Is there a reproducibility crisis? on the need for evidence-based approaches

The ‘Sixth Report—Reproducibility and Research Integrity’ (UK House of Commons Science, Innovation and Technology Committee 2023. ‘Sixth Report—Reproducibility and Research Integrity’) (‘The Report’) recommends measures designed to tackle an alleged ‘reproducibility crisis’ in scientific research. Our systematic analysis of the content of this report revealed that its findings and recommendations are consistent with the scientific literature, including the acknowledgement that conclusive evidence demonstrating the existence of a ‘reproducibility crisis’ is lacking. Though conceding that there is currently no way to determine the size of the crisis or whether it even exists, The Report nevertheless proposes actions to tackle the alleged crisis. However, without a quantitative understanding, the efficacy of the proposed measures cannot be verified. Hence, the current approach towards the alleged reproducibility crisis, here exemplified by The Report, does not adhere to the standards that would normally applied to the scientific method. An evidence-based approach requires the establishment of a quantitative understanding of whether data variability in the research literature exceeds technically achievable levels of reproducibility. If it does, the resulting understanding will enable the design of actions, whose success can be monitored. Our findings emphasise that the research environment requires the same level of rigour and scrutiny as the scientific experiments themselves

Balanced-detection visible optical coherence tomography with a low-noise supercontinuum laser

This paper comprehensively demonstrates the efficiency of balanced detection in a visible optical coherence tomography instrument employing a low-noise supercontinuum laser. By using an innovative technique for digitally aligning camera pixels, we achieved a noise floor reduction of up to 12.8 dB across the entire imaging depth range, particularly near the zero optical path difference between the interferometer arms. The instrument presented here operates at a central wavelength of 590 nm. It delivers high-resolution images with a sensitivity of up to 74 dB in a single spectrometer configuration and 92.8 dB in a balanced configuration. The enhancement in image contrast is exemplified through images of an optical phantom and in-vivo images of a human thumb and nail

Foundations of Expected Points in Rugby Union: A Methodological Approach

This study explores the feasibility of an Expected Points metric for rugby union, aiming to shift performance analysis from descriptive indicators to a predictive metric of possession quality. Notational analysis was conducted on 132 Premiership Rugby matches, producing a dataset of 35,199 unique phases of play containing variables such as team in possession, pitch location, type of play, score differences, time remaining, cards and the next scoring outcome. Four machine learning algorithms were explored to predict scoring outcomes: multinomial logistic regression, random forest, support vector machine and k-nearest neighbors. After extensive feature engineering and hyperparameter optimisation, the best-performing model (a random forest classifier) achieved 39.7% ±2.8 ppts accuracy. However, this did not meet a literature-derived baseline for practical usability (44.3%), thus the model was not suitable for applied contexts. A key challenge was predicting minority scoring outcomes due to severe class imbalance. SMOTE was explored to address this imbalance, resulting in a lower accuracy (35.7%) but an improved F1-score of 34.4%. This study highlights the inherent limitations of modelling scoring outcomes in dynamic, open-play team sports, challenging the predominant positivist paradigm in sports performance analysis. The methodology provides critical foundational groundwork and a benchmark for future research to build upon. It recommends exploring advanced samplers for minority classes, expanded feature sets and alternative modelling techniques, such as recurrent neural networks

A mixed method evaluation of a novel targeted health messaging intervention to promote COVID-19 protective behaviours and vaccination among black and South Asian communities living in the UK (The COBHAM study)

Aim: To evaluate an intervention (a film and electronic leaflet) disseminated via text message by general practices to promote COVID-19 preventative behaviours in Black and South Asian communities. Methods: We carried out a before-and-after questionnaire study of attitudes to and implementation of COVID-19 preventative behaviours and qualitative interviews about the intervention with people registered with 26 general practices in England who identified as Black or South Asian. Results: In the 108 people who completed both questionnaires, we found no significant change in attitudes to and implementation of COVID-19 preventative behaviours, although power was too low to detect significant effects. A key qualitative finding was that participants felt they did not ‘belong’ to the group targeted by the intervention. Conclusion: Interventions targeting ethnic minorities in the UK need to acknowledge the heterogeneity of experience and circumstances of the target group so that people feel that the intervention is relevant to them

