Making Waves: Early Childhood Teachers’ Experiences with Multicultural Picturebooks to Promote Equitable Classrooms

Abstract

The purpose of this phenomenological study was to explore the ways that early childhood teachers were “making waves” as they fostered equitable classrooms through multicultural picturebooks. Through a thematic analysis of one-on-one interviews and a virtual book selection simulation, five early childhood teachers offered their insights on the potential barriers teachers may face in selecting and using such books in their classrooms, as well as their suggestions for curating classroom libraries that highlight books that serve as mirrors, windows, and doors for children. These insights have the potential to support other early childhood teachers as they make waves of their own and leverage multicultural children’s literature to build equitable classrooms.

A Snapshot of Early Childhood Teachers’ Read-Aloud Selections

Abstract

The practice of reading aloud to children is ubiquitous in early childhood classrooms. Teachers read aloud to young children to entertain, to build early literacy skills, to develop domain specific content knowledge and vocabulary, to promote social and emotional development and well-being, and to draw children into community with each other and the world. The types of texts teachers decide to immerse children in matters: children need opportunities to examine fiction and nonfiction texts, to learn from and about history, to wonder about phenomena in their natural, physical, and social worlds. This study explores the range of titles that 445 early childhood teachers reported reading with their students at a single timepoint. It describes the variety of fiction and nonfiction texts teachers reported reading and surfaces rich culturally relevant literature selections use with young children.

Reviewing the science on 50 years of conservation: Knowledge production biases and lessons for practice

Abstract

Drawing on 662 studies from 102 countries, we present a systematic review of published empirical studies about site-level biodiversity conservation initiated between 1970 and 2019. Within this sample, we find that knowledge production about the Global South is largely produced by researchers in the Global North, implying a neocolonial power dynamic. We also find evidence of bias in reported ecological outcomes linked to lack of independence in scientific studies, serving to uphold narratives about who should lead conservation. We explore relationships in the sample studies between conservation initiative types, the extent of Indigenous Peoples’ and local communities’ influence in governance, and reported social and ecological outcomes. Findings reveal positive ecological and social outcomes are strongly associated with higher levels of influence of Indigenous Peoples and local communities and their institutions, implying equity in conservation practice should be advanced not only for moral reasons, but because it can enhance conservation effectiveness.

Physicochemical Properties of Two Mexican Stingless Bee Honeys to Strengthen Their Biocultural Value

Abstract

Honey production in Mexico is an ancient economic, social, and biocultural activity for indigenous communities, Mayas, and Totonacs, among others. Stingless bee honeys are used in traditional medicine to treat gastrointestinal, respiratory, dermatological, and ophthalmic ailments as they contain compounds with antioxidant, antibacterial, anti-inflammatory, and antifungal activities. Here, a comprehensive physicochemical characterization of Mexican stingless bee honeys is presented as a contribution to strengthening the practice of meliponiculture by native Mayan and Totonac communities. By delivering information on the main physicochemical parameters for the honeys of Melipona beecheii and Scaptotrigona mexicana, it is intended to increase their commercial and biocultural value in different regions of Mexico, taking into consideration the different zootechnical management techniques used by these communities. A statistical analysis was performed to identify if there were significant differences in the physicochemical parameters evaluated. Significant differences were observed in moisture level, pH, sugar composition, and HMF content between at least two of the honeys analyzed. Furthermore, a principal component analysis confirmed these differences by showing a distinct profile for Scaptotrigona mexicana honey when compared to the honeys of two other species. Here, the information that stingless beekeepers can use to classify and characterize the honeys they produce is provided. This information will complement the indisputable efforts by native populations to conserve biological biodiversity, the defense of their territories, and the various ancestral practices employed for the breeding, management, and reproduction of stingless bees. Furthermore, this information will help to increase the economic sustainability of meliponiculture by these communities.

