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Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission profiles. However, most of these parameters are unknown and must be estimated somehow. Some are estimated based on observed correlations and known volcano parameters. However, a more accurate estimate is often needed to bring the model into closer agreement with observations. This paper describes the inversion procedure implemented at the Norwegian Meteorological Institute for estimating ash emission rates from retrieved satellite ash column amounts and a priori knowledge. The overall procedure consists of five stages: (1) generate a priori emission estimates, (2) run forward simulations with a set of unit emission profiles, (3) collocate/match observations with emission simulations, (4) build system of linear equations, and (5) solve overdetermined systems. We go through the mathematical foundations for the inversion procedure, performance for synthetic cases, and performance for real-world cases. The novelties of this paper include a memory efficient formulation of the inversion problem, a detailed description and illustrations of the mathematical formulations, evaluation of the inversion method using synthetic known-truth data as well as real data, and inclusion of observations of ash cloud-top height. The source code used in this work is freely available under an open-source license and is able to be used for other similar applications.

GPUs have become a household name in High Performance Computing (HPC) systems over the last 15 years. However, programming GPUs is still largely a manual and arduous task, which requires expert knowledge of the physics, mathematics, and computer science involved. Even though there have been large advances in automatic parallelization and GPU execution of serial code, it is still difficult to fully utilize the GPU hardware with such approaches. Many core numeric GPU codes are therefore still mostly written using low level C/C++ or Fortran for the host code. Several studies have shown that using higher level languages, such as Python, can make software development faster and with fewer bugs. We have developed a simulator based on PyCUDA and mpi4py in Python for solving the Euler equations on Cartesian grids. Our framework utilizes the GPU, and can automatically run on clusters using MPI as well as on shared-memory systems. Our framework allows the programmer to implement low-level details in CUDA C/C++, which is important to achieve peak performance, whilst still benefiting from the productivity of Python. We show that our framework achieves good weak and strong scaling. Our weak scaling achieves more than 94% efficiency on a shared-memory GPU system and more than 90% efficiency on a distributed-memory GPU system, and our strong scaling is close to perfect on both shared-memory and distributed-memory GPU systems.

In this work, we take a modern high-resolution finite-volume scheme for solving the rotational shallow-water equations and extend it with features required to run real-world ocean simulations. Our contributions include a spatially varying north vector and Coriolis term required for large scale domains, moving wet-dry fronts, a static land mask, bottom shear stress, wind forcing, boundary conditions for nesting in a global model, and an efficient model reformulation that makes it well-suited for massively parallel implementations. Our model order is verified using a grid convergence test, and we show numerical experiments using three different sections along the coast of Norway based on data originating from operational forecasts run at the Norwegian Meteorological Institute. Our simulation framework shows perfect weak scaling on a modern P100 GPU, and is capable of providing tidal wave forecasts that are very close to the operational model at a fraction of the cost. All source code and data used in this work are publicly available under open licenses.

Quantifying and visualizing deformation and material fluxes is an indispensable tool for many geoscientific applications at different scales comprising for example global convective models (Burstedde et al., 2013), co-seismic slip (Leprince et al., 2007) or local slope deformation (Stumpf et al., 2014b). Within the European project IQmulus (http://www.iqmulus.eu) a special focus is laid on the efficient detection and visualization of submarine sand dune displacements. In this paper we present our approaches on the visualization of the calculated displacements utilizing modern GPU techniques to enable the user to interactively analyze intermediate and final results within the whole workflow.

