Hemorrhagic fever with renal syndrome (HFRS) is considered a globally distributed infectious disease which results in many deaths annually in Hubei Province, China. In order to conduct a better analysis and accurately predict HFRS incidence in Hubei Province, a new model named Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) was constructed. The SD-GTWR model, which integrates the analysis and relationship of seasonal difference, spatial and temporal characteristics of HFRS (HFRS was characterized by spatiotemporal heterogeneity and it is seasonally distributed), was designed to illustrate the latent relationships between the spatio-temporal pattern of the HFRS epidemic and its influencing factors. Experiments from the study demonstrated that SD-GTWR model is superior to traditional models such as GWR- based models in terms of the efficiency and the ability of providing influencing factor analysis.

Objective: Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors. Methods: We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics. Results: In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics. Conclusion: The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns.

Hemorrhagic fever with renal syndrome (HFRS) is a naturally-occurring, fecally transmitted disease caused by a Hantavirus (HV). It is extremely damaging to human health and results in many deaths annually, especially in Hubei Province, China. One of the primary characteristics of HFRS is the spatiotemporal heterogeneity of its occurrence, with notable seasonal differences. In view of this heterogeneity, the present study suggests that there is a need to focus on trend simulation and the spatiotemporal prediction of HFRS outbreaks. To facilitate this, we constructed a new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model. The SD-STARIMA model is based on the spatial and temporal characteristics of the Space-Time Autoregressive Integrated Moving Average (STARMA) model first developed by Cliff and Ord in 1974, which has proven useful in modelling the temporal aspects of spatially located data. This model can simulate the trends in HFRS epidemics, taking into consideration both spatial and temporal variations. The SD-STARIMA model is also able to make seasonal difference calculations to eliminate temporally non-stationary problems that are present in the HFRS data. Experiments have demonstrated that the proposed SD-STARIMA model offers notably better prediction accuracy, especially for spatiotemporal series data with seasonal distribution characteristics.

Fluorescence spectrophotometry was applied to the determination of fluorescent whitening agent in emulsion paint. At temperature of 30°C, linear relationship was found between fluorescence intensity and concentration of fluorescent whitening agent in the range of 0.025-0.25mg/ml when the emission wavelength was 437nm and excitation wavelength was 347nm, correlation conefficient was 0.998, detection limit was 0.0007mg/ml, relative standard deviation ranged from 0.49% to 0.72%, recovery of standard addition ranged from 98.3% to 104.5%. The influence of storage time and exposure time were also studied here. Measurement results indicated the feasibility and precision of using this method for quantitative analysis of fluorescent whitening agent.

This study employs a Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) model to examine the determinants, temporal patterns, and projected trajectories of Chinese Foreign Direct Investment (FDI) flows to Algeria over the period 2003-2021. Chinese capital investments have emerged as a critical factor in Algeria's economic transformation, with concentrated activity in construction, energy extraction, and infrastructure development sectors. Utilizing quarterly time-series data derived from national statistical agencies and international financial institutions, this research models the dynamic relationship between Chinese FDI inflows and key macroeconomic determinants, including international oil price volatility, exchange rate fluctuations, and bilateral trade volumes. The empirical analysis incorporates advanced econometric techniques to capture seasonal variations and external shocks affecting investment patterns. Through ex-ante forecasting methodologies, the study projects future FDI trajectories while identifying primary explanatory variables that influence investment decisions. The econometric findings provide empirical evidence for policymakers seeking to develop strategic frameworks for attracting sustainable and sectorally diversified foreign investment portfolios. The research contributes to the broader literature on South-South investment flows and offers practical insights for enhancing bilateral economic cooperation mechanisms between China and Algeria.