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Purpose: Investigate the most appropriate mathematical formula to objectively express upper eyelid contour symmetry. Methods: 62 eyes of 31 patients were included in the study. The upper eyelid contour symmetry of the patients was classified subjectively (independent of MRD1) as poor, acceptable, and good by three oculoplastic specialists (senior, expert, and junior surgeon). Bézier curves of the upper lid contour were drawn with ImageJ software (NIH, Bethesda, MA, USA). Using the algorithms created by Author SKC in Spyder (Python 3.7.9.), the symmetry of the Bézier curves of the left eyelids were obtained according to the y-axis, and the mid-pupils of both eyes were superimposed. The lower curve moved vertically to the equal height of the other curve to equalize MRD1's. R2 (Coefficient of determination), RMSE (Root-mean-square error), MSE (Mean squared error), POC (Percentage of co-efficiency), and MAE (Mean absolute error) were calculated. We evaluated the correlation between these objective formulas and the subjective grading of three surgeons using Spearman's rho (ρ). Results: The correlation coefficient of RMSE and MSE were the same for all surgeons grading. There was a strong correlation between the senior surgeon's subjective scoring (N; poor = 8, acceptable = 16, good = 8) and R2, RMSE, POC, MAE (ρ = 0.643, p < 0.001, ρ = −0.607, p < 0.001, ρ = 0.562, p < 0.001, ρ = −0.517, p < 0.001, respectively). We found a strong relationship between the expert surgeon's subjective scoring (N; poor = 9, acceptable = 13, good:10) and R2 (ρ = 0.611, p < 0.001), RMSE (ρ = −0.549, p < 0.001), POC (ρ = 0.511, p < 0.001), and MAE (ρ = −0.450, p < 0.05). We found a strong correlation between junior surgeon's subjective scoring (N; poor = 6, acceptable = 18, good = 8) and R2, RMSE, and POC (ρ: −0.517, p < 0.001; ρ: −0.470, p < 0.001; ρ: 0.521, p < 0.001; respectively) and moderate correlation between MAE (ρ:-0.394, p < 0.05). The highest correlation is observed with R2. Conclusions: RMSE, MSE, POC, MAE, and especially R2, may quantitatively express upper eyelid contour symmetry, comparable with the oculoplastic surgeon. The highest correlation was observed between the senior surgeon and R2, and decreases with the experience of the surgeon. [ABSTRACT FROM AUTHOR]

The major objective of this study is to establish a network of Ground Control Points-GCPs which can use it as a reference for any engineering project. Total Station (type: Nikon Nivo 5.C), Optical Level and Garmin Navigator GPS were used to perform traversing. Traversing measurement was achieved by using nine points covered the selected area irregularly. Near Civil Engineering Department at Baghdad University Al-jadiriya, an attempt has been made to assess the accuracy of GPS by comparing the data obtained from the Total Station. The average error of this method is 3.326 m with the highest coefficient of determination (R2) is 0.077 m observed in Northing. While in the Easting the average error is 4.634 m with (R2) 0.099 m observed. The average error 5.513 m with (R2) is 0.071 m is observed in Reduced Level. The accuracy assessments of the interpolation methods (Total Station and Level) have been calculated using the average error 0.240 m with (R2) is 0.735 m is observed in Reduced Level. The GIS 10.3 program can automatically transform the saved data to text format and produce digital plans with attribute.

Instead of relying on null-hypothesis significance testing (NHST), researchers are consistently advised to use effect sizes (ESs) and confidence intervals (CIs) to convey research findings. However, typical ES measures for most linear models (e.g., multiple regression) assume data normality, a condition that is often violated in behavioral research. This may lead to inaccurate interpretation of ES. In multiple regression models, Cohen's f2, R2 and R adj 2 are employed by researchers, but no study has systematically evaluated their robustness in practice. Thus, this Monte Carlo simulation study evaluates the robustness of f2, R2 and R adj 2 and the associated CIs based on manipulated levels of sample sizes, magnitudes of ESs, numbers of predictors, and data violations (i.e., heavy-tailed, skewed, contaminated, lognormal, and heteroscedastic distributions of errors). This study offers guidelines regarding how robust these ESs are so that researchers can report the most appropriate ES in their research studies. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave very similar results especially when studied with large sample sizes (n≥100), the Automatic linear modelling is preferred for analyzing data sets due to its simplicity in analyzing data and interpreting the results, ability to present results visually and providing more detailed information especially studying large complex data sets. It will be beneficial to use the Automatic linear modelling especially in analyzing massive and complex data sets for the purposes of investigating the relationships between one continuous dependent and 10 or more predictors and determine the factors that affect the response or target variable. At the same time, it will also be possible to evaluate the effect of each predictor with a more detailed response.

