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In the present study, soil erosion prioritization of sub-watersheds of the Kulfo River basin was conducted by adopting a drainage morphometric analysis along with a statistical correlation matrix-based weighted sum approach. The drainage network extracted and sub-watershed boundaries were demarcated through GIS techniques using advanced space-borne thermal emission and reflection–digital elevation model (ASTER–DEM). The Kulfo River basin was separated into six sub-watersheds (SW-1 to SW-6), and different morphometric criteria were calculated using the standard formula. And, morphometric parameters like drainage frequency, bifurcation ratio, drainage density, form factor, circulatory ratio, drainage texture, elongation ratio, compact coefficient, and length of overland flow have been considered for sub-watershed prioritization. Based on the results, the Kulfo River basin’s sub-watersheds were categorized into five priority classes: very low, low, medium, high, and very high. The results illustrate the sub-watersheds (SW-1, SW-2, SW-3, and SW-6) that approximately 65% of the Kulfo River basin’s total area fall under the very high, high, and medium soil erosion-prone areas, respectively. Therefore, the above-mentioned four sub-watersheds can be a value for the consideration of the soil protection plan. The outcomes derived from this study will be valuable information for several partners like agriculturists, surface and groundwater wealth administrators, and decision-makers for improving the soil management process. The current research shows that ASTER–DEM data, GIS approach, and a statistical correlation matrix-based weighted sum approach are vibrant tools for watershed prioritization in data-scarce regions.

Hydrochemical character of groundwater samples from a part of North-Central Nigeria has been investigated to decipher the physicochemical properties, sources of dissolved ions, hydrochemical facies, factors influencing the groundwater chemistry as well as the suitability of the waters for drinking and irrigation purposes. Sixty-seven (67) groundwater samples from hand-dug wells and boreholes were subjected to major ions and heavy metal analyses. The results were further analysed and studied using Pearson’s statistical correlation, ionic ratios and variation plots. The Pearson’s statistical correlation shows that there exists a very strong correlation between water hardness (TH) with Ca 2+ (0.99) and Mg 2+ (0.91), this indicates that both ions originate from similar sources. The ionic ratio plots of HCO 3 and Mg/Na versus Ca/Na; Ca 2+  + Mg 2+ /HCO 3 −  + SO 4 2− ; (Ca 2+  + Mg 2+ ) vs total cations (TZ); (Na + K) vs total cations (TZ) revealed that both carbonate dissolution and silicate weathering serve as major contributor of ions to groundwater chemistry in the study area. Piper and Schoeller’s plots revealed that majority of the water samples are of CaHCO 3 type, and few NaHCO 3 and CaCl 2 types. Gibbs plot suggested that chemical weathering of rocks, ionic exchange and secondary carbonates dissolution and precipitation influenced the groundwater chemistry. Comparison of the concentrations of the ions to World Health Organization (WHO) and Standard Organization of Nigeria (SON) showed that they are generally within permissible limits and therefore suitable for drinking purpose. Irrigation parameters such as, electrical conductivity, sodium adsorption ratio, Wilcox ratio, residual sodium carbonate (RSC), sodium solubility percentage (SSP), magnesium ratio/hazard (MR), permeability index (PI), Kelly ratio/index (KI), all revealed that > 90% of water samples fell into suitable, good and excellent water class and are thus generally suitable for irrigation purposes.

Association rule mining has shown outstanding capacity in extracting operation patterns from extensive building operational data. However, it typically generates a large number of association rules, most of which fail to reveal useful patterns. It is still unclear about how to develop effective methods to extract useful association rules. Additionally, it is challenging to extract clear insights from fragmented association rules, as a single rule can represent multiple meanings. To address these issues, advanced post mining methods are necessary for automatically eliminating most of worthless association rules. Therefore, this study proposes a causal discovery-based post mining method that uses Granger causality tests, based on the time series characteristics of building operational data, to replace traditional strong correlation tests in association rule mining. This method effectively integrates causality with statistical correlation to assess the value of association rules. The average causal effect is introduced to clarify the causal relationships between variables in association rules. By grouping rules with similar variables, it clarifies the expression of these relationships, enhancing interpretation efficiency and clarity. Causal strength is introduced to evaluate the intensity of each association rule, which can improve the robustness of rule assessment. The proposed method is evaluated using 20,564 association rules extracted from the historical operational data of an actual chiller plant in summer. Three common indexes (support, confidence, lift) are selected as benchmarks for comparison with the proposed method. The traditional method identifies 15,245 knowledge items as valuable, with an actual value density of 9.73%, while the proposed method identifies 625 knowledge Items with a value density of 95.52%. It proves that the proposed method has excellent performance in extracting valuable association rules, with significant advantages in clarity of meaning, precision, and user-friendliness.

