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Photocurrent Experiments as Probes and Prods in Large-Area Molecular Electronic Junctions
Trait emotion regulation similarity and well-being
Abstract The ways people manage their emotions (emotion regulation; ER) have been found to affect their own well-being and the well-being of others. In the present research, we extended prior work by examining whether similarity between people’s ER habits is also associated with well-being. The study consisted of 254 American romantic couples (N = 508) who completed self-report measures of habitual, or trait-level, use of a wide range of ER strategies (when regulating positive emotions and negative emotions), as well as psychological well-being and relational well-being indices. Contrary to our hypothesis, similarity in trait ER was not associated with any well-being index. This was the case across all ER strategies and the results converged across analyses that used three different metrics of similarity (difference scores, profile correlations, actor x partner interaction). The results suggest that although one’s own ER, and their romantic partner’s ER, may be tied to well-being, similarity between romantic partners’ ER habits may not matter as much.
Chemostructurally Stable Polyionomer Coatings Regulate Proton-Intermediate Landscape in Acidic CO<sub>2</sub> Electrolysis
Thermal, hardness, and tribological assessment of PEEK/CoCr composites
Shadowless amyloid imaging with quantitative birefringence contrast
Telecentric stereo 3D imaging with isotropic micrometer resolution bridges macro- and microscale in small Lepidopterans
Abstract We present a straightforward, application-driven telecentric stereo 3D-measurement system for high-precision measurements, designed for applications ranging from industrial quality control to biological research including scanning of Lepidoptera moths. Utilizing a dual-camera setup with telecentric lenses and structured illumination, our system achieves lateral resolution of 8.0 $$\upmu$$ m and axial resolution of 4.46 $$\upmu$$ m in a measurement volume of 11 mm $$\times 11$$ mm $$\times 6$$ mm. We address challenges typically encountered when using standard libraries like OpenCV, e.g. in extrinsic parameter estimation using a dedicated calibration method that corrects for a potential model mismatch due to telecentricity. Our approach adapts existing methods, such as telecentric stereo vision and structured illumination, into an optimized, user-friendly system tailored for life science research, enabling detailed 3D-reconstructions of scattering objects, such as small moths, with isotropic micrometer accuracy. This work presents an application-driven approach for biological 3D-metrology by integrating existing technologies (telecentric stereo vision, structured illumination) into a specialized imaging platform suitable for non-invasive morphological studies. Unlike conventional CT or microscopic approaches, our method provides a balance of precision, scalability, and practical usability for non-expert users with the aim to study developmental changes in species under varying environmental conditions, while also methodically bridging the gap between macroscopic and microscopic resolution in biological imaging.
Adaptive context biasing in transformer-based ASR systems
Statistical variability in comparing accuracy of neuroimaging based classification models via cross validation
Ultrasensitive detection of amlodipine using plasmonic optical fiber sensors enhanced with graphene oxide and chitosan nanocomposite
Modulating the Binding Kinetics of Bruton’s Tyrosine Kinase Inhibitors through Transition-State Effects
Prospective changes in lipocalin-2 and adipocytokines among adults with obesity
Intermolecular Interactions in Direct Air Capture Materials: Insights from Charge Density Analysis
An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments
Predicting soil organic carbon with ensemble learning techniques by using satellite images for precision farming
Adaptive laboratory evolution of Blakeslea trispora under acetoacetanilide stress leads to enhanced β-carotene biosynthesis
Process optimization and modeling research for the defluoridation of water using a novel adsorbent of cellulose and hydroxyapatite nanocomposite
Machine learning-driven framework for realtime air quality assessment and predictive environmental health risk mapping
Abstract This research introduces a practical and innovative approach for real-time air quality assessment and health risk prediction, focusing on urban, industrial, suburban, rural, and traffic-heavy environments. The framework integrates data from multiple sources, including fixed and mobile air quality sensors, meteorological inputs, satellite data, and localised demographic information. Using a combination of machine learning techniques such as Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks the system predicts pollutant concentrations and classifies air quality levels with high temporal accuracy. Interpretability is achieved through SHAP analysis, which provides insight into the most influential environmental and demographic variables behind each prediction. A cloud-based architecture enables continuous data flow and live updates through a web dashboard and mobile alert system. Visual risk maps and health advisories are generated every five minutes to support timely decision-making. The framework not only forecasts pollution trends but also identifies vulnerable populations through spatial overlays. Future validation will include real-world sensor deployment and comparison with health impact records to ensure both scientific accuracy and community relevance.