AbstractI present the moments of the nonnegative adjusted estimator of the squared multiple correlation ρ2, the coefficient of determination for random-predictor regression. This estimator, first proposed by Ezekiel, replaces with zero the negative estimates from the well-known adjusted estimator proposed by Fisher that, in turn, corrects the positive bias of the sample R2. Although Fisher’s version is presented in texts, Ezekiel’s version is used in practice. Each moment comprises a binomial sum of a negative binomial series of incomplete beta functions. Numerical computations, for which an R function is provided, are required to examine the moments. Ezekiel’s estimator is positively biased for smaller ρ2 and negatively biased for larger. It dominates Fisher’s via MSE. It does not dominate R2, but the MSE of Ezekiel’s estimator can be substantially smaller but at most negligibly larger. Possible applications to powers of ρ2 and to other adjusted estimators are briefly discussed. [ABSTRACT FROM AUTHOR]

Mining is one of the most daunting occupations gain the sector since it entails risk at any point in the operation. In its operation, the main focus is on slope stability. To avoid slope failures, work should be performed in line with both the regulations and the safety criteria. Slope stability is essential in mining activities owing to slope failure putting productivity and safety at risk. Prediction of slope failure is difficult because of the complexity of traditional engineering techniques. Through study, recent technologies have helped mining companies predict slope problems quickly and effectively. In this current research, an ensemble of machine learning intelligence algorithms was used to estimate and assess the Factor of Safety (FOS). In Ostapal Chromicte Mine, India, 79 experimental and failure slope occurrences were tracked to gather in-the-moment field data. The available data were split into training and testing sets at random to build algorithms. The five influenced factors such as the unit weight, the friction angle, the cohesiveness, the mining depth, as well as the slope angle used as input variables to estimate the FOS. Selected machine learning techniques such as Multiple Linear Regression (MLR), Decision Tree, Random Forest (RF), eXtreme Gradient Boosting (XGBoost) and ensemble hybrid model combining eXtreme Gradient Boosting and Random Forest (XGBoost-RF) were developed to evaluate the FOS. The validity and efficiency of created models can be evaluated using standard evaluation parameters such as coefficient of determination (R2), root mean square error (RMSE), mean square error (MSE), normalized root mean square error (NRMSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). The most precise model to assess the FOS across all models was discovered to be the XGBOOST-RF ensemble model, which had a high R² of 0.931, MSE of 0.009, NRMSE of 0.069, MAD of 0.037, MAPE of 3.581 and an RMSE of 0.098. [ABSTRACT FROM AUTHOR]

Blood is an important component produced by the human body. Blood is also a very vital part of human survival. When blood levels in the human body are less than they should be, the way to overcome this is by donating blood or blood transfusion. The health facilities that organize blood donations, provide blood and distribute blood are called Blood Transfusion Units (UTD). UTD in carrying out its duties encountered several obstacles, such as blood only having a shelf life of 35 days from donation. If it has passed the expiration date, it cannot be used anymore for blood transfusions. Meanwhile, regarding the demand for blood, the need for blood is greater than those donating. Making it difficult for UTD if the demand occurs when the existing blood stock is not sufficient. And if the stock in UTD experiences an axcess, it can cause losses because the blood is wasted due to expiration. Apart form that. The problem is that in everyday life, many people’s need for blood is reduced. Many of their families intervened directly to find available donors. They even search on social networks or social media such as WhatsApp, Facebook, Instagram and others. And this shows that many of them lack donors. To anticipate these problems. So it is necessary to carry out research on forecasting blood demand using the Support Vektor Regression method at UTD PMI Central Lombok. The aim of this research is to forecast or predict the demand for blood at UTD PMI Central Lombok in the coming period. To reduce the impact of lack or excess blood. SVR is the application of Support Vektor Machine (SVM) in the case of regression to find the best dividing line in the regression function. The advantage of the SVR model is that it can handle overfiting problems in the data. The tests used to measure the best model are Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2). The results of this research shows that the best model is Support Vektor Regression (SVR) with a polynomial kernel and based on the tuning results, the parameters used are C=10, degree=1, epsilon=1. The SVR model using a polynomial kernel produces a MAPE value of 18.7502% and RMSE value of 0.6919, which means the model has very good predictive ability. Prediction accuracy was achieved with an R2 value of 0.9936 or 99.36% and an MSE value of 0.4787, which means that the prediction of blood demand data at UTD PMI Central Lombok using SVR with a polynomial kernel function had very good prediction accuracy. With predicted result in january for blood type A it was 1654, B was 920, O was 2205 and AB was 1104

