Search Results (23)

Arctic sea ice variations exhibit relatively strong statistical associations with precipitation variability over northeastern and southern China. Using Arctic Ocean reanalysis data from the EU Copernicus Project, this study examines the time-lagged statistical relationships between winter Arctic sea ice conditions and subsequent spring precipitation variability over China through wavelet analysis and Granger causality tests. Singular value decomposition (SVD) identifies the Barents, Kara, East Siberian, and Chukchi Seas as key regions exhibiting strong associations with spring precipitation anomalies. Increased winter sea ice in the East Siberian and Chukchi Seas generates positive geopotential height anomalies over the Arctic and negative anomalies over Northeast Asia, adjusting upper-level jet streams and influencing precipitation patterns in Northeast China. Conversely, increased sea ice in the Barents–Kara Seas leads to persistent negative geopotential height anomalies simultaneously occurring over both the Arctic and South China regions, enhancing southern jet stream activity and intensifying warm-moist airflow at the 850 hPa level, thus favoring precipitation in southern China. Compared to considering only climate factors such as the Pacific Decadal Oscillation (PDO), El Niño–Southern Oscillation (ENSO), and Arctic Oscillation (AO), the inclusion of Arctic sea ice significantly enhances the influence of multiple climate factors on precipitation variability in China. Full article

In the modern era, CO₂ emissions is a popular and significant study topic. Environmental sustainability is adversely affected by CO₂ emissions, which have become the main cause of climate change. Using panel data analysis, this study investigated the connections between CO₂ emissions and economic development, capital accumulation, and the use of renewable energy. Long-term connections between variables were examined using the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators, taking into account heterogeneity and cross-sectional dependence. Additionally, the Dumitrescu–Hurlin Panel Granger Causality Test was used to assess dynamic interactions between variables. Although CH₄ emissions increase CO₂ emissions, the effects of economic growth and capital accumulation are not statistically significant, as determined using the AMG and CCEMG. Although the use of renewable energy was shown to have the potential to lower CO₂ emissions, this impact was not statistically significant. The results of the dynamic panel demonstrate that CO₂ emissions increase with capital accumulation. Although methane (CH₄) emissions significantly impact CO₂ emissions, economic growth, capital accumulation, and renewable energy use do not show statistically significant effects, highlighting the varying influences of these factors across nations. The findings of this study emphasize the need to integrate environmental regulations into capital investment strategies and adopt country-specific policies to effectively reduce CO₂ emissions. They also underscore the need to customize green legislation to the specific conditions of each nation while simultaneously advocating for further expenditures in clean energy and the formulation of policies to supplant fossil fuels. Full article

DNA damage repair (DDR) plays a key role in maintaining genomic stability and developing inflammatory bowel disease (IBD). However, no report about the causal association between DDR and IBD exists. Whether DDR-related genes are the precise causal association to IBD in etiology remains unclear. Herein, we employed a multi-omics summary data-based Mendelian randomization (SMR) approach to ascertain the potential causal effects of DDR-related genes in IBD. Methods: Summary statistics from expression quantitative trait loci (eQTL), DNA methylation QTL (mQTL), and protein QTL (pQTL) on European descent were included. The GWAS summarized data for IBD and its two subtypes, Crohn’s disease (CD) and ulcerative colitis (UC), were acquired from the FinnGen study. We elected from genetic variants located within or near 2000 DDR-related genes in cis, which are closely associated with DDR-related gene changes. Variants were selected as instrumental variables (IVs) and assessed for causality with IBD and its subtypes using both SMR and two-sample MR (TSMR) approaches. Colocalization analysis was employed to evaluate whether a single genetic variant simultaneously influences two traits, thereby validating the pleiotropy hypothesis. Results: We identified seven DDR-related genes (Arid5b, Cox5a, Erbb2, Ube2l3, Gpx1, H2bcl2, and Mapk3), 33 DNA methylation genes, and two DDR-related proteins (CD274 and FCGR2A) which were all causally associated with IBD and its subtypes. Beyond causality, we integrated the multi-omics data between mQTL-eQTL and conducted druggability values. We found that DNA methylation of Erbb2 and Gpx1 significantly impacted their gene expression levels offering insights into the potential regulatory mechanisms of risk variants on IBD. Meanwhile, CD247 and FCGR2A could serve as targets for potential pharmacological interventions in IBD. Conclusions: Our study demonstrates the causal role of DDR in IBD based on the data-driven MR. Moreover, we found potential regulatory mechanisms of risk variants on IBD and potential pharmacological targets. Full article

