Significant Autocorrelation Research Articles

How does regional economic integration affect carbon emission efficiency? Evidence from the Yangtze River Delta, China.

Rapid urbanization and industrialization promote economic growth as well as bring carbon emissions, which seriously threaten the eco-environment and socioeconomic sustainable development. Facing increasing resource constraints, improving carbon emissions efficiency (CEE) is conducive to promote coordinated development of economy and environmental protection. In recent years, regional economic integration (REI) has rapidly developed. It can not only promote factors flow between regions but also achieve industrial and economic agglomeration. However, few studies have been reported in the literature about the relationship between the REI and CEE. In this study, we first illustrate how the REI influences CEE in theory, then take the Yangtze River Delta (YRD) as a case study to conduct empirical research. The results show that (1) the overall CEE value in the YRD has exhibited an upward trend from 2000 to 2020, and its spatial distribution has revealed a significant auto-correlation pattern. (2) On the whole, the REI act a noteworthy positive impact on CEE. When considering types of cities, it is found to have significant positive impacts for the CEE in economically developed cities, while it exhibits a negative impact in the less-developed ones. (3) Upgrading industrial structure and increasing per capita GDP can promote the CEE, but hinder its growth in surrounding areas. Our findings suggest that the government should formulate a unified overall plan to facilitate REI development and establish a modern industrial system of clean and low-carbon to promote regional sustainable development.

Spatial variation and controls of soil microbial necromass carbon in a tropical montane rainforest

Soil microbial necromass carbon is an important component of the soil organic carbon (SOC) pool which helps to improve soil fertility and texture. However, the spatial pattern and variation mechanisms of fungal- and bacterial-derived necromass carbon at local scales in tropical rainforests are uncertain. This study showed that microbial necromass carbon and its proportion in SOC in tropical montane rainforest exhibited large spatial variation and significant autocorrelation, with significant high-high and low-low clustering patterns. Microbial necromass carbon accounted for approximately one-third of SOC, and the fungal-derived microbial necromass carbon and its proportion in SOC were, on average, approximately five times greater than those of bacterial-derived necromass. Structural equation models indicated that soil properties (SOC, total nitrogen, total phosphorus) and topographic features (elevation, convexity, and aspect) had significant positive effects on microbial necromass carbon concentrations, but negative effects on its proportions in SOC (especially the carbon:nitrogen ratio). Plant biomass also had significant negative effects on the proportion of microbial necromass carbon in SOC, but was not correlated with its concentration. The different spatial variation mechanisms of microbial necromass carbon and their proportions in SOC are possibly related to a slower accumulation rate of microbial necromass carbon than of plant-derived organic carbon. Geographic spatial correlations can significantly improve the microbial necromass carbon model fit, and low sampling resolution may lead to large uncertainties in estimating soil carbon dynamics at specific sites. Our work will be valuable for understanding microbial necromass carbon variation in tropical forests and soil carbon prediction model construction with microbial participation.

INNOVATION, COOPETITION AND SPILLOVER EFFECTS IN EUROPEAN REGIONS

Innovation and investment are critical to economic growth. In this article, we address the complex task of evaluating the capacity of regional innovation to increase investment and generate spillovers in regions of the European Union (EU) from both spatial and temporal perspectives. Using panel data estimation methods and exploring the effects of dynamic spatial autocorrelation, our findings show a positive spatial autocorrelation at the level of EU regions. We also observed spatial competition, both in terms of the distribution of investments and in terms of the diffusion of short-term innovation gains. We argue that, in the short term, EU regions tend to behave as competitors for investment fixing, but in the long run, innovation has the potential to generate spillover effects on neighbouring regions. Furthermore, we find that investment patterns were characterized by a significant temporal autocorrelation, showing that shocks to investment in regions tend to be absorbed in a few periods. This paper attempts to fill existing gaps by using estimation methods for dynamic spatial panel data to identify and explore the effects of regional innovation on investment for the 154 European Union regions, and reports original findings as regards the knowledge spillover across European regions.