Background: Studies indicate that nursing homes are special places that require getting used to feel comfortable. Changing staff might challenge the residents' individual needs and experience of recognition. The purpose of this study is to gain insight into the residents' perception of what they experience as significant in everyday life. Method: Qualitative, individual, semi-structured interviews were carried out with eight residents in nursing homes. The analysis is inspired by Malterud's systematic text condensation. Two main themes emerged: 1. Individual care: The importance of the staff in creating well-being and safety in the nursing home and 2: The importance of being together with like-minded residents and individually adapted activities. Conclusion: The findings indicate that it is of great importance that the staff show an interest in the residents as well as their interaction with the other residents. Activities can facilitate social contact, while the activities must meet the individual resident's unique preferences. [ABSTRACT FROM AUTHOR]

Alcohol consumption is an important risk factor for illness, disability, and mortality. Alcohol withdrawal symptoms have a significant health concern for individuals with alcohol dependence. This study aims to investigate the prevalence of withdrawal symptoms and liver function test abnormalities in this population and also to identify the factors associated with the severity of withdrawal symptoms. This cross-sectional study was conducted at a tertiary care facility involving 100 patients seeking treatment for alcohol dependence. Data collection included structured interviews and assessments using standardized scales and biochemical tests. Descriptive statistics, t-tests, and chi-square tests were employed to analyse the data. Individuals exhibiting mild/moderate alcohol withdrawal symptoms in this study displayed several distinctive features. They were more likely to have attained at least a high school education, their average alcohol consumption was notably lower, Importantly, their blood parameters, including RBC count, platelet count, total bilirubin levels, and liver enzyme levels (AST and ALT), generally exhibited fewer deviations from normal ranges. Family history of alcohol dependence and nicotine dependence was common among the participants. This research highlights the need for a holistic approach to address alcohol dependence, taking into consideration sociodemographic factors, clinical markers, and the diverse nature of this condition. The findings provide important insights for healthcare professionals to identify individuals at higher risk and guide treatment decisions. [ABSTRACT FROM AUTHOR]

Numerical simulation is an essential way to investigate the discharge behaviors of atmospheric low-temperature plasmas (LTPs). In this study, a deep neural network (DNN) with multiple hidden layers is constructed to surrogate the fluid model to investigate the discharge characteristics of atmospheric helium dielectric barrier discharges (DBDs) with very high computational efficiency, working as an example to show the ability and validity of DNN to explore LTPs. The DNN is trained by the well-formed training datasets obtained from a verified fluid model, and a designed loss function coupled in the DNN program is continuously optimized to achieve a better prediction performance. The predicted data show that the essential discharge characteristics of atmospheric DBDs such as the discharge current waveforms, spatial profiles of charged particles, and electric field can be yielded by the well-trained DNN program with great accuracy only in several seconds, and the predicted evolutionary discharge trends are consistent with the previous simulations and experimental observations. Additionally, the constructed DNN shows good generalization performance for multiple input attributes, which indicates a great potential promise for vastly extending the range of discharge parameters. This study provides a useful paradigm for future explorations of machine learning-based methods in the field of atmospheric LTP simulation without high-cost calculation. [ABSTRACT FROM AUTHOR]

The purpose of this study is to examine healthcare usage, morbidities, and alcohol consumption monitoring in patients before a diagnosis of mental manifestations to assist in the early identification of individuals at risk. Biological markers of alcoholism are separated into two groups: those biological variables that highlight with great confidence and validate the presence of a predisposition to alcoholism, also called trait markers, or those that highlight alcohol consumption, called markers of alcoholism ("status markers"). Biomarkers are the true "gold standard" for the diagnosis of alcoholism. They are valuable for tracking evolution and progress during biological and psychological therapy and for highlighting relapse. This review compiles the existing data from research on healthcare utilization, comorbidities, and alcohol consumption monitoring in patients before a diagnosis of mental manifestations to aid in the early identification of individuals at risk. This documentary study took place over three to four months by searching for terms on the Science Direct platform, PubMed, Web of Science, and Google Scholar such as alcoholism, alcohol use disorders, alcohol abuse, and biomarkers. Studies reporting on the development, characteristics, and utilization of blood biomarkers for alcohol consumption were included in the search. The initial search included a number of 11.019 articles that contained the keywords biomarkers and alcohol. Finally, a total of 50 research articles were considered. I am involved in clinical studies, meta-analyses, reviews, and case studies regarding alcohol consumption detection, as well as potential alcohol markers. [ABSTRACT FROM AUTHOR]

