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We study the economic integration of immigrants from Ottoman Syria and Turkey and their offspring in the United States using full count census data from 1900 to 1940. Immigrants initially achieved occupational premiums due to their selection into high-reward industries, but 1940 earnings data reveals significant and growing disadvantages over time, partly due to lower educational attainment. In co

With Gaia Data Release 3 (DR3), new and improved astrometric, photometric, and spectroscopic measurements for 1.8 billion stars have become available. Alongside this wealth of new data, however, there are challenges in finding efficient and accurate computational methods for their analysis. In this paper, we explore the feasibility of using machine learning regression as a method of extracting bas

Achieving sustainable water resource utilization requires a holistic management approach that recognizes the knowledge and values of different stakeholders through a comprehensive participation approach. This study evaluates the indigenous knowledge and technologies employed in watershed management by analyzing qualitative and quantitative data from six villages in the Mara River Basin area. Data

The Loess Plateau is one of the most severely affected regions in China by soil erosion, where vegetation restoration is the principal strategy for soil and water conservation. Runoff and sediment reduction benefits (RRB and SRB) are key indicators used to evaluate the effectiveness of vegetation measures. While numerous observational and experimental studies have explored the effects of different

[Background] The previous studies suggest that radioactive elements like Cs+and Sr may adversely affect marine ecosystems and the fishing industry. Traditional treatment systems for radioactive wastewater like the Advanced Liquid Processing System (ALPS) and Kurion have faced challenges in limiting concentration and achieving safety criteria. Studies suggest potential long-term impacts on benthic

A new metaheuristic optimizer combined with artificial neural networks is proposed for streamflow prediction. Hence, the study aimed to forecast monthly streamflow of the main rivers in Urmia, Iran, by considering data shortage and using artificial neural network (ANN) models. By combining three variables: temperature, precipitation, and streamflow, we formulated five patterns, where 70% of the da

Pan Evaporation (Ep) plays a pivotal role in water resource management, particularly in arid and semi-arid regions. This study assesses the predictive performance of a comprehensive range of advanced machine learning (ML) and deep learning (DL) algorithms for Ep prediction using readily available environmental sensing data. The models investigated include M5 Prime (M5P), M5Rule (M5R), Kstar, as we

Aquifer classification plays a pivotal role in understanding groundwater dynamics and informing sustainable water resource management, especially in regions under significant stress from over-extraction. This study presents a novel remote sensing-based methodology for classifying aquifers represented by monitoring wells within the study area. The approach integrates stress-strain analysis, incorpo

Groundwater drought, a significant natural disaster in arid and semi-arid regions, contributes to numerous consecutive issues. Due to the inherent complexity of groundwater flow systems, accurately quantifying and describing this phenomenon remains a challenging task. As a result of excessive agricultural development, the Marand Plain in northwestern Iran is experiencing both groundwater drought a

Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal alloca

The Baltic Sea is one of the fastest-warming marginal seas globally, and its temperature rise has adversely affected its physical and biochemical characteristics. In this study, forty years (1982–2021) of sea surface temperature (SST) data from the advanced very high resolution radiometer (AVHRR) were used to investigate spatial and temporal SST variability of the Baltic Sea. To this end, annual m

Groundwater contamination caused by the use of the aqueous film-forming foam (AFFF) containing per- and polyfluoroalkyl substances (PFAS) was investigated in southern Sweden. ΣPFAS concentrations in groundwater ranged between 20 and 20,000 ng L−1; PFAS composition was primarily represented by PFOS and PFHxS. The PFAS chain length was suggested to have an impact on the contaminant distribution and

In view of climate change, increasing soil salinity is expected worldwide. It is therefore important to improve prediction ability of plant salinity effects. For this purpose, brackish/saline irrigation water from two areas in central and coastal Tunisia was sampled. The water samples were classified as C3 (EC: 2.01–2.24 dS m−1) and C4 (EC: 3.46–7.00 dS m−1), indicating that the water was question

Land cover changes threaten biodiversity by impacting the natural habitats and require careful and continuous assessment. The standard approach for assessing these changes is land cover modeling. The present study investigated the spatio-temporal changes in Land Use Land Cover (LULC) in the Gorgan River Basin (GRB) during the 1990–2020 period and predicted the changes by 2040. First, a change anal

BACKGROUND: Emergency department (ED) crowding causes increased patient morbidity and mortality. ED occupancy rate (OR; patients by treatment beds) is a common measure of crowding, but the comparability of ORs between EDs is unknown. The objective of this investigation was to investigate differences in ORs between EDs using staff-perceived workload as reference.METHODS: This was a national cross-s

In Streamflow prediction the most important triggering/controlling variables are related to climate, physiography, and landscape patterns. This study investigated the effect of different landscape metrics to relate spatial patterns to surface runoff processes and predict monthly streamflow using climatic and physiographic variables for the 42 sub-basins of the Urmia Lake Basin in Iran. We develope

Climate change has directly and indirectly impacted natural and human systems across the globe in recent decades.

Water scarcity is one of the major challenges in semi-arid and arid areas. Drivers for water scarcity include climate change, population growth, economic, and agricultural development. This paper presents a framework for assessing water scarcity under the impact of climate, industry, and socio-economic changes in the Qazvin Plain, Iran. A system dynamics model is developed and calibrated using his