Browse Articles
Discover research articles across all indexed journals
Invulnerability bias in perceptions of artificial intelligence’s future impact on employment
Topochemical Reaction Involving Double-to-Single Layer Conversion: Mo<sub>3</sub>Ta<sub>2</sub>O<sub>10</sub>N with a Kagomé Lattice
Efficacy and safety of lateral approach laparoscopic spleen-preserving distal pancreatectomy: a multicenter retrospective cohort study
Entropy-Driven Structural Evolution in Ceramic Oxides
Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study
Abstract Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle study, a deep learning approach to multi-material decomposition was developed to quantify iodine, gadolinium, and calcium in spectral CT. A dual-phase network architecture was trained using synthetic datasets containing computational models of cylindrical and virtual patient phantoms. Classification and quantification performance was evaluated across a range of patient size and dose parameters. The model was found to accurately classify (accuracy: cylinders – 98%, virtual patients – 97%) and quantify materials (mean absolute percentage difference: cylinders – 8–10%, virtual patients – 10–15%) in both datasets. Performance in virtual patient phantoms improved as the hybrid training dataset included a larger contingent of virtual patient phantoms (accuracy: 48% with 0 virtual patients to 97% with 8 virtual patients). For both datasets, the algorithm was able to maintain strong performance under challenging conditions of large patient size and reduced dose. This study shows the validity of a deep-learning based approach to multi-material decomposition trained with in-silico images that can overcome the limitations of conventional material decomposition approaches.
Fully Recyclable and Remarkably Robust Cross-Linked Polyethylene Networks via Direct Free-Radical Copolymerization with Disulfide Dynamic Covalent Bonds
The practical impact of indoor temperature on the productivity of prefabricated construction workers using electroencephalogram data
Interfacial Electric Fields Modulate Redox Reactions in Abiological Coacervates
Air quality prediction-based big data analytics using hebbian concordance and attention-based long short-term memory
Dihapto-Coordinated Conjugated Carbocycles (η<sup>2</sup>-C<sub><i>n</i></sub>H<sub><i>n</i></sub> <i>n</i> = 5–8): Blurring the Line Between Aromatic and Antiaromatic Hydrocarbons
A two-wave longitudinal mediation study of the relationships between organizational ostracism, future anxiety, and work engagement among teachers
Tetracene Functionalized Si(111) Achieves Enhanced Solar-to-Chemical Energy Conversion via Molecular Acceptor States
Balancing mental health through predictive modeling for healthcare workers during public health crises
Mechanisms and Synthetic Applications of Cyclic, Nonstabilized Isodiazenes: Nitrogen-Atom Insertion into Pyrrolidines and Related Rearrangements
Quantum inspired qubit qutrit neural networks for real time financial forecasting
Abstract This research investigates the performance and efficacy of machine learning models in stock prediction, comparing Artificial Neural Networks (ANNs), Quantum Qubit-based Neural Networks (QQBNs), and Quantum Qutrit-based Neural Networks (QQTNs). By outlining methodologies, architectures, and training procedures, the study highlights significant differences in training times and performance metrics across models. While all models demonstrate robust accuracies above 70%, the Quantum Qutrit-based Neural Network consistently outperforms with advantages in risk-adjusted returns, measured by the Sharpe ratio, greater consistency in prediction quality through the Information Coefficient, and enhanced robustness under varying market conditions. The QQTN not only surpasses its classical and qubit-based counterparts in multiple quantitative and qualitative metrics but also achieves comparable performance with significantly reduced training times. These results showcase the promising prospects of Quantum Qutrit-based Neural Networks in practical financial applications, where real-time processing is critical. By achieving superior accuracy, efficiency, and adaptability, the proposed models underscore the transformative potential of quantum-inspired approaches, paving the way for their integration into computationally intensive fields.