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OBJECTIVES: The aim of this project was to assess malnutrition risk in a well-defined cohort of pancreatic diseases. METHODS: We performed a retrospective, cross-sectional study of 401 subjects with available malnutrition screening tool scores who received care at a single outpatient pancreas clinic during a six month study period. Univariate analyses were performed to compare demographic, anthropometric, symptoms/diseases, and risk for malnutrition characteristics across three strata of diseases: acute pancreatitis (n = 141), chronic pancreatitis (n = 193), and other pancreatic diagnoses (n = 67). RESULTS: A total of 18% of subjects were identified as at risk for malnutrition, including 25% who reported involuntary weight loss and/or decreased appetite. Subjects categorized as at risk for malnutrition were more likely to have gastrointestinal symptoms. Although the nutrition consultation rates were higher in subjects at risk for malnutrition (P = 0.03), 66% did not receive a clearly indicated dietary evaluation or management recommendations. One fifth of all patients in an ambulatory pancreas clinic are identified as at risk for malnutrition using a simple, validated tool. CONCLUSIONS: The majority of patients at increased risk for nutritional complications did not receive nutritional recommendations.

Due to its multiple advantages, the grid-forming, droop-controlled (GFDC) inverter is a strong contender for large-scale deployment in the future power systems. However, it is still unclear whether higher harmonics can play an important role in the dynamics of GFDC inverter networks. The major objective of this article is to gain insight into whether certain higher harmonics influence or interact with GFDC inverter network dynamics and to characterize this harmonic interaction. For this purpose, a multiple-harmonic dynamic phasor model (DPM) of a representative GFDC network is derived, which can be conveniently extended to hundreds of DPM equations in order to rigorously investigate the effects of dc offsets and/or second-harmonic content, six-step switching harmonics, and unbalance. For a representative network, the results from eigenvalue migration studies show that GFDC dynamics are not strongly influenced by the presence of six-step switching harmonics nor unbalance. For the first time, it is shown that under conditions of high droop slope and high-bandwidth power filtering, dc offsets and second-harmonic content can excite a resonance within the network and even influence the location of the eigenvalues of the linearized DPM. All DPM results are thoroughly validated using Simulink, and the selected results are validated experimentally using the Wisconsin Energy Institute (WEI) microgrid testbed.

This paper presents a new modeling and analysis approach to address the small-signal stability issues among interconnected grid-forming (GFM) inverters. A novel phasor domain “power impedance” model is developed to capture the terminal characteristics of GFM inverters. Based on the developed models, a sum-type impedance stability criterion is proposed to analyze the power oscillations between GFM Inverters. The developed models are validated with EMT simulations and the stability analysis results using the proposed stability criterion can match both EMT simulations and eigenvalue analysis. In addition, the application of the developed model can be extended to the stability analysis of large-scale power system with high penetration of GFM inverters.

There is an increasing need to apply rigorous model-order-reduction techniques in the analysis of large-scale networks of inverter-based distributed generation resources due to the limitations of existing simulation tools. Various coherency-based aggregation techniques have long been used to construct reduced-order dynamic models of large-scale synchronous machine (SM) networks. Such techniques have the advantage of preserving the nonlinear nature of the dynamic model throughout the order-reduction process, enabling the efficient and accurate analysis of large-scale network dynamics during large disturbances such as fault events. This paper proposes the application of a rigorous coherency-based aggregation technique to the analysis of large-scale networks of grid-forming droop-controlled inverters. A rapid and powerful generalized eigenvalue perturbation technique for coherency identification, previously only applied to SM networks, is adapted to grid-forming droop-controlled inverter networks. The resulting reduced-order models are physically insightful and are capable of accurately reproducing the system response in the aftermath of large disturbances. For some networks, a rigorously-derived condition of coherency can be difficult to achieve, given the expected range of L–C–L filter impedances. To remedy this limitation, the potential for high-bandwidth inverter control to enforce the conditions that allow for coherency of droop-controlled inverters has been investigated and confirmed using a controller hardware-in-the loop testbed. Using this approach, the use of simple nonlinear aggregate inverter models to accurately model large sections of the inverter network can be more rigorously justified.

The discovery of ‘event signatures’ and useful insights from very large historical Phasor Measurement Unit (PMU) datasets is predicated on offline Big Data analysis approaches that rely on the generation of predictive features on a massive scale. This paper presents lessons learned from a data platform perspective towards reducing barriers to adoption of Big Data analytics against a real dataset of almost half a trillion data points drawn from over 400 PMUs distributed across the North American power grid. We demonstrate software abstractions and targeted performance optimizations that can lead to significant productivity gains for power systems researchers seeking to perform offline exploratory temporal analysis and modeling tasks, with a focus on feature generation. We describe how our optimized approach goes beyond a naive application of mainstream Big Data technologies, enabling feature generation tasks, that previously took days or even weeks, to now be completed in just a few hours.

