Types Of Distribution Data Research Articles

Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue

This article aims <span lang="EN-US">to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved.<br /> A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles.</span>

Sampling and modelling rare species: Conceptual guidelines for the neglected majority.

Biodiversity conservation faces a methodological conundrum: Biodiversity measurement often relies on species, most of which are rare at various scales, especially prone to extinction under global change, but also the most challenging to sample and model. Predicting the distribution change of rare species using conventional species distribution models is challenging because rare species are hardly captured by most survey systems. When enough data are available, predictions are usually spatially biased towards locations where the species is most likely to occur, violating the assumptions of many modelling frameworks. Workflows to predict and eventually map rare species distributions imply important trade-offs between data quantity, quality, representativeness and model complexity that need to be considered prior to survey and analysis. Our opinion is that study designs need to carefully integrate the different steps, from species sampling to modelling, in accordance with the different types of rarity and available data in order to improve our capacity for sound assessment and prediction of rare species distribution. In this article, we summarize and comment on how different categories of species rarity lead to different types of occurrence and distribution data depending on choices made during the survey process, namely the spatial distribution of samples (where to sample) and the sampling protocol in each selected location (how to sample). We then clarify which species distribution models are suitable depending on the different types of distribution data (how to model). Among others, for most rarity forms, we highlight the insights from systematic species-targeted sampling coupled with hierarchical models that allow correcting for overdispersion and spatial and sampling sources of bias. Our article provides scientists and practitioners with a much-needed guide through the ever-increasing diversity of methodological developments to improve the prediction of rare species distribution depending on rarity type and available data.

The correlation between creatinine serum with total iron binding capacity (TIBC) on patients with sufferers chronic renal disease in Gambiran regional public hospital Kediri city

Chronic kidney disease (CKD) is kidney damage that occurs for more than 3 months with a glomerular filtration rate of less than 60 ml/sec/ 1.73 m2. WHO estimates that in Indonesia, there will be an increase in PGK in 1995-2025 by 41.4%. Over the past 40 years, Creatinine serum has become the most common and cheap serum marker for kidney function. One of the most common complications in patients with PGK is anemia. Anemia in PGK can be caused by several factors such as EPO deficiency, iron deficiency, etc. One of the usual parameters checking is the iron status of TIBC. This study aims to determine the relationship of serum Creatinine level with TIBC in chronic kidney patients in Gambiran regional public hospital Kediri and sampling taken by quota sampling. The study used cross-sectional using Rank Spearman statistic test. This test is used for abnormally distributed data types. The measured variable was serum Creatinine level with TIBC. The results of this study indicate that the value of p = (0.000)> α = (0.05), so H1 rejected and H0 accepted, then there is no relationship between serum Creatinine levels with TIBC in patients with chronic kidney disease in Gambiran Hospital Kediri.

Delta state replicated data types

Conflict-free Replicated Data Types (CRDTs) are distributed data types that make eventual consistency of a distributed object possible and non ad-hoc. Specifically, state-based CRDTs ensure convergence through disseminating the entire state, that may be large, and merging it to other replicas. We introduce Delta State Conflict-Free Replicated Data Types (δ-CRDT) that can achieve the best of both operation-based and state-based CRDTs: small messages with an incremental nature, as in operation-based CRDTs, disseminated over unreliable communication channels, as in traditional state-based CRDTs. This is achieved by defining δ-mutators to return a delta-state, typically with a much smaller size than the full state, that to be joined with both local and remote states. We introduce the δ-CRDT framework, and we explain it through establishing a correspondence to current state-based CRDTs. In addition, we present an anti-entropy algorithm for eventual convergence, and another one that ensures causal consistency. Finally, we introduce several δ-CRDT specifications of both well-known replicated datatypes and novel datatypes, including a generic map composition.

