Real Utility Research Articles

Molecular Subtypes of Pancreatic Cancer: A Proteomics Approach.

Through molecular subtyping, therapeutic vulnerabilities can be exploited for developing personalized medicine. The real utility of molecular subtyping lies in the clinical translation for disease characterization. Proteomic methods with defined marker sets hold great promise for improving therapy outcomes by improving the speed of predictions for devising treatment strategies.See related article by Son et al., p. 3370.

THE ROYAL PREROGATIVE REVISITED: SOME REFLECTIONS ON THE PROPOSED REPEAL OF THE FIXED TERM PARLIAMENTS ACT AND A (LENGTHY) CRITIQUE OF MILLER NO 2

THE ROYAL PREROGATIVE REVISITED: SOME REFLECTIONS ON THE PROPOSED REPEAL OF THE FIXED TERM PARLIAMENTS ACT AND A (LENGTHY) CRITIQUE OF MILLER NO 2

Long-Term Health Index Prediction for Power Asset Classes Based on Sequence Learning

Utility companies have widely adopted the concept of health index to describe asset health statuses and choose proper asset management actions. The existing application and research works have been focused on determining the current asset health index based on the current condition data. For long-term preventative asset management, it is highly desirable to predict asset health indices in the next few years, especially for asset classes in which the assets share similar electrical, mechanical and condition degradation characteristics. This important problem has not been sufficiently addressed. This paper proposes a sequence learning based method to predict long-term health indices for power asset classes. A comprehensive data-driven method based on sequence learning is presented and solid tests are conducted based on real utility data. The proposed method revealed superior performance with comparison to other prediction methods.

Temporal Solar Photovoltaic Generation Capacity Reduction From Wildfire Smoke

Wildfire smoke and other particulate matter can substantially inhibit solar photovoltaic (PV) generation production. While solar PV facilities may not be located in areas with a high fire risk, smoke from wildfires can travel hundreds of kilometers impacting a large number of facilities. This paper proposes a geospatial wildfire PV capacity model to quantify the anticipated temporal reduction in PV capacity due to wildfire smoke. A case study using data for two time periods from the 2020 California wildfires and real utility scale solar generation data evidences the model's high accuracy. Results argue that wildfire smoke can cause significant temporal solar generation capacity reductions over wide geographic regions. Application of the proposed model to inform power system resiliency planning is demonstrated for two use cases: generation scheduling and siting. With meteorological service providers beginning to release smoke forecasts, our geospatial wildfire PV capacity model enables balancing authorities to make use of this information to proactively schedule generation to compensate for reductions in PV capacity. The trained model also produces geospatial derate maps that can enable generation developers to consider historical capacity derates due to smoke when making siting or planning decisions.

Characteristics of Internet Use amongst Italian University Students.

Problematic Internet use (PIU), that may be defined as the inability to control one's use of Internet with negative consequences in daily life, is an emerging problem involving primarily, but not only young generations. Different studies have shown that students are particularly vulnerable to PIU. Given the paucity of information on PIU in our country, the aim of this paper was at investigating the characteristics of PIU amongst Italian University students. A self-assessment questionnaire, referred by the acronym QUNT ("Questionario sull'Utilizzo delle Nuove Tecnologie"), composed by 101 items grouped together to identify a series of factors, was developed and sent through e-mail invitation to several students from three Italian Universities. The returned questionnaires were 3324, out of a total of 51,304 sent, with no difference between the two sexes. On the contrary, the distribution of the QUNT factors was different in the two sexes, in people living alone and in overweight subjects. Men resulted to be more involved in online recreational activities, whereas women seemed more attracted to instant messaging and generally to social networks. PIU was significantly more present in men than women. The comparisons of QUNT factor scores in the four BMI categories showed that the greater the BMI the greater the score of some factors. The findings of the present study indicate that the use of Internet through new technologies may exceed its real utility amongst Italian university student, with some sex-related differences. Men seem more prone to use Internet for passing time and women for social relationships. Men are also at risk of developing PIU. Again, Internet use might be a basic vulnerability factor of increasing weight gain and obesity amongst young people.

