Public procurement is a fundamental aspect of public administration. Its vast size makes its oversight and control very challenging, especially in countries where resources for these activities are limited. To support decisions and operations at public procurement oversight agencies, we developed and delivered VigIA, a data-based tool with two main components: (i) machine learning models to detect inefficiencies measured as cost overruns and delivery delays, and (ii) risk indices to detect irregularities in the procurement process. These two components cover complementary aspects of the procurement process, considering both active and passive waste, and help the oversight agencies to prioritize investigations and allocate resources. We show how the models developed shed light on specific features of the contracts to be considered and how their values signal red flags. We also highlight how these values change when the analysis focuses on specific contract types or on information available for early detection. Moreover, the models and indices developed only make use of open data and target variables generated by the procurement processes themselves, making them ideal to support continuous decisions at overseeing agencies.

We propose a physics-constrained convolutional neural network (PC-CNN) to solve two types of inverse problems in partial differential equations (PDEs), which are nonlinear and vary both in space and time. In the first inverse problem, we are given data that is offset by spatially varying systematic error (i.e., the bias, also known as the epistemic uncertainty). The task is to uncover the true state, which is the solution of the PDE, from the biased data. In the second inverse problem, we are given sparse information on the solution of a PDE. The task is to reconstruct the solution in space with high resolution. First, we present the PC-CNN, which constrains the PDE with a time-windowing scheme to handle sequential data. Second, we analyze the performance of the PC-CNN to uncover solutions from biased data. We analyze both linear and nonlinear convection-diffusion equations, and the Navier–Stokes equations, which govern the spatiotemporally chaotic dynamics of turbulent flows. We find that the PC-CNN correctly recovers the true solution for a variety of biases, which are parameterized as non-convex functions. Third, we analyze the performance of the PC-CNN for reconstructing solutions from sparse information for the turbulent flow. We reconstruct the spatiotemporal chaotic solution on a high-resolution grid from only 1% of the information contained in it. For both tasks, we further analyze the Navier–Stokes solutions. We find that the inferred solutions have a physical spectral energy content, whereas traditional methods, such as interpolation, do not. This work opens opportunities for solving inverse problems with partial differential equations.

Urban communities rely on built utility infrastructures as critical lifelines that provide essential services such as water, gas, and power, to sustain modern socioeconomic systems. These infrastructures consist of underground and surface-level assets that are operated and geo-distributed over large regions where continuous monitoring for anomalies is required but challenging to implement. This article addresses the problem of deploying heterogeneous Internet of Things sensors in these networks to support future decision-support tasks, for example, anomaly detection, source identification, and mitigation. We use stormwater as a driving use case; these systems are responsible for drainage and flood control, but act as conduits that can carry contaminants to the receiving waters. Challenges toward effective monitoring include the transient and random nature of the pollution incidents, the scarcity of historical data, the complexity of the system, and technological limitations for real-time monitoring. We design a SemanTics-aware sEnsor Placement framework (STEP) to capture pollution incidents using structural, behavioral, and semantic aspects of the infrastructure. We leverage historical data to inform our system with new, credible instances of potential anomalies. Several key topological and empirical network properties are used in proposing candidate deployments that optimize the balance between multiple objectives. We also explore the quality of anomaly representation in the network through new perspectives, and provide techniques to enhance the realism of the anomalies considered in a network. We evaluate STEP on six real-world stormwater networks in Southern California, USA, which shows its efficacy in monitoring areas of interest over other baseline methods.

Many documents are produced over the years of managing assets, particularly those with long lifespans. However, during this time, the assets may deviate from their original as-designed or as-built state. This presents a significant challenge for tasks that occur in later life phases but require precise knowledge of the asset, such as retrofit, where the assets are equipped with new components. For a third party who is neither the original manufacturer nor the operator, obtaining a comprehensive understanding of the asset can be a tedious process, as this requires going through all available but often fragmented information and documents. While common knowledge regarding the domain or general type of asset can be helpful, it is often based on the experiences of engineers and is, therefore, only implicitly available. This article presents a graph-based information management system that complements traditional PLM systems and helps connect fragments by utilizing generic information about assets. To achieve this, techniques from systems engineering and data science are used. The overarching management platform also includes geometric analyses and operations that can be performed with geometric and product information extracted from STEP files. While the management itself is first described generically, it is also later applied to cabin retrofit in aviation. A mock-up of an Airbus A320 is utilized as the case study to demonstrate further how the platform can provide benefits for retrofitting such long-living assets.

