Divergence Index Research Articles

Racial residential segregation is associated with ambient air pollution exposure after adjustment for multilevel sociodemographic factors: Evidence from eight US-based cohorts.

We examined if racial residential segregation (RRS) - a fundamental cause of disease - is independently associated with air pollution after accounting for other neighborhood and individual-level sociodemographic factors, to better understand its potential role as a confounder of air pollution-health studies. We compiled data from eight large cohorts, restricting to non-Hispanic Black and White urban-residing participants observed at least once between 1999 and 2005. We used 2000 decennial census data to derive a spatial RRS measure (divergence index) and neighborhood socioeconomic status (NSES) index for participants' residing Census tracts, in addition to participant baseline data, to examine associations between RRS and sociodemographic factors (NSES, education, race) and residential exposure to spatiotemporal model-predicted PM2.5 and NO2 levels. We fit random-effects meta-analysis models to pool estimates across adjusted cohort-specific multilevel models. Analytic sample included eligible participants in CHS (N = 3,605), MESA (4,785), REGARDS (22,649), NHS (90,415), NHSII (91,654), HPFS (32,625), WHI-OS (77,680), and WHI-CT (56,639). In adjusted univariate models, a quartile higher RRS was associated with 3.73% higher PM2.5 exposure (95% CI: 2.14%, 5.32%), and an 11.53% higher (95% CI: 10.83%, 12.22%) NO2 exposure on average. In fully adjusted models, higher RRS was associated with 3.25% higher PM2.5 exposure (95% CI: 1.45%, 5.05%; P < 0.05) and 10.22% higher NO2 exposure (95% CI: 6.69%, 13.74%; P < 0.001) on average. Our findings indicate that RRS is associated with the differential distribution of poor air quality independent of NSES or individual race, suggesting it may be a relevant confounder to be considered in future air pollution epidemiology studies.

Adapting a style based generative adversarial network to create images depicting cleft lip deformity

Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken. Data augmentation maneuvers permitted input of merely 514 frontal photographs of cleft-affected faces adapted to a base model of 70,000 normal faces. The Frechet Inception Distance was used to measure the similarity of the newly generated facial images to the cleft training dataset. Perceptual Path Length and the novel Divergence Index of Normality measures also assessed the performance of the novel image generator. CleftGAN generates vast numbers of unique faces depicting a wide range of cleft lip deformity with variation of ethnic background. Performance metrics demonstrated a high similarity of the generated images to our training dataset and a smooth, semantically valid interpolation of images through the transfer learning process. The distribution of normality for the training and generated images were highly comparable. CleftGAN is a novel instrument that generates an almost boundless number of realistic facial images depicting cleft lip. This tool promises to become a valuable resource for the development of machine learning models to objectively evaluate facial form and the outcomes of surgical reconstruction.

Phylogenomics and species delimitation in the Lepidophyma sylvaticum complex (Squamata: Xantusiidae) using ddRADseq and morphological data.

The Lepidophyma sylvaticum complex occurs from west-central Nuevo León to the Sierra de Chiconquiaco in central Veracruz, Mexico. Morphological studies have revealed population groups that are "moderately divergent from each other" within the complex. In addition, a molecular phylogenetic study found L. sylvaticum to be strongly structured and paraphyletic with respect to L. micropholis. We used ddRADseq and morphological data to evaluate the phylogenetic relationships and species boundaries within the complex. Phylogenetic analyses performed using both concatenation and coalescent methods estimated the same well-supported tree, composed of five allopatric, strongly supported lineages of L. sylvaticum (from Nuevo León [NL]; Sierra de Álvarez, San Luis Potosí [ASLP]; southeastern San Luis Potosí, Querétaro, Hidalgo, and northwestern Veracruz [HSQV]; Tamaulipas [T]; and Puebla and northern Veracruz [PV]) branching off (in that order) from the base of the tree, with L. micropholis as the sister taxon to the last of them. A population genetics analysis revealed one pattern of genetic structure in L. micropholis and three in L. sylvaticum: one in the L. sylvaticum NL and ASLP lineages, another in the L. sylvaticum HSQV and T lineages, and a third in the L. sylvaticum PV lineage. The six lineages identified were genetically distinctive across our phylogenetic and population genetics analyses, and congruent with geography. Species tree and phylogenetic network analyses, considering the six lineages as potentially independent, inferred trees identical to the concatenated tree. Additionally, the phylogenetic network analysis detected a recent introgression event from the L. sylvaticum T lineage into L. micropholis. A time-calibrated tree indicated that the diversification of the complex began in the late Miocene and continued into the Pleistocene. A species delimitation analysis, based on the genealogical divergence index, suggested that the L. sylvaticum NL, ASLP, and PV lineages are independent evolutionary lineages. A morphological analysis showed that L. micropholis and the L. sylvaticum NL and ASLP lineages were distinguishable from each other and from the remaining L. sylvaticum lineages. We conclude that the L. sylvaticum NL and ASLP lineages, the two oldest in the complex, represent independent evolutionary lineages, and propose recognizing the other four lineages as subspecies (historical sublineages) of L. sylvaticum sensu stricto.

