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... Although it is the most accurate method, manual picking such a great amount of seismic and AE data has become a cumbersome and inefficient task and therefore has been less conducted. For decades, researchers have dedicated great efforts to developing automatic phase pickers with the goal of determining the arrival time of different phases of waveforms, both efficiently and accurately (Akaike, 1973;Allen, 1982;Baer & Kradolfer, 1987;Baillard et al., 2014;Boschetti et al., 1996;Han et al., 2009;Hinkley, 1971;Saragiotis, 2002;Tong & Kennett, 1996;Xu, 2011). ...
... Confidential manuscript submitted to Rock Mechanics and Rock Engineering 9 picking algorithm commonly used in the literature (Grosse et al., 2021) the Akaike Information Criterion (AIC) algorithm (Akaike, 1973). ...
The acoustic emission (AE) technique has been widely used in studying the cracking and frictional behavior of rocks in laboratory rock mechanics tests. Picking P-wave arrivals of AE waveforms is pivotal in analyzing AE data at an advanced level such as event localization, conducting acoustic tomography, and solving focal mechanisms. Deep-learning-based P-wave arrival pickers have outperformed traditional non-machine-learning algorithms in seismology, enhancing efficiency over manual picking. However, determining the optimal training data set size for these models in laboratory settings has been lacking. This evaluation is crucial for improving AE data analysis efficiency and ensuring the availability of open AE data sets with manually picked waveforms, currently limited by manual picking time constraints. We compiled ~50,000 manually picked AE waveforms from a Berea sandstone triaxial compression test to create a data set. Introducing AE-PNet, a deep-learning P-wave arrival picker based on modified PhaseNet architecture, we investigated the minimum number of manually picked waveforms needed for effective AE-PNet training. Our study revealed that a minimum of 1500 manually picked waveforms is necessary for adequate AE-PNet generalization, irrespective of total AE waveforms in data sets that AE-PNet processes. AE-PNet consistently outperformed the Akaike Information Criterion picker, even in picking low signal-to-noise waveforms.
... The Akaike information criterion (AIC) and the Bayesian information criterion (BIC) were used to estimate the quality of each model. The lower the AIC and BIC values and the higher the likelihood function (Loglik) values were, the better the model in question was [33]. Taking the soil properties (soil BD, water stable aggregates, soil saline-alkali characteristic parameters (EC 1:5 , SAR, ESP), CEC, SOM, clay, and silt content) or principal components as input variables, PTFs were established using a multiple regression method to evaluate the quantitative impact of the soil saline-alkali characteristics and aggregation structure on SHPs. ...
During coastal reclamation processes, land use conversion from natural coastal saline/sodic soils to agricultural land changes the soil’s physicochemical properties. However, the impact of soil structure evolution on soil hydraulic properties (SHPs, e.g., hydraulic conductivity and soil water retention curves) during long-term reclamation has rarely been reported. In this study, we aimed to evaluate the effect of reclamation duration and land use types on the soil aggregate stability and SHPs of coastal saline/sodic soils and incorporate the aggregate structures into the SHPs. In this study, a total of 90 soil samples from various reclaimed years (2007, 1960, and 1940) and land use patterns (cropland, grassland, forestland, and wasteland) were taken to analyze the quantitative effects of soil saline/sodic characteristics and the aggregate structure on SHPs through pedotransfer functions (PTFs). We found that soil macroaggregate contents in the old reclaimed areas (reclaimed in 1940 and 1960) were significantly larger than those in the new reclamation area (reclaimed in 2007). The soil saturated hydraulic conductivity (Ks) of forestland was larger than that of grassland in each reclamation year. Soil structure contributed to 22.13%, 24.52%, and 23.93% of the total variation in Ks and soil water retention parameters (α and n). The PTFs established in our study were as follows: log(Ks) = 0.524 − 0.177 × Yk3 − 0.093 × Yk1 + 0.135 × Yk4 − 0.054 × Yk2, 1/α = 477.244 − 91.732 × Yα2 − 81.283 × Yα4 + 38.106 × Yα3, and n = 1.679 − 0.086 × Yn2 + 0.045 × Yn1 − 0.042 × Yn3 (Yareprincipalcomponents). The mean relative errors of the prediction models for log(Ks), 1/α, and n were 79.30%, 36.1%, and 9.89%, respectively. Our findings quantify the vital roles of the aggregate structure on the SHPs of coastal saline/sodic soils, which will help us understand related hydrological processes.
... position at the top end of the log (m above the ground) of the jth individual tree, Y ijk is the measured property of the ith height position at the top end of the log (m above the ground) of the jth individual tree within the kth family, µ is the grand mean, X ij is the ith height position at the top end of the log (m above the ground) of the jth individual tree, X ijk is the ith height position at the top end of the log (m above the ground) of the jth individual tree within the kth family, α 0 is the fixed slope, α 1 is the fixed intercept, Family k is the random effect of the kth family, Family 0k and Family 1k are the random slope and intercept of the kth family, and e j ,e jk ,e ij and e ijk are residuals. The model with the minimum Akaike Information Criterion (AIC) (Akaike 1998) was regarded as the optimal model among the developed models. When the model including the random effects of the family (Eqs. ...
The growth traits (stem diameter at 1.3 m above the ground and tree height), stress-wave velocity of the stems, and log characteristics (taper and dynamic Young’s modulus of logs) were examined for 54 trees from 18 half-sib families planted in a seedling seed orchard of the first-generation Neolamarckia macrophylla (11-year-old) in Wonogiri, Central Java, Indonesia. The mean values for stem diameter and tree height were 20.2 cm and 20.0 m, respectively. The stress-wave velocity of the stems was 3.76 km s－1. Meanwhile, the taper and dynamic Young’s modulus of logs were 0.57 cm m－1 and 8.13 GPa, respectively. The heritability values of each trait were 0.412, 0.365, 0.101, <0.001, and 0.092 for the stem diameter, tree height, stress-wave velocity of stems, taper of logs, and dynamic Young’s modulus of logs, respectively, suggesting that the improvement of all traits is possible for the next generation, with the exception of the log taper. The 18 half-sib families could be classified into three groups for different potential uses based on the principal component analysis and cluster analysis results.
... O is the mean value of O i , and P is the mean value of P i (i = 1, 2, …, m). The Akaike information criterion (AIC) is applied as an evaluation indicator as well to quantify the effectiveness of DM (Akaike, 1973). ...
Empirical models for estimating ecosystem respiration (ER) are widely used in CO 2 flux partitioning algorithms that partition net ecosystem CO 2 exchange (NEE) into gross primary productivity (GPP) and ER due to advantages of simple structures. However, empirical ER models remain limited due to single-source conceptualization that doesn't discriminate different responses of aboveground respiration (AGR) and belowground respiration (BGR) to environmental factors (i.e., temperature and/or soil moisture). In this study, a dual-source module with only one parameter α was proposed and incorporated into six widely used ER models to enhance model capabilities. Long-term flux measurements of six typical terrestrial ecosystems and soil chamber respiration data at two sites were collected to evaluate models. Results showed that integration of the dual-source module can significantly improve the performance of empirical models in selected ecosystems with mean R 2 improvement of 0.10 ± 0.16. The site years with relative increased R 2 (ΔR 2) larger than 10 % range from 6 % to 79 % amongst different models. Further validation between soil respiration and estimated BGR showed good correlations (r > 0.7) and demonstrated that proposed method can provide robust estimate of above/belowground respiration. Calibrated α varies amongst ecosystem types. Further analysis indicates variation of α is largely influenced by ratio of above/belowground biomass and annual average moisture conditions. Our findings highlight the critical need for partitioning ER models into dual-source for developing CO 2 flux partitioning algorithms and support the approach as an effective means to enhance the understanding of global carbon cycles with changing climate.
