A liquefied natural gas (LNG) facility often incorporates replicate liquefaction trains. The performance of equivalent units across trains, designed using common numerical models, might be expected to be similar. In this article, we discuss statistical analysis of real plant data to validate this assumption. Analysis of operational data for end flash vessels from a pair of replicate trains at an LNG facility indicates that one train produces 2.8%–6.4% more end flash gas than the other. We then develop statistical models for train operation, facilitating reduced flaring and hence a reduction of up to 45% in CO2 equivalent flaring emissions, noting that flaring emissions for a typical LNG facility account for ~4%–8% of the overall facility emissions. We recommend that operational data-driven models be considered generally to improve the performance of LNG facilities and reduce their CO2 footprint, particularly when replica units are present.

This study introduces an advanced reinforcement learning (RL)-based control strategy for heating, ventilation, and air conditioning (HVAC) systems, employing a soft actor-critic agent with a customized reward mechanism. This strategy integrates time-varying outdoor temperature-dependent weighting factors to dynamically balance thermal comfort and energy efficiency. Our methodology has undergone rigorous evaluation across two distinct test cases within the building optimization testing (BOPTEST) framework, an open-source virtual simulator equipped with standardized key performance indicators (KPIs) for performance assessment. Each test case is strategically selected to represent distinct building typologies, climatic conditions, and HVAC system complexities, ensuring a thorough evaluation of our method across diverse settings. The first test case is a heating-focused scenario in a residential setting. Here, we directly compare our method against four advanced control strategies: an optimized rule-based controller inherently provided by BOPTEST, two sophisticated RL-based strategies leveraging BOPTEST’s KPIs as reward references, and a model predictive control (MPC)-based approach specifically tailored for the test case. Our results indicate that our approach outperforms the rule-based and other RL-based strategies and achieves outcomes comparable to the MPC-based controller. The second scenario, a cooling-dominated environment in an office setting, further validates the versatility of our strategy under varying conditions. The consistent performance of our strategy across both scenarios underscores its potential as a robust tool for smart building management, adaptable to both residential and office environments under different climatic challenges.

Since 1996, the incidence of rickettsiosis has been increasing in Yucatán, Mexico, but recent prevalence data are lacking. This study aimed to determine exposure to the Spotted Fever Group (SFG) and Typhus Group (TG) in human serum samples suspected of tick-borne diseases (TBD) between 2015 and 2022. A total of 620 samples were analysed using indirect immunofluorescence assay (IFA) to detect IgG antibodies against SFG (Rickettsia rickettsii) and TG (Rickettsia typhi), considering a titer of ≥64 as positive. Results showed that 103 samples (17%) were positive for R. rickettsii and 145 (24%) for R. typhi, while 256 (41%) and 229 (37%) were negative, respectively. There was a cross-reaction in 244 samples (39%). Individuals with contact with vectors, such as ticks, showed significant exposure to fleas (p = 0.0010). The study suggests a high prevalence of rickettsiosis and recommends prospective studies to assess the disease burden and strengthen surveillance and prevention in Yucatán, considering factors like temperature and ecological changes.

Numerical solutions of partial differential equations require expensive simulations, limiting their application in design optimization, model-based control, and large-scale inverse problems. Surrogate modeling techniques aim to decrease computational expense while retaining dominant solution features and characteristics. Existing frameworks based on convolutional neural networks and snapshot-matrix decomposition often rely on lossy pixelization and data-preprocessing, limiting their effectiveness in realistic engineering scenarios. Recently, coordinate-based multilayer perceptron networks have been found to be effective at representing 3D objects and scenes by regressing volumetric implicit fields. These concepts are leveraged and adapted in the context of physical-field surrogate modeling. Two methods toward generalization are proposed and compared: design-variable multilayer perceptron (DV-MLP) and design-variable hypernetworks (DVH). Each method utilizes a main network which consumes pointwise spatial information to provide a continuous representation of the solution field, allowing discretization independence and a decoupling of solution and model size. DV-MLP achieves generalization through the use of a design-variable embedding vector, while DVH conditions the main network weights on the design variables using a hypernetwork. The methods are applied to predict steady-state solutions around complex, parametrically defined geometries on non-parametrically-defined meshes, with model predictions obtained in less than a second. The incorporation of random Fourier features greatly enhanced prediction and generalization accuracy for both approaches. DVH models have more trainable weights than a similar DV-MLP model, but an efficient batch-by-case training method allows DVH to be trained in a similar amount of time as DV-MLP. A vehicle aerodynamics test problem is chosen to assess the method’s feasibility. Both methods exhibit promising potential as viable options for surrogate modeling, being able to process snapshots of data that correspond to different mesh topologies.

