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Background: Transitivity assumption is the cornerstone of network meta-analysis (NMA). Investigating the plausibility of transitivity can unveil the credibility of NMA results. The commonness of transitivity was examined based on study dissimilarities regarding several study-level aggregate clinical and methodological characteristics reported in the systematic reviews. The present study also demonstrated the disadvantages of using multiple statistical tests to assess transitivity and compared the conclusions drawn from multiple statistical tests with those from the approach of study dissimilarities for transitivity assessment. Methods: An empirical study was conducted using 209 published systematic reviews with NMA to create a database of study-level aggregate clinical and methodological characteristics found in the tracenma R package. For each systematic review, the network of the primary outcome was considered to create a dataset with extracted study-level aggregate clinical and methodological characteristics reported in the systematic review that may act as effect modifiers. Transitivity was evaluated by calculating study dissimilarities based on the extracted characteristics to provide a measure of overall dissimilarity within and between the observed treatment comparisons. Empirically driven thresholds of low dissimilarity were employed to determine the proportion of datasets with evidence of likely intransitivity. One-way ANOVA and chi-squared test were employed for each characteristic to investigate comparison dissimilarity at a significance level of 5%. Results: Study dissimilarities covered a wide range of possible values across the datasets. A 'likely concerning' extent of study dissimilarities, both intra-comparison and inter-comparison, dominated the analysed datasets. Using a higher dissimilarity threshold, a 'likely concerning' extent of study dissimilarities persisted for objective outcomes but decreased substantially for subjective outcomes. A likely intransitivity prevailed in all datasets; however, using a higher dissimilarity threshold resulted in few networks with transitivity for semi-objective and subjective outcomes. Statistical tests were feasible in 127 (61%) datasets, yielding conflicting conclusions with the approach of study dissimilarities in many datasets. Conclusions: Study dissimilarity, manifested from variations in the effect modifiers' distribution across the studies, should be expected and properly quantified. Measuring the overall study dissimilarity between observed comparisons and comparing it with a proper threshold can aid in determining whether concerns of likely intransitivity are warranted. [ABSTRACT FROM AUTHOR]

Mosses play a key role in Antarctic ecosystems. Understanding of moss diversity and its likely drivers across Antarctica is, however, limited, as is the extent to which Antarctic Specially Protected Areas (ASPAs) represent this diversity. Both are important given changing climates and direct human impacts in the region. Here we investigate variation in moss diversity, the frequency distribution of their range sizes, and their continent‐wide conservation. Richness is positively related to temperature, but negatively related to latitude, distance from bird colonies and geothermal sites; terrain roughness showed weak, yet positive, effects. Beta‐diversity is similar to that found for assemblages separated by long distances, dominated by species turnover. Multi‐site turnover (zeta diversity) suggests that niche‐related mechanisms are likely more responsible for diversity patterns than neutral mechanisms, despite the significant role wind‐driven dispersal is thought to play in structuring Antarctic biodiversity patterns. The frequency distribution of range sizes of mosses was right skewed, indicating that several moss species have very small range sizes, while a few species have larger ranges. Where ASPAs include mosses, richness varies between 1 and 41 species, with 65.1% (71 species) of the 109 species known from the continent included in the ASPA network. Twenty‐four species lie within 25 km2 radius of an ASPA, and 14 species beyond this distance could be considered relatively more difficult to protect. These findings lend support to the proposal that changing temperatures and expanding ice‐free areas will substantially increase Antarctica's diversity. Nonetheless, the mosses are reasonably well represented by the ASPA network, contrasting with other Antarctic taxa. [ABSTRACT FROM AUTHOR]

Negation provides a novel perspective for the representation of information. However, current research seldom addresses the issue of negation within the random permutation set theory. Based on the concept of belief reassignment, this paper proposes a method for obtaining the negation of permutation mass function in the of random set theory. The convergence of proposed negation is verified, the trends of uncertainty and dissimilarity after each negation operation are investigated. Furthermore, this paper introduces a negation-based uncertainty measure, and designs a multi-source information fusion approach based on the proposed measure. Numerical examples are used to verify the rationality of proposed method. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to introduce the basic concepts， calculation methods， two examples and the implementation of SAS calculation methods related to the cluster analysis of disordered samples. The basic concepts included similarity， dissimilarity， metric and non metric type. The calculation methods involved the second set of distance methods （12 in total） for measuring the distance between two samples and the centroid method for measuring the distance between two clusters of samples. The data in the two examples were "survey data on the expenditure of farmers in 16 regions of China in 1982" and "measurement values of 13 sports events on two quantitative indicators". Using SAS software， cluster analysis of disordered samples was performed on the data from two instances， and the SAS output results were explained. [ABSTRACT FROM AUTHOR]

Aim: We map global patterns of taxonomic and functional change between past (pre‐human impacts) and present (after anthropogenic extinctions and introductions) in large oceanic island bird assemblages and investigate if these patterns can be explained by island characteristics and anthropogenic factors. Location: Sixty‐four oceanic islands (>100 km2). Time Period: Late Holocene. Major Taxa Studied: Terrestrial and freshwater bird species. Methods: We compiled information on extinct, extant native and introduced bird species for all islands and used a probabilistic hypervolume approach to build a multi‐dimensional trait space and calculate several functional diversity metrics before and after extinctions and introductions. We identified which islands are global hotspots of human‐induced transformation by mapping multiple facets of biotic change and investigated intrinsic island characteristics and anthropogenic factors as drivers for these observed patterns. Results: The Hawaiian and Mascarene islands stand out as hotspots of taxonomic and functional change, but all islands changed taxonomically and functionally, mostly gaining species but losing functional richness. Taxonomic and functional changes vary across islands but are often consistent within the same archipelago. Island isolation and surface can explain some of the observed variations, but anthropogenic factors, namely human occupation, also shaped both taxonomic and functional changes. Islands with higher human pressure, as well as larger islands with high elevation ranges, tended to have greater losses in functional richness. Main Conclusions: Most biodiversity change assessments are still largely based exclusively on taxonomic diversity, which is particularly worrying in the case of oceanic islands given that the magnitude of functional diversity change is often considerably larger. We call for comprehensive assessments of changes in both taxonomic and functional diversity across oceanic islands in order to better understand the drivers of these changes and, in turn, predict future trends. [ABSTRACT FROM AUTHOR]

Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity. [ABSTRACT FROM AUTHOR]