A landscape-based ecological hazard evaluation and characterization of influencing factors in Laos

The study of the spatiotemporal evolution of landscape ecological hazard and human and natural influences is essential for conservative management and regional sustainable development. This study applied a landscape pattern analysis method and geodetector to multi-source data for 2000, 2010, and 2020 to analyze changes in and drivers of landscape ecological hazard in Laos. The results indicated that: (1) There were more prominent changes in landscape types in Laos. Forest area decreased, whereas the areas of other landscape types increased. There was an overall steady change in the landscape patterns of Laos. Besides for significant changes in the artificial surface landscape index, landscape indices remained stable; (2) The cumulative high and extreme ecological hazard areas increased by 1,947.81 km2, whereas the cumulative areas of low and minimal ecological hazard decreased by 8,461.8 km2. Areas of low and moderate ecological hazard accounted for > 85% of the total area. Areas of low ecological hazard were mainly in the northwest and southeast. The area of high ecological hazard was concentrated in the central and northeastern regions. The distributions of different landscape ecological hazards in Laos during the study period were similar, with general patterns of decreasing hazard from north to south; (3) A positive Moran’s I of landscape ecological hazard in Laos was obtained. While the agglomeration effect was pronounced, it decreased over time, resulting in a weakening in spatial autocorrelation. A significant positive autocorrelation was observed in the spatial distribution of landscape ecological hazard in the study area. Agglomerated areas of high and low ecological hazard were mainly concentrated in the northeast and southeast, respectively; (4) The spatiotemporal evolution of landscape ecological hazard in Laos over the last 20 years could be attributed to interactions between natural and anthropogenic influences. Natural influences were a significant driver of changes to landscape ecological hazard in Laos, with annual precipitation and average temperature being the most significant. Anthropogenic influences, including socioeconomic factors and regional accessibility, significantly impacted local ecological deterioration in Laos.

Performance and sustainability evaluation of rural digitalization and its driving mechanism: evidence from Hunan province of China

Quantitatively measuring rural digitalization performance and development sustainability, identifying their key influencing factors and figuring out their driving mechanisms are of great value to policy design for rural revitalization and management. This paper analyzed the sustainable development degree, spatial patterns, and influencing factors of rural digitization in Hunan Province, China, based on a combination of PSR, TOPSIS, ESDA, GWR and GeoDetector, in an attempt to provide a basis for the planning and policy design of rural management. The sustainability and construction performance of rural digitalization in Hunan were characterized by significant spatial inequality and positive autocorrelation, with coefficients of variation of 0.33 and 0.24, and Moran’s I values of 0.29 and 0.34, respectively. The rural digitalization in Hunan showed significant non-equilibrium across different dimensions and brought forward diversified combination patterns, including single dimensional leadership, dual dimensional leadership, three-dimensional leadership, and all-round development. The pattern dual dimensional leadership, especially PS (pressure + state), was dominant in the sustainability of rural digitalization, compared to the pattern single dimensional leadership dominant in the construction performance, especially I (rural infra-structure digitalization), IL (rural infrastructure + life digitalization), IG (rural infrastructure + governance digitalization). The sustainability and construction performance of rural digitalization in Hunan were subject to a complex driving mechanism, with different factors differing significantly in their action nature, force, spatial effects and interactions. Notably, economic development (gross domestic product) is a positive key factor, while government intervention capacity (fiscal self-sufficiency rate) is an important factor, and natural environment (relief amplitude) is a mixed auxiliary factor (both positive and negative). Factor interactions were mainly characterized by nonlinear enhancement and a large number of super factor pairs. Therefore, the policy design should take into account both localized and differentiated management; and also emphasize enhanced cooperation with adjacent counties and synergistic management. It is suggested to divide Hunan into four planning zonings of leading, potential, warning and general zone, and design the spatial policies for each of them according to the driving mechanism, so as to develop a more reasonable and practical combination of development projects and management policies.

Deep learning versus hybrid regularized extreme learning machine for multi-month drought forecasting: A comparative study and trend analysis in tropical region

Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.