Tropospheric ozone is a powerful oxidant that can damage living organisms; it is widely monitored, as air concentrations have more than doubled since the Industrial Revolution. However, in general air quality monitoring stations are limited spatially to large urban centres; accordingly, accurate prediction of concentrations outside of cities is important for protecting human and plant health. Land-use regression has been successfully used for modelling air pollutant concentrations by establishing a relationship between observed concentrations and landscape features representing sources and sinks. In this study, we developed a land-use regression model that explained 68% of the variance of summer average ozone concentrations in the Republic of Ireland. Ozone was measured at 14 active and 20 passive monitoring sites; air concentrations varied spatially, with the highest ozone measured in rural upland (64.5 µg/m3) and Atlantic coastal (50.2–60.5 µg/m3) sites and the lowest generally in urban centres (38.9–45.7 µg/m3). A total of 74 land-use predictor variables were tested, and their inclusion in the model was based on their impact on the coefficient of determination (R2). The final model included variables linked primarily to deposition processes and included "forest woodland and scrub area" and "distance to coast". The meteorological variable "rain" and an indicator for NOx emissions "distance to EPA Integrated Pollution Control facilities" were also included in the final model. Our results demonstrate the potential effectiveness of land-use regression modelling in predicting ozone concentrations, at a scale relevant for ecosystem protection. [ABSTRACT FROM AUTHOR]

Price informativeness is the amount of private information incorporated into stock prices and measured by R 2 from regressions of stock returns on systematic risk factors. While R 2 is closely related with idiosyncratic volatility, it is not interchangeable because magnitude of systematic volatility simultaneously changes. By controlling for the systematic volatility using the double-sorting portfolio approach, we suggest a potential involvement of price informativeness with the idiosyncratic volatility puzzle. Both cross-sectional evidence of monotonic and inverse relationship between idiosyncratic volatility and R 2 and time-series evidence of disappearing alphas of the low-minus-high idiosyncratic volatility portfolios during recessions support an explanation of the idiosyncratic volatility puzzle in association with price informativeness. [ABSTRACT FROM AUTHOR]

Lap joints of 1.5 mm thin sheets of dissimilar hot rolled E250 with polymer sandwich steel (MPM) were produced by cold metal transfer (CMT) process, closer to the real application. The polymer layer polystyrene-butadiene-styrene sandwiched between two DC06 sheets of MPM was found to be intact at significantly lower heat inputs (0.16 − 0.24 kJ/mm), which otherwise had been quite challenging during TIG welding leading to huge rejections. After several iterative trials for acceptable weld quality, experiments were conducted as per L9 orthogonal array, Taguchi technique with welding speed (WS), wire feed rate (WFR) and welding torch orientation (TO) as the process parameters. The joints were investigated through optical macrograph, micrographs, hardness, tensile lap shear tests and fractography on the fractured specimen. Optimum parameters were determined for maximizing shear strength. The sample with optimized parameters exhibited 6% improvement in shear strength achieving 152.09 MPa and polymer layer retention. ANOVA analysis suggested WS to be most significant parameter with 69.85% contribution affecting shear strength. Coefficient of determination (R2) for the shear strength was 87.26% derived from the linear regression model. Significantly, lower error 0.96% computed from the confirmatory test concluded very effective optimization. Thin sheets of polymer sandwich steel (MPM) sheets were joined successfully with hot rolled E250 sheet without thermal disengagement of the polymer layer in MPM by CMT welding. Under optimal parameters MPM/E250 joints exhibited excellent mechanical properties. Fractography analysis revealed ductile failure mode. The error between experimental and predicted results by Taguchi was significantly lower and very well within the acceptable range. [ABSTRACT FROM AUTHOR]