... The parameters for the B-spline were determined using the Akaike Information Criterion (AIC). 22) Since no suitable R package exists, we developed the code ourselves. For a more detailed explanation including mathematical formulas, refer to the supplementary materials (Supplemental Text 1). ...
In patients with acute cardiovascular disease, treatment aimed at reducing lactate levels is crucial for improving prognosis. Trends in blood lactate levels for each specific cardiovascular disease can provide an accurate evaluation of the patient's condition. We used functional data analysis with nonlinear mixed effects models to estimate the timeframe of lactate reduction in 3 cardiovascular diseases (acute heart failure, aortic dissection, and ischemic heart disease) by analyzing lactate trends.
Among 1,816 patients admitted to the intensive care unit (ICU) or intensive cardiovascular care unit (ICCU) of St. Luke's International Hospital for cardiology or cardiovascular surgery from December 31, 2010, to June 31, 2020, 1,249 adults with a diagnosis of acute heart failure (39%), aortic dissection (24%), or ischemic heart disease (37%) were included in the present study. Using functional data analysis with nonlinear mixed effects models, our study estimated the timeframe of lactate reduction based on blood lactate level trends. Lactate reduction took 30 hours (95% CI, 23-37 hours) in patients with acute heart failure, 40 hours (95% CI, 33-47 hours) in patients with aortic dissection, and 95 hours (95% CI, 49-failed estimate) in patients with ischemic heart disease.
We were able to estimate the timeframe of lactate reduction with different cardiovascular diseases. Recognizing the differences in lactate reduction may be useful in developing treatment plans tailored to each disease.
... (57) Comparison of the fitted models was basd on the following goodness-of-fit measures: the Akaike Information Criterion (AIC) due to Akaike (1992), given by ...
This paper introduces a new lifetime distribution called the Bimodal Extension of Suja
(BES) distribution using the Quadratic Rank Transmutation Map. The proposed distribution has Suja distribution as a special case. Some statistical and reliability properties of
the new distribution were derived and the method of maximum likelihood was employed for
estimating the model parameters. The usefulness and flexibility of the BES distribution were
illustrated with two real lifetime data sets. Results based on the log-likelihood and goodness
of fit statistics values showed that the BES provides a better fir to the data than the other
competing (lifetime) distributions considered in this study. Also, the consistency of the parameters of the new distribution was demonstrated through a simulation study. The BES
distribution is therefore recommended foe effective modelling of the unimodal or bimodal
continuous lifetime data with a non decreasing or bathtub shaped hazard rate function .
... This test checks the correlation between the two time series at different lags, i.e., offset by some commits forward and backward; • When a correlation at negative lags is detected, i.e., TD time series correlates with delayed microservices time series, we conduct the Granger Causality test [34] to determine if a microservices number change causes an increment or decrement of TD. We calculate the optimal lag order using the Akaike Information Criterion [1]; • We analyze the potential correlation between the derivative of TD time series and the microservices number to understand if the growth speed of TD depends on the number of microservices. In this case, we use CCF as well. ...
Context: Microservices are gaining significant traction in academic research and industry due to their advantages, and technical debt has long been a heavily researched metric in software quality context. However, to date, no study has attempted to understand how code technical debt evolves in such architectures.
Aim: This research aims to understand how technical debt evolves over time in microservice architectures by investigating its trends, patterns, and potential relations with microservices number.
Method: We analyze the technical debt evolution of 13 open-source projects. We collect data from systems through automated source code analysis, statistically analyze results to identify technical debt trends and correlations with microservices number, and conduct a subsequent manual commit inspection.
Results: Technical debt increases over time, with periods of stability. The growth is related to microservices number, but its rate is not. The analysis revealed trend differences during initial development phases and later stages. Different activities can introduce technical debt, while its removal relies mainly on refactoring.
Conclusions: Microservices independence is fundamental to maintain the technical debt under control, keeping it compartmentalized. The findings underscore the importance of technical debt management strategies to support the long-term success of microservices.
... The determination of the most fitted model for each trait considering Akaike's information criterion (AIC) was used with the log-likelihood ratio test. The formula for AIC is as follows (Akaike, 1973): ...
Changes in genetic variation in body measurements are a subject of interest. This study aimed to understand the changes in the genetic effects of body measurement at birth in Turkish Arabian foals over the years. Furthermore, estimating the sources of variation in body measurements at birth in Turkish Arabian foals, considering additive genetic, maternal genetic, and maternal permanent effects and the covariance between offspring and dams in animal models, was the objective of this study. The records for birth weight (BW), wither height (WH), chest circumference (CC), and cannon-bone circumference (CBC) of 2624 Arabian foals born between 1987 and 2007 in the Anadolu, Karacabey, and Sultansuyu agricultural enterprises were used in the analyses. Variance analysis for non-genetic effects showed that the effects of the farm, year of birth, sex, and dam age were significant (P<0.001) for all traits. Estimation of variance components and genetic parameters for body measurements was performed with the average information restricted maximum likelihood algorithm using six univariate animal models in the WOMBAT software. The best-fit model for each trait was identified based on Akaike's information criterion (AIC). Genetic trends were determined by performing linear regression analysis on the estimated breeding value (EBV) of the animals based on their year of birth. Additive direct heritabilities for BW, WH, CC, and CBC were 0.10 ± 0.04, 0.41 ± 0.07, 0.06 ± 0.03, and 0.30 ± 0.07, respectively. The estimates of maternal heritability for the corresponding traits were 0.24 ± 0.03, 0.05 ± 0.03, 0.09 ± 0.03, and 0.13 ± 0.03, respectively. Additive–maternal genetic correlations for BW, WH, CC, and CBC were 0.33, −0.13, −0.19, and −0.22, respectively. Genetic and phenotypic correlations were analyzed with multivariate animal models considering additive genetic, maternal genetic, and maternal permanent effects and ranged from 0.340 to 0.924. The low to moderate direct and maternal heritabilities with additive–maternal genetic correlations showed that the variation in morphometric traits in foals could be affected by these factors and needs to be considered. Genetic trends showed increased weight and chest circumference in foals at birth. Based on these findings, breeders may consider these traits when selecting horses in future breeding programs.
... The number of latent classes was selected on the basis of Akaike information criterion (AIC) [20] and Bayesian information criterion (BIC) [21]. Models with lower AIC and BIC are preferred. ...
... The masses displayed are obtained by extrapolating the measurements toward continuum and massless limits, using a sim- plified approach inspired by Wilson [57,58] and heavy-baryon [59][60][61] chiral perturbation theory. The Akaike Information Criterion (AIC) [62,63] is used to evaluate the fit quality and identify the optimal fitting procedure for each chimera baryon in such extrapolations. Figure 4 presents the dependence of chimera baryon masses on pseudoscalar masses in the continuum limit. ...
We report progress on our lattice calculations for the mass spectra of low-lying composite states in the Sp(4) gauge theory coupled to two and three flavors of Dirac fermions transforming in the fundamental and the two-index antisymmetric representations, respectively. This theory provides an ultraviolet completion to the composite Higgs model with Goldstone modes in the SU(4)/Sp(4) coset and with partial compositeness for generating the top-quark mass. We measure the meson and chimera baryon masses. These masses are crucial for constructing the composite Higgs model. In particular, the chimera baryon masses are important inputs for implementing top partial compositeness. We employ Wilson fermions and the Wilson plaquette action in our simulations. Techniques such as APE and Wuppertal smearing, as well as the procedure of generalised eigenvalue problem, are implemented in our analysis.
