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Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature. Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type. Results: After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables. Conclusions: ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.

Since illegal wildlife trade poses challenges to biodiversity and public security, improving people’s wildlife conservation through environmental education has become an important issue. This study analyzes the intervention effect of labeling in wildlife conservation education and explores the underlying mechanism by targeting adolescents. Specifically, it is found that (1) the labeling in wildlife conservation education can improve teenagers’ willingness to protect wildlife; (2) the environmental self-identity is highly correlated with wildlife conservation.

A charge-based model is described for long-channel cylindrical surrounding-gate (SRG) MOSFETs from an intrinsic channel to a heavily doped body. The model is verified by numerical simulations with a wide range of SRG MOSFET geometric parameters and channel doping concentrations, including the undoped channel.

In recent years, more and more metal oxides have been finding critical tribo-catalytic applications. Presently, we have explored the tribo-catalytic conversion of H2O and CO2 using Co3O4 nanoparticles and obtained some surprising results. In an as-received 150 mL glass reactor enclosed with 10 mL of H2O, 0.10 g of Co3O4 nanoparticles, 1 atm of CO2, and a Teflon magnetic rotary disk, we observed the production of as much as 57.41 µmol/L of H2, 0.15 µmol/L of CH4, and 0.21 µmol/L of CO after 5 h of magnetic stirring. Metallic coatings of Cu, Ni, SUS316, Ti, Nb, Mo, and W were further introduced on reactor bottoms separately. For those coatings of Ni, SUS316, Ti, and Nb, the reduction of CO2 was dramatically enhanced, and C2+ products of C2H6 and C2H4 were observed. Especially for the Ti coating, the amounts of H2 and CH4 were increased by 2 and 26 times from those for the glass bottom, respectively, and the amounts of C2H6 and C2H4 were very impressive. The Co3O4 nanoparticles were proven chemically stable under magnetic stirring in water, and hydroxyl radicals and superoxide radicals have been detected for the Co3O4 nanoparticles under magnetic stirring through fluorescence spectroscopy and electron paramagnetic resonance spectroscopy analyses. These findings not only reveal outstanding capability of Co3O4 to generate multicarbon products from H2O and CO2 through tribo-catalysis but also highlight a promising potential of tribo-catalysis as a whole to harness mechanical energy for addressing energy shortages and environmental pollution.

Gate underlap structure can be utilized to improve the immunity to short channel effects in MOSFET devices. In this work, gate underlap design scheme in cylindrical gate-all-around MOSFETs is explored based on an analytical model. This model takes into account the fringing field from gate electrode to underlap regions based on conformal mapping and a channel length transformation method. By solving Poisson equations in the underlap and channel regions and matching the boundary conditions, this model reproduces the channel potential profile in subthreshold operation region. Both symmetric and asymmetric underlap structures are covered. The developed model is verified extensively with TCAD simulations. A gate underlap design scheme is then provided based on this analytical model. [ABSTRACT FROM AUTHOR]

An analytic surface potential-based non-charge-sheet core model for intrinsic nanowire surrounding-gate (SRG) MOSFETs is presented in this paper. Starting from the Poisson-Boltzmann equation in the cylindrical SRG MOSFETs, a surface potential equation is derived. Based on the exact surface potential solutions evaluated at the source and drain ends, a single set of the analytic drain current expression is obtained from the Pao-Sah's dual integral without the charge-sheet approximation. The analytical trans-capacitance model is also obtained from Ward-Dutton's charge partition method within the surface potential-based model framework. It is shown that the proposed drain current model and trans-capacitance model are valid for all operation regions, allowing the nanowire SRG MOSFET characteristics to be adequately described from the linear to saturation and from the sub-threshold to strong inversion region without any fitting-parameters. Moreover, the model prediction is verified by the three-dimensional numerical simulation. [ABSTRACT FROM AUTHOR]

A universal framework for describing the temperature enhanced negative bias temperature instability (NBTI) is developed in this paper. Analytical time evolution models of the NBTI mechanisms, as Pb center generation and hole-transport in the oxygen vacancies, are proposed based on careful investigation of atom-level microscopic pictures. A logarithmic time function is derived to describe the interface state (Pb center) generation and recovery evolution by revealing a fact that the activation energy is significantly modified by the accumulation of generated defects. Corresponding coefficients, including the generation amplitudes and time constant, are identified depending on temperature linearly and exponentially. Moreover, the unrecoverable oxide hole-trapping is proposed resulted from the hole-transport among deep-level oxygen vacancies driven by electrical field within the gate oxide. A power-law time function is derived to describing this evolution, with time exponent linear to temperature. Parameters calculated by the proposed analytical models reveal good consistent with the parameters directly extracted from the measured data, indicating the validation and universality of the physical based framework in reproducing the parametric shift of the NBTI degradation under various temperature conditions and process technologies. [ABSTRACT FROM AUTHOR]

Reliability of FinFETs is studied in this paper using the forward gated-diode generation-recombination (G-R) current. The current-voltage characteristics of a FinFET are measured for parameter extraction and a mathematical algorithm is used to extract the stress-induced interface states and oxide traps of the FinFET from the G-R current measurement. It is observed that the stress-induced interface states and oxide traps can be distinguished by observing the shift of the peak G-R current in the body current (Ib) versus gate voltage (Vg) characteristic. The interface states cause a change in the maximum G-R current (DIpeak) while the oxide traps result in a shift of the gate voltage (DVg) corresponding to the DIpeak. Unlike bulk MOSFETs, the dominant degradation mechanism of FinFETs is found to be the oxide traps formation rather than the interface states generation. [ABSTRACT FROM AUTHOR]