Scabies outbreaks cause significant morbidity and disruption in aged care facilities and other institutional settings. Failure to manage scabies outbreaks may be attributable to low awareness amongst healthcare workers. A survey was distributed to healthcare workers across aged care facilities in South-East Queensland, Australia. The survey captured demographics, prior scabies experience, knowledge-based questions, and attitudes. Scabies was common in aged care facilities, with 41% of 128 respondents encountering the disease while working in aged care. Participants demonstrated sound theoretical knowledge regarding scabies (median knowledge score 82%). Scabies knowledge was not associated with years of experience in the sector or educational level but was associated with respondent age (p = 0.017). Knowledge gaps were evident regarding diagnosis, incubation periods, and treatment. Respondents demonstrated an inconsistent ability to identify atypical clinical presentations of scabies, showing discordance between theoretical knowledge and its practical application. The ability to identify crusted scabies was low, reflecting the high frequency of misdiagnosis of index cases in scabies outbreaks. Respondents considered scabies to be a problem and were supportive of improved management guidelines. These study outcomes will inform the design of accessible, targeted educational resources for scabies to help prevent and reduce the impact of outbreaks.

We present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighboring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter’s belief into the ego’s but by adjusting the ego’s decision-making criteria. We provide an instance from the framework in which social influence between agents generalizes majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarization and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus.

While the number of international students attending UK universities has been increasing in recent years, the 2021/22 and 2022/23 academic years saw a decline in applications from EU-domiciled students. However, the extent and varying impact of this decline remain to be estimated and disentangled from the impacts of the COVID-19 pandemic. Using difference-in-differences (DID) in a hierarchical regression framework and Universities and Colleges Admissions Service (UCAS) data, we aim to quantify the decline in the number of student applications post-Brexit. We find evidence of an overall decline of 65% in the 2021 academic year in successful applications from EU students as a result of Brexit. This decline is more pronounced for non-Russell Group institutions, as well as for Health and Life Sciences and Arts and Languages. Furthermore, we explore the spatial heterogeneity of the impact of Brexit across EU countries of origin, observing the greatest effects for Poland and Germany, though this varies depending on institution type and subject. We also show that higher rates of COVID-19 stringency in the country of origin led to greater applications for UK higher education institutions. Our results are important for government and institutional policymakers seeking to understand where losses occur and how international students respond to external shocks and policy changes. Our study quantifies the distinct impacts of Brexit and COVID-19 and offers valuable insights to guide strategic interventions to sustain the UK’s attractiveness as a destination for international students.

Causal machine learning tools are beginning to see use in real-world policy evaluation tasks to flexibly estimate treatment effects. One issue with these methods is that the machine learning models used are generally black boxes, that is, there is no globally interpretable way to understand how a model makes estimates. This is a clear problem for governments who want to evaluate policy as it is difficult to understand whether such models are functioning in ways that are fair, based on the correct interpretation of evidence and transparent enough to allow for accountability if things go wrong. However, there has been little discussion of transparency problems in the causal machine learning literature and how these might be overcome. This article explores why transparency issues are a problem for causal machine learning in public policy evaluation applications and considers ways these problems might be addressed through explainable AI tools and by simplifying models in line with interpretable AI principles. It then applies these ideas to a case study using a causal forest model to estimate conditional average treatment effects for returns on education study. It shows that existing tools for understanding black-box predictive models are not as well suited to causal machine learning and that simplifying the model to make it interpretable leads to an unacceptable increase in error (in this application). It concludes that new tools are needed to properly understand causal machine learning models and the algorithms that fit them.

The walk matrix associated to an $n\times n$ integer matrix $\mathbf{X}$ and an integer vector $b$ is defined by ${\mathbf{W}} \,:\!=\, (b,{\mathbf{X}} b,\ldots, {\mathbf{X}}^{n-1}b)$. We study limiting laws for the cokernel of $\mathbf{W}$ in the scenario where $\mathbf{X}$ is a random matrix with independent entries and $b$ is deterministic. Our first main result provides a formula for the distribution of the $p^m$-torsion part of the cokernel, as a group, when $\mathbf{X}$ has independent entries from a specific distribution. The second main result relaxes the distributional assumption and concerns the ${\mathbb{Z}}[x]$-module structure.

The motivation for this work arises from an open problem in spectral graph theory, which asks to show that random graphs are often determined up to isomorphism by their (generalised) spectrum. Sufficient conditions for generalised spectral determinacy can, namely, be stated in terms of the cokernel of a walk matrix. Extensions of our results could potentially be used to determine how often those conditions are satisfied. Some remaining challenges for such extensions are outlined in the paper.