Alcoholism presents a significant health concern with notable socioeconomic implications. Alcohol withdrawal syndrome (AWS) can manifest when individuals cease or drastically reduce their alcohol consumption after prolonged use. Non-alcoholic fatty liver disease (NAFLD) is characterized by substantial lipid accumulation in the liver cells of individuals with no history of alcohol consumption. There is evidence suggesting an association between cognitive impairment and both conditions. This study aimed to evaluate cognitive impairment in patients with NAFLD and AWS using the Mini-Mental State Examination (MMSE). This study involved 120 patients admitted to two hospitals in Craiova, Romania. Results indicated that patients with NAFLD did not exhibit cognitive impairment as measured by MMSE (Mean = 29.27, SD = 0.785). Conversely, patients with AWS showed more pronounced cognitive dysfunction, with a mean MMSE score at admission of 16.60 ± 4.097 and 24.60 ± 2.832 after 2 weeks under treatment with Vitamins B1 and B6 and Cerebrolysin. Additionally, our findings suggested that cognitive dysfunction among alcohol consumers was correlated with the severity of clinical symptoms, as demonstrated by the severity of tremors in our study. The two-week period under treatment and alcohol withdrawal was insufficient for cognitive function to return to normal levels. Observational studies on longer periods of time are advised. [ABSTRACT FROM AUTHOR]

Background: Delirium Tremens (DT) is known to be a serious complication of alcohol withdrawal syndrome (AWS). Neurotransmitter abnormalities, inflammation, and increased permeability are associated with the pathogenesis of AWS and DT. However, the biomarkers of these conditions are still poorly understood. Methods: In this work, biochemical, hematologic, inflammatory, and gut permeability biomarkers were investigated in the following three groups: healthy controls (n = 75), severe AWS patients with DT (n = 28), and mild/moderate AWS without DT (n = 97). Blood sampling was performed after resolution of the acute condition (on 5 ± 1 day after admission) to collect clinical information from patients and to investigate associations with clinical scales. Biomarker analysis was performed using automated analyzers and ELISA. Inflammatory biomarkers included the erythrocyte sedimentation rate (ESR), high-sensitivity C-reactive protein (hsCRP), and platelet-to-lymphocyte ratio (PLR). Results: Among the biochemical biomarkers, only glucose, total cholesterol, and alanine aminotransferase (ALT) changed significantly in the analyzed groups. A multiple regression analysis showed that age and ALT were independent predictors of the CIWA-Ar score. Hematologic biomarker analysis showed an increased white blood cell count, and the elevated size and greater size variability of red blood cells and platelets (MCV, RDWc, and PDWc) in two groups of patients. Gut permeability biomarkers (FABP2, LBP, and zonulin) did not change, but were associated with comorbid pathologies (alcohol liver disease and pancreatitis). The increase in inflammatory biomarkers (ESR and PLR) was more evident in AWS patients with DT. Cluster analysis confirmed the existence of a subgroup of patients with evidence of high inflammation, and such a subgroup was more frequent in DT patients. Conclusions: These findings contribute to the understanding of biomarker variability in AWS patients with and without DT and support the heterogeneity of patients by the level of inflammation. [ABSTRACT FROM AUTHOR]

Background: Long-term care services for older adults are characterised by increasing needs and scarce resources. Political strategies have led to the reorganisation of long-term care services, with an increased focus on "ageing in place" and efficient use of resources. There is currently limited research on the processes by which resource allocation decisions are made by service allocators of long-term care services for older adults. The aim of this study is to explore how three political principles for priority setting in long-term care, resource, severity and benefit, are expressed in service allocation to older adults. Methods: This qualitative study uses data from semi-structured individual interviews, focus groups and observations of service allocators who assess needs and assign long-term care services to older adults in Norway. The data were supplemented with individual decision letters from the allocation office, granting or denying long-term care services. The data were analysed using reflexive thematic analysis. Results: The allocators drew on all three principles for priority setting when assessing older adults' long-term care needs and allocating services. We found that the three principles pushed in different directions in the allocation process. We identified six themes related to service allocators' expression of the principles: (1) lowest effective level of care as a criterion for service allocation (resource), (2) blanket allocation of low-cost care services (resource), (3) severity of medical and rehabilitation needs (severity), (4) severity of care needs (severity), (5) benefit of generous service allocation (benefit) and (6) benefit of avoiding services (benefit). Conclusions: The expressions of the three political principles for priority setting in long-term care allocation are in accordance with broader political trends and discourses regarding "ageing in place", active ageing, an investment ideology, and prioritising those who are "worse off". Increasing attention to the rehabilitation potential of older adults and expectations that they will take care of themselves increase the risk of not meeting frail older adults' care needs. Additionally, difficulties in defining the severity of older adults' complex needs lead to debates regarding "worse off" versus potentiality in future long-term care services allocation. Trial registration: Not applicable. [ABSTRACT FROM AUTHOR]

