Search Results (1,368)

The design of sustainable hydrogel materials with tunable mechanical and thermal properties is essential for emerging applications in flexible and wearable electronics. In this study, hydrogels based on natural gums such as Guar, Tara, and Xanthan and their composites with Cellulose Citrate were developed through a mild physical crosslinking process, ensuring environmental compatibility and structural integrity. The effect of cellulose citrate pretreatment under different alkaline conditions (0.04%, 5%, and 10% NaOH) was systematically investigated using Fourier Transform Infrared Spectroscopy (FT-IR), Thermogravimetric Analysis (TGA), and dynamic rheology. Overall, the results show that the composites exhibit different properties of the hydrogel networks compared to the pure hydrogel gums, strongly depending on the alkaline treatment. In all composite hydrogels, a significant increase in the number of interacting rheological units occurs, though the strength of the interactions decreases in Guar and Tara composites, which exhibit partial structural destabilization. In contrast, Xanthan–Cellulose Citrate hydrogels display enhanced strong gel character, and crosslinking density. These improvements reflect stronger intermolecular associations and a more compact polymer network, due to the favorable H-bonding and ionic interactions among Xanthan, Cellulose and Citrate mediated by water and sodium ions. Overall, the results demonstrate that Xanthan–Cellulose Citrate systems represent a new class of eco-friendly, mechanically robust hydrogels with controllable viscoelastic and thermal responses, features highly relevant for the next generation of flexible, self-supporting, and responsive soft materials suitable for wearable and stretchable electronic devices. Full article

The rapid growth of distributed photovoltaic (PV) resources is transforming distribution networks into active systems with highly variable net loads, while the rising frequency and severity of extreme weather events is increasing outage risk and restoration challenges. In this context, utilities require reliability assessment tools that jointly represent operational variability and climate-driven stressors beyond stationary assumptions. This paper presents a weather-aware probabilistic framework to quantify the reliability of active distribution networks with high PV penetration. The approach synthesizes realistic residential demand and PV time series at 15-min resolution, models extreme weather as a low-probability/high-impact escalation of component failure rates and restoration uncertainty, and computes IEEE Std 1366–2022 indices (SAIFI, SAIDI, ENS) through Monte Carlo simulation. The methodology is validated on a modified IEEE 33-bus feeder with parameter values representative of urban/suburban overhead networks. Compared with classical reliability modeling, the proposed framework captures in a unified pipeline the joint effects of load/PV stochasticity, weather-dependent failure escalation, and repair-time dispersion, providing a consistent statistical interpretation supported by kernel density estimation and convergence diagnostics. The results show that (i) extreme weather shifts the distributions of SAIFI, SAIDI and ENS to the right and thickens upper tails (higher exceedance probabilities); (ii) PV penetration yields a non-monotonic response with measurable improvements up to intermediate levels and saturation/partial degradation at very high penetrations; and (iii) compound risk is nonlinear, as the mean ENS surface over $(r_{\mathrm{PV}},P_{\mathrm{ext}})$ exhibits a valley at moderate PV and a ridge for large storm probability. A tornado analysis identifies the base failure rate, storm escalation factor and storm exposure as dominant drivers, in line with resilience literature. Overall, the framework provides an auditable, scenario-based tool to co-design DER hosting and resilience investments. Full article