ANALISIS PERBANDINGAN INVESTASI SAHAM MENGGUNAKAN INDIKATOR MOVING AVERAGE CONVERGENCE DIVERGENCE (MACD) DAN RELATIVE STRENGTH INDEX (RSI) PADA PT. PERUSAHAAN GAS NEGARA (PERSERO) TBK TAHUN 2022 - 2023

This research aims to analyze investment decisions on the shares of PT Perusahaan Gas Negara Tbk (PGAS) using the Moving Average Convergence Divergence and Relative Strength Index indicators. The Indonesia Stock Exchange provided panel data (time series) for the qualitative research method. The sampling technique used purposive sampling with daily data and a research period from 2022 to 2023. The data analysis technique was technical analysis using the Moving Average Convergence Divergence and Relative Strength Index indicators with the assistance of the TradingView program. The results of this study indicate that the stock price movement of PT Perusahaan Gas Negara Tbk (PGAS) using the Moving Average Convergence Divergence (MACD) indicator during the years 2022–2023 yielded a total of 7 (seven) true buy signals, 7 (seven) true sell signals, 7 (seven) false buy signals, and 5 (five) false sell signals. Additionally, using the Relative Strength Index (RSI) indicator yielded a total of 2 (two) true buy signals, 2 (two) true sell signals, 3 (three) false buy signals, and 2 (two) false sell signals. This research obtained an accuracy rate of 54% using the Moving Average Convergence Divergence (MACD) indicator and 44% using the Relative Strength Index (RSI).

Hierarchical Heuristic Species Delimitation Under the Multispecies Coalescent Model with Migration.

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data from closely related species to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for 1 species versus MSC for 2 species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively, heuristic criteria based on population parameters (such as population split times, population sizes, and migration rates) estimated from genomic data may be used to delimit species. Here, we develop hierarchical merge and split algorithms for heuristic species delimitation based on the genealogical divergence index (gdi) and implement them in a Python pipeline called hhsd. We characterize the behavior of the gdi under a few simple scenarios of gene flow. We apply the new approaches to a dataset simulated under a model of isolation by distance as well as 3 empirical datasets. Our tests suggest that the new approaches produced sensible results and were less prone to oversplitting. We discuss possible strategies for accommodating paraphyletic species in the hierarchical algorithm, as well as the challenges of species delimitation based on heuristic criteria.

Rapid evolution of recombination landscapes during the divergence of cichlid ecotypes in Lake Masoko.

Variation of recombination rate along the genome is of crucial importance to rapid adaptation and organismal diversification. Many unknowns remain regarding how and why recombination landscapes evolve in nature. Here, we reconstruct recombination maps based on linkage disequilibrium and use subsampling and simulations to derive a new measure of recombination landscape evolution: the Population Recombination Divergence Index (PRDI). Using PRDI, we show that fine-scale recombination landscapes differ substantially between two cichlid fish ecotypes of Astatotilapia calliptera that diverged only ~2,500 generations ago. Perhaps surprisingly, recombination landscape differences are not driven by divergence in terms of allele frequency (FST) and nucleotide diversity (Δπ): although there is some association, we observe positive PRDI in regions where FST and Δπ are zero. We found a stronger association between evolution of recombination and 47 large haplotype blocks that are polymorphic in Lake Masoko, cover 21% of the genome, and appear to include multiple inversions. Among haplotype blocks, there is a strong and clear association between the degree of recombination divergence and differences between ecotypes in heterozygosity, consistent with recombination suppression in heterozygotes. Overall, our work provides a holistic view of changes in population recombination landscapes during early stages of speciation with gene flow.