Excessive and uncontrolled consumption of alcohol can cause alcohol use disorder (AUD), but its pharmacological mechanisms are not fully understood. Inhibiting the reverse mode activity of the sodium–calcium exchanger (NCX) can reduce the risk of alcohol withdrawal seizures, suggesting that NCX could play a role in controlling alcohol consumption. Here, we investigated how two potent inhibitors of NCX reverse mode activity, SN-6 (NCX1) and KB-R7943 (NCX3), affect voluntary alcohol consumption in adult male and female rats using the intermittent alcohol access two-bottle choice paradigm. Initially, animals were trained to drink 7.5% ethanol and water for four weeks before administering SN-6 and KB-R7934. Afterward, their alcohol intake, preference, and water intake were recorded 2 and 24 h after exposure to water and 7.5% ethanol. SN-6 significantly reduced alcohol consumption by 48% in male and 36% in female rats without affecting their water intake. Additionally, SN-6 significantly reduced alcohol preference in females by 27%. However, KB-R7943 reduced alcohol consumption by 42% in female rats and did not affect alcohol preference or water intake. These findings suggest that alcohol exposure increased NCX reverse activity, and targeting NCX1 could be an effective strategy for reducing alcohol consumption in subjects susceptible to withdrawal seizures. [ABSTRACT FROM AUTHOR]

The provision of long-term care services for older adults is characterised by increasing needs and scarce resources, leading to ethical dilemmas. This qualitative study explored the ethical dilemmas experienced by healthcare professionals when allocating long-term care services to older adults and the strategies used to handle ethical dilemmas. Data from semi-structured individual interviews, focus group interviews, and observations of service allocators assessing needs and assigning long-term care services to older adults were analysed using content analysis. The overarching theme was the struggle for safe and equitable service allocation. The identified dilemmas were: (i) Struggles with A Just Allocation of Services due to Limited Time and Trust, (ii) Pressure on Professional Values Concerning Safety and Dignity, and (iii) Difficulties in Prioritising One Group Over Another. The strategies to deal with ethical dilemmas were: (i) Assessing Needs Across the Entire Municipality, (ii) Ensuring Distance to Service Recipients, (iii) Working as a Team, and (iv) Interprofessional Decision-Making. Scarce resources, organisational limitations, and political expectations drive the ethical dilemmas in long-term care service allocation. An open public discussion regarding the acceptable minimum standard of long-term care is needed to reduce the ethical pressure on service allocators. [ABSTRACT FROM AUTHOR]

Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission profiles. However, most of these parameters are unknown and must be estimated somehow. Some are estimated based on observed correlations and known volcano parameters. However, a more accurate estimate is often needed to bring the model into closer agreement with observations. This paper describes the inversion procedure implemented at the Norwegian Meteorological Institute for estimating ash emission rates from retrieved satellite ash column amounts and a priori knowledge. The overall procedure consists of five stages: (1) generate a priori emission estimates, (2) run forward simulations with a set of unit emission profiles, (3) collocate/match observations with emission simulations, (4) build system of linear equations, and (5) solve overdetermined systems. We go through the mathematical foundations for the inversion procedure, performance for synthetic cases, and performance for real-world cases. The novelties of this paper include a memory efficient formulation of the inversion problem, a detailed description and illustrations of the mathematical formulations, evaluation of the inversion method using synthetic known-truth data as well as real data, and inclusion of observations of ash cloud-top height. The source code used in this work is freely available under an open-source license and is able to be used for other similar applications. [ABSTRACT FROM AUTHOR]

