Posten (Norway)

Hydrogen is exposed to competition from abated fossil fuels, imports, and electrification A European target of 25 Mt/a of green hydrogen production by 2040 hedges against risk 25

Die Psalmen gehören zu den bekanntesten und beliebtesten Büchern der Bibel. Nach den zwölf Untersuchungen zu den Asaph-Psalmen im Vorgängerband ÖBS 57 werden hier weitere zwanzig Studien (vier davon auf Englisch) zu Psalmen und Psalter publiziert. Erstmals veröffentlicht wird eine Studie zu Psalm 91 und seiner frühen Nachgeschichte. Darüber hinaus werden in diesem Sammelband verstreut erschienene Beiträge in überarbeiteter, aktualisierter und teils erweiterter Form neu zugänglich gemacht. Die Beiträge sind unter die folgenden vier Abschnitte gegliedert: I. Psalmenkommunikation und Redekonstellationen; II. Zu den Psalmen 25–34 und ihrer Konfiguration; III. Psalter, Kanon und Theologie; IV. Verschiedenes.

Globally, continental shelf environments, and the marine sediments therein, have been recognised as having significant roles to play in the sequestration, cycling and storage of. Recently, shelf sediments have been identified as the largest, but most uncertain, stock of carbon stored on the continental shelf, citing a lack of empirical data. Moreover, seabeds are coming under increased pressure through anthropogenic impacts, such as offshore renewable energy development, trawling and dredging, and climate change effects. To fully understand, and effectively manage the seabed in terms of maximising this Blue Carbon potential requires a thorough understanding of carbon cycling in the marine environment over time, physical processes at the seafloor and high-quality spatial mapping. The QUEST project scope aims to conduct a multidisciplinary research programme to qualify, quantify and elucidate the provenance of carbon stocks in offshore marine sediments in Irelands EEZ. Furthermore, the research will examine and characterise threats to the stability of Blue Carbon in these settings and support the development of long-term management strategies. This programme will comprise spatial predictive modelling along with offshore surveying and sampling, laboratory analysis and hydrodynamic modelling, with past and new data to deliver comprehensive geochemical, geological, geotechnical, environmental and morphodynamical assessments of Blue Carbon ‘hotspots’ in the Irish offshore, as identified in the National Marine Planning Framework.To date, this programme has worked with a variety of stakeholders to collate historical data relevant as predictors for sediment OC and generated new geochemical data sets through analysis of legacy sediment samples. The combined data sets have been used to produce spatial predictive modelling maps providing preliminary baselines for sediment OC stocks in Ireland’s EEZ.Spatial mapping has identified knowledge gaps in the spatial extent and resolution of available sediment data. Additionally, the data collation and mapping exercise has highlighted a sparsity of physio-geochemical data essential for the accurate estimation and upscaling of OC stocks including OC content, Bulk density, and grain size analysis. Likewise, the paucity of available data extends to deeper sediments, consequently inhibiting the determination of OC stocks to the desired standard of 1 meter depth in assessment of BC ecosystems.  The first off-shore sampling survey for QUEST was completed in October 2023 providing a selection of grab (surface ~10cm depth), gravity (1-2m depth ), Vibro- ( 2-5m) and box core ( 10-25cm) samples from a series of near to off-shore transects from Ireland’s East coast encompassing the high sedimentary region of the Western Irish Sea Mudbelt (WISMB). Initial work carried out on cores has generated data describing updated OC stocks for Irish Sea sediments up to a 1m depth. Furthermore, data sets have been used to produce a region-specific conversion equation to calculate OC content using values attained from Loss on ignition analysis (LOI). This conversion factor has been applied to convert historical data LOI data for spatial predictive modelling. 

BackgroundPrematurity and enlarged subarachnoid spaces are both hypothesised to represent an increased risk of subdural haemorrhages (SDHs) in infancy, both with and without a history of abuse.ObjectiveTo examine the prevalence of a previous haemorrhage, particularly SDHs, in infants born extremely prematurely around term-equivalent age; to examine intra- and inter-observer agreement for identification of haemorrhages; and to examine the width of the subarachnoid spaces.Materials and methodsA total of 121 extremely premature infants had cerebral magnetic resonance imaging (MRI) performed around term-equivalent age (mean chronological age 14.7 weeks, range 10.3–24.0 weeks). There were no infants investigated for abuse in our cohort. Intracranial haemorrhages were classified as isolated germinal matrix-haemorrhages, parenchymal haemorrhages (cerebellar- and cerebral haemorrhages), or extra-axial haemorrhages (subarachnoid haemorrhages, SDHs, or epidural haemorrhages). Sinocortical width and interhemispheric distance were measured.ResultsNo appreciable SDH was detected with the performed sequences. Haemorrhage/blood products related to prematurity were seen in 60 (49.5%) of the neonates. Agreement was good to very good for identification of haemorrhage. The mean sinocortical width was 3.5 mm with a standard deviation (SD) of 1.4 mm on the right side and 3.3 mm (SD 1.2 mm) on the left side. The mean interhemispheric distance was 3.1 mm (SD 1.1 mm). 61.1% of the infants had a sinocortical width > 3 mm on one or both sides.ConclusionOur study does not support the hypothesis that premature infants are more prone to SDH unrelated to abusive head trauma during the first 3–4 months of life. A large percentage of the ex-premature infants had prominent subarachnoid spaces.Graphical

BackgroundThe implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals' behavioral intention to use EHR systems.MethodsA quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (β), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05.ResultsThe proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R2) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (β = 0.215, p-value < 0.018), performance expectancy (β = 0.245, p-value < 0.001), and attitude (β = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention.ConclusionThe extended UTAUT3 model accurately predicts health professionals' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.

BackgroundAdverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective interventions for birth outcomes. Accordingly, this study aimed to develop a predictive model for adverse birth outcomes among childbearing women in Sub-Saharan Africa using advanced machine learning techniques. Additionally, this study aimed to employ a novel data science interpretability techniques to identify the key risk factors and quantify the impact of each feature on the model prediction.MethodsThe study population involved women of childbearing age from 26 Sub-Saharan African countries who had given birth within five years before the data collection, totaling 139,659 participants. Our data source was a recent Demographic Health Survey (DHS). We utilized various data balancing techniques. Ten advanced machine learning algorithms were employed, with the dataset split into 80% training and 20% testing sets. Model evaluation was conducted using various performance metrics, along with hyperparameter optimization. Association rule mining and SHAP analysis were employed to enhance model interpretability.ResultsBased on our findings, about 28.59% (95% CI: 28.36, 28.83) of childbearing women in Sub-Saharan Africa experienced adverse birth outcomes. After repeated experimentation and evaluation, the random forest model emerged as the top-performing machine learning algorithm, with an AUC of 0.95 and an accuracy of 88.0%. The key risk factors identified were home deliveries, lack of prenatal iron supplementation, fewer than four antenatal care (ANC) visits, short and long delivery intervals, unwanted pregnancy, primiparous mothers, and geographic location in the West African region.ConclusionThe region continues to face persistent adverse birth outcomes, emphasizing the urgent need for increased attention and action. Encouragingly, advanced machine learning methods, particularly the random forest algorithm, have uncovered crucial insights that can guide targeted actions. Specifically, the analysis identifies risky groups, including first-time mothers, women with short or long birth intervals, and those with unwanted pregnancies. To address the needs of these high-risk women, the researchers recommend immediately providing iron supplements, scheduling comprehensive prenatal care, and strongly encouraging facility-based deliveries or skilled birth attendance.

We provide a descriptive comparative analysis of features related to emergence and design among 14 Rights of Nature (RoN) case studies worldwide. For analysis, we develop a schematic roadmap in which we categorise RoN into case studies with public guardianship and ones with appointed guardians (termed Environmental Legal Personhoods (ELPs) with further sub-categories of indirect, direct and living ELPs). Our findings suggest that RoN case studies emerged under similar circumstances where existing governance structures had been unable to protect natural environments from continued economic (urban, agricultural and industrial) activity by multiple economic actors. The strong role of local community and Indigenous Peoples in advocacy for RoN point to a divide between in situ communities and external economic agents, allowing for eco-centric value systems to emerge in juxtaposition to existing governance structures. We find that the design of RoN, however, varies in geographical entity, legal framework, legal status and guardianship. Poorly defined liability of guardians and economic agents have led to the overturning of two case studies, which stands in contrast to well-defined rights and liabilities in other case studies, suggesting that attention to liability may be an important building block for the effectiveness of RoN to protect biodiversity.

Humans build mental models of the world and utilize them for various cognitive tasks. The exact form of cognitive maps is not fully understood, especially for novel and complex environments beyond the flat Euclidean environment. To address this gap, we investigated path integration - a critical process underlying cognitive mapping - and spatial memory capacity on the spherical (non-Euclidean) and planar (Euclidean) environments in young healthy adults (n=20) using immersive virtual reality. We observed a strong Euclidean bias during the path integration task on the spherical surface, even among participants who possessed knowledge of non-Euclidean geometry. Notably, despite this bias, participants demonstrated reasonable navigation ability on the sphere. This observation and simulation suggest that humans navigate non-flat surfaces by constructing locally confined Euclidean maps and flexibly combining them. This insight sheds light on potential neural mechanisms and behavioral strategies for solving complex cognitive tasks.

10-VE A≡A' Diatomics, such as N≡N, C≡O, etc., have a strong σ2π4 triple bond plus a lone pair at each end. In our studies on 14-VE A≡B systems, such as C≡Pt, we find a similar bonding system plus a (5dδ)4 ring. Here, the A atom belongs to groups 13-17 and the B atom to groups 7-11. Also the BB' combinations, triatomics, such as PtCO or DsCO or uranyl, and longer chains, such as AuCN and [NC-Au-CN]-, are discussed. The δ ring directly contributes to nuclear quadrupole coupling constants, and DFT calculations using the BH and H or mPW1K functionals reproduce the experimental trends of the NQCC.