Update propagation in chimera, an active DOOD language

Propagating updates is an important task to be performed within many database services such as integrity checking, maintenance of materialized views, and condition monitoring. This paper is concerned with the propagation of updates in an active DOOD language. The approach proposed is to make use of Chimera triggers for computing induced updates. It will be shown how a subset of Chimera's deductive rules can be compiled to update propagation triggers. In its expressiveness the rule set considered corresponds to that of Datalog with sets and negation. Using triggers for implementing update propgation has the advantage that no special component has to be implemented as a trigger mechanism has. to exist anyway. In this paper we will not propose new techniques for computing induced updates but will transfer the techniques - well-known for the relational model - to the object-oriented case

Assessment of the cortisol awakening response: Real-time analysis and curvilinear effects of sample timing inaccuracy

The cortisol awakening response (CAR) is typically measured in the domestic setting. Moderate sample timing inaccuracy has been shown to result in erroneous CAR estimates and such inaccuracy has been shown partially to explain inconsistency in the CAR literature. The need for more reliable measurement of the CAR has recently been highlighted in expert consensus guidelines where it was pointed out that less than 6% of published studies provided electronic-monitoring of saliva sampling time in the post-awakening period. Analyses of a merged data-set of published studies from our laboratory are presented. To qualify for selection, both time of awakening and collection of the first sample must have been verified by electronic-monitoring and sampling commenced within 15 min of awakening. Participants (n = 128) were young (median age of 20 years) and healthy. Cortisol values were determined in the 45 min post-awakening period on 215 sampling days. On 127 days, delay between verified awakening and collection of the first sample was less than 3 min (‘no delay’ group); on 45 days there was a delay of 4–6 min (‘short delay’ group); on 43 days the delay was 7–15 min (‘moderate delay’ group). Cortisol values for verified sampling times accurately mapped on to the typical post-awakening cortisol growth curve, regardless of whether sampling deviated from desired protocol timings. This provides support for incorporating rather than excluding delayed data (up to 15 min) in CAR analyses. For this population the fitted cortisol growth curve equation predicted a mean cortisol awakening level of 6 nmols/l (±1 for 95% CI) and a mean CAR rise of 6 nmols/l (±2 for 95% CI). We also modelled the relationship between real delay and CAR magnitude, when the CAR is calculated erroneously by incorrectly assuming adherence to protocol time. Findings supported a curvilinear hypothesis in relation to effects of sample delay on the CAR. Short delays of 4–6 min between awakening and commencement of saliva sampling resulted an overestimated CAR. Moderate delays of 7–15 min were associated with an underestimated CAR. Findings emphasize the need to employ electronic-monitoring of sampling accuracy when measuring the CAR in the domestic setting

Response to Therapeutic Sleep Deprivation: A Naturalistic Study of Clinical and Genetic Factors and Post-treatment Depressive Symptom Trajectory

Research has shown that therapeutic sleep deprivation (SD) has rapid antidepressant effects in the majority of depressed patients. Investigation of factors preceding and accompanying these effects may facilitate the identification of the underlying biological mechanisms. This exploratory study aimed to examine clinical and genetic factors predicting response to SD and determine the impact of SD on illness course. Mood during SD was also assessed via visual analogue scale. Depressed inpatients (n = 78) and healthy controls (n = 15) underwent ~36 h of SD. Response to SD was defined as a score of ≤ 2 on the Clinical Global Impression Scale for Global Improvement. Depressive symptom trajectories were evaluated for up to a month using self/expert ratings. Impact of genetic burden was calculated using polygenic risk scores for major depressive disorder. In total, 72% of patients responded to SD. Responders and non-responders did not differ in baseline self/expert depression symptom ratings, but mood differed. Response was associated with lower age (p = 0.007) and later age at life-time disease onset (p = 0.003). Higher genetic burden of depression was observed in non-responders than healthy controls. Up to a month post SD, depressive symptoms decreased in both patients groups, but more in responders, in whom effects were sustained. The present findings suggest that re-examining SD with a greater focus on biological mechanisms will lead to better understanding of mechanisms of depression

Principal component analysis for fast and automated thermographic inspection of internal structures in sandwich parts

Rising demand and increasing cost pressure for lightweight materials – such as sandwich structures – drives the manufacturing industry to improve automation in production and quality inspection. Quality inspection of honeycomb sandwich components with infrared (IR) thermography can be automated using image classification algorithms. This paper shows how principal component analysis (PCA) via singular value decomposition (SVD) is applied to compress data in an IR-video sequence in order to save processing time in the subsequent step of image classification. According to PCA theory, an orthogonal transformation can project data into a lower dimensional subspace with linearly uncorrelated principal components preserving all original information. The effect of data reduction is confirmed with experimental data from IR-video sequences of simple square-pulsed thermal loadings on aramid honeycomb-sandwich components with CFRP/GFRP (carbon-/glass-fiber-reinforced plastic) facings and GFRP inserts. Hence, processing time for image classification can be saved by reducing the dimension of information used by the classification algorithm without losing accuracy

Exploring the effect of an 8-week AI-composed exercise program on pain intensity and well-being in patients with Spinal pain : Retrospective cohort analysis