Graphical Abstract

Assessment of Seasonal Rainfall Prediction in Ethiopia: Evaluating a Dynamic Recurrent Neural Network to Downscale ECMWF-SEAS5 Rainfall

Abstract

Seasonal rainfall plays a vital role in both environmental dynamics and decision-making for rainfed agriculture in Ethiopia, a country often impacted by extreme climate events such as drought and flooding. Predicting the onset of the rainy season and providing localized rainfall forecasts for Ethiopia is challenging due to the changing spatiotemporal patterns and the country’s rugged topography. The Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), ERA5-Land total precipitation and temperature data are used from 1981–2022 to predict spatial rainfall by applying an artificial neural network (ANN). The recurrent neural network (RNN) is a nonlinear autoregressive network with exogenous input (NARX), which includes feed-forward connections and multiple network layers, employing the Levenberg Marquait algorithm. This method is applied to downscale data from the European Centre for Medium-range Weather Forecasts fifth-generation seasonal forecast system (ECMWF-SEAS5) and the Euro-Mediterranean Centre for Climate Change (CMCC) to the specific locations of rainfall stations in Ethiopia for the period 1980–2020. Across the stations, the results of NARX exhibit strong associations and reduced errors. The statistical results indicate that, except for the southwestern Ethiopian highlands, the downscaled monthly precipitation data exhibits high skill scores compared to the station records, demonstrating the effectiveness of the NARX approach for predicting local seasonal rainfall in Ethiopia’s complex terrain. In addition to this spatial ANN of the summer season precipitation, temperature, as well as the combination of these two variables, show promising results.

Estimation of Sentinel-1 derived soil moisture using modified Dubois model

Abstract

Surface soil moisture plays a crucial role in various fields such as climate change, agronomy, water resources, and many other scientific and engineering domains. Accurately measuring soil moisture at both regional and global scales, with high spatial and temporal resolution, is essential for predicting and managing floods, droughts, and agricultural productivity to ensure food security. The launch of Sentinel operational satellites has significantly advanced remote sensing observations, enabling scientists to estimate soil moisture more accurately at improved spatial and temporal resolutions. This study aims to assess the potential of utilizing Sentinel-1A satellite images for soil moisture estimation in a semi-arid region using the Modified Dubois Model (MDM) semi-empirical model with Topp’s model. The soil moisture estimated is validated by comparing it with field measurements, which helps in understanding the spatial variability of soil moisture across various land use classes. Results concluded that the Sentinel 1 derived soil moisture on 3rd and 15th January 2022 in comparison with the soil moisture measured using soil moisture probe (R2 = 0.68 and 0.63) and laboratory measurement (R2 = 0.72 and 0.72) are found to be well correlated and can be adapted for monitoring drought and managing water resources. The study offers a robust accuracy assessment of Sentinel 1 derived soil moisture using soil moisture probe and laboratory analysis and suggests that the framework has the potential for operational monitoring of drought conditions and water resource management in semi-arid regions at a higher spatial and temporal resolution.

Visual MODFLOW, solute transport modeling, and remote sensing techniques for adapting aquifer potentiality under reclamation and climate change impacts in coastal aquifer

Abstract

Global environmental changes, such as climate change and reclamation alterations, significantly influence hydrological processes, leading to hydrologic nonstationarity and challenges in managing water availability and distribution. This study introduces a conceptual underpinning for the rational development and sustainability of groundwater resources. As one of the areas intended for the development projects within the Egyptian national plan for the reclamation of one and a half million acres; hundreds of pumping wells were constructed in the Moghra area to fulfill the reclamation demand. This study investigates the long-term impacts of exploiting the drilled pumping wells under climate change. The approach is to monitor the groundwater levels and the salinity values in the Moghra aquifer with various operational strategies and present proposed sustainable development scenarios. The impact of global warming and climate change is estimated for a prediction period of 30 years by using satellite data, time series geographical analysis, and statistical modeling. Using MODFLOW and Solute Transport (MT3DMS) modules of Visual MODFLOW USGS 2005 software, a three-dimensional (3D) finite-difference model is created to simulate groundwater flow and salinity distribution in the Moghra aquifer with the input of forecast downscaling (2020–2050) of main climatic parameters (PPT, ET, and Temp). The optimal adaptation-integrated scenario to cope with long-term groundwater withdrawal and climate change impacts is achieved when the Ministry of Irrigation and Water Resources (MWRI) recommends that the maximum drawdown shouldn’t be more significant than 1.0 m/ year. In this scenario, 1,500 pumping wells are distributed with an equal space of 500 m, a pumping rate of 1,200 m3/day and input the forecast of the most significant climatic parameters after 30 years. The output results of this scenario revealed a drawdown level of 42 m and a groundwater salinity value of 16,000 mg/l. Climate change has an evident impact on groundwater quantity and quality, particularly in the unconfined coastal aquifer, which is vulnerable to saltwater intrusion and pollution of drinking water resources. The relationship between climate change and the hydrologic cycle is crucial for predicting future water availability and addressing water-related issues.

