtration ditch, infiltration trench, and permeable pavement) in Seoul, South Korea. The framework combines annual benefits in four key sectors: water management (runoff reduction), energy savings (building cooling/heating demands), air quality (pollutant deposition and avoided emissions) and climate change (carbon sequestration and mitigation). Applying a transparent, localized spreadsheet model, the results indicate significant multifunctional value for LID systems. While water management provides the primary benefit, there is substantial added value in energy, air quality, and climate co-benefits. In the case of green roofs, such ancillary benefits can exceed hydrological values. The analysis further reveals a consistent scale-benefit relationship and a clear trade-off between the magnitude of benefits and the cost of implementation. This provides evidence of the need for context-sensitive, portfolio-based LID planning. The proposed framework is a practical decision support tool for urban planners and policymakers to consider LID not only as a stormwater solution but also as multifunctional green infrastructure that simultaneously promotes urban water security, energy efficiency, environmental quality, and climate resilience. Full article

Atopic dermatitis (AD) and canine atopic dermatitis (CAD) are common allergic and pruritic skin diseases characterized by immune dysregulation and epidermal barrier dysfunction. To delineate how Th2 cytokines contribute to CAD pathogenesis, canine primary epidermal organoids (cPEOs) were established from keratinocytes, and exposure to IL-4/IL-13 induced morphologic changes characteristic of CAD. RNA sequencing analysis comparing IL-4/IL-13-treated cPEOs to untreated controls identified 224 differentially expressed genes (DEGs). Further rigorous filtering narrowed this down to 69 key DEGs, with the majority being associated with atopic dermatitis in both dogs and humans. Pathway enrichment analyses demonstrated the activation of immune and inflammatory signalling and suppression of epidermal differentiation, keratinisation, and lipid metabolism, recapitulating key features of atopic skin. Additional Th2-driven alterations included dysregulation of neuro-immune signalling, calcium homeostasis, apoptosis, extracellular matrix remodelling, and metabolic/epigenetic regulations. Together, these findings demonstrate that Th2 cytokines orchestrate multifaceted transcriptomic alterations relevant to AD/CAD. By mapping each key DEG to its known or putative role in AD/CAD, this study also provides a gene-level functional framework to inform future mechanistic studies and targeted therapeutic development. These findings also underscore the value of this model as a comparative tool for investigating both human and canine atopic dermatitis. Full article

Background: Unplanned 30-day hospital readmissions, a key healthcare quality metric, are common and costly. Prediction models built on structured data often perform modestly, and the added value of unstructured clinical notes remains unclear. Methods: This retrospective cohort study included 4135 general medicine admissions to a tertiary Australian hospital between July 2022 and June 2023. Structured predictors included demographics, comorbidities, frailty, prior healthcare utilisation, length-of-stay, inflammatory markers, socioeconomic indicators, and lifestyle factors. We developed deep learning models using structured data alone, unstructured text alone, and a combined multimodal architecture integrating both modalities. For benchmarking, multiple classical machine learning models trained on structured features were evaluated using identical data splits, including logistic regression, XGBoost, random forest, gradient boosting, extra trees, and HistGradient Boosting. Model performance was assessed on a hold-out test set using ROC-AUC, accuracy, precision, recall, and F1-score. Results: Unplanned readmissions occurred in 24.3% of admissions. Among classical machine learning models, logistic regression achieved the highest discrimination (ROC-AUC 0.64), with no substantial improvement observed from ensemble methods. Structured-only deep learning achieved ROC-AUC 0.62. Unstructured text-only and multimodal models achieved ROC-AUCs of 0.52 and 0.58, respectively. Although overall discrimination of the multimodal model was lower than structured-only performance, it demonstrated improved sensitivity and F1-score for identifying patients who were readmitted. Prior hospitalisations, emergency department visits, and comorbidity burden were the strongest predictors. Conclusions: Structured EMR variables remain the main drivers of 30-day readmission risk. More complex classical machine learning models did not outperform logistic regression, and incorporating unstructured clinical text provided only modest improvement in identifying high-risk patients without enhancing overall discrimination. Full article