Maximizing social welfare in a competitive diffusion model

Influence maximization (IM) has garnered a lot of attention in the literature owing to applications such as viral marketing and infection containment. It aims to select a small number of seed users to adopt an item such that adoption propagates to a large number of users in the network. Competitive IM focuses on the propagation of competing items in the network. Existing works on competitive IM have several limitations. (1) They fail to incorporate economic incentives in users' decision making in item adoptions. (2) Majority of the works aim to maximize the adoption of one particular item, and ignore the collective role that different items play. (3) They focus mostly on one aspect of competition - pure competition. To address these concerns we study competitive IM under a utility-driven propagation model called UIC, and study social welfare maximization. The problem in general is not only NP-hard but also NP-hard to approximate within any constant factor. We, therefore, devise instant dependent efficient approximation algorithms for the general case as well as a (1 - 1/ e - ∈ )-approximation algorithm for a restricted setting. Our algorithms outperform different baselines on competitive IM, both in terms of solution quality and running time on large real networks under both synthetic and real utility configurations.

Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments.

The significant growth of wireless communications systems in the last years has led to the adoption of a wide range of applications not only for the general public but, also, including utilities and administrative authorities. In this context, the notable expansion of new services for smart cities requires, in some specific cases, the construction of underground tunnels in order to enable the maintenance and operation works of utilities, as well as to reduce the visual impact within the city center. One of the main challenges is that, inherently, underground service tunnels lack coverage from exterior wireless communication systems, which can be potentially dangerous for maintenance personnel working within the tunnels. Accordingly, wireless coverage should be deployed within the underground installation in order to guarantee real-time connectivity for safety maintenance, remote surveillance or monitoring operations. In this work, wireless channel characterization for complex urban tunnel environments was analyzed based on the assessment of LoRaWAN and ZigBee technologies operating at 868 MHz. For that purpose, a real urban utility tunnel was modeled and simulated by means of an in-house three-dimensional ray-launching (3D-RL) code. The utility tunnel scenario is a complex and singular environment in terms of radio wave propagation due to the limited dimensions and metallic elements within it, such as service trays, user pathways or handrails, which were considered in the simulations. The simulated 3D-RL algorithm was calibrated and verified with experimental measurements, after which, the simulation and measurement results showed good agreement. Besides, a complete wireless sensor network (WSN) deployment within the tunnels was presented, providing remote cloud data access applications and services, allowing infrastructure security and safety work conditions. The obtained results provided an adequate radio planning approach for the deployment of wireless systems in complex urban utility scenarios, with optimal coverage and enhanced quality of service.

Lipoprotein-associated phospholipase A2 (Lp-PLA2) – possible diagnostic and risk biomarker in chronic ischaemic heart disease

In a group of 208 patients with chronic ischaemic heart disease, the variation of A2-associated-LDL phosphatase (Lp-PLA2) serum concentration values was analysed in dynamics at a two-week interval. The conclusion of the study is that the values of serum concentration of Lp-PLA2 can be accepted as a biomarker with diagnostic specificity for chronic ischaemic heart disease, a parameter of real utility in medical practice, both in situations where the patient, although clinically reporting the existence of angina pectoris, does not show specific changes on an EKG, and for the assessment of the response to personalised therapy.

Extracting Resilience Metrics From Distribution Utility Data Using Outage and Restore Process Statistics

Resilience curves track the accumulation and restoration of outages during an event on an electric distribution grid. We show that a resilience curve generated from utility data can always be decomposed into an outage process and a restore process and that these processes generally overlap in time. We use many events in real utility data to characterize the statistics of these processes, and derive formulas based on these statistics for resilience metrics such as restore duration, customer hours not served, and outage and restore rates. The formulas express the mean value of these metrics as a function of the number of outages in the event. We also give a formula for the variability of restore duration, which allows us to predict a maximum restore duration with 95% confidence. Overall, we give a simple and general way to decompose resilience curves into outage and restore processes and then show how to use these processes to extract resilience metrics from standard distribution system data.