This paper aims at exploring the dynamic interplay between advanced technological developments in AI and Big Data and the sustained relevance of theoretical frameworks in scientific inquiry. It questions whether the abundance of data in the AI era reduces the necessity for theory or, conversely, enhances its importance. Arguing for a synergistic approach, the paper emphasizes the need for integrating computational capabilities with theoretical insight to uncover deeper truths within extensive datasets. The discussion extends into computational social science, where elements from sociology, psychology, and economics converge. The application of these interdisciplinary theories in the context of AI is critically examined, highlighting the need for methodological diversity and addressing the ethical implications of AI-driven research. The paper concludes by identifying future trends and challenges in AI and computational social science, offering a call to action for the scientific community, policymakers, and society. Being positioned at the intersection of AI, data science, and social theory, this paper illuminates the complexities of our digital era and inspires a re-evaluation of the methodologies and ethics guiding our pursuit of knowledge.

Urban logistics has emerged as a priority to improve goods distribution and mobility within urban centers worldwide. Brazil presents a unique set of challenges in this regard due to issues such as excessive reliance on road transportation, lack of regulations, inadequate infrastructure, cargo theft, and the intricate interplay of cargo transportation with urban traffic. These challenges collectively exert a substantial influence on the economic, urban, and environmental performance of cities. This article introduces a novel approach aimed at assessing and benchmarking urban logistics performance between Brazilian cities with potential applicability to other contexts. The methodology was based on data envelopment analysis to evaluate efficiency based on key indicators, including GDP Gross Domestic Product, population size, commercial establishments, urban area coverage, cargo fleet size, and travel time. By applying this methodology to 12 Brazilian cities, the study improves the understanding of their relative efficiency levels concerning urban logistics and provides key insights for policymaking. The results also show the relevance of the proposed methodology and contribute to provide a perspective of different administrative and logistical facets through the lens of macroeconomic indicators, contributing to a holistic understanding of urban logistics dynamics.

We aimed to estimate the overall apparent prevalence, true prevalence, and the spatial, temporal, and test-specific burden of bovine tuberculosis in Bangladesh. PubMed, Web of Science, Scopus, Google Scholar, and BanglaJOL were searched for bovine tuberculosis publications in Bangladesh from 1 January 1970 to 23 June 2023. Of 142 articles screened, systematic review and meta-analysis were performed on 22 (15.5%) articles. The apparent estimated bovine tuberculosis prevalence was 7%. The apparent Bayesian pooled mean bovine tuberculosis prevalences based on caudal fold test and single intradermal comparative tuberculin test were 7.83% and 9.89%, respectively, and the true pooled mean prevalences were 10.39% and 10.48%, respectively. Targeted interventions are recommended for districts with higher prevalence to effectively reduce the bovine tuberculosis burden in those areas. Current diagnostic practices employed in Bangladesh may not accurately reflect the bovine tuberculosis burden. Our findings highlight the need for better diagnostic tools and supplemental testing methods to ensure accurate diagnosis and surveillance. Efforts should prioritize obtaining ‘true’ prevalence estimates corrected for misclassification bias, rather than relying solely on apparent prevalence. Underestimating the bovine tuberculosis burden could result in inadequate resource allocation and hinder the implementation of effective control measures.

Legionellosis is a respiratory infection caused by Legionella sp. that is found in water and soil. Infection may cause pneumonia (Legionnaires’ Disease) and a milder form (Pontiac Fever). Legionella colonizes water systems and results in exposure by inhalation of aerosolized bacteria. The incubation period ranges from 2 to 14 days. Precipitation and humidity may be associated with increased risk. We used Medicare records from 1999 to 2020 to identify hospitalizations for legionellosis. Precipitation, temperature, and relative humidity were obtained from the PRISM Climate Group for the zip code of residence. We used a time-stratified bi-directional case-crossover design with lags of 20 days. Data were analyzed using conditional logistic regression and distributed lag non-linear models. A total of 37 883 hospitalizations were identified. Precipitation and relative humidity at lags 8 through 13 days were associated with an increased risk of legionellosis. The strongest association was precipitation at day 10 lag (OR = 1.08, 95% CI = 1.05–1.11 per 1 cm). Over 20 days, 3 cm of precipitation increased the odds of legionellosis over four times. The association was strongest in the Northeast and Midwest and during summer and fall. Precipitation and humidity were associated with hospitalization among Medicare recipients for legionellosis at lags consistent with the incubation period for infection.