Industrial structure optimization and upgrading driven by information infrastructure facilitates resource-efficient allocation, which is crucial for advancing China’s economic development toward sustainability. This paper constructs a simplified multi-sector general equilibrium model to theoretically reveal the mechanism of information infrastructure’s impact on industrial structure. Theoretical results indicate that among various factors, information infrastructure investment scale, its effect on industrial sector factor productivity, and the capital factor output elasticity of industrial sectors are three key determinants of industrial structure rationalization. Based on this, the paper uses China’s provincial panel data from 2009 to 2022 and adopts the fixed effect estimation method to empirically verify the theoretical conclusions. Empirical results show that information infrastructure characteristics play a pivotal role in promoting industrial structure optimization. They exert a positive effect on the free flow of production factors across industrial sectors and efficient resource allocation. Specifically, fixed information infrastructure has a stronger impact on industrial structure rationalization than mobile information infrastructure. Neither mobile nor fixed information infrastructure exerts a significant impact on industrial structure upgrading. To fully leverage information infrastructure and its investment, further efforts are needed to strengthen their role in high-value-added industrialization and high-tech industrialization, thereby consolidating the foundation for sustainable economic development. Full article

Background: Imaging-transcriptomic analysis, through the integration of multimodal magnetic resonance imaging (MRI) and transcriptomic data, provides complementary structural, functional, and molecular information that is crucial for the early detection and mechanistic exploration of Alzheimer’s disease (AD). However, effectively extracting features from heterogeneous multimodal data and capturing the associations between microscopic molecular variations and macroscopic brain alterations remain key challenges. Recent advances in deep learning and multimodal integration have enhanced the ability to model nonlinear cross-modal relationships, enabling more accurate identification of imaging-transcriptomic biomarkers and subtypes. Developing robust multimodal frameworks is therefore essential for early AD detection, subtype identification, and advancing precision medicine in neurodegenerative diseases. Methods: In this study, a two-stage method of multimodal Feature Extraction based on Association Analysis and Graph Convolutional Network with Self-Attention and Self-Expression framework (MFEAA-GCNSASE) for early diagnosis of AD and effective identification of subtypes of MCI with different progression to AD is proposed. In the first stage, the MFEAA model is applied to integrate multiple association analysis methods on sMRI, PET, and transcriptomic data to identify key multimodal biomarkers for AD and mild cognitive impairment (MCI). In the second stage, the GCNSASE model enhances classification accuracy between AD and MCI patients through self-attention and self-expression layers. Additionally, unsupervised clustering was performed on MCI samples using top multimodal biomarkers to explore subtype heterogeneity and conversion risk. Reliable MCI subtypes were also identified through a consensus clustering approach. Results: The proposed algorithm integrates sMRI, PET, and transcriptomic data, identifying robust biomarkers including the Left Hippocampus, Left Angular Gyrus, and key genes such as SLC25A5 and GABARAP. To ensure statistical robustness given the extreme class imbalance, we employed a rigorous repeated stratified cross-validation (RSCV) framework. GCNSASE achieved state-of-the-art discrimination performance with mean AUC values ranging from 0.946 to 0.961 across feature subsets (10–50%), significantly outperforming MOGONET (mean AUC: 0.844–0.875, p < 0.001) and conventional machine learning models with tighter 95% confidence intervals, indicating superior stability despite the limited AD sample size. Clustering analysis revealed two distinct MCI subtypes with divergent molecular landscapes: Subtype A was enriched in energy metabolism and cellular maintenance pathways, whereas Subtype B was enriched in neuroinflammatory and aberrant signaling pathways. Notably, the majority of MCI patients who subsequently converted to AD were concentrated in the immune-inflammatory Subtype B. These findings highlight that neuroinflammation coupled with bioenergetic failure constitutes a critical mechanism driving the conversion from MCI to AD. Conclusions: The proposed methods not only provide the key multimodal biomarkers and enhance the accuracy of the classification model for early AD diagnosis but also identify biologically and clinically meaningful MCI subtypes with distinct molecular signatures and conversion risks. Exploring these associated multimodal biomarkers and MCI subtypes is of great significance, as they help elucidate the heterogeneous mechanisms underlying AD onset and progression, enable the identification of high-risk individuals likely to convert to AD, and provide a foundation for targeted therapeutic strategies and individualized clinical management. These findings have important implications for understanding disease heterogeneity, discovering potential intervention targets, and advancing precision medicine in neurodegenerative diseases. Full article