The association between economic variables and the frequency and duration of disability income insurance (DII) claims is well established. Across many jurisdictions, heightened levels of unemployment have been associated with both a higher incidence and a longer duration of DII claims. This motivated us to derive an asset portfolio for which the total asset value moves in line with the level of unemployment, thus, providing a natural match for the DII portfolio liabilities. To achieve this, we develop an economic tracking portfolio where the asset weights in the portfolio are chosen so that the portfolio value changes in a way that reflects, as closely as possible, the level of unemployment. To the best of our knowledge, this is the first paper applying economic tracking portfolios to hedge economic risk in DII. The methodology put forward to establish this asset-liability matching portfolio is illustrated using DII data from the UK between 2004 and 2016. The benefits of our approach for claims reserving in DII portfolios are illustrated using a simulation study.

Rapid urbanization poses several challenges, especially when faced with an uncontrolled urban development plan. Therefore, it often leads to anarchic occupation and expansion of cities, resulting in the phenomenon of urban sprawl (US). To support sustainable decision–making in urban planning and policy development, a more effective approach to addressing this issue through US simulation and prediction is essential. Despite the work published in the literature on the use of deep learning (DL) methods to simulate US indicators, almost no work has been published to assess what has already been done, the potential, the issues, and the challenges ahead. By synthesising existing research, we aim to assess the current landscape of the use of DL in modelling US. This article elucidates the complexities of US, focusing on its multifaceted challenges and implications. Through an examination of DL methodologies, we aim to highlight their effectiveness in capturing the complex spatial patterns and relationships associated with US. This work begins by demystifying US, highlighting its multifaceted challenges. In addition, the article examines the synergy between DL and conventional methods, highlighting the advantages and disadvantages. It emerges that the use of DL in the simulation and forecasting of US indicators is increasing, and its potential is very promising for guiding strategic decisions to control and mitigate this phenomenon. Of course, this is not without major challenges, both in terms of data and models and in terms of strategic city planning policies.

We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine-learning enhancements. As a demonstrative application, we pursue the modeling of cathodic electrophoretic deposition, commonly known as e-coating. Our approach illustrates a systematic procedure for enhancing physical models by identifying their limitations through inference on experimental data and introducing adaptable model enhancements to address these shortcomings. We begin by tackling the issue of model parameter identifiability, which reveals aspects of the model that require improvement. To address generalizability, we introduce modifications, which also enhance identifiability. However, these modifications do not fully capture essential experimental behaviors. To overcome this limitation, we incorporate interpretable yet flexible augmentations into the baseline model. These augmentations are parameterized by simple fully-connected neural networks, and we leverage machine-learning tools, particularly neural ordinary differential equations, to learn these augmentations. Our simulations demonstrate that the machine-learning-augmented model more accurately captures observed behaviors and improves predictive accuracy. Nevertheless, we contend that while the model updates offer superior performance and capture the relevant physics, we can reduce off-line computational costs by eliminating certain dynamics without compromising accuracy or interpretability in downstream predictions of quantities of interest, particularly film thickness predictions. The entire process outlined here provides a structured approach to leverage data-driven methods by helping us comprehend the root causes of model inaccuracies and by offering a principled method for enhancing model performance.