In 2022, the largest ever virgin soil outbreak of Japanese encephalitis (JE) occurred in Australia resulting in 45 reported human cases of JE, with seven fatalities. Japanese encephalitis virus (JEV) was detected in 84 piggeries across Australia. In response, states implemented targeted vaccination programs for those individuals at the highest risk of JEV exposure. A mixed methods approach, including geospatial mapping of JEV vaccine distribution in Queensland, a case series of Queensland human cases and interviews with Queensland Health staff, assessed the JEV vaccination response program. Five notified human cases were reviewed, with three having occupational outdoor risk and local travel-related exposure. Vaccine coverage ranged from 0 to 7.4 doses per 100 people after 12 months of the program. The highest uptake was in southern Queensland, where 95% of the state’s commercial pig population is located. The vaccination program was limited by a heavy reliance on general practitioners, vast geographical distribution of eligible populations, difficulties mobilising and engaging eligible cohorts, and suboptimal One Health collaboration. Population and climate factors make it possible for the virus to become endemic. Targeted vaccination programs remain an important strategy to protect people at the highest risk of exposure, however, program improvements are required to optimize vaccine accessibility.

A multi-year process of debate around draft articles for a Crimes Against Humanity Treaty is underway and calls to categorize gender-based persecution as a stand-alone crime and to codify gender apartheid form fundamental aspects of discussion. These developments in international criminal law are significant to anticipate forced migration as recent changes in asylum regulations across the EU suggest. Between December 2022 and February 2023, Sweden, Finland, and Denmark moved to grant asylum to women and girls from Afghanistan on general risks of gender-based persecution. This falls in line with the EU Agency for Asylum establishing that the accumulation of repressive measures against women and girls in the country, which have been described as gender apartheid, amounts to persecution. In efforts to offer new perspectives on foresight in forced migration, I use case study method and legal-institutional analysis to delineate migration scenarios for gender apartheid and asylum. On the example of Afghanistan, I compare Sweden, Finland, and Denmark as case studies in which asylum is granted to women and girls on general risks of gender-based persecution in contrast to Germany and France as case studies for main destination countries of Afghan asylum-seekers absent of such policies. I explore factors towards policy in/action and provide outlooks for further lines of inquiry regarding anticipatory methods in forced migration.

Abortion is one of the major threats to the livestock industry, and it also poses significant threats to public health since some of the abortifacient agents are considered zoonotic. Chlamydia abortus (C. abortus), Coxiella burnetii (C. burnetii), Listeria monocytogenes (L. monocytogenes), and Cache Valley virus (CVV) are recognized as important zoonotic and abortifacient agents of reproductive failure in small ruminants. This study determined the prevalence of these agents in ovine and caprine foetuses in Türkiye. A total of 1 226 foetuses were collected from the sheep (n = 1 144) and goats (n = 82) from different flocks between 2012 and 2017. Molecular detection methods were used to detect C. abortus, C. burnetii, and L. monocytogenes DNA and CVV RNA in aborted foetuses. In this study, C. abortus was the most prevalent abortifacient agent among the investigated ovine (264/1144) and caprine (12/82) foetuses, followed by C. burnetii with a frequency of 2.8% (32/1144) and 8.5% (7/82) in ovine and caprine foetuses, respectively. L. monocytogenes DNA was detected in 28 (2.4%) and 2 (2.4%) of the ovine and caprine foetuses, respectively. However, CVV RNA was not detected. Although the predominant mixed infection was C. abortus and C. burnetii, mixed infection of C. abortus and L. monocytogenes, and C. burnetii and L. monocytogenes were also found. The information presented in this study contributes to the understanding of the roles of C. abortus, C. burnetii, L. monocytogenes, and CVV in abortions in small ruminants, and could be beneficial for developing more effective control strategies.