The day-to-day operation of modern power systems is highly reliant on prompt and adequate situational-awareness. This can be achieved via various system monitoring functions such as anomaly detection, in which static thresholds are commonly utilized to distinguish the normal and the abnormal system states. However, a predetermined static threshold usually lacks the flexibility to adapt to unobserved scenarios. In this paper, we propose two self-adaptive synchrophasor data driven anomaly detection approaches based on Chebyshev’s Inequality. The proposed approaches have been evaluated with Kundur’s 2area system and Mini-WECC system. Experimental results verify that the proposed approaches can dynamically adapt to unprecedented scenarios, and detect anomalous events with lower false alarm rate compared to static threshold based detection.

We present a hierarchical optimization architecture for large-scale power networks that overcomes limitations of fully centralized and fully decentralized architectures. The architecture leverages principles of multigrid computing schemes, which are widely used in the solution of partial differential equations on massively parallel computers. The top layer of the architecture uses a coarse representation of the entire network while the bottom layer is composed of a family of decentralized optimization agents each operating on a network subdomain at full resolution. We use an alternating direction method of multipliers (ADMM) framework to drive coordination of the decentralized agents. We show that state and dual information obtained from the top layer can be used to accelerate the coordination of the decentralized optimization agents and to recover optimality for the entire system. We demonstrate that the hierarchical architecture can be used to manage large collections of microgrids.

Scholars have recently suggested that communicating levels of scientific consensus (e.g. the percentage of scientists who agree about human-caused climate change) can shift public opinion toward the dominant scientific opinion. Initial research suggested that consensus communication effectively reduces public skepticism. However, other research failed to find a persuasive effect for those with conflicting prior beliefs. This study enters this contested space by experimentally testing how different levels of consensus shape perceptions of scientific certainty. We further examine how perceptions of certainty influence personal agreement and policy support. Findings indicate that communicating higher levels of consensus increases perceptions of scientific certainty, which is associated with greater personal agreement and policy support for non-political issues. We find some suggestive evidence that this mediated effect is moderated by participants’ overall trust in science, such that those with low trust in science fail to perceive higher agreement as indicative of greater scientific certainty.

The wide area monitoring system (WAMS) is critical for power system situational awareness, but represents a growing cybersecurity vulnerability. Malicious adversaries may seek to compromise one or more PMUs in order to effect control decisions that unnecessarily disrupt typical grid operations. One example of a particularly pernicious attack vector is the spoofing or replaying of a fault event using one or more compromised PMUs. This work documents the development and validation of a coherency-based cyberattack detection algorithm that integrates a sliding-window singular value decomposition (SVD) with physics-based partitioning analysis to achieve accurate classification of events. Special consideration is given to discerning a sophisticated fault-replay or fault spoofing attack from actual faults. A software-based cybersecurity testbed has been developed for rigorous testing of the algorithm. The algorithm is further validated using simulated synchrophasor datasets obtained from a MinniWECC 63-bus test system. Results show that the algorithm can successfully detect fault-replay attacks even when over half of the PMUs are compromised.

In this article, we examine framing effects regarding unconventional oil and gas extraction using hydraulic fracturing (or fracking): an issue involving considerable controversy over potential impacts as well as terminology used to describe it. Specifically, we explore how two commonly used terms to describe this issue – fracking or shale oil or gas development – serve as issue frames and influence public opinion. Extending existing research, we suggest that these frames elicit different top-of-mind associations that reflect positive or negative connotations and resonate with people's political ideology. These associations, in turn, help explain direct and indirect framing effects on support/opposition as well as whether these effects differ by political ideology. Results of a split-ballot, national U.S. survey (n=1000) reveal that people are more supportive of the energy extraction process when it is referred to as shale oil or gas development versus fracking, and this relationship is mediated by greater perceptions of benefit versus risk. Political ideology did not moderate these effects. Further analysis suggests that these findings are partly explained by the tendency to associate fracking more with negative thoughts and impacts and shale oil or gas development more with positive thoughts and impacts. However, these associations also did not vary by political ideology. We discuss implications for communicating risk regarding energy development.

