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In this paper, we innovatively stack multiple resonant units of photoconductive silicon to design an ultra-broadband metamaterial absorber. By manipulating the conductivity of the silicon with a pump beam, adjustments are made to the amplitude of the wide absorption spectrum spanning 6.6 THz, enabling functional switching from total reflection to near-perfect ultra-broadband absorption. By integrating vanadium dioxide as an intermediary layer, a dual-mode switchable absorber is realized, offering dual control functionalities. Temperature changes enable the absorber to switch between dual-band absorption and ultra-broadband absorption, while variations in pump beam intensity allow for further amplitude adjustments within the absorption spectrum. Impedance matching theory and near-field analysis provide the necessary physical foundation for understanding broadband absorption. Structural parameters, incident angle, and polarization angle of the incident electromagnetic waves are also studied to demonstrate the device's robustness. Our proposed absorbers not only greatly broaden the absorption bandwidth of silicon-based absorbers, but also offer versatility, polarization insensitivity, and robustness over a wide range of incidence angles. Moreover, our design ideas are useful for broadening the bandwidth and enhancing absorption, which enables wider applications in ultra-broadband terahertz absorption and promises extensive prospects. [ABSTRACT FROM AUTHOR]

A tunable dual broadband switchable terahertz absorber based on vanadium dioxide and graphene is proposed. The tunability of graphene and the phase transition properties of vanadium dioxide are used to switch broadband absorption between low-frequency and high-frequency, as well as the absorption rate tuning function. The simulation results indicate that when vanadium dioxide is in the insulating phase and the graphene Fermi energy is 0.7 eV, the absorber achieves low-frequency broadband absorption within the range of 2.6–4.2 THz with an absorptance greater than 90%; when vanadium dioxide is in the metallic phase and the graphene Fermi energy is 0 eV, the absorber achieves high-frequency broadband absorption within the range of 4.9–10 THz with an absorptance greater than 90%. Furthermore, the absorptance can be tuned by adjusting the conductivity of vanadium dioxide or the Fermi energy of graphene. Due to the central symmetry of the proposed structure, the absorber is completely insensitive to polarization. For TE and TM polarized waves, both low and high-frequency broadband absorption are maintained over a range of incident angles from 0° to 50°. The simple structure, tunable absorption rate, insensitivity to polarization angle and incident angle properties are advantages of our proposed absorber. It has broad application prospects in adjustable filters and electromagnetic shielding. [ABSTRACT FROM AUTHOR]

In this work, a simple and sensitive electrochemical aptasensor for protein (thrombin – TB used as the model) was developed by using cubic Cu2O nanocages (Cu2O-NCs) loaded with Au nanoparticles (AuNPs@Cu2O-NCs) as non-enzymatic electrocatalysts and robust redox probes. Through the specific sandwich-type reaction between TB and TB aptamers (TBA), the formed AuNPs@Cu2O-NCs bound with NH2-TBA were captured onto the electrode surface modified with SH-TBA. Based on the inherent redox activity of AuNPs@Cu2O-NCs with cubic nanostructures, a detectable electrochemical signal was generated which was dependent on the analyte concentration. Meanwhile, AuNPs@Cu2O-NCs showed an efficient electrocatalytic capability in the reduction of H2O2, resulting in a significant enhancement of the response signal. Thus, the simplification of the proposed strategy and the improvement of analytical performances were easily achieved with a sub-picomolar sensitivity (the limit of detection was 0.066 pmol L−1). The applicability of the simple and sensitive aptasensor was successfully demonstrated by assaying TB in human serum samples. This non-enzymatic detection platform would be potential and promising in clinical diagnostics and protein analysis techniques. [ABSTRACT FROM AUTHOR]

Missed, wrong or delayed diagnosis has a direct effect on patient safety. Diagnostic errors have been discussed at length, however it still lacks a systematic approach. This study proposed a more systematic way of studying diagnostic errors by using a causal loop diagram. A systematic review was used to find the key factors which may cause diagnostic errors and their interrelationships. A causal loop diagram, as a qualitative model at the first stage of system dynamics modeling, was produced to map all the factor and interrelationships. The diagram provides not only the direct and indirect factors affecting correct diagnosis, but also a clear view of how the change of one factor in the model triggers changes of other factors and then the change of the number of final diagnostic errors. [ABSTRACT FROM AUTHOR]