SAC-ConvLSTM: A novel spatio-temporal deep learning-based approach for a short term power load forecasting

The short-term spatiotemporal load forecasting of power distribution networks has been extensively studied in light of high importance in distribution system control and consumption management. Load forecasting is dependent on temporal and spatial parameters e.g., temperature, rainfall, and land use. Additionally, load time series have spatial autocorrelation in cities. Deep learning has been recently demonstrated to be effective and efficient in time-series and load forecasting in power distribution networks. The present study proposes a new algorithm based on Spatial Auto Correlation and Convolutional Long Short-Term Memory (SAC-ConvLSTM) algorithm via spatiotemporal power load autocorrelation modeling and deep learning. Load time-series signals in a power distribution network are decomposed into sub-signals using the discrete wavelet transform (DWT). Then, the spatio-temporal autocorrelation of sub-signals is calculated, alleviating prediction model complexity using spatial statistics. The areas with significant positive/negative/no significant autocorrelation in each time-series sub-signal are separately used as the input of the SAC-ConvLSTM algorithm. Finally, the sub-signals are reconstructed into the load signals. This algorithm models the spatial relationships in time-series at different time intervals for power consumption forecasting. The proposed methodology was found to outperform the support vector machine (SVM), Long Short Term Memory (LSTM), Convolutional LSTM (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Planar Flow-Based Variational Auto-Encoder (PFVAE) and Federated Averaging (FedAVG) algorithms in short-term power load forecasting for short term forecasting of up to two weeks, based on the evaluation metrics of root-mean-square error (RMSE) and mean absolute error (MAE), producing values of 11.08% and 7.02% respectively.

Five Centuries of Groundwater Elevations Provide Evidence of Shifting Climate Drivers and Human Influences on Water Resources in North Central Florida

AbstractGroundwater depletion is a concern around the world with implications for food security, ecological resilience, and human conflict. Long‐term perspectives provided by tree ring‐based reconstructions can improve understanding of factors driving variability in groundwater elevations, but such reconstructions are rare to date. Here, we report a set of new 546‐year tree‐ring chronologies developed from living and remnant longleaf pine (Pinus palustris) trees that, when combined with existing bald cypress (Taxodium distichum) tree‐ring chronologies, create a set of nested reconstructions of mean annual groundwater elevation for North Central Florida that together explain 66% of the variance in instrumental measurements and span 1498–2015. Split calibration confirms the skill of the reconstructions, but coefficient of efficiency metrics and significant autocorrelation in the regression residuals indicate a weakening relationship between tree growth and groundwater elevation over recent decades. Comparison to data from nearby groundwater wells suggests extraction of groundwater is likely contributing to this weakening signal. Periodicity within the reconstruction and comparison with global sea surface temperatures (SST) highlight the role of El Niño‐Southern Oscillation (ENSO) in driving groundwater elevations, but the strength of this role varies substantially over time. Atlantic and Pacific SST modulate ENSO influences, and comparisons to multiple proxy‐based reconstructions indicate an inconsistent and weaker influence of ENSO prior to the 1800s. Our results highlight the dynamic influence of ocean‐atmospheric phenomena on groundwater resources in North Central Florida and build on instrumental records to better depict the long‐term range of groundwater elevations.

Dynamic Interaction of Coconut Oil and Crude Oil Prices: Insights from a Vector Error Correction Model

Understanding the relationship between commodity prices is paramount for investors, policymakers, and industries reliant on these markets. This study delves into the intricate dynamics between coconut oil and crude oil prices using a Vector Error Correction Model (VECM). The VECM approach allows for exploring these commodities' short-term adjustments and long-term equilibrium relationships. The analysis reveals that while there may not be a strong long-term interdependence between coconut oil and crude oil prices, robust short-term adjustment mechanisms exist. The cointegration rank two (2) highlights the presence of two cointegrating vectors, indicating a stable equilibrium relationship. The alpha and beta coefficients shed light on the speed and direction of adjustments, emphasizing how the system corrects deviations from the equilibrium relationship. Various model selection criteria, including the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), validate the VECM's efficacy in capturing the complexities of these markets while maintaining model simplicity. Moreover, significant error correction terms emphasize the system's self-correcting nature, ensuring long-term stability. Interpreting the coefficients of the lagged terms reveals short-term dynamics, showcasing how coconut oil and crude oil prices mutually influence and respond to changes in one another. The absence of significant autocorrelation in the model's residuals validates the model's accuracy in capturing underlying dynamics.