... However, applying traditional goodness-of-fit tests in a non-stationary context is challenging as these tests must be performed at each time step due to time-varying parameters. Therefore, we employ the corrected Akaike information criterion (AICc) (Akaike, 1973;Burnham and Anderson, 2004) to measure the relative quality of the two models in fitting the data, as recommended in several studies (Cannon, 2010;Villarini et al., 2009;Kim et al., 2017). AICc, like other information criteria, does not provide direct measures of goodness of fit. ...
We present a novel non-stationary regional weather generator (nsRWG) based on an auto-regressive process and marginal distributions conditioned on climate variables. We use large-scale circulation patterns as a latent variable and regional daily mean temperature as a covariate for marginal precipitation distributions to account for dynamic and thermodynamic changes in the atmosphere, respectively. Circulation patterns are classified using ERA5 reanalysis mean sea level pressure fields. We set up the nsRWG for the central European region using data from the E-OBS dataset, covering major river basins in Germany and riparian countries. The nsRWG is meticulously evaluated, showing good results in reproducing at-site and spatial characteristics of precipitation and temperature. Using time series of circulation patterns and the regional daily mean temperature derived from general circulation models (GCMs), we inform the nsRWG about the projected future climate. In this approach, we utilize GCM output variables, such as pressure and temperature, which are typically more accurately simulated by GCMs than precipitation. In an exemplary application, the nsRWG statistically downscales precipitation from nine selected models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), generating long synthetic but spatially and temporally consistent weather series. The results suggest an increase in extreme precipitation over the German basins, aligning with previous regional analyses. The nsRWG offers a key benefit for hydrological impact studies by providing long-term (thousands of years) consistent synthetic weather data indispensable for the robust estimation of probability changes in hydrologic extremes such as floods.
... Picking the AE onset point (i.e., first arrival time) is crucial for AE relocation and source mechanism inversion. Based on the Akaike Information Criterion (Akaike 1998), the intervals of an AE signal before and after the trigger point are separated into two different locally stationary datasets through an auto-regressive process: ...
Shearing stimulation with proppant is widely used in geothermal and hydrocarbon reservoirs. However, the shear behavior associated with proppant-proppant and proppant-fracture interactions has not been clearly elaborated. This paper investigates surface damage and the interactions between proppant and surface roughness through direct shear tests with acoustic emission (AE) monitoring. The AE events show distinct spatial and temporal distribution patterns under the influence of proppant. The small magnitude AE events, representing proppant slipping and crushing, start to occur in large areas from the beginning of the shear deformation, and the large magnitude AE events occur mostly at the peak and during the residual phase and are concentrated on asperities. Crushed proppant grains and asperities form a gouge layer that prevents further damage to the fracture surface, reduces shear dilation and promotes aseismic creep. Fine proppant grains tend to remain intact, while the coarse proppant grains tend to be crushed. Our results suggest that acoustic emission characteristics can be used to infer different stages of shear behavior of propped fractures. These findings enhance our understanding of the shear behavior of propped fractures and provide evidence for monitoring their conditions using seismic signals.
... Lower values of AICc and BIC indicate a more suitable model. In this experiment, considering the sample sizes ranging from 250 to 408 for each group (Table 1) and 8 parameters for GMM when k = 3, the AICc was used [31,39]. Tables 2-4 present AICc and BIC values for three models (single normal distribution, GMM k = 2, GMM k = 3) across three cultivars (GW, MP, SY). ...
Grain weight is one of the key phenotypic traits in crops, closely related to yield. However, the actual structure of grain weight distribution is often overlooked. In this paper, to analyze the characteristics of grain weight, we interpret the weight distribution and structure of individual grains of triticale (× Triticosecale Wittmack) from the perspective of a sum of normal distributions, rather than a single normal distribution, using the Gaussian Mixture Model (GMM). We analyzed the individual grain weight distribution of three triticale cultivars (Gwangyoung, Minpung, Saeyoung) bred in Republic of Korea, cultivated under three different seeding rates (150 kg grains per ha, 225 kg grains per ha, and 300 kg grains per ha), over time from 2 to 5 weeks post-heading. Each distribution was fitted using a GMM and evaluated using the Corrected Akaike Information Criterion (AICc) and Bayesian Information Criterion (BIC). It suggests that the distribution of the grain weight is not a single normal distribution, but rather more closely to the distribution composed of two normal distributions. This is hypothesized to be due to the physiological characteristics of the spikelet of Poaceae, including triticale, wheat, rye, and oats. Through these results, we recognize the importance of understanding the distribution structure of data and their physiological traits, which is often overlooked in measuring the characteristics of crops.
... [97] within RStudio version 2022.07.2 (RStudio, Inc, Boston, MA, USA). For each developmental temperature and population, we calculated the Akaike Information Criterion (AIC) [98] to compare model fit. To evaluate overall model performance, we calculated the mean AIC value across all developmental temperatures and populations. ...
Background
Understanding how ectotherms manage energy in response to temperature is crucial for predicting their responses to climate change. However, the complex interplay between developmental and adult thermal conditions on total energy stores remains poorly understood. Here, we present the first comprehensive quantification of this relationship in Drosophila melanogaster, a model ectotherm, across its entire thermal tolerance range. To account for potential intraspecific variation, we used flies from two distinct populations originating from different climate zones. Utilizing a full factorial design, we assessed the effects of both developmental and adult temperatures on the amount of key energy macromolecules (fat, glycogen, trehalose, and glucose). Importantly, by quantifying these macromolecules, we were able to calculate the total available energy.
Results
Our findings reveal that the dynamic interplay between developmental and adult temperatures profoundly influences the energy balance in Drosophila. The total energy reserves exhibited a quadratic response to adult temperature, with an optimal range of 18–21 °C for maximizing energy levels. Additionally, the temperature during development considerably affected maximum energy stores, with the highest reserves observed at a developmental temperature of approximately 20–21 °C. Deviations from this relatively narrow optimal thermal range markedly reduced energy stores, with each 1 °C increase above 25 °C diminishing energy reserves by approximately 15%.
Conclusions
This study highlights the critical and interacting roles of both developmental and adult thermal conditions in shaping Drosophila energy reserves, with potentially profound implications for fitness, survival, and ecological interactions under future climate scenarios.
... The system then converts the x and y rainfall time series into scale-free measures by taking their probability values from a Cumulative Distribution Function (CDF) (Fig. 3). This CDF, denoted by F x (x), is de ned such that it considers the probability of obtaining zero rainfall value and by tting a Gamma distribution (G x (x)) to the non-zero rainfall values, according to the following Eq., The best-t copula among Frank, Clayton, and Gumbel is selected using the empirical copula test and the upper tail dependence test based on Akaike Information Criteria (AIC) (Akaike, 1973) and Bayesian Information Criteria (BIC) (Stone, 1979) values. This procedure is iterated over all the pixels in the study region. ...
Flood forecasting is an effective disaster management and risk reduction tool, especially as climate change and urbanisation increase the frequency and intensity of flood events worldwide. We propose a flood inundation forecasting system incorporating a copula-corrected forecast rainfall mechanism to rectify the spatio-temporal inconsistency between observed (from the Indian Meteorological Department – IMD) and forecast rainfall (Global Ensemble Forecast System – GEFS) patterns. The Dynamic Budyko hydrological model and a conceptual flood inundation model were coupled successively to this corrective mechanism and executed continuously to map the inundation extent for a 1 in 100-year flood event across Kerala, India. The forecast inundation was mapped with a spatial accuracy between 61% and 48% for lead times between 1 and 7 days, respectively, for the peak flood day on August 16, 2018. We tested the conceptual inundation modelling framework across Kerala for its capability to be operationally deployed for emergency flood mitigation purposes with runtimes of 2 ~ 3 hours/lead day.
