PRISMA database machine: A distributed, main-memory approach

The PRISMA project is a large-scale research effort in the design and implementation of a highly parallel machine for data and knowledge processing. The PRISMA database machine is a distributed, main-memory database management system implemented in an object-oriented language that runs on top of a multi-computer system. A prototype that is envisioned consists of 64 processing elements

Comparison of the meteorology and surface energy balance at Storbreen and Midtdalsbreen, two glaciers in southern Norway

We compare 5 years of meteorological records from automatic weather stations (AWSs) on Storbreen and Midtdalsbreen, two glaciers in southern Norway, located approximately 120 km apart. The records are obtained from identical AWSs with an altitude difference of 120 m and cover the period September 2001 to September 2006. Air temperature at the AWS locations is found to be highly correlated, even with the seasonal cycle removed. The most striking difference between the two sites is the difference in wind climate. Midtdalsbreen is much more under influence of the large-scale circulation with wind speeds on average a factor 1.75 higher. On Storbreen, weaker katabatic winds are dominant. The main melt season is from May to September at both locations. During the melt season, incoming and net solar radiation are larger on Midtdalsbreen, whereas incoming and net longwave radiation are larger on Storbreen, primarily caused by thicker clouds on the latter. The turbulent fluxes are a factor 1.7 larger on Midtdalsbreen, mainly due to the higher wind speeds. Inter-daily fluctuations in the surface energy fluxes are very similar at the AWS sites. On average, melt energy is a factor 1.3 larger on Midtdalsbreen, a result of both larger net radiation and larger turbulent fluxes. The relative contribution of net radiation to surface melt is larger on Storbreen (76%) than on Midtdalsbreen (66%). As winter snow depth at the two locations is comparable in most years, the larger amount of melt energy results in an earlier disappearance of the snowpack on Midtdalsbreen and 70% more ice melt than on Storbreen. We compare the relative and absolute values of the energy fluxes on Storbreen and Midtdalsbreen with reported values for glaciers at similar latitudes. Furthermore, a comparison is made with meteorological variables measured at two nearby weather stations, showing that on-site measurements are essential for an accurate calculation of the surface energy balance and melt rate

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi–CSI Data

Unobtrusive sensing (device-free sensing) aims to embed sensing into our daily lives. This is achievable by re-purposing communication technologies already used in our environments. Wireless Fidelity (Wi-Fi) sensing, using Channel State Information (CSI) measurement data, seems to be a perfect fit for this purpose since Wi-Fi networks are already omnipresent. However, a big challenge in this regard is CSI data being sensitive to ‘domain factors’ such as the position and orientation of a subject performing an activity or gesture. Due to these factors, CSI signal disturbances vary, causing domain shifts. Shifts lead to the lack of inference generalization, i.e., the model does not always perform well on unseen data during testing. We present a domain factor-independent feature-extraction pipeline called ‘mini-batch alignment’. Mini-batch alignment steers a feature-extraction model’s training process such that it is unable to separate intermediate feature-probability density functions of input data batches seen previously from the current input data batch. By means of this steering technique, we hypothesize that mini-batch alignment (i) absolves the need for providing a domain label, (ii) reduces pipeline re-building and re-training likelihood when encountering latent domain factors, and (iii) absolves the need for extra model storage and training time. We test this hypothesis via a vast number of performance-evaluation experiments. The experiments involve both one- and two-domain-factor leave-out cross-validation, two open-source gesture-recognition datasets called SignFi and Widar3, two pre-processed input types called Doppler Frequency Spectrum (DFS) and Gramian Angular Difference Field (GADF), and several existing domain-shift mitigation techniques. We show that mini-batch alignment performs on a par with other domain-shift mitigation techniques in both position and orientation one-domain leave-out cross-validation using the Widar3 dataset and DFS as input type. When considering a memory-complexity-reduced version of the GADF as input type, mini-batch alignment shows hints of recuperating performance regarding a standard baseline model to the extent that no additional performance due to weight steering is lost in both one-domain-factor leave-out and two-orientation-domain-factor leave-out cross-validation scenarios. However, this is not enough evidence that the mini-batch alignment hypothesis is valid. We identified pitfalls leading up to the hypothesis invalidation: (i) lack of good-quality benchmark datasets, (ii) invalid probability distribution assumptions, and (iii) non-linear distribution scaling issues

Mini-Batch Alignment: A Deep-Learning Model for Domain Factor-Independent Feature Extraction for Wi-Fi–CSI Data