China’s export-oriented economic expansion has substantially influenced transport-sector CO₂ emissions, raising critical concerns about the environmental impacts of sustained industrial growth and global trade integration. Understanding the interplay between macroeconomic dynamics, trade composition, and industrial structure is essential for aligning economic development with climate mitigation objectives. This study examines transport-related CO₂ emissions in China over the period 1990–2023, employing a hybrid methodological framework that combines econometric modeling—including Autoregressive Distributed Lag (ARDL) bounds testing, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS)—with machine-learning techniques using Extreme Gradient Boosting (XGBoost) interpreted through SHapley Additive exPlanations (SHAP). The analysis confirms a long-run cointegration relationship between transport emissions and the selected macroeconomic variables. Short-run dynamics indicate a strong sensitivity of emissions to GDP growth, while long-run estimates reveal that higher export-to-GDP ratios and industrial value added contribute to reducing transport emissions, reflecting the efficiency gains from industrial upgrading and cleaner trade practices. By contrast, the expansion of medium- and high-technology exports increases emissions due to the energy- and logistics-intensive nature of high-value goods. The XGBoost model achieves high predictive performance, with an out-of-sample R² of 0.9975 and a Root Mean Square Error (RMSE) of 87.16, confirming the dominant contribution of medium- and high-technology exports to transport-sector emissions. The results underscore the critical role of aligning trade structure, industrial productivity, and low-carbon logistics within China’s policy agenda. Implementing strategies that enhance industrial energy efficiency and develop sustainable transport infrastructure can substantially reduce the environmental impacts associated with export-driven economic expansion. Full article

One-step hydrothermal synthesis of zeolites is a common synthesis technology for zeolites. Las-NaP1 zeolite was synthesized with fly ash (FA) as the silica-alumina source under low-alkalinity conditions for aqueous adsorption. Furthermore, H-NaP1 modified zeolite, a high-efficiency chloride ion (Cl⁻) adsorbent, was fabricated using hollow glass microspheres (HGMs) and FA as a silica-alumina source. The structure of the material was characterized by XRD, SEM, TEM, BET, XPS, FT-IR, Zeta, and other techniques. Effects of the synthesis process and adsorption conditions on the adsorption performance of Cl⁻ and its mechanism were systematically studied. The maximum adsorption capacity of H-NaP1 for Cl⁻ (193.57 mg/g) is 12 times that of Las-NaP1 (15.48 mg/g). The adsorption process conformed to the pseudo-second-order kinetic model and the Freundlich isotherm model. The addition of HGMs effectively inhibited the agglomeration of zeolite particles. This research provided a new idea for the synthesis of efficient dechlorination materials with low alkali and realized the high-value-added utilization of FA. Full article

Background: Older adults with Alzheimer’s disease (AD) face a heightened risk of adverse hospital outcomes, including mortality. However, early identification of high-risk patients remains a challenge. While regression models provide interpretable associations, they may miss non-linear interactions that machine learning can uncover. Objective: To identify key predictors of in-hospital mortality among AD patients using both survey-weighted logistic regression and explainable machine learning. Methods: We analyzed hospitalizations among AD patients aged ≥60 in the 2017 Nationwide Inpatient Sample (NIS). The outcome was in-hospital death. Predictors included demographics, hospital variables, and 15 comorbidities. Logistic regression used survey weighting to generate nationally representative inference; XGBoost incorporated NIS discharge weights as sample weights during 5-fold hospital-grouped cross-validation and used the same weights in performance evaluation. Missing-value imputation and feature scaling were performed within the cross-validation pipelines to prevent data leakage. Model performance was assessed using AUROC, AUPRC, Brier score, and log loss. Feature importance was assessed using adjusted odds ratios and SHapley Additive exPlanations (SHAP). A sensitivity analysis excluded palliative care and DNR status and was re-evaluated under the same grouped cross-validation. Results: In the full model, logistic regression achieved AUROC 0.879 and AUPRC 0.310, while XGBoost achieved AUROC 0.887 and AUPRC 0.324. Palliative care (aOR 6.19), acute respiratory failure (aOR 5.15), DNR status (aOR 2.20), and sepsis (aOR 2.26) were the strongest logistic predictors. SHAP analysis corroborated these findings and additionally emphasized dysphagia, malnutrition, and pressure ulcers. In sensitivity analysis excluding palliative care and DNR status, logistic regression performance declined (AUROC 0.806; AUPRC 0.206), while XGBoost performed similarly (AUROC 0.811; AUPRC 0.206). SHAP corroborated the dominant signals from end-of-life documentation and acute organ failure in the full model; in the restricted model (excluding DNR and palliative care), SHAP highlighted physiologic and frailty-related features (e.g., dysphagia, malnutrition, aspiration risk) that may be more actionable when end-of-life documentation is absent. Conclusions: Combining regression with explainable machine learning enables robust mortality risk stratification in hospitalized AD patients. Restricted models excluding end-of-life indicators provide actionable risk signals when such documentation is absent, while the full model may better support resource allocation and goals-of-care workflows. Full article