Recycled concrete aggregate (RC A) is considered a sustainable material; however, its porosity and interfacial properties are poor due to adhering mortar. This study investigates the influence of synergistic surface treatments in terms of improving RCA quality and the resulting compressive strength of recycled aggregate concrete (RAC). A machine learning (ML) model was also developed to predict the compressive strength of recycled aggregate concrete (RAC) with different surface treatments, not just untreated RCA. In this study, three different RCA surface treatments were investigated. In this regard, acetic acid, silica fume, and sodium silicate treatments were combined. The properties of concrete and fresh concrete were investigated using slump and compressive tests at 28 and 90 days. The performance of various ML models, incorporating Gradient Boosting, Random Forest, XGBoost, and Extra Trees, was investigated. The performance of different models was also evaluated using R2, MAE, and RMSE. SHAP analysis was used to evaluate the performance of different models. It has been observed that the use of surface treatment leads to lower water absorption values and higher interfacial bonding, as well as substantial improvements in compressive strength. Specifically, the use of acetic acid and silica fume for treating RCA produced compressive strengths similar to those achieved from natural aggregates at lower costs. XGBoost has the highest accuracy among all models. The R2 value of XGBoost was 0.909. The SHAP analysis indicates that cement and curing age are the main features. RCA treatment parameters are considered modifiers. A user-friendly online tool was created to estimate compressive strength using different types of RCA treatment. The RCA treatment with sodium silicate and silica fume performed best in terms of embodied carbon among the treated mixes; it was deemed the best alternative from an environmental standpoint.
Stigma towards People Who Use Drugs (PWUD) is reinforced through social structures and intersectional identities (e.g., gender, race, class). This often manifests as PWUD being The Other in society. This study aimed to explore how stigma manifests within the community environment using Community-Based Participatory Research framework and photovoice. Peer advocates (n = 15) from various Irish community-based organizations were trained in the photovoice technique to capture experiences of stigma. A series of seven focus group interviews were conducted. Photographs were analyzed using visual analysis, and interview data were analyzed using deductive codebook Thematic Analysis informed by Intersectional Stigma frameworks. The findings identified three major themes that demonstrate the multiple pathways through which social structures are used to reinforce stigma across intersectional identities possessed by PWUD. Peers experienced class-based discrimination in the accommodation sector, and urban hostile architecture reinforced internalized stigma for those who were unhoused. Being unhoused and unemployed was perceived to justify social rejection, public aggression, and violence towards PWUD. Peers described being unfairly targeted by police based on living in economically deprived neighborhoods. Structural stigma was evident in strict rules for entry into mental health clinics and was reinforced based on class and limited income. False complaints to family and social services appeared to justify police surveillance and invasion of privacy of woman identified peers. Stigma campaigns should account for the nuances and additive effects of intersectional identity, with an emphasis on gender and class of PWUD. Health professionals providing mental health services would benefit from reflexivity to explore how their position of power relates to the therapeutic process.
The demand for concrete has led to increased use of raw materials and significant waste generation. Recycled aggregate concrete (RAC) offers a viable approach to sustainable concrete; however, the use of weakly bonded mortar on aggregate leads to low strength and crack formation. Fiber reinforcement, specifically hybrid fiber reinforcement combining steel, glass, basalt, and polypropylene fibers, can increase the tensile and flexural properties of RAC. This study developed machine learning models to enable the prediction of hybrid fiber-reinforced RAC's compressive, splitting tensile, and flexural strength performance; these new models overcome the limitations of previous research, which relied on only one fiber type and regular methods of optimization. Two models (a deep neural network (DNN) and an XGBoost model) were trained and optimized using bald eagle search (BES), particle swarm optimization (PSO), and the Bayesian optimization (BO) algorithm to improve performance. Among the three optimization analyses, PSO-XGBoost achieved the highest accuracy for compressive strength and splitting tensile strength, while BES-XGBoost achieved the highest accuracy for flexural strength. The most significant influences on the compressive strength were curing age and silica fume, while the main drivers of splitting tensile strength and flexural strength were fiber volume and fiber characteristics. The use of SHAP-based methodology with a user-friendly interface further improved the design of RAC mixtures, reducing waste from raw materials, enhancing the structural performance of RAC, and enabling data-driven decision-making in the manufacturing of eco-friendly concrete products.
PurposeComparative policy analysis has become a popular tool for comparing policy developments and outcomes in different contexts. This paper compares policy responses to novel psychoactive substances (NPS) in two cases, Poland and Britain.Design/methodology/approachDrawing on frameworks from Habermas, Stevens and Zampini, this paper compares NPS policy developments in both countries, focusing on the roles of morality and power. The analysis is based on 33 qualitative interviews with senior police officers, academics, ministers, non-governmental organisations (NGOs) workers and other relevant stakeholders.FindingsThe analysis shows a clash between actors and organisations with contrasting normative understandings of NPS in Poland and Britain, including those who favoured prohibition and those who sought to use the NPS "crisis" as an opportunity to reform existing drug policies. Most notably, it shows differences in how power was deployed in each case, with Polish stakeholders tending to use institutional and legal power in a more open and direct way.Originality/valueThis paper shows that the more mediated and "covert" use of power in Britain may be partly shaped by longer traditions of the public sphere and democratic governance.
Generative Pretrained Transformers (GPTs) are foundational Large Language Models (LLMs) for text generation. However, individual LLMs often produce inconsistent outputs and exhibit biases, limiting their representation of diverse language patterns. The closed-source nature of many powerful LLMs further restricts industry applications due to data privacy concerns. Inspired by successes in text generation, LLM ensemble techniques are now increasingly explored for code generation. This article reviews these emerging ensemble approaches to enhance understanding, encourage further research, and promote practical implementation in both text and code generation. We categorize LLM ensembles into seven main methods - weight merging, knowledge fusion, mixture-of-experts, reward ensemble, output ensemble, routing, and cascading - analyzing capabilities of those approaches. Our findings highlight key benefits such as improved diversity representation, enhanced output quality, and greater application flexibility. These insights aid model selection for real-world tasks and crucially, lay groundwork for extending ensemble strategies to multimodal LLMs.