Ongoing habitat loss and fragmentation caused by human activities represent one of the greatest causes of biodiversity loss. However, the effects of habitat loss and fragmentation are not felt equally among species. Here, we examined how habitat loss influenced the diversity and abundance of species from different trophic levels, with different traits, by taking advantage of an inadvertent experiment that created habitat islands from a once continuous forest via the creation of the Thousand Island Lake, a large reservoir in China. On 28 of these islands with more than a 9000‐fold difference in their area (0.12–1154 ha), we sampled plants, herbivorous insects, and predatory insects using effort‐controlled sampling and analyses. This allowed us to discern whether any observed differences in species diversity were due to passive sampling alone or to demographic effects that disproportionately influenced some species relative to others. We found that while most metrics of sampling effort‐controlled diversity increased with island area, the strength of the effect was exacerbated for species in higher trophic levels. When we more explicitly examined differences in species composition among islands, we found that the pairwise difference in species composition among islands was dominated by species turnover but that nestedness increased with differences in island area, indicating that some species are more likely to be absent from smaller islands. Furthermore, by examining trends of several dispersal‐related traits of species, we found that species with lower dispersal propensity tended to be those that were lost from smaller islands, which was observed for herbivorous and predatory insects. Our results emphasize the importance of incorporating within‐patch demographic effects, as well as the taxa and traits of species when understanding the influence of habitat loss on biodiversity. [ABSTRACT FROM AUTHOR]

The Mexican Republic ranks fifth in the world in terms of amphibian diversity, and within Mexico, the state of Oaxaca has the greatest amphibian richness and endemism. Unfortunately, various factors, such as land use change and global warming, have caused a global crisis that threatens the conservation of this class. In the face of these threats, an analysis of beta diversity provides information that can be applied to conservation strategies, since its study reveals the spatial scaling of diversity loss and clarifies the mechanisms of regional diversity maintenance. In this work, we analyzed the beta diversity at the species and higher taxa level (order, family, subfamily, genus and species) for the amphibians of Oaxaca and their replacement components and the differences in richness for anurans and caudates separately between physiographic subprovinces. Very high beta diversity was recorded, with higher diversity occurring among caudates (0.92) than among anurans (0.84). Species replacement was the component that most contributed to this result, and the subprovinces with substantial environmental differences had the most dissimilar amphibian communities. The results of this study show the need to implement conservation strategies in subprovinces with high amphibian richness and endemism levels, following the example of Sierra Madre de Oaxaca (SMO), where local communities have developed conservation actions in most of the territory. [ABSTRACT FROM AUTHOR]

Fisheries of the Mekong Basin are crucial to regional food security. They rely on seasonal monsoon rains that inundate rice fields and reconnect water bodies. Species assemblages vary over time and space, but infrastructure developments and climate change are negatively impacting the timing and magnitude of the flood pulse as well as fish migration routes and populations. In lowland rice field landscapes of Cambodia, community fish refuges (CFRs) are used to maintain fish abundance and biodiversity, with varying success. There is little knowledge of the drivers of this variation to guide management interventions. We used beta diversity to explore the effectiveness of 40 CFRs in maintaining species abundance and diversity during the dry seasons from 2012 to 2015. Results show CFR connectivity to the floodplain is important and suggest the type of inlet/outlet influences fish retention. CFRs connected to a large area of rice fields during the wet season had lower losses in species abundance through the dry season. However, large CFRs, or those part of a larger body of water, had higher losses in species abundance. Key design features of CFRs must be coupled with strong management capacity to guide the conservation and fisheries strategy in the Mekong Basin. [ABSTRACT FROM AUTHOR]

A functional approach to understand natural plant communities helps elucidate the dynamics of environmental filters and community assembly. Species classification into functional groups allows the translation of ecological complexity into simplified and applicable knowledge. Here, we developed a plant functional type (PFT) system to investigate whether functional structure (PFT composition and abundance) of plant communities varies between substrate types and plant physiognomies in an old climatically buffered infertile landscape (OCBIL), namely Brazilian campo rupestre. We sampled the herbaceous and shrubby vegetation and collected soil variables in four different plant physiognomies growing in quartzitic and ferruginous substrates. We classified the sampled species according to life‐history traits related to species persistence, reproduction, and biological interactions. Functional dissimilarity among plant physiognomies and the two substrate types and the role of soil factors affecting the dissimilarities were assessed. Pollination and seed dispersal syndromes, mycorrhizal association, and reproductive phenology were the main traits defining the PFTs. We found distinct functional structure between plant communities of different substrates and physiognomies, with a marked influence of chemical and physical soil factors. Also, we defined main PFTs profiles for each physiognomy, identifying core PFTs with basic combinations of traits, common to all or almost all physiognomies. The functional perspective proposed by this study provides applicable knowledge to the restoration and conservation of campo rupestre ecosystem, reinforcing the importance of habitat heterogeneity for maintaining its functioning. [ABSTRACT FROM AUTHOR]

Abstract Automakers spend billions of dollars annually towards warranty costs, and warranty reduction is typically high on their priorities. An accurate understanding of warranty performance plays a critical role in controlling and steering the business, and it is of crucial importance to fully understand the actual situation as well as be able to predict future performance, for example, to set up adequate financial reserves or to prioritize improvement actions based on expected forthcoming claims. Data maturation, a major nuisance causing changes in performance metrics with observation time, is one of the factors complicating warranty data analysis and typically leads to over‐optimistic conclusions. In this paper, we propose a sequence of steps, decomposing and addressing the main reasons causing data maturation. We first compensate for reporting delay effects using a Cox regression model. For the compensation of heterogeneous build quality, sales delay, and warranty expiration rushes, a constrained quadratic optimization approach is presented, and finally, a sales pattern forecast is provided to properly weigh adjusted individual warranty key performance indicators. The results are shown to dramatically improve prior modeling approaches.

The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient’s seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance.

Data analysis is performed to examine, interpret, and extract information from data series, and it includes applying various methods and techniques to understand patterns and compare data. An approach to compare data is to use rank metrics that help identify how distinct two data series are when compared to each other according to patterns, formats, criteria, and dimensions in both data series. Among these metrics, Kendall's Tau metric stands out, as it is robust and inexpensive, widely used in analyzing sequences and genomes, to detect errors in flash memories, and to compare distributions and top-k ranked values. However, a challenge arises when comparing lists with different lengths or when lists do not share the same elements. This happens, for example, when lists are defined by top-k elements, commonly called k-list. In this case, there is no guarantee that two k-lists share the same set of elements. Traditional metrics like Kendall's Tau are designed to quantify differences only between shared elements in lists. Recognizing this limitation, a possible solution is to apply the metric to the shared elements of the lists. Another solution, named the generalization of Kendall's Tau, proposed by Fagin et al., considers all elements in two lists. However, this generalization of Kendall Tau is a semi-metric, as it does not satisfy the triangular inequality. To solve this problem, we propose the Extended Kendall Tau (EKT) metric that meets all the conditions of a metric and simultaneously considers the distinct elements of the compared lists. The proposed metric was evaluated by applying conventional Kendall's Tau and the extended Kendall's Tau over 40 text files divided into five different languages (eight files per language). We compared KT and EKT measures within the "same language" and across "other language" files for the two scenarios. The results revealed that both methods could accurately identify the differences between the groups of texts of the "same language" and "other language". However, the numerical results show that EKT is able to more significantly highlight the difference between groups of texts of different languages. [ABSTRACT FROM AUTHOR]