Unobtrusive sensing (device-free sensing) aims to embed sensing into our daily lives. This is achievable by re-purposing communication technologies already used in our environments. Wireless Fidelity (Wi-Fi) sensing, using Channel State Information (CSI) measurement data, seems to be a perfect fit for this purpose since Wi-Fi networks are already omnipresent. However, a big challenge in this regard is CSI data being sensitive to ‘domain factors’ such as the position and orientation of a subject performing an activity or gesture. Due to these factors, CSI signal disturbances vary, causing domain shifts. Shifts lead to the lack of inference generalization, i.e., the model does not always perform well on unseen data during testing. We present a domain factor-independent feature-extraction pipeline called ‘mini-batch alignment’. Mini-batch alignment steers a feature-extraction model’s training process such that it is unable to separate intermediate feature-probability density functions of input data batches seen previously from the current input data batch. By means of this steering technique, we hypothesize that mini-batch alignment (i) absolves the need for providing a domain label, (ii) reduces pipeline re-building and re-training likelihood when encountering latent domain factors, and (iii) absolves the need for extra model storage and training time. We test this hypothesis via a vast number of performance-evaluation experiments. The experiments involve both one- and two-domain-factor leave-out cross-validation, two open-source gesture-recognition datasets called SignFi and Widar3, two pre-processed input types called Doppler Frequency Spectrum (DFS) and Gramian Angular Difference Field (GADF), and several existing domain-shift mitigation techniques. We show that mini-batch alignment performs on a par with other domain-shift mitigation techniques in both position and orientation one-domain leave-out cross-validation using the Widar3 dataset and DFS as input type. When considering a memory-complexity-reduced version of the GADF as input type, mini-batch alignment shows hints of recuperating performance regarding a standard baseline model to the extent that no additional performance due to weight steering is lost in both one-domain-factor leave-out and two-orientation-domain-factor leave-out cross-validation scenarios. However, this is not enough evidence that the mini-batch alignment hypothesis is valid. We identified pitfalls leading up to the hypothesis invalidation: (i) lack of good-quality benchmark datasets, (ii) invalid probability distribution assumptions, and (iii) non-linear distribution scaling issues

Debris cover and surface melt at a temperate maritime alpine glacier: Franz Josef Glacier, New Zealand

Melt rates on glaciers are strongly influenced by the presence of supraglacial debris, which can either enhance or reduce ablation relative to bare ice. Most recently, Franz Josef Glacier has entered into a phase of strong retreat and downwasting, with the increasing emergence of debris on the surface in the ablation zone. Previously at Franz Josef Glacier, melt has only been measured on bare ice. During February 2012, a network of 11 ablation stakes was drilled into locations of varying supraglacial debris thickness on the lower glacier. Mean ablation rates over 9 days varied over the range 1.2–10.1 cm d−1, and were closely related to debris thickness. Concomitant observations of air temperature allowed the application of a degree-day approach to the calculation of melt rates, with air temperature providing a strong indicator of melt. Degree-day factors (d f) varied over the range 1.1–8.1 mm d−1 °C−1 (mean of 4.4 mm d−1 °C−1), comparable with rates reported in other studies. Mapping of the current debris cover revealed 0.7 km2 of the 4.9 km2 ablation zone surface was debris-covered, with thicknesses ranging 1–50 cm. Based on measured debris thicknesses and d f, ablation on debris-covered areas of the glacier is reduced by a total of 41% which equates to a 6% reduction in melt overall across the entire ablation zone. This study highlights the usefulness of a short-term survey to gather representative ablation data, consistent with numerous overseas ablation studies on debris-covered glaciers

Incidence and risk factors of late right heart failure in chronic mechanical circulatory support

BACKGROUND: Late right heart failure (LRHF) is a common complication during long-term left ventricular assist device (LVAD) support. We aimed to identify risk factors for LRHF after LVAD implantation. METHODS: Patients undergoing primary LVAD implantation between 2006 and 2019 and surviving the perioperative period were included for this study (n = 261). Univariate Cox proportional hazards analysis was used to assess the association of clinical covariates and LRHF, stratified for device type. Variables with p < 0.10 entered the multivariable model. In a subset of patients with complete echocardiography or right catheterization data, this multivariable model was extended. Postoperative cardiopulmonary exercise test data were compared in patients with and without LRHF. RESULTS: Nineteen percentage of patients suffered from LRHF after a median of 12 months, of which 67% required hospitalization. A history of atrial fibrillation (AF) (HR: 2.06 [1.08–3.93], p = 0.029), a higher preoperative body mass index (BMI) (HR: 1.07 [1.01–1.13], p = 0.023), and intensive care unit (ICU) duration (HR: 1.03 [1.00–1.06], p = 0.025) were independent predictors of LHRF in the multivariable model. A significant relation between the severity of tricuspid regurgitation (TR) and LRHF (HR: 1.91 [1.13–3.21], p = 0.016) was found in patients with echocardiographic data. Patients with LRHF demonstrated a lower maximal workload and peak VO2 at 6 months postoperatively. CONCLUSION: A history of AF, BMI, and longer ICU stay may help identify patients at high risk for LRHF. Severity of TR was significantly related to LRHF in a subset of patient