Small area analysis methods in an area of limited mapping: exploratory geospatial analysis of firearm injuries in Port-au-Prince, Haiti

BackgroundThe city of Port-au-Prince, Haiti, is experiencing an epidemic of firearm injuries which has resulted in high burdens of morbidity and mortality. Despite this, little scientific literature exists on the topic. Geospatial research could inform stakeholders and aid in the response to the current firearm injury epidemic. However, traditional small-area geospatial methods are difficult to implement in Port-au-Prince, as the area has limited mapping penetration. Objectives of this study were to evaluate the feasibility of geospatial analysis in Port-au-Prince, to seek to understand specific limitations to geospatial research in this context, and to explore the geospatial epidemiology of firearm injuries in patients presenting to the largest public hospital in Port-au-Prince.ResultsTo overcome limited mapping penetration, multiple data sources were combined. Boundaries of informally developed neighborhoods were estimated from the crowd-sourced platform OpenStreetMap using Thiessen polygons. Population counts were obtained from previously published satellite-derived estimates and aggregated to the neighborhood level. Cases of firearm injuries presenting to the largest public hospital in Port-au-Prince from November 22nd, 2019, through December 31st, 2020, were geocoded and aggregated to the neighborhood level. Cluster analysis was performed using Global Moran’s I testing, local Moran’s I testing, and the SaTScan software. Results demonstrated significant geospatial autocorrelation in the risk of firearm injury within the city. Cluster analysis identified areas of the city with the highest burden of firearm injuries.ConclusionsBy utilizing novel methodology in neighborhood estimation and combining multiple data sources, geospatial research was able to be conducted in Port-au-Prince. Geospatial clusters of firearm injuries were identified, and neighborhood level relative-risk estimates were obtained. While access to neighborhoods experiencing the largest burden of firearm injuries remains restricted, these geospatial methods could continue to inform stakeholder response to the growing burden of firearm injuries in Port-au-Prince.

Spatial Characteristics and Obstacle Factors of Cultivated Land Quality in an Intensive Agricultural Region of the North China Plain

Cultivated land quality (CLQ) is at the core of the trinity protection of cultivated land in China. Scientific evaluation of CLQ and identification of its obstacle factors are the foundation for the construction and improvement of the quality of cultivated land. The main objective of this study was to evaluate CLQ and identify its obstacle factors, and Quzhou County, an intensive agricultural region in the North China Plain (NCP), was selected as a case study. The evaluation index system of CLQ was constructed based on five dimensions, including climate condition, topographic characteristic, soil property, farming status, and environmental condition, by analyzing the logical evolution of elements, processes, functions, and quality of cultivated land. A methodological system based on the Weighted Summation Method (WSM) and the “1 + X” model was developed to evaluate the CLQ. Then, the obstacle diagnosis model constructed based on the Cask Law and relevant academic studies was used to identify the obstacle factors of CLQ. The results showed that the proportion of high-, medium-, and low-quality cultivated land in Quzhou County was 36.19%, 33.60%, and 30.21%, respectively, and the average grade of CLQ was 2.97, which was considered to be at a medium level. Moran’s I of global spatial autocorrelation in Quzhou County was 0.8782, indicating a significant positive autocorrelation of the cultivated land quality index (CLQI). The main obstacle factors of CLQ in Quzhou County were soil profile constitution, irrigation guarantee rate, groundwater depth, and soil microbial biomass carbon. Therefore, based on the stable and dynamic characteristics of the obstacle factors, suggestions were provided to improve the quality of cultivated land in terms of strengthening the consolidation of cultivated land, transforming the concept of agricultural fertilization, and carrying out cultivated land recuperation. This study provides a new perspective on the cognition, evaluation, and identification of obstacle factors of CLQ, and the findings of this study can provide a reference for the consolidation and improvement of CLQ in the NCP.