In Turkey, the housing market is affected by various factors, and housing prices are shaped according to current conditions. In this study, the relationship between the housing price index (HPI) and economic variables (inflation and exchange rate) in Turkey was investigated. We used a 10-year time period for this study, from January-2010 to December-2019, with monthly data frequency. For our research, we employ the Wavelet Coherence Transformation (WCT) method. The results show that there is a positive relationship between inflation and housing prices in the short and long run. In addition, a continuous rise in inflation led to an increase in housing prices all over the period from 2010 to 2019. The findings of this study can aid in achieving the goal of the research by offering evidence-based perceptions of how housing prices and different economic variables are related. Housing costs in Turkey increased as a result of the substantial likelihood that the Turkish lira would weaken. The expansion of global inflation is a further anticipated factor contributing to the rise in housing costs in Turkey. This study can also be used by investors to help them decide whether to engage in the Turkish real estate industry.

This study tests the association between financial inclusion and energy poverty in Indonesia. It adopts multi-dimensional energy poverty and financial inclusion measures employing the 2018 Statistics Indonesia data. The energy poverty measures cover six indicators, i.e., the primary cooking fuel, indoor air pollution, lighting, ownership of household appliances, and supporting essential services such as education, entertainment, and communication. On the other hand, the financial inclusion variable involves household access to banks, credit, and insurance. In testing the financial inclusion-energy poverty association, the present study performs the Two-Stage Least Square (2SLS) method, involving household distance to the nearest financial facilities as an instrument variable (IV) to overcome the potential endogeneity problem from the reverse causality between the financial inclusion and energy poverty. This analysis reports a robust negative and significant association between financial inclusion and energy poverty, bringing potential critical notes for Indonesia's policy formulation against energy poverty.

ABSTRACTR2 and adjusted R2 may exaggerate a model’s true ability to predict the dependent variable in the presence of overfitting, whereas leave-one-out R2 (LOOR2) is robust to overfitting. We demonstrate this by replicating 279 regressions from 100 papers in top economics journals, where the median increases of R2 and adjusted R2 over LOOR2 reach 40.2% and 21.4% respectively. The inflation of test errors over training errors increases with the severity of overfitting as measured by the number of regressors and nonlinear terms, and the presence of outliers, but decreases with the sample size. These results are further validated by Monte Carlo simulations.

Studies of anthropological genetics and bioarcheology often examine the degree of among‐group variation in quantitative traits such as craniometrics and anthropometrics. One comparative index of among‐group differentiation is the minimum value of Wright's FST as estimated from quantitative traits. This measure has been used in certain population‐genetic applications such as comparison with FST estimated from genetic data, although some inferences are limited by how well the data and study design fit the underlying population‐genetic model. In many cases, all that is needed is a simple measure of among‐group variation. One such measure is R2, the proportion of total phenotypic variation accounted for by among‐group phenotypic variation, a measure easily obtained from analysis of variance and regression methods. This paper shows that R2 and minimum FST are closely related as MinFST≈R2/2−R2. R2 is computationally easy and may be useful in cases where all we need is a simple measure of relative among‐group differentiation. [ABSTRACT FROM AUTHOR]

This article explores the cross-sectional properties of household's food and non-food consumption expenditures conceptualized as inter-dependent spatial random processes within the West African Economic and Monetary Union (WAEMU). To this end, the paper applies spatio-temporal econometric methods to data extracted from the first edition of the Harmonized Survey on Household Living Conditions (EHCVM, 2018/2019). The findings from the unconditional distribution of household economic wellbeing (measured by nominal per-capita food and non-food expenditures) using the inequality indicators of the Atkinson and Gini coefficients, suggest the presence of cross-sectional inequalities in household food and non-food wellness within WAEMU. However, food wellness inequality appeared lower than that of non-food wellness across the 8 countries. In addition, the economic security analysis, based on the indices of Watts, Sen, and Foster (aplha=1), highlight important spatial heterogeneity in household's vulnerability to poverty within-and-between WAEMU country members. Moreover, estimation of the semiparametric bivariate Gaussian copula model of food and non-food consumption expenditures, shows their significant dependence with household's demographic and geospatial characteristics, as well as the economic and health characteristics of the household head. Indeed, among other key findings, a gender based gradient in household's food and non-food wellness at respectively 3.6 and 4.4% is observed in favor of male headed households; along with an Urban/Rural divide in food and non-food wellness at 20.7% and 27.3%, respectively, in favor of urban households. Therefore, in line with goals 1, 2, 4, 5, 9 and 10 of the United Nations 2030 Agenda for sustainable development, the paper provides key insights for evidence based policy making within WAEMU.