Tooth point cloud resampling method based on divergence index and improved Euclidean clustering rule.

In endodontic therapy, 3D Cone Beam Computerized Tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data.&#xD;Approach: Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence Index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean Clustering Rule (IECR) downsamples each point using its weight and joins the two halves using Iterative Nearest Neighbour (ICP) to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth.&#xD;Main results: Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas.&#xD;Significance: Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.

Divergence and Similarity Characteristics for Two Fuzzy Measures Based on Associated Probabilities

Abstract: The article deals with the definitions of the distance, divergence, and similarity characteristics between two finite fuzzy measures, which are generalizations of the same definitions between two finite probability distributions. As is known, a fuzzy measure can be uniquely represented by the so-called its associated probability class (APC). The idea of generalization is that new definitions of distance, divergence, and similarity between fuzzy measures are reduced to the definitions of distance, divergence, and similarity between the APCs of fuzzy measures. These definitions are based on the concept of distance generator. The proof of the correctness of generalizations is provided. Constructed distance, similarity, and divergence relations can be used in such applied problems as: determining the difference between Dempster-Shafer belief structures; Constructions of collaborative filtering similarity relations; non-additive and interactive parameters of machine learning in phase space metrics definition, object clustering, classification and other tasks. In this work, a new concept is used in the fuzzy measure identification problem for a certain multi-attribute decision-making (MADM) environment. For this, a conditional optimization problem with one objective function representing the distance, divergence or similarity index is formulated. Numerical examples are discussed and a comparative analysis of the obtained results is presented.

Unravelling spatial scale effects on elevational diversity gradients: insights from montane small mammals in Kenya

BackgroundMontane ecosystems play crucial roles as global biodiversity hotspots. However, climatic changes and anthropogenic pressure increasingly threaten the stability of montane community dynamics, such as diversity-elevation interactions, creating a challenge in understanding species biogeography and community ecology dynamics in these crucial conservation areas. We examined how varying sampling spatial grains influence small mammal diversity patterns within Kenya’s tallest montane ecosystems.MethodsEmploying a combination of multidimensional alpha diversity metrics and multisite beta diversity characteristics (species richness, phylogenetic and functional diversity and divergence, and multisite beta diversity) alongside spatial generalized additive multivariate regression analyses, we tested how spatial scaling influences elevational diversity gradient patterns and their associations with environmental and human activity variables.ResultsThe diversity-elevation associations were generally homogeneous across spatial grains; however, idiosyncratic patterns emerged across mountains. The total (taxonomic, phylogenetic, and functional) beta diversity, nestedness, and turnover resultant components monotonically increased or decreased with varying spatial grains. The associations between the diversity patterns and the environmental and human footprint variables increased with spatial grain size but also presented variations across mountains and indices. Species richness and phylogenetic and functional richness indices were more strongly influenced by spatial scale variations than were the divergence and community structure indices in both the diversity distribution patterns and their associations with the environmental and human variables.ConclusionsThe diversity-elevation and diversity-environment (including human activity pressure) relationships across spatial grains suggest that montane small mammal diversity patterns portray subtle but systematic sensitivity to sampling spatial grain variation and underscore the importance of geographical context in shaping these elevational diversity gradients. For improved effectiveness, conservation efforts should consider these spatial effects and the unique geographical background of individual montane ecosystems.

Opinion divergence, investor sentiment, and stock liquidity: Evidence from social media

ABSTRACT This paper developed a mediating effect model of retail opinion divergence, investor sentiment, and stock liquidity to investigate how investor comments and bullish-bearish-polling activities on social media affect investors’ attitudes and behaviours, thus, the stock market. The paper first used the Python programming language to scrape the bullish-bearish-polling outcomes for each stock and developed an investor opinion divergence index. Next, the study collected online investor comments, from which an online investor sentiment index was developed through machine-learning-based ways. The analysis results show that both investor sentiment and retail opinion divergence significantly impact stock liquidity, and investor sentiment plays a mediating role in it.

