Optimized strength predictions for 3D printed fiber-reinforced concrete:machine learning-driven insights

3D printed concrete (3DPC) has reshaped the building industry by producing intricate, tailored, and quick constructions in recent years. However, due to the complex interactions between the concrete's individual components, determining the compressive strength (CS) and flexural strength (FS) of 3D printed fiber-reinforced concrete (3DP-FRC) is challenging. The present study predicts the CS and FS using Soft Computing and Machine Learning (ML) models. Gene Expression Programming (GEP), K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), and Gradient Boosting (GB) were the predictive ML models that were employed. This analysis extends to measuring accuracy and interpretability, thus combining multi-model comparative ML analysis with GEP-based sensitivity analysis and SHapley Additive exPlanations (SHAP)-based interpretability into a single framework.The GB model was the most accurate in predicting CS, with mean absolute errors (MAE) and root mean square errors (RMSE) of 6.21 MPa and 7.83 MPa in the training stage, and 7.03 MPa and 8.84 MPa in the testing stage, respectively. The coefficients of determination (R2) remained above 0.92. For FS, the GB model again outperformed other models, yielding MAEs and RMSEs of 1.81 MPa and 2.18 MPa in training and 2.21 MPa and 2.18 MPa in evaluation, respectively, with R2 higher than 0.94. The second-best models were those from GEP, which achieved nearly equal accuracy and, moreover, produced explicit symbolic regression equations that can be used directly in engineering without requiring retraining.According to the sensitivity and SHAP analyses, the parameters most influential on CS were fiber volume fraction (Fvolf), water/binder ratio (W/B), and silica fume content (SF). For FS, Fvolf, W/B, and fly ash content (FA) were the dominant parameters. These results demonstrated the intricate relationships between mix design parameters and mechanical responses, providing an engineering-transparent, data-driven approach for optimizing 3DP-FRC mixtures.Among the early studies that integrate SHAP interpretability with multi-model ML benchmarking and GEP symbolic regression for 3DP-FRC, this work aims to fill the gap between accuracy and practicality with an approach that empowers engineers to design optimized mixes that satisfy determinate strength requirements, taking into account material and process variability. The acquired knowledge thus directs the path toward establishing protocols for quality control, enhanced performance prediction of structures, and ultimately, the faster implementation of 3DP-FRC into sustainable construction applications

The self-memory system:exploring developmental links between self and memory across early to late childhood

This study tests whether developments in self-knowledge and autobiographical memory across early to late childhood are related. Self-descriptions and autobiographical memory reports were collected from 379 three- to eleven-year-old predominantly white Scottish children, Mage = 90.3 months, SD = 31.1, 54% female. Episodic memory was measured in an enactment task involving recall and source monitoring of performed and witnessed actions. The volume and complexity of self-knowledge and autobiographical memory reports increased with age, as did source monitoring ability and recall bias for own actions. Regression analyses and structural equation modeling confirmed a close association between these developments. These results inform our theoretical understanding of the development of the self-memory system in childhood, which may contribute to the gradual offset of childhood amnesia.</p

Followership identity construction:an insight on the non-profit sector

PurposeThe purpose of this study was to understand and examine the relationship between follower self-identity and organisational commitment, with an examination of how trust indirectly influences the relationship between follower self-identities and organisational commitment.Design/methodology/approachTo test the established hypotheses, partial least squares structural equation modelling (PLS-SEM) was utilised. The data were obtained from 612 European board members in the non-profit sector from organisations in the UK, France, Germany and Switzerland.FindingsThe results show that the relationship between follower identity factor with trust and commitment are positive and significant. Results also show follower identity had an indirect effect on commitment through trust. The presence of a strong level of follower identity can help reinforce the trust between the chief executive and the governors/board members in the organisation.Originality/valueThis study is innovative in the sense that it seeks to gain a better understanding of the mediating role of trust between follower identify and commitment in the non-profit sector

ANN prediction analysis of engine performance and emissions characteristics using a new set of plastic pyrolysis oil blended with pure diesel

Artificial Neural Network a soft tool enabled by Artificial intelligence was utilized to accurately predict engine performance and emissions analysis using a new set of plastic pyrolysis oil blended with pure diesel. Under various loads, blends, and speeds, this chapter analyzed the effect of plastic pyrolysis oil (PPO) on a diesel engine's performance and emissions, which were used to forecast performance and emissions. The ANN models have the potential to produce a more accurate estimate of the brake thermal efficiency, brake-specific fuel consumption, and emission performance parameters

