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This research demonstrated a Geographic Information System (GIS) of licensed fish farms in Rondônia state, Brazil. Based on structuring of the GIS, spatial analyzes of location and distribution of fish farms were carried out in relation to highway network; to drainage; to microregions of Rondônia and the verification of the density. Methodological procedure consisted of modeling the Database (DB), whose information was obtained from Secretaria do Estado de Rondônia para Desenvolvimento Ambiental (SEDAM/RO), which holds the references of licensed fish farms processed in SPRING and ARCGIS 9 Arcmap 9.3 software. For spatial statistics, the Kernel density estimator was applied. The main result is the fact that GIS made it quick and easy to search for data and information about the fish farms studied. The highest density was 4937.64 fish farms per unit area in Ji-Paraná microregion, which is located in the Central region of Rondônia state. In thematic mapping, the fish farms showed some spatial dependencies, as follows: I – They depend on main access, highway BR 364. II – The cluster of fish farms is arranged where there is greater availability of water, that is, they depend on water courses. Therefore, positioning and distribution of fish farms take place in the three main microregions, Ji-Paraná 40.30% of licensed fish farms, followed by microregions of Cacoal 16.02% and Ariquemes 15.87%.

Copula modeling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametric density estimator, the kernel estimator is not suitable for the unit-square-supported copula densities, mainly because it is heavily affected by boundary bias issues. In addition, most common copulas admit unbounded densities, and kernel methods are not consistent in that case. In this paper, a kernel-type copula density estimator is proposed. It is based on the idea of transforming the uniform marginals of the copula density into normal distributions via the probit function, estimating the density in the transformed domain, which can be accomplished without boundary problems, and obtaining an estimate of the copula density through back-transformation. Although natural, a raw application of this procedure was, however, seen not to perform very well in the earlier literature. Here, it is shown that, if combined with local likelihood density estimation methods, the idea yields very good and easy to implement estimators, fixing boundary issues in a natural way and able to cope with unbounded copula densities. The asymptotic properties of the suggested estimators are derived, and a practical way of selecting the crucially important smoothing parameters is devised. Finally, extensive simulation studies and a real data analysis evidence their excellent performance compared to their main competitors.

Given random samples drawn i.i.d. from a probability measure ℙ (defined on say, ℝd), it is well-known that the empirical estimator is an optimal estimator of ℙ in weak topology but not even a consistent estimator of its density (if it exists) in the strong topology (induced by the total variation distance). On the other hand, various popular density estimators such as kernel and wavelet density estimators are optimal in the strong topology in the sense of achieving the minimax rate over all estimators for a Sobolev ball of densities. Recently, it has been shown in a series of papers by Giné and Nickl that these density estimators on ℝ that are optimal in strong topology are also optimal in || · ||ℱ for certain choices of ℱ such that || · ||ℱ metrizes the weak topology, where ||ℙ||ℱ ≔ sup{∫ f dℙ: f ∈ ℱ]. In this paper, we investigate this problem of optimal estimation in weak and strong topologies by choosing ℱ to be a unit ball in a reproducing kernel Hubert space (say ℱH defined over ℝd), where this choice is both of theoretical and computational interest. Under some mild conditions on the reproducing kernel, we show that $\\parallel \\cdot {\\parallel _{{F_H}}}$ metrizes the weak topology and the kernel density estimator (with L¹ optimal bandwidth) estimates ℙ at dimension independent optimal rate of n-1/2 in $\\parallel \\cdot {\\parallel _{{F_H}}}$ along with providing a uniform central limit theorem for the kernel density estimator.

Germination and emergence assays represent the most notable examples of time-to-event data in agriculture and related disciplines. In spite of the peculiar characteristics of this type of data, there has been little effort to establish a specific and comprehensive framework for their analyses. Indeed, a brief survey of the literature shows that germination and emergence data, along with other phenological measurements such as flowering time, have been analyzed through myriad approaches, giving rise to confusion and uncertainty among scientists and practitioners as to what may represent the best statistical practice. This lack of coherence in statistical approach may reduce the efficiency of research, while making the communication of results and the cross-study comparisons extremely challenging. Here, we attempt to provide a coherent framework and protocol for the analyses of germination/emergence and other time-to-event data in weed science and related disciplines, together with a software implementation in the form of a new R package. We propose a similar approach to biological assays in ecotoxicology, based on: (1) fitting a time-to-event model to describe the whole time course of events; (2) comparing time-to-event curves across experimental treatments, and (3) deriving further information from the fitted model to better focus on some traits of interest. The most appropriate methods to accomplish this procedure were carefully selected from the framework of survival analysis and related sources and were modified to comply with the specific needs of weed, seed, and plant sciences. Finally, they were implemented in the new R package drcte. In this article, we describe the procedure and its limitations by way of providing examples of several types of germination/emergence assays. We highlight that our proposed procedure can also serve as the first step of data analyses, with its output subsequently submitted to traditional or meta-analytic approaches.

