Antimicrobial resistance (AMR) is a pressing global health challenge, with sub-Saharan Africa experiencing the highest burden of AMR-related deaths. Inappropriate prescribing and rising antibiotic consumption drive AMR, while limited local data hampers antimicrobial stewardship efforts. This study analysed Global Point Prevalence Survey of Antimicrobial Consumption and Resistance (Global-PPS) data from Tygerberg Hospital to identify antimicrobial use trends and inform stewardship priorities. Standard Global-PPS methodology was employed at three distinct time points. All inpatients prescribed at least one antimicrobial on the day of each survey were included in the analysis. Among 3,524 hospitalized patients, 25.9% (911/3,524) received antimicrobial therapy. Overall antimicrobial use decreased significantly (p < 0.05), with the largest reduction among paediatric patients (p < 0.01). Community-acquired infections accounted for the majority of prescriptions (50.7%; 483/952) and empirical antibiotic use was high (85.3%, 872/1022). ‘Access’ antibiotics constituted 62.7% (750/1196) of prescriptions. Single-dose prescriptions for surgical prophylaxis accounted for 17.6% (15/85). This study demonstrates progress in stewardship, particularly among paediatric inpatients. Ongoing monitoring of broad-spectrum antibiotic use and adherence to single-dose surgical prophylaxis guidelines are essential priorities. Continued Global-PPS surveillance is crucial to track trends and guide future AMS interventions.

Let $ ([0,1]^d,T,\mu ) $ be a measure-preserving dynamical system so that the correlations decay exponentially for Hölder continuous functions. Suppose that $ \mu $ is absolutely continuous with a density function $ h\in L^q(\mathcal L^d) $ for some $ q>1 $, where $ \mathcal L^d $ is the $ d $-dimensional Lebesgue measure. Under suitable conditions on the underlying dynamical system, we obtain a strong dynamical Borel–Cantelli lemma for recurrence: for any sequence $ \{R_n\} $ of hyperrectangles centered at the origin, with sides parallel to the axes and diameter going to $0$ as $n\to \infty $, where $ \mathbf {x}\in [0,1]^d $ and $ R_n+\mathbf {x} $ is the translation of $ R_n $. The result applies to the Gauss map, $\beta $-transformations, and expanding toral endomorphisms.

Global health law as a field has been deeply unequal, reflecting colonial histories, geopolitics, and power plays. The current turmoil and fundamental shifts occurring in international politics has uncertain outcomes for global public health. Looking at the field from the prism of recent and ongoing global health negotiations serves to illustrate the unpredictability of the direction the governance of global health can take. Recent global health law reforms in the aftermath of COVID-19 have aimed to create obligations governing health emergencies. While much of the discussions have unfolded in the realm of health emergencies, many of the resulting legal obligations will have a normative impact on the field. Some of these new obligations are considerably caveated and weakened compared to what was originally articulated by some member states of the World Health Organization (WHO).

There is a consensus in the literature that the Marxism of the Second International (1889–1916) lacked philosophical sophistication and that understanding of Marxism’s Hegelian origins was lost soon after Karl Marx’s death, only to be recovered with the emergence of Western Marxism in the 1920s. This article challenges this consensus, urging revision of the basic outlines of the intellectual history of Marxism. It begins by sketching two ways contemporary scholars understand the Hegel-Marx connection. It then shows that these views were anticipated before World War I in the work of Max Adler. Against the view that Hegel was “put back into Marxism” in the 1920s or 1970s, then, this article maintains that there have always been sophisticated as well as simplifying accounts of the Hegel-Marx connection.