A flow analysis system integrating an optoelectronic detector for the quantitative determination of active ingredients in pharmaceutical formulations

A simple, compact and low-cost optoelectronic detector was developed for complete turbidimetric measurements under continuous flow injection analysis. The device was fabricated by integrating white snow light-emitting diodes as a light source with solar cells as a detector (WLEDs-SCs) of attenuated light that is passed through the analyte. The device is made from ten of WLEDs and three solar cells only without any filters, lenses and fibers. The device operates according to the turbidimetric principle which measures the attenuated light at 180° of the incident light. The developed detector was adapted under flow injection analysis conditions and its real analytical utility has been successfully confirmed by validation and the development of a turbidimetric method for the quantitative determination of Cyproheptadine hydrochloride (CPH) in pure and pharmaceutical formulations. The proposed method is based on forming a turbid, yellow ion-pair association complex in a closed flow system as a result of a reaction between the drug and potassium chromate which was used as a precipitation reagent. Under optimized conditions, the analysis system offers linear range 2.2–160 µg ml−1 with a limit of detection at 0.6 µg ml−1, limit of quantification 1.8 µg ml−1 and correlation coefficient of 0.9986. The intra-day and inter-day precision for two serial estimations of 30 and 60 µg ml−1 of CPH exhibited a relative standard deviation of 0.94%, 0.29 and 0.68%, 0.21% respectively. While the accuracy of the proposed method has expressed as a recovery percentage (Rec %) and error % which were between 100.45–100.67 and 0.45% to 0.67% respectively. The proposed method has been successfully applied for the CPH determination in pharmaceutical preparations with high throughput tests (60 injections per hour) with no interference effect of the common excipients.

Impact of hospitalization for checking medication adherence due to resistant and refractory hypertension suspicious on short and mid-term blood pressure control

Abstract Background Resistant (HR) and refractory hypertension (HRef) are associated with increased cardiovascular events and target-organ damage. However, appropriate HR and HRef diagnosis require good drug adherence. In this context, the “gold standard” method for assessing adherence is supervised medication intake. However, it is not clear the real utility of supervised medication intake in clinical practice. Purpose To evaluate whether hospitalization for confirming anti-hypertensive adherence in patients with HR and HRef may impact blood pressure (BP) control after hospital discharge in patients with HR or HRef suspicious at a tertiary outpatient clinic. Methods We recruited consecutive patients with HR or HRef suspicious admitted to the Hospital for confirming treatment adherence. HR was defined as uncontrolled office BP (≥140 and/or ≥90mmHg) despite using ≥3 classes at optimal doses (one of them being diuretic) or controlled BP using ≥4 classes. HRef was defined as no BP control despite using ≥5 antihypertensive drugs. Patients with suspected HRef who did not meet the criteria but full field the HR definition were named HRNoRef. During hospitalization, all patients used low sodium diet and had supervised taking of prescribed drugs by the medical team aiming BP control. We defined not only the rate of adherence and HF/HRef status but also BP and number of antihypertensive drugs at hospital discharge and in the two first return outpatient's visits. Results We studied a total of 83 patients with suspected HR/HRef (age 53±14 years; 76% females; pre-hospitalization systolic and diastolic BP: 177±28 and 106±21mmHg, respectively). Of these, 68.7% (57 patients) had suspected HRef in the outpatient clinic. The average number of antihypertensive drugs on admission was 5.3±1.3 classes. After hospitalization, the overall frequency of HR fell to 80% (66 patients). The average number of antihypertensive drugs at hospital discharge as well as systolic and diastolic BP was 4.5±1.3 classes, 131±17mmHg and 80±12mmHg, respectively (p<0.001 vs. pre-hospitalization for all comparisons). Among the HR types, HRef was confirmed in only 27 patients (32.5%). During the outpatient follow-up, the patients remained with lower number of antihypertensive drugs as well as lower systolic and diastolic BP at first outpatient visit (mean returned time: 2.1±1.7months) and second outpatient visit post-discharge (mean returned time 7.1±2.6months) as compared to pre-hospitalization data: First visit: 4.3±1.2 classes, systolic: 152±24mmHg, diastolic BP: 89±17mmHg; second visit: 4.5±1.3 classes, systolic: 150±26mmHg, diastolic BP: 89±15mmHg; (p<0.001 vs. pre-hospitalization for all comparisons). Conclusion Supervised medication intake during hospitalization may help not only to define the HR and HRef status but also to have impact on the number of antihypertensive drugs and lower BP values at short and mid-term follow-up. Funding Acknowledgement Type of funding source: None