Background/Objectives: Acute heart failure (AHF) is a heterogeneous syndrome with phenotype-dependent prognosis. NT-proBNP is the reference biomarker, but standard echocardiographic measures (TAPSE, RV–RA gradient, LVOT VTI) offer only partial prognostic insight. The Virtue Index, defined as (RV–RA gradient)/(TAPSE × LVOT VTI), was introduced to integrate right–left ventricular interaction. This study evaluated its clinical and prognostic performance in AHF and its behavior across ejection-fraction phenotypes. Methods: We retrospectively analyzed 222 patients with AHF; complete data for Virtue calculation were available in 168 (99 HFrEF, 69 HFpEF) patients. HFmrEF patients were excluded from subgroup prognostic analyses. Correlation with NT-proBNP was assessed using Spearman testing with bootstrap intervals, and in-hospital mortality prediction was evaluated using ROC analysis with DeLong comparisons. Results: In HFpEF, the Virtue Index correlated moderately with NT-proBNP (ρ = 0.38, p = 0.002) and showed fair prognostic discrimination (AUC 0.704), similar to the RV–RA gradient (0.724) and higher than TAPSE or LVOT VTI. In HFrEF, correlation was weak (ρ = 0.19, p = 0.06) and predictive accuracy was modest (AUC 0.584), while LVOT VTI performed best (AUC 0.700). NT-proBNP outperformed all echocardiographic parameters in both groups. Conclusions: The Virtue Index reflects integrated hemodynamics and shows phenotype-dependent prognostic value in AHF, being more informative in HFpEF than in HFrEF. Although NT-proBNP remained superior, Virtue may complement biomarker-based risk assessment by offering a rapid, bedside estimate of short-term mortality risk. Full article

The replacement of ionizable functional groups that are predominantly charged at physiological pH with neutral bioisosteres is a common strategy in medicinal chemistry; however, its impact on binding affinity is often context-dependent. Here, we investigated a series of amide derivatives of a glycomimetic E-selectin ligand, in which the carboxylate group of the lead compound is substituted with a range of amide and isosteric analogs. Despite the expected loss of the salt-bridge interaction with Arg97, several amides retained or even improved the binding affinity. Co-crystal structures revealed conserved binding poses across the series, with consistent interactions involving the carbonyl oxygen of the amide and the key residues Tyr48 and Arg97. High-level quantum chemical calculations ruled out a direct correlation between carbonyl partial charges and affinity. Instead, a moderate correlation was observed between ligand binding and the out-of-plane pyramidality of the amide nitrogen, suggesting a favorable steric adaptation within the binding site. Molecular dynamics (MD) simulations revealed that high-affinity ligands exhibit enhanced solution-phase pre-organization toward the bioactive conformation, likely reducing the entropic penalty upon binding. Further analysis of protein–ligand complexes using Molecular mechanics/Generalized born surface area (MM-GB/SA) decomposition suggested minor lipophilic contributions from amide substituents. Taken together, this work underscores the importance of geometric and conformational descriptors, beyond classical electrostatics, in driving affinity in glycomimetic ligand design and provides new insights into the nuanced role of amides as carboxylate isosteres in protein–ligand recognition. Full article

Variants in CLCN3 and CLCN4, encoding the neuronal endosomal Cl⁻/H⁺ antiporters ClC-3 and ClC-4, are linked to neurodevelopmental disorders with broad phenotypic variability. Over sixty CLCN4 variants have been functionally characterized, showing gain- or loss-of-function (GoF or LoF) effects. While ClC-3 can function as a homodimer, ClC-4 depends on heterodimerization with ClC-3 for efficient endosomal trafficking. CLCN4, located on the X chromosome, exhibits diverse pathogenic outcomes: complete LoF variants often cause non-syndromic presentations in hemizygous males and are asymptomatic in heterozygous females, whereas certain missense variants with partial or complete LoF produce severe syndromic phenotypes in both sexes. Here, we demonstrate dominant effects of three CLCN4 variants within ClC-3/ClC-4 heterodimers using two-electrode voltage-clamp recordings in Xenopus laevis oocytes and whole-cell patch-clamp recordings in mammalian cells co-expressing both proteins via a bicistronic IRES construct. Our findings provide the first evidence of dominant-negative CLCN4 effects within ClC-3/ClC-4 complexes and establish a platform for functional analysis of additional disease-associated variants. Full article