ObjectivesTo evaluate the efficacy of rituximab (RTX) in IgG4-related disease (IgG4-RD) in an open-label pilot trial.MethodsWe treated 30 IgG4-RD patients with two doses of RTX (1000 mg each). The participants were either treated with RTX alone (n = 26; 87%) or required to discontinue baseline glucocorticoids (GC) within 2 months (n = 4; 13%). Disease activity was measured by the IgG4-RD Responder Index (IgG4-RD RI) and physician's global assessment (PGA). Disease response was defined as the improvement of the IgG4-RD RI by two points. The primary outcome, measured at 6 months, was defined as: (1) decline of the IgG4-RD RI ≥2 points compared with baseline; (2) no disease flares before month 6; and (3) no GC use between months 2 and 6. Complete remission was defined as an IgG4-RD RI score of 0 with no GC use.ResultsDisease responses occurred in 97% of participants. The baseline IgG4-RD RI and PGA values, 11±7 and 63±22 mm, respectively, declined to 1±2 and 11±16 mm at 6 months (both pConclusionsRTX appears to be an effective treatment for IgG4-RD, even without concomitant GC therapy.Trial registration numberClinicalTrials.gov identifier: NCT01584388.

Autoimmune pancreatitis is the prototypical manifestation of immunoglobulin G4-related disease, a fibroinflammatory syndrome that can affect virtually any organ. Rarely, skin involvement has been reported in immunoglobulin G4-related disease. Isolated case reports have described other distinct associated dermatoses.Our objective was to determine the frequency and nature of dermatologic manifestations of immunoglobulin G4-related disease in patients with autoimmune pancreatitis.Retrospective analysis of dermatologic conditions of patients with autoimmune pancreatitis.Among the 118 identified patients, 31 (26.3%) had a dermatologic diagnosis [mean (standard deviation; SD) follow-up 5.9 (4.6) years]. Two patients (1.7%) had necrobiotic xanthogranuloma; three (2.5%) had another xanthomatous or xanthogranulomatous process. One patient (0.8%) had pemphigus erythematosus. No patient had immunoglobulin G4-related skin disease.Skin involvement in immunoglobulin G4-related disease appears to be rare. A disproportionately high number of patients had xanthomatous or xanthogranulomatous processes, including necrobiotic xanthogranuloma. It remains unclear whether the association between immunoglobulin G4-related disease and necrobiotic xanthogranuloma or other xantho(granulo)matous processes represents shared pathophysiology, a mutual underlying driver, or coincidence, though the results of this study cast doubt on the latter. Although pemphigus was not reported in immunoglobulin G4-related disease previously, the prominent role of the immunoglobulin G4 subclass in each condition makes this association intriguing.

There is an increasing need to model large networks of inverter-based distributed generation due to the limits of existing simulation tools. A deliberate enforcement of intra-feeder coherency between adjacent droop-controlled inverters is a worthwhile objective, as phase angle coherency will allow for a deliberate partitioning of fast and slow dynamics within the network. This partitioning, in turn, results in simpler and more tractable reduced-order network dynamics that are easier to model. This paper presents the adaptation of the generalized eigenvalue perturbation approach to the identification of coherent groups within grid-forming, droop-controlled inverter networks. For many networks, a rigorously-derived condition of coherency can be difficult to achieve, given the expected range of L-C-L filter and grid-tie impedances. To remedy this limitation, this paper investigates the use of multi-loop voltage regulation and virtual impedance to enforce the conditions that allow for coherency, and thereby more rigorously justify the use of simple nonlinear aggregate inverter models to accurately model large sections of the inverter network.

BACKGROUND Soluble signaling molecules may play an important role in malignant pathogenesis. We hypothesize that perioperative cytokine levels are associated with outcomes in patients with pancreatic adenocarcinoma (PDAC) undergoing surgical resection. METHODS One hundered and eighteen patients with benign or malignant pancreatic disease were enrolled in a prospective study through a protocol for banking biologic samples. Peripheral blood was drawn at time of operation, and a multiplex cytokine assay was performed. Statistical analysis was via χ2 and Kaplan Meier methods. RESULTS Of 118 patients enrolled, 85 (72%) had a diagnosis of PDAC, and 60 (70%) ultimately underwent partial pancreatectomy. Cytokine levels were not associated with postoperative complications in this initial cohort. A plasma level of monocyte chemoattractant protein-1 (MCP-1) pg/mL ≤118 was associated with better overall survival (OS) (median survival 21 months vs 12.8 months, P = 0.023), as was non-detectable interleukin-8 (IL-8) (19 months) versus detectable IL-8 (12.8 months, P = 0.05). Patients with both MCP-1 >118 pg/mL and detectable IL-8 had a median survival of 10.6 months (P = 0.028). CONCLUSIONS MCP-1 and IL-8 cytokine levels are associated with decreased survival following pancreatectomy for PDAC, and may be useful biomarkers. Measurement of these cytokine levels at different time points in future investigations will be important to validate these findings.

