Parameter estimate for three-parameter kappa distribution using LHmoments approach

The method of higher-order L-moments (LH-moment) was proposed as a more robust alternative compared to classical L-moments to characterize extreme events. The new derivation will be done for Mielke-Johnson’s Kappa and Three-Parameters Kappa Type-II (K3D-II) distributions based on the LH-moments approach. The data of maximum monthly rainfall for Embong station in Terengganu were used as a case study. The analyses were conducted using the classical L-moments method with η=0 and LH-moments methods with η=1, η=2, η=3 and η=4 for a complete data series and upper parts of the distributions. The most suitable distributions were determined based on the Mean Absolute Deviation Index (MADI), Mean Square Deviation Index (MSDI), and Correlation (r). Also, L-moment and LH-moment ratio diagrams were used to represent visual proofs of the results. The analysis showed that LH-moments methods at a higher order of K3D-II distribution best fit the data of maximum monthly rainfalls for the Embong station for the upper parts of the distribution compared to L-moments. The results also proved that whenever η increases, LH-moments reflect more and more characteristics of the upper part of the distribution. This seems to suggest that LH-moments estimates for the upper part of the distribution events are superior to L-moments in fitting the data of maximum monthly rainfalls.

Long-Term Changes in Ionospheric Climate in Terms of foF2

There is not only space weather; there is also space climate. Space climate includes the ionospheric climate, which is affected by long-term trends in the ionosphere. One of the most important ionospheric parameters is the critical frequency of the ionospheric F2 layer, foF2, which corresponds to the maximum ionospheric electron density, NmF2. Observational data series of foF2 have been collected at some stations for as long as over 60 years and continents are relatively well covered by a network of ionosondes, instruments that measure, among others, foF2. Trends in foF2 are relatively weak. The main global driver of long-term trends in foF2 is the increasing concentration of greenhouse gases, namely CO2, in the atmosphere. The impact of the other important trend driver, the secular change in the Earth’s main magnetic field, is very regional, being positive in some regions, negative in others, and neither in the rest. There are various sources of uncertainty in foF2 trends. One is the inhomogeneity of long foF2 data series. The main driver of year-to-year changes in foF2 is the quasi-eleven-year solar cycle. The removal of its effect is another source of uncertainty. Different methods might provide somewhat different strengths among trends in foF2. All this is briefly reviewed in the paper.

Model-free detection of unique events in time series

Recognition of anomalous events is a challenging but critical task in many scientific and industrial fields, especially when the properties of anomalies are unknown. In this paper, we introduce a new anomaly concept called “unicorn” or unique event and present a new, model-free, unsupervised detection algorithm to detect unicorns. The key component of the new algorithm is the Temporal Outlier Factor (TOF) to measure the uniqueness of events in continuous data sets from dynamic systems. The concept of unique events differs significantly from traditional outliers in many aspects: while repetitive outliers are no longer unique events, a unique event is not necessarily an outlier; it does not necessarily fall out from the distribution of normal activity. The performance of our algorithm was examined in recognizing unique events on different types of simulated data sets with anomalies and it was compared with the Local Outlier Factor (LOF) and discord discovery algorithms. TOF had superior performance compared to LOF and discord detection algorithms even in recognizing traditional outliers and it also detected unique events that those did not. The benefits of the unicorn concept and the new detection method were illustrated by example data sets from very different scientific fields. Our algorithm successfully retrieved unique events in those cases where they were already known such as the gravitational waves of a binary black hole merger on LIGO detector data and the signs of respiratory failure on ECG data series. Furthermore, unique events were found on the LIBOR data set of the last 30 years.

Forecasting mean monthly maximum and minimum air temperature of Jalandhar district of Punjab, India using seasonal ARIMA model

The long-term air temperature data series from 1971-2019 was analyzed and used for forecasting mean monthly air temperature at the district level. The Augmented Dickey-Fuller test, Kwiatkowski-Phillips-Schmidt-Shin test, and Mann-Kendall test were employed to test the stationarity and trend of the time series. The mean monthly maximum air temperature did not show any significant variation while an increasing trend of 0.04°C per annum was observed in mean monthly minimum air temperature, which was detrended. Box-Jenkins autoregressive integrated moving–averages were used to forecast the forthcoming 5 years (2020-2024) air temperature in the district Jalandhar of Punjab. The goodness of fit was tested against residuals, the autocorrelation function, and the histogram. The fitted model was able to capture dynamics of the time series data and produce a sensible forecast.

A review of the impact of mining operations on sustainable development

Global annual extraction has increased due to market demand. It is anticipated that this aspect will continue in the future. The mining industry is one of the important industries at national level. It also anticipates certain aspects of sustainability that must be seriously evaluated. The main objective of this paper is to evaluate mine waste reuse, pollution, and recycling in mining industries. At the same time, mining operations are evaluated to carry out a review of the implications for sustainability. In order to achieve the research aspects, a qualitative evaluation and a semi-quantitative evaluation of some data series are used. For the entire research approach, the objectives of sustainable development and its principles are used. At the same time, the results emphasize the importance of the existence of an efficient waste and pollution management.