The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient’s seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance. [ABSTRACT FROM AUTHOR]

In modern data analysis and machine learning, data are often represented in the form of pairwise comparisons of the elements of the data set. The pairwise comparisons immediately correspond to the similarity or dissimilarity of objects under investigation, and such a situation regularly arises in the domains of image and signal analysis, bioinformatics, expert evaluation, etc. The practical pairwise comparison functions may be incorrect in terms of potentially using them as scalar products or distances. In contrast to other approaches, we develop in this paper a technique based on the so-called metric approach, which proposes to modify the values of empirical functions so as to get scalar products or distances. The methods for obtaining the correct matrices of pairwise comparisons and for improving their conditionality are developed here. [ABSTRACT FROM AUTHOR]

Introduction: Bacterial communities are important for soil functions, but the effect of clomazone on network complexity, composition, and stability is not well studied. Method: In this study, two agricultural soils were used to test the impact of clomazone on bacterial communities, and the two soils were treated with three concentrations of clomazone (0, 0.8, 8, and 80 mg kg1) in an incubator. Results and discussion: Bacterial network nodes, links, and average degrees were all decreased by 9-384, 648-829, and 0.703-2.429, respectively. Based on keystone nodes, the topological roles of the nodes were also influenced by clomazone. Bacterial network composition was also impacted based on the analysis of similarity (ANOSIM) and network dissimilarity. Compared with control and clomazone treatments in both soils, the ANOSIM between control and all clomazone treatments was higher than 0.6, network dissimilarities were 0.97-0.98, shared nodes were 131-260, and shared links were 12-100. The bacterial network stability was decreased by clomazone, with decreased robustness by 0.01-0.016 and increased vulnerability by 0.00023-0.00147 in both soils. There were fewer bacterial network modules preserved after clomazone treatment, and the bacterial network community functions were also impacted in both soils. Based on these results, soil bacterial species connections, modularization, and network stability were significantly impacted by clomazone. [ABSTRACT FROM AUTHOR]

Data clustering is a commonly used data processing technique in many fields, which divides objects into different clusters in terms of some similarity measure between data points. Comparing to partitioning clustering methods which give a flat partition of the data, hierarchical clustering methods can give multiple consistent partitions of the data at different levels for the same data without rerunning clustering, it can be used to better analyze the complex structure of the data. There are usually two kinds of hierarchical clustering methods: divisive and agglomerative. For the divisive clustering, the key issue is how to select a cluster for the next splitting procedure according to dissimilarity and how to divide the selected cluster. For agglomerative hierarchical clustering, the key issue is the similarity measure that is used to select the two most similar clusters for the next merge. Although both types of the methods produce the dendrogram of the data as output, the clustering results may be very different depending on the dissimilarity or similarity measure used in the clustering, and different types of methods should be selected according to different types of the data and different application scenarios. So, we have reviewed various hierarchical clustering methods comprehensively, especially the most recently developed methods, in this work. The similarity measure plays a crucial role during hierarchical clustering process, we have reviewed different types of the similarity measure along with the hierarchical clustering. More specifically, different types of hierarchical clustering methods are comprehensively reviewed from six aspects, and their advantages and drawbacks are analyzed. The application of some methods in real life is also discussed. Furthermore, we have also included some recent works in combining deep learning techniques and hierarchical clustering, which is worth serious attention and may improve the hierarchical clustering significantly in the future. [ABSTRACT FROM AUTHOR]

In recent years, researchers have grappled with the phenomenon that public demand for redistribution has not systematically increased in response to rising inequality. Meritocratic beliefs have been suggested as an explanation for this observation, because they can help legitimize inequalities. Past research has identified local-level inequality, segregation, or diversity as important factors for how these beliefs might be formed and maintained. Different theoretical approaches have been advanced and tested to determine the direction and extent of these effects, producing mixed results. We put these theories to the test by focusing on a country in which changes in the level of inequality have indeed been met with equal changes in justice perceptions: Germany. Furthermore, we broaden the scope by focusing on local segregation between different socioeconomic status groups, rather than income inequality. To do so, we utilize geocoded individual-level data from the German Socio-Economic Panel (SOEP), microm data, and relevant geospatial indicators from the INKAR database. We find some indication that residential segregation of status groups and isolation of high status groups is associated with less support of meritocratic beliefs, contradicting previous work. Additionally, we find evidence of urban–rural differences in the effects of segregation. [ABSTRACT FROM AUTHOR]

Routine evaluations of cooking time trait in common bean (Phaseolus vulgaris L.) can be performed in different ways resulting in different variables. At the same time, the univariate statistical analysis does not consider the interdependencies between the variables, and may omit important information regarding the genotypes. With this, the objective of this work was to present an alternative proposal for analysis of the cooking time in common bean, allowing the discrimination between genotypes. The experiment used for this approach was conducted under field conditions in the 2017/18 agricultural season in Lages, Santa Catarina, Brazil. The treatments consisted of twelve genotypes, (four parents, structured in two crossings BAF50 x BAF07 and BAF09 x IPR 88 Uirapuru, with their generations F2, F3, F8 and F9). The design used was randomized blocks, with two blocks and two observations in each experimental unit. After the harvest, the response variable cooking time of the grains was measured with a Mattson cooker, considering the cooking time of the 13 initial stems. In the multivariate analysis, the variables cooking time of the second (TCH2), twelfth (TCH12) and thirteenth stem (TCH13) were used based on their significance by the stepwise variable selection method. Multivariate analysis of variance showed differences between genotypes (P<0.05). From the dissimilarity matrix with the Mahalanobis distances and the clustering dendrogram, it was possible to verify the distances of the genotypes derived from crosses BAF50 x BAF07 and BAF09 x IPR 88 Uirapuru. With that, the multivariate analysis enabled the genotypes, additionally the crossing BAF50 x BAF07 showed higher estimates of dissimilarity in the progenies. [ABSTRACT FROM AUTHOR]