... The optimal bandwidth can be found by minimizing a number of model fit goodnessof-fit diagnostics, and the choice of bandwidth values can be made by either CV (crossvalidation) [31] or the AIC (Akaike information criterion) [32,33]. CV generally considers only the accuracy of the prediction, whereas the AIC will consider parsimony, which balances accuracy with complexity. ...
This paper aims to analyze the influence mechanism of built environment factors on passenger flow by predicting the passenger flow of Shenzhen rail transit in the morning peak hour. Based on the classification of built environment factors into socio-economic variables, built environment variables, and station characteristics variables, eight lines and one hundred sixty-six stations in Shenzhen Railway Transportation are taken as research objects. Based on the automatic fare collection (AFC) system data and the POI data of AMAP, the multiple regression model (OLS) and the geographically weighted regression (GWR) model based on the least squares method are established, respectively. The results show that the average house price is significantly negatively correlated with passenger flow. The GWR model considering the house price factor has a high prediction accuracy, revealing the spatial characteristics of the built-up environment in the administrative districts of Shenzhen, which has shifted from the industrial structure in the east to the commercial and residential structure in the west. This paper provides a theoretical basis for the synergistic planning of house price regulation and rail transportation in Shenzhen, which helps to develop effective management and planning strategies.
... In terms of model performance, the model achieves an AIC (Akaike, 1998;Bozdogan, 1987) of 808.68, and excellent predictive power, with an AUC (Hanley and McNeil, 1982) of 0.9425 and an accuracy of 0.92. Sensitivity (0.954) and precision (0.951) are both high, indicating that the model is very good at correctly identifying students who are at risk of dropping out. ...
This paper analyzes the dynamics of higher education dropouts through an innovative approach that integrates recurrent events modeling and point process theory with functional data analysis. We propose a novel methodology that extends existing frameworks to accommodate hierarchical data structures, demonstrating its potential through a simulation study. Using administrative data from student careers at Politecnico di Milano, we explore dropout patterns during the first year across different bachelor's degree programs and schools. Specifically, we employ Cox-based recurrent event models, treating dropouts as repeated occurrences within both programs and schools. Additionally, we apply functional modeling of recurrent events and multilevel principal component analysis to disentangle latent effects associated with degree programs and schools, identifying critical periods of dropout risk and providing valuable insights for institutions seeking to implement strategies aimed at reducing dropout rates.
... (7) nolu denklemde, : log olabilirliği : tahmin edici veya parametre sayısını : modeldeki gözlem sayısını (Akaike, 1973;Hilbe, 2014) göstermektedir. En küçük AIC değerine sahip modelin tercih edilmesi gerekmektedir. ...
Yoksulluk çok boyutlu bir kavramdır. Yoksulluk göstergelerden bir tanesi hanenin sahip olduğu konut sayısıdır. Bu çalışmada, hane halkı bireyinin sahip olduğu konut sayısına etki eden faktörleri belirlemek için sayıma dayalı regresyon modelleri kullanılmıştır. Ayrıca, veriye en iyi uyum sağlayan regresyon modeli araştırılmıştır. Sayıma dayalı regresyon modellerinden en sık kullanılanlar klasik sayıma dayalı regresyon modelleri ve sıfır yığılmalı sayıma dayalı regresyon modelleridir. Ancak literatürde önerilmiş diğer bir regresyon modeli sıfır kesilmiş sayıma dayalı regresyon modelleridir. Bu modeller tüm veriyi analiz etmenin yarattığı zaman ve maliyet kaybının önüne geçmektedir. Bu nedenle, bu modeller sayıma dayalı verilerin olduğu durumlarda modellemede kullanılmak için iyi bir seçenektir. Çeşitli sayıma dayalı regresyon modelleri uygulamasını TÜİK’in yaptığı Gelir ve Yaşam Koşulları Araştırması veri setine uygulanmıştır. Çalışmada ele alınan modellerin performans değerlendirilmesi yapılmıştır. Bu değerlendirmeler için Akaike Bilgi Kriteri ve Log olabilirlik değeri kullanılmıştır. Sonuç olarak, sıfır kesilmiş negatif binom regresyon modeli gerçek veri setine en iyi uyum gösteren modeldir.
... The aforementioned methods are covariance-based techniques that demand sufficient data snapshots for accurate DOA estimation and often assume known NOS. To ensure consistency with the experimental setup of other methods where the NOS is unknown, we applied the Akaike Information Criterion (AIC) to estimate the NOS before performing DOA estimation [62]. Multi-snapshot Newtonized Orthogonal Matching Pursuit (MNOMP) uses Newton refinement and feedback strategy for DOA estimation, leveraging Fast Fourier Transform (FFT) to keep computation complexity low [63]. ...
There are numerous methods in the literature for Direction-of-Arrival (DOA) estimation, including both classical and machine learning-based approaches that jointly estimate the Number of Sources (NOS) and DOA. However, most of these methods do not fully leverage the potential synergies between these two tasks, which could yield valuable shared information. To address this limitation, in this article, we present a multi-task Convolutional Neural Network (CNN) capable of simultaneously estimating both the NOS and the DOA of the signal. Through experiments on simulated data, we demonstrate that our proposed model surpasses the performance of state-of-the-art methods, especially in challenging environments characterized by high noise levels and dynamic conditions.
... Selecting the model that minimizes the negative likelihood penalized by the number of parameters as given in the equation as follows is the rationale behind AIC [20]: ...
Tree biomass and volume estimation based on allometric equations is a widely used non-destructive technique for estimating biomass, sequestered carbon, and volume worldwide. Non-linear models for the biomass and volume of individual trees in Nigeria's Cross River State's Agoi-Ibami Forest Reserve were fitted and validated in this study. In this study, two parallel lines transect of 1500 meters in length, separated by 500 meters, were established using the systematic line transects sampling method. Along each transect, ten sample plots, each measuring 50 m by 50 m, were placed alternately at 100 m intervals. Twenty sample plots in all were thus marked for the study. The estimation of biomass using a non-destructive method was used. To calculate the aboveground green biomass for each, the diameter at breast height and total height were employed. Agoi-Ibami Forest Reserve had a total value of 391N ha-1 for number of stem per hectare, 14 tree families, mean dbh of 26.04cm, height of 15.9m, and basal area of 50.21m2ha-1. Conversion factors were used to estimate stand biomass, carbon sink, and sequestered carbon dioxide (CO2). Non-linear models were fitted for volume and aboveground biomass estimation in the study area. All of the models were evaluated and validated using some assessment statistical criteria and residual graphs, and models with good fit were suggested for use. Curve Expert Software was used for the development of the non-linear regression models. According to the assessment criteria, the forest reserve's best non-linear volume and aboveground biomass models were the Ratkowsky, Weibull, and Logistic models. Fitted models should therefore be employed for the forest reserve's efficient and successful management.
... A smaller −2LL indicates better fit to the data compared to models with larger −2LLs; Chi-square tests were then carried out to test whether −2LL differences were statistically significant. Akaike information criterion (AIC) and weights (Akaike, 1973(Akaike, , 1974 were used to compare the fit of non-nested models. Greater AIC differences provide stronger evidence for the model with the lower AIC, with differences of 0-2, 4-7, and 10 indicating no difference, some support and greater support, respectively (Burnham & Anderson, 2004). ...
