Month: April 2024

Embedding Social Justice into Clinical Practice: A Framework for Early Career Social Workers

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Abstract

This article offers a framework to help graduating MSW students and early career clinical social workers become social justice practitioners. Beginning with the assertion that clinical social work has the potential to be a powerful force in the movement to create a more just and humane world, the authors outline and discuss key components that can provide the foundation for clinical social workers to meet that potential. The authors first provide a definition of social justice, anchored in an ethic of love, and discuss the compatibility of clinical social work and social justice. They then summarize a framework for social workers to develop a critical consciousness through ongoing critical reflection and action. Each component is discussed using personal examples to illustrate how it is part of a process that helps us critically understand ourselves as social workers, how we show up and use ourselves in practice, how we attempt to come into community with our clients, and how we try to disrupt practices that reinforce systems of control and hierarchy. The article ends by recommending resources to help enhance early career social workers’ personal and professional growth.

Socioeconomic anxieties and electoral polarization: insights from Belgian federal elections at the municipal level

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Abstract

Polarization is increasingly seen as a phenomenon with far reaching and precarious consequences that extend beyond politics. Despite the extensive attention polarization has received, two crucial aspects of this phenomenon remain relatively understudied: electoral polarization and its socioeconomic drivers. We argue that socioeconomic anxieties—the fear of losing one’s social position due to economic insecurity—play an important role in driving electoral polarization. To measure the level of electoral polarization, we develop an intuitive metric based on the dyadic ideological distance between voters. Using elections data from federal elections in Belgium, we study the electoral outcomes of the 300 Flemish municipalities, where we find a significant link between risk of poverty, income, and unemployment, on the one hand, and the level of electoral polarization on the other. This provides empirical support to our overarching hypothesis that socioeconomic anxiety plays a centrifugal electoral impact, amplifying the appeal of polar parties, thereby increasing the level of electoral polarization.

Socioeconomic anxieties and electoral polarization: insights from Belgian federal elections at the municipal level

Latest Results

Abstract

Polarization is increasingly seen as a phenomenon with far reaching and precarious consequences that extend beyond politics. Despite the extensive attention polarization has received, two crucial aspects of this phenomenon remain relatively understudied: electoral polarization and its socioeconomic drivers. We argue that socioeconomic anxieties—the fear of losing one’s social position due to economic insecurity—play an important role in driving electoral polarization. To measure the level of electoral polarization, we develop an intuitive metric based on the dyadic ideological distance between voters. Using elections data from federal elections in Belgium, we study the electoral outcomes of the 300 Flemish municipalities, where we find a significant link between risk of poverty, income, and unemployment, on the one hand, and the level of electoral polarization on the other. This provides empirical support to our overarching hypothesis that socioeconomic anxiety plays a centrifugal electoral impact, amplifying the appeal of polar parties, thereby increasing the level of electoral polarization.

Analysing the Determinants of Surface Solar Radiation with Tree-Based Machine Learning Methods: Case of Istanbul

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Abstract

This study estimates both hourly and daily Downward Surface Solar Radiation (SSR) in Istanbul while determining the importance of variables on SSR using tree-based machine learning methods, namely Decision Tree (DT), Random Forest (RF), and Gradient Boosted Regression Tree (GBRT). The hourly and daily data of climatic factors for the period between January 2016 and December 2020 are gathered from the European Centre for Medium-Range Weather Forecasts' (ECMWF) ERA5 reanalysis data sets. In addition to the meteorology data, hourly data of selected aerosols are obtained from the Ministry of Environment, Urbanization and Climate Change. Temperature, cloud coverage, ozone level, precipitation, pressure, and two components of wind speeds, PM10, PM2.5, and SO2 are utilized to train and test the established models. The model performances are determined with the out-of-bag errors by calculating R-squared, MSE, RMSE, and MBE. The GBRT model is found to be the most accurate model with the lowest error rates. Furthermore, this study provides the variable importance in determining the SSR. Although all models provide different values for the variable importance; temperature, ozone level, cloud coverage, and precipitation are found to be the most important variables in estimating daily SSR. For the hourly estimation, the time of day (hour) becomes the most important factor in addition to temperature, ozone level, and cloud coverage. Finally, this study shows that the tree-based machine learning methods used with these variables to estimate hourly and daily SSR results are very accurate when it is not possible to measure the SSR values directly.