The global number of individuals experiencing forced displacement has reached its highest level in the past decade. In this context, the provision of services for those in need requires timely and evidence-based approaches. How can mobile phone data (MPD) based analyses address the knowledge gap on mobility patterns and needs assessments in forced displacement settings? To answer this question, in this paper, we examine the capacity of MPD to function as a tool for anticipatory analysis, particularly in response to natural disasters and conflicts that lead to internal or cross-border displacement. The paper begins with a detailed review of the processes involved in acquiring, processing, and analyzing MPD in forced displacement settings. Following this, we critically assess the challenges associated with employing MPD in policy-making, with a specific focus on issues of user privacy and data ethics. The paper concludes by evaluating the potential benefits of MPD analysis for targeted and effective policy interventions and discusses future research avenues, drawing on recent studies and ongoing collaborations with mobile network operators.

In 2020, the COVID-19 pandemic resulted in a rapid response from governments and researchers worldwide, but information-sharing mechanisms were variable, and many early efforts were insufficient for the purpose. We interviewed fifteen data professionals located around the world, working with COVID-19-relevant data types in semi-structured interviews. Interviews covered both challenges and positive experiences with data in multiple domains and formats, including medical records, social deprivation, hospital bed capacity, and mobility data. We analyze this qualitative corpus of experiences for content and themes and identify four sequential barriers a researcher may encounter. These are: (1) Knowing data exists, (2) being able to access that data, (3) data quality, and (4) ability to share data onwards. A fifth barrier, (5) human throughput capacity, is present throughout all four stages. Examples of these barriers range from challenges faced by single individuals to non-existent records of historic mingling/social distance laws, and up to systemic geopolitical data suppression. Finally, we recommend that governments and local authorities explicitly create machine-readable temporal “law as code” for changes in laws such as mobility/mingling laws and changes in geographical regions.

Surrogate models of turbulent diffusive flames could play a strategic role in the design of liquid rocket engine combustion chambers. The present article introduces a method to obtain data-driven surrogate models for coaxial injectors, by leveraging an inductive transfer learning strategy over a U-Net with available multifidelity Large Eddy Simulations (LES) data. The resulting models preserve reasonable accuracy while reducing the offline computational cost of data-generation. First, a database of about 100 low-fidelity LES simulations of shear-coaxial injectors, operating with gaseous oxygen and gaseous methane as propellants, has been created. The design of experiments explores three variables: the chamber radius, the recess-length of the oxidizer post, and the mixture ratio. Subsequently, U-Nets were trained upon this dataset to provide reasonable approximations of the temporal-averaged two-dimensional flow field. Despite the fact that neural networks are efficient non-linear data emulators, in purely data-driven approaches their quality is directly impacted by the precision of the data they are trained upon. Thus, a high-fidelity (HF) dataset has been created, made of about 10 simulations, to a much greater cost per sample. The amalgamation of low and HF data during the the transfer-learning process enables the improvement of the surrogate model’s fidelity without excessive additional cost.

Currently, artificial intelligence (AI) is integrated across various segments of the public sector, in a scattered and fragmented manner, aiming to enhance the quality of people’s lives. While AI adoption has proven to have a great impact, there are several aspects that hamper its utilization in public administration. Therefore, a large set of initiatives is designed to play a pivotal role in promoting the adoption of reliable AI, including documentation as a key driver. The AI community has been proactively recommending a variety of initiatives aimed at promoting the adoption of documentation practices. While currently proposed AI documentation artifacts play a crucial role in increasing the transparency and accountability of various facts about AI systems, we propose a code-bound declarative documentation framework that aims to support the responsible deployment of AI-based solutions. Our proposed framework aims to address the need to shift the focus from data and models being considered in isolation to the reuse of AI solutions as a whole. By introducing a formalized approach to describing adaptation and optimization techniques, we aim to enhance existing documentation alternatives. Furthermore, its utilization in the public administration aims to foster the rapid adoption of AI-based applications due to the open access to common use cases in the public sector. We further showcase our proposal with a public sector-specific use case, such as a legal text classification task, and demonstrate how the AI Product Card enables its reuse through the interactions of the formal documentation specifications with the modular code references.