PERBANDINGAN RETURN PENGGUNAAN MOVING AVERAGE CONVERGENCE DIVERGENCE DAN RELATIVE STRANGE INDEX PADA INDEKS SAHAM SYARIAH INDONESIA

This study compares the return levels from the use of the technical indicators Moving Average Convergence Divergence (MACD) and Relative Strength Index (RSI) applied to the simulation of the movement of the Indonesia Sharia Stock Index (ISSI). This research is a population study using panel data in the form of daily closing values over 12 years of the Indonesia Sharia Stock Index movement. The data was processed using a technical analysis approach with two momentum indicators, MACD and RSI, to obtain buy and sell signals based on the principles of each indicator, applied to the ISSI. The use of the MACD and RSI technical indicators on the ISSI shows that MACD yielded a profit of 207% with a success rate (win rate) of 72%, while RSI provided a profit of 125% with a win rate of 55%.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

The Limits of the Metapopulation: Lineage Fragmentation in a Widespread Terrestrial Salamander (Plethodon cinereus)

Abstract In vicariant species formation, divergence results primarily from periods of allopatry and restricted gene flow. Widespread species harboring differentiated, geographically distinct sublineages offer a window into what may be a common mode of species formation, whereby a species originates, spreads across the landscape, then fragments into multiple units. However, incipient lineages usually lack reproductive barriers that prevent their fusion upon secondary contact, blurring the boundaries between a single, large metapopulation-level lineage and multiple independent species. Here, we explore this model of species formation in the Eastern Red-backed Salamander (Plethodon cinereus), a widespread terrestrial vertebrate with at least 6 divergent mitochondrial clades throughout its range. Using anchored hybrid enrichment data, we applied phylogenomic and population genomic approaches to investigate patterns of divergence, gene flow, and secondary contact. Genomic data broadly match most mitochondrial groups but reveal mitochondrial introgression and extensive admixture at several contact zones. While species delimitation analyses in Bayesian Phylogenetics and Phylogeography supported 5 lineages of P. cinereus, genealogical divergence indices (gdi) were highly sensitive to the inclusion of admixed samples and the geographic representation of candidate species, with increasing support for multiple species when removing admixed samples or limiting sampling to a single locality per group. An analysis of morphometric data revealed differences in body size and limb proportions among groups, with a reduction of forelimb length among warmer and drier localities consistent with increased fossoriality. We conclude that P. cinereus is a single species, but one with highly structured component lineages of various degrees of independence.

Investigation on the propagation of uncertainties of a timber floor under human excitation

Due to the characteristics of high stiffness-weight ratio, timber floors are prone to annoying vibrations under human excitation. Given the natural origin of timber, its mechanical properties exhibit significant variability. The randomness inherent in human excitation cannot be overlooked during structural dynamic analysis. Consequently, the adoption of a stochastic approach is imperative for attaining reliable serviceability evaluation results. However, current research on human-induced vibrations in the timber floor, accounting for this randomness, remains inadequate. In this paper, an experimental investigation is conducted on the dynamic properties and human-induced responses of a timber floor composed of glued laminated timber and oriented strand board. A finite element model is developed and subsequently validated for accuracy in terms of modal properties and dynamic responses. The probability density evolution method is introduced for stochastic analysis, which demonstrates that both the natural frequency and dynamic responses of the floor exhibit considerable variability when uncertainty factors are considered. The Kullback–Leibler divergence indices are used to assess the impact of each uncertain variable quantitatively. The results indicate that the longitudinal elastic modulus has the greatest influence on the natural frequency, while the first dynamic load factor, αz1, exerts the most significant impact on dynamic responses. Notably, both material mechanical properties and load model parameters contribute to the uncertainty of dynamic responses, with the influence of the load model parameters being significantly greater than that of material mechanical properties.