Biological soil crusts (biocrusts) are communities of microorganisms, fungi, algae, lichens and mosses inhabiting on the soil surface and within the uppermost soil millimetres. They play an important ecological role in drylands, determining physical and chemical soil properties and reducing soil erosion. Studies on biocrust natural recovery establish highly variable recovery times. The different objectives and methodologies of experimentation and analysis, strongly influence these predictions. The main purpose of this research is to analyze the recovery dynamics of four biocrust communities and their relationship with microclimatic variables. In 2004, in Tabernas Desert, some of us removed the biocrust in central 30 cm 30 cm area of three 50 cm x 50 cm plots in each of four biocrust communities (Cyanobacteria, Squamarina, Diploschistes, and Lepraria), installing a microclimatic station in each one with sensors for temperature and humidity of the soil and air, dew point, PAR and rain. Yearly, the 50 cm < 50 cm plots were photographed, and the cover of every species was monitored in every 5 cm <5 cm cell of a 36-cells grid covering the removed central area. We analyzed different functions to fit the cover recovery, the differences in cover recovery speed between communities, the recovery dynamics from the spatial analysis of the plot, the changes in dissimilarity and biodiversity and the possible relationships with the climatic variables. The recovery of the biocrust cover fits to a sigmoidal function. The community dominated by Cyanobacteria developed faster than those dominated by lichens. The Squamarina and Diploschistes communities recovered faster than that of Lepraria and appears to be influenced by the surrounding undisturbed areas. Species-based dissimilarity between consecutive inventories fluctuated and decreased over time, while biodiversity increases in a similar way. The speed of recovery of the biocrust in each community, along with the order in which the species appeared, support the hypothesis about the succession, which would include three phases: firstly Cyanobacteria, then Diploschistes and/or Squamarina and finally Lepraria. The relationship between biocrust recovery and microclimate is complex and this work highlights the need to carry out further research on this topic and on biocrust dynamics in general. [ABSTRACT FROM AUTHOR]

Introduction: The composition and stability of soil fungal network are important for soil function, but the effect of trifluralin on network complexity and stability is not well understood Methods: In this study, two agricultural soils were used to test the impact of trifluralin on a fungal network. The two soils were treated with trifluralin (0, 0.84, 8.4, and 84mg kg-1) and kept in artificial weather boxes. Results and discussion: Under the impact of trifluralin, the fungal network nodes, edges, and average degrees were increased by 6-45, 134-392, and 0.169-1.468 in the two soils, respectively; however, the average path length was decreased by 0.304-0.70 in both soils. The keystone nodes were also changed in trifluralin treatments in the two soils. In the two soils, trifluralin treatments shared 219-285 nodes and 16-27 links with control treatments, and the network dissimilarity was 0.98-0.99. These results indicated that fungal network composition was significantly influenced. After trifluralin treatment, fungal network stability was increased. Specifically, the network robustness was increased by trifluralin with 0.002-0.009, and vulnerability was decreased by trifluralin with 0.0001-0.00032 in the two soils. Fungal network community functions were also impacted by trifluralin in both soils. Trifluralin significantly impacts the fungal network. [ABSTRACT FROM AUTHOR]

Fault detection of nonlinear dynamic processes can ensure the safety of industrial production processes. Industrial process data are mostly autocorrelated along with strong nonlinear characteristics. And these significant characteristics interact with each other and limit the fault detection performance of traditional methods. Therefore, this paper presents a novel adaptive fault detection method for nonlinear dynamic processes based on kernel entropy component analysis (KECA) integrating the moving window of dissimilarity (DMW) (KECA-DMW). The KECA is used to map the raw data and capture the nonlinear features of the data, which combine with moving window techniques to build the fault detection model. In the process of updating the data in the moving window, the data information of the historical window is combined with that of the current window to obtain a more comprehensive judgment of the current moment. Then a dynamic update fusion method with adaptive weight allocation based on the dissimilarity index is proposed by analyzing the data characteristics of window information at different moments through the dissimilarity. Finally, three example studies with a numerical example, a closed-loop continuously stirred tank reactor and a Tennessee-Eastman process are used to validate the effectiveness of the proposed method. Compared with other nonlinear dynamic process fault detection methods, the results verify the effectiveness of the proposed method in the process monitoring performance of nonlinear dynamic processes in terms of false alarm rate and fault detection rate, where the false alarm rates of the proposed method are only 2%, 1.83%, and 4.33%, while the fault detection rates are 97.4%, 96.83%, and 86.25%, respectively. [Display omitted] • Novel adaptive fault detection method based on the KECA-DMW is proposed. • The proposed method adaptively assigns weights from different windows. • The proposed method captures structural changes in complex process data. • The proposed method reduces the impact of autocorrelation on fault detection. [ABSTRACT FROM AUTHOR]

Synchronic variations in abundance in populations of the same species are common phenomena encountered in various environments, including lakes, and different taxa of freshwater fishes. This phenomenon can be caused by similar environmental conditions across physically separated populations. In the context of the ongoing climate change, it is essential to test this hypothesis, identify the factors driving the synchrony and elucidate the mechanisms, in the attempt to improve fisheries management. This study investigates synchronic variations in European whitefish (Coregonus spp.) populations in five peri-alpine lakes. The hypothesis suggests that shared biotic or abiotic factors contribute to similar trends in whitefish landings. Environmental and seasonal variables impacting the early life stages of the species were analyzed, and the Euclidean distances between the multivariate time series were calculated to identify similarities or dissimilarities in lake environmental parameters. We found that regional winter and spring temperatures were consistent across the lakes, but these factors did not fully account for variations in landings statistics. Wind intensity, water level and zooplankton abundance showed lake-specific patterns that could better explain local conditions and dynamics. Linear models did not reveal a coherent correlation with a common environmental variable across all lakes. However, distinct relationships were found in four of the lakes, with local factors significantly contributing to abundance variations. The spring abundance of Daphnia spp., a primary food source for whitefish larvae, was the main factor correlated with fish landing trends in Lake Geneva and Lake Bourget. Higher availability of Daphnia spp. may decrease intraspecific competition and density-dependent mortality. In Lake Neuchâtel, winter water temperature was negatively correlated with fish abundance proxies, suggesting that warmer winters may compromise reproduction success. Lake Annecy saw an increase in whitefish landings following a substantial reduction in fishing efforts during the late 2000s. A significant negative correlation was found between whitefish landings and fishing efforts. No relationship was found for Lake Aiguebelette, maybe due to a lack of zooplankton data. In conclusion, the observed synchrony in the European whitefish population is likely driven by a combination of interacting environmental and anthropogenic factors rather than a single common variable. Further research and a more detailed dataset are needed to better understand these complex relationships. Article cover image: Whitefish (Credit: Rémi Masson)