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes.

Myrmeleontiformia are an ancient group of lacewing insects characterized by predatory larvae with unusual morphologies and behaviours. Mostly soil dwellers with a soft cuticle, their larvae fossilize only as amber inclusions, and thus their fossil record is remarkably sparse. Here, we document a disparate assemblage of myrmeleontiform larvae from the mid-Cretaceous amber (99 Ma) of Myanmar, evidence of a considerable diversification. Our cladistic analysis integrating extant and extinct taxa resolves the fossils as both stem- and crown-groups. Similarities between extinct and extant species permit inferences of larval ethology of the fossil species through statistical correlation analyses with high support, implying that morphological disparity matched behavioural diversity. An improved understanding of the evolutionary history of antlions and relatives supports the conclusion that hunting strategies, such as camouflage and fossoriality, were acquired early within the lineage. Larvae of the Myrmeleontiformia, which include antlions, are not well preserved in much of the fossil record. Here, Badano et al. describe a collection of predatory myrmeleontiform larvae from Cretaceous amber, resolving their evolutionary relationships and inferring their ecology.

Key observational indicators of climate change in the Arctic, most spanning a 47 year period (1971-2017) demonstrate fundamental changes among nine key elements of the Arctic system. We find that, coherent with increasing air temperature, there is an intensification of the hydrological cycle, evident from increases in humidity, precipitation, river discharge, glacier equilibrium line altitude and land ice wastage. Downward trends continue in sea ice thickness (and extent) and spring snow cover extent and duration, while near-surface permafrost continues to warm. Several of the climate indicators exhibit a significant statistical correlation with air temperature or precipitation, reinforcing the notion that increasing air temperatures and precipitation are drivers of major changes in various components of the Arctic system. To progress beyond a presentation of the Arctic physical climate changes, we find a correspondence between air temperature and biophysical indicators such as tundra biomass and identify numerous biophysical disruptions with cascading effects throughout the trophic levels. These include: increased delivery of organic matter and nutrients to Arctic near-coastal zones; condensed flowering and pollination plant species periods; timing mismatch between plant flowering and pollinators; increased plant vulnerability to insect disturbance; increased shrub biomass; increased ignition of wildfires; increased growing season CO2 uptake, with counterbalancing increases in shoulder season and winter CO2 emissions; increased carbon cycling, regulated by local hydrology and permafrost thaw; conversion between terrestrial and aquatic ecosystems; and shifting animal distribution and demographics. The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with implications not only within but beyond the Arctic. The indicator time series of this study are freely downloadable at AMAP.no.

Surface ozone (O3) pollution levels are strongly correlated with daytime surface temperatures, especially in highly polluted regions. This correlation is nonlinear and occurs through a variety of temperature-dependent mechanisms related to O3 precursor emissions, lifetimes, and reaction rates, making the reproduction of temperature sensitivities – and the projection of associated human health risks – a complex problem. Here we explore the summertime O3–temperature relationship in the United States and Europe using the chemical transport model GEOS-Chem. We remove the temperature dependence of several mechanisms most frequently cited as causes of the O3–temperature “climate penalty”, including PAN decomposition, soil NOx emissions, biogenic volatile organic compound (VOC) emissions, and dry deposition. We quantify the contribution of each mechanism to the overall correlation between O3 and temperature both individually and collectively. Through this analysis we find that the thermal decomposition of PAN can explain, on average, 20 % of the overall O3–temperature correlation in the United States. The effect is weaker in Europe, explaining 9 % of the overall O3–temperature relationship. The temperature dependence of biogenic emissions contributes 3 % and 9 % of the total O3–temperature correlation in the United States and Europe on average, while temperature-dependent deposition (6 % and 1 %) and soil NOx emissions (10 % and 7 %) also contribute. Even considered collectively these mechanisms explain less than 46 % of the modeled O3–temperature correlation in the United States and 36 % in Europe. We use commonality analysis to demonstrate that covariance with other meteorological phenomena such as stagnancy and humidity can explain the bulk of the remainder of the O3–temperature correlation. Thus, we demonstrate that the statistical correlation between O3 and temperature alone may greatly overestimate the direct impacts of temperature on O3, with implications for the interpretation of policy-relevant metrics such as climate penalty.

