The Impact of Place of Residence on the Sense of Coherence of Population Aged 60–89: Evidence from Poland

Place of living is one of the most important socio-demographic factors which characterizes the lives of older people. The importance of with whom and under what conditions older adults live to a large extent determines their health and standard of living. The goal of the study was to find the relationship between the place of residence and housing condition of older adults in Poland and their sense of coherence and health problems. The 29-item Antonovsky SOC questionnaire was used. In the research 303 people (76% women and 24% men) aged 60–89 were evaluated: 158 lived in their own houses/flats, while 145 resided in Daily Homes of Social Assistance (DPS). The overall result for the sense of coherence was 129.65 for older adults living in their own homes and 126.48 for these living in DPS. Statistical dependence between the place of residence and sense of manageability and meaningfulness was found. There is no dependence between gender and the overall score, nor the three components of the sense of coherence. Statistical dependence was determined in the criterion of age. A higher level of meaningfulness was observed in people aged 60–74. Taking into account the place of residents, 52% of the respondents living in their own houses/flats experience loneliness and among the people living in Daily Homes of Social Assistance, 46% experience loneliness. The type of place of residence is one of the most important personal factors affecting the sense of coherence, chronic health problems, and sense of loneliness. The last factor, especially, can adversely affect community sustainability and undermine social cohesion.

Water cut forecast after downhole pumping equipment optimization based on displacement characteristics

The article deals with the issue of water cut predicting when downhole pumping equipment optimizing. In practice, an expert assessment of this parameter is used as a rule, which does not take into account the degree of planned optimization relative to the current mode. The paper proposes a methodology allowing taking into account the dynamics of planned fluid withdrawals in predicting water cut based on displacement characteristics. To solve the described problem, four characteristics were selected with a certain type of statistical dependence, where, in one part of the equation, fluid withdrawals do not depend on oil withdrawals. This allows, by setting different values of fluid production, to predict oil production and water cut at any time period. On the example of deposits of one of the regions of the Ural-Volga region, the most suitable for certain geological conditions displacement characteristics were determined. Look back analysis shows a high degree of convergence between the calculated and actual water cut indicators – the average absolute deviation is 1.9%, which allows forecasting with sufficient accuracy. Keywords: oil fields development; production stimulation; displacement characteristics; water cut.

Paradigmatic relations between lexical-semantic groups of adjectives in the scientific style of the english language

The article is dedicated to the study of paradigmatic relations of adjectives on the semantic level in the scientific style. Paradigmatic ties in the nucleus and the first periphery are determined and compared. The object of study is adjectives in the scientific style. The subject of the study is paradigmatic ties on the semantic level. The goal is to investigate and compare paradigmatic ties of adjectives in the scientific style. The topicality is caused by the necessity of study of paradigmatic ties of adjectives in the scientific style as they have not been investigated yet. In our study the statistical method was used such as the correlation analysis which helps to determine intensity and character of ties. The more there is the dependence between signs, the more the quantity of coefficient of correlation will approach to 1. In case of the absence of the statistical dependence the quantity of coefficient of correlation will be about zero or equals zero. Using the quantity of usage LSG of adjectives were divided into the nucleus and the first periphery. Due to the correlation analysis paradigmatic ties of LSG of adjectives within the nucleus and within the first periphery and between them were found and analyzed. In our study the statistical method was used such as χІ -which shows presence or absence of a tie. The coefficient K indicates the force (intensity) of ties. Due to it the ties are divided into strong, mean and weak ones. Statistically meaningful ties of lexical-semantic groups (LSG) of adjectives were analyzed. A scientific novelty is in distinguishing the nucleus and the first periphery and investigating paradigmatic ties between them. Conclusions. LSG of adjectives within the first periphery create not a big number of ties in comparison with the number of ties with LSG of adjectives of the nucleus. LSG of adjectives “Natural and physical state” and “Space value of distance and duration” are distinguished as making up the biggest number of ties with nucleus. The study shows that LSG of adjectives within of the first periphery are more independent between themselves and are able to have free ties. Paradigmatic ties of LSG of adjectives within the nucleus have the tendency to dependence and a mutual influence.

Basis-Function Models in Spatial Statistics

Spatial statistics is concerned with the analysis of data that have spatial locations associated with them, and those locations are used to model statistical dependence between the data. The spatial data are treated as a single realization from a probability model that encodes the dependence through both fixed effects and random effects, where randomness is manifest in the underlying spatial process and in the noisy, incomplete measurement process. The focus of this review article is on the use of basis functions to provide an extremely flexible and computationally efficient way to model spatial processes that are possibly highly nonstationary. Several examples of basis-function models are provided to illustrate how they are used in Gaussian, non-Gaussian, multivariate, and spatio-temporal settings, with applications in geophysics. Our aim is to emphasize the versatility of these spatial-statistical models and to demonstrate that they are now center-stage in a number of application domains. The review concludes with a discussion and illustration of software currently available to fit spatial-basis-function models and implement spatial-statistical prediction. Expected final online publication date for the Annual Review of Statistics and Its Application, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Partitioning the uncertainty contributions of dependent offshore forcing conditions in the probabilistic assessment of future coastal flooding at a macrotidal site