Biological soil crusts (biocrusts) are communities of microorganisms, fungi, algae, lichens and mosses inhabiting on the soil surface and within the uppermost soil millimetres. They play an important ecological role in drylands, determining physical and chemical soil properties and reducing soil erosion. Studies on biocrust natural recovery establish highly variable recovery times. The different objectives and methodologies of experimentation and analysis, strongly influence these predictions. The main purpose of this research is to analyze the recovery dynamics of four biocrust communities and their relationship with microclimatic variables. In 2004, in Tabernas Desert, some of us removed the biocrust in central 30 cm × 30 cm area of three 50 cm × 50 cm plots in each of four biocrust communities (Cyanobacteria, Squamarina, Diploschistes, and Lepraria), installing a microclimatic station in each one with sensors for temperature and humidity of the soil and air, dew point, PAR and rain. Yearly, the 50 cm × 50 cm plots were photographed, and the cover of every species was monitored in every 5 cm × 5 cm cell of a 36-cells grid covering the removed central area. We analyzed different functions to fit the cover recovery, the differences in cover recovery speed between communities, the recovery dynamics from the spatial analysis of the plot, the changes in dissimilarity and biodiversity and the possible relationships with the climatic variables. The recovery of the biocrust cover fits to a sigmoidal function. The community dominated by Cyanobacteria developed faster than those dominated by lichens. The Squamarina and Diploschistes communities recovered faster than that of Lepraria and appears to be influenced by the surrounding undisturbed areas. Species-based dissimilarity between consecutive inventories fluctuated and decreased over time, while biodiversity increases in a similar way. The speed of recovery of the biocrust in each community, along with the order in which the species appeared, support the hypothesis about the succession, which would include three phases: firstly Cyanobacteria, then Diploschistes and/or Squamarina and finally Lepraria. The relationship between biocrust recovery and microclimate is complex and this work highlights the need to carry out further research on this topic and on biocrust dynamics in general.

Clarifying the effect of the sampling protocol on the detection of environmental DNA (eDNA) is essential for appropriately designing biodiversity research. However, technical issues influencing eDNA detection in the open ocean, which consists of water masses with varying environmental conditions, have not been thoroughly investigated. This study evaluated the sampling effort for the metabarcoding‐based detection of fish eDNA using replicate sampling with filters of different pore sizes (0.22 and 0.45 μm) in the subtropical and subarctic northwestern Pacific Ocean and Arctic Chukchi Sea. The asymptotic analysis predicted that the accumulation curves for detected taxa did not saturate in most cases, indicating that our sampling effort (7 or 8 replicates, corresponding to 10.5–40 L of filtration in total) was insufficient to fully assess the species diversity in the open ocean and that tens of replicates or a substantial filtration volume were required. The Jaccard dissimilarities between filtration replicates were comparable with those between the filter types at any site. In subtropical and subarctic sites, turnover dominated the dissimilarity, suggesting that the filter pore size had a negligible effect. In contrast, nestedness dominated the dissimilarity in the Chukchi Sea, implying that the 0.22 μm filter could collect a broader range of eDNA than the 0.45 μm filter. Therefore, the effect of filter selection on the collection of fish eDNA likely varies depending on the region. These findings highlight the highly stochastic nature of fish eDNA collection in the open ocean and the difficulty of standardizing the sampling protocol across various water masses. [ABSTRACT FROM AUTHOR]

Habitat change affects both taxonomic and functional biodiversity, and beta-diversity is often used as a metric to describe these changes. Furthermore, spatially closer communities tend to have more similar species compositions (lower beta-diversity). These changes in community composition can be revealed with taxonomic and functional aspects of diversity. We assessed the responses of ant taxonomic and functional beta-diversity to changes in forest cover and spatial distance. We expected that changes in taxonomic and functional beta-diversity along a forest cover gradient would be caused by the replacement of open-habitat ant species by forest-habitat ant species. We sampled ants within twelve landscapes with different forest cover percentages in the southwestern Amazon of Brazil. Both taxonomic and functional beta-diversity of pairwise samples (βBC) were partitioned into their turnover (βBal) and nestedness (βGra) components. Increasing forest cover correlated with increases in taxonomic and functional βBC, however, βBal had a greater contribution to taxonomic βBC and βGra to functional βBC. Taxonomic βBC and βBal and functional βBal increased with spatial distance. Forest-habitat species richness increased, and open-habitat species richness decreased with increasing forest cover, while the richness of habitat-use generalist species did not vary. The loss of environmental heterogeneity may be responsible for generalist species dominance and open-habitat species presence in less-forested landscapes. This leads to great taxonomic replacement, but a nestedness gradient of function. Better land use planning is needed to ensure biodiversity and ecosystem functions of forest habitats in human-modified landscapes.

Introduction: Soil fungal network composition and stability are important for soil functions, but there is less understanding of the impact of clomazone on network complexity and stability. Methods: In this work, two agricultural soils were used to investigate the impact of clomazone on fungal network complexity, composition, and stability. The two soils were treated with clomazone solution (0, 0.8, 8, and 80 mgkg-1) and kept in an incubator. Results and Discussion: Under the influence of clomazone, the fungal network nodes were decreased by 12-42; however, the average degree was increased by 0.169-1.468 and fungal network density was increased by 0.003-0.054. The keystone nodes were significantly changed after clomazone treatment. Network composition was also impacted. Specifically, compared with control and clomazone treatments in both soils, the shared edges were fewer than 54 in all comparisons, and network dissimilarity was 0.97-0.98. These results suggested that fungal network composition was significantly impacted. The network robustness was increased by 0.0018-0.0209, and vulnerability was decreased by 0.00018-0.00059 in both soils, which indicated that fungal network stability was increased by clomazone. In addition, the functions of network communities were also changed in both soils. These results indicated that clomazone could significantly impact soil fungal networks. [ABSTRACT FROM AUTHOR]

Genetic variability in pepper genotypes has been widely assessed using phenotypic traits and molecular markers, which are essential to estimate the genetic divergence of the species and thus recommend parental individuals for breeding programs. In this scenario, this study aimed to evaluate the genetic divergence between ornamental pepper accessions (C. annuum L.) using RAPD molecular markers. Seventeen ornamental pepper accessions and one plant of the commercial cultivar Calypso were used in the study. The accessions were subjected to RAPD analysis with 15 primers. The analysis revealed high variability between the accessions and evidenced that accessions UFPB-355 and UFPB-348 were the most dissimilar, whereas accessions UFPB-443 and UFPB-77.3 were the most similar. The accessions were grouped into three different groups, evidencing their genetic divergence The accessions characterized in this study are promising and can be used in future pepper breeding programs due to their high genetic variability. Moreover, the results obtained in this study allow recommending accessions UFPB-45, UFPB-77.3, UFPB-137, UFPB-356, and UFPB-443 as parent individuals since they are the most divergent. Finally, the RAPD markers were highly sensitive in driscriminating pepper accessions and should be used as an auxiliary tool for choosing parents. [ABSTRACT FROM AUTHOR]

