Online prediction and control of post-fault transient stability based on PMU measurements and multi-task learning

The combined usage of phasor measurement units and machine learning algorithms provides the opportunity for developing response-based wide-area system integrity protection scheme against transient instability in power systems. However, only the transient stability status is usually predicted in the literature, which is not enough for real-time decision-making for response-based emergency control. In this paper, an integrated approach is proposed. The GRU-based predictor is firstly proposed for post-disturbance transient stability prediction. On this basis, a multi-task learning framework is proposed for the identification of unstable machines and also the estimation of generation shedding. Case study on the IEEE 39-bus system demonstrates that, apart from the basic task of transient stability prediction, the proposed GRU-based multi-task predictor can predict the grouping of unstable machines correctly. Moreover, based on the estimated amount of generation shedding, the generated remedial control actions can retain the synchronism of the power system

Moisture Changes in the Northern Xinjiang Basin Over the Past 2400 years as Documented in Pollen Records of Jili Lake

Regional humidity is important for terrestrial ecosystem development, while it differs from region to region in inland Asia, knowledge of past moisture changes in the lower basin of northern Xinjiang remainly largely unclear. Based on a pollen record from Jili Lake, the Artemisia/(Amaranthaceae + Ephedra) (Ar/(Am + E)) ratio, as an index of regional humidity, has recorded four relatively dry phases: 1) 400 BCE to 1 CE, 2) the Roman Warm Period (RWP; c. 1–400 CE), 3) the Medieval Warm Period (MWP; c. 850–1200 CE) and 4) the Current Warm Period (CWP; since 1850 CE). In contrast, the Dark Age Cold Period (DACP; c. 400–850 CE) and the Little Ice Age (LIA; c. 1200–1850 CE) were relatively wet. Lower lake levels in a relatively humid climate background indicated by higher aquatic pollen (Typha and Sparganium) after c. 1700 CE are likely the result of intensified irrigation for agriculture in the catchment as documented in historical records. The pollen Ar/(Am + E) ratio also recorded a millennial-scale wetting trend from 1 CE to 1550 CE which is concomitant with a long-term cooling recorded in the Northern Hemisphere

Protective effects of resveratrol on the inhibition of hippocampal neurogenesis induced by ethanol during early postnatal life

AbstractEthanol (EtOH) exposure during early postnatal life triggers obvious neurotoxic effects on the developing hippocampus and results in long-term effects on hippocampal neurogenesis. Resveratrol (RSV) has been demonstrated to exert potential neuroprotective effects by promoting hippocampal neurogenesis. However, the effects of RSV on the EtOH-mediated impairment of hippocampal neurogenesis remain undetermined. Thus, mice were pretreated with RSV and were later exposed to EtOH to evaluate its protective effects on EtOH-mediated toxicity during hippocampal development. The results indicated that a brief exposure of EtOH on postnatal day 7 resulted in a significant impairment in hippocampal neurogenesis and a depletion of hippocampal neural precursor cells (NPCs). This effect was attenuated by pretreatment with RSV. Furthermore, EtOH exposure resulted in a reduction in spine density on the granular neurons of the dentate gyrus (DG), and the spines exhibited a less mature morphological phenotype characterized by a higher proportion of stubby spines and a lower proportion of mushroom spines. However, RSV treatment effectively reversed these responses. We further confirmed that RSV treatment reversed the EtOH-induced down-regulation of hippocampal pERK and Hes1 protein levels, which may be related to the proliferation and maintenance of NPCs. Furthermore, EtOH exposure in the C17.2 NPCs also diminished cell proliferation and activated apoptosis, which could be reversed by pretreatment of RSV. Overall, our results suggest that RSV pretreatment protects against EtOH-induced defects in neurogenesis in postnatal mice and may thus play a critical role in preventing EtOH-mediated toxicity in the developing hippocampus

Chronic Microcystin-LR Exposure Induces Hepatocarcinogenesis via Increased Gankyrin in Vitro and in Vivo

Background/Aims: Our recent study indicated that the serum microcystin-LR (MC-LR) level is positively linked to the risk of human hepatocellular carcinoma (HCC). Gankyrin is over-expressed in cancers and mediates oncogenesis; however, whether MC-LR induces tumor formation and the role of gankyrin in this process is unclear. Methods: We induced malignant transformation of L02 liver cells via 35 passages with exposure to 1, 10, or 100 nM MC-LR. Wound healing, plate and soft agar colony counts, and nude mice tumor formation were used to evaluate the tumorigenic phenotype of MC-LR-treated cells. Silencing gankyrin was used to confirm its function. We established a 35-week MC-LR exposure rat model by twice weekly intraperitoneal injection with 10 μg/kg body weight. In addition, 96 HCC patients were tested for tumor tissue gankyrin expression and serum MC-LR levels. Results: Chronic low-dose MC-LR exposure increased proliferation, mobility, clone and tumor formation abilities of L02 cells as a result of gankyrin activation, while silencing gankyrin inhibited the carcinogenic phenotype of MC-LR-treated cells. MC-LR also induced neoplastic liver lesions in Sprague-Dawley rats due to up-regulated gankyrin. Furthermore, a trend of increased gankyrin was observed in humans exposed to MC-LR. Conclusion: These results suggest that MC-LR induces hepatocarcinogenesis in vitro and in vivo by increasing gankyrin levels, providing new insight into MC-LR carcinogenicity studies

Off-line evaluation of indoor positioning systems in different scenarios: the experiences from IPIN 2020 competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001

Off-Line Evaluation of Indoor Positioning Systems in Different Scenarios: The Experiences From IPIN 2020 Competition

Every year, for ten years now, the IPIN competition has aimed at evaluating real-world indoor localisation systems by testing them in a realistic environment, with realistic movement, using the EvAAL framework. The competition provided a unique overview of the state-of-the-art of systems, technologies, and methods for indoor positioning and navigation purposes. Through fair comparison of the performance achieved by each system, the competition was able to identify the most promising approaches and to pinpoint the most critical working conditions. In 2020, the competition included 5 diverse off-site off-site Tracks, each resembling real use cases and challenges for indoor positioning. The results in terms of participation and accuracy of the proposed systems have been encouraging. The best performing competitors obtained a third quartile of error of 1 m for the Smartphone Track and 0.5 m for the Foot-mounted IMU Track. While not running on physical systems, but only as algorithms, these results represent impressive achievements.Track 3 organizers were supported by the European Union’s Horizon 2020 Research and Innovation programme under the Marie Skłodowska Curie Grant 813278 (A-WEAR: A network for dynamic WEarable Applications with pRivacy constraints), MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE), INSIGNIA (MICINN ref. PTQ2018-009981), and REPNIN+ (MICINN, ref. TEC2017-90808-REDT). We would like to thanks the UJI’s Library managers and employees for their support while collecting the required datasets for Track 3. Track 5 organizers were supported by JST-OPERA Program, Japan, under Grant JPMJOP1612. Track 7 organizers were supported by the Bavarian Ministry for Economic Affairs, Infrastructure, Transport and Technology through the Center for Analytics-Data-Applications (ADA-Center) within the framework of “BAYERN DIGITAL II. ” Team UMinho (Track 3) was supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope under Grant UIDB/00319/2020, and the Ph.D. Fellowship under Grant PD/BD/137401/2018. Team YAI (Track 3) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 109-2221-E-197-026. Team Indora (Track 3) was supported in part by the Slovak Grant Agency, Ministry of Education and Academy of Science, Slovakia, under Grant 1/0177/21, and in part by the Slovak Research and Development Agency under Contract APVV-15-0091. Team TJU (Track 3) was supported in part by the National Natural Science Foundation of China under Grant 61771338 and in part by the Tianjin Research Funding under Grant 18ZXRHSY00190. Team Next-Newbie Reckoners (Track 3) were supported by the Singapore Government through the Industry Alignment Fund—Industry Collaboration Projects Grant. This research was conducted at Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU). Team KawaguchiLab (Track 5) was supported by JSPS KAKENHI under Grant JP17H01762. Team WHU&AutoNavi (Track 6) was supported by the National Key Research and Development Program of China under Grant 2016YFB0502202. Team YAI (Tracks 6 and 7) was supported by the Ministry of Science and Technology (MOST) of Taiwan under Grant MOST 110-2634-F-155-001.Peer reviewe