This study investigates the morphometric and anthropogenic controls governing the occurrence and spatial distribution of tributary–junction fans (TJFs) along the Chaudière River, Québec, Canada. Using GIS-based morphometric analysis, field validation, and multivariate statistics (PCA, CART, LDA), 142 tributary watersheds were analyzed, of which 41 display fan-shaped depositional features. Basin relief, drainage density, contributing area, and slope–area coupling emerge as the dominant predictors of TJF development, delineating an intermediate energy domain where sediment supply and transport capacity become balanced enough to allow partial geomorphic coupling at confluence nodes. CART analysis identified approximate slope and area thresholds (slope < 9°, area > 20 km²; 66% accuracy), while LDA achieved 76%, indicating that morphometry provides useful but incomplete predictive power. These moderate performances reflect the additional influence of event-scale hydrological forcing and unquantified Quaternary substrate heterogeneity typical of postglacial terrain. Beyond morphometry, anthropogenic disturbance exerts a secondary but context-dependent influence, with moderately disturbed watersheds (10–50% altered) showing higher frequencies of fans than both highly engineered (>50%) and minimally disturbed (<10%). This pattern suggests that land-use modification can locally reinforce or offset morphometric predisposition by altering sediment-routing pathways. Overall, TJFs function as localized sediment-storage buffers that may be periodically reactivated during high-magnitude floods. The combined effects of basin geometry, land-use pressures, and hydroclimatic variability explain their spatial distribution. The study provides an indicative, process-informed framework for evaluating sediment connectivity and depositional thresholds in cold-region fluvial systems, with implications for geomorphic interpretation and hazard management. Full article

Soil acidity severely constrains coffee production by reducing nutrient availability and promoting aluminum toxicity and oxidative stress. Foliar zinc oxide nanoparticles (ZnO NPs) have been proposed as redox modulators that can improve nutrient homeostasis under abiotic stress. However, the safe and effective range of Coffea arabica L. remains unclear. In this study, seedlings were grown in acidic soil and sprayed twice with ZnO NPs at 10, 25, 50, and 100 mg L⁻¹. Morphophysiological, biochemical, and ionomic parameters were evaluated fifty days after treatment. Moderate ZnO NPs doses led to intermediate stomatal conductance values, whereas net photosynthesis showed intermediate but non-significant responses only at 10–25 mg L⁻¹, with higher doses (50–100 mg L⁻¹) causing a marked decline. These doses did not significantly modify hydrogen peroxide (H₂O₂) or malondialdehyde (MDA) levels in leaves or roots. In contrast, the highest dose (100 mg L⁻¹) induced a marked increase in H₂O₂ without affecting MDA, indicating a partial oxidative response rather than clear lipid peroxidation. Foliar analysis showed that 50 mg L⁻¹ ZnO NPs significantly increased P compared with the optimal soil, while Ca and K remained statistically similar across treatments. Na in the optimal soil was comparable to the 10–25 mg L⁻¹ ZnO NPs treatments, whereas Na at 50–100 mg L⁻¹ ZnO NPs was significantly reduced and foliar Zn increased markedly with increasing nanoparticle dose. Proline accumulation reflected a dose-dependent osmotic adjustment, and chlorophyll ratios indicated adaptive photoprotection. Overall, foliar ZnO NPs mitigated acidity-induced stress through physiological and ionomic adjustment, with 10–25 mg L⁻¹ identified as the physiologically safe range for C. arabica seedlings grown under acidic conditions. Full article