OBJECTIVE: Melatonin regulates the mammalian circadian rhythm and plays metabolic functions such as glucose homeostasis. Both melatonin receptors (MTNR1A and MTNR1B, encoded by the MTNR1A and MTNR1B genes, respectively) are expressed in pancreatic beta cells and mediate the glucometabolic roles of melatonin as well as insulin secretion. The MTNR1B gene is a well-known genetic risk factor in type 2 diabetes (T2D); however, little is known about the involvement of the MTNR1A gene in here T2D. We aimed to investigate whether MTNR1A is linked to and/or associated with familial T2D. SUBJECTS AND METHODS: We genotyped 14 single nucleotide polymorphisms within the MTNR1A gene in 212 peninsular Italian families with T2D. We performed parametric linkage and linkage disequilibrium analyses to investigate the role of MTNR1A variants in conferring T2D risk. We considered variants statistically significant if conferring linkage or linkage disequilibrium with p < 0.05. RESULTS: We found 3 novel variants (rs62350392, rs2119883, and rs13147179) significantly linked to and/or associated with T2D in multigenerational Italian families. CONCLUSIONS: This is the first study to report MTNR1A as a novel risk gene in T2D. Functional studies are needed to confirm these results. [ABSTRACT FROM AUTHOR]

This paper examines the non-linear integration between the real estate and stock market for a series of developed markets namely UK, Germany, Australia, Hong-Kong, Japan, Singapore and the US. The period of analysis covers different market phases for these countries. We examine the volatility dynamics of the real estate and stock market in the UK and Germany within a novel FIGARCH-BEKK model. Our results reveal evidence of a common long-term fractional integration between real estate and stock market for these two countries. Moreover, when there is a lower common order of fractional integration, there might also be a significant bilateral or unilateral volatility spillover effect between real estate and stock market. Robustness tests confirm the consistency of the FIGARCH-BEKK model even during the global financial crisis. Additional tests capture the existence of volatility spillovers and fractional integration in the rest of countries (Australia, Hong-Kong, Japan, Singapore and the US) under examination. Our findings entail significant implications for investors and policy makers. [ABSTRACT FROM AUTHOR]

Porosity (ɸ) and permeability (k) are two important rock properties used in oil, gas, and water resources calculations. The mathematical relationship between porosity and permeability is not easily demonstrated. Due to the heterogeneity of rock properties, there is no exact mathematical formula from which permeability could be calculated depending on porosity. Some researchers depend on the core analysis to introduce a general mathematical relation between ɸ and k, while others depend on the flow zone indicator (FZI) method to find such a relation. Recently, supervised machine learning has gained much popularity in establishing a relationship between complex non-linear datasets. This type of machine learning algorithm has shown its superiority over petroleum engineering regression techniques in terms of prediction errors for high dimensional data, computational power, and memory. In this work, the FZI method was applied to a data set for Khasib formation in the East Baghdad oil field to present a mathematical formula relating ɸ and k. In addition an AI algorithm was used to predict k depending on ɸ for the formation under study. Results proved that the predicted values of k had better agreement with the actual k values compared to the k values calculated using the FZI method. The accuracy of results is measured by calculating the coefficient of determination (R²). [ABSTRACT FROM AUTHOR]

Adsorption is kinetically time-dependence controlled retention/ release mobility as a natural phenomenon in base and applicable in industry or in science. It is well-studied and modulated by known Langmuir, Temkin, Freundlich, and other models to describe how it occurred and explains kinetic- thermodynamic material behaviour. Linear and/ or non- linear expressions may take place according to the theoretical base of these models to conclude the layer formation, uniformity besides reaction reversibility, and favourability from kinetic- thermodynamic principles. Coefficient of determination (R²) is a mean variation of data or a degree of proper or fitting as mostly used in kinetic and isotherm literatures. In adsorption investigations, experimental physical- chemical conditions and error sources are the main influenced factors, for example, at surface coverage (or inhibition efficiency) in corrosion treatments or adsorption capacity in pollution subject. Linearity variation will govern R² to predict adsorbate behaviour on adsorbent surface that is highly influenced by concentration, temperature, pH, type of measuring method, physical and chemical structures of the adsorbent and adsorbate, and error sources in each experiment. [ABSTRACT FROM AUTHOR]

