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Cinema Theatres from Within: Giornale dello Spettacolo’s Success, Longevity, and Data Abundance
Get PDFIn 1945, the Associazione generale italiana dello spettacolo (General Italian Association for Entertainment, or AGIS) was established in Italy with the aim of uniting national cinema, theater, music, opera, and dance associations in order to represent their needs and interests. Simultaneously, the association’s biweekly trade journal, Bollettino di informazione, emerged, and it continues to represent the perspective of the entertainment industry today. Focusing on the editorial commitments of the journal, whose name became Il Giornale dello Spettacolo in 1957, this essay aims to investigate its audience, circulation, emergence, and strategies. A unique wealth of information for researchers investigating the history of cinema and the film industry, the Giornale dello Spettacolo (GdS) is now available online. This chapter will first appraise the emergence of the journal in the historical context of postwar Italy, the most significant period of modernization of the national cultural industry. It will then briefly provide an overview of the different phases of the journal from 1945 to today, highlighting the significant changes it went through and the ways in which the cultural sectors engaged with it. Lastly, it will present the 1950s cinema section as a case study to investigate the journal’s unique traits in the mediascape of that time and to finally explore what resources it has provided today to scholars interested in analyzing and commenting on the figures of the film industry in Italy
How to install LibKey Nomad
Get PDFInstructions for Oxford Brookes University members on how to install the LibkKey Nomad browser plugin. This plugin provides instant links to the full text of articles via the Library when searching or browsing onlin
Evaluation of thermal performance of the MGU-K for 2026 F1 power unit regulations
Get PDFThe 2026 Formula One (F1) power unit (PU) regulations introduce significant changes, particularly in the Motor Generator Unit-Kinetic (MGU-K), allowing for increased energy recovery under braking and greater energy deployment per lap. These changes are expected to lead to higher heat generation within the electric motor during the energy recovery and deployment phases. This paper presents a methodology for assessing the thermal performance of the MGU-K under the 2026 regulations, with a comparative analysis against the 2024 powertrain configuration. A hybrid F1 powertrain model, coupled with rule-based control logic, was developed in GT-Drive, adhering to both FIA 2024 and 2026 regulations. Validation of the powertrain model was performed using 2023 Q3 data. The heat loss data derived from the simulations were integrated into a hybrid thermal model, combining lumped parameter networks with finite element thermal analysis, to predict temperature increases. Based on these findings, the paper proposes cooling system specifications designed to meet the thermal demands of the 2026 regulations, per IEC 60034-1 insulation classifications
Modelling and simulation of a fuel cell powertrain for extreme H applications
Get PDFThis paper aims to model and simulate a design specification for a fuel cell electric powertrain tailored for Extreme H motorsport applications. A comprehensive numerical model of the powertrain was constructed using GT-Suite, integrating the 2025 Extreme H regulations, which include specifications for the fuel cell stack, electric motors, hydrogen storage, and battery systems. A detailed drive cycle representing the real-world driving patterns of Extreme E vehicles was developed, utilising kinematic parameters derived from literature and real-world data. The performance of the Extreme H powertrain was benchmarked against the Toyota Mirai fuel cell vehicle to validate the simulation accuracy under the same racing conditions. The proposed design delivers a maximum power output of 400 kW, with 75 kW supplied by the fuel cell and 325 kW by the battery, ensuring optimal performance within the constraints set by the Extreme H 2025 regulations. Additionally, the design maintains an optimal fuel cell operating temperature of 81°C, as indicated in the literature. The logical methodology employed for developing the powertrain, which includes integrating regulations, designing the drive cycle, and optimising the performance envelope, is elaborated in this paper
Does how I feel change how I move? : the influence of anxiety, self-efficacy and resilience on movement in adults with developmental coordination disorder
Get PDFBackground
Anxiety and movement consistency both influence movement in individuals with Developmental Coordination Disorder (DCD).
Aims.
This study investigated the influence of anxiety, self-efficacy, resilience and movement variability on perceptions and actions of adults with and without DCD.
Methods.
17 adults with DCD and 17 adults with typical motor skills (TMS) (age and sex-matched) completed a questionnaire and three experimental tasks: two perceptual judgement tasks (static and dynamic conditions), and an executed action task involving judging and walking through different-sized gaps between doors.
Results.
No significant relationships were detected between general or movement-specific anxiety, self-efficacy or resilience and perceptual judgement or movement behaviour; however, movement consistency did significantly relate to movement execution in both groups. Correlations showed adults with DCD with lower movement-specific self-efficacy left bigger safety margins, and indicated stronger links between perception and action in TMS adults. In the adults with DCD there was no significant correlation between the point of behaviour change (critical ratio) in the perceptual judgement and executed action tasks, suggesting a less smoothly linked perception-action cycle than in the TMS adults.
Conclusions and implications.