Background: Spinal pain, one of the most common musculoskeletal disorders (MSDs), significantly impacts the quality of life due to chronic pain and disability. Physical activity has shown promise in managing spinal pain, although optimizing adherence to exercise remains a challenge. The digital development of artificial intelligence (AI)-driven applications offers a possibility for guiding and supporting patients with MSDs in their daily lives. Objective: The trial aimed to investigate the effect of an 8-week AI-composed exercise program on pain intensity and well-being in patients with spinal pain. It also examined the relationship between exercise frequency, pain intensity, and well-being. In addition, app usage frequency was examined as a proxy for app engagement. Methods: Data from users who met the inclusion criteria were collected retrospectively from the medicalmotion app between January 1, 2020, and June 30, 2023. The intervention involved the use of the medicalmotion app, which provides 3‐5 personalized exercises for each session based on individual user data. The primary outcomes assessed pain intensity and well-being using the numeric rating scale (NRS) and the Likert scale. Data were collected at baseline (t0), 4 weeks (t1), and 8 weeks (t2). The correlation between exercise frequency, pain intensity, and well-being was analyzed as a secondary outcome. In addition, average session length and frequency were measured to determine app engagement. Statistical analysis included ANOVA and Spearman correlation analysis. Results: The study included 379 participants with a mean age of 50.96 (SD 12.22) years. At t2, there was a significant reduction of 1.78 points on the NRS (P<.001). The score on the Likert scale for well-being improved by 3.11 points after 8 weeks. Pain intensity showed a negative correlation with the number of daily exercises performed at t1 and t2. Well-being had a small negative correlation with the average number of exercises performed per day. The average number of exercises performed per day was 3.58. The average session length was approximately 10 minutes, and the average interaction with the app was 49.2% (n=27.6 days) of the 56 available days. Conclusions: Overall, the study demonstrates that an app-based intervention program can substantially reduce pain intensity and increase well-being in patients with spinal pain. This retrospective study showed that an app that digitizes multidisciplinary rehabilitation for the self-management of spinal pain significantly reduced user-reported pain intensity in a preselected population of app users

Transportation Noise and Blood Pressure in a Population-Based Sample of Adults

Background: There is some evidence for an association between traffic noise and ischemic heart disease; however, associations with blood pressure have been inconsistent, and little is known about health effects of railway noise

A Transdisciplinary Research Agenda for Light Pollution Policy

Although the invention and widespread use of artificial light is clearly one of the most important human technological advances, the transformation of nightscapes is increasingly recognized as having adverse effects. Night lighting may have serious physiological consequences for humans, ecological and evolutionary implications for animal and plant populations, and may reshape entire ecosystems. However, knowledge on the adverse effects of light pollution is vague. In response to climate change and energy shortages, many countries, regions, and communities are developing new lighting programs and concepts with a strong focus on energy efficiency and greenhouse gas emissions. Given the dramatic increase in artificial light at night (0 - 20% per year, depending on geographic region), we see an urgent need for light pollution policies that go beyond energy efficiency to include human well-being, the structure and functioning of ecosystems, and inter-related socioeconomic consequences. Such a policy shift will require a sound transdisciplinary understanding of the significance of the night, and its loss, for humans and the natural systems upon which we depend. Knowledge is also urgently needed on suitable lighting technologies and concepts which are ecologically, socially, and economically sustainable. Unless managing darkness becomes an integral part of future conservation and lighting policies, modern society may run into a global self-experiment with unpredictable outcomes.Peer Reviewe

Consequences of the Timing of Menarche on Female Adolescent Sleep Phase Preference

Most parents experience their children's puberty as a dramatic change in family life. This is not surprising considering the dynamics of physical and psychosocial maturation which occur during adolescence. A reasonable question, particularly from the parents' perspective, is: when does this vibrant episode end and adulthood finally start? The aim of the present study was to assess the relationship between puberty and the changes in sleep phase preferences during female maturation and adulthood by a cross-sectional survey. The results from 1'187 females aged 5 to 51 years based on self-report measures of sleep preferences on weekdays and on free days as well as the occurrence of menarche, show that in contrast to prepubertal children, adolescent females exhibit a striking progression in delaying their sleep phase preference until 5 years after menarche. Thereafter, the sleep phase preference switches to advancing. The current study provides evidence that a clear shift in sleep-wake cycles temporally linked to menarche heralds the beginning of “adult-like” sleep-wake behaviour in women and can be used as a (chrono)biological marker for the onset of adulthood

Dose finding of melatonin for chronic idiopathic childhood sleep onset insomnia: an RCT

Contains fulltext : 86695.pdf (publisher's version ) (Open Access)Rationale Pharmacokinetics of melatonin in children might differ from that in adults. Objectives This study aims to establish a dose–response relationship for melatonin in advancing dim light melatonin onset (DLMO), sleep onset (SO), and reducing sleep onset latency (SOL) in children between 6 and 12 years with chronic sleep onset insomnia (CSOI). Methods The method used for this study is the randomized, placebo-controlled double-blind trial. Children with CSOI (n=72) received either melatonin 0.05, 0.1, and 0.15 mg/kg or placebo during 1 week. Sleep was assessed with log and actigraphy during this week and the week before. Outcomes were the shifts in DLMO, SO, and SOL. Results Treatment with melatonin significantly advanced SO and DLMO by approximately 1 h and decreased SOL by 35 min. Within the three melatonin groups, effect size was not different, but the circadian time of administration (TOA) correlated significantly with treatment effect on DLMO (rs=-0.33, p=0.022) and SO (rs=-0.38, p=0.004), whereas clock TOA was correlated with SO shift (r=-0.35, p=0.006) and not with DLMO shift. Conclusions No dose–response relationship of melatonin with SO, SOL, and DLMO is found within a dosage range of 0.05–0.15 mg/kg. The effect of exogenous melatonin on SO, SOL, and DLMO increases with an earlier circadian TOA. The soporific effects of melatonin enhance the SO shift. This study demonstrates that melatonin for treatment of CSOI in children is effective in a dosage of 0.05 mg/kg given at least 1 to 2 h before DLMO and before desired bedtime.13 p