Climate Change Anxiety Focus Groups: Executive Summary

During the year 2020, we were considering the problem of climate change anxiety in the Lawrence, Kansas, and Kansas City metro areas. In September of 2020, we partnered to conduct focus groups with environmentally engaged participants to understand their experience of climate change anxiety. We conducted 14 semi-structured focus groups with 46 community members to understand their emotions, behaviors, and perceptions of community in light of the climate crisis. We asked participants, many of whom were local environmental activists, to engage in a group discussion via Zoom videoconference which lasted between 60 and 90 minutes. After the discussion, we sent participants a brief survey. This executive summary is a preliminary report of the findings of that investigation. We present charts detailing participants’ responses to the focus group questions, followed by select excerpts from the conversations and some statistical relationships of interest.

Quality assessment of anatomical MRI images from Generative Adversarial Networks: human assessment and image quality metrics

Background: Generative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human eye to more subtle statistical relationships, a more automated approach towards evaluating GANs is required. New method: We investigated to what extent visual quality can be assessed using image quality metrics and we used group analysis and spatial independent components analysis to verify that the GAN reproduces multivariate statistical relationships found in real data. Reference human data was obtained by recruiting neuroimaging experts to assess real Magnetic Resonance (MR) images and images generated by a Wasserstein GAN. Image quality was manipulated by exporting images at different stages of GAN training. Results: Experts were sensitive to changes in image quality as evidenced by ratings and reaction times, and the generated images reproduced group effects (age, gender) and spatial correlations moderately well. We also surveyed a number of image quality metrics which consistently failed to fully reproduce human data. While the metrics Structural Similarity Index Measure (SSIM) and Naturalness Image Quality Evaluator (NIQE) showed good overall agreement with human assessment for lower-quality images (i.e. images from early stages of GAN training), only a Deep Quality Assessment (QA) model trained on human ratings was sensitive to the subtle differences between higher-quality images. Conclusions: We recommend a combination of group analyses, spatial correlation analyses, and both distortion metrics (SSIM, NIQE) and perceptual models (Deep QA) for a comprehensive evaluation and comparison of brain images produced by GANs.

Estimation Of Water Quality Using Geomatics Techniques for The Garraf River Basin

This study was conducted for the purpose of estimating the soil and water quality of the Gharraf River Basin in the north of Dhi Qar Governorate using geomatics techniques represented by geographic information systems (GIS), remote sensing (RS) and positioning systems (GNSS). Which is about (90 km) from the city center. The chemical analyzes of the water samples showed that the degree of interaction was between (7.84-7.7) and the electrical conductivity (dS.m¯1.1-1.05), and the total dissolved substances were between (1106-1051ppm), and the mathematical statistical relationships were weakly correlated with the ratios of the visible space bands. pH, electrical conductivity and total dissolved materials. Calcium ratios in the study area ranged between (ppm 47.4-107) and there was a significant correlation with the range (B/R + B) with a value of (R2 = 0.51), and the results showed the ratios of magnesium in the study area between (ppm 9.67 - 26.61.) Between it and the band ratio (B/R + B)), a correlation relationship with a value of (R2=0.525), potassium recorded an average between (3.1-ppm 5.5), and there was a significant correlation between it and the band ratio (B/R +R) and it reached (R2=0.665). ), found a statistical relationship between sodium and the ratio of the band (B/R + R)) and a significant correlation was recorded with a value of (R2 = 0.527). - 102.52) And there was a correlation between the presence of chloride and the ratio of the range (B/NIR + G) as it was recorded (R2 = 0.593), the bicarbonate recorded ratios between (ppm 1.8-2.7), and there was a statistical relationship between the bicarbonate and the ratio of the range (C / R). ) amounted to (R2 = 0.573), nitrate values were recorded in the study area between (4 - 3.45 ppm) and there was a significant correlation between them and the range (B5) as it reached (R2 = 0.581), sulfate values were recorded between (207.25 - 277.5 ppm) and through Statistical analysis found that there is a correlation between The presence of sulfate with the ratio (C + B + G + R + NIR) which amounted to (R2 = 0.596), the sodium adsorption ratio (SAR) was calculated, as its values ranged between (3.192 - 0.147) and most of the statistical relationships were weakly related to the spatial ratios and were gradually The hardness values in the study area are between (99.7 - 198.1)(

