THE NATURAL AND MIGRATORY MOVEMENT OF THE RURAL POPULATION OF THE BANAT MOUNTAINS (1993-2016)

The article is based on the study of population dynamics in the rural area of the Banat Mountains. The study period ranged from 1993 to 2016. The aim of the paper was to observe the numerical evolution of the population and to make calculations on the evolution of some demographic indicators. Demographic indicators such as the natality, mortality, natural growth, natural, migratory movement and migratory growth of communes have been analyzed over the period mentioned. A first objective was to physically and then administratively delineate the Banat Mountains. The second objective was to obtain the necessary numerical data, then to process the data for each indicator. Data processing resulted in a series of indicator rates, and in addition, graphs were developed with the numerical evolution of newborns and deaths. The numerical data on the number of newborns and deaths were taken from the website of the National Institute of Statistics. There have been introduced in the Microsoft Excel 2013 program a few graphs. Also, through Microsoft Excel 2013, rates for demographic indicators were calculated, and then the results obtained were introduced into Arc Gis version 10.3. Finally, a series of maps on the territorial distribution of values for each indicator was generated.

On the Asymptotics and Distribution of Values of the Jacobi Theta Functions and the Estimate of the Type of the Weierstrass Sigma Functions

A refined asymptotics of the Jacobi theta functions and their logarithmic derivatives have been received. The asymptotics of the Nevanlinna characteristics of the indicated functions and the arbitrary elliptic function have been found. The estimation of the type of the Weierstrass sigma functions has been given.

Rating Opportunity for Long-Acting Injectable Antipsychotic Initiation Index (ROLIN)

Background: Schizophrenia is a severe psychiatric condition with devastating consequences for the individual's functionality and leading to severe disability. Lack of insight and non-adherence to treatment remain the most important factors in the progression of the disease to chronicity. Despite their proven effectiveness in preventing relapses, reducing morbidity and mortality, long-acting injectable antipsychotics (LAIs) are still underused. One of the causes invoked is the lack of guidelines or protocols for initiating LAIs.Objective: The aim of this article is to present Rating Opportunity for Long-Acting Injectable Antipsychotic Initiation Index (ROLIN), a clinician-rated index that rates the important factors of the disorder across seven items: age, duration of illness, relapses, antipsychotic treatment response, family support, antipsychotic existing formulation and adherence.Method: A retrospective study in which all patients with schizophrenia discharged on oral antipsychotics without LAIs treatment lifetime were evaluated with ROLIN for opportunity for LAIs initiation.Results: Of 225 consecutive patients, 126 patients (56%) had a strong indication for initiating LAI (score between 25 and 35). Kolmogorov-Smirnov test was used for checking the normal distribution of values (95% CI for the mean = 9.5781 to 20.4219; 95% CI for the median = 6.5920 to 24.8161; SD = 9.7907; Coefficient of Skewness = 0.0743; Coefficient of Kurtosis = −1.1377).Conclusion: This paper proposed an instrument designed to improve treatment in schizophrenia using a simple conceptual model which integrates important predictors of good or poor outcomes.

Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.

An Analysis of the Operation of Distribution Networks Using Kernel Density Estimators

Efficiency in the operation of distribution networks is one of the commonly recognised goals of the Smart Grid aspect. Novel approaches are needed to assess the level of energy loss and reliability in electricity distribution. Transmission of electricity in the power system is invariably accompanied by certain physical phenomena and random events causing losses. Identifying areas where excessive energy losses or excessive grid failure occur is a key element for energy companies in resource management. The study presented in the article is based on data obtained from distribution system operators concerning 41 distribution regions in Poland for a period of 5 years. The first part of the article presents an analysis of the distribution of values for the introduced energy density and energy losses in the lines of medium- and low-voltage networks and in transformers supplying the low-voltage network. The second part of the article presents the assessment of the network reliability of the same distribution regions based on analysis of the distributions of System Average Interruption Duration Index (SAIDI) and System Average Interruption Frequency Index (SAIFI) values for planned and unplanned outages. Data analysis is performed by non-parametric methods by means of kernel estimators.