To evaluate the variations in COVID-19 case fatality rates (CFRs) across different regions and waves, and the impact of public health interventions, social and economic characteristics, and demographic factors on COVID-19 CFRs, we collected data from 30 countries with the highest incidence rate in three waves. We summarized the CFRs of different countries and continents in each wave through meta-analysis. Spearman’s correlation and multiple linear regression were employed to estimate the correlation between influencing factors and reduction rates of CFRs. Significant differences in CFRs were observed among different regions during the three waves (P < 0.001). An association was found between the changes in fully vaccinated rates (rs = 0.41), population density (rs = 0.43), the proportion of individuals over 65 years old (rs = 0.43), and the reduction rates of case fatality rate. Compared to Wave 1, the reduction rates in Wave 2 were associated with population density (β = 0.19, 95%CI: 0.05–0.33) and smoking rates (β = −4.66, 95%CI: −8.98 – −0.33), while in Wave 3 it was associated with booster vaccine rates (β = 0.60, 95%CI: 0.11–1.09) and hospital beds per thousand people (β = 4.15, 95%CI: 1.41–6.89). These findings suggest that the COVID-19 CFRs varied across different countries and waves, and promoting booster vaccinations, increasing hospital bed capacity, and implementing tobacco control measures can help reduce CFRs.

A series of papers by Hickey (1982, 1983, 1984) presents a stochastic ordering based on randomness. This paper extends the results by introducing a novel methodology to derive models that preserve stochastic ordering based on randomness. We achieve this by presenting a new family of pseudometric spaces based on a majorization property. This class of pseudometrics provides a new methodology for deriving the randomness measure of a random variable. Using this, the paper introduces the Gini randomness measure and states its essential properties. We demonstrate that the proposed measure has certain advantages over entropy measures. The measure satisfies the value validity property, provides an adequate extension to continuous random variables, and is often more appropriate (based on sensitivity) than entropy in various scenarios.

Given an $n\times n$ symmetric matrix $W\in [0,1]^{[n]\times [n]}$, let ${\mathcal G}(n,W)$ be the random graph obtained by independently including each edge $jk\in \binom{[n]}{2}$ with probability $W_{jk}=W_{kj}$. Given a degree sequence $\textbf{d}=(d_1,\ldots, d_n)$, let ${\mathcal G}(n,\textbf{d})$ denote a uniformly random graph with degree sequence $\textbf{d}$. We couple ${\mathcal G}(n,W)$ and ${\mathcal G}(n,\textbf{d})$ together so that asymptotically almost surely ${\mathcal G}(n,W)$ is a subgraph of ${\mathcal G}(n,\textbf{d})$, where $W$ is some function of $\textbf{d}$. Let $\Delta (\textbf{d})$ denote the maximum degree in $\textbf{d}$. Our coupling result is optimal when $\Delta (\textbf{d})^2\ll \|\textbf{d}\|_1$, that is, $W_{ij}$ is asymptotic to ${\mathbb P}(ij\in{\mathcal G}(n,\textbf{d}))$ for every $i,j\in [n]$. We also have coupling results for $\textbf{d}$ that are not constrained by the condition $\Delta (\textbf{d})^2\ll \|\textbf{d}\|_1$. For such $\textbf{d}$ our coupling result is still close to optimal, in the sense that $W_{ij}$ is asymptotic to ${\mathbb P}(ij\in{\mathcal G}(n,\textbf{d}))$ for most pairs $ij\in \binom{[n]}{2}$.

This article focuses on measuring the impact of artificial intelligence (AI) on the peace and security agenda, taking stock of recent initiatives and progress in this area. While there is a keen awareness of the fact that AI can be weaponized to become a tool of power politics and military competition, there is comparatively less systematic attention paid to what technology can do for peace. While it is important to address risk mitigation, equal space should be given to thinking about how to harness the peace potential of AI on a large scale. This study follows a series of publications that aim to assess the impact of technological innovation on peace, also referred to as PeaceTech, Global PeaceTech, peace innovation, or digital peacebuilding. The first section provides an overview of the debate on the impact of AI on peace and conflict. The second section examines conceptual frameworks and measures of the impact of AI on peace and conflict. The third section looks at the risks to peace and conflict posed by the use of AI and possible governance measures to mitigate them. The fourth section provides examples of AI-enabled initiatives that are having a positive impact on peace, providing a compass for public and private investment. The conclusion offers policy recommendations to advance the AI for peace agenda.