Regional Disparities in Inflation Persistence: Unpacking the Dynamics of Price Growth in Portugal

<p><big>This paper investigates the degree of inflation persistence across regions in Portugal by analyzing the Consumer Price Index (CPI) growth rates for NUTS II regions. The study employs the Augmented Dickey-Fuller (ADF) test to determine whether the CPI data for Portugal is stationary or non-stationary. The results of the ADF test reveal that the IPC data for Portugal is non-stationary, indicating that inflation exhibits persistence in the long run. The study further assesses the persistence of inflation by estimating an autoregressive integrated moving average (ARIMA) model for each region. The Ljung-Box test is used to test for autocorrelation in the time series data, and the Hurst exponent is calculated to evaluate the presence of long-term memory in the time series data. The study finds that there is significant autocorrelation in the time series data for all regions, supporting the presence of persistence in inflation at the regional level in Portugal. The Hurst exponent also shows that the time series data for each region exhibits a high degree of persistence in inflation. Finally, the study applies the ARIMA model to each CPI division's data and uses the Ljung-Box test to test for autocorrelation in the time series data. The results show that some CPI divisions exhibited higher levels of persistence compared to others. For example, the "Housing, water, electricity, gas and other fuels" division exhibited high persistence, while the "Communication" division exhibited low persistence. This study contributes to the existing literature by exploring regional inflation persistence in Portugal and its implications for policymaking. The results provide insights into the inflation persistence patterns across regional levels in Portugal, by emphasizing the need to consider regional differences in inflation dynamics when formulating effective policy interventions. Understanding the persistence of inflation is crucial for policymakers to ensure price stability and sustainable economic growth.</big></p>

Harnessing Unresolved Lensed Quasars: The Mathematical Foundation of the Fluctuation Curve

Strong gravitational lensed quasars (QSOs) have emerged as powerful and novel cosmic probes as they can deliver crucial cosmological information, such as a measurement of the Hubble constant, independent of other probes. Although the upcoming LSST survey is expected to discover 103–104 lensed QSOs, a large fraction will remain unresolved due to seeing. The stochastic nature of the quasar intrinsic flux makes it challenging to identify lensed quasars and measure the time delays using unresolved light-curve data only. In this regard, Bag et al. introduced a data-driven technique based on the minimization of the fluctuation in the reconstructed image light curves. In this article, we delve deeper into the mathematical foundation of this approach. We show that the lensing signal in the fluctuation curve is dominated by the autocorrelation function (ACF) of the derivative of the joint light curve. This explains why the fluctuation curve enables the detection of the lensed QSOs only using the joint light curve, without making assumptions about QSO flux variability, nor requiring any additional information. We show that the ACF of the derivative of the joint light curve is more reliable than the ACF of the joint light curve itself because intrinsic quasar flux variability shows significant autocorrelation up to a few hundred days (as they follow a red power spectrum). In addition, we show that the minimization of fluctuation approach provides even better precision and recall as compared to the ACF of the derivative of the joint light curve when the data have significant observational noise.

Geographic Information System Based Economic And Financial Risk Analysis: The Case Of Europe And Central Asia

The study has two purposes. The first purpose is, using the economic and financial risk factors determined by the International Country Risk Guide (ICRG) rating agency, to reclassify the scores that countries get from these factors with the Jenks Natural Breaks (JNB) classification technique and to compare countries by creating their thematic maps according to this classification. The second purpose of our study is to create economic and financial risk maps of countries by using the Geographical Weighted Regression (GWR) method, which is one of the Geographical Information System (GIS) analysis techniques, based on the economic and financial risk factors determined by the ICRG. Before the GWR analysis for the variables included in the research, Moran Index analysis was performed as the main measurement of spatial autocorrelation. As a result of the Moran analysis, it was determined that there was a statistically significant positive autocorrelation between the countries. In other words, it has been seen that the countries examined have spatial dependence on each other in terms of economic and financial risk. According to the results of the GWR analysis, risk maps of the examined countries were created and more dynamic, more meaningful or more sensitive, more specific and visually easier to understand results were revealed. And according to these results, it has been seen that the GWR technique can also be used in the fields of economy and finance. In addition, the study brought a different interdisciplinary perspective by bringing together the fields of economy, finance and geography.