Results highlight the importance of movement variability and motor control in the movement behaviour of adults with DCD, while illustrating the importance of studying perception and action together, especially when comparing populations, to elucidate how these may be constrained differently by individual-, task- and environmental-based constraints
Leveraging explainable AI for early risk prediction and type classification for leukemia : insights using clinical data from Pakistan
Get PDFLeukemia, a prevalent childhood cancer affecting the blood and bone marrow, necessitates a proactive approach to risk mitigation through lifestyle adaptations. Despite previous investigations into similar aspects across different cancer types, a comprehensive inquiry focusing on all leukemia subtypes within Pakistan remains a significant research gap. Acknowledging the influence of regional variations on individuals' lifestyles, this study aims to identify lifestyle and demographic factors associated with leukemia development and predict specific leukemia subtypes using clinical data. Our data collection included 364 leukemia cases and 896 control subjects, gathered from different cancer or tertiary care hospitals in Islamabad and Peshawar, Pakistan. The data was meticulously categorized into laboratory results, demographic characteristics, and lifestyle parameters. For demographic and lifestyle analysis, we employed advanced techniques of Machine Learning, and statistical and graph-based methodologies to assess leukemia development risk. This study highlights factors associated with a higher risk of leukemia, including passive smoking, rural residence, and poor nutrition. These insights emphasize the promotion of healthier lifestyle choices to potentially reduce leukemia incidences. Additionally, we transformed the clinical dataset into graph data, which was utilized for leukemia classification and subtype prediction. We conducted classification on both graph and structured (tabular) data, with the structured data, achieving a 96% accuracy rate, notably on oversampled data. To enhance the interoperability of our classification outcomes, we employed the SHapley Additive exPlanations (SHAP) algorithm to explain the classification, offering comprehensive insights into the rationale behind leukemia classification
CreINNS : credal-set interval neural networks for uncertainty estimation in classification tasks
Get PDFEffective uncertainty estimation is becoming increasingly attractive for enhancing the reliability of neural networks. This work presents a novel approach, termed Credal-Set Interval Neural Networks (CreINNs), for classification. CreINNs retain the fundamental structure of traditional Interval Neural Networks, capturing weight uncertainty through deterministic intervals. CreINNs are designed to predict an upper and a lower probability bound for each class, rather than a single probability value. The probability intervals can define a credal set, facilitating estimating different types of uncertainties associated with predictions. Experiments on standard multiclass and binary classification tasks demonstrate that the proposed CreINNs can achieve superior or comparable quality of uncertainty estimation compared to variational Bayesian Neural Networks (BNNs) and Deep Ensembles. Furthermore, CreINNs significantly reduce the computational complexity of variational BNNs during inference. Moreover, the effective uncertainty quantification of CreINNs is also verified when the input data are intervals
Credal learning theory
Get PDFStatistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution. In actual deployment, however, the data distribution may (and often does) vary, causing domain adaptation/generalization issues. In this paper we lay the foundations for a `credal' theory of learning, using convex sets of probabilities (credal sets) to model the variability in the data-generating distribution. Such credal sets, we argue, may be inferred from a finite sample of training sets. Bounds are derived for the case of finite hypotheses spaces (both assuming realizability or not), as well as infinite model spaces, which directly generalize classical results
Uncertainty measures: A critical survey
No full textClassical probability is not the only mathematical theory of uncertainty, or the most general. Many authors have argued that probability theory is ill-equipped to model the ‘epistemic’, reducible uncertainty about the process generating the data. To address this, many alternative theories of uncertainty have been formulated. In this paper, we highlight how uncertainty theories can be seen as forming clusters characterised by a shared rationale, are connected to each other in an intricate but interesting way, and can be ranked according to their degree of generality. Our objective is to propose a structured, critical summary of the research landscape in uncertainty theory, and discuss its potential for wider adoption in artificial intelligence
Exploring the impact of mixed reality on architectural basic design education : a systematic review
Get PDFThis systematic review examines the integration of Mixed Reality (MR) encompassing Virtual Reality (VR) and Aug-mented Reality (AR) in architectural basic design education. A comprehensive search across five academic databases iden-tified 96 studies published between 2013 and 2023. Our analysis organized these findings into a five-dimensional valueframework through a two-stage screening and thematic coding process. The framework encompasses five dimensions:Cognition & Design Thinking, Experience, Pedagogy & Practice, Design Process, and Creativity. The analysis reveals thatMR enhances spatial reasoning, accelerates design iteration, and enriches experiential and studio-based learning. Thesedimensions frequently overlap, particularly between cognition and experience, with creativity serving as a bridging factorthat links technological interaction to learning outcomes. To represent this dynamic relationship, this review introduces aflow-based conceptual model that traces the progression from technological inputs to experiential processes and learningoutcomes. Despite its pedagogical potential, MR faces challenges related to curricular integration, digital dependency,and the lack of longitudinal empirical research. This review consolidates fragmented findings into a coherent framework,providing both theoretical insight and methodological guidance for future studies. By focusing exclusively on MR appli-cations, the review also highlights the need for broader comparative analyses that include extended XR approaches toenhance generalizability