Model selection (MS) and model averaging (MA) are two popular approaches when many candidate models exist. Theoretically, the estimation risk of an oracle MA is not larger than that of an oracle MS because the former is more flexible, but a foundational issue is this: Does MA offer a substantial improvement over MS? Recently, seminal work by Peng and Yang (2022) has answered this question under nested models with linear orthonormal series expansion. In the current paper, we further respond to this question under linear nested regression models. A more general nested framework, heteroscedastic and autocorrelated random errors, and sparse coefficients are allowed in the current paper, giving a scenario that is more common in practice. A remarkable implication is that MS can be significantly improved by MA under certain conditions. In addition, we further compare MA techniques with different weight sets. Simulation studies illustrate the theoretical findings in a variety of settings.

In 2023, Bangladesh experienced its largest and deadliest outbreak of the Dengue virus (DENV), reporting the highest-ever recorded annual cases and deaths. Historically, most of the cases were recorded in the capital city, Dhaka. We aimed to characterize the geographical transmission of DENV in Bangladesh. From 1 January–31 December 2023, we extracted and analyzed daily data on dengue cases and deaths from the Management Information System of the Ministry of Health and Family Welfare. We performed a generalized linear mixed model to identify the associations between division-wise daily dengue counts and various geographical and meteorological covariates. The number of dengue cases reported in 2023 was 1.3 times higher than the total number recorded in the past 23 years (321,179 vs. 244,246), with twice as many deaths than the total fatalities recorded over the past 23 years (1705 vs. 849). Of the 1,705 deaths in 2023, 67.4% (n = 1,015) died within one day after hospital admission. The divisions southern to Dhaka had a higher dengue incidence/1000 population (2.30 vs. 0.50, p <0.01) than the northern divisions. Festival-related travel along with meteorological factors and urbanization are likely to have contributed to the shift of dengue from Dhaka to different districts in Bangladesh.

Highly pathogenic avian influenza (HPAI) outbreaks have repeatedly occurred in two districts of Kerala state, India, over the last few years. The outbreaks in the wetland areas coincided with the arrival of migratory birds. At the time, the factors responsible for local transmission in ducks were not known. This study aimed to identify the socio-economic factors responsible for spatial variation in the occurrence of HPAI outbreaks in the two districts using Bayesian network modelling (BNM) and Stochastic Partial Differential Equation (SPDE) model. Further, information was collected on the duck rearing practices in rice paddy fields to identify the risk factors for local – spread of the outbreaks. We found that the SPDE model without covariates explained variation in occurrence of outbreaks. The number of rice paddy fields used by the duck farmers was identified as risk factor. We concluded based on BNM and SPDE that the infected migratory birds were the source of infection for the first few duck farms in the wetland areas and subsequent transmission was driven by shifting of ducks from one rice paddy field to other fields. There is a probability of persistent and recurrent infections in the ducks and possible spill over to humans. Hence, it is important to have surveillance in ducks to prevent recurrent outbreaks in the region.