Evaluation and spatial-temporal evolution of ecosystem service value of cascade hydropower project reservoir area in the Jinsha River, China

The Jinsha River basin, full of hydropower resources, is the largest hydropower energy base in China. From 2005 to 2018, four giant cascade hydropower stations (Wudongde, Baihetan, Xiluodu, and Xiangjiaba) were built along the Jinsha River. The reservoir area of four hydropower stations involves 26 counties (districts). The ecological environment of the reservoir area has a close relationship with hydropower projects, and ecosystem service value is an important standard to measure the quality of the ecological environment. Taking the reservoir area formed by four cascade hydropower stations in Jinsha River (Jinsha River Reservoir Area, JRRA) as the research object, the essay analyzed the spatial and temporal pattern of ecosystem service value in the reservoir area in 2005, 2010, 2015, and 2018. The results showed that (1) The ecosystem service value of JRRA reached 94 billion yuan in 2018, and the forestland took the largest proportion of ecosystem service value, accounting for 46.93%, followed by grassland, water area, cropland, and unused land. (2) From 2005 to 2018, the total ecosystem service value in JRRA increased by 3.374 billion yuan, and the spatial pattern of ecosystem service value showed a spatial distribution characteristic of high in the middle and low on both sides, and the spatial distribution had significant positive autocorrelation. (3) Because the area of water increased a lot, the ecosystem service value of JRRA showed a trend of overall increase which mainly occurred in the 3-km buffer zone along the river. The results further proved that the implementation of hydropower projects could improve the ecosystem service function in the reservoir area and provide technical support for the sustainable utilization of land resources and ecological compensation in the reservoir area.

Forecasting Groundnut Area, Production and Productivity in Rajasthan, India using ARIMA Model

This paper presents an analysis of the area, production and productivity of groundnut in Rajasthan over the last thirty years and a forecast of these variables using the auto regressing integrated moving average (ARIMA) model. Descriptive statistics show that there was a large fluctuation in the lowest and maximum values of area, production, and productivity of groundnut in Rajasthan over the period of last thirty years. The ARIMA model was used to forecast the area, production, and productivity of groundnut in Rajasthan. The parameter estimates of the ARIMA model were used to determine the model fit statistics, including the R-squared value, which indicates how well the model fits the data. The Ljung-Box Q Statistics and the corresponding Sig. indicate that there is no significant autocorrelation in the residuals of the model. Finally, forecasts for 2021, 2022, 2023, 2024, and 2025 are presented, along with their corresponding upper and lower confidence limits. The results indicate that there is a considerable upward trend in area, production, and productivity of groundnut in Rajasthan over the last thirty years. The ARIMA model was found to be successful in forecasting the area, production, and productivity of ground. The findings of this paper can help in the formulation of better policies for groundnut production in Rajasthan.

Evaluation and Analysis of Development Status of Yellow River Beach Area Based on Multi-Source Data and Coordination Degree Model

The Yellow River beach area is the basic component of the Yellow River Basin. Promoting the comprehensive improvement and high-quality development of the Yellow River beach area is an important guarantee of the long-term stability of the Yellow River and an important part of promoting the high-quality development and ecological protection of the Yellow River Basin. In this paper, four new indexes (flood risk intensity index, accessibility index, biological abundance index, and remote sensing ecological index) were extracted from geospatial data and remote sensing images, and a quantitative evaluation model (Ecology-Economy -Society-Flood, EESF) for the development of the Yellow River beach area were constructed based on social statistics, such as flood control and control in the beach area. The coordinated development level of the Yellow River beach area was evaluated by combining the “CRITIC–entropy weight method” and “‘single index quantification–multi-index synthesis–multi-criteria integration’ (SMI-P)—coordination degree model” methods. The spatial autocorrelation model was used to analyze the spatial distribution characteristics of the coordinated development level, and the global sensitivity and uncertainty analysis (GSUA) was carried out for the sensitivity and uncertainty of the parameters. Taking the Yellow River beach area in Shandong Province in 2009 and 2019 as the study object, the research results showed that during this period, the coordinated development level of the Yellow River beach area in Shandong Province showed a gradual upward trend, from 0.344 to 0.580, reaching a basic coordinated state; the overall coordinated development level of the beach area showed significant autocorrelation and small spatial heterogeneity. Grain production was the most sensitive parameter in the coordinated development model of the beach area. The beach area should rationally develop and utilize agricultural resources and promote the integration of ecological industries.