Although it requires simple computations, provides good performance on linear classification tasks and offers a suitable environment for active learning strategies, the Hebbian learning rule is very sensitive to how the training data relate to each other in the input space. Since this spatial arrangement is inherent to each set of samples, the practical application of this learning paradigm is limited. Thus, representation learning may play an important role in projecting the input data into a new space where linear separability is improved. Earlier methods based on orthogonal coding addressed this issue but presented many side effects, impoverishing the generalization of the model. Hence, this paper considers a recently proposed method based on kernel density estimators, which performs a likelihood-based projection where linear separability and generalization capacity are enhanced in an autonomous fashion. Results show that this novel method allows one to use linear classifiers to solve many binary classification problems and overcome the performance of well-established classifiers.

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short‐lived in practice. Thus, SWC is neither designed to capture transient dynamic changes nor heterogeneity across subjects/time. We propose a state‐space time series summarization framework called “statelets” to address these shortcomings. It models functional connectivity dynamics at fine‐grained timescales, adapting time series motifs to changes in connectivity strength, and constructs a concise yet informative representation of the original data that conveys easily comprehensible information about the phenotypes. We leverage the earth mover distance in a nonstandard way to handle scale differences and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC of patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. Statelets in the HC group show an increased recurrence across the dFNC time‐course compared to the SZ. Analyzing the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced approach also enables handling dynamic information in cross‐modal and multimodal applications to study healthy and disordered brains. We proposed a novel method for analyzing dynamic functional connectivity via extracting high‐frequency texture from the connectivity space. The analysis of those motifs enables measuring the characteristics of brain circuitry and network organization. The experiments don't he summary motifs facilitate the observation of distinguishing connectivity signatures and the interplay among the hubs to process information

Renewable energy plays a key role in reducing greenhouse gas emissions. However, the expansion of wind farms has raised concerns about risks for bird collisions. We tested different methods used to understand whether birds’ flight occurs over wind turbines and found kernel density estimators outperform other methods. Previous studies using kernel utilization distribution (KUD) have considered only the 2 horizontal dimensions (2D). However, if altitude is ignored, an unrealistic depiction of the situation may result because birds move in 3 dimensions (3D). We quantified the 3D space use of the Griffon vulture (Gyps fulvus) in El Estrecho natural park in Tarifa (southern Spain, on the northern shore of the Strait of Gibraltar) during 2012–2013, and, for the first time, their risk of collision with wind turbines in an area in the south of Spain. The 2D KUD showed a substantial overlap of the birds’ flight paths with the wind turbines in the study area, whereas the 3D kernel estimate did not show such overlap. Our aim was to develop a new approach using 3D kernel estimation to understand the space use of soaring birds; these are killed by collision with wind turbines more often than any other bird types in southern Spain. We determined the probability of bird collision with an obstacle within its range. Other potential application areas include airfields, plane flight paths, and tall buildings.

Jaguars (Panthera onca) and pumas (Puma concolor) coexist throughout the Neotropics. Using camera trapping in four Brazilian biomes, we compare the daily activity patterns of the jaguar and puma, and their relationships with their main prey species. We used a kernel density method to quantify daily activity patterns and to investigate overlap between these predators and their main prey. Both cats showed intensive nocturnal and crepuscular activity (0.69 and 0.14 kernel density, respectively, for jaguars; 0.68 and 0.19 kernel density, respectively, for pumas). Only in the Pantanal did we observe a pattern of concentrated diurnal activity for both species. We found little temporal segregation between jaguars and pumas, as they showed similar activity patterns with high coefficients of overlapping (average Δ1 = 0.86; SE = 0.15). We also observed a significant overlap between the activity patterns of the predators and their main prey species, suggesting that both predators adjust their activity to reduce their foraging energy expenditure. Our findings suggest that temporal partitioning is probably not a generalized mechanism of coexistence between jaguars and pumas; instead, the partitioning of habitat/space use and food resources may play a larger role in mediating top predator coexistence. Knowledge about these behavior aspects is crucial to elucidating the factors that enable coexistence of jaguars and pumas. Furthermore, an understanding of their respective activity periods is relevant to management and associated research efforts.

This study investigates the changes in precipitation and evaporation patterns and their impact on Climatic Water Balance and river discharges in central Poland from 1961 to 2020. The analysis focuses on two “normal” periods, 1961–1990 and 1991–2020 (according to the World Meteorological Organization). Bartlett’s test and the Kruskal–Wallis rank sum test were used to assess the homogeneity of variances and compare distributions of analyzed variables over two “normal” periods. The probability density functions were estimated using a kernel density estimator with a Gaussian kernel function. Significant findings indicate alterations in evaporation rates and shifts in water balance dynamics. Mean evaporation increased from 530.8 to 637.9 mm, leading to a notable decrease in the mean Climatic Water Balance from 1.1 to − 107.5 mm (in the periods 1961–1990 and 1991–2020, respectively). Additionally, rivers showed reduced mean annual discharges (from 4.28 to 3.01 m3·s−1 and 1.25 to 0.87 m3·s−1, for the Zgłowiączka and Skrwa Lewa rivers, respectively). These climatic changes in central Poland have substantial implications for regional water resources, especially in spring and summer and particularly in agricultural areas, potentially exacerbating drought conditions and impacting agricultural productivity mainly in the warm half-year.