Background
ADHD symptoms are associated with emotional problems such as depressive and anxiety symptoms from early childhood to adulthood, with the association increasing with age. A shared aetiology and/or a causal relationship could explain their correlation. In the current study, we explore these explanations for the association between ADHD symptoms and emotional problems from childhood to adulthood.
Methods
Data were drawn from the Twins Early Development Study (TEDS), including 3675 identical and 7063 non-identical twin pairs. ADHD symptoms and emotional symptoms were reported by parents from childhood to adulthood. Self-report scales were included from early adolescence. Five direction of causation (DoC) twin models were fitted to distinguish whether associations were better explained by shared aetiology and/or causal relationships in early childhood, mid-childhood, early adolescence, late adolescence, and early adulthood. Follow-up analyses explored associations for the two subdomains of ADHD symptoms, hyperactivity-impulsivity and inattention, separately.
Results
The association between ADHD symptoms and emotional problems increased in magnitude from early childhood to adulthood. In the best-fitting models, positive genetic overlap played an important role in this association at all stages. A negative causal effect running from ADHD symptoms to emotional problems was also detected in early childhood and mid-childhood. When distinguishing ADHD subdomains, the apparent protective effect of ADHD symptoms on emotional problems in childhood was mostly driven by hyperactivity-impulsivity.
Conclusions
Genetic overlap plays an important role in the association between ADHD symptoms and emotional problems. Hyperactivity-impulsivity may protect children from emotional problems in childhood, but this protective effect diminishes after adolescence.
... It began with the null model (Step 0, containing only the intercept) and at each subsequent step added one more predictor. Models were selected based on corrected Akaike's Information Criterion (AICc), which penalizes model complexity to balance fit and parsimony (Akaike, 1973;Akaike, 1974;Burnham and Anderson, 2002). The process continued until the maximum number of six predictors was reached, ensuring that the selected models provided the best approximation of the data without overfitting. ...
... According to Hu and Bentler (1998), CFI and TLI values >0.90 reveal a good model fit, as well as RMSEA values below 0.08. Also, Akaike information criterion (AIC; Akaike, 1998) and the Bayesian information criteria (BIC; Schwarz, 1978) were used to account for model complexity with models with smaller AIC and BIC values being the most suitable. Model specification analyses were performed, and modification indices (MI; cutoff of >25) were included in the models only when theoretically sustained. ...
In M. Greenberg (Coord.), International perspectives on assessment and intervention with Educator SEL [Symposium].
The study aimed to address the pressing need for an effective assessment tool to evaluate adults' Social and Emotional Competence (SEC), resulting in the development of the Social and Emotional Competence Assessment Battery for Adults (SECAB-A). A total of 796 adults participated in the study, with 63 elementary school teachers included in a subsample for further validation. Despite sample size variation, the SECAB-A was subjected to rigorous examination for its factor structure, reliability, and validity. No statistically significant differences between groups were observed across various scales of the SECAB-A, affirming its consistency across different demographics. Factor analysis confirmed the anticipated structures, while coefficient omegas indicated strong internal consistency. The test–retest reliability was demonstrated through highly correlated scores across data waves. Additionally, the SECAB-A exhibited promising convergent and discriminant validities when compared to external measures assessing related constructs. These findings underscore the potential of the SECAB-A as a parsimonious and reliable tool for evaluating adult SEC. The study contributes to bridging the gap in research and practice by offering a comprehensive instrument tailored to assess SEC in adults. Future research endeavors will focus on further validating the SECAB-A, including its English, Spanish and Iranian versions currently under development. Overall, our study highlights the importance of investing in the promotion of SEC in adults and provides a valuable resource for researchers and practitioners in this field.
... On the other hand, however, simulations have shown that the statistical power to detect significant fixed effects can in fact be increased when opting for a random effect structure that fits the data better, as compared to implementing the full model (and more so for complex models; Matuschek et al., 2017; for a similar assessment of model selection in repeated-measures designs see also Stroup, 2013). To balance the Type I error rates and statistical power, Matuschek et al. (2017) suggest to select a model based on a selection criterion such as the Akaike information criterion (AIC; Akaike, 1998) or the Bayes information criterion (BIC; Schwarz, 1978) that assess goodness-of-fit. We decided to follow this line of argument to identify the most parsimonious model while balancing the Type I error and power. ...
With attentional mechanisms, humans select and de-select information from the environment. But does selective attention modulate implicit learning? We tested whether the implicit acquisition of contingencies between features are modulated by the task-relevance of those features. We implemented the contingencies in a novel variant of the contextual cueing paradigm. In such a visual search task, participants could use non-spatial cues to predict target location, and then had to discriminate target shapes. In Experiment 1, the predictive feature for target location was the shape of the distractors (task-relevant). In Experiment 2, the color feature of distractors (task-irrelevant) cued target location. Results showed that participants learned to predict the target location from both the task-relevant and the task-irrelevant feature. Subsequent testing did not suggest explicit knowledge of the contingencies. For the purpose of further testing the significance of task-relevance in a cue competition situation, in Experiment 3, we provided two redundantly predictive cues, shape (task-relevant) and color (task-irrelevant) simultaneously, and subsequently tested them separately. There were no observed costs of single predictive cues when compared to compound cues. The results were not indicative of overshadowing effects, on the group and individual level, or of reciprocal overshadowing. We conclude that the acquisition of contingencies occurs independently of task-relevance and discuss this finding in the framework of the event coding literature.
... By penalizing additional parameters, it quantifies prediction accuracy (Schwarz, 1978). We chose the BIC over the Akaike Information Criterion (AIC) due to its more pronounced penalty on overfitting with numerous features (Akaike, 1973). ...
... R 2 increases as components are added, because the rank of the two constituent matrices are increasing, capturing more variance. Akaike Information Criterion (AIC) has been adapted for NMF to inform the rank to which the dimensionality should be reduced 42,58 , with the optimal model minimizing it 59 ; for our implementation of NMF: ...
Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Nonnegative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.
Deriving trend functions from observed time series is one of the main tasks in the field of signal processing. Separating the observation into a deterministic function, a stochastic residual signal and a stochastic noise component using dedicated model representations enables to further study these individual components, e.g. screening for climate signals. Whereas the deterministic part is modelled as a linear combination of basis functions, the use of autoregressive processes to model the noise and signal is proposed. Within an iterative estimation scheme, the uncertainty information of the observed variables is properly modelled and carefully propagated to the resulting parameters. This enables the use of statistical testing and Least-Squares Collocation in further investigations of the separated signal components. In this study, the proposed iterative procedure is applied to relatively short total water storage time series derived from measurements of the satellite mission GRACE. The trend in total water storage is for instance relevant for climate studies, identifying regions getting drier or wetter. Accounting for the aforementioned covariance information based on autoregressive processes allows to use Hypothesis tests to identify regions with significant trends. On the contrary, the smoothed stochastic signal components are required to identify extreme/anomalous events like floods and droughts in the observed time series. Additionally, to improve the estimation of the stochastic signal, data from numerical models are used to estimate the process characteristics.
Making decisions based on noisy sensory information is a crucial function of the brain. Various decisions take each sensory signal's uncertainty into account. Here, we investigated whether perceptual inferences rely on accurate estimates of sensory uncertainty. Participants completed a set of auditory, visual, and audiovisual spatial as well as temporal tasks. We fitted Bayesian observer models of each task to every participant's complete dataset. Crucially, in some model variants the uncertainty estimates employed for perceptual inferences were independent of the actual uncertainty associated with the sensory signals. Model comparisons and analysis of the best-fitting parameters revealed that, in unimodal and bimodal contexts, participants' perceptual decisions relied on overconfident estimates of auditory spatial and audiovisual temporal uncertainty. These findings challenge the ubiquitous assumption that human behavior optimally accounts for sensory uncertainty regardless of sensory domain.
