Which game narratives do adolescents of different gameplay and sociodemographic backgrounds prefer? a mixed-methods analysis

OBJECTIVE: The aim of this study was to investigate which narrative elements of digital game narratives are preferred by the general adolescent population, and to examine associations with gender, socioeconomic status (SES), and gameplay frequency. Further, the study aims to discuss how results can be translated to serious digital games. MATERIALS AND METHODS: Adolescents were recruited through school to complete a survey on narrative preferences in digital games. The survey included questions on sociodemographic information, frequency of gameplay, and an open-ended question on what could be an appealing narrative for them. Data were analyzed in a mixed-methods approach, using thematic analysis and chi-square analyses to determine narrative preferences and the associations between game narrative elements and player characteristics (gender, SES, and frequency of gameplay). RESULTS: The sample consisted of 446 adolescents (12-15 years old) who described 30 narrative subthemes. Preferences included human characters as protagonists; nonhuman characters only as antagonists; realistic settings, such as public places or cities; and a strong conflict surrounding crime, catastrophe, or war. Girls more often than boys defined characters by their age, included avatars, located the narrative in private places, developed profession-related skills, and included a positive atmosphere. Adolescents of nonacademic education more often than adolescents of academic education defined characters by criminal actions. Infrequent players more often included human characters defined by their age than frequent players. After performing a Bonferroni correction, narrative preferences for several gender differences remained. CONCLUSION: Different narrative elements related to subgroups of adolescents by gender, SES, and frequency of gameplay. Customization of narratives in serious digital health games should be warranted for boys and girls; yet, further research is needed to specify how to address girls in particular

Inventory control for point-of-use locations in hospitals

Most inventory management systems at hospital departments are characterised by lost sales, periodic reviews with short lead times, and limited storage capacity. We develop two types of exact models that deal with all these characteristics. In a capacity model, the service level is maximised subject to a capacity restriction, and in a service model the required capacity is minimised subject to a service level restriction. We also formulate approximation models applicable for any lost-sales inventory system (cost objective, no lead time restrictions etc). For the capacity model, we develop a simple inventory rule to set the reorder levels and order quantities. Numerical results for this inventory rule show an average deviation of 1% from the optimal service levels. We also embed the single-item models in a multi-item system. Furthermore, we compare the performance of fixed order size replenishment policies and (R, s, S) policies

Association between COVID-19 lockdown measures and the incidence of iatrogenic versus spontaneous very preterm births in the Netherlands:a retrospective study

Background: The COVID-19 pandemic led to regional or nationwide lockdowns as part of risk mitigation measurements in many countries worldwide. Recent studies suggest an unexpected and unprecedented decrease in preterm births during the initial COVID-19 lockdowns in the first half of 2020. The objective of the current study was to assess the effects of the two months of the initial national COVID-19 lockdown period on the incidence of very and extremely preterm birth in the Netherlands, stratified by either spontaneous or iatrogenic onset of delivery, in both singleton and multiple pregnancies. Methods: Retrospective cohort study using data from all 10 perinatal centers in the Netherlands on very and extremely preterm births during the initial COVID-19 lockdown from March 15 to May 15, 2020. Incidences of very and extremely preterm birth were calculated using an estimate of the total number of births in the Netherlands in this period. As reference, we used data from the corresponding calendar period in 2015–2018 from the national perinatal registry (Perined). We differentiated between spontaneous versus iatrogenic onset of delivery and between singleton versus multiple pregnancies. Results: The incidence of total preterm birth < 32 weeks in singleton pregnancies was 6.1‰ in the study period in 2020 versus 6.5‰ in the corresponding period in 2015–2018. The decrease in preterm births in singletons was solely due to a significant decrease in iatrogenic preterm births, both < 32 weeks (OR 0.71; 95%CI 0.53 to 0.95) and < 28 weeks (OR 0.53; 95%CI 0.29 to 0.97). For multiple pregnancies, an increase in preterm births < 28 weeks was observed (OR 2.43; 95%CI 1.35 to 4.39). Conclusion: This study shows a decrease in iatrogenic preterm births during the initial COVID-19-related lockdown in the Netherlands in singletons. Future studies should focus on the mechanism of action of lockdown measures and reduction of preterm birth and the effects of perinatal outcome