A droop-controlled, hybrid ac/dc microgrid represents a robust architecture that can coordinate the operation of multiple distributed sources while minimizing the power conversion stages. While the reduced-order nonlinear dynamics of grid-forming, droop-controlled inverter-based microgrids have been rigorously shown to be stable for a wide range of operating parameters, the problem of analyzing the transient stability characteristics of droop-controlled hybrid ac/dc architectures has not been adequately addressed. This work reviews the link between the Virtual Synchronous Machine control concept and grid-forming droop control, and introduces a new droop control strategy for ac/dc hybrid microgrids, termed ‘symmetric droop control’ (SDC). SDC better addresses the dynamic interactions between the ac and dc sub-grids of the hybrid microgrid, by ensuring that the interfacing inverter appropriately represents the dc sub-grid dynamics when interacting with other inverters. SDC can help to ensure that the reduced-order, nonlinear dynamics in both the ac and dc networks remain predictable and well-behaved during large disturbances in the ac network.

This work describes the development of a reduced-order nonlinear dynamic phasor modeling tool for distribution networks containing a large number of P-f and Q-v droop-controlled inverters. Attention is focused on constructing reduced-order models that accurately predict the nonlinear behavior of coherent clusters of droop-controlled inverters in response to a large disturbance, such as an asymmetric fault. In such a scenario, dynamic phasor models are shown to offer valuable insights into how the signal conditioning associated with local power measurement can have a significant effect on the large-signal stability of a droop-controlled inverter or inverter cluster. An algorithm conventionally used to identify coherent groups of synchronous generators is adopted for the investigation of coherency between large numbers of droop-controlled inverters within a distribution network. The coherency results help guide the aggregation of droop-controlled inverters and enable nonlinear reduced-order dynamic phasor modeling. For large disturbances, a reduced-order dynamic phasor simulation of a 50-inverter system is validated against full-order simulation results obtained using an OPAL-RT power system simulator.

Appendix G, the Performance Rating Method in ASHRAE Standard 90.1 has been updated to make two significant changes for the 2016 edition, to be published in October of 2016. First, it allows Appendix G to be used as a third path for compliance with the standard in addition to rating beyond code building performance. This prevents modelers from having to develop separate building models for code compliance and beyond code programs. Using this new version of Appendix G to show compliance with the 2016 edition of the standard, the proposed building design needs to have a performance cost index (PCI) less than targets shown in a new table based on building type and climate zone. The second change is that the baseline design is now fixed at a stable level of performance set approximately equal to the 2004 code. Rather than changing the stringency of the baseline with each subsequent edition of the standard, compliance with new editions will simply require a reduced PCI (a PCI of zero is a net-zero building). Using this approach, buildings of any era can be rated using the same method. The intent is that any building energy code or beyond code program can usemore » this methodology and merely set the appropriate PCI target for their needs. This report discusses the process used to set performance criteria for compliance with ASHRAE Standard 90.1-2016 and suggests a method for demonstrating compliance with other codes and beyond code programs.« less

Moving to the ASHRAE Standard 90.1-2013 (ASHRAE 2013) edition from Standard 90.1-2010 (ASHRAE 2010) is cost-effective for the State of California. The table below shows the state-wide economic impact of upgrading to Standard 90.1-2013 in terms of the annual energy cost savings in dollars per square foot, additional construction cost per square foot required by the upgrade, and life-cycle cost (LCC) per square foot. These results are weighted averages for all building types in all climate zones in the state, based on weightings shown in Table 4. The methodology used for this analysis is consistent with the methodology used in the national cost-effectiveness analysis. Additional results and details on the methodology are presented in the following sections. The report provides analysis of two LCC scenarios: Scenario 1, representing publicly-owned buildings, considers initial costs, energy costs, maintenance costs, and replacement costs—without borrowing or taxes. Scenario 2, representing privately-owned buildings, adds borrowing costs and tax impacts.

Moving to the ASHRAE Standard 90.1-2013 (ASHRAE 2013) edition from Standard 90.1-2010 (ASHRAE 2010) is cost-effective for the State of Arkansas. The table below shows the state-wide economic impact of upgrading to Standard 90.1-2013 in terms of the annual energy cost savings in dollars per square foot, additional construction cost per square foot required by the upgrade, and life-cycle cost (LCC) per square foot. These results are weighted averages for all building types in all climate zones in the state, based on weightings shown in Table 4. The methodology used for this analysis is consistent with the methodology used in the national cost-effectiveness analysis. Additional results and details on the methodology are presented in the following sections. The report provides analysis of two LCC scenarios: Scenario 1, representing publicly-owned buildings, considers initial costs, energy costs, maintenance costs, and replacement costs—without borrowing or taxes. Scenario 2, representing privately-owned buildings, adds borrowing costs and tax impacts.