The sustainable development of the hog industry has significant implications for agricultural development, farmers’ income, and the daily lives of residents. Precise hog supply forecasts are essential for both government to ensure food security and industry stakeholders to make informed decisions. This study proposes an integrated framework for hog supply forecast. Granger causality analysis is utilized to simultaneously investigate the causal relationships among piglet, breeding sow, and hog supply, as well as to ascertain the uncertain time lags associated with these variables, facilitating the extraction of valuable time lag features. The Seasonal and Trend decomposition using Loess (STL) is leveraged to decompose hog supply into three components, and Autoregressive Integrated Moving Average (ARIMA) and Xtreme Gradient Boosting (XGBoost) are utilized to forecast the trends, i.e., seasonality and residuals, respectively. Extensive experiments are conducted using monthly data from all the large-scale pig farms in Chongqing, China, covering the period from July 2019 to November 2023. The results demonstrate that the proposed model outperforms the other five baseline models with more than 90% reduction in Mean Squared Logarithm (MSL) loss. The inclusion of the piglet feature can enhance the accuracy of hog supply forecasts by 42.1% MSL loss reduction. Additionally, the findings reveal statistical time lag periods of 4–6 months for piglet and 11–13 months for breeding sow, with significance levels of 99%. Finally, policy recommendations are proposed to promote the sustainability of the pig industry, thereby driving the sustainable development of both upstream and downstream sectors of the swine industry and ensuring food security. Full article

Simultaneous causality occurs when two variables mutually influence each other, creating empirical contexts where cause and effect are not clearly unidirectional. Crime and policing often appear in urban studies presenting the following characteristic: sometimes, increased police patrols can reduce criminal activities, and other times, higher crime rates can prompt law enforcement administrations to increase patrols in affected areas. This study aims to explore the relationships between patrol dynamics and crime locations using spatial regression to support public policies. We identify spatial patterns and the potential impact of crime on policing and vice versa. Data on crimes and patrol locations were collected from the database provided by the Planning and Management Secretariat and the Social Defense Secretariat of Pernambuco, Brazil. The study employed Ordinary Least Squares (OLS) to create a spatial simultaneous regression model for integrated security zones within the Brazilian geography. This approach provides a holistic visualization, enhancing our understanding and predictive capabilities regarding the intricate relationship between police presence and crime. The results report a significant relationship, with crime locations explaining police patrols (varying in geographic domain and type of crime). No statistically significant results from most geographic locations point to the inverse relation. The quantitative analysis segregated by typology presents a potential for effective public decision support by identifying the categories that most influence the patrol security time. Full article

The Italian territory is subject to a high level of hydrogeological instability that periodically results in the loss of lives, buildings and productive activities. Therefore, the recognition of areas susceptible to hydrogeological instability is the basis for preparing countermeasures. In this context, landslide susceptibility in the mid-Adriatic slope was analyzed using a statistical method, the weight of evidence (WoE), which uses information from several independent sources to provide sufficient evidence to predict possible system developments. Only flows, slides, debris flows and mud flows were considered, with a total of 14,927 landslides obtained from the IFFI (Inventory of Franous Phenomena in Italy) database. Seven climatic–environmental factors were used for mapping landslide susceptibility in the study area: slope, aspect, extreme precipitation, normalized difference vegetation index (NDVI), CORINE land cover (CLC), and topographic wetness index (TWI). The introduction of these factors into the model resulted in rasters that allowed calculation by GIS-type software of a susceptibility map. The result was validated by the ROC curve method, using a group of landslides, equal to 20% of the total, not used in the modeling. The performance of the model, i.e., the ability to predict the presence or absence of a landslide movement correctly, was 0.75, indicating a moderately accurate model, which nevertheless appears innovative for two reasons: the first is that it analyzes an inhomogeneous area of more than 9000 km², which is very large compared to similar analyses, and the second reason is the causal factors used, which have high weights for some classes despite the heterogeneity of the area. This research has enabled the simultaneous introduction of unconventional factors for landslide susceptibility analysis, which, however, could be successfully used at larger scales in the future. Full article

