scholarly journals Failure in building and remedies

2021 ◽  
Author(s):  
SAMUEL OKOLO KENECHI
Keyword(s):  
Environmental Factors ◽  
Drainage System ◽  
Common Factors ◽  
The Common ◽  
Causes Of Failure ◽  
Poor Drainage

ABSTRACT The research investigates the causes of failure in buildings (failure on floors, walls or block works and roofing) for the purpose of this research, the surveying of the various sites of the areas under study was undertaken from the sites surveying, it was found that the common factors that are causing these failures in the area under study are environmental factors, poor drainage system and poor execution method. Thus, the study recommends the possible solutions to the identified problems on the study area.

scholarly journals N-soliton solutions and the Hirota conditions in (1 + 1)-dimensions

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Wen-Xiu Ma
Keyword(s):  
Common Factors ◽  
Soliton Solutions ◽  
Higher Order ◽  
Kdv Equations ◽  
The Common ◽  
Bilinear Formulation ◽  
Generalized Kdv Equations ◽  
Hirota Bilinear

Abstract We analyze N-soliton solutions and explore the Hirota N-soliton conditions for scalar (1 + 1)-dimensional equations, within the Hirota bilinear formulation. An algorithm to verify the Hirota conditions is proposed by factoring out common factors out of the Hirota function in N wave vectors and comparing degrees of the involved polynomials containing the common factors. Applications to a class of generalized KdV equations and a class of generalized higher-order KdV equations are made, together with all proofs of the existence of N-soliton solutions to all equations in two classes.

scholarly journals A Spatial Panel Structural Vector Autoregressive Model with Interactive Effects and Its Simulation

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 883
Author(s):  
Yaqing Liu ◽  
Hongbing Ouyang ◽  
Xiaolu Wei
Keyword(s):  
Autoregressive Model ◽  
Common Factors ◽  
Interactive Effects ◽  
Vector Autoregressive Model ◽  
Finite Sample ◽  
Vector Autoregressive ◽  
Spatial Panel ◽  
The Common ◽  
Structural Vector Autoregressive Model ◽  
Structural Vector

The existing spatial panel structural vector auto-regressive model can effectively capture the time and spatial dynamic dependence of endogenous variables. However, the hypothesis that the common factors have the same effect for all spatial units is unreasonable. Therefore, incorporating time effects, spatial effects, and time-individual effects, this paper develops a more general spatial panel structural vector autoregressive model with interactive effects (ISpSVAR) that can reflect the different effects of common factors on different spatial units. Additionally, based on whether or not the common factors can be observed, this paper proposes procedures to estimate ISpSVAR separately and studies the finite sample properties of estimators by Monte Carlo simulation. The simulation results show the effectiveness of the proposed ISpSVAR model and its estimation procedures.

scholarly journals Genetic or Psychogenic? A Case Study of “Folie à Quatre” Including Twins

2015 ◽  
Vol 2015 ◽  
pp. 1-3 ◽  
Author(s):  
Tohru Ohnuma ◽  
Heii Arai
Keyword(s):  
Environmental Factors ◽  
Social Isolation ◽  
Schizophrenia Patient ◽  
Monozygotic Twins ◽  
The Other ◽  
Positive Symptoms ◽  
Short Period ◽  
The Common ◽  
Shared Psychotic Disorder

Shared psychotic disorder, characterized by shared delusion among two or more subjects (termed “Folie à deux,” “trois,” etc.), is often associated with strong religious beliefs or social isolation, factors creating strong psychological sympathy. Recently, we treated a rare familial case of “Folie à quatre” in central Tokyo without such influences. The proband was a schizophrenia patient and younger brother within monozygotic twins. Positive symptoms were “transmitted” to remaining family members, his elder brother, mother, and father father, in a relatively short period of three months. Although the pathophysiology of these positive symptoms (delusions and hallucinations) remains unclear, the transmission pattern suggests the primacy of social and environmental factors (and/or their interaction), while genetics appeared less influential in this “Folie à famille.” Although undiagnosed psychoses in the whole family cannot be excluded, they did not share the other negative schizophrenia symptoms of the proband. A strong familial connection appeared to be the most important factor for the common delusion and hallucination.

scholarly journals Płatności prośrodowiskowe jako instrument ochrony użytkowej biosfery w rolnictwie

2020 ◽  
Vol 29 (2) ◽  
pp. 37
Author(s):  
Beata Jeżyńska
Keyword(s):  
Environmental Factors ◽  
Soil Quality ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Agricultural Production ◽  
Environmental Aspects ◽  
The Common ◽  
New Guidelines ◽  
Quality Assessments ◽  
The Impact

<p>The need to keep the expected level of production in agriculture generates a serious burden on the environment. The most important environmental factors exposed to the impact of agriculture include biodiversity and water, air, and soil quality. Assessments of all these environmental aspects related to agricultural production are negative. The condition of the agricultural environment has been subject to rapid deterioration. In such a situation, environmental instruments have drawn particular attention from the European legislature when developing new guidelines of the Common Agricultural Policy to be applicable after 2020.</p>

Determinants of total factor productivity in Visegrad Group Nuts-2 regions

2018 ◽  
Vol 68 (1) ◽  
pp. 31-50 ◽  
Author(s):  
Barbara Danska-Borsiak
Keyword(s):  
Panel Data ◽  
Total Factor Productivity ◽  
Latent Variables ◽  
Fixed Effects ◽  
Orthogonal Transformation ◽  
Common Factors ◽  
Dynamic Panel Data ◽  
Factor Productivity ◽  
The Common ◽  
Visegrad Group

This article attempts to estimate the total factor productivity (TFP) for 35 NUTS-2 regions of the Visegrad Group countries and to identify its determinants. The TFP values are estimated on the basis of the Cobb-Douglas production function, with the assumption of regional differences in productivity. The parameters of the productivity function were analysed with panel data, using a fixed effects model. There are many economic variables that influence the TFP level. Some of them are highly correlated, and therefore the factor analysis was applied to extract the common factors – the latent variables that capture the common variance among those observed variables that have similar patterns of responses. This statistical procedure uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. Each component is interpreted using the contributions of variables to the respective component. I estimated a dynamic panel data model describing TFP formation by regions. An attempt was made to incorporate the common factors among the model’s explanatory variables. One of them, representing the effects of research activity, proved to be significant.

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