Introduction
The COVID-19 pandemic placed many restrictions on families and affected the mental health of parents and children. The present study examines how the restrictions imposed during the pandemic and parental mental health affect early childhood psychopathology.
Method
From September 2019 to December 2021, the Outpatient Department of Family Therapy at the Institute for Psychosocial Prevention, Heidelberg surveyed a clinical sample of 249 families who sought consultation for early childhood psychopathology. Early childhood psychopathology in children aged 0–3 years was assessed using the German Questionnaire for Crying, Feeding and Sleeping and the German version of the Child Behavior Checklist 1½–5. The Patient Health Questionnaire provided information on parental depressiveness and generalized anxiety. At the same time, the Stringency Index as part of the Oxford Coronavirus Government Response Tracker indicated the severity of COVID restrictions in Germany.
Results
Dependent comparisons did not reveal significant differences in the infants' regulatory problems ( n = 165, mean age = 8 months) during the lockdown compared to reopening phases. However, older children ( n = 84, mean age = 25 months) exhibited more behavioral problems during lockdowns compared to reopening phases (Cohen's d = 0.32, p = .04). Subsequent regression analyses confirmed a slight increase in behavioral problems only among children aged 1.5–3 years ( p = .047, R ² = .08), but did not indicate any increase in parental mental health problems when more restrictions were in place. However, parental depressiveness had a strong independent effect on early childhood psychopathology. A hierarchical regression analysis indicated that psychopathology in children aged 1.5–3 years is best explained by female child gender, high parental depressiveness, and more severe restrictions during the COVID-19 pandemic ( p < .001, R ² = .17) whereas early childhood psychopathology in infants aged 0-1.5 years is more prevalent in younger and male children with parents experiencing higher levels of depressiveness ( p < .001, R ² = .26).
Discussion
The study found no increase in infant regulatory disorders or parental depressiveness and generalized anxiety during the pandemic. However, older children exhibited more behavioral problems during more severe pandemic restrictions. The study supports the provision of parent-child support during crises and beyond, as early childhood psychopathology was strongly associated with parental depressiveness.
This chapter evaluates the common gap-filling methods as well as flexible machine learning methods for the reconstruction of cloud-free Sentinel-2 maps based on a multi-year time series of established vegetation indicators, such as leaf area index (LAI) and Normalized Difference Vegetation Index (NDVI). It addresses the trends in fitting methods for filling up cloud-based gaps or for the reconstruction of regular cloud-free composite maps, outlines a novel fusion method that blends multi-source time series data and presents some methods and tools for further processing steps, such as the reconstruction of multiple years satellite products (e.g. LAI and NDVI), and the calculation of phenological indicators along multiple seasons. These indicators quantify the vegetation dynamics and can eventually be used to estimate valuable and quantitative agricultural information such as day of harvesting and crop yield. The chapter also reviews the recent advances available in image time series processing for the extraction of information about phenology trends.
This chapter presents the first of two programs developed in the integrated training-and-research project that constitutes the core of this book: a program for preceptors focused on interpersonal skills and related themes as discussed in Chapters 1, 2, 4, and 5, incorporating all methodological concepts from Chapter 3. In line with the stone-in-the-lake metaphor introduced in Chapter 1, the rationale behind this program has been that empowering preceptors through the development of knowledge, skills, and attitudes that help them to develop and maintain good relations with their residents will result in these residents developing and maintaining better relations with their colleagues as well as with their patients and caregivers. As argued in Chapter 1, healthcare teams with better relations are better equipped to facilitate job satisfaction, clinical competence, and patient satisfaction, as well as to reduce the risk of burnout of professionals and increase confidence on the part of patients and their caregivers to share all information needed for good person-centered care. This chapter reports on the development and implementation of the preceptor program and how it has been contributing to relations between preceptors and residents, and between residents and their colleagues as well as their patients and caregivers. A group of 10 preceptors from different specialties at Marqués de Valdecilla University Hospital was followed during 7 months with four blocks of weekly questionnaires (one questionnaire per week, six weeks per block) spaced around three training periods of 2–4 days each. The questionnaire was the same throughout the period and combined series of Likert-type items and an open-ended question regarding the perceived relation with their resident. Simultaneously, their resident (in the second, third or fourth year of their residency program)—who did not know if their preceptor was a participant in a training program or what content was covered during the program—responded on a weekly basis to a similar questionnaire aimed at the same relation between preceptor and resident. Finally, at the beginning and towards the end of the 7-month period, colleagues and patients (or their caregivers) of the residents—who did not know about the preceptors’ training program either—were contacted with similar questions regarding the relation with their resident. The narrative data resulting from the open-ended questions was subjected a multi-stage multi-coder procedure, which resulted in a range of qualitative examples relating to the content of the program and possible effects on preceptors and other stakeholders studied as well as metrics along with the rating data which were subjected to both group-level analysis (i.e., multilevel analysis) and individual analysis. Succinctly put, the findings indicate moderate positive effects of training at group level for both preceptors and residents, and larger positive effects for some individuals. For these two groups, conform the stone-in-the-lake metaphor, there is some evidence of a delay in the occurrence of the positive effect at the level of residents. Implications of these findings and limitations are discussed briefly at the end of this chapter and in more detail in Chapter 9.
This seventh chapter builds forth on Chapters 5 (preparatory interviews with preceptors for designing the training programs for preceptors and residents) and 6 (training program for preceptors) and presents a second program that was developed in the project that constitutes the core of this book: a resident training program focused on interpersonal skills and related themes. In line with the stone-in-the-lake metaphor introduced in Chapter 1, the rationale behind this program has been that empowering residents through the development of knowledge, skills, and attitudes that help them to develop and maintain good relations with others will benefit their relations with their colleagues, preceptors as well as with their patients and caregivers. As argued in Chapter 1, healthcare teams with better relations are better equipped to facilitate job satisfaction, clinical competence, and patient satisfaction, as well as to reduce the risk of burnout of professionals and increase confidence on the part of patients and their caregivers to share all information needed for good person-centered care. This chapter reports on the development and implementation of the resident program and how it has been contributing to relations with their colleagues, preceptors as well as with their patients and caregivers. A group of 8 residents from different specialties at Marqués de Valdecilla University Hospital was followed during 16 months with six blocks of weekly questionnaires (one questionnaire per week, six weeks per block) spaced around three training periods of two days in a specific week each. The questionnaire was the same throughout the period and combined series of Likert-type items and an open-ended question regarding the perceived relation with their patients (or caregivers). Simultaneously, some of their colleagues and patients (or caregivers)—who did not know about the resident training program—responded on a weekly basis to a similar questionnaire aimed at their relations with the resident in question. Finally, at the beginning and towards the end of the 16-month period, preceptors of the residents were contacted with similar questions regarding the relation with the resident in question. The narrative data resulting from the open-ended questions was subjected to a multi-stage multi-coder procedure, which resulted in a range of qualitative examples relating to the content of the program and possible effects on the different types of stakeholders studied as well as metrics along with the rating data which were subjected to both group-level analysis (i.e., multilevel analysis) and individual analysis. Briefly put, no effects of training were found at the group level for residents, colleagues or preceptors, although for some individual residents a positive effect was found either in self-reports or in reports from patients or a colleague. At the level of patients (and caregivers), a positive effect of training was found, with more training modules predicting better performance by the resident in terms of program content-related behaviors that are known to contribute to better relations. Finally, several residents reported changes in the context—sometimes favorable, sometimes not so favorable—at some point during the study, unrelated to the training program. There is some evidence that these differences in context can facilitate (favorable context change) or hinder (not so favorable context change) the display of program content-related behaviors that are known to contribute to better relations. Implications of these findings and limitations are discussed at the end of this chapter and in more detail in Chapter 9.