In the context of the deepening of population aging and the trial implementation of a progressive retirement delay policy in China, understanding the relationship between the labor force participation and health status of the elderly will not only enrich relevant research but also help the elderly better achieve their goals of active aging and aging. Using the 2018 China Health and Retirement Longitudinal Study, this paper first established multiple linear regression models to analyze the impact of labor force participation on the health status of elderly people in China and then established simultaneous equation models using households living on minimum living allowances and the community average of labor participation as instrumental variables to deal with the endogeneity caused by two-way causality. The findings confirmed significant positive correlations between labor force participation and physical and mental health, while caring for grandchildren and participating in social activities were found to be negatively moderated the relationship between labor force participation and the physical and mental health of older adults. The impact of labor force participation on the physical health status of older men and the mental health status of older women may be greater. In addition, labor force participation may have a greater impact on the physical health of the rural elderly, and its impact on mental health was not found to be statistically significant between urban and rural areas. Full article

We examined the impact of agricultural intensification on the wellbeing of rural communities in a developing country on a sub-communal scale. To measure the interactions within this complex causal relationship, a statistical approach was applied, using partial least squares path modeling (PLS-PM) in its formative structure. Using PLS-PM to simultaneously relate the measured variables (manifest variables) and conceptual variables (latent variables), while incorporating other variables, such as the bioclimate and demography, we characterized the spatial structure of the links between intensive agriculture and wellbeing. The aim was to facilitate government intervention aiming to improve the wellbeing of rural households, while avoiding cumbersome and costly surveys when the scope of public action is extended to a region or a country. Our findings show that the generalization of the productivist system is not always appropriate in developing countries. In our case study, employment in the secondary and tertiary sectors is insufficient to accommodate the rural exodus. In such situations, agricultural intensification leads to poverty and migration to the areas of production and increases disparities in social wellbeing in rural areas. Full article

In genome-wide association studies (GWAS), efficient incorporation of linkage disequilibria (LD) among densely typed genetic variants into association analysis is a critical yet challenging problem. Functional linear models (FLM), which impose a smoothing structure on the coefficients of correlated covariates, are advantageous in genetic mapping of multiple variants with high LD. Here we propose a novel constrained generalized FLM (cGFLM) framework to perform simultaneous association tests on a block of linked SNPs with various trait types, including continuous, binary and zero-inflated count phenotypes. The new cGFLM applies a set of inequality constraints on the FLM to ensure model identifiability under different genetic codings. The method is implemented via B-splines, and an augmented Lagrangian algorithm is employed for parameter estimation. For hypotheses testing, a test statistic that accounts for the model constraints was derived, following a mixture of chi-square distributions. Simulation results show that cGFLM is effective in identifying causal loci and gene clusters compared to several competing methods based on single markers and SKAT-C. We applied the proposed method to analyze a candidate gene-based COGEND study and a large-scale GWAS data on dental caries risk. Full article

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain–behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5–18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role ‘between’ brain and behavior. We discuss implications and possible avenues for future studies. Full article

Russia’s ecosystems and ecosystem services (ES) are critical not only for the country’s economy and well-being of the people but also for maintaining biodiversity and biosphere regulation around the world. Thus, the introduction of ecosystem accounting in Russia is an urgent national and international goal to which the TEEB-Russia project is dedicated. In this publication, we briefly review and discuss the main project results. Based on currently available open statistical and cartographic data, TEEB-Russia project conducted the first national assessment of terrestrial ES in Russia to derive methodological approaches to national ecosystem accounting. A range of indicators were used to assess the ES provided by ecosystems (potential) as well as the level of demand and consumption of ES by Russia’s regions, both for populations and economies. Indicators of ecosystem assets include extent (ecosystem size) and condition (productivity, phytomass, bird and plant species diversity). An analysis of the correlations between indicators of ES and ecosystem assets showed that a system of national ecosystem accounting in Russia should be regionally differentiated to take account of the strong heterogeneity of natural conditions and the socio-economic development at this level. Decision-making in spatial planning and ecosystem management should carefully consider the difference between causal relationships between indicators and correlations that arise from the simultaneous response of indicators to changes in other factors. Differences in relationships between indicators at different spatial scales should also be taken into account. Full article

This article examines the impact of applied metacognition on the development of geographical causal structures by students in the geography classroom. For that, three different metacognitive strategies were designed: a. action plan, activating meta-knowledge prior to problem-solving and simultaneously visualizing action steps for dealing with the task (A); b. circular thinking (C), a loop-like, question-guided procedure applied during the problem-solving process that supports and controls content-related and linguistic cognition processes; c. reflexion (R), aiming at evaluating the effectivity and efficiency of applied problem-solving heuristics after the problem-solving process and developing strategies for dealing with future tasks. These strategies were statistically tested and assessed as to their effectiveness on the development of complex geographical causal structures via a quasi-experimental pre-posttest design. It can be shown that metacognitive strategies strongly affect students’ creation of causal structures, which depict a multitude of elements and relations at a high degree of interconnectedness, thus enabling a contentually and linguistically coherent representation of system-specific properties of the human–environment system. On the basis of the discussion of the results, it will be demonstrated that metacognitive strategies can provide a significant contribution to initiating systemic thinking-competences and what the implications might be on planning and teaching geography lessons. Full article