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by a lack of targetable receptors, leading to limited treatment options and a critical need for novel therapeutic strategies. This study aimed to evaluate the potential of azelastine, a clinically approved H1-antihistamine, for drug repositioning against TNBC and to elucidate its underlying HRH1-independent mechanism of action. Cell viability assays (CCK-8) were performed on TNBC cell lines (MDA-MB-231 and BT-549) following treatment with azelastine and its major metabolite, desmethyl azelastine. After observing ambiguous clinical associations between HRH1 expression and patient prognosis, HRH1 dependency was assessed through histamine stimulation and HRH1 knockdown (siRNA). Subsequently, the role of ADP-ribosylation factor 1 (ARF1), found to be overexpressed in TNBC and linked to poor prognosis, was investigated using ARF1 knockdown (siRNA), co-treatment with the Golgi-specific brefeldin A-resistance guanine nucleotide exchange factor 1 (GBF1) inhibitor golgicide A (GCA), and co-treatment with the Drp1 inhibitor M-divi 1. Azelastine and desmethyl azelastine potently reduced MDA-MB-231 cell viability in a dose- and time-dependent manner, achieving cell survivals of 61.3 ± 6.1% (30 µM) and 34.9 ± 3.7% (50 µM) for azelastine, and 52.4 ± 12.5% (30 µM) for desmethyl azelastine, respectively, after 72 h, with an IC₅₀ of 35.93 µM determined for azelastine in MDA-MB-231 cells. Additionally, azelastine significantly reduced the viability of BT-549 cells. Bioinformatic analysis of clinical datasets revealed HRH1 downregulation in tumors and, functionally, neither histamine stimulation nor HRH1 knockdown mediated azelastine cytotoxicity in cell culture. Importantly, ARF1 expression was significantly upregulated in TNBC and associated with poor prognosis. Co-treatment with GCA, preventing ARF1 activation, restored viability to near-control levels, supporting dependence on the GBF1–ARF1 activation axis of azelastine, whereas the Dynamic-related protein 1 (Drp1) inhibitor M-divi 1 not only partially rescued CCK-8-based cell viability but also normalized azelastine-induced loss of MitoTracker™ Red CMXRos signal and partially preserved (4′,6-diamidino-2-phenylindole) DAPI-based cell density, indicating Drp1-dependent mitochondrial dysfunction. Furthermore, azelastine selectively reduced p-ERK phosphorylation in the cell signaling pathway. Azelastine exerts potent anticancer effects in TNBC cells via an HRH1-independent, ARF1-dependent mechanism that attenuates the Extracellular signal-regulated kinase (ERK)–Drp1 axis, and induces Drp1-dependent mitochondrial dysfunction, independent of its canonical HRH1 receptor function. This ARF1-dependent mechanism provides strong scientific rationale for the drug repositioning of azelastine as an effective therapeutic agent for ARF1-driven TNBC. Full article

Anammox is a promising approach for biological nitrogen removal, but the differences in microbial community structure across different systems and their response mechanisms to environmental factors remain unclear. In this study, 206 microbial samples and 2126 environmental factor data points from three different anammox systems, including the upflow anaerobic sludge blanket (UASB), integrated fixed-film activated sludge-partial nitritation/anammox (IFAS-PN/A), and integrated fixed-film activated sludge-simultaneous nitrification, anammox and denitrification (IFAS-SNAD), were analyzed using 16S rRNA sequencing analysis, bioinformatics, and machine learning (ML) techniques. The results revealed significant differences in microbial composition among three systems, evidenced by the enrichment of Candidatus_Brocadia in IFAS-PN/A, the high-diversity community in IFAS-SNAD, and the low-diversity communities dominated by Candidatus_Kuenenia in the UASB. Co-occurrence network analysis demonstrated more tightly connected and complex interactions in IFAS-SNAD networks. Machine learning predictions further showed that the stacked model (ST-RF) achieved the highest accuracy in predicting the nitrogen removal rate (NRR), with determination coefficients (R²) exceeding 0.987 across all testing datasets. Moreover, SHapley Additive exPlanations (SHAP) analysis based on the stacked model revealed that the influence of key environmental factors on NRR varied by system type. These results suggested that NRR of different systems depended on the control of key environmental factors, while the significance of these environmental factors was determined by the type of system. Overall, this study enhanced the ecological and functional understanding of anammox-based processes and provided a data-driven framework for optimizing mainstream nitrogen removal. Full article