The land-ice contribution to 21st-century dynamic sea level rise

Climate change has the potential to influence global mean sea level through a number of processes including (but not limited to) thermal expansion of the oceans and enhanced land ice melt. In addition to their contribution to global mean sea level change, these two processes (among others) lead to local departures from the global mean sea level change, through a number of mechanisms including the effect on spatial variations in the change of water density and transport, usually termed dynamic sea level changes. In this study, we focus on the component of dynamic sea level change that might be given by additional freshwater inflow to the ocean under scenarios of 21st-century land-based ice melt. We present regional patterns of dynamic sea level change given by a global-coupled atmosphere–ocean climate model forced by spatially and temporally varying projected ice-melt fluxes from three sources: the Antarctic ice sheet, the Greenland Ice Sheet and small glaciers and ice caps. The largest ice melt flux we consider is equivalent to almost 0.7m of global mean sea level rise over the 21st century. The temporal evolution of the dynamic sea level changes, in the presence of considerable variations in the ice melt flux, is also analysed. We find that the dynamic sea level change associated with the ice melt is small, with the largest changes occurring in the North Atlantic amounting to 3 cm above the global mean rise. Furthermore, the dynamic sea level change associated with the ice melt is similar regardless of whether the simulated ice fluxes are applied to a simulation with fixed CO2 or under a business-as-usual greenhouse gas warming scenario of increasing CO2

Perceived information provision and satisfaction among lymphoma and multiple myeloma survivors—results from a Dutch population-based study

To improve posttreatment care for (long-term) lymphoma survivors in the Netherlands, survivorship clinics are being developed. As information provision is an important aspect of survivorship care, our aim was to evaluate the current perceived level of and satisfaction with information received by non-Hodgkin’s lymphoma (NHL), Hodgkin’s lymphoma (HL) and multiple myeloma (MM) survivors, and to identify associations with sociodemographic and clinical characteristics. The population-based Eindhoven Cancer Registry was used to select all patients diagnosed with NHL, HL and MM from 1999 to 2009. In total, 1,448 survivors received a questionnaire, and 1,135 of them responded (78.4 %). The EORTC QLQ-INFO25 was used to evaluate the perceived level of and satisfaction with information. Two thirds of survivors were satisfied with the amount of received information, with HL survivors being most satisfied (74 %). At least 25 % of survivors wanted more information. Young age, having had chemotherapy, having been diagnosed more recently, using internet for information and having no comorbidities were the most important factors associated with higher perceived levels of information provision. Although information provision and satisfaction with information seems relatively good in lymphoma and MM survivors, one third expressed unmet needs. Furthermore, variations between subgroups were observed. Good information provision is known to be associated with better quality of life. Survivorship care plans could be a way to achieve this

Spatio-Temporal Characteristics of Global Warming in the Tibetan Plateau during the Last 50 Years Based on a Generalised Temperature Zone - Elevation Model

Temperature is one of the primary factors influencing the climate and ecosystem, and examining its change and fluctuation could elucidate the formation of novel climate patterns and trends. In this study, we constructed a generalised temperature zone elevation model (GTEM) to assess the trends of climate change and temporal-spatial differences in the Tibetan Plateau (TP) using the annual and monthly mean temperatures from 1961-2010 at 144 meteorological stations in and near the TP. The results showed the following: (1) The TP has undergone robust warming over the study period, and the warming rate was 0.318°C/decade. The warming has accelerated during recent decades, especially in the last 20 years, and the warming has been most significant in the winter months, followed by the spring, autumn and summer seasons. (2) Spatially, the zones that became significantly smaller were the temperature zones of -6°C and -4°C, and these have decreased 499.44 and 454.26 thousand sq km from 1961 to 2010 at average rates of 25.1% and 11.7%, respectively, over every 5-year interval. These quickly shrinking zones were located in the northwestern and central TP. (3) The elevation dependency of climate warming existed in the TP during 1961-2010, but this tendency has gradually been weakening due to more rapid warming at lower elevations than in the middle and upper elevations of the TP during 1991-2010. The higher regions and some low altitude valleys of the TP were the most significantly warming regions under the same categorizing criteria. Experimental evidence shows that the GTEM is an effective method to analyse climate changes in high altitude mountainous regions