P030 The effects of continuous positive airway pressure (CPAP) therapy in moderate to severe obstructive sleep apnea (OSA): A high-density electroencephalography (EEG) study

Introduction A previous high-density EEG investigation in OSA showed regional sleep EEG deficits particularly slow wave activity (SWA) in the parietal region. It is unclear whether CPAP treatment can reverse local sleep EEG abnormalities, and whether any recovery is related to improved cognitive function. Methods Fifteen males with moderate-severe OSA (age 50.4±6.5yrs, AHI 51.7±23.5/h) underwent polysomnography with 256-channel high-density EEG at baseline and following 3 months of CPAP. Tasks assessing cognitive performance and sleep-dependent memory were administered. Topographical spectral power maps were calculated for standard frequency ranges for sleep stages. Differences in normalized power between baseline and treatment were determined by statistical nonparametric mapping. Results In 11 CPAP compliant patients (data loss: intolerant of CPAP[n=3]/high-density EEG [n=1]), total sleep time did not change after CPAP but N1 (baseline vs. treatment: 66.9 vs. 39.5 mins, p=0.008) and N2 (195.0 vs. 150.6 mins, p=0.002) sleep was lower and N3 (89.8 vs. 128.7 mins, p=0.003) was higher. Topographic high-density EEG analysis revealed a regional increase in SWA (1–4.5Hz) during N3 sleep in a cluster of 22 electrodes overlying the parietal cortex (paired t-test, t(10)=-3.9, p=0.0029). The change in N3 SWA in the parietal cluster after CPAP was correlated with improved overnight procedural memory on the motor sequence task (rho=0.79, p=0.03) and better executive functioning (Stroop accuracy, rho=0.73, p=0.01). Conclusion CPAP treatment reduces localised deficits in sleep EEG, and specific regional recovery relates to short-term improvements in memory and executive function. These data also highlight the potential for long-term therapeutic effects on cognitive outcomes.

Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines

Wind turbines are typically organised as a fleet in a wind park, subject to similar, but varying, environmental conditions. This makes it possible to assess and benchmark a turbine’s output performance by comparing it to the other assets in the fleet. However, such a comparison cannot be performed straightforwardly on time series production data since the performance of a wind turbine is affected by a diverse set of factors (e.g., weather conditions). All these factors also produce a continuous stream of data, which, if discretised in an appropriate fashion, might allow us to uncover relevant insights into the turbine’s operations and behaviour. In this paper, we exploit the outcome of two inherently different discretisation approaches by statistical and visual analytics. As the first discretisation method, a complex layered integration approach is used. The DNA-like outcome allows us to apply advanced visual analytics, facilitating insightful operating mode monitoring. The second discretisation approach is applying a novel circular binning approach, capitalising on the circular nature of the angular variables. The resulting bins are then used to construct circular power maps and extract prototypical profiles via non-negative matrix factorisation, enabling us to detect anomalies and perform production forecasts.

Current Developments in Corneal Topography and Tomography

Introduction: Accurate assessment of the corneal shape is important in cataract and refractive surgery, both in screening of candidates as well as for analyzing postoperative outcomes. Although corneal topography and tomography are widely used, it is common that these technologies are confused. The aim of this study was to present the current developments of these technologies and particularly distinguish between corneal topography and tomography. Methods: The PubMed, Web of Science and Embase databases were the main resources used to investigate the medical literature. The following keywords were used in various combinations: cornea, corneal, topography, tomography, Scheimpflug, Pentacam, optical coherence tomography. Results: Topography is the study of the shape of the corneal surface, while tomography allows a three-dimensional section of the cornea to be presented. Corneal topographers can be divided into large- and small-cone Placido-based devices, as well as devices with color-LEDs. For corneal tomography, scanning slit or Scheimpflug imaging and optical coherence tomography may be employed. In several devices, corneal topography and tomography have been successfully combined with tear-film analysis, aberrometry, optical biometry and anterior/posterior segment optical coherence tomography. Conclusion: There is a wide variety of imaging techniques to obtain corneal power maps. As different technologies are used, it is imperative that doctors involved in corneal surgery understand the science and clinical application of devices for corneal evaluation in depth.

Smooth parameterizations of power-subanalytic sets and compositions of Gevrey functions

We show that if $X$ is an $m$ -dimensional definable set in $\mathbb {R}_\text {an}^\text{pow}$ , the structure of real subanalytic sets with real power maps added, then for any positive integer $r$ there exists a $C^{r}$ -parameterization of $X$ consisting of $cr^{m^{3}}$ maps for some constant $c$ . Moreover, these maps are real analytic and this bound is uniform for a definable family.

Empirical mapping for evaluating an LPWAN (LoRa) wireless network sensor prior to installation in a vineyard

The main aim of this study was to use Empirical mapping to test the efficiency of local low cost wireless network sensors (LPWAN - Low-Power Wide Area Network) before being applied in real wine-growing conditions. The second aim was to obtain information on the communication distances to be expected from a LPWAN, taking into account the specific needs and real conditions of a vineyard. A hand-held autonomous end-device was specifically built to simulate short messages sent by sensors via a locally designed LPWAN. This device was used to test the quality of the network from different locations within an entire vineyard and also inside the cellar. Two parameters were used to test the quality of reception of the messages: i) The Received Signal Strength Indication (RSSI), which is the received signal power measured in decibels (dB or dBm), and ii) the Signal-to-Noise Ratio (SNR), which is the ratio of the received signal power to the ambient noise power. Maps of signal reception and errors between the observed and the theoretical signal highlighted how vineyard environment (e.g., hedges, topography, and buildings) affects the signal. The results show that the maximum communication distance differed considerably from distances published in the literature. In the open field, the signal, although attenuated by the distance, was received up to 600 meters away, or even more in favourable conditions. Meanwhile, in urban areas the signal was attenuated by buildings and the electro-magnetic environment and therefore communication distances were very short (< 50 m). Empirical mapping has great potential for determining how the local environment affects signal quality and as a decision support tool for identifying the optimal location for the sensors and gateway. With a single well-positioned gateway, such low cost wireless sensor networks (LPWAN-LoRa) could be used by small to medium-sized vineyards to collect information from sensors either outside in the fields or indoors in the vineyard cellar. This paper proposes a very cheap method (< 40 €) for testing and spatialising the quality of a low cost wireless sensor network before its implementation, and it also provides information on zones with low quality reception.

Dense images of the power maps for a disconnected real algebraic group

Let 𝐺 be a complex algebraic group defined over ℝ, which is not necessarily Zariski-connected. In this article, we study the density of the images of the power maps g → g k g\to g^{k} , k ∈ N k\in\mathbb{N} , on real points of 𝐺, i.e., G ⁢ ( R ) G(\mathbb{R}) equipped with the real topology. As a result, we extend a theorem of P. Chatterjee on surjectivity of the power map for the set of semisimple elements of G ⁢ ( R ) G(\mathbb{R}) . We also characterize surjectivity of the power map for a disconnected group G ⁢ ( R ) G(\mathbb{R}) . The results are applied in particular to describe the image of the exponential map of G ⁢ ( R ) G(\mathbb{R}) .

High-resolution offshore wind resource assessment at turbine hub height with Sentinel-1 SAR data and machine learning

This paper presents a method to calculate offshore wind power at turbine hub height from Sentinel-1 Synthetic Aperture Radar (SAR) data using machine learning. The method is tested in two 70 km × 70 km areas off the Dutch coast where Lidar measurements are available. Firstly, SAR winds at surface level are improved with a machine learning algorithm using geometrical characteristics of the sensor and parameters related to the atmospheric stability extracted from a high-resolution numerical model. The wind speed bias at 10 m above sea level is reduced from −0.42 m s−1 to 0.02 m s−1 and its standard deviation from 1.41 m s−1 to 0.98 m s−1. After improvement, SAR surface winds are extrapolated at higher altitudes with a separate machine learning algorithm trained with the wind profiles measured by the Lidars. We show that, if profiling Lidars are available in the area of study, these two steps can be combined into a single one, in which the machine learning algorithm is trained directly at turbine hub height. Once the wind speed at turbine hub height is obtained, the extractible wind power is calculated using the method of the moments and a Weibull distribution. The results are given assuming an 8 MW turbine typical power curve. The accuracy of the wind power derived from SAR data is in the range ±3–4 % when compared with Lidars. Then, wind power maps at 200 m are presented and compared with the raw outputs of the numerical model at the same altitude. The maps based on SAR data have a much better level of detail, in particular regarding the coastal gradient. The new revealed patterns show differences with the numerical of as much as 10 % in some locations. We conclude that SAR data combined with a high-resolution numerical model and machine learning techniques can improve the wind power estimation at turbine hub height, and thus provide useful insights for optimizing wind farm siting and risk management.

“Advise and Rule” or “Rule by Advising”: The Changing Nature of the Advisory Jurisdiction of the Constitutional Court of Kosovo

Constitutional courts play an essential role in authoritatively interpreting constitutions. Oftentimes they go beyond the constitutional text by inventing so-called judge-made law. Their authority to interpret the text covers not only substantive parts but also the clause authorizing their jurisdiction. Such power, namely the power to interpret the limits of their jurisdiction, is often used to intervene in the interpretation of the constitution more vigorously than explicitly authorized. One example is the invention, designation, and development of the advisory jurisdiction by the Constitutional Court of the Republic of Kosovo. On that basis, the Court has, for almost ten years of its existence, pronounced on numerous fundamental issues relating to the governing system, power maps, and entitlements on political authority. The Court developed its advisory jurisdiction in a rather unpredictable and impulsive fashion; however, it steadily revealed its willingness to engage with interpretations that sought to resolve high-stakes issues. Such braveness also had a credibility cost for the Court. The year 2018 marked a major shift in the Court’s interpretation of its own jurisdiction to “advise.” In the Central Election Commission case, it abandoned its previous precedent and commenced a passive, restrained attitude in engaging with the constitutional interpretation on the basis of case or controversy. This Article analyzes the Court’s path and change of course in this cycle.