Learning involves estimating if new observations are meaningful. This depends on beliefs about distinct but interconnected sources of uncertainty: volatility and noise (stochasticity). While psychosis has been linked to altered volatility processing, studies have not accounted for the computationally interdependent nature of noise. We developed and tested a novel learning task that manipulated uncertainty using “ground truth” probability distributions, and incentivized participants to provide explicit trial-by-trial estimates of noise and volatility. Capitalizing on the presence of psychotic-like traits in the general population, the task was applied in two online experiments (Ns=580/147) and one in-person sample (N=19). While most participants learnt according to a normative account of statistical inference, psychometric schizotypy and delusional ideation displayed non-normative learning patterns, whereas poorer performance in paranoid ideation was underpinned by a poorer grasp of underlying statistical contingencies. All psychosis traits showed inflexible belief updating to changes in uncertainty. Computational modeling suggested that non-normative learning may stem from difficulties inferring noise, causing noisy inputs to be misinterpreted as meaningful. Capturing the multifaceted nature of uncertainty offers valuable insights for understanding psychosis and developing clinically meaningful computational phenotypes.
The behavioral characteristics of species may result in certain populations being inherently more susceptible to fragmentation. For example, species exhibiting spatial sexual segregation or those constrained to elongated and narrow habitats. We studied the fragmentation threats, spatial dynamics, resource utilization, and movement ecology of a particularly vulnerable species that is both sexually segregated and constrained to elongated and narrow habitat—the north Judean Desert population of Nubian ibex (Capra nubiana). From 2016‒2020 we tracked 48 marked ibex (27 male, 21 female), of which 38 (20 male, 18 female) also had global position system (GPS) collars. Using GPS‐collar and camera‐trap data in zones delineated around perennial water sources (PWSs), we calculated ibex drinking frequencies and individual utilization distributions by season and sex, focusing on their overall (95% isopleth) and core (50% isopleth) home ranges. We quantified joint space use between sexes using a utilization distribution overlap index (UDOI) and ibex daily movements and space use via movement indices. Female groups formed philopatric activity centers that were anchored around PWSs year‐round and arranged in a metapopulation‐like structure, with no female movement detected between them. Conversely, movement of adult males changed seasonally, with the cores of male groups anchored around PWSs only during the dry season, and long-range movement between female activity centers during the rut. Female groups also spent more time at steeper terrain and higher elevations compared with male groups. Outside the rut, groups of males and groups of females exhibited minimal joint space use (i.e., average dry season UDOI was 0.06). These patterns indicate high sensitivity of this population to intersexual fragmentation by obstacles (physical or virtual). Management strategies to mitigate fragmentation threats for such populations should be sex‐specific and landscape‐oriented.
The dynamics of coexistence among species sharing resources in the same space is a key aspect of understanding their ecological interactions, which can influence their distribution and fitness. The quantification of niche overlap allows us to elucidate the coexistence relationships among species, as this measure can be considered indicative of resource partitioning and potentially highlights competition within a particular habitat. The ecological strategies that promote coexistence are not fully described for many sympatric species, including common and widely distributed species such as coyotes (Canis latrans) and gray foxes (Urocyon cinereoargenteus) in North America. For these species, our interest was to determine the similarity of their ecological niches and identify which of the main axes (i.e., spatial, trophic, temporal) had the greatest influence on differentiation in temperate forests. Given both species’ sympatry and similar ecological roles, we expected to find significantly different ecological niches but with high overlap. Additionally, we anticipated that the temporal axis would have the greatest influence on niche segregation, as has recently been described for these species in the area. To investigate this, we conducted a unified analysis of niche overlap between coyotes and gray foxes in temperate forests, averaging data from the spatial, trophic, and temporal niche axes. The unified analysis revealed significant differences in the niche spaces of coyotes and gray foxes, despite a high overlap between them, the daily activity patterns (temporal axis) was the variable that had the most influence on niche segregation. The dynamics of resource partitioning in temperate forests between coyotes and gray foxes are complex. Despite the high degree of niche overlap, coexistence is facilitated by differentiation across the main ecological niche axes at different scales, with the most pronounced segregation occurring along the temporal axis and, to a lesser extent, through fine-scale segregation along the spatial and trophic axes.
Full-text access to a view-only version of our paper:
https://rdcu.be/d0oOv
The temporal resolution of adults’ visual attention has been linked to the frequency of alpha-band oscillations in electroencephalogram (EEG) signal, with higher Peak Alpha Frequency (PAF) being associated with better visual temporal processing skills. However, relatively less is known about neural mechanisms underlying individual differences in the temporal resolution of visual attention in infancy. This study investigated the role of PAF in visual temporal processing in early infancy. In a sample of 6-month-old infants (n = 62) we examined the relationship between PAF extracted from resting-state EEG, and saccadic latencies in a predictive cueing task where the appearance of a reward was predicted by higher or lower frequency of two flickering objects. Results showed that higher PAF was associated with shorter saccadic latencies in a condition with higher differences between the two flickering frequencies, speaking for the involvement of PAF in visual temporal attention in early development. Additionally, we found that infants were generally faster to orient to the reward in trials where both peripheral stimuli were flickering at relatively lower frequencies, roughly corresponding to the theta frequency band. Our findings support theoretical accounts highlighting the role of PAF in visual attention processing and extend this framework to early infancy.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-79129-0.
The rapid changes in the shipping fleet during the last decades has increased the ship-induced loads and, thus, their impact on infrastructures, margin protections and ecosystems. Primary waves have been pointed out as the cause of those impacts, with heights that can exceed 2 m and periods around 2 minutes. Consequently, extensive literature can be found on their estimation mainly from a deterministic perspective with methods based on datasets limited to one location, making difficult their generalization. These studies propose either computationally expensive numerical models or empirical equations which often underestimate the extreme primary waves, hindering their use for design purposes. Moreover, a framework to allow the design of infrastructure under ship-wave attack based on probabilistic concepts such as return periods is still missing. In this study, a probabilistic model based on bivariate copulas is proposed to model the joint distribution of the primary wave height, the peak of the total energy flux, the ship length, the ship width, the relative velocity of the ship and the blockage factor. This model, a vine-copula, is developed and validated for four different deployments along the Savannah river (USA), with different locations and times. To do so, the model is quantified using part of the data in one deployment and validated using the rest of the data from this deployment and data of the other three. The vine-copula is validated from both a predictive performance point of view and with respect to the statistical properties. We prove that the probabilistic dependence of the data is preserved spatially and temporally in the Savannah river.
Otsuka (2023) argues for a correspondence between data science and traditional epistemology: Bayesian statistics is internalist; classical (frequentist) statistics is externalist, owing to its reliabilist nature; model selection is pragmatist; and machine learning is a version of virtue epistemology. Where he sees diversity, I see an opportunity for unity. In this article, I argue that classical statistics, model selection, and machine learning share a foundation that is reliabilist in an unconventional sense that aligns with internalism. Hence a unification under internalist reliabilism.