Getting a deep insight into the role of coastal flooding drivers is of high interest for the planning of adaptation strategies for future climate conditions. Using global sensitivity analysis, we aim to measure the contributions of the offshore forcing conditions (wave/wind characteristics, still water level and sea level rise (SLR) projected up to 2200) to the occurrence of the flooding event (defined when the inland water volume exceeds a given threshold YC) at Gâvres town on the French Atlantic coast in a macrotidal environment. This procedure faces, however, two major difficulties, namely (1) the high computational time costs of the hydrodynamic numerical simulations; (2) the statistical dependence between the forcing conditions. By applying a Monte-Carlo-based approach combined with multivariate extreme value analysis, our study proposes a procedure to overcome both difficulties through the computation of sensitivity measures dedicated to dependent input variables (named Shapley effects) with the help of Gaussian process (GP) metamodels. On this basis, our results outline the key influence of SLR over time. Its contribution rapidly increases over time until 2100 where it almost exceeds the contributions of all other uncertainties (with Shapley effect > 40 % considering the representative concentration pathway RCP4.5 scenario). After 2100, it continues to linearly increase up to > 50 %. The SLR influence depends however on our modelling assumptions. Before 2100, it is strongly influenced by the digital elevation Model (DEM); with a DEM with lower topographic elevation (before the raise of dykes in some sectors), the SLR effect is smaller by ~40 %. This influence reduction goes in parallel with an increase in the importance of wave/wind characteristics, hence indicating how the relative effect of the flooding drivers strongly change when protective measures are adopted. By 2100, the joint role of RCP and of YC impacts the SLR influence, which is reduced by 20–30 % when the mode of the SLR probability distribution is high (for RCP8.5 in particular) and when YC is low (of 50 m3). Finally, by showing that these results are robust to the main uncertainties in the estimation procedure (Monte-Carlo sampling and GP error), the combined GP-Shapley effect approach proves to be a valuable tool to explore and characterize uncertainties related to compound coastal flooding under SLR.

Measuring Avalanche Properties on RC4 Stream Cipher Variants

In the last three decades, the RC4 has been the most cited stream cipher, due to a large amount of research carried out on its operation. In this sense, dissimilar works have been presented on its performance, security, and usability. One of the distinguishing features that stand out the most is the sheer number of RC4 variants proposed. Recently, a weakness has been reported regarding the existence of statistical dependence between the inputs and outputs of the RC4, based on the use of the strict avalanche criterion and the bit independence criterion. This work analyzes the influence of this weakness in some of its variants concerning RC4. The five best-known variants of RC4 were compared experimentally and classified into two groups according to the presence or absence of such a weakness.

PyMVPD: A toolbox for multivariate pattern dependence

Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate Pattern Dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for Multivariate Pattern Dependence. The toolbox includes pre-implemented linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models.

Synchronous development in open-source projects: A higher-level perspective

Mailing lists are a major communication channel for supporting developer coordination in open-source software projects. In a recent study, researchers explored temporal relationships (e.g., synchronization) between developer activities on source code and on the mailing list, relying on simple heuristics of developer collaboration (e.g., co-editing files) and developer communication (e.g., sending e-mails to the mailing list). We propose two methods for studying synchronization between collaboration and communication activities from a higher-level perspective, which captures the complex activities and views of developers more precisely than the rather technical perspective of previous work. On the one hand, we explore developer collaboration at the level of features (not files), which are higher-level concepts of the domain and not mere technical artifacts. On the other hand, we lift the view of developer communication from a message-based model, which treats each e-mail individually, to a conversation-based model, which is semantically richer due to grouping e-mails that represent conceptually related discussions. By means of an empirical study, we investigate whether the different abstraction levels affect the observed relationship between commit activity and e-mail communication using state-of-the-art time-series analysis. For this purpose, we analyze a combined history of 40 years of data for three highly active and widely deployed open-source projects: QEMU, BusyBox, and OpenSSL. Overall, we found evidence that a higher-level view on the coordination of developers leads to identifying a stronger statistical dependence between the technical activities of developers than a less abstract and rather technical view.

Analysis of time series by the example of registration of variations in the gravitational field

In this work, a time series model is used to study the structure of gravimetric data series to identify patterns in the change in the levels of the series and build its model in order to predict and study the relationships between the levels of gravimetric data. Observations of the activity of geophysical processes showed that the periods of variations in geophysical processes are scattered chaotically on the time axis. According to their schedule, it is impossible to definitely speak about the regularity in the duration of the periods of variations, and in the alternation of periods of seismic calm with a period of high seismic activity. The impetus for this study was the desire to analyze the structure of a number of formal methods to search for statistical patterns in the variations of geophysical parameters over time. Time series models were used to study the dynamics of geophysical events. Forecasting was carried out using the SPSS 20 package and EXCEL 2016. The accuracy of the forecast is indicated by the comparison of the forecast series with the actual data. The predicted values of the gravimetric data are within the confidence intervals. If you start forecasting too early, the forecast may differ from the forecast based on all statistical data. If the data shows seasonal trends, it is recommended to start forecasting from the date before the last point of the statistical data. Spatial and time series models can be used to study the dynamics of geophysical events. A spatial model describes a set of geophysical parameters at a given point in time. A time series is a series of regular observations of a certain parameter at successive points in time or at intervals of time. In this work, the time series model is used: to identify the statistical relationship between the frequency and depth of occurrence of earthquakes, as well as to identify the statistical dependence of these data on gravimetric variations; determination of patterns in the change in the levels of the series and the construction of its model in order to predict and study the relationships between geophysical phenomena.