Alertness is an important part of attention in human development and behavior. This system allows us to have different behaviors depending on the stimuli and the environment around us. Within the area of cognitive architectures, alertness is a system that in many cases is not explicitly developed or is not addressed; for many of these architectures, attention is simply a filtering of information. In this work a model of the alert system was made taking into account the biological processes within the human brain, that is, the brain areas involved and their interconnections. This system modulates the state of alertness in which it is, depending on the circadian rhythm and auditory stimuli. The results obtained through experimentation both with humans and with different configurations of the model show that there is a similar behavior between the responses of the model and those of humans. In conclusion, the model presented can produce behavior in a manner similar to human behavior. [ABSTRACT FROM AUTHOR]

Synchronic variations in abundance in populations of the same species are common phenomena encountered in various environments, including lakes, and different taxa of freshwater fishes. This phenomenon can be caused by similar environmental conditions across physically separated populations. In the context of the ongoing climate change, it is essential to test this hypothesis, identify the factors driving the synchrony and elucidate the mechanisms, in the attempt to improve fisheries management. This study investigates synchronic variations in European whitefish (Coregonus spp.) populations in five peri-alpine lakes. The hypothesis suggests that shared biotic or abiotic factors contribute to similar trends in whitefish landings. Environmental and seasonal variables impacting the early life stages of the species were analyzed, and the Euclidean distances between the multivariate time series were calculated to identify similarities or dissimilarities in lake environmental parameters. We found that regional winter and spring temperatures were consistent across the lakes, but these factors did not fully account for variations in landings statistics. Wind intensity, water level and zooplankton abundance showed lake-specific patterns that could better explain local conditions and dynamics. Linear models did not reveal a coherent correlation with a common environmental variable across all lakes. However, distinct relationships were found in four of the lakes, with local factors significantly contributing to abundance variations. The spring abundance of Daphnia spp., a primary food source for whitefish larvae, was the main factor correlated with fish landing trends in Lake Geneva and Lake Bourget. Higher availability of Daphnia spp. may decrease intraspecific competition and density-dependent mortality. In Lake Neuchâtel, winter water temperature was negatively correlated with fish abundance proxies, suggesting that warmer winters may compromise reproduction success. Lake Annecy saw an increase in whitefish landings following a substantial reduction in fishing efforts during the late 2000s. A significant negative correlation was found between whitefish landings and fishing efforts. No relationship was found for Lake Aiguebelette, maybe due to a lack of zooplankton data. In conclusion, the observed synchrony in the European whitefish population is likely driven by a combination of interacting environmental and anthropogenic factors rather than a single common variable. Further research and a more detailed dataset are needed to better understand these complex relationships. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Family Research (JFR) is the property of University of Bamberg Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The ecology of benthic diatoms is scarce in diatom reviews, and it seems that the loss of interest in their local ecology (populations–communities) coincides with an increase in metacommunity studies. We include a review of the latter to highlight some unresolved issues. We aim to demonstrate the relevance of local population–community ecology for a better understanding of the metacommunity by addressing gaps such as the relevance of biotic relationships. We analyzed 132 assemblages of benthic diatoms from two neighboring catchments, with varying altitudes, lentic and lotic waters and substrates. Population–community features (e.g., populations' relative abundance and alpha diversity) and metacommunity descriptors (e.g., beta diversity indices) were related to likely control factors such as space, catchment features, local physico-chemistry and biotic environment. Our results confirm the relevant role of local interactions between diatoms and with the biotic environment as the mechanism in assembly communities. Moreover, abiotic habitat stability enhances alternative assemblages, which are the base of the metacommunity structure, mostly by taxa sorting and mass effects. Our results suggest that in order to better disclose factors controlling metacommunities, we must study their communities at local scales where mechanisms that explain their assemblage occur, as this is the bridge to a better understanding of benthic diatom ecology. [ABSTRACT FROM AUTHOR]

Biodiversity analyses across continental extents are important in providing comprehensive information on patterns and likely drivers of diversity. For vascular plants in Australia, community‐level diversity analyses have been restricted by the lack of a consistent plot‐based survey dataset across the continent. To overcome these challenges, we collated and harmonised plot‐based vegetation survey data from the major data sources across Australia and used them as the basis for modelling species richness (α‐diversity) and community compositional dissimilarity (β‐diversity), standardised to 400 m2, with the aim of mapping diversity patterns and identifying potential environmental drivers. The harmonised Australian vegetation plot (HAVPlot) dataset includes 219 552 plots, of which we used 115 083 to analyse plant diversity. Models of species richness and compositional dissimilarity both explained approximately one‐third of the variation in plant diversity across Australia (D2 = 33.0% and 32.7%, respectively). The strongest environmental predictors for both aspects of diversity were a combination of temperature and precipitation, with soil texture and topographic heterogeneity also important. The fine‐resolution (≈ 90 m) spatial predictions of species richness and compositional dissimilarity identify areas expected to be of particular importance for plant diversity, including south‐western Australia, rainforests of eastern Australia and the Australian Alps. Arid areas of central and western Australia are predicted to support assemblages that are less speciose or unique; however, these areas are most in need of additional survey data to fill the spatial, environmental and taxonomic gaps in the HAVPlot dataset. The harmonised data and model predictions presented here provide new insight into plant diversity patterns across Australia, enabling a wide variety of future research, such as exploring changes in species abundances, linking compositional patterns to functional traits or undertaking conservation assessments for selected components of the flora. [ABSTRACT FROM AUTHOR]

The "similarity" (gongtong 共通) and "dissimilarity" (chayi 差異) between the Buddhist precepts and Confucian rites in the Wei, Jin, and Southern and Northern Dynasties reflected a "dialogue of civilizations" (wenming duihua 文明對話) at the levels of concept, system, and life. During these periods, the Chinese system of rites were compared and interpreted with the Buddhist monastic codes by Buddhist monks and Confucian scholars, so a history of the ideas interpretation process of "interpreting precepts with rites" (yi li shi jie 以禮釋戒) was achieved. The result of such a process was two-fold: from the perspective of lay Buddhist ethics, they were in common with each other; from the perspective of monastic precepts, they were irreconcilable contradictions. Thus, on the one hand, the eminent Chinese monks "were emulating the Confucian rites to justify Buddhist precepts" (ni li yi zheng jie 擬禮義證戒) to stress their commonalities. On the other hand, the differences between the precepts and rites continued to be discovered, and the Buddhist subjective consciousness (zhuti yishi 主體意識) of "the distinction between precepts and rites" (jie li you bie 戒禮有別) was gradually established. [ABSTRACT FROM AUTHOR]

This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity to catch more anisotropic information of an image. The normalised NSST subband features are next decomposed into bit-plane slices in order to capture very fine to coarse subband details. Then each bit-plane slices of all the subbands are encoded by exploiting the dissimilarity relationship between each neighbouring pixel and its adjacent neighbours. Experiments on two computed tomography (CT) and one magnetic resonance imaging (MRI) image datasets confirms the superior results of NSST-LBNDP when compared to many recent well known relevant descriptors both in terms of average retrieval precision (ARP) and average retrieval recall (ARR).