Incorporating spatial population structure in gap analysis reveals inequitable assessments of species protection

AbstractAimGap analysis measures the percentage of protected species distribution and generally compares it to a representation target (i.e. minimum area to be protected). The results are used to identify species that need further protection, providing a quantitative baseline to address a possible expansion of a given protected area (PA) systems. However, the achievement of the same representation target may have different implications in terms of species persistence depending on population spatial structure and conservation needs. The aim was to investigate to what extent and why gap analysis may provide inequitable assessments of species protection.LocationEurope.MethodsWe performed three gap analyses on 27 European species of carnivores and ungulates, measuring the level of protection according to three different types of distribution data: geographical ranges, habitat suitability models and habitat suitability models that incorporate the potential spatial structuring in populations within PAs.ResultsThe estimated degree of species protection depends on the distribution proxy and the target adopted. When the analyses are based on areas able to support viable populations (irrespective of how these areas are quantified), the perceived relative protection of different species changes considerably. The ability of different species to persist in PA systems mostly depends on their population density and dispersal abilities, as well as the interaction between these two features, which eventually determines the number and relative size of the populations.Main conclusionsThe achievement of the same representation target for different species may imply protecting different numbers of individuals in populations having different spatial structures and may consequently lead to different probabilities of persistence across species. If species spatial structuring is disregarded, gap analysis may thus lead to inequitable assessments of PA coverage.

Fine-scale sites of global conservation importance in the Atlantic forest of Brazil

The Brazilian Atlantic Forest is a highly threatened biodiversity hotspot that has been the subject of several complementary conservation assessments and priority-setting initiatives in the last 30 years. Results of these initiatives have relied on distinct types of distribution data for biodiversity features and differ in the objectivity and repeatability of their methodologies. Here we refine earlier priority-setting exercises using the key biodiversity areas (KBA) approach. We evaluate how well these KBAs are represented in the existing protected areas system, prioritize among them, and analyze critical aspects of the KBA methodology in the Brazilian Atlantic Forest context, such as its ability to guide specific conservation strategies. Building upon an extensive database with 1,636 species records and 122 previously identified Important Bird Areas, we demonstrate that conservation assessments in highly fragmented landscapes may be benefited by high resolution species data as is required by the KBA process. We identify 538 KBAs for 141 globally threatened vertebrate species. Prioritizing among these KBA, we highlight the 24 most irreplaceable sites for terrestrial vertebrate species conservation in the Atlantic Forest, based on existing data.

Identifying Conservation Areas on the Basis of Alternative Distribution Data Sets

Distribution data on biodiversity features is a major component of conservation planning that are often inaccurate; thus, the true distribution of each feature is commonly over- or underrepresented. The selection of distribution data sets may therefore lead to variability in the spatial configuration and size of proposed reserve networks and uncertainty regarding the extent to which these networks actually contain the biodiversity features they were identified to protect. Our goals were to investigate the impact on reserve selection of choosing different distribution data sets and to propose novel methods to minimize uncertainty about target attainment within reserves. To do so, we used common prioritization methods (richness mapping, systematic reserve design, and a novel approach that integrates multiple types of distribution data) and three types of data on the distribution of mammals (predicted distribution models, occurrence records, and a novel combination of the two) to simulate the establishment of regional biodiversity reserves for the state of Arizona (U.S.A.). Using the results of these simulations, we explored variability in reserve placement and size as a function of the distribution data set. Spatial overlap of reserve networks identified with only predicted distribution data or only occurrence distribution data never exceeded 16%. In pairwise comparisons between reserves created with all three types of distribution data, overlap never achieved 50%. The reserve size required to meet conservation targets also varied with the type of distribution data used and the conservation goal; the largest reserve system was 10 times the smallest. Our results highlight the impact of employing different types of distribution data and identify novel tools for application to existing distribution data sets that can minimize uncertainty about target attainment.

Simulating the effects of using different types of species distribution data in reserve selection

In a perfect world, systematic conservation planning would use complete information on the distribution of biodiversity. However, information on most species is grossly incomplete. Two main types of distribution data are frequently used in conservation planning: observed and predicted distribution data. A fundamental question that planners face is – which kind of data is better under what circumstances? We used simulation procedures to analyse the effects of using different types of distribution data on the performance of reserve selection algorithms in scenarios using different reserve selection problems, amounts of species distribution known, conservation targets and costs. To compare these scenarios we used occurrence data from 25 amphibian and 41 reptile species of the Iberian Peninsula and assumed the available data represented the whole truth. We then sampled fractions of these data and either used them as they were, or converted them to modelled predicted distributions. This enabled us to build three other types of species distribution data sets commonly used in conservation planning: “predicted”, “transformed predicted” and “mixed”. Our results suggest that reserve selection performance is sensitive to the type of species distribution data used and that the most cost-efficient decision depends most on the reserve selection problem and on how much we have of the species distribution data. Choosing the most appropriate type of distribution data should start by evaluating the scenario circumstances. While there is no one best approach for every scenario, we discovered that using a mixed approach usually provides an acceptable compromise between species representation and cost.