With the advent of Artificial Intelligence for healthcare, data synthesis methods present crucial benefits in facilitating the fast development of AI models while protecting data subjects and bypassing the need to engage with the complexity of data sharing and processing agreements. Existing technologies focus on synthesising real-time physiological and physical records based on regular time intervals. Real health data are, however, characterised by irregularities and multimodal variables that are still hard to reproduce, preserving the correlation across time and different dimensions. This paper presents two novel techniques for synthetic data generation of real-time multimodal electronic health and physical records, (a) the Temporally Correlated Multimodal Generative Adversarial Network and (b) the Document Sequence Generator. The paper illustrates the need and use of these techniques through a real use case, the H2020 GATEKEEPER project of AI for healthcare. Furthermore, the paper presents the evaluation for both individual cases and a discussion about the comparability between techniques and their potential applications of synthetic data at the different stages of the software development life-cycle.

We consider two division problems on narrow strips of square and hexagonal lattices. In both cases we compute the bivariate enumerating sequences and the corresponding generating functions, which allowed us to determine the asymptotic behavior of the total number of such subdivisions and the expected number of parts. For the square lattice we extend results of two recent references by establishing polynomiality of enumerating sequences forming columns and diagonals of the triangular enumerating sequence. In the hexagonal case, we find a number of new combinatorial interpretations of the Fibonacci numbers and find combinatorial proofs of some Fibonacci related identities. We also show how both cases could be treated via the transfer matrix method and discuss some directions for future research.

Significance Mars has a liquid iron alloy core at its center. Using seismic data gathered by the InSight mission, we have made the first observations of seismic waves traveling through Mars’ core. We use the travel times of core-transiting seismic waves, relative to ones which remain in the mantle, to constrain properties of the core and construct the first models of the elastic properties of the entire planet. Our results are consistent with a core rich in sulfur, with smaller fractions of oxygen, carbon and hydrogen.

To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribution of operational data—so‐called distributional shifts. Distributional shifts can harm the performance of predictive models when deployed to future customer orders with new specifications. The literature provides limited advice on how such distributional shifts can be addressed in operations management. Here, we propose a data‐driven approach based on adversarial learning, which allows us to account for distributional shifts in manufacturing settings with high degrees of product customization. We empirically validate our proposed approach using real‐world data from a job shop production that supplies large metal components to an oil platform construction yard. Across an extensive series of numerical experiments, we find that our adversarial learning approach outperforms common baselines. Overall, this paper shows how production managers can improve their decision making under distributional shifts.

Hibiscus is a widely used plant, which has been proven to have numerous positive effects on human health, such as lowering blood pressure, maintaining optimal blood cholesterol levels, liver protection, prevention of oxidative stress, etc. In this study, the content of polyphenols, flavonoids and antioxidant capacity of aqueous, ethanolic and hydroethanolic (50/50 v/v) hibiscus extracts, prepared by maceration and ultrasonic extraction, was analyzed. Analysis of antioxidant activity was performed in vitro, using FRAP and DPPH methods. The results showed that the mixture of water and ethanol had a significantly higher effect of extraction of bioactive components from hibiscus than the remaining two solvents. The lowest content of polyphenols and flavonoids, and thus the weakest antioxidant activity was recorded in extracts prepared in absolute ethanol. By comparing the efficiency of the techniques used, maceration proved to be slightly more efficient in the case of aqueous and hydroethanol extracts, while higher polyphenol content and higher antioxidant activity were observed in ethanolic extracts prepared by ultrasonic extraction.

Apple accessions, currently maintained within the two main ex situ collections in Bosnia and Herzegovina (B&H), have previously been genotyped using microsatellite markers. The obtained molecular data provided insight into mislabeled accessions and redundancies, as well as the overall genetic structure of the germplasm. The available dataset enabled the creation of a core collection consisting of 52 accessions. The reliability and usefulness of microsatellites has made this low-density marker system a norm in studies on apple germplasm. However, the increased access to medium- and high-density SNP arrays, developed specifically for apples, has opened new avenues of research into apple genetic resources. In this study, 45 apple genotypes consisting of 33 diploid core collection accessions from B&H and 12 international reference cultivars were genotyped using an Axiom® Apple 480 K SNP array in order to examine their genetic relationships, population structure and diversity, as well as to compare the obtained results with those calculated on previously reported SSR profiles. The SNPs displayed a better ability to differentiate apple accessions based on their origin, as well as to cluster them according to their pedigree. Calculating identity by descent revealed 16 pairings with first-degree relationships and uncovered the introgression of ‘Delicious’ and ‘Golden Delicious’ into the core collection.