Random bridges have gained significant attention in recent years due to their potential applications in various areas, particularly in information-based asset pricing models. This paper aims to explore the potential influence of the pinning point’s distribution on the memorylessness and stochastic dynamics of the bridge process. We introduce Lévy bridges with random length and random pinning points, and analyze their Markov property. Our study demonstrates that the Markov property of Lévy bridges depends on the nature of the distribution of their pinning points. The law of any random variables can be decomposed into singular continuous, discrete, and absolutely continuous parts with respect to the Lebesgue measure (Lebesgue’s decomposition theorem). We show that the Markov property holds when the pinning points’ law does not have an absolutely continuous part. Conversely, the Lévy bridge fails to exhibit Markovian behavior when the pinning point has an absolutely continuous part.

The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. Machine learning has previously been used to reliably “screen” articles for review – that is, identify relevant articles based on reviewers’ inclusion criteria. The application of machine learning techniques to subsequent stages of a review, however, such as data extraction and evidence mapping, is in its infancy. We, therefore, set out to develop a series of tools that would assist in the profiling and analysis of 1952 publications on the theme of “outcomes-based contracting.” Tools were developed for the following tasks: assigning publications into “policy area” categories; identifying and extracting key information for evidence mapping, such as organizations, laws, and geographical information; connecting the evidence base to an existing dataset on the same topic; and identifying subgroups of articles that may share thematic content. An interactive tool using these techniques and a public dataset with their outputs have been released. Our results demonstrate the utility of machine learning techniques to enhance evidence accessibility and analysis within the systematic review processes. These efforts show promise in potentially yielding substantial efficiencies for future systematic reviewing and for broadening their analytical scope. Beyond this, our work suggests that there may be implications for the ease with which policymakers and practitioners can access evidence. While machine learning techniques seem poised to play a significant role in bridging the gap between research and policy by offering innovative ways of gathering, accessing, and analyzing data from systematic reviews, we also highlight their current limitations and the need to exercise caution in their application, particularly given the potential for errors and biases.

We consider the following problem: the drift of the wealth process of two companies, modelled by a two-dimensional Brownian motion, is controllable such that the total drift adds up to a constant. The aim is to maximize the probability that both companies survive. We assume that the volatility of one company is small with respect to the other, and use methods from singular perturbation theory to construct a formal approximation of the value function. Moreover, we validate this formal result by explicitly constructing a strategy that provides a target functional, approximating the value function uniformly on the whole state space.

Carbon neutrality cannot be achieved without different economic sectors, individuals and households, and the government making serious efforts. Green finance in different forms including environmental, social and governance investment and carbon emissions trading are used to measure the reduction in carbon emissions and place a monetary value on them. However, because of inconsistencies or even manipulation in the monitoring/measurement, reporting and verification (MRV) of air quality and carbon emissions data, the effectiveness of green finance has been largely compromised. Environmental MRV is a technology-based engineering task, which is also heavily impacted by institutional design and professionalism. This commentary will draw upon principal–agent theory and the practical arrangements of environmental MRV to discuss why professionalism is badly needed and how to bridge the missing link for achieving carbon neutrality and sustainability transitions.

We study computational aspects of repulsive Gibbs point processes, which are probabilistic models of interacting particles in a finite-volume region of space. We introduce an approach for reducing a Gibbs point process to the hard-core model, a well-studied discrete spin system. Given an instance of such a point process, our reduction generates a random graph drawn from a natural geometric model. We show that the partition function of a hard-core model on graphs generated by the geometric model concentrates around the partition function of the Gibbs point process. Our reduction allows us to use a broad range of algorithms developed for the hard-core model to sample from the Gibbs point process and approximate its partition function. This is, to the extent of our knowledge, the first approach that deals with pair potentials of unbounded range.

Let $r$ be any positive integer. We prove that for every sufficiently large $k$ there exists a $k$-chromatic vertex-critical graph $G$ such that $\chi (G-R)=k$ for every set $R \subseteq E(G)$ with $|R|\le r$. This partially solves a problem posed by Erdős in 1985, who asked whether the above statement holds for $k \ge 4$.

We consider an $\mathrm{M}/\mathrm{G}/\infty$ queue with infinite expected service time. We then provide the transience/recurrence classification of the states (the system is said to be at state n if there are n customers being served), observing also that here (unlike irreducible Markov chains, for example) it is possible for recurrent and transient states to coexist. We also prove a lower bound on the growth speed in the transient case.