High-resolution cropland mapping in China’s Huang-Huai-Hai Plain: The coupling of machine learning methods and prior information

Numerous map products containing cropland data are currently available. However, due to their low temporal and spatial resolutions and limited accuracy, their practical applications are limited. Thus, research on the precise mapping of croplands with high temporal and spatial resolutions is still urgently needed. The Huang-Huai-Hai Plain, a critical grain production area in China, was used as the research area to produce high-resolution annual cropland maps from 2018 to 2022. Sentinel-2 imagery, digital elevation model (DEM) data, and China Agricultural Ecological Zoning (CAEZ) data were collected. Based on these data, the study area was stratified into different zones, and then the distinct characteristics and optimal timing for differentiating cropland from other land cover types within each zone were analyzed and used as prior information for guiding time-series image compositing. Furthermore, the spectral information divergence (SID) index was used to modify the existing sample migration technique to transfer training samples across years. Via the Google Earth Engine (GEE) platform, the random forest (RF) classification method and positive and unlabeled learning (PUL) method were combined to construct cropland maps from composited images and terrain information from DEMs. Compared with the seasonal or spectral-temporal metric (STM) image composition methods, the image composition method based on prior information significantly improved the distinction between cropland and other land cover types. Moreover, the newly proposed sample migration strategy substantially increased the accuracy of training samples transferred across years. Compared with existing cropland maps from products such as the ESA, ESRI, GlobeLand30, GLAD, and CLCD, the cropland maps generated in this study demonstrated superior accuracy, with average overall accuracy (OA) values from 88.81% to 91.66% for different zones, thus outperforming other products with OA values from 77.53% to 88.90%. This study supports the development of related agricultural and ecological research and provides a valuable reference for cropland mapping.

Spatiotemporal Variations in Trophic Diversity of Fish Communities in a Marine Bay Ecosystem Based on Stable Isotope Analysis

Climate change has led to significant fluctuations in marine ecosystems. As a component of the food web, the trophic diversity and spatiotemporal changes of fish communities are crucial for understanding ecosystems. In recent years, stable isotope analysis has been increasingly used as a comprehensive tool for quantitative assessment of trophic diversity to explore spatiotemporal variations in fish community diversity. This study is based on carbon (δ13C) and nitrogen (δ15N) stable isotope analysis using different biomass-weighted isotope diversity indices, including isotopic divergence index (IDiv), isotopic dispersion index (IDis), isotopic evenness index (IEve), and isotopic uniqueness index (IUni). The overall results indicate that IDis, IEve, and IUni values of fish communities were relatively low, while IDiv was relatively high in the Haizhou Bay ecosystem. IDiv, IDis, IEve, and IUni were lower in autumn than in spring; IDiv and IDis were relatively higher in offshore waters, while IEve and IUni were relatively higher in inshore waters. The changes in species composition and intensive pelagic–benthic coupling in Haizhou Bay may lead to significant spatiotemporal variations in the trophic diversity of fish communities in the area. These findings highlight the importance of incorporating trophic relationships into ecosystem models, which will help to enhance our understanding of the complexity of the trophic structure of fish communities.

The link between digital gaming and creativity: a meta analysis

ABSTRACT This study explored the relationship between digital gaming and creativity by conducting a meta-analysis of effect sizes using data from 78 effect sizes in 19 studies encompassing 3,172 samples. We employed a two-level multivariate model with a robust variance estimator and conducted separate analyses for between-subject and within-subject designs. For the between-subject designs, the mean effect size was r = .109, 95% [−0.036, 0.249], indicating non-significance. We conducted moderator analyses to explore potential sources of variation in the observed effects, including creativity test modality, creativity test type, divergent thinking index, analytical approach, age, gender, and year of publication. None of the examined moderators were found to be statistically significant. In contrast, we observed a significant mean effect size for within-subject designs based on 29 effects from seven studies (r = .772, 95% CI [0.595, 0.878]). Variation in mean effects by study design warrants caution when making inferences about the impact of digital gaming on creativity. The implications and limitations of the findings are discussed.