As astroviral infection rapidly increased in the summer of 2022 in Korea, this study aimed to determine the cause and genotype of astroviruses during this period. From January to December 2022, we tested 43,312 stool samples from patients with acute gastroenteritis utilizing multiplex PCR to detect HAstV. For the HAstV-positive samples, we determined the genotypes of the HAstVs by PCR and sequencing. The monthly positive rate from 2015 to 2022 showed a notable and abrupt increase of HAstV infection between June and August 2022, peaking at 9.8% in July 2022. The annual positivity rate of HAstV remained at 2–3% between 2015 and 2019, and then decreased to 0.5% in 2020, followed by an increase to 1.5% in 2021 and 3.6% in 2022.The genotyped astroviruses in 2022 were all identified as HAstV-1 type, and the nucleotide identity% among them was >99%. The GenBank accession number for the strain genetically closest to the strains identified in our study was ON571597.1, which was HAstV-1 isolated from Pingtan in 2019. Our results provide recent epidemiological data on HAstVs in Korea. The decline and surge in astrovirus positivity in recent years may be related to the COVID-19 pandemic.

This paper extends previous research on using quantum computers for risk management to a substantial, real-world challenge: constructing a quantum internal model for a medium-sized insurance company. Leveraging the author’s extensive experience as the former Head of Internal Model at a prominent UK insurer, we closely examine the practical bottlenecks in developing and maintaining quantum internal models. Our work seeks to determine whether a quadratic speedup, through quantum amplitude estimation can be realised for problems at an industrial scale. It also builds on previous work that explores the application of quantum computing to the problem of asset liability management in an actuarial context. Finally, we identify both the obstacles and the potential opportunities that emerge from applying quantum computing to the field of insurance risk management.

Extraintestinal pathogenic Escherichia coli (ExPEC) causes invasive E. coli disease (IED), including bacteraemia and (uro)sepsis, resulting in a high disease burden, especially among older adults. This study describes the epidemiology of IED in England (2013–2017) by combining laboratory surveillance and clinical data. A total of 191 612 IED cases were identified. IED incidence increased annually by 4.4–8.2% across all ages and 2.8–7.6% among adults ≥60 years of age. When laboratory-confirmed urosepsis cases without a positive blood culture were included, IED incidence in 2017 reached 149.4/100 000 person-years among all adults and 368.4/100 000 person-years among adults ≥60 years of age. Laboratory-confirmed IED cases were identified through E. coli-positive blood samples (55.3%), other sterile site samples (26.3%), and urine samples (16.6%), with similar proportions observed among adults ≥60 years of age. IED-associated case fatality rates ranged between 11.8–13.2% among all adults and 13.1–14.7% among adults ≥60 years of age. This study reflects the findings of other published studies and demonstrates IED constitutes a major and growing global health concern disproportionately affecting the older adult population. The high case fatality rates observed despite available antibiotic treatments emphasize the growing urgency for effective intervention strategies. The burden of urosepsis due to E. coli is likely underestimated and requires additional investigation.

With the ongoing emergence of SARS-CoV-2 variants, there is a need for standard approaches to characterize the risk of vaccine breakthrough. We aimed to estimate the association between variant and vaccination status in case-only surveillance data. Included cases were symptomatic adult laboratory-confirmed COVID-19 cases, with onset between January 2021 and April 2022, reported by five European countries (Estonia, Ireland, Luxembourg, Poland, and Slovakia) to The European Surveillance System. Associations between variant and vaccination status were estimated using conditional logistic regression, within strata of country and calendar date, and adjusting for age and sex. We included 80,143 cases including 20,244 Alpha (B.1.1.7), 152 Beta (B.1.351), 39,900 Delta (B.1.617.2), 361 Gamma (P.1), 10,014 Omicron BA.1, and 9,472 Omicron BA.2. Partially vaccinated cases were more likely than unvaccinated cases to be Beta than Alpha (adjusted odds ratio [aOR] 2.48, 95% CI 1.29–4.74), and Delta than Alpha (aOR 1.75, 1.31–2.34). Fully vaccinated cases were relative to unvaccinated cases more frequently Beta than Alpha (aOR 4.61, 1.89–11.21), Delta than Alpha (aOR 2.30, 1.55–3.39), and Omicron BA.1 than Delta (aOR 1.91, 1.60–2.28). We found signals of increased breakthrough infections for Delta and Beta relative to Alpha, and Omicron BA.1 relative to Delta.