Currently, the outbreak of COVID-19 is rapidly spreading especially in Wuhan city, and threatens 14 million people in central China. In the present study we applied the Moran index, a strong statistical tool, to the spatial panel to show that COVID-19 infection is spatially dependent and mainly spread from Hubei Province in Central China to neighbouring areas. Logistic model was employed according to the trend of available data, which shows the difference between Hubei Province and outside of it. We also calculated the reproduction number R0 for the range of [2.23, 2.51] via SEIR model. The measures to reduce or prevent the virus spread should be implemented, and we expect our data-driven modeling analysis providing some insights to identify and prepare for the future virus control.

Objective: Early disease screening and diagnosis are important for improving patient survival. Thus, identifying early predictive features of disease is necessary. This paper presents a comprehensive comparative analysis of different Machine Learning (ML) systems and reports the standard deviation of the results obtained through sampling with replacement. The research emphasises on: (a) to analyze and compare ML strategies used to predict Breast Cancer (BC) and Cardiovascular Disease (CVD) and (b) to use feature importance ranking to identify early high-risk features. Results: The Bayesian hyperparameter optimization method was more stable than the grid search and random search methods. In a BC diagnosis dataset, the Extreme Gradient Boosting (XGBoost) model had an accuracy of 94.74% and a sensitivity of 93.69%. The mean value of the cell nucleus in the Fine Needle Puncture (FNA) digital image of breast lump was identified as the most important predictive feature for BC. In a CVD dataset, the XGBoost model had an accuracy of 73.50% and a sensitivity of 69.54%. Systolic blood pressure was identified as the most important feature for CVD prediction.

The novel coronavirus (COVID-19) that has been spreading worldwide since December 2019 has sickened millions of people, lock down major cities and some countries, prompted unprecedented global travel restrictions. Real data-driven modeling is an effort to help evaluate and curb the spread of the novel virus. Lockdowns and the effectiveness of reduction in the contacts in Italy has been measured via our modified model, with the addition of auxiliary and state variables that represent, contacts with infected, conversion rate and latent propagation. Results show the decrease in infected people due to stay-at-home orders and tracing quarantine intervention. The effect of quarantine and centralized medical treatment was also measured through numerical modeling analysis.

Accurate photovoltaic (PV) power generation forecasting is crucial for optimizing grid management and enhancing the reliability of sustainable energy systems. This study creates a novel hybrid model—MPA-VMD-BiGRU-MAM—designed to improve PV power forecasting accuracy through advanced decomposition and deep learning techniques. Initially, the Kendall correlation coefficient is applied to identify key influencing factors, ensuring robust feature selection for the model inputs. The Marine Predator Algorithm (MPA) optimizes the hyperparameters of Variational Mode Decomposition (VMD), effectively segmenting the PV power time series into informative sub-modes. These sub-modes are processed using a bidirectional gated recurrent unit (BiGRU) enhanced with a multi-head attention mechanism (MAM), enabling dynamic weight assignment and comprehensive feature extraction. Empirical evaluations on PV datasets from Alice Springs, Australia, and Belgium indicate that our hybrid model consistently surpasses baseline methods and achieves a 38.34% reduction in Mean Absolute Error (MAE), a 19.6% reduction in Root Mean Square Error (RMSE), a 4.41% improvement in goodness of fit, and a 33.91% increase in stability (STA) for the Australian dataset. For the Belgian dataset, the model attains a 96.32% reduction in MAE, a 95.84% decrease in RMSE, an 11.92% enhancement in goodness of fit, and an STA of 92.08%. We demonstrate the model’s effectiveness in capturing seasonal trends and addressing the inherent variability in PV power generation, offering a reliable solution to the challenges of instability, intermittency, and unpredictability in renewable energy sources.

Introduction: The spatiotemporal patterns of Corona Virus Disease 2019 (COVID-19) is detected in the United States, which shows temperature difference (TD) with cumulative hysteresis effect significantly changes the daily new confirmed cases after eliminating the interference of population density. Methodology: The nonlinear feature of updated cases is captured through Generalized Additive Mixed Model (GAMM) with threshold points; Exposure-response curve suggests that daily confirmed cases is changed at the different stages of TD according to the threshold points of piecewise function, which traces out the rule of updated cases under different meteorological condition. Results: Our results show that the confirmed cases decreased by 0.390% (95% CI: -0.478 ~ -0.302) for increasing each one degree of TD if TD is less than 11.5°C; It will increase by 0.302% (95% CI: 0.215 ~ 0.388) for every 1°C increase in the TD (lag0-4) at the interval [11.5, 16]; Meanwhile the number of newly confirmed COVID-19 cases will increase by 0.321% (95% CI: 0.142 ~ 0.499) for every 1°C increase in the TD (lag0-4) when the TD (lag0-4) is over 16°C, and the most fluctuation occurred on Sunday. The results of the sensitivity analysis confirmed our model robust. Conclusions: In US, this interval effect of TD reminds us that it is urgent to control the spread and infection of COVID-19 when TD becomes greater in autumn and the ongoing winter.