“News Cabaret”: Live Journalism and Theatre “Making Human Contact Again with the News Agenda”

At a time of political polarisation, social fragmentation and continuing mistrust in journalism, the practice of “live journalism” is flourishing. The practice aims to reconnect audiences and rebuild trust in news organisations through interactive events in public spaces. The authors of this paper experimented with a new format of putting news on stage involving both journalists and actors using theatrical and comic techniques to tell exclusive, unpublished stories in a show called News Cabaret. This article explores the reactions of the audience and participants to the event. The show consisted of eight dramatic pieces of journalism involving verbatim theatre techniques, stand-up comedy, sketches, monologues, songs, masks and improvisation. Discussions between cast, crew and audience took place during and after the show. We adopted a Reflective Practitioner Case Study approach and analysed surveys, recordings, observations and interviews. Our research suggests that using actors and theatrical devices to deliver content did not detract from quality journalism and resulted in some unexpected positive outcomes. The audience reported that the event challenged them to think and prompted some action, albeit limited. Results show that such events could be useful to revitalise journalism practice, challenge social and political norms and re-engage hard-to-reach audiences

Troubled ontologies: an economisation approach to climate risk and its politics

Whether as economisation or performativity, scholars in market studies have problematised various entanglements between financial markets and climate change. Studies have identified, for instance, how notions of climate change were subjugated to the concepts and needs of financial actors in the form of climate risk. While some scholars have cast doubt on whether such an approach to govern climate change can succeed, these doubts rest on an implicit assumption of ontological stability in existing market arrangements. By contrast, and drawing on the economisation framework, we provide a theorisation of climate risk as a performative project in-the-making shaped by marketizing framing processes, highlighting its potential to successfully transform relations, identities and ontologies. Nevertheless, we also identify misfires and counterperformative moments, as well as instances where this transformative drive reinforces the unequal relations of financialised capitalism. Thus, our paper contributes to market studies by demonstrating the value of an economisation approach to climate risk. Furthermore, it advances a nascent post-performativity scholarship by proposing a novel conceptualisation of the politics of economisation

PROTECTION: Provably Robust Intrusion Detection system for IoT through recursive Delegation

The security of Internet of Things (IoT) ecosystems is crucial for maintaining user trust and facilitating widespread adoption. Machine Learning (ML) based Intrusion Detection and Prevention Systems (IDS/IPS) are frequently used to protect IoT networks, yet they are susceptible to adversarial attacks (AAs) and lack formal verifiability of their robustness. It has been demonstrated that meticulously designed AAs can alter the classification of ML-based IDSs, rendering them ineffective and posing risks to lives and physical infrastructure in safety-critical systems. This paper addresses these issues by introducing PROTECTION: a Provably RObust Intrusion DeTECTion system for IoT through recursive delegatION, which combines formal methods with ensemble machine learning. To enhance the robustness of ensemble ML models, we utilise Satisfiability-Modulo-Theory (SMT) to formally verify the classifier’s robustness, ensuring that output probabilities remain outside a thick decision boundary even when small perturbations are applied to the inputs. If a classifier fails to meet this criterion on any training sample, we reassign the training task to other classifiers that are iteratively trained until all samples are trained in accordance with the required property. The efficacy of the final ensemble model is thoroughly tested against various input perturbations and AAs using SMT based formal verification

Bayes or Pascal? The computations underlying motivated reasoning

The construct of motivated reasoning has inspired an influential body of research. However, most theories of this construct are expressed in a verbal form. This is somewhat limited in light of contemporary research in cognitive science that emphasizes the insight afforded by employing computational modeling. To address this, the paper introduces a computational model of motivated reasoning. The model builds on previous accounts of belief formation based on Bayesian inference by adding computations concerning value or utility. The result is an interpretation of motivated reasoning as being akin to a process reflecting an unconscious Bayesian decision, in a way that is reminiscent of the famous Pascal’s wager. This framework is broadly consistent with empirical evidence, especially about the effect of loss function asymmetries on probability judgments, about the confirmation bias, and about the backfire effect. Moreover, it is compatible with evolutionary explanations of motivated reasoning that interpret this phenomenon as ensuing from self-deception. The model helps understanding the computational principles behind the concept of motivated reasoning. Moreover, it facilitates the comparison between perspectives that downplay motivated reasoning and theories that emphasize its role. This may inform empirical research aimed at establishing the real contribution of motivated reasoning during belief formation

Race and redistribution in the United States: An experimental analysis

Scholars have suggested that White American support for welfare is influenced by their beliefs about the racial composition of welfare recipients. In this paper, we test this hypothesis using two experiments (n = 9,775) that induce random variation in participants’ beliefs about the racial distribution of welfare recipients. In both experiments, we obtain evidence that exogenously increasing beliefs about the share of welfare recipients who are Black reduces support for welfare. We also use our experiments to study the effect of ‘priming’ participants to think about race, the accuracy of beliefs about the racial distribution of welfare recipients, and the mechanisms that underpin our results

UC-PUAL: A universally consistent classifier of positive-unlabelled data

Positive-unlabelled (PU) learning is a challenging task in pattern recognition, as there are only labelled-positive instances and unlabelled instances available for the training of a classifier. The task becomes even harder when the PU data show an underlying trifurcate pattern that positive instances roughly distribute on both sides of ground-truth negative instances. To address this issue, we propose a universally consistent PU classifier with asymmetric loss (UC-PUAL) on positive instances. We also propose two three-block algorithms for non-convex optimisation to enable UC-PUAL to obtain linear and kernel-induced non-linear decision boundaries, respectively. Theoretical and experimental results verify the superiority of UC-PUAL. The code for UC-PUAL is available at https://github.com/tkks22123/UC-PUAL