Introduction
Increasing soil organic carbon (SOC) in croplands is a natural climate mitigation effort that can also enhance crop yields. However, there is a lack of comprehensive field studies examining the impact of SOC on crop yields across wide climatic, soil, and farming gradients. Furthermore, it is largely unknown how water retention, soil microbial diversity, and nutrient availability modulate the SOC‐crop yield relationship.
Materials and Methods
We conducted an observational study across 127 cereal fields along a 3000 km north‐south gradient in Europe, measured topsoil (0–20 cm) organic C content, and collected data on climate, soil properties, crop yield and farming practices. Additionally, we explored the relationship between crop yield, particulate organic carbon (POC) and mineral‐associated organic carbon (MAOC) contents at three soil depths (0–20, 20–40 and 40–60 cm) in a subset of sites.
Results
Relative yield increases levelled off at 1.4% SOC, indicating an optimal SOC content for cereals along a European gradient. The quadratic relationship between SOC and cereal yield was conspicuous even after controlling for large differences in climate, soil and farming practices across countries. The relationship varied significantly across soil depths and C fractions. MAOC dominated the SOC pool, and was significantly related to relative yield up to an optimal level that varied with soil depth. Soil microbial diversity and nutrient availability emerged as main drivers of the SOC‐yield relationship, while water retention did not exhibit a notable influence.
Conclusions
Our study demonstrates that SOC is as a key determinant of cereal yield along a European gradient, and identifying this threshold can inform soil management strategies for improved carbon capture based on initial SOC levels. Nevertheless, the complex SOC‐yield relationship highlights the necessity for tailored soil management strategies that consider specific site conditions to optimize C storage and crop yield.
Selecting a suitable equation to represent a set of multifactor data that was collected for other purposes in a plant, pilot-plant, or laboratory can be troublesome. If there are k independent variables, there are 2 possible linear equations to be examined; one equation using none of the variables, k using one variable, k(k – 1)/2 using two variables, etc. Often there are several equally good candidates. Selection depends on whether one needs a simple interpolation formula or estimates of the effects of individual independent variables. Fractional factorial designs for sampling the 2 possibilities and a new statistic proposed by C. Mallows simplify the search for the best candidate. With the new statistic, regression equations can be compared graphically with respect to both bias and random error.
Summary The use of a multidimensional extension of the minimum final prediction error (FPE) criterion which was originally developed
for the decision of the order of one-dimensional autoregressive process [1] is discussed from the standpoint of controller
design. It is shown by numerical examples that the criterion will also be useful for the decision of inclusion or exclusion
of a variable into the model. Practical utility of the procedure was verified in the real controller design process of cement
rotary kilns.
In a recent paper by the present author [1] a simple practical procedure of predictor identification has been proposed. It is the purpose of this paper to provide a theoretical and empirical basis of the procedure.
A fully computerized cement rotary kiln process control was tested in a real production line and the results are presented in this paper. The controller design was based on the understanding of the process behavior obtained by careful statistical analyses, and it was realized by using a very efficient statistical identification procedure and the orthodox optimal controller design by the statespace method. All phases of analysis, design and adjustment during the practical application are discussed in detail. Technical impact of the success of the control on the overall kiln installation is also discussed. The computational procedure for the identification is described in an Appendix.
Incluye bibliografía e índice
The foundations of a general theory of statistical decision functions, including the classical non-sequential case as well as the sequential case, was discussed by the author in a previous publication [3]. Several assumptions made in [3] appear, however, to be unnecessarily restrictive (see conditions 1-7, pp. 297 in [3]). These assumptions, moreover, are not always fulfilled for statistical problems in their conventional form. In this paper the main results of [3], as well as several new results, are obtained from a considerably weaker set of conditions which are fulfilled for most of the statistical problems treated in the literature. It seemed necessary to abandon most of the methods of proofs used in [3] (particularly those in section 4 of [3]) and to develop the theory from the beginning. To make the present paper self-contained, the basic definitions already given in [3] are briefly restated in section 2.1.
Sherman [8] and Stein [9] have shown that a method given by the author [1] for comparing two experiments is equivalent, for experiments with a finite number of outcomes, to the original method introduced by Bohnenblust, Shapley, and Sherman [4]. A new proof of this result is given, and the restriction to experiments with a finite number of outcomes is removed. A class of weaker comparisons--comparison in k-decision problems--is introduced, in three equivalent forms. For dichotomies, all methods are equivalent, and can be described in terms of errors of the first and second kinds.
The principle of maximum entropy, together with some generalizations, is interpreted as a heuristic principle for the generation of null hypotheses. The main application is to m-dimensional population contingency tables, with the marginal totals given down to dimension ("restraints of the rth order"). The principle then leads to the null hypothesis of no "rth-order interaction." Significance tests are given for testing the hypothesis of no rth-order or higher-order interaction within the wider hypothesis of no sth-order or higher-order interaction, some cases of which have been treated by Bartlett and by Roy and Kastenbaum. It is shown that, if a complete set of rth-order restraints are given, then the hypothesis of the vanishing of all rth-order and higher-order interactions leads to a unique set of cell probabilities, if the restraints are consistent, but not only just consistent. This confirms and generalizes a recent conjecture due to Darroch. A kind of duality between maximum entropy and maximum likelihood is proved. Some relationships between maximum entropy, interactions, and Markov chains are proved.
Thesis (Ph. D. in Statistics)--University of California, Berkeley, June 1952. Bibliography: p. 125-128.
Standard real business cycle models must rely on total factor productivity (TFP) shocks to explain the observed comovement of consumption, investment, and hours worked. This paper shows that a neoclassical model consistent with observed heterogeneity in labor supply and consumption can generate comovement in the absence of TFP shocks. Intertemporal substitution of goods and leisure induces comovement over the business cycle through heterogeneity in the consumption behavior of employed and unemployed workers. This result owes to two model features introduced to capture important characteristics of U.S. labor market data. First, individual consumption is affected by the number of hours worked: Employed agents consume more on average than the unemployed do. Second, changes in the employment rate, a central factor explaining variation in total hours, affect aggregate consumption. Demand shocks--such as shifts in the marginal efficiency of investment, as well as government spending shocks and news shocks--are shown to generate economic fluctuations consistent with observed business cycles.
The problems of statistics are broadly classified into problems of specification and problems of inference, and a brief recapitulation is given of some standard methods in statistics, based on the use of the probability p (S/H) of the data S on the specification H (or on the use of the equivalent likelihood function). The general problems of specification and inference for time-series are then also briefly surveyed. To conclude Part I, the relation is examined between the information (entropy) concept used in communication theory, associated with specification, and Fisher's information concept used in statistics, associated with inference. In Part II some detailed methods of analysis are described with special reference to stationary time-series. The first method is concerned with the analysis of probability chains (in which the variable X can assume only a finite number of values or 'states', and the time t is discrete). The next section deals with autoregressive and autocorrelation analysis, for series defined either for discrete or continuous time, including proper allowance for sampling fluctuations; in particular, least-squares estimation of unknown coefficients in linear autogressive representations, and Quenouille's goodness of fit test for the correlogram, are illustrated. Harmonic or periodogram analysis is theoretically equivalent to autocorrelation analysis, but in the case of time-series with continuous spectra is valueless in practice without some smoothing device, owing to the peculiar distributional properties of the observed periodogram; one such arithmetical device is described in Section 7. Finally the precise use of the likelihood function (when available) is illustrated by reference to two different theoretical series giving rise to the same autocorrelation function.
On a semi-automatic power spectrum estimation procedure
- H Akaike
Determination of the number of factors by an extended maximum likelihood principle
- H Akaike
Tests of statistical hypotheses concerning several parameters when the number of observations is large
- A Wald