Long-Term Outcome of Alcohol Withdrawal Seizures Sansone G, Megevand P, Vulliémoz S, Corbetta M, Picard F, Seeck M. Eur J Neurol. 2024;31(1):e16075. doi:10.1111/ene.16075 Background and purpose: Alcohol withdrawal seizures (AWS) are a well-known complication of chronic alcohol abuse, but there is currently little knowledge of their long-term relapse rate and prognosis. The aims of this study were to identify risk factors for AWS recurrence and to study the overall outcome of patients after AWS. Methods: In this retrospective single-center study, we included patients who were admitted to the Emergency Department after an AWS between January 1, 2013 and August 10, 2021 and for whom an electroencephalogram (EEG) was requested. AWS relapses up until April 29, 2022 were researched. We compared history, treatment with benzodiazepines or antiseizure medications (ASMs), laboratory, EEG and computed tomography findings between patients with AWS relapse (r-AWS) and patients with no AWS relapse (nr-AWS). Results: A total of 199 patients were enrolled (mean age 53 ± 12 years; 78.9% men). AWS relapses occurred in 11% of patients, after a median time of 470.5 days. Brain computed tomography (n = 182) showed pathological findings in 35.7%. Risk factors for relapses were history of previous AWS (p = 0.013), skull fractures (p = 0.004) at the index AWS, and possibly epileptiform EEG abnormalities (p = 0.07). Benzodiazepines or other ASMs, taken before or after the index event, did not differ between the r-AWS and the nr-AWS group. The mortality rate was 2.9%/year of follow-up, which was 13 times higher compared to the general population. Risk factors for death were history of AWS (p < 0.001) and encephalopathic EEG (p = 0.043). Conclusions: Delayed AWS relapses occur in 11% of patients and are associated with risk factors (previous AWS >24 h apart, skull fractures, and pathological EEG findings) that also increase the epilepsy risk, that is, predisposition for seizures, if not treated. Future prospective studies are mandatory to determine appropriate long-term diagnostic and therapeutic strategies, in order to reduce the risk of relapse and mortality associated with AWS. [ABSTRACT FROM AUTHOR]

Background and purpose: Alcohol withdrawal seizures (AWS) are a well‐known complication of chronic alcohol abuse, but there is currently little knowledge of their long‐term relapse rate and prognosis. The aims of this study were to identify risk factors for AWS recurrence and to study the overall outcome of patients after AWS. Methods: In this retrospective single‐center study, we included patients who were admitted to the Emergency Department after an AWS between January 1, 2013 and August 10, 2021 and for whom an electroencephalogram (EEG) was requested. AWS relapses up until April 29, 2022 were researched. We compared history, treatment with benzodiazepines or antiseizure medications (ASMs), laboratory, EEG and computed tomography findings between patients with AWS relapse (r‐AWS) and patients with no AWS relapse (nr‐AWS). Results: A total of 199 patients were enrolled (mean age 53 ± 12 years; 78.9% men). AWS relapses occurred in 11% of patients, after a median time of 470.5 days. Brain computed tomography (n = 182) showed pathological findings in 35.7%. Risk factors for relapses were history of previous AWS (p = 0.013), skull fractures (p = 0.004) at the index AWS, and possibly epileptiform EEG abnormalities (p = 0.07). Benzodiazepines or other ASMs, taken before or after the index event, did not differ between the r‐AWS and the nr‐AWS group. The mortality rate was 2.9%/year of follow‐up, which was 13 times higher compared to the general population. Risk factors for death were history of AWS (p < 0.001) and encephalopathic EEG (p = 0.043). Conclusions: Delayed AWS relapses occur in 11% of patients and are associated with risk factors (previous AWS >24 h apart, skull fractures, and pathological EEG findings) that also increase the epilepsy risk, that is, predisposition for seizures, if not treated. Future prospective studies are mandatory to determine appropriate long‐term diagnostic and therapeutic strategies, in order to reduce the risk of relapse and mortality associated with AWS. [ABSTRACT FROM AUTHOR]