Plant species diversity and functional diversity relations in the degradation process of desert steppe in an arid area of northwest China

Species and functional diversity play a major role in the stability and sustainability of grassland ecosystems. However, changes in species and functional diversity during grassland degradation in arid areas as well as the underlying mechanisms remain unclear. In this study, we surveyed the vegetation and soil properties of arid regions across a degradation gradient to explore the shifts in species and functional diversity in plant communities, their relationships and key determinants during desert steppe degradation. Our results found significant variability in species diversity and functional diversity across degradation stages. Species diversity (Shannon-Wiener index (H), and Pielou index) and functional diversity (functional evenness (FEve) index, and Rao's quadratic entropy (RaoQ) index) tended to increase initially and then decrease with increasing grassland degradation. The Patrick index, Simpson index, functional richness (FRic) index, functional divergence (FDiv) index, and functional dispersion (FDis) index declined as grassland degradation increased. The relationships between species diversity and functional diversity indices at different stages of degradation in the desert steppe were inconsistent. From no to heavy degradation grasslands, the correlation between species diversity and functional diversity gradually weakened. Specifically, there was a significant correlation between Patrick (R) and FRic indices (R2 > 0.7) on both non-degraded and light degraded grasslands, but there was no significant correlation between R and FRic indices in moderately and heavily degraded grasslands (R2 < 0.7), and R2 gradually decreased. Redundancy analysis and partial least squares path modeling showed that grassland degradation has a significant direct effect on the species diversity and functional diversity. In addition grassland degradation has direct and indirect effects on the species diversity through soil available nitrogen, organic matter and total nitrogen. Functional diversity is directly or indirectly affected by species diversity, soil available nitrogen, organic matter and total nitrogen, soil moisture content, soil bulk density, and pH value. In summary, the relationship between species and functional diversity indices gradually weakened from areas with no degradation to heavy degradation in arid desert grasslands. Our study reveals the patterns and relationships between species diversity and functional diversity throughout the process of grassland degradation, demonstrating a gradual decrease in ecosystem stability and sustainability as degradation advances. Our results have significant implications for the restoration of grassland degradation and the management of ecosystem services in arid steppe regions.

Nonparametric CD-CAT for multiple-choice items: Item selection method and Q-optimality.

Computerized adaptive testing for cognitive diagnosis (CD-CAT) achieves remarkable estimation efficiency and accuracy by adaptively selecting and then administering items tailored to each examinee. The process of item selection stands as a pivotal component of a CD-CAT algorithm, with various methods having been developed for binary responses. However, multiple-choice (MC) items, an important item type that allows for the extraction of richer diagnostic information from incorrect answers, have been underemphasized. Currently, the Jensen-Shannon divergence (JSD) index introduced by Yigit etal. (Applied Psychological Measurement, 2019, 43, 388) is the only item selection method exclusively designed for MC items. However, the JSD index requires a large sample to calibrate item parameters, which may be infeasible when there is only a small or no calibration sample. To bridge this gap, the study first proposes a nonparametric item selection method for MC items (MC-NPS) by implementing novel discrimination power that measures an item's ability to effectively distinguish among different attribute profiles. A Q-optimal procedure for MC items is also developed to improve the classification during the initial phase of a CD-CAT algorithm. The effectiveness and efficiency of the two proposed algorithms were confirmed by simulation studies.

A multi-task spatio-temporal generative adversarial network for prediction of travel time reliability in peak hour periods

Travel time reliability (TTR) serves as a crucial indicator for evaluating the efficiency and service quality of a road traffic network. This paper proposes a multi-task spatio-temporal generative adversarial network (MTST-GAN) model that simultaneously predicts the TTR in morning and evening peak hour periods. The model incorporates multi-graph convolutional networks to extract spatial correlations from travel time data, while long short-term memory neural networks are employed to consider temporal correlations. Additionally, self-attention mechanisms are applied to the proposed MTST-GAN model to further capture spatial and temporal features. A feature fusion bridge is constructed to integrate the spatial and temporal features learned by each task. Through a numerical experiment conducted on a road network in a Chinese city, our findings demonstrate that the proposed model outperforms several state-of-the-art approaches in terms of Jensen-Shannon divergence, mean, standard deviation, and buffer time indices. Finally, we provide conclusions and suggest areas for further research.