A cost-benefit multiobjective approach for placement of meters in electrical distribution systems

The present work proposes a multiobjective approach for optimal allocation of metering devices in Electrical Distribution Systems. The proposed approach seeks to obtain solutions that represent attractive investment options for utilities, while providing good performance for State Estimation (SE) when processing measurements from the allocated meters. The requirements of low investment costs and good SE performance are conflicting ones. In this paper, the Pareto multiobjective method, associated with the Modified Monkey Search (MMS) metaheuristic, is employed to obtain concurrent candidate solutions for optimal allocation of smart meters and phasor measurement units. The proposed method, called MMS-Pareto, is employed along with an efficient tool for SE in distribution networks, known as Enhanced Optimal Power Flow. The proposed method is assessed using benchmark systems of the literature, as well as a real Brazilian utility network, being adopted practical and updated costs for the meter devices. Comparisons with a weighted mono-objective approach are presented and the obtained results show that the MMS-Pareto can lead to good trade-off solutions, useful for system planners. Moreover, comparisons with the results obtained when employing other metaheuristics are presented.

Are Regulators Effective at Unraveling Accounting Manipulation? Evidence from Public Utility Commissions

Prior research proposes that a monopolist with private information inflates its reported costs under rate regulation to extract an informational rent. Using a sample of U.S. electric utilities from 1990–2011, we first confirm an unexpected increase in operating expense during rate review periods, then decompose operating expense into its cash and accrual components, and find the cash component accounts for 89% of this increase. The observed pattern is consistent with some combination of real activities management and utility managers misrepresenting transitory expense shocks as permanent. We then focus on identifying regulators’ effectiveness at unraveling this manipulation and minimizing the rent. We estimate that, on average, regulators allow 17¢ out of every dollar of abnormal cash expense to be recovered in future annual revenue, a statistically significant amount. Next, we study the effects of regulators’ ability (proxied by experience) and motivation (proxied by whether they were elected) to unravel accounting manipulation. We find that whereas inexperienced and politically appointed regulators allow a significant portion of abnormal cash expense to be recovered (41¢ and 24¢ out of every dollar, respectively), experienced and elected regulators do not (although the difference between appointed and elected regulators is not statistically significant). Our findings suggest that regulators differ in their ability to identify manipulation—with experience enhancing this ability—and that, on average, state regulators effectively unravel most of the effect of accounting manipulation.This paper was accepted by Suraj Srinivasan, accounting.

When should we order a next generation sequencing test in a patient with cancer?

Technical advances in genome sequencing and the implementation of next-generation sequencing (NGS) in clinical oncology have paved the way for individualizing cancer patient therapy based on molecular profiles. When and how to use NGS testing in the clinic is at present an unsolved issue, although new research results provide evidence favoring this approach in some types of advanced cancer. Clinical research is evolving rapidly, from basket and umbrella trials to adaptative design precision oncology clinical studies, and genomic and molecular data often displace the classical clinical validation procedures of biomarkers. In this context, physicians must be aware of the clinical evidence behind these new biomarkers and NGS tests available, in order to use them in the right moment, and with a critical point of view. This review will present the status of currently available targeted drugs that can be effective based on actionable molecular alterations, and the NGS tests that are currently available, offering a practical guide for the application of Clinical Precision Oncology in the real world routine practice.

Bariatric Surgery for Resistant Hypertension: Working in Progress!

To discuss the recent evidence pointing the benefits of the bariatric surgery on blood pressure control in patients with obesity and hypertension. Particular focus is devoted to discuss the potential impact on resistant hypertension. Growing evidence suggest that bariatric surgery promotes not only a significant reduction in the anti-hypertensive medication while maintained blood pressure control but also a significant proportion of hypertension remission as compared to the usual care. In a sub-analysis of the GATEWAY trial using both office and 24-h ambulatory blood pressure monitoring, the prevalence of resistant hypertension significantly decreased after 12months in the surgical group whereas the numbers remained stable in the control group. Despite the lack of robust evidence, preliminary findings underscore the strong need to explore the potential role of bariatric surgery on resistant hypertension in patients with obesity. This statement is justified not only for the burden of obesity in this scenario but also for the unmet demands in managing resistant hypertension appropriately by multiple drug-therapy or the lack of real utility of procedures like renal denervation and carotid baroreflex activation.

