Статистический анализ и моделирование изменчивости качества сточных вод в системе производственного водоотведения

This article presents results of the study on economic and statistical justification for improvement of water and environmental management of an industrial enterprise. As a main tool the authors applied - was the method for modeling time series using stationary stochastic processes. The models of the integrated auto-regression and moving average, seasonally adjusted were used as the base. The models of fractionally integrated processes and models of autoregressive conditional heteroskedasticity were tested to reflect the long memory in time series of indicators. Analysis of dynamic links was based on vector autoregression model. The authors demonstrated that for all the analyzed indicators of pollution, along with the apparent lack of mid-level trend, there is a considerable variability of values, which manifested in both annual and non-seasonal cyclical and structural changes. The longstanding interrelations between the individual indicators were revealed - for most of them the damping effect of a single excess discharge of any other indicator lasted for at least a year. The article proves sufficiency of the applied econometric tools which have determined the possibility for reliable forecasting the wastewater quality along with optimization of the measures for preventing excessive discharges. Identifying the character of the periodicity of the discharges with account to seasonality, as well as the synergistic effect of contamination indicated the possibility of increasing the efficiency of water treatment process by selecting the optimum costs. The identification of the inertia of the processes of pollution of individual indicators, testified to their possible aggregation from different sources to the necessity of strengthening of control over wastewater discharges for each anthropogenic source and the natural background contamination. Determining the dynamic interrelations between the individual polluters justified a reasonable opportunity to improve the pool cleanability with regard to the structure and duration of those relations.Представлены результаты исследования по решению задачи экономико-статистического обоснования совершенствования водно-экологического менеджмента промышленного предприятия. В качестве основного инструментария в работе использовалась методика моделирования временных рядов с помощью стационарных случайных процессов. Базовыми моделями являлись модели интегрированных процессов авторегрессии и скользящего среднего с учетом сезонности. Для отражения длинной памяти во временных рядах показателей тестировались модели дробно интегрированных процессов, а также модели авторегрессии с условной гетероскедастичностью. Анализ динамических связей производился на основе модели векторной авторегрессии. Обнаружено, что для всех анализируемых показателей загрязнения, наряду с явным отсутствием тренда среднего уровня, имеет место большая вариабельность значений, проявляющаяся как в годовой, так и несезонной цикличности. Выявлены долговременные связи между отдельными показателями, в частности для большинства из них затухание влияния единичного сверхнормативного сброса любого другого показателя длилось не менее года. Продемонстрированная в работе адекватность применяемого эконометрического инструментария определила возможность достоверного прогнозирования качества сточных вод, а также оптимизации превентивных мер по предотвращению сверхнормативных сбросов. Оценка характера периодичности контролируемых показателей с учетом сезонности, синергетического эффекта загрязнения указала на возможность повышения эффективности процесса водоочистки с выбором оптимального режима затрат. Выявление инерционности временных рядов отдельных показателей свидетельствует о возможной агрегации загрязнения из различных источников сбросов и, как следствие, о необходимости усиления контроля над сбросами сточных вод как по каждому антропогенному источнику, так и по фоновому природному загрязнению. Определение динамических связей между отдельными загрязнителями обосновало возможность повышения очищающей способности пруда с учетом структуры и длительности этих связей

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

Rationale, design, and baseline characteristics in Evaluation of LIXisenatide in Acute Coronary Syndrome, a long-term cardiovascular end point trial of lixisenatide versus placebo

BACKGROUND: Cardiovascular (CV) disease is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). Furthermore, patients with T2DM and acute coronary syndrome (ACS) have a particularly high risk of CV events. The glucagon-like peptide 1 receptor agonist, lixisenatide, improves glycemia, but its effects on CV events have not been thoroughly evaluated. METHODS: ELIXA (www.clinicaltrials.gov no. NCT01147250) is a randomized, double-blind, placebo-controlled, parallel-group, multicenter study of lixisenatide in patients with T2DM and a recent ACS event. The primary aim is to evaluate the effects of lixisenatide on CV morbidity and mortality in a population at high CV risk. The primary efficacy end point is a composite of time to CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for unstable angina. Data are systematically collected for safety outcomes, including hypoglycemia, pancreatitis, and malignancy. RESULTS: Enrollment began in July 2010 and ended in August 2013; 6,068 patients from 49 countries were randomized. Of these, 69% are men and 75% are white; at baseline, the mean ± SD age was 60.3 ± 9.7 years, body mass index was 30.2 ± 5.7 kg/m(2), and duration of T2DM was 9.3 ± 8.2 years. The qualifying ACS was a myocardial infarction in 83% and unstable angina in 17%. The study will continue until the positive adjudication of the protocol-specified number of primary CV events. CONCLUSION: ELIXA will be the first trial to report the safety and efficacy of a glucagon-like peptide 1 receptor agonist in people with T2DM and high CV event risk