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines

Defect structure and charge transport in solid solutions Ba

In this paper we have investigated the defect structure and the charge-transport properties of solid solutions of the type Ba1-xLaxF2+x. The defect structure has been studied by means of samples which had been doped slightly with trivalent gadolinium ions. These probes have been employed to investigate the surrounding crystal lattice, which contains in some cases large amounts of trigonal La3+-Fi- dipoles. It appears that the Gd3+ impurities do not participate in an eventual clustering process, because we have not observed EPR signals with significant intensities which can be assigned to clusters. In the solid solutions studied in this paper we have observed two different dipolar defects: (a) the nearest-neighbor (NN) or tetragonal La3+-Fi- dipole and (b) the nextnearest-neighbor (NNN) or trigonal La3+-Fi- dipole, the latter being the more dominant defect. The concentration ratio of the NN and NNN dipoles varies with the concentration of La3+ ions in the sample. With increasing La3+ concentration the above-mentioned ratio changes in favor of the NNN dipoles. In our ionic thermocurrent experiments on the system Ba1-xLaxF2+x we have observed three peaks: a weak one at about 137 K, which is associated with NN dipoles; a stronger one at about 190 K, which is due to NNN complexes; and a very strong one, which shifts to lower temperatures with increasing values of x. This strong peak is due to space charges which are produced by the polarizing field. The conductivity mechanism will be discussed in terms of the two-jump mechanism proposed in an earlier paper. In the range of low concentrations the eventual role of monovalent cations and oxide impurities is discussed. In order to obtain more information about the defect structure of the solid solutions Ba1-xLaxF2+x, we have investigated the development of the linewidth of the different resonances observed for trigonal Gd3+ probes which had been introduced into the samples. The observations have been analyzed, and it has been concluded that the Gd3+ probes are perturbed by distant La3+-Fi- dipoles. The broadening of the EPR lines will be calculated using a statistical model; the electrostatic interactions of the dipoles are found to shift the fine lines of the Gd3+ probes. The theoretical model employed here is found to give reasonable agreement with the experimental results

On Double-Boundary Non-Crossing Probability for a Class of Compound Processes with Applications

We develop an efficient method for computing the probability that a non-decreasing, pure jump (compound) stochastic process stays between arbitrary upper and lower boundaries (i.e., deterministic functions, possibly discontinuous) within a finite time period. The compound process is composed of a process modelling the arrivals of certain events (e.g., demands for a product in inventory systems, customers in queuing, or claims/capital gains in insurance/dual risk models), and a sequence of independent and identically distributed random variables modelling the sizes of the events. The events arrival process is assumed to belong to the wide class of point processes with conditional stationary independent increments which includes (non-)homogeneous Poisson, binomial, negative binomial, mixed Poisson and doubly stochastic Poisson (i.e., Cox) processes as special cases. The proposed method is based on expressing the non-exit probability through Chapman-Kolmogorov equations, re-expressing them in terms of a circular convolution of two vectors which is then computed applying fast Fourier transform (FFT). We further demonstrate that our FFT-based method is computationally efficient and can be successfully applied in the context of inventory management (to determine an optimal replenishment policy), ruin theory (to evaluate ruin probabilities and related quantities) and double-barrier option pricing or simply computing non-exit probabilities for Brownian motion with general boundaries

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines

Clinical characteristics of women captured by extending the definition of severe postpartum haemorrhage with 'refractoriness to treatment': a cohort study

Background: The absence of a uniform and clinically relevant definition of severe postpartum haemorrhage hampers comparative studies and optimization of clinical management. The concept of persistent postpartum haemorrhage, based on refractoriness to initial first-line treatment, was proposed as an alternative to common definitions that are either based on estimations of blood loss or transfused units of packed red blood cells (RBC). We compared characteristics and outcomes of women with severe postpartum haemorrhage captured by these three types of definitions. Methods: In this large retrospective cohort study in 61 hospitals in the Netherlands we included 1391 consecutive women with postpartum haemorrhage who received either ≥4 units of RBC or a multicomponent transfusion. Clinical characteristics and outcomes of women with severe postpartum haemorrhage defined as persistent postpartum haemorrhage were compared to definitions based on estimated blood loss or transfused units of RBC within 24 h following birth. Adverse maternal outcome was a composite of maternal mortality, hysterectomy, arterial embolisation and intensive care unit admission. Results: One thousand two hundred sixty out of 1391 women (90.6%) with postpartum haemorrhage fulfilled the definition of persistent postpartum haemorrhage. The majority, 820/1260 (65.1%), fulfilled this definition within 1 h following birth, compared to 819/1391 (58.7%) applying the definition of ≥1 L blood loss and 37/845 (4.4%) applying the definition of ≥4 units of RBC. The definition persistent postpartum haemorrhage captured 430/471 adverse maternal outcomes (91.3%), compared to 471/471 (100%) for ≥1 L blood loss and 383/471 (81.3%) for ≥4 units of RBC. Persistent postpartum haemorrhage did not capture all adverse outcomes because of missing data on timing of initial, first-line treatment. Conclusion: The definition persistent postpartum haemo

Parametric replenishment policies for inventory systems with lost sales and fixed order cost

In this paper we consider a single-item inventory system with lost sales and fixed order cost. We numerically illustrate the lack of a clear structure in optimal replenishment policies for such systems. However, policies with a simple structure are preferred in practical settings. Examples of replenishment policies with a simple parametric description are the (s, S) policy and the (s, nQ) policy. Besides these known policies in literature, we propose a new type of replenishment policy. In our modified (s, S) policy we restrict the order size of the standard (s, S) policy to a maximum. This policy results in near-optimal costs. Furthermore, we derive heuristic procedures to set the inventory control parameters for this new replenishment policy. In our first approach we formulate closed-form expressions based on power approximations, whereas in our second approach we derive an approximation for the steady-state inventory distribution. As a result, the latter approach could be used for inventory systems with different objectives or service level constraints. The numerical experiments illustrate that the heuristic procedures result on average in 2.4 percent and 1.8 percent cost increases, respectively, compared to the optimal replenishment policy. Therefore, we conclude that the heuristic procedures are very effective to set the inventory control parameters