Evaluation and comparison of the performances of the CMIP5 and CMIP6 models in reproducing extreme rainfall in the Upper Blue Nile basin of Ethiopia

Abstract

Understanding the characteristics of extreme rainfall is vital for planning effective adaptation and mitigation measures. Thus, this study aims to evaluate the performances of 16 general circulation models (GCMs) of the coupled model intercomparison project (8 CMIP5 and 8 CMIP6) in reproducing observed extreme rainfall indices (ERFIs) and monthly rainfall in the Upper Blue Nile (UBN) basin of Ethiopia (1981–2005). The observed ERFIs were computed based on rainfall estimates of the random forest merging (RF-MERGE) algorithm, which combines ground-based rainfall with three gridded rainfall products (GRFPs). The GCMs were evaluated using statistical performance measures such as the Pearson correlation coefficient (R), root mean square error (RMSE), and percent bias (PBIAS). Using the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS), the GCMs were ranked from least skilled to most skilled. Accordingly, MIROC5 of the CMIP5 and MPI-ESM1-2-LR of the CMIP6 models were found to be the most suitable models. Furthermore, the top-ranked models were selected and bias corrected using quantile mapping (QM), and their ensembles (CMIP5-ensemble and CMIP6-ensemble) were used for projecting future extremes under representative concentration pathways (RCP4.5 and RCP8.5) and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5), respectively, for the periods 2031–2055 and 2056–2080. Most of the ERFIs exhibited high variability and inconsistent trends for both the observations and future periods. The findings of this study provide valuable insights the the impacts of climate change on ERFIs, and the developed framework can serves as a useful reference for future research.

Implementation Science for HIV Prevention and Treatment in Indigenous Communities: a Systematic Review and Commentary

Abstract

Purpose of Review

We systematically reviewed implementation research conducted in Indigenous communities in the Americas and the Pacific that focused on improving delivery of HIV preventive or treatment services. We highlight strengths and opportunities in the literature and outline principles for Indigenous-led, HIV-related implementation science.

Recent Findings

We identified 31 studies, revealing a consistent emphasis on cultural tailoring of services to Indigenous communities. Common barriers to implementation included stigma, geographic limitations, confidentiality concerns, language barriers, and mistrust. Community involvement in intervention development and delivery emerged as a key facilitator, and nearly half of the studies used community-based participatory research methods. While behavioral HIV prevention, especially among Indigenous youth, was a major focus, there was limited research on biomedical HIV prevention and treatment. No randomized implementation trials were identified.

Summary

The findings underscore the importance of community engagement, the need for interventions developed within Indigenous communities rather than merely adapted, and the value of addressing the social determinants of implementation success. Aligned to these principles, an indigenized implementation science could enhance the acceptability and reach of critical HIV preventive and treatment services in Indigenous communities while also honoring their knowledge, wisdom, and strength.

System Design, Automatic Data Collection Framework and Embedded Software Development of Internet of Things (IoT) for Air Pollution Monitoring of Nagpur Metropolis

Abstract

In the era of smart computing, edge computing, and machine intelligence, the Internet of Things (IoT) is playing a greater role in establishing hyper connective, cost-effective infrastructure for monitoring the environment. With the increase in the level of urbanization and population density in very fast-growing cities like Nagpur, the increase in air pollution needs to be monitored. This requires a network of Pollution monitoring systems for carrying out spatio-temporal analysis of pollution in the city on a real-time basis. Such established networks can be a key to understand the sources of pollution under various city conditions. To monitor and manage air pollutants, it is essential to put in place monitoring stations at multiple places. Although commercial pollution monitoring stations exist, they are limited in number. In this study, an attempt has been made to develop and implement a network of IoT devices using cost effective Metal Oxide Semiconductor based gas sensors integrated with ATMEGA 328P Microcontroller. Commercial systems are found to be space, energy and cost expensive. The developed pollution monitoring system can be replicated easily since they are compact in size, cost-effective, network and energy independent. This study discusses the development and implementation of a network of 10 smart IoT sensors in the Nagpur metropolis. The developed smart air pollution monitoring system combines IoT technology with real-time pollution monitoring systems. It measures and monitors temperature, humidity and pollutant concentration of Carbon Monoxide, Ozone, Carbon Dioxide, Sulphur Dioxide and PM2.5 and Nitrous oxides simultaneously. The study envisages to support Sustainable Development Goals – SDG11 which aims to reduce the environmental impact of cities by improving air quality.