This paper develops and compares four distinct constraint-aware and adaptive matheuristic approaches based on tabu search, simulated annealing, bacterial foraging optimisation and multi-ant colony optimisation. By integrating metaheuristic search with a mixed-integer linear programming (MILP) model, a hybrid framework is designed to optimise aircraft departure sequencing with rigorous precision. The core novelty lies in how this integration reconciles the exactness of mathematical programming with the interpretability of metaheuristics. Standard MILP models offer precise solutions but function as opaque ‘black boxes’, while standard metaheuristics operate via understandable moves but often rely on simplified feasibility checks. The proposed solution merges these strengths, employing adaptive metaheuristics to generate candidate sequences using interpretable moves while a restricted MILP acts as a high-fidelity evaluator. Fixing the sequence enables the MILP to focus solely on optimising continuous timings and holds. This ensures that every candidate step is strictly feasible and optimally timed, turning the solution process into a sequence of human-readable queue adjustments backed by the numerical precision of an exact solver. In a calibrated Antalya Airport case study with 40 aircraft, all algorithms attain the MILP optimum or remain within 1% of it, achieving a 29% reduction in hold fuel compared to first-come-first-served (FCFS). In denser 50-aircraft scenarios, the approach maintains high solution quality within feasible time limits, proving that explainability does not require sacrificing computational efficiency.

While much scholarship has considered the role of racial attitudes in shaping public opinion, this paper extends this line of research by examining how racial resentment influences voter turnout and further, how this effect differs across religious and racial groups. Building on conflict decision theory and theories of racialized social pressure, the paper develops expectations about the conditional influence of racial attitudes on voter participation, depending on these compounding religious and racial identities. Analysis of the 2020 Collaborative Multiracial Post-Election Survey (CMPS) reveals that racial resentment is associated with higher voter turnout among White evangelicals, Asian American evangelicals, and Latino evangelicals, while having no impact on White, Latino, and Asian Americans who do not identify as evangelical. In contrast, holding more conservative racial attitudes is associated with a lower, rather than higher, likelihood of voting among both Black evangelicals and Black non-evangelicals. The paper concludes by underscoring the need to examine how multiple social identities can structure the factors that influence political decision-making in American politics.

Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perform these tasks, limiting their broader applicability. Inspired by the transformative success of Large Language Models (LLMs), Time-Series Foundation Models (TSFMs), trained on diverse datasets, have the potential to change this. Were TSFMs to achieve a level of generalizability across tasks and contexts akin to LLMs, they could fundamentally address the scalability challenges pervasive in BEM. To understand where they stand today, we evaluate TSFMs across four dimensions: (1) generalizability in zero-shot univariate forecasting, (2) forecasting with covariates for thermal behavior modeling, (3) zero-shot representation learning for classification tasks, and (4) robustness to performance metrics and varying operational conditions. Our results reveal that TSFMs exhibit limited generalizability, performing only marginally better than statistical models on unseen datasets and modalities for univariate forecasting. Similarly, inclusion of covariates in TSFMs does not yield performance improvements, and their performance remains inferior to conventional models that utilize covariates. While TSFMs generate effective zero-shot representations for downstream classification tasks, they may remain inferior to statistical models in forecasting when statistical models perform test-time fitting. Moreover, TSFMs’ forecasting performance is sensitive to evaluation metrics, and they struggle in more complex building environments compared to statistical models. These findings underscore the need for targeted advancements in TSFM design, particularly their handling of covariates and incorporating context and temporal dynamics into prediction mechanisms, to develop more adaptable and scalable solutions for BEM.

We study the generalised Chvátal–Sankoff constant $\gamma _{k,d}$, which represents the normalised expected length of the longest common subsequence of d independent uniformly random strings over an alphabet of size k. We derive asymptotically tight bounds for $\gamma _{2,d}$, establishing that $\gamma _{2,d} = \tfrac 12 + \Theta ({1}/{\sqrt {d}})$. We also derive asymptotically near-optimal bounds on $\gamma _{k,d}$ for $d\ge \Omega (\log k)$.

This commentary suggests that the meaning and content of dignity is bound to the broader question of who is said to have personhood and sovereignty, and thus protection and rights under the law, and who is excluded from our legal community.

Examining the “heathen Chinee” craze prompted by Bret Harte’s poem and its transnationalization uncovers what happened to the minstrel form and the violence of slavery that the figure evokes. As the “heathen Chinee” figure became racialized as distinctly “Oriental,” it obscured the antiblackness of the minstrel form.