A Clinical Prognostic Model Based on Machine Learning from the Fondazione Italiana Linfomi (FIL) MCL0208 Phase III Trial

Background: Multicenter clinical trials are producing growing amounts of clinical data. Machine Learning (ML) might facilitate the discovery of novel tools for prognostication and disease-stratification. Taking advantage of a systematic collection of multiple variables, we developed a model derived from data collected on 300 patients with mantle cell lymphoma (MCL) from the Fondazione Italiana Linfomi-MCL0208 phase III trial (NCT02354313). Methods: We developed a score with a clustering algorithm applied to clinical variables. The candidate score was correlated to overall survival (OS) and validated in two independent data series from the European MCL Network (NCT00209222, NCT00209209); Results: Three groups of patients were significantly discriminated: Low, Intermediate (Int), and High risk (High). Seven discriminants were identified by a feature reduction approach: albumin, Ki-67, lactate dehydrogenase, lymphocytes, platelets, bone marrow infiltration, and B-symptoms. Accordingly, patients in the Int and High groups had shorter OS rates than those in the Low and Int groups, respectively (Int→Low, HR: 3.1, 95% CI: 1.0–9.6; High→Int, HR: 2.3, 95% CI: 1.5–4.7). Based on the 7 markers, we defined the engineered MCL international prognostic index (eMIPI), which was validated and confirmed in two independent cohorts; Conclusions: We developed and validated a ML-based prognostic model for MCL. Even when currently limited to baseline predictors, our approach has high scalability potential.

Índice de Tendências Climáticas de Longo Prazo em Área Urbana na Amazônia Oriental

Esta pesquisa tem como objetivo analisar a variabilidade e a tendência de variáveis meteorológicas no longo prazo para caracterizar o clima urbano da cidade de Macapá-AP. Compreender a variabilidade dos índices climáticos em ambientes urbanos tende a mostrar possíveis interferências na qualidade de vida dos moradores locais, bem como torna possível comparar a realidade das cidades amazônicas em um contexto regional, nacional e mundial, contribuindo ao debate acadêmico. No presente caso as variáveis-chave são a temperatura do ar e a precipitação pluviométrica. A metodologia consiste nas seguintes etapas: a) coleta e consistência da série de dados por um período contínuo de 52 anos para o Estado do Amapá (1968 – 2020), b) a utilização do aplicativo RClimDex 1.1/IPCC para estimar as variações e as tendências climáticas locais utilizando-se 27 parâmetros climáticos extremos previstos pela equipe de peritos do CCI/CLIVAR e Climate Change Detection Monitoring and Indices (ETCCDMI). Os resultados obtidos acusaram treze indicadores estatísticos significativos (p<0,05), sugerindo tendência generalizada da elevação da temperatura média do ar na zona urbana da cidade. Como consequência, estes indicadores mostraram não somente uma significativa elevação das temperaturas máximas, médias e mínimas, mas também quais são os indicadores mais coerentemente associados com tendências de aquecimento temporal de cidades amazônicas, tanto para períodos diurnos quanto para períodos noturnos. Esse comportamento dos indicadores confirma a hipótese de predisposição a formação de ilha de calor em Macapá. Esta tendência mudou significativamente a partir de 2010. Index of Long Term Climate Trends in Urban Area in the Eastern AmazonA B S T R A C T This research aims to analyze the variability and trend of meteorological variables in the long term to characterize the urban climate of the city of Macapá-AP. Understanding the variability of climate indices in urban environments reveals possible interferences in the quality of life of the inhabitants, especially in urban locations. However, it has been relatively difficult to quantify trends in historical series that reliably represent climate indices relevant to the reality of Amazonian cities, both at a local and regional level. In the present case, the key variables analyzed were air temperature and rainfall. The methodology followed the following steps: a) collection and consistency of the data series over a continuous period of 52 years for the State of Amapá (1968–2020), b) using the data series by the RClimDex 1.1/IPCC application to estimate the local climate variations and trends using 27 extreme weather parameters predicted by the CCI/CLIVAR and Climate Change Detection Monitoring and Indices (ETCCDMI) team of experts. The results showed thirteen significant statistical indicators (p<0.05), suggesting a general trend towards an increase in the average air temperature in the urban area of the city. As a consequence, these indicators showed not only a significant increase in maximum, average and minimum temperatures, but also the indicators most coherently associated with temporal warming trends in Amazonian cities. So many that these effects seem to affect both the day and night periods, confirming the hypothesis of a predisposition to the formation of an urban heat island, with a significant change in this trend from 2010 onwards. Keywords: RClimDex 1.1, climate change indice, Macapá, Amapá

Complexing the hydraulic fracturing simulation results when hybrid acid-propant treatment performing and with the simultaneous hydraulic fracture initiation in separated intervals

The purpose of the work is to substantiate and formulate the principles of data generation with multiple results of hydraulic fracturing (HF) modeling. Qualitative data for assessment, intercomparison and subsequent statistical analysis are characterized by a single numerical value for each considered hydraulic fracturing parameter. For a number of hydraulic fracturing technologies, uncertainty may arise due to obtaining several values for the parameter under consideration. The scientific novelty of the work lies in the substantiation of a new approach for evaluating the obtained data series during hydraulic fracturing modeling. A number of data can be obtained both during the formation and modeling of several hydraulic fractures, and for one fracture when calculating in different modules of the simulator. As a result, an integration technique was developed that allows forming a uniform data array regardless of the number of elements in the hydraulic fracturing modeling results. Keywords: hydraulic fracturing; acid-proppant hydraulic fracturing; hydraulic fracturing of layered rocks; hydraulic fracturing modeling; pseudo-three-dimensional fracture model; data preparation; statistical analysis.

Decadal trends in climate over India

Decadal variations of meteorological parameters, vig, temperature (surface air maximum temperature, minimum temperature and upper air up to middle troposphere), station level pressure and seasonal and annual rainfall are studied for the period 1901 to 1986 (upper air data available from 1951 onwards), Tests of significance applied to data series (stationwise as well as country as a whole) show that the temperatures are showing a decreasing trend in almost all the northern parts of the country (north of 23" N) and a rising trend in southern parts (south of 23"N), For the country as a whole, however, there is a small warming trend Atmospheric pressure shows a fall between second and third decades but does not indicate any significant change after 1930, Decadal analysis of seasonal (Jun-Sep) and annual rainfall indicates that the variations in rainfall are within the statistical limits.