This study was conducted to explore physical education students’ intrinsic motivation and clarify the influence mechanism of cooperative learning methods on learning intrinsic motivation through meta-analysis. In accordance with the Preferred Reporting Items for Systematic Reviews (PRISMA) criteria, we screened literature from the years 2000–2020. The included literature underwent bias analysis on the basis of the five criteria proposed herein. Data were extracted and summarized from the included literature to analyze the causality before and after cooperative learning intervention. Statistical analysis was conducted to determine principal factors affecting physical education students’ learning intrinsic motivation. Simultaneously, the influencing mechanism of cooperative learning on physical education students’ intrinsic motivation was clarified. Results revealed that intrinsic motivation had a high total effect amount. In the experimental group, only three documents determined the significant influence of cooperative learning on physical education students’ intrinsic motivation. Moreover, the time and age differences needed to be considered thoroughly during the intervention. Therefore, cooperative learning intervention can improve physical education students’ intrinsic motivation significantly, and meta-analysis provided a theoretical foundation for applying cooperative learning to the teaching of physical education majors. Full article

Rolling element bearings are a vital part of rotating machines and their sudden failure can result in huge economic losses as well as physical causalities. Popular bearing fault diagnosis techniques include statistical feature analysis of time, frequency, or time-frequency domain data. These engineered features are susceptible to variations under inconsistent machine operation due to the non-stationary, non-linear, and complex nature of the recorded vibration signals. To address these issues, numerous deep learning-based frameworks have been proposed in the literature. However, the logical reasoning behind crack severities and the longer training times needed to identify multiple health characteristics at the same time still pose challenges. Therefore, in this work, a diagnosis framework is proposed that uses higher-order spectral analysis and multitask learning (MTL), while also incorporating transfer learning (TL). The idea is to first preprocess the vibration signals recorded from a bearing to look for distinct patterns for a given fault type under inconsistent working conditions, e.g., variable motor speeds and loads, multiple crack severities, compound faults, and ample noise. Later, these bispectra are provided as an input to the proposed MTL-based convolutional neural network (CNN) to identify the speed and the health conditions, simultaneously. Finally, the TL-based approach is adopted to identify bearing faults in the presence of multiple crack severities. The proposed diagnostic framework is evaluated on several datasets and the experimental results are compared with several state-of-the-art diagnostic techniques to validate the superiority of the proposed model under inconsistent working conditions. Full article

This study introduces a framework for the information-theoretic analysis of brain functional connectivity performed at the level of electroencephalogram (EEG) sources. The framework combines the use of common spatial patterns to select the EEG components which maximize the variance between two experimental conditions, simultaneous implementation of vector autoregressive modeling (VAR) with independent component analysis to describe the joint source dynamics and their projection to the scalp, and computation of information dynamics measures (information storage, information transfer, statistically significant network links) from the source VAR parameters. The proposed framework was tested on simulated EEGs obtained mixing source signals generated under different coupling conditions, showing its ability to retrieve source information dynamics from the scalp signals. Then, it was applied to investigate scalp and source brain connectivity in a group of children manifesting episodes of focal and generalized epilepsy; the analysis was performed on EEG signals lasting 5 s, collected in two consecutive windows preceding and one window following each ictal episode. Our results show that generalized seizures are associated with a significant decrease from pre-ictal to post-ictal periods of the information stored in the signals and of the information transferred among them, reflecting reduced self-predictability and causal connectivity at the level of both scalp and source brain dynamics. On the contrary, in the case of focal seizures the scalp EEG activity was not discriminated across conditions by any information measure, while source analysis revealed a tendency of the measures of information transfer to increase just before seizures and to decrease just after seizures. These results suggest that focal epileptic seizures are associated with a reorganization of the topology of EEG brain networks which is only visible analyzing connectivity among the brain sources. Our findings emphasize the importance of EEG modeling approaches able to deal with the adverse effects of volume conduction on brain connectivity analysis, and their potential relevance to the development of strategies for prediction and clinical treatment of epilepsy. Full article