We studied a patient with mitochondrial DNA depletion in skeletal muscle and a multiorgan phenotype, including fatal encephalomyopathy, retinopathy, optic atrophy, and sensorineural hearing loss. Instead of pathogenic variants in the mitochondrial maintenance genes, we identified previously unpublished variant in DHX16 gene, a de novo heterozygous c.1360C>T (p. Arg454Trp). Variants in DHX16 encoding for DEAH‐box RNA helicase have previously been reported only in five patients with a phenotype called as neuromuscular oculoauditory syndrome including developmental delay, neuromuscular symptoms, and ocular or auditory defects with or without seizures. We performed functional studies on patient‐derived fibroblasts and skeletal muscle revealing, that the DHX16 expression was decreased. Clinical features together with functional data suggest, that our patient's disease is associated with a novel pathogenic DHX16 variant, and mtDNA depletion could be a secondary manifestation of the disease. [ABSTRACT FROM AUTHOR]

Background: Many healthcare professionals have left their professions recently because of increased moral distress, and the COVID‐19 pandemic has had a further major impact on the ever‐changing healthcare environment. Aim: The purpose of the study was to examine care leaders' experiences of moral distress in their daily work in older adult care. Methodology: A qualitative design was used. The data consisted of texts from interviews with care leaders (N = 8) in an older adult care context. Content analysis was used to analyse the data. Findings: Five themes emerged: (1) moral distress arises from a lack of time, (2) moral distress contributes to a sense of inadequacy but also a sense of responsibility, (3) moral distress arises from an imbalance in values, (4) increased knowledge and open discussion help reduce moral distress and (5) reflection, increased support and increased resources can reduce moral distress. Conclusion: Moral distress is something that care leaders, according to this study, experience daily in an older adult care context and it is considered to have increased. Care leaders can experience moral distress from a lack of time; patient‐related, relative‐related or other ethically difficult situations or an imbalance between own values and an organisation's, other caregivers', patients' and/or patients' relatives values. Increased staffing resources, more knowledge (training and lectures) and time for reflection individually, in groups or with an outside expert could increase care leaders' insights into and ability to reduce moral distress. Although situations that are characterised by moral distress are burdensome, care leaders have the opportunity to learn from such situations through reflection and discussion and can develop strategies for future ethical challenges. Future research could focus on exploring caregivers' experiences of moral distress. [ABSTRACT FROM AUTHOR]

In the past decade, Graphics Processing Units have played an important role in the field of high-performance computing and they still advance new fields such as IoT, autonomous vehicles, and exascale computing. It is therefore important to understand how to extract performance from these processors, something that is not trivial. This survey discusses various optimization techniques found in 450 articles published in the last 14 years. We analyze the optimizations from different perspectives which shows that the various optimizations are highly interrelated, explaining the need for techniques such as auto-tuning. [ABSTRACT FROM AUTHOR]

Many individuals, both in the public and within the field of psychology, often perceive aging as a burden that negatively impacts intellectual and mental health. Our present study aims to challenge this notion by identifying the crucial components of positive mental health in later life. These components not only promote positive mental health but also actively contribute to it, even under difficult circumstances. To accomplish this, we first offer a concise review of well-being and mental health models that highlight the psychological aspects of flourishing in late life. We then introduce a psychological competence-based model for positive mental health, which aligns with the concept of positive aging. Subsequently, we present a measurement tool suitable for practical applications. Finally, we provide a comprehensive overview of positive aging, drawing on methodological guidelines and existing research findings concerning sustainable positive mental health in later life. We examine the evidence indicating that psychological resilience (the capacity to adapt and recover from adversity or stress) and competence (skills and abilities to effectively cope with challenges across various life domains) significantly contribute to slowing down biological aging processes. Furthermore, we discuss insights into the relationship between psychological factors and aging derived from research on Blue Zones (regions characterized by a higher proportion of individuals experiencing longer, healthier lives). [ABSTRACT FROM AUTHOR]