Study on Failure Mechanism and Dynamic Response of RC Shear Wall in Tall Buildings under Impact Load

Currently, there are few studies on the impact resistance of reinforced concrete (RC) shear walls in tall buildings. To this end, the dynamic response and failure mode of RC shear walls under impact load were investigated experimentally and numerically. Six specimens were tested using a specialized pendulum impact rig. The parametric study was conducted to reveal the effects of wall height, impact position, reinforcement ratio, drop height, and energy consumption. Based on the experimental results, an analytical model is established to predict the maximum displacement under impact load. Furthermore, more parameters were quantified by the verified numerical model using LS-DYNA. The obtained results show that the drop height and reinforcement ratio have a significant effect on the peak impact force. When the impact energy is constant, the energy absorption performance of the specimen is negatively correlated to the overall wall stiffness. The parametric results of LS-DYNA show that an increment of the axial compression ratio and wall width will significantly reduce the maximum displacement at the center of the wall. When the impact energy is low, increasing the impact velocity has a more significant effect on the displacement difference than the impact mass

Inverse Machine Learning for the Design of Perforated Beams: Parent Section and Material Prediction

This study introduces a novel machine learning-based inverse design methodology for predicting the parent cross-sectional and material properties of perforated steel beams with elliptically-based openings. Unlike conventional forward design or optimization-based methods, the proposed ap-proach frames structural design as an inverse problem. It enables the direct mapping of opening ge-ometry and resistance parameters to essential properties such as section depth, flange width, web thickness, flange thickness, and yield strength. Five advanced supervised machine learning mod-els— Histogram-Based Gradient Boosting, Random Forest, Extreme Gradient Boosting, k-Nearest Neighbours, and Support Vector Regression—were trained on a dataset generated through forward structural analysis. This methodology develops non-iterative surrogate models that enhance the gen-eralizability and accessibility of the structural design process. The models demonstrated excellent predictive performance, with coefficient of determination (R²) values exceeding 0.99 for most out-puts. Shapley Additive Explanations (SHAP) analysis identified web-post buckling resistance and section height as the most influential input features, with other variables contributing depending on the output. The proposed inverse learning framework was also benchmarked against an analytical design model to assess accuracy and consistency. To support practical implementation, a user-friendly web tool was developed, allowing engineers and researchers to access instant prediction. Overall, this research offers an efficient and explainable data-driven solution for structural design, demonstrating the practical value of artificial intelligence in engineering applications

Behavior of two-storey welded steel frames strengthened with external prestressed strands

A possible solution for enhancing the resistance of welded steel frames against progressive collapse is external installation of prestressed strands. Although several experimental studies have been conducted on steel frames with prestressed strands, most of these studies have focused only on individual joints or single-story substructures, while neglecting the interaction between different stories in multi-story frames. The objective of this research is to conduct a detailed study on the impact of prestressed strands on the progressive collapse resistance of welded steel frame structures. Pushdown tests were performed on two-story steel frame substructures with and without prestressed strand strengthening. Additionally, corresponding numerical models were established using LS-DYNA. Further parametric analyses were conducted to investigate the effects of prestressing level, strand diameter, layout type, and lateral restraint stiffness on the collapse resistance. The results show that the load-carrying capacity of frames strengthened with prestressed strands is higher than that of bare steel frames, which is mainly attributed to the significant enhancement of the frame's catenary action (CA) capacity after prestressed strand strengthening. Furthermore, analysis of horizontal reaction forces indicates that there are differences in the CA resistance among different stories. Through numerical analyses, it is concluded that compared with polyline and diagonal layouts, the straight and parallel layout provide a more significant enhancement in the load resistance of steel frames

Validation of clinical tools to measure grating acuity and contrast sensitivity in children with cerebral visual impairment

There is a lack of validated clinical tools to measure visual functions in children with cerebral visual impairment (CVI). This study addresses this gap. Children aged 6 months-7 years with and without CVI (CVI, n = 111, mean age: 3.0 ± 1.9 years; 70.2 % male and without CVI, n = 50, mean age: 3.4 ± 1.9 years; 38 % male) were recruited. Grating acuity (GA) was evaluated using Teller Acuity Cards-II (TAC-II) and the Peekaboo Vision app (PV app), and contrast sensitivity (CS) using Hiding Heidi low contrast face cards (HH cards) and Ohio Contrast Cards (OCC). Retests were conducted within one month. The mean difference between the PV app and TAC-II was significant (CVI: −0.25 ± 0.40 logMAR, 95 % LoA: −1.03 to 0.53 logMAR; controls: −0.14 ± 0.30 logMAR, 95 % LoA: −0.72 to 0.44 logMAR). The median difference between the HH cards and OCC was also significant (CVI: 0.00 logCS, IQR: 0.25 logCS, 95 % LoA: −0.43 to 0.67 logCS; controls: 0.25 logCS, IQR: 0.00 logCS, 95 % LoA: −0.01 to 0.56 logCS). Intra-examiner repeatability analysis in children with CVI (n = 21) and controls (n = 16) revealed that TAC-II (CR, CVI = 0.47, controls = 0.27) had better repeatability than the PV app (CR, CVI = 0.99, controls = 0.41), while OCC (CR, CVI = 0.45, controls = 0.19) had better repeatability than HH cards (CR, CVI = 0.90, controls = 0.60). TAC-II and OCC demonstrated better repeatability and comparable testability, testing time, and engagement scores for GA and CS tests respectively in children with CVI. Findings indicate that clinical tools should not be used interchangeably, and clinicians must carefully interpret results based on each test’s repeatability indices