Accurate altimetry is essential for location-based services in commercial and industrial applications. However, current altimetry methods only provide low-accuracy measurements, particularly in multistorey buildings with irregular structures, such as hollow areas found in various industrial and commercial sites. This paper innovatively proposes a tightly coupled indoor altimetry system that utilizes floor identification to improve height measurement accuracy. The system includes two optimized algorithms that improve floor identification accuracy through activity detection and address the problem of difficult convergence of z-axis coordinates due to indoor coplanarity by applying constraints to iterative least squares (ILS). Two experiments were conducted in a teaching building and a laboratory, including an irregular environment with a hollow area. The results show that our proposed method for identifying floors based on activity detection outperforms other methods. In dynamic experiments, our method effectively eliminates repeated transformations during the up- and downstairs process, and in static experiments, it minimizes the impact of barometric drift. Furthermore, our proposed altimetry method based on constrained ILS achieves significantly improved positioning accuracy compared to ILS, 1D-CNN, and WC. Specifically, in the teaching building, our method achieves improvements of 0.84 m, 0.288 m, and 0.248 m, respectively, while in the laboratory, the improvements are 2.607 m, 0.696 m, and 0.625 m.

The objective of this paper is to introduce some new logarithm operational laws for intuitionistic fuzzy sets. Some structure properties have been developed and based on these, various aggregation operators, namely confidence logarithmic intuitionistic fuzzy Einstein weighted geometric (CLIFEWG) operator, confidence logarithmic intuitionistic fuzzy Einstein ordered weighted geometric (CLIFEOWG) operator, confidence logarithmic intuitionistic fuzzy Einstein hybrid geometric (CLIFEHG) operator, confidence logarithmic intuitionistic fuzzy Einstein weighted averaging (CLIFEWA) operator, confidence logarithmic intuitionistic fuzzy Einstein ordered weighted averaging (CLIFEOWA) operator, confidence logarithmic intuitionistic fuzzy Einstein hybrid averaging (CLIFEHA) operator have been presented. To show the validity and the superiority of the proposed operators, we compared these methods with the existing methods and concluded from the comparison and sensitivity analysis our proposed techniques are more effective.

In this paper, we provide different variants of the Hermite–Hadamard (H⋅H) inequality using the concept of a new class of convex mappings, which is referred to as up and down harmonically s-convex fuzzy-number-valued functions (UDH s-convex FNVM) in the second sense based on the up and down fuzzy inclusion relation. The findings are confirmed with certain numerical calculations that take a few appropriate examples into account. The results deal with various integrals of the 2ρσρ+σ type and are innovative in the setting of up and down harmonically s-convex fuzzy-number-valued functions. Moreover, we acquire classical and new exceptional cases that can be seen as applications of our main outcomes. In our opinion, this will make a significant contribution to encouraging more research.

Introduction Although children seem to be less susceptible to COVID-19, some of them develop a rare but serious hyperinflammatory condition called multisystem inflammatory syndrome in children (MIS-C). While several studies describe the clinical conditions of acute MIS-C, the status of convalescent patients in the months after acute MIS-C is still unclear, especially the question of persistence of changes in the specific subpopulations of immune cells in the convalescent phase of the disease. Methods We therefore analyzed peripheral blood of 14 children with MIS-C at the onset of the disease (acute phase) and 2 to 6 months after disease onset (post-acute convalescent phase) for lymphocyte subsets and antigen-presenting cell (APC) phenotype. The results were compared with six healthy age-matched controls. Results All major lymphocyte populations (B cells, CD4 + and CD8+ T cells, and NK cells) were decreased in the acute phase and normalized in the convalescent phase. T cell activation was increased in the acute phase, followed by an increased proportion of γ/δ-double-negative T cells (γ/δ DN Ts) in the convalescent phase. B cell differentiation was impaired in the acute phase with a decreased proportion of CD21 expressing, activated/memory, and class-switched memory B cells, which normalized in the convalescent phase. The proportion of plasmacytoid dendritic cells, conventional type 2 dendritic cells, and classical monocytes were decreased, while the proportion of conventional type 1 dendritic cells was increased in the acute phase. Importantly the population of plasmacytoid dendritic cells remained decreased in the convalescent phase, while other APC populations normalized. Immunometabolic analysis of peripheral blood mononuclear cells (PBMCs) in the convalescent MIS-C showed comparable mitochondrial respiration and glycolysis rates to healthy controls. Conclusions While both immunophenotyping and immunometabolic analyzes showed that immune cells in the convalescent MIS-C phase normalized in many parameters, we found lower percentage of plasmablasts, lower expression of T cell co-receptors (CD3, CD4, and CD8), an increased percentage of γ/δ DN Ts and increased metabolic activity of CD3/CD28-stimulated T cells. Overall, the results suggest that inflammation persists for months after the onset of MIS-C, with significant alterations in some immune system parameters, which may also impair immune defense against viral infections.

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries.