Philips (India)

ABSTRACTBackgroundAbdominal aortic aneurysms (AAAs) are an important cause of death. Small AAAs are surveyed with ultrasound (US) until a defined diameter threshold, often triggering a CT scan and surgical repair. Nevertheless, 5-10 % of AAA ruptures are below threshold, and some large AAAs never rupture. AAA wall biomechanics may reveal vessel wall degradation with potential for patient-centred risk assessment. This clinical study investigated AAA vessel wall biomechanics and deformation patterns, including reproducibility.MethodsIn 50 patients with AAA, 183 video clips were recorded by two sonographers. Prototype software extracted AAA vessel wall principal strain characteristics and patterns. Functional principal component analysis (FPCA) derived strain pattern statistics.ResultsStrain patterns demonstrated reduced AAA wall strains close to the spine. The strain pattern ‘topography’ (i.e., curve phases or ‘peaks’ and ‘valleys’) had a 3.9 times lower variance than simple numeric assessment of strain amplitudes, which allowed for clustering in two groups with FPCA. A high mean reproducibility of these clusters of 87.6 % was found. Median pulse pressure-corrected mean principal strain (PPPS) was 0.038 %/mmHg (interquartile range: 0.029 to 0.051 %/mmHg) with no correlation to AAA size (Spearman’s ρ = 0.02, FDR-P = 0.15). Inter-operator reproducibility of PPPS was poor (limits of agreement: ±0.031 %/mmHg).DiscussionStrain patterns challenge previous numeric stiffness measures based on AP-diameter and are reproducible for clustering. This study’s PPPS aligned with prior findings, although clinical reproducibility was poor. In contrast, US-based strain patterns hold promising potential to enhance AAA risk assessment beyond traditional diameter-based metrics.

Abstract Introduction Automatic servo-ventilation (Auto SV) treats Central sleep apnea (CSA) and obstructive sleep apnea (OSA) by monitoring airflow and intervening with changes or pauses in airflow patterns. Evidence on adherence to Auto SV therapy and its effects on symptoms, sleep quality and quality of life (QOL) in real world Auto SV users is sparse. The aim of this registry study was to describe the adherence to Auto SV (Philips DreamStation BiPAP auto SV) and to evaluate Auto SV therapy effects on sleepiness, sleep quality and QOL. Methods Data were collected at six sites across Germany from 2018 through 2023 at 6, 12, and/or 24 months. Auto SV adherence data were collected through Philips EncoreAnywhere™ (EA) software and examined according to intent-to-treat and subgroup analyses accounting for data gaps. Average adherence was calculated from the first to the final date of first device usage. Sleepiness, sleep quality and general QOL were assessed with the Epworth Sleep Scale (ESS), the Pittsburgh Sleep Quality Index (PSQI) and the European Quality of Life-5 level questionnaire (EQ 5D-5L), respectively. Changes from baseline (CFB) were calculated for questionnaires collected at enrollment and at visits at 6, 12 and 24 months (Mixed-Models including Sidak adjustment). Results One hundred and twenty-five participants were enrolled. Average age was 65 ± 12 years; 106 (85%) were males. Average adherence over days used was 6.0 ± 1.9 hours/day (n = 103) and 5.0 ± 2.4 hours/day (n = 103) among all days (average follow-up 478 days). Percent days used averaged 78.5 ± 27.1%. ESS and global PSQI scores showed significant improvement from baseline at 24 months in completed-cases and last-observation-carried-forward analyses (ESS [Mean difference: CC -1.9 ± 3.9 (N=57), LOCF -1.8 ± 4.5 (N=103), p ≤0.001], PSQI [Mean difference: CC -1.7± 3.5, LOCF (N=55), -1.6 ± 3.5 (N=98), p < 0.001]). The EQ-5D-5L did not significantly differ from baseline. Conclusion Auto SV use averaged 5 hours per night and Auto SV significantly improved sleepiness and sleep quality, but not general QOL, in a real-world sleep clinic population. ESS approached clinical significance (MCID 2.0) while the PSQI did not (MCID 3). Support (if any)

Recent advancements in cyber technologies have created a demand for smart vehicles. The network of these smart vehicles is often called as Internet of Vehicles (IoV). The swift increase in the IoV has led to security challenges which can lead to unwarranted and lethal accidents. The security of IoV is needed faster. Lightweight and accurate algorithms to stop any malicious attacks at the vehicle of IoV. This article proposes MICORD Intrusion Detection Systems (IDS). The proposed IDS system is based on the feature selection technique involving Mutual Information with the further reduction of features using Correlation and a Decision Tree classifier. The performance of the proposed system was tested using the CICIDS2018 dataset. The system secured an exceptionally good accuracy and an incredibly good F1 Score of 98.6% which is comparable to the latest available studies.

Between 1950 and 1980, television in the Netherlands became a mass medium, which attracted a large audience, in part by broadcasting football matches and news. Scholars have theorised how the rise of a new medium creates anxiety and feelings of rivalry with established media as they are confronted with a new competitor. This paper focuses on this premise by examining to what extent and how such feelings of rivalry between television and newspapers are part of the metadiscourse on football and television within the ranks of Dutch dailies between 1950 and 1980. Based on the results of this qualitative discourse analysis, this paper argues that newspapers certainly discussed the rise of football coverage on television, but did not show any signs of being afraid that television would offer a harmful competition. Much rather, in defending the right to free news gathering as well as the interests of the audience, newspapers positioned themselves by and large as great supporters of football coverage on television.

New model combining global forecasting of substorms based on solar wind and local forecasting based on all sky imagers was created • Previous global substorm forecasting study was successfully reproduced as baseline comparison for new model • Combined forecasting model performed below necessary precision for scientific purposes