Decision support for distribution automation : data analytics for automated fault diagnosis and prognosis

Distribution Automation (DA) is deployed to reduce outage times, isolate the faulted area, and rapidly restore customer supplies following network faults. Recent developments in Supervisory Control and Data Acquisition (SCADA) and intelligent DA equipment have sought to improve reliability and security of supply. The introduction of such ‘intelligent’ technologies on distribution networks, where investment in dedicated condition monitoring equipment remains difficult to justify, presents an opportunity to capture constant streams of operational data which can offer a useful insight into underlying circuit conditions if utilised and managed appropriately. The primary function of the NOJA Pole-Mounted Auto-Recloser (PMAR) is to isolate distribution circuits from detected faults, while attempting to minimise outages due to transient faults. However, in this process the PMAR also captures current and voltage measurements that can be analysed to inform any subsequent fault diagnosis, and potentially detect the early onset of circuit degradation, and monitor and predict its progression. This paper details the design and development of an automated decision support system for fault diagnosis and prognosis, which can detect and diagnose evolving faults by analysing PMAR data and corresponding SCADA alarm data. A knowledge based system has been developed, utilising data science and data mining techniques, to implement diagnostic and prognostic algorithms which automate the existing manual process of post fault diagnosis and anticipation, and circuit condition assessment

A data analytic approach to automatic fault diagnosis and prognosis for distribution automation

Distribution Automation (DA) is deployed to reduce outages and to rapidly reconnect customers following network faults. Recent developments in DA equipment have enabled the logging of load and fault event data, referred to as ‘pick-up activity’. This pick-up activity provides a picture of the underlying circuit activity occurring between successive DA operations over a period of time and has the potential to be accessed remotely for off-line or on-line analysis. The application of data analytics and automated analysis of this data supports reactive fault management and post fault investigation into anomalous network behavior. It also supports predictive capabilities that identify when potential network faults are evolving and offers the opportunity to take action in advance in order to mitigate any outages. This paper details the design of a novel decision support system to achieve fault diagnosis and prognosis for DA schemes. It combines detailed data from a specific DA device with rule-based, data mining and clustering techniques to deliver the diagnostic and prognostic functions. These are applied to 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) as provided by a leading UK network operator. This novel automated analysis system diagnoses the nature of a circuit’s previous fault activity, identifies underlying anomalous circuit activity, and highlights indications of problematic events gradually evolving into a full scale circuit fault. The novel contributions include the tackling of ‘semi-permanent faults’ and the re-usable methodology and approach for applying data analytics to any DA device data sets in order to provide diagnostic decisions and mitigate potential fault scenarios

A systemic transformation of an arts and sciences curriculum to nurture inclusive excellence of all students through course-based research experiences

IntroductionWe describe herein a large-scale, multidisciplinary course-based undergraduate research experience program (CRE) developed at Lawrence Technological University (LTU). In our program, all students enrolled in CRE classes participate in authentic research experiences within the framework of the curriculum, eliminating self-selection processes and other barriers to traditional extracurricular research experiences.MethodsSince 2014, we have designed and implemented more than 40 CRE courses in our College of Arts and Sciences involving more than 30 instructors from computer science, mathematics, physics, biology, chemistry, English composition, literature, philosophy, media communication, nursing, and psychology.ResultsAssessment survey data indicates that students who participate in CRE courses have an enhanced attitude towards research and discovery, as well as increased self-efficacy. This intervention is particularly relevant for non-traditional students, such as students who commute and/or have significant work or childcare commitments, who often experience limited access to research activities.DiscussionHerein we highlight the importance of a systemic institutional change that has made this intervention sustainable and likely to outlast the external funding phase. Systemic change can emerge from a combination of conditions, including: (1) developing a critical mass of CRE courses by providing instructors with both incentives and training; (2) developing general principles on which instructors can base their CRE activities; (3) securing and maintaining institutional support to promote policy changes towards a more inclusive institution; and (4) diversifying the range of the intervention, both in terms of initiatives and disciplines involved

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Outgassing through magmatic fractures enables effusive eruption of silicic magma

Several mechanisms have been proposed to allow highly viscous silicic magma to outgas efficiently enough to erupt effusively. There is increasing evidence that challenges the classic foam-collapse model in which gas escapes through permeable bubble networks, and instead suggests that magmatic fracturing and/or accompanying localized fragmentation and welding within the conduit play an important role in outgassing. The 2011–2012 eruption at Cordón Caulle volcano, Chile, provides direct observations of the role of magmatic fractures. This eruption exhibited a months-long hybrid phase, in which rhyolitic lava extrusion was accompanied by vigorous gas-and-tephra venting through fractures in the lava dome surface. Some of these fractures were preserved as tuffisites (tephra-filled veins) in erupted lava and bombs. We integrate constraints from petrologic analyses of erupted products and video analyses of gas-and-tephra venting to construct a model for magma ascent in a conduit. The one-dimensional, two-phase, steady-state model considers outgassing through deforming permeable bubble networks, magmatic fractures, and adjacent wall rock. Simulations for a range of plausible magma ascent conditions indicate that the eruption of low-porosity lava observed at Cordón Caulle volcano occurs because of significant gas flux through fracture networks in the upper conduit. This modeling emphasizes the important role that outgassing through magmatic fractures plays in sustaining effusive or hybrid eruptions of silicic magma and in facilitating explosive-effusive transitions

Serum Bile Acids Improve Prediction of Alzheimer's Progression in a Sex-Dependent Manner

Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n = 660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n = 578) with cerebrospinal fluid amyloid-beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction