During the 1519–1522 Magellan expedition, the astronomer Andrés de San Martín made two remarkably accurate longitude measurements, an order of magnitude better than what was typical for the 16th century. How he managed to do so remained shrouded in mystery for the past 500 years. Using modern ephemerides, we have retraced San Martín's observations and calculated their error signatures, clarifying the method he used (a simplified version of lunar distances) and why two out of his six measurements were accurate (a rather fortuitous cancellation of errors). It would be rash to dismiss San Martín's work as sheer luck though, as he was an exceedingly rare combination of a capable astronomer and a knowledgeable mariner.

Ion homeostasis is a crucial process in plants that is closely linked to the efficiency of nutrient uptake, stress tolerance and overall plant growth and development. Nevertheless, our understanding of the fundamental processes of ion homeostasis is still incomplete and highly fragmented. Especially at the mechanistic level, we are still in the process of dissecting physiological systems to analyse the different parts in isolation. However, modelling approaches have shown that it is not individual transporters but rather transporter networks (homeostats) that control membrane transport and associated homeostatic processes in plant cells. To facilitate access to such theoretical approaches, the modelling of the potassium homeostat is explained here in detail to serve as a blueprint for other homeostats. The unbiased approach provided strong arguments for the abundant existence of electroneutral H+/K+ antiporters in plants.

Syphilis remains a serious public health problem in mainland China that requires attention, modelling to describe and predict its prevalence patterns can help the government to develop more scientific interventions. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, hybrid SARIMA-LSTM model, and hybrid SARIMA-nonlinear auto-regressive models with exogenous inputs (SARIMA-NARX) model were used to simulate the time series data of the syphilis incidence from January 2004 to November 2023 respectively. Compared to the SARIMA, LSTM, and SARIMA-LSTM models, the median absolute deviation (MAD) value of the SARIMA-NARX model decreases by 352.69%, 4.98%, and 3.73%, respectively. The mean absolute percentage error (MAPE) value decreases by 73.7%, 23.46%, and 13.06%, respectively. The root mean square error (RMSE) value decreases by 68.02%, 26.68%, and 23.78%, respectively. The mean absolute error (MAE) value decreases by 70.90%, 23.00%, and 21.80%, respectively. The hybrid SARIMA-NARX and SARIMA-LSTM methods predict syphilis cases more accurately than the basic SARIMA and LSTM methods, so that can be used for governments to develop long-term syphilis prevention and control programs. In addition, the predicted cases still maintain a fairly high level of incidence, so there is an urgent need to develop more comprehensive prevention strategies.

Due to the rising multidisciplinarity and connectivity of products especially modular product families, a sophisticated handling of the information is crucial for reducing complexity during the development. System modelling techniques have evolved to assist engineers with managing information. However, nowadays, it is rarely focusing on modular product families. This paper introduces a meta-model based on an ontology, which improves the creation and management of modular product family and its occurring data. The meta-model is presented using the example of a Passenger Service Unit (PSU).

Anticipating all technical requirements that a product must meet throughout its lifespan has become difficult due to a rise in market, regulatory, and technological uncertainty. As a result, the attribute values of these requirements may be highly uncertain at the start of product development. We propose a mathematical model that captures and quantifies this uncertainty in a clear and comprehensive manner. We evaluate the approach by encoding uncertain requirements for an automotive project. Misconceptions regarding probabilities are alleviated and the requirements are unambiguously defined.

This study examines the use of graph centrality to identify critical components in assembly models, a method typically dominated by computationally intense analyses. By applying centrality measures to simulated assembly graphs, components were ranked to assess their criticality. These rankings were compared against Monte Carlo sensitivity analysis results. Preliminary findings indicate a promising correlation, suggesting graph centrality as a valuable tool in assembly analysis, enhancing efficiency and insight in critical component identification.

Quantitative analyses and models are required to connect a plant’s cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels.

Recent research has shown the potential of speleothem δ13C to record a range of environmental processes. Here, we report on 230Th-dated stalagmite δ13C records for southwest Sulawesi, Indonesia, over the last 40,000 yr to investigate the relationship between tropical vegetation productivity and atmospheric methane concentrations. We demonstrate that the Sulawesi stalagmite δ13C record is driven by changes in vegetation productivity and soil respiration and explore the link between soil respiration and tropical methane emissions using HadCM3 and the Sheffield Dynamic Global Vegetation Model. The model indicates that changes in soil respiration are primarily driven by changes in temperature and CO2, in line with our interpretation of stalagmite δ13C. In turn, modelled methane emissions are driven by soil respiration, providing a mechanism that links methane to stalagmite δ13C. This relationship is particularly strong during the last glaciation, indicating a key role for the tropics in controlling atmospheric methane when emissions from high-latitude boreal wetlands were suppressed. With further investigation, the link between δ13C in stalagmites and tropical methane could provide a low-latitude proxy complementary to polar ice core records to improve our understanding of the glacial–interglacial methane budget.

Bentonites are proposed to be used as buffers in high-level radioactive waste repositories. The elevated temperatures in repositories may, however, affect bentonites’ desired properties. For instance, heating under dry conditions can cause cation fixation, potentially affecting swelling properties. The kinetics of mineral dissolution and precipitation reactions will equally be influenced by temperature. Redistributions of Ca-sulphates and -carbonates have been observed, as well as illitization of smectite. Illitization, however, has only been observed in laboratory experiments at large solution/solid ratios, whereas it has not yet been unambiguously identified in large-scale experiments. In many large-scale tests, cation exchange is the first observable geochemical reaction. In addition, an enrichment of Mg close to the heater is found in many such tests. The thermal gradient and (incongruent) smectite dissolution are suspected to play a role with respect to the Mg enrichment, but the underlying mechanism has not been unravelled so far. To predict the long-term performance of a bentonite buffer, numerical modelling is required in order to be able to simulate the reactions of all accompanying mineral phases. Smectites, which dominate the bentonite composition, are therefore particularly difficult to characterise, as their dissolution is often observed to be non-stoichiometric. Various model approaches exist to simulate smectite reactions, mostly based on kinetic rate reactions, ideally considering the effect of pH (congruent or incongruent dissolution), temperature and the degree of saturation of the solution. Reassessing and improving the thermodynamic/kinetic data of smectites are prerequisites for improving long-term buffer performance assessment.

Country-wide social distancing and suspension of non-emergency medical care due to the COVID-19 pandemic will undoubtedly have affected public health in multiple ways. While non-pharmaceutical interventions are expected to reduce the transmission of several infectious diseases, severe disruptions to healthcare systems have hampered diagnosis, treatment, and routine vaccination. We examined the effect of this disruption on meningococcal disease and vaccination in the UK. By adapting an existing mathematical model for meningococcal carriage, we addressed the following questions: What is the predicted impact of the existing MenACWY adolescent vaccination programme? What effect might social distancing and reduced vaccine uptake both have on future epidemiology? Will catch-up vaccination campaigns be necessary? Our model indicated that the MenACWY vaccine programme was generating substantial indirect protection and suppressing transmission by 2020. COVID-19 social distancing is expected to have accelerated this decline, causing significant long-lasting reductions in both carriage prevalence of meningococcal A/C/W/Y strains and incidence of invasive meningococcal disease. In all scenarios modelled, pandemic social mixing effects outweighed potential reductions in vaccine uptake, causing an overall decline in carriage prevalence from 2020 for at least 5 years. Model outputs show strong consistency with recently published case data for England.