Clima urbano: episódios de inverno e primavera em Rio Verde-GO

A perda de áreas florestadas causa extremos climáticos, como picos de temperatura e quedas da umidade relativa do ar, comprometendo a qualidade ambiental. Em Rio Verde, Goiás, com a expansão da área urbana, áreas de floresta foram reduzidas, trazendo um desconforto térmico na população. Assim, o objetivo deste artigo foi estimar as variações de temperatura e umidade relativa do ar, em três pontos de coletas distintos na cidade de Rio Verde-GO, considerando-se as características da superfície (uso da terra, vegetação, relevo) durante os meses de julho (inverno) e outubro (primavera) de 2018. Para isso foram processadas relações entre as temperaturas (T) e umidade relativa (UR) do ar com as áreas (vegetada; construída/pavimento e solo exposto) num raio de 200 m entre os pontos de coletas. Com essas informações fundamentadas no estudo na Teoria do Clima Urbano, por meio do subsistema termodinâmico (relativo à temperatura e umidade relativa do ar) foram feitas relações estatísticas entre a variação dos atributos climáticos e observamos os parâmetros geográficos, tais como porcentagem de vegetação, área construída, solo exposto, e a atuação e dinâmica atmosférica da região no período analisado. Os resultados mostraram que os aspectos do meio físico dos pontos amostrais, principalmente a vegetação, contribuiu para a variação dos registros da temperatura do ar mínima (T. mín) e máxima (T. máx) absoluta , com oscilação de 1,4 a 2,5°C e 2,8 a 4°C, enquanto os valores da umidade relativa do ar mínima e máxima absoluta variaram de 0,6 a 11,6% e 2,2 a 5,4%, respectivamente.Urban climate: winter and spring episodes in Rio Verde-GOA B S T R A C TThe loss of forested areas causes climatic extremes, such as temperature peaks and drops in relative humidity, compromising environmental quality. In Rio Verde, Goiás, with the population growth and the expansion of the urban area, forest areas were decimated, bringing a thermal discomfort to the population. The objective of this article was to estimate the variations in temperature and relative humidity in three distinct collection points in the city of Rio Verde-GO, considering the surface characteristics (land use, vegetation, relief) during the months of July (winter) and October (spring) 2018. For this purpose, relationships between the temperatures (T) and relative humidity (RH) of the air were processed with the areas (vegetated; constructed/paved and exposed soil) within a radius of 200 m between the collection points. With this information based on the study in the Theory of Urban Climate, through the thermodynamic subsystem (relative to the temperature and relative humidity of the air), statistical relationships were made between the variation of climatic attributes and we observed the geographical parameters, such as percentage of vegetation, area constructed, exposed soil, and the performance and atmospheric dynamics of the region in the analyzed period. The results showed that the physical aspects of the sample points, mainly the vegetation, contributed to the variation of the minimum (T. min) and maximum (T. max) absolute air temperature records, with an oscillation of 1,4 to 2,5°C and 2,8 to 4°C, while the minimum and maximum absolute relative humidity values varied from 0,6 to 11,6% and 2,2 to 5,4%, respectively.Keywords: Urban climate, climatic attributes, physical environment, land use and occupation

Potential GDP and its factors assessment

The object of the study is the reserves of economic growth in the country on the example of Ukraine. One of the problems of such studies is the calculation of potential GDP, which is not observed, but is calculated on the basis of various methods. Also problematic is the choice of method/methods of calculating potential GDP and potential values of its factors. Any estimate of the potential value of a variable is based on one or more statistical relationships and therefore contains an element of uncertainty. In order to reduce uncertainty, 2 methods were used to determine the potential values of the components of GDP – the growth rate of employment, fixed capital and TFP (total factor productivity). The study used the methods of one-dimensional statistical filters Hodrick-Prescott and Baxter-King to estimate the potential values of GDP and the model of the production function to calculate potential GDP based on the potential values of its factors. The main reasons for the slowdown in Ukraine's GDP have been identified, the main of which is low capital productivity due to budget constraints. The second place in this ranking was taken by labor productivity, the last third – by TFP. Weak productivity and investment growth reinforced each other. Capital has the highest growth potential in Ukraine. Therefore, measures to stimulate capital investment, including in research and innovation and human capital, are important. Other factors that affect GDP through labor productivity and TFP are population aging, emigration, and tight lending conditions. To neutralize these factors, it is necessary to create new jobs, facilitate the conditions for obtaining loans by enterprises, stimulate advanced training and lifelong learning. The proposed approach to the separate calculation of potential values of GDP factors and their analysis find reserves for GDP growth. This provides the advantages of this method over other approaches.

Effect of varying silica-limestone sand fines on the physical-mechanical performance of concrete

The use of crushed limestone sand in the concrete industry will be quite possible and imperative for environmental reasons. Many researchers around the world have found that concrete based on 50% substitution of river sand by limestone sand gives better physico-mechanical characteristics. The main objective of this investigation is to search for an optimal percentage of silica-limestone fines resulting from the substitution of half in quantity of alluvial sand by crushed limestone sand in ordinary concrete. The proportions of fines that were tested in this work are 6%, 8%, 10%, 12% and 14%. The obtained results revealed that concrete based on silica-limestone sand and containing 14% of the same type of fines strongly improves the different mechanical strengths and participates in the reduction of 10% and 13%, of the coefficient of capillary absorption and of the porosity accessible to water, respectively, compared to the control concrete. In addition, good statistical relationships between the studied parameters were also found

