Herramienta web de análisis de eficiencia energética para la ANDE

Presentación realizada en el marco del Proyecto PINV18-661: Análisis de la eficiencia energética en edificios no residenciales mediante técnicas metaheurísticas y de inteligencia artificial.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI

Parallel evolutionary biclustering of short-term electric energy consumption

Presentación realizada en el marco del Proyecto PINV18-661: Análisis de la eficiencia energética en edificios no residenciales mediante técnicas metaheurísticas y de inteligencia artificial.CONACYT - Consejo Nacional de Ciencias y TecnologíaPROCIENCI

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters

Non-ischemic cerebral enhancing lesions after intracranial aneurysm endovascular repair: a retrospective French national registry

International audienceBackground Non-ischemic cerebral enhancing (NICE) lesions are exceptionally rare following aneurysm endovascular therapy (EVT). Objective To investigate the presenting features and longitudinal follow-up of patients with NICE lesions following aneurysm EVT. Methods Patients included in a retrospective national multicentre inception cohort were analysed. NICE lesions were defined, using MRI, as delayed onset punctate, nodular or annular foci enhancements with peri-lesion edema, distributed in the vascular territory of the aneurysm EVT, with no other confounding disease. Results From a pool of 58 815 aneurysm endovascular treatment procedures during the study sampling period (2006–2019), 21/37 centres identified 31 patients with 32 aneurysms of the anterior circulation who developed NICE lesions (mean age 45±10 years). Mean delay to diagnosis was 5±9 months, with onset occurring a month or less after the index EVT procedure in 10 out of 31 patients (32%). NICE lesions were symptomatic at time of onset in 23 of 31 patients (74%). After a mean follow-up of 25±26 months, 25 patients (81%) were asymptomatic or minimally symptomatic without disability (modified Rankin Scale (mRS) score 0–1) at last follow-up while 4 (13%) presented with mild disability (mRS score 2). Clinical follow-up data were unavailable for two patients. Follow-up MRI (available in 27 patients; mean time interval after onset of 22±22 months) demonstrated persistent enhancement in 71% of cases. Conclusions The clinical spectrum of NICE lesions following aneurysm EVT therapy spans a wide range of neurological symptoms. Clinical course is most commonly benign, although persistent long-term enhancement is frequent

Contact Aspiration Versus Stent Retriever in Patients With Acute Ischemic Stroke With M2 Occlusion in the ASTER Randomized Trial (Contact Aspiration Versus Stent Retriever for Successful Revascularization)

International audienc