Validation of selected gridded potential evapotranspiration datasets with ground-based observations over the Ogun-Osun River Basin, Nigeria

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Abstract

The impact of climate change on the hydrological cycle has spurred extensive research, particularly regarding potential evapotranspiration (PET), a crucial variable linking water, energy, carbon cycles, and ecosystem services. PET estimation usually relies on in situ weather station data, but data scarcity in regions like Nigeria’s Ogun-Osun Basin poses challenges. With few in situ ET monitoring stations, researchers have turned to alternative PET sources, such as satellite and reanalysis products. In this study, we evaluated four PET products in the Ogun-Osun Basin: Global Land Evaporation Amsterdam Model (GLEAM), hourly potential evapotranspiration (hPET), amine early warning systems network (NET) Land Data Assimilation System (FLDAS), and Global Land Data Assimilation System (GLDAS). We assessed monthly and annual timescales using statistical indicators such as the Pearson correlation coefficient (PCC/r), mean absolute error (M.A.E.), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBIAS). The results showed that hPET outperformed other PET datasets at the monthly scale, with the highest correlation, lowest errors, and minimal bias values (P.C.C. = 0.80, RMSE = 25.55, PBIAS = 13.62%). GLDAS dataset showed closer performance to the hPET dataset (P.C.C. = 0.61, RMSE = 94.76, PBIAS = 71.1%) and GLEAM (P.C.C. = 0.12, RMSE = 64.67, PBIAS = 73.52%). Moreover, the FLDAS dataset performed least compared to other assessed PET datasets. hPET’s overall better performance was further certified at the annual scale, again outperforming the other products across all performance indicators (PCC = 0.34, M.A.E. = 258.10, RMSE = 263.05). The performance of the other products was quite poor, but the GLEAM product came closest to hPET compared to the other assessed products (P.C.C. =  − 0.20, M.A.E. – 711.57, RMSE = 716.97). Overall, the hPET dominated all statistical indicators at both timescales, making it the best PET product among the ones evaluated by this study. The findings indicate that hPET is a reliable alternative source of PET data, which can greatly support future hydrological research and modelling in the Ogun-Osun Basin.

Modeling of temperature distribution and clad geometry of the molten pool during laser cladding of CoCrCuFeNi alloys

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Abstract

This work simulated a modified three-dimensional single-track finite element model and a temperature discrimination mechanism in order to investigate the variation of temperature field in stainless steel and the effect of technological parameters on the coating. Through theoretical calculation, the distinction between the thermal characteristics of CoCrCuFeNi powders and CoCrCuFeNi alloys was made to improve the precision of simulation findings. In addition, to more precisely represent the heat transfer, an asymmetric Gaussian was used to distributed heat source and exponentially change the laser energy to account for the attenuation of laser power along the z-axis. The absorption rate of the laser beam at different temperatures of the material was also considered. The data was curve-fitted to examine the impact of laser power and scanning speed on the cladding layer morphologies. The laser power was found to be proportional to the width and depth of the clad layer, whereas the laser scanning speed was found to be inversely related to the width and depth of the clad layer. The simulation results were basically matched to the experiment.

Modeling of temperature distribution and clad geometry of the molten pool during laser cladding of CoCrCuFeNi alloys

Latest Results

Abstract

This work simulated a modified three-dimensional single-track finite element model and a temperature discrimination mechanism in order to investigate the variation of temperature field in stainless steel and the effect of technological parameters on the coating. Through theoretical calculation, the distinction between the thermal characteristics of CoCrCuFeNi powders and CoCrCuFeNi alloys was made to improve the precision of simulation findings. In addition, to more precisely represent the heat transfer, an asymmetric Gaussian was used to distributed heat source and exponentially change the laser energy to account for the attenuation of laser power along the z-axis. The absorption rate of the laser beam at different temperatures of the material was also considered. The data was curve-fitted to examine the impact of laser power and scanning speed on the cladding layer morphologies. The laser power was found to be proportional to the width and depth of the clad layer, whereas the laser scanning speed was found to be inversely related to the width and depth of the clad layer. The simulation results were basically matched to the experiment.