Healthcare workers’ (HCWs) safety and availability to care for patients are critical during a pandemic such as the one caused by severe acute respiratory syndrome coronavirus 2. Among providers of different specialities, it is critical to protect those working in hospital settings with a high risk of infection. Using an agent-based simulation model, various staffing policies were developed and simulated for 90 days using data from the largest health systems in South Carolina. The model considers staffing policies that include geographic segregation, interpersonal contact limits, and a combination of factors, including the patient census, transmission rates, vaccination status of providers, hospital capacity, incubation time, quarantine period, and interactions between patients and providers. Comparing the existing practices to various risk-adjusted staffing policies, model predictions show that restricted teaming and rotating schedules significantly (p-value <0.01) reduced weekly HCW unavailability and the number of infected HCWs by 22% and 38%, respectively, when the vaccination rates among HCWs were lower (<75%). However, as the vaccination rate increases, the benefits of risk-adjusted policies diminish; and when 90% of HCWs were vaccinated, there were no significant (p-value = 0.09) benefits. Although these simulated outcomes are specific to one health system, our findings can be generalised to other health systems with multiple locations.

After the winter of 2021/2022, the coronavirus disease 2019 (COVID-19) pandemic had reached a phase where a considerable number of people in Germany have been either infected with a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, vaccinated or both, the full extent of which was difficult to estimate, however, because infection counts suffer from under-reporting, and the overlap between the vaccinated and recovered subpopulations is unknown. Yet, reliable estimates regarding population-wide susceptibility were of considerable interest: Since both previous infection and vaccination reduce the risk of severe disease, a low share of immunologically naïve individuals lowers the probability of further severe outbreaks, given that emerging variants do not escape the acquired susceptibility reduction. Here, we estimate the share of immunologically naïve individuals by age group for each of the sixteen German federal states by integrating an infectious-disease model based on weekly incidences of SARS-CoV-2 infections in the national surveillance system and vaccine uptake, as well as assumptions regarding under-ascertainment. We estimate a median share of 5.6% of individuals in the German population have neither been in contact with vaccine nor any variant up to 31 May 2022 (quartile range [2.5%–8.5%]). For the adult population at higher risk of severe disease, this figure is reduced to 3.8% [1.6%–5.9%] for ages 18–59 and 2.1% [1.0%–3.4%] for ages 60 and above. However, estimates vary between German states mostly due to heterogeneous vaccine uptake. Excluding Omicron infections from the analysis, 16.3% [14.1%–17.9%] of the population in Germany, across all ages, are estimated to be immunologically naïve, highlighting the large impact the first two Omicron waves had until the beginning of summer in 2022. The method developed here might be useful for similar estimations in other countries or future outbreaks of other infectious diseases.

Climate change is resulting in global changes to sea level and wave climates, which in many locations significantly increase the probability of erosion, flooding and damage to coastal infrastructure and ecosystems. Therefore, there is a pressing societal need to be able to forecast the morphological evolution of our coastlines over a broad range of timescales, spanning days-to-decades, facilitating more focused, appropriate and cost-effective management interventions and data-informed planning to support the development of coastal environments. A wide range of modelling approaches have been used with varying degrees of success to assess both the detailed morphological evolution and/or simplified indicators of coastal erosion/accretion. This paper presents an overview of these modelling approaches, covering the full range of the complexity spectrum and summarising the advantages and disadvantages of each method. A focus is given to reduced-complexity modelling approaches, including models based on equilibrium concepts, which have emerged as a particularly promising methodology for the prediction of coastal change over multi-decadal timescales. The advantages of stable, computationally-efficient, reduced-complexity models must be balanced against the requirement for good generality and skill in diverse and complex coastal settings. Significant obstacles are also identified, limiting the generic application of models at regional and global scales. Challenges include the accurate long-term prediction of model forcing time-series in a changing climate, and accounting for processes that can largely be ignored in the shorter term but increase in importance in the long term. Further complications include coastal complexities, such as the accurate assessment of the impacts of headland bypassing. Additional complexities include complex structures and geology, mixed grain size, limited sediment supply, sources and sinks. It is concluded that with present computational resources, data availability limitations and process knowledge gaps, reduced-complexity modelling approaches currently offer the most promising solution to modelling shoreline evolution on daily-to-decadal timescales.