A Risk-Based Approach to Assess the Operational Resilience of Transmission Grids

Modern risk analysis studies of the power system increasingly rely on big datasets, either synthesized, simulated, or real utility data. Particularly in the transmission system, outage events have a strong influence on the reliability, resilience, and security of the overall energy delivery infrastructure. In this paper we analyze historical outage data for transmission system components and discuss the implications of nearby overlapping outages with respect to resilience of the power system. We carry out a risk-based assessment using North American Electric Reliability Corporation (NERC) Transmission Availability Data System (TADS) for the North American bulk power system (BPS). We found that the quantification of nearby unscheduled outage clusters would improve the response times for operators to readjust the system and provide better resilience still under the standard definition of N-1 security. Finally, we propose future steps to investigate the relationship between clusters of outages and their electrical proximity, in order to improve operator actions in the operation horizon.

Semiparametric copula-based analysis for treatment effects in the presence of treatment switching.

In controlled trials, "treatment switching" occurs when patients in one treatment group switch to alternative treatments during the trial, and poses challenges to treatment effect evaluation owing to crossover of the treatments groups. In this work, we assume that treatment switching can occur after some disease progression event and view the progression and death events as two semicompeting risks. The proposed model consists of a copula model for the joint distribution of time-to-progression (TTP) and overall survival (OS) up to the earlier of the two events, as well as a conditional hazard model for OS subsequent to progression. The copula model facilitates assessing the marginal distributions of TTP and OS separately from the association between the two events, and, in particular, the treatment effect on OS in the absence of treatment switching. The proposed conditional hazard model for death subsequent to progression allows us to assess the treatment switching (crossover) effect on OS given occurrence of progression and covariates. Semiparametric proportional hazards models are employed in the marginal models for TTP and OS. A nonparametric maximum likelihood procedure is developed for model inference, which is verified through asymptotic theory and simulation studies. The proposed analysis is applied to a lung cancer dataset to illustrate its real utility.

Optimizing initial oil production of horizontal Wolfcamp wells utilizing data analytics

Machine learning techniques have fundamentally altered how oil and gas industry practitioners design fracture operations. In this paper, we perform data analytics utilizing response surface methodology (RSM), a group of statistical techniques that develop a functional relationship between an output variable of interest and several associated input variables, to optimize the output. We apply RSM to optimize horizontal well production based on initial production (IP) of horizontal oil wells for 180 days (IP180 Oil), as a function of five input variables: reservoir type, fracturing fluid (gal/ft), proppants (lbm/ft), cluster spacing, and stage length (ft). The RSM model correlates the initial production of each well to the input variables via a single equation, thus allowing for exploration of the fitted response surface in order to maximize production. Although the choice of the five inputs is made based primarily after consultation with industry professionals, we validate our selection by also applying an assortment of data-analytics-based methods that attempt to rank variable importance and thereby identify completion variables that may be predictive of initial production. The findings rank all five variables above the 50th percentile, thus indicating that the chosen variables have merit. This procedure is applied to a dataset of 201 horizontal wells from the Wolfcamp formations. The model fits reasonably well, with R2 = 61%, a very significant F-statistic p value, and a predicted versus observed values scatterplot indicating a good fit. The RSM analysis suggests that, within the feasible space defined by this dataset, maximum values of IP 180 Oil may be obtained by setting the fracturing fluid in gal/ft at approximately 1972, while simultaneously maximizing the remaining input variables (proppant loading, cluster spacing, stage length). The outcome indicates the possible directions to be taken in seeking a global optimum initial production for the setting of completion variables. Iteration of this scheme may lead to a near-optimum global solution. The real utility of this work may be indicating the way different studies may be designed to optimize production, each with its own selection of inputs, and ultimately be combined in a meta-analysis.