A novel teeth segmentation on three-dimensional dental model using adaptive enhanced googlenet classifier

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Abstract

This study introduces a novel approach for the segmentation and classification of tooth types in 3D dental models, leveraging the Adaptive Enhanced GoogLeNet (AEG) classifier and a Custom Mask Convolutional Neural Network (CM-CNN). Motivated by the complexities and potential misinterpretations in manual or semi-automatic tooth segmentation, our method aims to automate and enhance the accuracy of dental image analysis. The 3D dental models are processed using CM-CNN for segmentation, followed by classification using the AEG classifier. The significance of this work lies in its potential to provide a reliable and efficient solution for precise tooth categorization, contributing to improved diagnostic procedures and treatment planning in dentistry. Comparative analyses demonstrate the superiority of the proposed method, achieving high accuracy, precision, recall, and F-measure when compared to conventional techniques such as traditional geometrical transformation and deep convolutional generative adversarial networks (TD-model) and deep CNN (DCNN). The outcomes suggest the method's practical applicability in real-world dental scenarios, offering a promising contribution to the ongoing integration of AI in dentistry. This study employs a dataset of 3D dental models, and its significance lies in addressing the challenges associated with tooth segmentation and classification in dental images. Dental X-ray pictures play a crucial role in predicting dental disorders, and precise tooth segmentation is essential for effective diagnosis and treatment planning. Manual or semi-automatic segmentation processes are prone to misinterpretations due to image noise and similarities between tooth types. The proposed method, integrating CM-CNN and AEG, aims to automate this process, providing accurate tooth categorization. The significance of achieving this automation is notable in enhancing the overall efficiency of dental image analysis, contributing to quicker and more accurate diagnoses. The outcomes of the study showcase the method's superiority over conventional techniques, indicating its potential impact on improving dental diagnostic procedures and treatment strategies, ultimately benefiting both dental practitioners and patients.

A novel teeth segmentation on three-dimensional dental model using adaptive enhanced googlenet classifier

Latest Results

Abstract

This study introduces a novel approach for the segmentation and classification of tooth types in 3D dental models, leveraging the Adaptive Enhanced GoogLeNet (AEG) classifier and a Custom Mask Convolutional Neural Network (CM-CNN). Motivated by the complexities and potential misinterpretations in manual or semi-automatic tooth segmentation, our method aims to automate and enhance the accuracy of dental image analysis. The 3D dental models are processed using CM-CNN for segmentation, followed by classification using the AEG classifier. The significance of this work lies in its potential to provide a reliable and efficient solution for precise tooth categorization, contributing to improved diagnostic procedures and treatment planning in dentistry. Comparative analyses demonstrate the superiority of the proposed method, achieving high accuracy, precision, recall, and F-measure when compared to conventional techniques such as traditional geometrical transformation and deep convolutional generative adversarial networks (TD-model) and deep CNN (DCNN). The outcomes suggest the method's practical applicability in real-world dental scenarios, offering a promising contribution to the ongoing integration of AI in dentistry. This study employs a dataset of 3D dental models, and its significance lies in addressing the challenges associated with tooth segmentation and classification in dental images. Dental X-ray pictures play a crucial role in predicting dental disorders, and precise tooth segmentation is essential for effective diagnosis and treatment planning. Manual or semi-automatic segmentation processes are prone to misinterpretations due to image noise and similarities between tooth types. The proposed method, integrating CM-CNN and AEG, aims to automate this process, providing accurate tooth categorization. The significance of achieving this automation is notable in enhancing the overall efficiency of dental image analysis, contributing to quicker and more accurate diagnoses. The outcomes of the study showcase the method's superiority over conventional techniques, indicating its potential impact on improving dental diagnostic procedures and treatment strategies, ultimately benefiting both dental practitioners and patients.

“Talking about something no one wants to talk about”—navigating hepatitis B-related work in remote Australian Aboriginal communities: a decade of learning and growth

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Abstract

Background

Chronic hepatitis B (CHB) is one of the leading causes of liver cirrhosis and liver cancer globally. In Australia, Aboriginal and Torres Strait Islander people of the Northern Territory (NT) have the highest prevalence of CHB (6%) and are six times more likely than non-Aboriginal people to be diagnosed with liver cancer. In 2010, a “liver one-stop shop” model of specialised care and research was initiated to address this disparity. Despite many challenges, the program was accepted in NT Aboriginal communities. This study aimed to identify the key elements linked to this success.

Methods

We conducted a retrospective case study using Stake’s methodology to understand the hepatitis B phenomenon. A constructivist approach allowed a holistic understanding from the real-life perspectives of those involved in the hepatitis B work. Information was sourced from the Aboriginal workforce, patients of remote clinics, interested community members and service providers.

Results

We identified six elements critical to the successful conduct of our hepatitis B program, which included the essential role of the local Aboriginal workforce, providing health education in a patient’s preferred language, addressing shame and stigma, respecting culture, taking time, and building trust in the community.

Conclusions

Commitment over the long term was crucial for the success of our hepatitis B program. Adhering to the identified elements was essential to create a culturally safe environment and engage more Aboriginal people in clinical care and research. This study provides powerful lessons and insights that can be applied to other programs and comparable settings worldwide.