The global repercussions of the COVID-19 pandemic on economies and public health worldwide have been profound. This study aims to examine the developmental trends of the COVID-19 pandemic, establish predictive models, and provide insights for effective control measures against potential future disease outbreaks. Considering the coexistence of both linear and nonlinear factors in COVID-19 data, conventional single-machine learning and traditional forecasting models encounter challenges in accurately predicting pandemic trends. To enhance the precision of COVID-19 pandemic predictions by integrating linear and nonlinear factors, this study proposes three combined forecasting models: CNN-LSTM-ARIMA, TCN-LSTM-ARIMA, and SSA-LSTM-ARIMA. These models leverage the strengths of deep learning in capturing nonlinear factors and the capabilities of the traditional ARIMA model in handling linear factors. Initially, LSTM and ARIMA models are used to model and predict the COVID-19 pandemic in Quebec, Canada. Subsequently, CNN models, TCN models, and the Sparrow Search Algorithm are employed to integrate predictions from the LSTM and ARIMA models. Comparative analyses of the three combined models, it was found that the CNN-LSTM-ARIMA model exhibits the highest predictive accuracy, with an MSE of 7048.26, RMSE of 83.95, MAE of 61.18, MAPE of 0.16, and R ² of 0.95. To validate the applicability and stability of the CNN-LSTM-ARIMA model in predicting COVID-19 pandemics, Italian COVID-19 pandemic data was employed. The three combined forecasting models are established and evaluated using model evaluation metrics. The results affirm that the CNN-LSTM-ARIMA model remains the optimal choice, underscoring its high stability and suitability for COVID-19 pandemic forecasting endeavors.

The purpose of this study is to study the spread of COVID-19, establish a predictive model, and provide guidance for its prevention and control. Considering the high complexity of epidemic data, we adopted an ARIMA-LSTM combined model to describe and predict future transmission. A new method of the ARIMA-LSTM model paralleling by weight of regression coefficient was proposed. Then, we used the ARIMA-LSTM model paralleling by weight of regression coefficient, ARIMA model, and ARIMA-LSTM series model to predict the epidemic data in China, and we found that the ARIMA-LSTM model paralleling by weight of regression coefficient had the best prediction accuracy. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 4049.913, RMSE = 63.639, MAPE = 0.205, R² = 0.837, MAE = 44.320. In order to verify the effectiveness of the ARIMA-LSTM model paralleling by weight of regression coefficient, we compared the ARIMA-LSTM model paralleling by weight of regression coefficient with the SVR model and found that ARIMA-LSTM model paralleling by weight of regression coefficient has better prediction accuracy. It was further verified with the epidemic data of India and found that the prediction accuracy of the ARIMA-LSTM model paralleling by weight of regression coefficient was still higher than that of the SVR model. In the ARIMA-LSTM model paralleling by weight of regression coefficient, MSE = 744,904.6, RMSE = 863.079, MAPE = 0.107, R² = 0.983, MAE = 580.348. Finally, we used the ARIMA-LSTM model paralleling by weight of regression coefficient to predict the future epidemic situation in China. We found that in the next 60 days, the epidemic situation in China will become a steady downward trend.

Since the end of 2019, a novel coronavirus known as COVID-19 has caused a severe outbreak worldwide. Due to the complexity of epidemic data, traditional algorithms have struggled to accurately predict the development of the pandemic. The Autoregressive Integrated Moving Average (ARIMA) model is capable of capturing time-based trends in epidemic data, including seasonality, cyclic patterns, and long-term trends, which helps improve the accuracy of forecasting future epidemic trajectories. The Bidirectional Long Short-Term Memory (BiLSTM) network, a variant of the Recurrent Neural Network (RNN), is highly effective in handling sequential data. In epidemic data analysis, BiLSTM models can be applied to forecast future trends or conduct time series predictions. BiLSTM is able to capture temporal relationships and sequential patterns within data, thereby providing more accurate predictions. Genetic Algorithms (GA), inspired by biological evolution through operations such as selection, crossover, and mutation, offer an efficient approach to identifying the best-fit models and parameter configurations. By using GA, we can iteratively optimize epidemic forecasting models and enhance their performance over time. In this study, we proposed a hybrid model called GA-BiLSTM-ARIMA. Using COVID-19 case data from Japan, we calculated the GA-BiLSTM-ARIMA model's evaluation metrics: RMSE, MAE, MAPE , and R ² , which were 2,262.42, 1,672.07, 6.81, and 0.9764, respectively. The results demonstrate that the hybrid model outperforms both the standalone BiLSTM and ARIMA models in predictive performance. The GA-BiLSTM-ARIMA model successfully integrates the strengths of different models through a systematic and intelligently optimized hybrid strategy. When forecasting infectious disease time series data, this model achieves higher and more robust predictive accuracy compared to traditional single models or partial hybrid models. This type of analysis supports the development of more effective prevention and control strategies and delivers accurate information and early warnings to the public and policymakers, contributing to a better global response to pandemic challenges.