Post-great recession municipal budgeting and governance: A mixed methods analysis of budget stress and reform

The Great Recession hit several U.S. cities hard. Facing large revenue losses, these cities undertook dramatic spending cuts and utilized rarely used restructuring tools. This led some to speculate that these were exemplars of austerity urbanism. Subsequent work has contested this interpretation, arguing instead that cities have generally pursued pragmatic, not austere, reform. This paper seeks to move beyond this impasse, developing a mixed methods longitudinal analysis of quantitative and qualitative municipal budget data. Quantitative data is drawn from the U.S. Census of Local Government (2006–2016) and is used identify statistical relationships between budget health and budget composition in a nationwide sample ( n = 1,449) of municipalities. Then follows a qualitative analysis of budget narrative data from the six most fiscally distressed large and medium sized U.S. cities. The paper therefore identifies commonalities in post-Great Recession urban governance (i) in a large nationwide sample of cities and (ii) within a small group of extreme cases. The research found weak nationwide trends in budgetary change and divergent budget narratives in cases of severe municipal fiscal distress. We argue this means three things for understanding U.S. urban governance. First, the tracing of superficially similar “local” budget reforms to a single political economic descriptor is misplaced. Second, U.S. municipal budgetary reforms are relational, outcomes of both local and extra-local diagnosis, interpretation, and mediation. Third, and finally, decisions to introduce local austerity policies stem not just from “outside.” This paper therefore shows the potential intellectual returns of in-depth, case-study research on U.S. urban governance and finance.

Monitoring and control of polymer production line based on machine learning

The work is devoted to the development of an application for monitoring and controlling the state of equipment (extruder) for the petrochemical industry based on sensor readings using a machine learning model. The statistical relationships of the technological process parameters are analyzed, the most significant parameters influencing the occurrence of failures are determined using SHAP values. The hypotheses regarding the effectiveness of various machine learning algorithms in relation to the real problem of predicting the technical state of the extruder are tested. A gradient boosting model has been developed to predict the probability of extruder shutdown due to the formation of polypropylene agglomerates. The developed application allows interpreting the results of the model, which makes it possible to select the most important process parameters that have the greatest impact on the probability of extruder failure, and also proposing a prototype of an extruder monitoring system based on sensor readings using a machine learning model.

The Binding of Aripiprazole to Plasma Proteins in Chronic Renal Failure Patients

The binding of drugs to plasma protein is frequently altered in certain types of renal diseases. We recently reported on the effects of oxidation and uremic toxins on the binding of aripiprazole (ARP) to human serum albumin. In our continuing investigations, we examined the binding of ARP to plasma pooled from patients with chronic renal dysfunction. We examined the issue of the molecular basis for which factors affect the changes in drug binding that accompany renal failure. The study was based on the statistical relationships between ARP albumin binding and biochemical parameters such as the concentrations of oxidized albumin and uremic toxins. The binding of ARP to plasma from chronic renal patients was significantly lower than healthy volunteers. A rational relationship between the ARP binding rate and the concentration of toxins, including indoxyl sulphate (IS) and p-cresyl sulphate (PCS), was found, particularly for IS. Moreover, multiple regression analyses that involved taking other parameters such as PCS or oxidized albumin ratio to IS into account supports the above hypothesis. In conclusion, the limited data reported in this present study indicates that monitoring IS in the blood is a very important determinant in the dosage plan for the administration of site II drugs such as ARP, if the efficacy of the drug in renal disease is to be considered.

Effect of indoor environmental quality on visual comfort and productivity in office buildings

Purpose The Purpose of this paper is to identify statistical relationships between visual environment and occupant productivity. Visual environment is one of the most important indoor environmental quality (IEQ) parameters, and it directly impacts occupant productivity in offices. The literature outlines the significance of the impact. Still, there is a lack of investigation, statistical analysis and inter-relationships between the independent variables (IEQ factors), especially in the hot and arid climate. Design/methodology/approach This study presents a research study investigating the effects and shows statistical relationships between IEQ on occupant comfort and productivity. The study was conducted in the Middle East, and data was collected for 12 months. It used the response surface analysis to perform analysis. Findings This study outlined seven unique relationships highlighting the recommended range, inter-dependencies. Results include that illumination has maximum effect on visual comfort and temperature, daylight having direct influence and relative humidity, wall type next to the seat and kind of workspace also impact visual comfort and productivity. These findings would help to improve occupant comfort and productivity in office buildings. It is recommended to include results and recommendations on design guidelines for office buildings. Originality/value This study presents the unique effects of non-visual IEQ parameters on visual comfort and productivity. This investigation also provides a unique method to develop the statistical relationship between various indoor environmental factors and productivity in different contexts and buildings.