On an Improved Iterative Reweighted Least Squares Algorithm in Robust Estimation

In geodesy,classical least squares (LS) estimation methods rely heavily on assumptions which are often not met in practice.In particular,it is often assumed that the data errors are zero mean distributed,at least appproximately.Unfortunately,when there are outliers in the data,the classical LS estimators frequently have meaningless performance.In this case,robust estimation such as M-type estimation is usually applied,which is numerically implemented by a so called iterative reweighted least squares algorithm.In the current reweighting process,however,the equivalent normal matrix is required to be inverted in every iteration,which needs an expensive computation demand,especially when the number of the unknown parameters is large.Therefore,in this contribution,the numerical process of the iterative reweighted least squares algorithm is essentially improved,which is mainly represented by avoiding the inversion of the equivalent normal matrix.The numerical example shows that the improved version is performed much superior to the previous one

Parallelizing Gene Expression Programming Algorithm in Enabling Large-Scale Classification

As one of the most effective function mining algorithms, Gene Expression Programming (GEP) algorithm has been widely used in classification, pattern recognition, prediction, and other research fields. Based on the self-evolution, GEP is able to mine an optimal function for dealing with further complicated tasks. However, in big data researches, GEP encounters low efficiency issue due to its long time mining processes. To improve the efficiency of GEP in big data researches especially for processing large-scale classification tasks, this paper presents a parallelized GEP algorithm using MapReduce computing model. The experimental results show that the presented algorithm is scalable and efficient for processing large-scale classification tasks

Path Analysis on Environmental Factors Controlling Runoff and Sediment Yields in Shelter Forests in Three Gorges Reservoir Region

Effects of environmental factors such as climate, topography, vegetation and soil in shelter forests in Three Gorges Reservoir Region on runoff and sediment yields were monitored to identify dominant environmental factors controlling runoff and sediment yields in 15 runoff plots in study area by soil sampling, laboratory analysis, stepwise regression analysis and path analysis, and to establish the main control environmental factors that affect runoff and sediment yields. The results showed that soil bulk density, herbaceous cover, slope, and canopy density were the significant factors controlling runoff, and the direct path coefficient of each factor was ranked as canopy closure (-0.628) > litter thickness (-0.547) > bulk density (0.509) > altitude (0.289). The indirect path coefficient was ranked as soil bulk density (0.354) > litter thickness (-0.169) > altitude (0.126) > canopy closure (-0.104). Therefore, canopy closure and litter thickness mainly had direct effects on runoff, while soil bulk density mainly had indirect effects through their contributions to other factors. Herbaceous cover, litter thickness, slope, canopy density, and altitude were the significant factors controlling sediment yields. The direct path coefficient of each factor was ranked as herbaceous cover (-0.815) > litter thickness (-0.777) > canopy closure (-0.624) > slope (0.620). The indirect path coefficient was ranked as slope (0.272) > litter thickness (-0.131) > canopy closure (-0.097) > herbaceous cover (-0.084). Therefore, herbaceous cover and litter thickness mainly had direct effects on sediment yields, while slope mainly had indirect effects through their contributions to other factors. All the selected environmental factors jointly explained 85.5% and 78.3% of runoff and sediment yield variability, respectively. However, there were large values of remaining path coefficients of other factors influencing runoff and sediment yields, which indicated that some important factors are not included and should be taken into account