Browse Articles

Real-Time Tracking of Photoinduced Metal–Metal Bond Formation in a d⁸d⁸ Di-Iridium Complex by Vibrational Coherence and Femtosecond Stimulated Raman Spectroscopy

A simplified approach to 3D spherical cloaking using shell based dielectric metamaterials

An Alloy Engineering Strategy toward Helical Microstructures of Achiral π-Conjugated Molecules for Circularly Polarized Luminescence

Cloud-enabled e-commerce negotiation framework using bayesian-based adaptive probabilistic trust management model

Abstract Enforcing a trust management model in the broker-based negotiation context is identified as a foremost challenge. Creating such trust model is not a pure technical issue, but the technology should enhance the cloud service negotiation framework for improving the utility value and success rate between the bargaining participants (consumer, broker, and service provider) during their negotiation progression. In the existing negotiation frameworks, trusts were established using reputation, self-assessment, identity, evidence, and policy-based evaluation techniques for maximizing the negotiators (cloud participants) utility value and success rate. To further maximization, a Bayesian-based adaptive probabilistic trust management model is enforced in the future broker-based trusted cloud service negotiation framework. This adaptive model dynamically ranks the service provider agents by estimating the success rate, cooperation rate and honesty rate factors to effectively measure the trustworthiness among the participants. The measured trustworthiness value will be used by the broker agents for prioritization of trusted provider agents over the non-trusted provider agents which minimizes the bargaining conflict between the participants and enhance future bargaining progression. In addition, the proposed adaptive probabilistic trust management model formulates the sequence of bilateral negotiation process among the participants as a Bayesian learning process. Finally, the performance of the projected cloud-enabled e-commerce negotiation framework with Bayesian-based adaptive probabilistic trust management model is compared with the existing frameworks by validating under different levels of negotiation rounds.

Concise Biosynthesis of Antifungal Papulacandins

A path forward on online misinformation mitigation based on current user behavior

Mobile Constituent-Boosted Dynamic Separation of C₂H₂/C₂H₄/CO₂ Ternary Mixtures in Metal–Organic Frameworks

Early prediction of microvascular obstruction prior to percutaneous coronary intervention

Abstract Early prediction of microvascular obstruction (MVO) occurrence in acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention (PCI) can facilitate personalized management and improve prognosis. This study developed a prediction model for MVO occurrence using preoperative clinical data and validated its performance in a prospective cohort. A total of 504 AMI patients were included, with 406 in the exploratory cohort and 98 in the prospective cohort. Feature selection was performed using random forest recursive feature elimination (RF-RFE), identifying five key predictors: High-Sensitivity Troponin T, Neutrophil Count, Creatine Kinase-MB, Fibrinogen, and Left Ventricular Ejection Fraction. Among the models developed, logistic regression demonstrated the highest predictive performance, achieving an AUC score of 0.800 in the exploratory cohort and 0.792 in the prospective cohort. This model has been integrated into a user-friendly online platform, providing a practical tool for guiding personalized perioperative management and improving patient prognosis.

Chiral-Polar Photovoltage-Driven Efficient Self-Powered Circularly Polarized Light Detection in Three-Dimensional Hybrid Perovskites

Retraction Note: Anti-proliferative and immunomodulatory potencies of cinnamon oil on Ehrlich ascites carcinoma bearing mice

[Co₃@Ge₆Sn₁₈]^5–: A Giant σ-Aromatic Cluster Analogous to H₃⁺ and Li₃⁺

A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales

Abstract The use of quantitative real-time PCR (qPCR) to monitor pathogens is common; however, quantitative frameworks that consider the observation process, dynamics in pathogen presence, and pathogen load are lacking. This can be problematic in the early stages of disease progression, where low level detections may be treated as ‘inconclusive’ and excluded from analyses. Alternatively, a framework that accounts for imperfect detection would provide more robust inferences. To better estimate pathogen dynamics, we developed a hierarchical multi-scale dynamic occupancy hurdle model (MS-DOHM). The model used data gathered during sampling for Pseudogymnoascus destructans (Pd), the causative agent of white-nose syndrome, a fungal disease that has cause severe declines in several species of hibernating bats in North America. The model allowed us to estimate initial occupancy, colonization, persistence and prevalence of Pd at bat hibernacula. Additionally, utilizing the relationship between cycle threshold and pathogen load, we estimated pathogen detectability and modeled expected colony and bat pathogen loads. To assess the ability of MS-DOHM to estimate pathogen dynamics, we compared MS-DOHM’s results to those of a dynamic occupancy model and naïve detection/non-detection. MS-DOHM’s estimates of site-level pathogen presence were up to 11.9% higher than estimates from the dynamic occupancy model and 35.7% higher than naïve occupancy. Including prevalence and load in our modeling framework resulted in estimates of pathogen arrival that were two to three years earlier compared to the dynamic occupancy and naïve detection/non-detection, respectively. Compared to naïve values, MS-DOHM predicted greater pathogen loads on colonies; however, we found no difference between model estimates and naïve values of prevalence. While the model predicted no declines in site-level prevalence, there were instances where pathogen load decreased in colonies that had been Pd positive for longer periods of time. Our findings demonstrate that accounting for pathogen load and prevalence at multiple scales changes our understanding of Pd dynamics, potentially allowing earlier conservation intervention. Additionally, we found that accounting for pathogen load and prevalence within hibernacula and among individuals resulted in a better fitting model with greater predictive ability.

Atomically Dispersed Catalytic Platinum Anti-Substitutions in Molybdenum Ditelluride

A rapid approach for linear epitope vaccine profiling reveals unexpected epitope tag immunogenicity

Room-Temperature H₂ Splitting and N₂-Hydrogenation Induced by a Neutral Lu^II Complex

FDP/FIB ratio serves as a novel biomarker for diagnosing bone marrow invasion in gastric cancer and predicting patient prognosis\

D−π–A Fluorophores with Strong Solvatochromism for Single-Molecule Ratiometric Thermometers

Investigating Fe and Cr doping effects on thermoelectric efficiency in Mg3Sb2 through first-principles calculations for sustainable energy solutions

Enhancing T-Cell Infiltration and Immunity in Solid Tumors via DNA Nanolinker-Mediated Monocyte Hitchhiking

Self-supervised VICReg pre-training for Brugada ECG detection