Borders, boats and barges:a UK post-election analysis

This paper considers policy and rhetoric surrounding the United Kingdom’s stance on asylum seekers and undocumented migrants. This is considered from a discourse analytic perspective by examining a corpus of data drawn from three sources at critical time points in recent months. One source of data is drawn from the record of a key debate in January 2024 in the House of Commons on the Rwanda Plan Cost and Asylum System. Another data source is the Safety of Rwanda (Asylum and Immigration) Bill itself and explanatory notes, presented in April 2024. The final data source is news media reporting of asylum seekers and undocumented migrants as an issue in the May-through July UK election campaign period and immediately post-election as the new Labour administration took office. The analysis reveals the different political stances taken on the issue of asylum seekers and the ways in which these are bound up with rhetorical constructions of deterrence, human rights, and practicality

The many paths ahead:toward an interdisciplinary framework for Critical Cycling Studies

This essay outlines our vision for Critical Cycling Studies (CCS), a framework for cycling-related research. At the core of CCS is a focus on interdisciplinarity and the reconceptualisation of cycles as “interfaces” that mediate the experiences of cycle users. CCS focuses not only on the object of the cycle as a physical interface, but also on the experience of cycling as a figurative “interface technology”: that is, cycling as a form of situated knowledge rooted in practice that mediates between cyclists, non-cyclists, and the ecological, social, political, and cultural environments in which cycling occurs, and which shapes the bodily, sensory, cognitive, and emotional states of cyclists. In moving from an object-oriented focus to a broader conceptual perspective on cycling, CCS adopts a fundamentally interdisciplinary approach that accounts for cycling as a complex, multi-valent experience and phenomenon. Research in cycling studies is already to some degree interdisciplinary; however, many existing approaches in cycling studies more closely resemble “cross-disciplinary” work, insofar as such research involves exchange between related disciplines with shared methodologies and objectives. To address this problem, this essay explores some fundamental difficulties of interdisciplinarity as such and proposes that CCS should in part adopt perspectives from the humanities, which have a long history of bridging distinct methodologies and overcoming disciplinary boundaries. On this basis, CCS is introduced as a humanities-informed, self-reflexive, and dynamic framework for research into cycles and cycling that invites revision and expansion by all contributors. CCS advocates an openness to boundary-crossing forms of interdisciplinarity, a willingness to reflect critically on one’s methods and disciplinary assumptions, and an awareness of the ways in which issues of power, class, gender, access, geography, culture, and identity have shaped and continue to shape cycling-related research and the experiences of cycle users themselves

Golden goal:altruism versus toxicity and the soul of football (soccer) simulators

This paper explores contrasting player behaviors in two of the largest football (soccer) franchises -EA Sports FC (formerly FIFA) and eFootball (formerly Pro Evolution Soccer). Each game has built up substantial global online player bases (Guins 2022) and offer a wide range of official tools and paratextual materials to engage with their respective communities. Outside the official ecosystem, players have found their own ways to create, improve, subvert and re-engage with the games. Whilst toxic in-game behaviors often attract attention, players often demonstrate altruistic behaviors and collective action that mirrors both wider fan behavior regarding the sport (e.g. commemoration or remembrance activities), standing up to the perceived owners and authorities that police the game (e.g. fan protests towards both game companies and football owners)or reflect a range of social, activism, political and non-political causes (e.g. support for Ukraine, Palestine, LGBT+ rights and various charities)

Understanding security practitioners’ experiences of investigating compromised email accounts

Cybersecurity breaches are a serious threat to economies and organisations across the globe in the digital landscape of today. Phishing attacks are one of the most common ways that these threats infiltrate businesses as they have developed into sophisticated strategies that make use of compromised accounts and exploit legitimate credentials for advanced attacks like lateral phishing. This paper investigates the processes employed by security practitioners in verifying the identity of account owners when suspecting a compromised account. Through semi-structured interviews with 13 cybersecurity professionals, we report on how practitioners are using diverse strategies for contacting suspected employees, including direct and indirect contact through line managers. We discuss the complexities in communication strategies during security incidents

A novel flow through assay and smartphone application based prototype for point-of-care diagnosis of tuberculosis

Affordable point-of-care test sensors with automated result recording are essential for reducing undetected tuberculosis cases in remote, resource-limited areas. Therefore, this study addresses this need with three key aims. First, we aimed to lower the costs of a patented flow-through assay (Kit and method: WO2016/024116A1) by developing an in-house method for producing antibody-coated gold nanoparticles (anti-IgG-AuNPs). These anti-IgG-AuNPs demonstrated specific binding with performance comparable to existing antibody-capped gold nanoparticles. The second aim was to transform the flow-through assay into a multi-disease screening tool by incorporating multiple antigen test spots. A newly designed wax-printed background allows for simultaneous testing of up to five antigens, delivering results within 15 min at the point-of-care, while also reducing assay costs by 70 %. Lastly, we developed a smartphone application (RAP-TBS) to provide quantitative analysis of the flow-through assay results. This user-friendly app requires no additional hardware and addresses the limitations of subjective visual interpretation.</p