Despite the clinical significance of prepulse inhibition (PPI), the mechanisms are not well understood. Herein, we present our investigation of PPI in the R1 component of electrically induced blink reflexes. The effect of a prepulse was explored with varying prepulse test intervals (PTIs) of 20–600 ms in 4 females and 12 males. Prepulse–test combinations included the following: stimulation of the supraorbital nerve (SON)–SON [Experiment (Exp) 1], sound–sound (Exp 2), the axon of the facial nerve–SON (Exp 3), sound–SON (Exp 4), and SON–SON with a long trial–trial interval (Exp 5). Results showed that (1) leading weak SON stimulation reduced SON-induced ipsilateral R1 with a maximum effect at a PTI of 140 ms, (2) the sound– sound paradigm resulted in a U-shaped inhibition time course of the auditory startle reflex (ASR) peaking at 140 ms PTI, (3) facial nerve stimulation showed only a weak effect on R1, (4) a weak sound prepulse facilitated R1 but strongly inhibited SON-induced late blink reflexes (LateRs) with a similar U-shaped curve, and (5) LateR in Exp 5 was almost completely absent at PTIs .80 ms. These results indicate that the principal sensory nucleus is responsible for R1 PPI. Inhibition of ASR or LateR occurs at a point in the startle reflex circuit where auditory and somatosensory signals converge. Although the two inhibitions are different in location, their similar time courses suggest similar neural mechanisms. As R1 has a simple circuit and is stable, R1 PPI helps to clarify PPI mechanisms. [ABSTRACT FROM AUTHOR]

We propose and evaluate two imputation methods for missing data of fine particulate matter on air. We assume a 24- variate normal distribution, one per weekday, due to the fact that the imputed data are hourly data on a 24-hour daily cycle. From this distribution properties, the imputation methods are based on the conditional distributions for missing hours, starting from hours with available records. We estimate the weekday variancecovariance matrix using two methods: maximum likelihood (denoted by ∑), and shrinkage (denoted ∑*). Afterwards, we verify the missing completely at random (MCAR) assumption using the Little’s test, and also de multivariate normality using the Mardia´s test. Finally, we evaluate the proposed methods through a simulation trial, generating suitable scenarios for this kind of problems. We use two evaluation criteria: the coefficient of determination (R 2 ) and the square root of the mean square error (RMSE). We use a 2018 data set from Cali, Colombia, to illustrate how to use the proposed methods. We reach R² values of around 0,70 and 0,49, and RMSE values of around 5,7 and 8,5, for the methods based on ∑ and ∑*, respectively. [ABSTRACT FROM AUTHOR]

Tropospheric ozone is a powerful oxidant that can damage living organisms; it is widely monitored, as air concentrations have more than doubled since the Industrial Revolution. However, in general air quality monitoring stations are limited spatially to large urban centres; accordingly, accurate prediction of concentrations outside of cities is important for protecting human and plant health. Land-use regression has been successfully used for modelling air pollutant concentrations by establishing a relationship between observed concentrations and landscape features representing sources and sinks. In this study, we developed a land-use regression model that explained 68% of the variance of summer average ozone concentrations in the Republic of Ireland. Ozone was measured at 14 active and 20 passive monitoring sites; air concentrations varied spatially, with the highest ozone measured in rural upland (64.5 µg/m3) and Atlantic coastal (50.2–60.5 µg/m3) sites and the lowest generally in urban centres (38.9–45.7 µg/m3). A total of 74 land-use predictor variables were tested, and their inclusion in the model was based on their impact on the coefficient of determination (R2). The final model included variables linked primarily to deposition processes and included “forest woodland and scrub area” and “distance to coast”. The meteorological variable “rain” and an indicator for NOx emissions “distance to EPA Integrated Pollution Control facilities” were also included in the final model. Our results demonstrate the potential effectiveness of land-use regression modelling in predicting ozone concentrations, at a scale relevant for ecosystem protection.