At present, the problem of an aging population in China is severe. The integration of existing healthcare services with elderly care services is inefficient and cannot meet the needs of the elderly. As such, China urgently needs the concerted efforts of various social forces to cope with the increasingly serious problem of aging. In accordance with Andersen’s behavioral model, a survey was conducted in Tangshan City among seniors 60 years of age and older. Using logistic regression models, decision tree models, and random forest models, we examined the factors impacting senior people’s desire to choose the integrated medical care and nursing care model. The results of the three models displayed that the elderly’s propensity to choose the combined medical care and nursing care model is significantly influenced by the amount of insurance, life care needs, and healthcare needs. Moreover, the study found that the willingness of the elderly in Tangshan to improve the combined medical and nursing care service system is low. The government should appeal to the community to participate in multiple developments to improve the integrated medical and nursing service system.

Dissipative particle dynamics (DPD) simulations is used to study the effect of A_m/2B_mA_m/2 and H-shaped (A_m/4)₂B_m(A_m/4)₂ block copolymers on the interfacial properties of ternary blends. Our simulations show the following: (i) The capacity of block copolymers to diminish interfacial tension is closely linked to their compositions. With identical molecular weights and concentrations, H-shaped block copolymers outperform triblock copolymers in mitigating interfacial tension. (ii) The interfacial tension within the blends correlates positively with the escalation in H-shaped block copolymer molecular weight. This correlation suggests that H-shaped block copolymers featuring a low molecular weight demonstrate superior efficacy as compatibilizers when contrasted with those possessing a high molecular weight. (iii) Enhancing the concentration of H-shaped block copolymers fosters their accumulation at the interface, leading to a reduction in correlations between immiscible homopolymers and a consequent decrease in interfacial tension. (iv) As the length of the homopolymer chains increases, there is a concurrent elevation in interfacial tension, suggesting that H-shaped block copolymers perform more effectively as compatibilizers in blends characterized by shorter homopolymer chain lengths. These findings elucidate the associations between the efficacy of H-shaped block copolymer compatibilizers and their specific molecular characteristics.

Rice (Oryza sativa) is one of the key staple crops, providing food for nearly half of the world’s population. The past twenty years have seen significant advances in understanding Oryza species through genome sequencing efforts. However, the stability of Oryza genomes during their divergence has not been well characterized. Here, by performing gene collinearity and comparative genomics analysis, we selected ten Oryza species and three other Poaceae species to check their genome stability, with Leersia perrieri as the reference. Intra- and intergenomic analysis showed a ~30% difference in homologous block numbers and a 35.7% difference in collinear gene numbers per block, indicating that Oryza genomes have undergone extensive DNA permutations. Notably, we found that Oryza chromosomes with smaller ordinal numbers have often preserved larger percentages of genes, while those with bigger numbers have undergone more gene losses. This unique observation may be explained by elevated gene losses incurred by illegitimate or homoeologous recombination between homoeologous chromosomes produced by the grass-common tetraploidization (GCT) ~100 million years ago (Mya), e.g., Chro. 11 and 12. However, the lowered gene loss rates in Chro. 1–3 could be explained by earlier restriction of illegitimate recombination after the GCT due to there often being (larger) neo-chromosomes produced by the fusion of ancestral chromosomes. The enriched NBS-LRR (nucleotide-binding site and leucine-rich repeat) genes in chromosomes 11 and 12 are another explanation for the above observation. Further evidence was obtained from other Poaceae plants. Moreover, we revealed around twice as many differences in tandem genes and their densities among Oryza plants, further showing their divergent levels of genome stability. The present efforts may contribute to the understanding of the stability of the Oryza genome and its formation, evolution, and functional innovation.