In the new era, many people seek their health-related information through the Internet due to the increasing access to this technology. Searching online health information can affect the health behavior. This study aimed to investigate the correlation between online health information-seeking behavior and a healthy lifestyle during pregnancy in a sample of Iranian pregnant women. This cross-sectional study was conducted among pregnant women admitted to health centers of Eghlid city, Fars province, Iran in 2019. A total of 193 women participated in the study. The required data were gathered using two validated questionnaires to measure the online health information-seeking behavior and the healthy lifestyle practices of the participants. The collected data were analyzed through descriptive statistics and Pearson correlation coefficient using SPSS version 22. Online health information experience and its subscales showed no statistical correlation with a healthy lifestyle. Age and education did not correlate with online health information-seeking behavior. Age had a statistical correlation with a healthy lifestyle, but education had the same correlation only with some subscales of a healthy lifestyle. The findings were surprising, suggesting that online health information-seeking behavior does not affect the lifestyle of pregnant women. These finding and probable explanations are discussed, but due to the limited literature on the subject, further studies are recommended to be conducted.

Real-time tool wear prediction and its remaining useful life (RUL) estimation is an important part of the development of a smart machining system while it is practically complex. A two-step framework proposed based on the statistical correlation of the experimentally measured cutting tool vibration data with the flank wear progression and estimation of the cutting tool RUL by the construction of stochastic tool life probability predictive models. The machining experiments are conducted on the IN718 superalloy with uncoated WC tools under the varied conditions of cutting speed and feed to acquire the data of flank wear and associated tool vibration data. The results of confirmation experiments show the statistical correlation constructed is practically viable for in-process flank wear prediction at any time of instance during machining with any set cutting conditions using the real-time tool vibration monitoring. The in-process observation of 1.5 g tool acceleration during machining with 60 m/min cutting speed and 0.1 mm/tooth feed signifies 15% of the cutting tool failure probability, and its remaining useful life is 12.91 min. For 50% of tool reliability machining with 0.1 mm/tooth feed and 60, 90 and 120 m/min cutting speed, tool accelerations of 2.01, 3.08 and 3.98 g reflect that the respective exhausted tool lives are 12, 8 and 6 min and the respective remaining useful lives are 8, 6 and 5 min. Hence, based on the presented analysis and results, it is envisaged the proposed framework is reliable and robust for in-process cutting tool condition prediction based on the real-time tool vibration monitoring for its adoption to develop a smart machining system with autonomous decision-making capability.

The aim of our study is to quantify the relationship between ecosystem and climate variables in southwest China. We further examined spatiotemporal distribution patterns of daily reference evapotranspiration (ET0) and ecosystem types through integrated approaches, including spatiotemporal interpolation, Penman–Monteith, Mann–Kendall test, statistical correlation analysis and transition matrix based on those datasets including observation climate data, satellite remote sensing images (MODIS and Landsat) and observed ecosystem data. The following results are achieved. First, changes of ET0 were greatly influenced by the combined effects of precipitation (with a decrease rate of −13 mm/10 years) and temperature (with a decrease rate of + 0.17 ℃/10 years). The annual average ET0 increased by + 2.1 mm/10 years, and the increased ET0 are more than 25% of the total area. Second, evapotranspiration was regarded as a sensitive indicator of climate and ecosystem feedbacks, and these ecosystem types have a great transformation, including forest, agriculture, and grass. Forest and grass were distributed primarily in the southern and eastern mountain areas, grass was in high mountains area while agriculture was prevalent in basin areas respond to climate changes. The area of forest converted to grass was 3670 km2, which was greater than transition from grass to forest (1720 km2). Correlation coefficients of evapotranspiration and NDVI were positive in forest and negative in agriculture. Third, the effects of these changes on climate vegetation and ecosystem process feedbacks on the quickly warming southwest China are potentially significant. Although the variation in ecosystem types was combined effects caused by climate variation and human activities, an effective ecological restoration program “Grain for Green” has improved the environmental conditions in southwest China.