We document a reduction in police reporting of domestic violence during shelter-in-place (SIP) orders issued by city governments in March and April of 2020. Using data from 18 large U.S. city police departments, we exploit the unique timing of city-specific SIP orders to identify a decrease as high as 11 percent in police reports of domestic violence. Lockdowns, explicitly designed to reduce contact among civilian populations, may also lead to reduced contact between emergency responders and civilians. Our findings suggest police change their own behaviors in response to government-issued SIP orders leading to a decrease in agency reports of domestic violence. While previous scholarship on domestic violence has found an increase in emergency calls for service from households during SIP, we consistently find a decrease in police reporting of domestic violence. By centering our analysis on the responses of law enforcement agencies, we highlight the divergence in the consequences of SIP orders on household behaviors versus institutional behaviors. We present robust findings using event studies in addition to both staggered treatment and conventional difference-in-differences estimators. The implications of our study extend to city officials who are tasked with assessing the unique repercussions of SIP orders on both households and the responsiveness of local government agencies. This research underscores the multifaceted nature of the effects induced by external shocks such as COVID-19, shedding light on the intricate interplay between societal dynamics and institutional responses.

Near-infrared materials find extensive applications in bio -sensing, photodynamic treatment, anti-counterfeiting and opto -electronics. Their progress has notably expanded possibilities in optical communication systems, non-invasive imaging and targeted therapy, benefiting fields such as material science, medicine, tele-communication and biology. In light of these advancements, developments of near-infrared region (NIR) based probes are highly desirable. Moreover, the prediction of the optical properties of a compound prior to its synthesis can diminish the need for expensive experimental testing. Considering the importance of prior prediction, we herein present QSPR models for the prediction of absorption maxima using a dataset of 384 compounds. The aim of the present study is to identify molecular features that could shift their λ max in the near-infrared region. The Monte Carlo Optimization approach along with the index of ideality of correlation (TF 2) has been utilized using CORAL 2019 software for the development of ten splits. The predictability of the resulting ten models was assessed using various validation metrics. The model derived from the tenth split proved to be efficient, exhibiting R V a l i d a t i o n 2 = 0.8561 , I I C = 0.7849 a n d Q 2 = 0.8512. Good and bad fragments were also identified that are responsible for the change in absorption maxima (λ max). Identified fragments were utilized for designing ten new molecules to evaluate their reliability. It was observed that molecules designed using positive attributes shifted the absorption maxima towards the near-infrared region, specifically between 711 and 893 nm. This study opens up new possibilities for the advancement of NIR-based chromophores and will contribute significantly by reducing the overall cost of chromophore development. • QSPR models were developed for the prediction of absorption maxima. • The best QSPR model was observed with R2 val = 0.8561 & IIC = 0.7849. • Good and bad fragments were identified that are responsible for change in endpoint. • Identified fragments were utilized for designing of ten new molecules. • The positive attributes shifted the absorption maxima towards NIR region. [ABSTRACT FROM AUTHOR]

The aim of study was to investigate whether CASP8 (CASPASE8) could be a biomarker for prognosis in neuroblastoma. The prognostic value of CASP8 was determined by analyzing CASP8 methylation status and gene expressions in the tumor tissues of 37 neuroblastoma patients. Bisulfite and quantitative multiplex-methylation-specific polymerase chain reaction (PCR) were used to identify the methylation status. CASP8 messenger ribonucleic acid (RNA) expression levels were determined using reverse transcriptase–quantitative PCR. CASP8 expression levels associated with prognostic value were also analyzed using the TARGET NBL (141 cases) database through PDX for Childhood Cancer Therapeutics (PCAT) and SEQC (498 cases) via the R2 platform. CASP8 methylation status was associated with risk groups, MYCN amplification, and 17q gain status. CASP8 expression was found to be statistically different between high- and low-risk neuroblastoma groups. Low expression of CASP8 was associated with MYCN amplification status. Low expression of CASP8 has shown statistically significant prognostic value through TARGET NBL and SEQC-498 data sets. CASP8 messenger RNA expressions and methylation status were associated with the MYCN amplified high-risk group in neuroblastoma. CASP8 messenger RNA expressions may be considered as a clinical prognostic marker in neuroblastoma. [ABSTRACT FROM AUTHOR]