Distinguishing the Social Sector: A Buffalo-Niagara Labor Market Study

The study focuses on the two largest parts of Buffalo-Niagara’s social sector: nonprofit and government employers

Ten Bee Species New to Green Roofs in the Chicago Area

Green roofs increasingly provide habitat for many insects in urban environments. Pollinators such as bees may utilize foraging and nesting resources provided by green roofs but few studies have documented which species occur in these novel habitats. This study identified bees from 26 species, 11 genera and 5 families collected from 7 green roofs using pan trapping methods over 2 years. Ten of these species have not previously been recorded on green roofs in the Chicago region. Although the majority of bee species collected were solitary, soil-nesting, and native to Illinois, the proportion of exotic species was high compared to previous collections from Chicago area green roofs and urban parks. Urban green roofs may enhance populations of both native and exotic bees, but their ability to support the same range of native diversity recorded from other urban habitats requires further investigation

Spatial modeling and uncertainty analysis for subsurface feature mapping : integration of geostatistical concepts and image-based machine learning model validation

Spatial modeling of subsurface features and uncertainty analysis plays a pivotal role in the integration of data analytics and machine learning techniques in the petroleum industry. As the energy landscape is always changing, and new technologies are emerging, the demand for accurate assessments of uncertainty to inform high-value decision-making is of utmost importance. Nonetheless, the same longstanding methods are used due to their simplicity and the lack of immediate necessity for change. However, with improvements and the implementation of proper workflows, the current methods for calculating uncertainties and validating machine learning models can be more effectively addressed. We developed multiscale methods for data analytics and machine learning. These approaches integrate geostatistical concepts to enhance the precision and reliability of subsurface modeling techniques. We address the challenge of integrating multiple datasets with varying accuracies and volume support sizes. We emphasize the importance of accounting for different sources of uncertainty in spatial modeling workflows. Leveraging geostatistical concepts, such as semivariograms, and dispersion variance, a novel approach is introduced to calculate a more precise measure of error when imputing smaller scale datasets with larger scale datasets. This refined measure of error allows for the direct integration of these datasets in spatial modeling workflows. Once all the uncertainty in our models is accounted for, we must check if our models are accurate. Therefore, we focus on the validation of machine learning models, particularly those tailored for image data. Image-based models often necessitate pre-processing steps, such as resizing and augmentation, to improve data quality for training. To ensure the performance and suitability of these models for real-world datasets, proper validation techniques are imperative. We propose integrating the concept of minimum acceptance criteria with the multi-scale Structural Similarity Index (MS-SSIM) for improved model checking. This enables a more accurate evaluation of model performance in reproducing original images and predicting new ones, surpassing conventional approaches such as mean squared error (MSE) and single-scale SSIM. Our multiscale approaches for data analytics and machine learning establish a comprehensive framework for addressing uncertainty and validating image-based models. The incorporation of geostatistical principles in calculating uncertainty and proper selection criteria for image-based model validation are showcased on subsurface data; however, they are versatile and applicable across various domains. Ultimately, they contribute to the safe and effective deployment of machine learning models for spatial modeling, advancing the field towards more reliable and informed decision-making.Petroleum and Geosystems Engineerin

Oscar Romero (Assignment #8)

The author was assigned to live in Oscar Romero hall of the Christian Witness Commons in his freshman year at Sacred Heart University. This was the inspiration for the student to delve deeper into the life of Oscar Romero and all the contributions he made to the world

Benchmark tests on heuristic methods in the darts game

Games are among problems that can be reduced to optimization, for which one of the most universal and productive solving method is a heuristic approach. In this article we present results of benchmark tests on using 5 heuristic methods to solve a physical model of the darts game. Discussion of the scores and conclusions from the research have shown that application of heuristic methods can simulate artificial intelligence as a regular player with very good results

Ten Bee Species New to Green Roofs in the Chicago Area

Green roofs increasingly provide habitat for many insects in urban environments. Pollinators such as bees may utilize foraging and nesting resources provided by green roofs but few studies have documented which species occur in these novel habitats. This study identified bees from 26 species, 11 genera and 5 families collected from 7 green roofs using pan trapping methods over 2 years. Ten of these species have not previously been recorded on green roofs in the Chicago region. Although the majority of bee species collected were solitary, soil-nesting, and native to Illinois, the proportion of exotic species was high compared to previous collections from Chicago area green roofs and urban parks. Urban green roofs may enhance populations of both native and exotic bees, but their ability to support the same range of native diversity recorded from other urban habitats requires further investigation

Oxidative stress in neurodegenerative diseases and ageing

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