Hunting High or Low: Evaluating the Effectiveness of High-Interaction and Low-Interaction Honeypots

Honeypots are cybersecurity mechanisms that are set up as decoys in networks to lure and monitor attackers trying to compromise vulnerable systems. Two commonly used honeypot designs are high-interaction and low-interaction honeypots, which differ in the amount of interplay that the attackers are allowed to do. So far, the effectiveness of high-interaction and low-interaction honeypots has been understudied, making it difficult for security teams to choose between different honeypot technologies. The aim of this paper is to compare the effectiveness of high-interaction and low-interaction honeypots through real-world data. We deployed multiple Elasticsearch honeypot implementations to collect data: a closed-source high-interaction honeypot developed by the authors, and three types of open-source low-interaction honeypots (namely Elastichoney, Delilah and Elasticpot). The collected data came from 48 instances of high-interaction honeypots and 111 instances of low-interaction honeypots, over a period of 14 days. We found that low-interaction honeypots captured only a fraction of the attacks that high-interaction honeypots can catch. On the other hand, low-interaction honeypots are simpler, more efficient to run due to their low usage of resources, and easier to deploy. In our dataset, high-interaction honeypots captured 76.12% of the total attack packets and attracted 70.61% of the unique attacker IPs. In comparison, low-interaction honeypots performed a lot worse in collecting attack data; they only managed to capture 23.88% of the total attack packets and attracted 29.39% of the unique attacker IPs. In this paper, we present an experiment that evaluated and compared the effectiveness of high-interaction and low-interaction honeypots in terms of the amount and the type of information collected from attacks targeting them. It follows from our findings that it would be wiser to either concentrate solely on using high-interaction honeypots, or to increase the effectiveness of low-interaction ones by automatically changing each static value during deployment and/or by increasing the mimicking capabilities of low-interaction honeypots

Harnessing transient CAAC-stabilized mesitylborylenes for chalcogen activation

Newly synthesized adducts of CAAC-bound mesitylborylene with carbon monoxide (CO) and trimethylphosphine (PMe3) are established as efficient precursors for the in situ generation of the dicoordinate borylene [(CAAC)BMes] (CAAC = cyclic(alkyl)(amino)carbene), as demonstrated by their ability to activate elemental chalcogens. Upon thermal or photolytic activation, these precursors readily react with sulfur and selenium, yielding boron chalcogenides characterized by terminal boron–chalcogen double bonds. In contrast, the reaction with tellurium leads to the formation of an unusual diradical ditelluride species with a Te–Te bond. Quantum chemical calculations of its electronic structure indicate an open-shell singlet ground state characterized by significant diradical character. Further investigations into the redox behavior of these boron chalcogenides reveal intriguing transformations, including the redox-induced formation and cleavage of E–E bonds

Understanding the role of metabolic syndrome in prostate cancer risk: A UK Biobank prospective cohort study

Predictive value of metabolic syndrome for prostate cancer risk is not clear. We aimed to assess the association between metabolic syndrome and its components with prostate cancer incidence. The primary outcome was prostate cancer incidence, i.e., incidence rate ratios and adjusted cumulative incidence curves derived from flexible parametric survival models. Adjusted cumulative incidence curves were derived using a flexible survival parametrical modeling framework. We analysed UK Biobank data including 242,349 adult males, recruited during 2006–2010 and followed up until 2021, during which 6,467 (2.7%) participants were diagnosed with prostate cancer. Our findings indicate that metabolic syndrome, as a whole, was not associated with prostate cancer risk (incidence rate ratios, 1.07; 95% confidence interval, 0.94–1.22). However, specific components such as hypertension and obesity increased the risk (incidence rate ratios, 1.22; 95% confidence interval, 1.03–1.44 and incidence rate ratios, 1.24; 95% confidence interval, 1.05–1.46, respectively). Other components, such as prediabetes/diabetes and low cholesterol, were associated with a reduced risk (incidence rate ratios, 0.80; 95% confidence interval, 0.67–0.94 and incidence rate ratios, 0.82; 95% confidence interval, 0.69–0.97, respectively), while hyperlipidaemia showed no significant effect (incidence rate ratios, 1.07; 95% confidence interval, 0.93–1.24). Further research is needed to understand the underlying mechanisms behind these relationships. Prostate cancer prevention strategies might benefit from targeting modifiable risk factors, particularly hypertension and obesity