RANTAI NILAI (VALUE CHAIN) AGRIBISNIS KOPI ROBUSTA RAKYAT: STUDI KASUS DI KABUPATEN EMPAT LAWANG PROVINSI SUMATERA SELATAN

The absence of routine counseling and cooperatives that can work together also makes farmers still have to independently decide the direction of their business. In addition, the coffee processing industry is still very minimal. The majority of existing coffee products are sold in the form of dried coffee bean directly to collector traders. This study aims to analyze the value chain of coffee produced by value chain actors, and the potential for agribusiness development of robusta coffee in Karang Caya Village, Pendopo Barat District, Empat Lawang Regency. In this study, primary and secondary data were used which were analyzed using value chain analysis (VCA) and discussed in descriptive analysis. Research respondents involved were smallholder coffee farmers, agro-industry players, traders, and local government officials who understand the prospects for developing coffee farming in the research location. Based on the results of the study, it is known that there are 3 marketing channels for coffee, namely: farmers à subdistrict collectors à wholesalers à end consumers; farmers à end consumers; and farmers à coffee processors à end consumers. The dominance of farmers as the main actors in the activities of supply to marketing of coffee is very visible, the role of groups and other institutions is very minimal. Uneven distribution of values results in inefficient channels. This condition results in the price setting being carried out by the buyer (price setter) while the seller acts as the price taker.Key words: Robusta coffee, smallholder farmer, value chain

Uncertainty quantification and sensitivity analysis of COVID-19 exit strategies in an individual-based transmission model

Many countries are currently dealing with the COVID-19 epidemic and are searching for an exit strategy such that life in society can return to normal. To support this search, computational models are used to predict the spread of the virus and to assess the efficacy of policy measures before actual implementation. The model output has to be interpreted carefully though, as computational models are subject to uncertainties. These can stem from, e.g., limited knowledge about input parameters values or from the intrinsic stochastic nature of some computational models. They lead to uncertainties in the model predictions, raising the question what distribution of values the model produces for key indicators of the severity of the epidemic. Here we show how to tackle this question using techniques for uncertainty quantification and sensitivity analysis. We assess the uncertainties and sensitivities of four exit strategies implemented in an agent-based transmission model with geographical stratification. The exit strategies are termed Flattening the Curve, Contact Tracing, Intermittent Lockdown and Phased Opening. We consider two key indicators of the ability of exit strategies to avoid catastrophic health care overload: the maximum number of prevalent cases in intensive care (IC), and the total number of IC patient-days in excess of IC bed capacity. Our results show that uncertainties not directly related to the exit strategies are secondary, although they should still be considered in comprehensive analysis intended to inform policy makers. The sensitivity analysis discloses the crucial role of the intervention uptake by the population and of the capability to trace infected individuals. Finally, we explore the existence of a safe operating space. For Intermittent Lockdown we find only a small region in the model parameter space where the key indicators of the model stay within safe bounds, whereas this region is larger for the other exit strategies.

Historical Benchmarks for Quality Tolerance Limits Parameters in Clinical Trials

Background In 2016, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use updated its efficacy guideline for good clinical practice and introduced quality tolerance limits (QTLs) as a quality control in clinical trials. Previously, TransCelerate proposed a framework for QTL implementation and parameters. Historical data can be important in helping to determine QTL thresholds in new clinical trials. Methods This article presents results of historical data analyses for the previously proposed parameters based on data from 294 clinical trials from seven TransCelerate member companies. The differences across therapeutic areas were assessed by comparing Alzheimer’s disease (AD) and oncology trials using a separate dataset provided by Medidata. Results TransCelerate member companies provided historical data on 11 QTL parameters with data sufficient for analysis for parameters. The distribution of values was similar for most parameters with a relatively small number of outlying trials with high parameter values. Medidata provided values for three parameters in a total of 45 AD and oncology trials with no obvious differences between the therapeutic areas. Conclusion Historical parameter values can provide helpful benchmark information for quality control activities in future trials.

On the vertical distribution of values of L-functions in the Selberg class

We prove explicit formulae for [Formula: see text]-points of [Formula: see text]-functions from the Selberg class. Next we extend a theorem of Littlewood on the vertical distribution of zeros of the Riemann zeta-function [Formula: see text] to the case of [Formula: see text]-points of the aforementioned [Formula: see text]-functions. This result implies the uniform distribution of subsequences of [Formula: see text]-points and from this a discrete universality theorem in the spirit of Voronin is derived.