Many biological applications are essentially pairwise comparison problems, such as evolutionary relationships on genomic sequences, contigs binning on metagenomic data, cell type identification on gene expression profiles of single-cells, etc. To make pair-wise comparison, it is necessary to adopt suitable dissimilarity metric. However, not all the metrics can be fully adapted to all possible biological applications. It is necessary to employ metric learning based on data adaptive to the application of interest. Therefore, in this study, we proposed MEtric Learning with Triplet network (MELT), which learns a nonlinear mapping from original space to the embedding space in order to keep similar data closer and dissimilar data far apart. MELT is a weakly supervised and data-driven comparison framework that offers more adaptive and accurate dissimilarity learned in the absence of the label information when the supervised methods are not applicable. We applied MELT in three typical applications of genomic data comparison, including hierarchical genomic sequences, longitudinal microbiome samples and longitudinal single-cell gene expression profiles, which have no distinctive grouping information. In the experiments, MELT demonstrated its empirical utility in comparison to many widely used dissimilarity metrics. And MELT is expected to accommodate a more extensive set of applications in large-scale genomic comparisons. MELT is available at https://github.com/Ying-Lab/MELT. [ABSTRACT FROM AUTHOR]

Guatemala has a great diversity of butterflies, although there have been few intensive surveys on Lepidoptera in the country so far. We present an updated list of 218 species in 149 genera, 19 subfamilies, and six families of butterflies sampled at two seasonally dry forests in the Salamá and Motagua valleys in central and eastern Guatemala, by integrating new data from field surveys conducted in 2014-2021 into our previously published data (Yoshimoto et al. 2018, 2019), with Amblyscirtes elissa elissa Godman, 1900, Repens florus (Godman, 1900), and Niconiades nikko Hayward, 1948 (Hesperiidae: Hesperiinae) as new country records. We collected a hairstreak species, Chalybs hassan (Stoll, 1790) (Lycaenidae: Theclinae), at the Motagua Valley site, representing the second record for Guatemala since the early 20th century, after we rediscovered it at the Salamá Valley site in 2011 and 2012 (Yoshimoto and Salinas-Gutiérrez 2015). Nymphalidae and Hesperiidae had larger numbers of species than the other four families at both sites. In Pieridae and Nymphalidae, species composition was similar between the sites, whereas in Lycaenidae, Riodinidae, and Papilionidae it differed more greatly between the sites. These results confirm the relatively high lepidopteran diversity of Guatemalan dry forests, noteworthy for the small areas that comprise the study sites, and represent marked similarities and differences in butterfly fauna and phenology within these forests. [ABSTRACT FROM AUTHOR]

• Increasing forest cover correlated with increases in taxonomic and functional beta diversity. • Species turnover had a greater contribution to taxonomic beta diversity while species loss had a great effect on functional beta diversity. • Forest-habitat species richness increased, and open-habitat species decreased with the increase in forest cover, while richness of generalist species did not vary. Habitat change affects both taxonomic and functional biodiversity, and beta-diversity is often used as a metric to describe these changes. Furthermore, spatially closer communities tend to have more similar species compositions (lower beta-diversity). These changes in community composition can be revealed with taxonomic and functional aspects of diversity. We assessed the responses of ant taxonomic and functional beta-diversity to changes in forest cover and spatial distance. We expected that changes in taxonomic and functional beta-diversity along a forest cover gradient would be caused by the replacement of open-habitat ant species by forest-habitat ant species. We sampled ants within twelve landscapes with different forest cover percentages in the southwestern Amazon of Brazil. Both taxonomic and functional beta-diversity of pairwise samples (β BC) were partitioned into their turnover (β Bal) and nestedness (β Gra) components. Increasing forest cover correlated with increases in taxonomic and functional β BC , however, β Bal had a greater contribution to taxonomic β BC and β Gra to functional β BC. Taxonomic β BC and β Bal and functional β Bal increased with spatial distance. Forest-habitat species richness increased, and open-habitat species richness decreased with increasing forest cover, while the richness of habitat-use generalist species did not vary. The loss of environmental heterogeneity may be responsible for generalist species dominance and open-habitat species presence in less-forested landscapes. This leads to great taxonomic replacement, but a nestedness gradient of function. Better land use planning is needed to ensure biodiversity and ecosystem functions of forest habitats in human-modified landscapes. [ABSTRACT FROM AUTHOR]

Background. Despite the great concern triggered by the environmental crisis worldwide, the loss of temporal key functions and processes involved in biodiversity maintenance has received little attention. Species are restricted in their life cycles by environmental variables because of their physiological and behavioral properties; thus, the timing and duration of species' presence and their activities vary greatly between species within a community. Despite the ecological relevance of such variation, there is currently no measure that summarizes the key temporal aspects of biological diversity and allows comparisons of community phenological patterns. Here, we propose a measure that synthesizes variability of phenological patterns using the Hill numbersbased attribute diversity framework. Methods. We constructed a new phenological diversity measure based on the aforementioned framework through pairwise overlapping distances, which was supplemented with wavelet analysis. The Hill numbers approach was chosen as an adequate way to define a set of diversity values of different order q, a parameter that determines the sensitivity of the diversity measure to abundance. Wavelet transform analysis was used to model continuous variables from incomplete data sets for different phenophases. The new measure, which we call Phenological Hill numbers (PD), considers the decouplings of phenophases through an overlapping area value between pairs of species within the community. PD was first tested through simulations with varying overlap in phenophase magnitude and intensity and varying number of species, and then by using one real data set. Results. PD maintains the diversity patterns of order q as in any other diversity measure encompassed by the Hill numbers framework. Minimum PD values in the simulated data sets reflect a lack of differentiation in the phenological curves of the community over time; by contrast, the maximumPDvalues reflected the most diverse simulations in which phenological curves were equally distributed over time.PDvalues were consistent with the homogeneous distribution of the intensity and concurrence of phenophases over time, both in the simulated and the real data set. Discussion. PD provides an efficient, readily interpretable and comparable measure that summarizes the variety of phenological patterns observed in ecological communities. PD retains the diversity patterns of order q characteristic of all diversity measures encompassed by the distance-based Hill numbers framework. In addition, wavelet transform analysis proved useful for constructing a continuous phenological curve. This methodological approach to quantify phenological diversity produces simple and intuitive values for the examination of phenological diversity and can be widely applied to any taxon or community's phenological traits. [ABSTRACT FROM AUTHOR]