The persistent Hubble tension—marked by a notable disparity between early- and late-universe determinations of the Hubble constant $H_0$—poses a serious challenge to the standard cosmological framework. Closely linked to this is the $H_0-r_d$ tension, which stems from the fact that BAO-based estimates of $H_0$ are intrinsically dependent on the assumed value of the sound horizon at the drag epoch, $r_d$. In this study, we construct a scalar field dark energy model within the framework of a spatially flat Friedmann–Lemaitre–Robertson–Walker model to explore the dynamics of cosmic acceleration. To solve the field equations, we introduce a generalized extension of the standard Lambda Cold Dark Matter model that allows for deviations in the expansion history. Employing advanced Markov Chain Monte Carlo techniques, we constrain the model parameters using a comprehensive combination of observational data, including Baryon Acoustic Oscillations, Cosmic Chronometers, and Standard Candle datasets from Pantheon, Quasars, and Gamma-Ray Bursts (GRBs). Our analysis reveals a transition redshift from deceleration to acceleration at $z_{\mathrm{tr}}=0.69$ and a present-day deceleration parameter value of $q_0=-0.64$. The model supports a dynamical scalar field interpretation, with an equation of state parameter satisfying $-1<{\omega }_0^{\varphi }<0$, consistent with quintessence behavior, and signaling a deviation from the $\mathrm{\Lambda }$. While the model aligns closely with the Lambda Cold Dark Matter scenario at lower redshifts ($z\lesssim 0.65$), notable departures emerge at higher redshifts ($z\gtrsim 0.65$), offering a potential window into modified early-time cosmology. Furthermore, the evolution of key cosmographic quantities such as energy density ${\rho }^{\varphi }$, pressure $p^{\varphi }$, and the scalar field equation of state highlights the robustness of scalar field frameworks in describing dark energy phenomenology. Importantly, our results indicate a slightly higher value of the Hubble constant $H_0$ for specific data combinations, suggesting that the model may provide a partial resolution of the current $H_0$ tension. Full article

Background/Objectives: This paper presents the results of predicting chronological age from psychophysiological tests using machine learning regressors. Methods: Subjects completed a series of psychological tests measuring various cognitive functions, including reaction time and cognitive conflict, short-term memory, verbal functions, and color and spatial perception. The sample included 99 subjects, 68 percent of whom were men and 32 percent were women. Based on the test results, 43 features were generated. To determine the optimal feature selection method, several approaches were tested alongside the regression models using MAE, $R^2$, and $CV\text{\_}{R}^2$ metrics. SHAP and Permutation Importance (via Random Forest) delivered the best performance with 10 features. Features selected through Permutation Importance were used in subsequent analyses. To predict participants’ age from psychophysiological test results, we evaluated several regression models, including Random Forest, Extra Trees, Gradient Boosting, SVR, Linear Regression, LassoCV, RidgeCV, ElasticNetCV, AdaBoost, and Bagging. Model performance was compared using the determination coefficient ($R^2$) and mean absolute error (MAE). Cross-validated performance ($CV\text{\_}{R}^2$) was estimated via 5-fold cross-validation. To assess metric stability and uncertainty, bootstrapping (1000 resamples) was applied to the test set, yielding distributions of MAE and RMSE from which mean values and 95% confidence intervals were derived. Results: The study identified RidgeCV with winsorization and standardization as the best model for predicting cognitive age, achieving a mean absolute error of 5.7 years and an $R^2$ of 0.60. Feature importance was evaluated using SHAP values and permutation importance. SHAP analysis showed that stroop_time_color and stroop_var_attempt_time were the strongest predictors, followed by several task-timing features with moderate contributions. Permutation importance confirmed this ranking, with these two features causing the largest performance drop when permuted. Partial dependence plots further indicated clear positive relationships between these key features and predicted age. Correlation analysis stratified by sex revealed that most features were significantly associated with age, with stronger effects generally observed in men. Conclusions: Feature selection revealed Stroop timing measures and task-related metrics from math and campimetry tests as the strongest predictors, reflecting core cognitive processes linked to aging. The results underscore the value of careful outlier handling, feature selection, and interpretable regularized models for analyzing psychophysiological data. Future work should include longitudinal studies and integration with biological markers to further improve clinical relevance. Full article