P0153IS THERE ANY VALUE FOR HOSPITALIZATION FOR CHECKING MEDICATION ADHERENCE DUE TO RESISTANT AND REFRACTORY HYPERTENSION SUSPICIOUS? IMPLICATIONS FOR MID-TERM FOR BLOOD PRESSURE CONTROL

Abstract Background and Aims Resistant hypertension (RH) and refractory hypertension (RefH, an extreme phenotype of RH) are associated with increased cardiovascular events and target-organ damage. However, appropriate RH and RefH diagnosis require good drug adherence. In this context, the “gold standard” method for assessing adherence is supervised medication intake. However, it is not clear the real utility of supervised medication intake during a hospital stay. Therefore, this study was aimed to evaluate whether hospitalization for confirming antihypertensive adherence in patients with RH and RefH may impact blood pressure (BP) values and BP control after hospital discharge in patients with RH or RefH suspicious at a tertiary outpatient clinic. Method From June 2015 to June 2017, we recruited consecutive patients with RH or RefH suspicious admitted to the Hospital for confirming treatment adherence and the respective diagnosis. We excluded patients with secondary forms of hypertension. RH was defined as uncontrolled office BP (≥140 and/or ≥90mmHg) despite using ≥3 classes at optimal doses (one of them being diuretic) or controlled BP using ≥4 classes. RefH was defined as no BP control despite using ≥5 antihypertensive drugs. During hospitalization, all patients used low sodium diet (∼3grs of salt/day) and had supervised taking of prescribed drugs by the medical team aiming BP control. We defined not only the rate of adherence and RH/RefH status but also BP and number of antihypertensive drugs at hospital discharge and in the two first outpatients visits returns. Results We studied a total of 83 patients with suspected RH/RefH (age 53±14 years; 76% female gender; body mass index 28.8±6.2 Kg/m2; pre-hospitalization systolic 177±28mmHg and diastolic BP 106±21mmHg). Of these, 68.7% (57 patients) had suspected RefH in the outpatient clinic. The average number of antihypertensive drugs on admission was 5.3±1.3 classes. After hospitalization, the overall frequency of RH fell to 80% (66 patients). The average number of antihypertensive drugs at hospital discharge as well as systolic and diastolic BP was 4.5±1.3 classes, 131±17mmHg and 80±12mmHg, respectively (p<0.001 vs. pre-hospitalization for all comparisons). Among the RH types, RefH was confirmed in only 27 patients (32.5%). As compared to pre-hospitalization data, we noticed consistent lower number of antihypertensive drugs and lower levels of systolic and diastolic BP both at first outpatient visit post-discharge (mean returned time 2.1±1.7 months) and second outpatient visit post-discharge (mean returned time 7.1±2.6 months): First visit: 4.3±1.2 classes, systolic BP: 152±24 mmHg, diastolic BP: 89±17 mmHg; second visit: 4.5±1.3 classes, systolic BP: 150±26 mmHg, diastolic BP: 89±15 mmHg; (p<0.001 vs. pre-hospitalization for all comparisons). As a consequence, the rate of patients that reached at least 140x90mmHg increased from 0% to 35% (first visit post-discharge) and 38.5% (second visit post-discharge). Conclusion Supervised medication intake with controlled salt diet during hospitalization may help not only to define the RH and RefH status in uncontrolled hypertensive outpatients but also to have impact on the number of antihypertensive drugs and lower BP values in the mid-term follow-up.

A Data-driven Long-Term Dynamic Rating Estimating Method for Power Transformers

This paper presents a data-driven method for producing annual continuous dynamic rating of power transformers to serve the long-term planning purpose. Historically, research works on dynamic rating have been focused on real-time/near-future system operations. There has been a lack of research for long-term planning oriented applications. Currently, most utility companies still rely on static rating numbers when planning power transformers for the next few years. In response, this paper proposes a novel and comprehensive method to analyze the past 5-year temperature, loading and load composition data of existing power transformers in a planning region. Based on such data and the forecasted area load composition, a future power transformer loading profile can be constructed by using Gaussian Mixture Model. Then according to IEEE std. C57.91-2011, a power transformer thermal aging model can be established to incorporate future loading and temperature profiles. As a result, annual continuous dynamic rating profiles under different temperature scenarios can be determined. The profiles can reflect the long-term thermal overloading risk in a much more realistic and granular way, which can significantly improve the accuracy of power transformer planning. A real utility application example in Canada has been presented to demonstrate the practicality and usefulness of this method.