This paper proposes a simulated design for a versatile terahertz absorber that can be actively tuned. The absorber utilizes the unique tuning capabilities of graphene and vanadium dioxide, enabling it to alternate between ultra-broadband absorption, broadband absorption, and almost complete reflection. In the metallic phase of vanadium dioxide, coupled with a graphene Fermi level at 0 eV, the absorber achieves ultra-broadband absorption. This spans an extensive frequency range from 3.85 THz to 9.73 THz, exhibiting an absorption rate surpassing 90%. As we shift to the insulating phase of vanadium dioxide and adjust the graphene Fermi level to 1 eV, the absorber operates in a broadband absorption mode. This mode spans 2.98 THz to 4.63 THz, demonstrating an absorption rate exceeding 90%. In the insulating state of vanadium dioxide with a graphene Fermi level at 0 eV, the absorber metamorphoses into a nearly total reflector. Its maximum absorption rate is a mere 0.52%. The unique adjustability of vanadium dioxide and graphene independently enables the fine-tuning of absorption rates for both ultra-broadband and broadband absorption without encountering interference. Additionally, thanks to the central symmetry inherent in the proposed structure, the absorber exhibits insensitivity to alterations in polarization angles and remains stable under a broad range of incident angles. With these benefits, the absorber shows promising potential for applications in electromagnetic stealth, wireless communication, and so on.

Atrial fibrillation (AF) is associated with an increased risk of acute ischemic stroke (AIS). Accurately predicting AIS and planning effective treatment pathways for AIS prevention are crucial for AF patients. Because of the temporality of patients' disease progressions, sequential disease and treatment patterns have the potential to improve risk prediction performance and contribute to effective treatment pathways. This paper integrates temporal pattern mining into the AF study of AIS prediction and treatment pathway discovery. We combine temporal pattern mining with feature selection to identify temporal risk factors that have predictive ability, and integrate temporal pattern mining with treatment efficacy analysis to discover temporal treatment patterns that are statistically effective. Results show that our approach has identified new potential temporal risk factors for AIS that can improve the prediction performance, and has discovered treatment pathway patterns that are statistically effective to prevent AIS for AF patients.

Atrial fibrillation (AF) is a common cardiac rhythm disorder, which increases the risk of ischemic stroke and other thromboembolism (TE). Accurate prediction of TE is highly valuable for early intervention to AF patients. However, the prediction performance of previous TE risk models for AF is not satisfactory. In this study, we used integrated machine learning and data mining approaches to build 2-year TE prediction models for AF from Chinese Atrial Fibrillation Registry data. We first performed data cleansing and imputation on the raw data to generate available dataset. Then a series of feature construction and selection methods were used to identify predictive risk factors, based on which supervised learning methods were applied to build the prediction models. The experimental results show that our approach can achieve higher prediction performance (AUC: 0.71~0.74) than previous TE prediction models for AF (AUC: 0.66~0.69), and identify new potential risk factors as well.

Atrial fibrillation (AF) patients suffer a high risk of ischemic stroke and other thromboembolism (TE). Warfarin is a long-term oral medication and is effective in reducing TE for AF patients. Identifying the trajectory patterns of warfarin use in AF patients and discovering how different trajectories are associated with different TE outcomes are important for understanding long-term use of warfarin. Also, finding the factors affecting future warfarin use and predicting the warfarin use trajectory for new patients can help to efficiently target the specific patient groups and propose relevant interventions for warfarin use. This paper, combining group-based trajectory modeling and predictive modeling, has successfully discovered three patient groups with distinct warfarin use trajectories. Also, results suggest that the warfarin use trajectory has potential association with the TE outcomes. Moreover, results show factors affecting the future trajectory, which have been used to build prediction models for the warfarin use group.

The diagnostic process involves a series of stages in the patient pathway. Any errors or misleading information from any stage could lead to errors in the final decision-making. System dynamics modeling maps the diagnostic process as a whole and seeks to provide a quantitative way of analyzing different errors at each stage as well as relevant key factors. This paper provides a framework based on system dynamics for modeling the tracing of errors inside of the system from where errors initially occur, the routes of errors inside of the system, and how errors are delivered out of the system. Also, a detailed illustration of the phase history and physical examinations is provided as an example to explain how relevant factors can be interpreted and how this affects errors according to the framework.

Although many researches have been carried out to analyze laboratory test errors during the last decade, it still lacks a systemic view of study, especially to trace errors during test process and evaluate potential interventions. This study implements system dynamics modeling into laboratory errors to trace the laboratory error flows and to simulate the system behaviors while changing internal variable values. The change of the variables may reflect a change in demand or a proposed intervention. A review of literature on laboratory test errors was given and provided as the main data source for the system dynamics model. Three "what if" scenarios were selected for testing the model. System behaviors were observed and compared under different scenarios over a period of time. The results suggest system dynamics modeling has potential effectiveness of helping to understand laboratory errors, observe model behaviours, and provide a risk-free simulation experiments for possible strategies.