Amid China’s push for digital transformation, green technology innovation has become a vital pathway to achieving its carbon neutrality goals. Using panel data from Chinese A-share-listed companies between 2012 and 2023, sourced from the CNRDS and CSMAR databases, this study employs a two-way fixed effects model to examine how digital transformation affects green innovation. In this model, carbon information disclosure serves as a mediator and is measured through text analysis and entropy weighting, while media attention is included as a moderator. The results show that: (1) Digital transformation significantly promotes green technology innovation, with a one-unit increase in the digitalization index raising green patent applications by 4.45%; upon controlling for potential path dependence, the effect remains stable at 3.76%. (2) Carbon information disclosure plays a partial mediating role. (3) Media attention moderates both the direct effect of digital transformation and the first stage of the indirect effect through carbon information disclosure. (4) Heterogeneity analyses, supplemented by inter-group difference tests, reveal stronger effects in state-owned enterprises, firms in western China, and larger firms. The study concludes with practical recommendations for corporate practice and public policy. Full article

Small object localization is one of the most challenging tasks owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. Recent advances in localizing small objects are mainly dependent on regression-based counting approaches, which require considerable annotations for training. As a contrast, human learners can quickly master labeling skills from only a few annotation examples. In this paper, we attempt to simulate this training mechanism and propose a novel positive-unlabeled (PU) learning based approach that can localize small objects by learning from partial point annotations. We evaluate our approach on five typical datasets of small objects involving a single cell, an animal/insect, and human crowds. Quantitative experimental results show that our approach has achieved inspiring localization performance (F1 score > 0.75) even under the supervision of less than 10% of the overall point annotations. This approach paves the way for low-annotation-cost single-cell analysis within microfluidic droplets. Full article

The article’s main focus is on identifying the key enablers that are making Industry 4.0 adoption easier, utilizing structural equation modeling via SPSS version 26. A comprehensive examination of previous studies led to the identification of 10 main enablers and 35 associated sub-enablers. Data collected from 182 manufacturing companies in India, selected by simple random sampling, was used for quantitative research. The analysis basically depends on PLS-SEM and CB-SEM (Partial Least Squares and Covariance-Based Structural Equation Modeling) path modeling. The findings indicate that technological enablers such as data analytics and artificial intelligence, computational power and connectivity, technologies that integrate physical and digital systems, and other enabling technologies are crucial to Industry 4.0 adoption. Additionally, organizational enablers (including a supportive organization, government efforts and promotions, and human resources) are also found to be significant contributors to Industry 4.0 implementation. Additionally, the study identified a significant mediating effect between technological and organizational enablers, emphasizing the importance of collaborative visualization mechanisms, established through bootstrapping with bias-corrected confidence intervals. Strengthening technological, organizational, and collaborative capabilities through Industry 4.0 adoption allows firms to attain improved operational performance while advancing sustainability objectives. These results contribute to the present understanding of Industry 4.0 adoption by offering useful implications for policymakers and industry practitioners. These insights guide managers and policymakers in structuring digital transformation initiatives. Full article

Digitalization is rapidly transforming organizational strategies and structures; disruptive technologies now play a central role in driving this transformation. However, the impact of disruptive technologies on digital transformation remains a complex and context-dependent phenomenon, particularly in highly regulated sectors. This study examines the impact of disruptive technologies on digital transformation in the banking sector in Turkey within the framework of the Technology–Organization–Environment (TOE) model. Data were collected from 513 participants at the managerial level (managers, IT staff, and experts) working in public and private banks through a standardized questionnaire and analyzed using Confirmatory Factor Analysis (CFA), Structural Equation Modeling (SEM), and Hayes PROCESS Macro (Model 4) techniques. The findings show that disruptive technologies have a meaningful and direct effect on digital transformation; technological, organizational, and environmental factors play a partial mediating role in this relationship. The results reveal that this transformative effect varies across TOE dimensions depending on the context. The study contributes to the literature by extending the TOE model in a tightly regulated context and provides practical implications for managers and policymakers to develop sustainability-focused digital transformation strategies. Full article