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Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods.

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A landslide involves the downward movement of a mass of rock, debris, earth, or soil. Landslides happen when gravitational forces and other types of shear stresses on a slope surpass the shear strength of the materials. Additionally, landslides can be triggered by processes that weaken the shear strength of the slope\'s material. Shear strength primarily depends on two factors such as frictional strength, which is the resistance to movement between the interacting particles of the slope material, and cohesive strength, which is the bonding between those particles. A landslide is a terrible natural disaster that causes much damage to both human life and the economy. It often occurs in steep mountainous areas or hilly regions, ranging in scale from medium to large. It progresses slowly (20-50 mm/year), but when it occurs, it can move at a speed of 3 m/s. Therefore, early detection or prevention of this disaster is an essential and significant task. This paper developed a method to collect and analyze data, with the purpose of determining the possibility of landslide occurrences to reduce its potential losses.•The proposed method is convenient for users to grasp information of landslide phenomenon.•A machine learning model is applied to forecast landslide phenomenon.•Internet of things (IoT) system is utilized to manage and send a warning text to individual email address and mobile devices.

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Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

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BACKGROUND: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China.
METHODS: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk.
RESULTS: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E-H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China.
CONCLUSIONS: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease.

OBJECTIVE: To study the historical global incidence and mortality trends of gastric cancer and predicted mortality of gastric cancer by 2035.
METHODS: Incidence data were retrieved from the Cancer Incidence in Five Continents (CI5) volumes I-XI, and mortality data were obtained from the latest update of the World Health Organization (WHO) mortality database. We used join-point regression analysis to examine historical incidence and mortality trends and used the package NORDPRED in R to predict the number of deaths and mortality rates by 2035 by country and sex.
RESULTS: More than 1,089,000 new cases of gastric cancer and 769,000 related deaths were reported in 2020. The average annual percent change (AAPC) in the incidence of gastric cancer from 2003 to 2012 among the male population, South Korea, Japan, Malta, Canada, Cyprus, and Switzerland showed an increasing trend (P > 0.05); among the female population, Canada [AAPC, 1.2; (95%Cl, 0.5-2), P < 0.05] showed an increasing trend; and South Korea, Ecuador, Thailand, and Cyprus showed an increasing trend (P > 0.05). AAPC in the mortality of gastric cancer from 2006 to 2015 among the male population, Thailand [3.5 (95%cl, 1.6-5.4), P < 0.05] showed an increasing trend; Malta Island, New Zealand, Turkey, Switzerland, and Cyprus had an increasing trend (P > 0.05); among the male population aged 20-44, Thailand [AAPC, 3.4; (95%cl, 1.3-5.4), P < 0.05] showed an increasing trend; Norway, New Zealand, The Netherlands, Slovakia, France, Colombia, Lithuania, and the USA showed an increasing trend (P > 0.05). It is predicted that the mortality rate in Slovenia and France\'s female population will show an increasing trend by 2035. It is predicted that the absolute number of deaths in the Israeli male population and in Chile, France, and Canada female population will increase by 2035.
CONCLUSIONS: In the past decade, the incidence and mortality of gastric cancer have shown a decreasing trend; however, there are still some countries showing an increasing trend, especially among populations younger than 45 years. Although mortality in most countries is predicted to decline by 2035, the absolute number of deaths due to gastric cancer may further increase due to population growth.