Investigating Mobile Technology for Experiential Outdoor Heritage Practices

This thesis investigates the potential of innovative mobile guides to enhance heritage experiences during walks in historic urban precincts. Focusing on the effects of smartphone-based and mobile projector-based guides, the research explores how different display modalities influence visitors' embodied and social engagement, behaviour, and meaning-making processes in complex, multi-sensory heritage environments. Through a series of empirical studies conducted in Canterbury's historic high street, involving 66 participants, the research employs a multi-method approach including surveys, interviews, observations, and participant-generated materials. The findings reveal that while smartphone guides often create a 'bubble' effect, isolating users from their surroundings, mobile projector guides foster more exploratory, playful, and socially engaged interactions with the heritage site. The thesis proposes a novel approach to designing mobile guides through the lens of 'playful walking', emphasizing the importance of supporting not only cognitive engagement but also multi-sensory, embodied, and social interactions. This approach is synthesized into actionable design considerations and four innovative design directions, illustrated with conceptual examples. Key contributions include technology design, empirical observations on the affordances of different mobile guide types, and a set of design principles for creating devices that support embodied heritage interpretation practices. The research highlights the potential of mobile projector guides to transform static heritage sites into dynamic spaces for interaction, fostering deeper connections between visitors and their surroundings. While primarily focused on historic urban precincts, the findings offer broader implications for designing mobile guides that enhance heritage experiences in contexts such as archaeological sites, living history museums, etc

Development of a Microcontroller-Based Recurrent Neural Network Predictive System for Lower Limb Exoskeletons

Practical deployments of exoskeletons can often be limited by cost, limiting access to their usage by those that would benefit from them. Minimising cost whilst not harming effectiveness is therefore desirable for exoskeleton development. For Control Systems governing assistive and rehabilitative exoskeletons that react to the wearer’s movements, there will inevitably be some delay between when their wearer intends to move and when the exoskeleton can assist with this movement. This can lead to situations where a user may be limited by their own assistive exoskeleton, reducing their ability to move freely. A potential solution to this is to provide a proactive method of control, where the most likely path of the wearer’s movement is predicted ahead of the wearer making the motion themselves. This can be used to give the user assistance immediately as they are walking, as well as potentially pre-emptively adjust their gait if they suffer from predictable gait deficiencies. The purpose of this paper is to investigate the Data Collection, Implementation, and Effectiveness of an LSTM Recurrent Neural Network dynamically predicting future movement based off of prior movement. These methods were developed to use off the shelf, Low-Cost Microcontrollers as to minimise their Financial, Weight, and Power Impact on an overall Low-Cost exoskeleton design, as well as to evaluate how effective such an implementation would be when compared to running such a Neural Network on a more powerful processor. The created model was capable of achieving similar accuracies to far more powerful models on High-Powered Laptops

Explainable artificial intelligence for business and economics: methods, applications and challenges

In recent years, artificial intelligence (AI) has made significant strides in research and shown great potential in various application fields, including business and economics (B&E). However, AI models are often black boxes, making them difficult to understand and explain. This challenge can be addressed using eXplainable Artificial Intelligence (XAI), which helps humans understand the factors driving AI decisions, thereby increasing transparency and confidence in the results. This paper aims to provide a comprehensive understanding of the state-of-the-art research on XAI in B&E by conducting an extensive literature review. It introduces a novel approach to categorising XAI techniques from three different perspectives: samples, features and modelling method. Additionally, the paper identifies key challenges and corresponding opportunities in the field. We hope that this work will promote the adoption of AI in B&E, inspire interdisciplinary collaboration, foster innovation and growth and ensure transparency and explainability