A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection.

Feature selection (FS) is a popular data pre-processing technique in machine learning to extract the optimal features to maintain or increase the classification accuracy of the dataset, which is a combinatorial optimization problem, requiring a powerful optimizer to obtain the optimum subset. The equilibrium optimizer (EO) is a recent physical-based metaheuristic algorithm with good performance for various optimization problems, but it may encounter premature or the local convergence in feature selection. This work presents a self-adaptive quantum EO with artificial bee colony for feature selection, named SQEOABC. In the proposed algorithm, the quantum theory and the self-adaptive mechanism are employed into the updating rule of EO to enhance convergence, and the updating mechanism from the artificial bee colony is also incorporated into EO to achieve appropriate FS solutions. In the experiments, 25 benchmark datasets from the UCI repository are investigated to verify SQEOABC, which is compared with several state-of-the-art metaheuristic algorithms and the variants of EO. The statistical results of fitness values and accuracy demonstrate that SQEOABC has better performance than the compared algorithms and the variants of EO. Finally, a real-world FS problem from COVID-19 illustrates the effectiveness and superiority of SQEOABC.

The relationships between trait anger, forgiveness, and subjective well-being during the COVID-19 pandemic: a moderated mediating model under lockdown situation.

Although previous studies have found relationships between anger, forgiveness, and well-being, none have examined whether forgiveness mediates the relationship between trait anger and subjective well-being. To address this gap, this study constructed and tested a relevant moderated mediating model. We also considered the moderating effects of the COVID-19 lockdown situation, which has indirectly deteriorated well-being. The participants included 1,274 individuals who were recruited in April 2022. In sum, the results showed negative relationships between the trait anger and forgiveness and well-being, with a positive association between forgiveness and well-being. Moreover, forgiveness mediated the association between trait anger and subjective well-being, whereas the lockdown situation regulated the effects of trait anger on forgiveness and subjective well-being; specifically, forgiveness and well-being were more susceptible to trait anger among individuals under the lockdown situation. These findings suggest that forgiveness mediates the relationship between trait anger and well-being, while trait anger negatively predicts forgiveness and subjective well-being. Furthermore, the lockdown situation increases the negative predictive effects of anger on forgiveness and subjective well-being. Supplementary information: The online version contains supplementary material available at 10.1007/s12144-023-04500-9.

Presence of anti-nuclear antibody associated with worse clinical outcomes of anti-NMDAR encephalitis.

Purpose: Systemic autoantibodies are important for the diagnosis of autoimmune diseases, but their roles in anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis are unknown. The purpose of our study is to investigate the characteristics and a prognosis of anti-NMDAR encephalitis with the prevalence of autoantibodies. Methods: Systemic autoantibodies were evaluated in 64 patients with anti-NMDAR encephalitis and 14 patients with autoimmune encephalitis with other forms. Then, according to systemic autoantibodies, patients with anti-NMDAR encephalitis were divided into an anti-nuclear antibody (ANA) positive group and an ANA negative group. The clinical outcome was assessed by a modified Rankin score at 12 months after the disease onset. Results: A total of 64 patients with anti-NMDAR encephalitis were enrolled, of which 28.13% (18/64) were positive for ANA. The titers of a positive anti-NMDAR antibody in CSF (p = 0.041) and serum (p = 0.031) in the ANA-positive group were significantly higher than the ANA-negative group. Patients with ANA positive than those with ANA negative showed lower rates of headache (p = 0.047) and speech disorder (p = 0.049). The presence of ANA was associated with a worse clinical outcome at 12 months (p = 0.043). Conclusion: ANA was prevalent in patients with anti-NMDAR encephalitis, and associated with a worse prognosis and impaired neurological recovery.

Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case

It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process.

Effectiveness of Digital Cognitive Behavior Therapy for the Treatment of Insomnia: Spillover Effects of dCBT.

The effects of digital Cognitive Behavior Therapy for insomnia (dCBT-i) on sleep quality have been previously demonstrated but the spillover effects on fatigue, flow (a state of immersion in activities of interest), and cognitive flexibility remain unclear. The current study examined the effectiveness of dCBT-i. A total of 97 college students (20.96 ± 1.87 years, 73.1% female students) were randomly selected from a shortlist and divided into sleep intervention (n = 39), conventional education (n = 37), and healthy control (n = 21) groups. Task switching paradigm, Fatigue Severity Scale (FSS), Flow Experience Scale (FES), and the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) were measured pre- and post-intervention. Results show that the sleep quality of the intervention group improved, and fatigue was relieved. Participants in the sleep intervention group had increased flow experience scores post-intervention and improved cognitive flexibility. The control group's sleep quality deteriorated and fatigue level increased. dCBT-i can not only achieve a significant improvement in sleep quality and reduce fatigue, but also improve learning abilities, quality of life, flow, and cognitive flexibility. Future research should pay attention to indicators such as work efficiency, sedative use, and the durability and stability of such effects.