The subject of this article is the still relevant issue of the adequate choice of comparables in evaluation procedures within the sales comparison approach. The text features the results of an experiment that makes it possible to validate the dependence of the accuracy of the final estimation of the price of valued objects in relation to the criteria of significance of similarity as a determinant of accepting individual objects as sufficiently similar, and the dissimilarity coefficients describing the created reference sets. Based on the example of simulated data, it has been shown that there is a clear relationship between the accuracy of the estimates made and the selection of comparables. In a set of all possible calculation variations, estimations based on selected data are characterized by more accurate calculations relative to parallel calculations carried out on the widest possible sets of comparables.

The objective of the present work was to evaluate the influence of the genotype–environment interaction on genetically divergent soybean grown under semiarid conditions. Four experiments were carried out in randomized blocks with four replicates to evaluate twenty-one soybean genotypes. The following descriptors were used to quantify divergence: plant height, dry matter, oil content, number of pods per plant, number of grains per pod, the weight of 100 seeds, yield, days for flowering, and days for maturation. The unweighted pair group method with arithmetic mean was used to group the genotypes from the Mahalanobis distance matrix estimated using the genotypic means estimated by the REML/BLUP method. The grouping of genotypes depended mainly on the effects of the interaction between genotypes and years. The joint analysis, without the effect of the interaction, allowed us to obtain two groups of genotypes. The most recommended crosses were those of the lines BRS Tracajá, BRS Pérola, BRS Carnaúba, M 8644 IPRO, BRS 8590, and BMX OPUS IPRO with the genotype BRS Sambaíba, especially the one between BMX OPUS IPRO and BRS Sambaíba. Highlights • The grouping of genotypes is dependent on the evaluation conditions, mainly due to the effects of the genotype x years interaction. • The use of estimates of the free genotypic means of the interaction obtained in the joint analysis may be a more adequate alternative to define the heterotic groups and, consequently, the crossings to be performed. • The joint analysis, without the presence of the interaction effect, allows obtaining two groups of genotypes.

Identifying relationships between entities in data is a central topic across various industries and businesses, from social networks to supply chain and heavy manufacturing industries. In this paper we present data from a database of machinery represented in terms of machine parts. The machine parts are originally organised in tree structures where the vertices are machine part types, and the edges are “part-of” relations. Hence, each tree represents a type of machinery broken down into its machine part constituent types. The data we present is the union over these trees, making up a directed acyclic graph describing the type hierarchy of the machine parts.The motivation for publishing the dataset is the following real-world industry problem: Each tree represents a mechanical design, and over time some designs have been copy-pasted with minor modifications. The new instances have been given new identifiers with no reference to where from they were copied. In hindsight, it is desirable to recover the copy-paste links to for interchange between essentially identical designs. However, telling which parts are copies of which other parts has turned out to be difficult. In particular, the metadata has a tendency of displaying higher similarities within a composite part than between a part and its copy. Due to non-disclosure, we cannot provide the metadata, but we provide element wise dissimilarities that are generated based on the metadata using classical methods such as Jaccard similarity on description texts, material types etc. The dissimilarities are obtained from a data science project in the company owning the data, trying to tackle the very problem of recovering the copy-paste links.Availability of labeled data on this data set is limited, so based on our in-depth knowledge of the problem domain, we present a data synthesisation method that can generate arbitrarily large problem instances of the copy-paste problem based on the sample data, that provides a realistic representation of the real world problem. The problems are presented as planted partitions of vertices of directed acyclic graphs with vertex dissimilarities, and thus constitutes a typical classification problem along the lines of graph- or network clustering.The type of industry data we present is usually company confidential, bound by intellectual property rights, and generally not available to scientists. We therefore publish this anonymised dataset to offer real world sample data and generated problem instances for researchers that are interested in this type of classification problems, and on which theories and algorithms can be tested.The data and the problem generation methodology are backed by a Python implementation, providing both data access and an API for parameterised problem generation. The data is also available as raw files.

Background Despite the great concern triggered by the environmental crisis worldwide, the loss of temporal key functions and processes involved in biodiversity maintenance has received little attention. Species are restricted in their life cycles by environmental variables because of their physiological and behavioral properties; thus, the timing and duration of species’ presence and their activities vary greatly between species within a community. Despite the ecological relevance of such variation, there is currently no measure that summarizes the key temporal aspects of biological diversity and allows comparisons of community phenological patterns. Here, we propose a measure that synthesizes variability of phenological patterns using the Hill numbers-based attribute diversity framework. Methods We constructed a new phenological diversity measure based on the aforementioned framework through pairwise overlapping distances, which was supplemented with wavelet analysis. The Hill numbers approach was chosen as an adequate way to define a set of diversity values of different order q, a parameter that determines the sensitivity of the diversity measure to abundance. Wavelet transform analysis was used to model continuous variables from incomplete data sets for different phenophases. The new measure, which we call Phenological Hill numbers (PD), considers the decouplings of phenophases through an overlapping area value between pairs of species within the community. PD was first tested through simulations with varying overlap in phenophase magnitude and intensity and varying number of species, and then by using one real data set. Results PD maintains the diversity patterns of order q as in any other diversity measure encompassed by the Hill numbers framework. Minimum PD values in the simulated data sets reflect a lack of differentiation in the phenological curves of the community over time; by contrast, the maximum PD values reflected the most diverse simulations in which phenological curves were equally distributed over time. PD values were consistent with the homogeneous distribution of the intensity and concurrence of phenophases over time, both in the simulated and the real data set. Discussion PD provides an efficient, readily interpretable and comparable measure that summarizes the variety of phenological patterns observed in ecological communities. PD retains the diversity patterns of order q characteristic of all diversity measures encompassed by the distance-based Hill numbers framework. In addition, wavelet transform analysis proved useful for constructing a continuous phenological curve. This methodological approach to quantify phenological diversity produces simple and intuitive values for the examination of phenological diversity and can be widely applied to any taxon or community’s phenological traits.

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)