Estimation of evaporator valve sizes in supermarket refrigeration cabinets

In many applications, e.g. fault diagnostics and optimized control of supermarket refrigeration systems, it is important to determine the heat demand of the cabinets. This can easily be achieved by measuring the mass flow through each cabinet, however that is expensive and not feasible in large-scale deployments. Therefore it is important to be able to estimate the valve sizes from the monitoring data, which is typically measured. The valve size is measured by an area, which can be used to calculate mass flow through the valve — this estimated value is referred to as the valve constant. A novel method for estimating the cabinet evaporator valve constants is proposed in the present paper. It is demonstrated using monitoring data from a refrigeration system in a supermarket in Otterup (Denmark), consisting of data sampled at a one-minute sampling rate, however it is shown that a sampling time of around 10–20 min is adequate for the method. Through thermodynamic analysis of a two stage CO2 refrigeration system, a linear regression model for estimating valve constants is developed using time series data. The linear regression requires that transient dynamics are not present in the data, which depends on multiple factors e.g. the sampling time. If dynamics are not modelled it can be detected by a significant auto-correlation of the residuals. In order to include the dynamics in the model, an Auto-Regressive Moving Average model with eXogenous variables (ARMAX) is applied, and it is shown how it effectively eliminates the auto-correlation and provides less bias and higher accuracy of the estimates. Furthermore, it is shown that the sample time has a huge impact on the estimates. Thus, a method for selecting the optimal sampling time is introduced. It works individually for each of the evaporators, by exploring their respective frequency spectrum. That way, a reliable estimate of the actual valve constant can be achieved for each evaporator in the system and it is shown that sampling times above 10 min are optimal for the analysed systems.

Exploratory Spatio-Temporal Data Analysis (ESTDA) of Dengue and its association with climatic, environmental, and sociodemographic factors in Punjab, India

Routine health data is rich in information but underutilised for research in Low and Middle-Income countries. The present study was carried out to understand spatiotemporal patterns of dengue and its association with risk factors using routine data from health and allied sectors. ESTDA included estimation of dengue incidence rates, time series features and correlation coefficients up to the sub-district level. Scatter plots and correlation coefficients were used to identify relationships between covariates. Dengue incidence in 2015–19 was 47.76, 33.64, 52.03, 49.71, and 33.36 per 100,000, respectively, with a mean (SD) age of 34.33 (16.78) years and the majority being males (63.94%). Dengue had significant cross-correlation, non-linear relationships, and spatio-temporal associations with climatic, environmental, and socio-demographic risk factors. Significant autocorrelation of dengue occurrence was present at a lag of one month with seasonal patterns. The reproducible open-source algorithms add value to existing Routine Health Information Systems, and the findings will enable the development of Spatio-temporal models in future research. The research was done using R version 4.1.0.

Ants combine systematic meandering and correlated random walks when searching for unknown resources

Animal search movements are typically assumed to be mostly random walks, although non-random elements may be widespread. We tracked ants (Temnothorax rugatulus) in a large empty arena, resulting in almost 5km of trajectories. We tested for meandering by comparing the turn autocorrelations for empirical ant tracks and simulated, realistic Correlated Random Walks. We found that 78% of ants show significant negative autocorrelation around 10mm (3 body lengths). This means that turns in one direction are likely followed by turns in the opposite direction after this distance. This meandering likely makes the search more efficient, as it allows ants to avoid crossing their own paths while staying close to the nest, avoiding return-travel time. Combining systematic search with stochastic elements may make the strategy less vulnerable to directional inaccuracies. This study is the first to find evidence for efficient search by regular meandering in a freely searching animal.