Transient stability analysis using potential energy indices for determining critical generator sets

In this paper, we propose the enhancement of existing power system stability analysis techniques through the use of a proposed set of potential energy indices, applied for observing the separation of generators into critical sets during transient events. This proposed potential-energy-based description of system transient stability behavior permits the formation of a critical generator cutset, which is then used in a quantitative single machine equivalent (SIME) energy-function analysis of system stability. The derivation of the method will show that the proposed potential energy indices do not rely on a detailed representation of the network model, making the indices suitable for use in a variety of applications. This method enhances the current capabilities of SIME analysis for pre-fault offline stability studies, but may also be useful for near-real-time stability analysis, owing to the lack of dependence of the proposed potential energy indices on the network parameters. The ability to utilize the proposed indices without the need for network parameters or fault location information, typically obtained from updated SCADA data, potentially allows the proposed method to be applied for real-time stability analysis utilizing only PMU input data

Improving Service Quality in the New Public Sector

No abstract availabl

Implementation of a national HIV pre-exposure prophylaxis service is associated with changes in characteristics of people with newly diagnosed HIV: a retrospective cohort study

OBJECTIVES: To review characteristics of individuals newly diagnosed with HIV following implementation of a national pre-exposure prophylaxis (PrEP) programme (comprehensive PrEP services, delivered in sexual health clinics) to inform future delivery and broader HIV prevention strategies. METHODS: We extracted data from national HIV databases (July 2015-June 2018). We compared sociodemographic characteristics of individuals diagnosed in the period before and after PrEP implementation, and determined the proportion of 'potentially preventable' infections with the sexual health clinic-based PrEP delivery model used. RESULTS: Those diagnosed with HIV before PrEP implementation were more likely to be male (342/418, 81.8% vs 142/197, 72.1%, p=0.005), be white indigenous (327/418, 78.2% vs 126/197, 64.0%, p<0.001), report transmission route as sex between men (219/418, 52.4% vs 81/197, 41.1%, p=0.014), and have acquired HIV in the country of the programme (302/418, 72.2% vs 114/197, 57.9% p<0.001) and less likely to report transmission through heterosexual sex (114/418, 27.3% vs 77/197, 39.1%, p=0.002) than after implementation.Pre-implementation, 8.6% (36/418) diagnoses were 'potentially preventable' with the PrEP model used. Post-implementation, this was 6.6% (13/197), but higher among those with recently acquired HIV (49/170, 28.8%). Overall, individuals with 'potentially preventable' infections were more likely to be male (49/49, 100% vs 435/566, 76.9%, p<0.001), aged <40 years (37/49, 75.5% vs 307/566, 54.2%, p=0.004), report transmission route as sex between men (49/49, 100% vs 251/566, 44.3%, p<0.001), have previously received post-exposure prophylaxis (12/49, 24.5% vs 7/566, 1.2%, p<0.001) and less likely to be black African (0/49, 0% vs 67/566, 11.8%, p=0.010) than those not meeting this definition. CONCLUSIONS: The sexual health clinic-based national PrEP delivery model appeared to best suit men who have sex with men and white indigenous individuals but had limited reach into other key vulnerable groups. Enhanced models of delivery and HIV combination prevention are required to widen access to individuals not benefiting from PrEP at present

Forecasting in the light of Big Data

Predicting the future state of a system has always been a natural motivation for science and practical applications. Such a topic, beyond its obvious technical and societal relevance, is also interesting from a conceptual point of view. This owes to the fact that forecasting lends itself to two equally radical, yet opposite methodologies. A reductionist one, based on the first principles, and the naive inductivist one, based only on data. This latter view has recently gained some attention in response to the availability of unprecedented amounts of data and increasingly sophisticated algorithmic analytic techniques. The purpose of this note is to assess critically the role of big data in reshaping the key aspects of forecasting and in particular the claim that bigger data leads to better predictions. Drawing on the representative example of weather forecasts we argue that this is not generally the case. We conclude by suggesting that a clever and context-dependent compromise between modelling and quantitative analysis stands out as the best forecasting strategy, as anticipated nearly a century ago by Richardson and von Neumann

Improving HIV pre-exposure prophylaxis (PrEP) adherence and retention in care: Process evaluation and recommendation development from a nationally implemented PrEP programme

Introduction HIV pre-exposure prophylaxis (PrEP), in which people take HIV medication to prevent HIV acquisition, underpins global HIV transmission elimination strategies. Effective prevention needs people to adhere to PrEP and remain in care during periods of risk, but this is difficult to achieve. We undertook a process evaluation of Scotland’s PrEP programme to explore barriers and facilitators to PrEP adherence and retention in care and to systematically develop evidence-based, theoretically-informed recommendations to address them. Methods We conducted semi-structured interviews and focus groups (09/2018-07/2019) with patients who identified as gay or bisexual men and were either using PrEP, had declined the offer of PrEP, had stopped PrEP, or had been assessed as ineligible for PrEP (n = 39 of whom n = 5 (13%) identified as trans, median age 31 years and interquartile range 14 years), healthcare professionals involved in PrEP provision (n = 54 including specialist sexual health doctors and nurses of various grades, PrEP prescribing general practitioners, health promotion officers, midwifes, and a PrEP clinical secretary), and clients (n = 9) and staff (n = 15) of nongovernmental organisations with an HIV prevention remit across Scotland. We used thematic analysis to map key barriers and facilitators to priority areas that could enhance adherence and retention in care. We used implementation science analytic tools (Theoretical Domains Framework, Intervention Functions, Behaviour Change Technique Taxonomy, APEASE criteria) and expert opinion to systematically generate recommendations. Results Barriers included perceived complexity of on-demand dosing, tendency for users to stop PrEP before seeking professional support, troublesome side-effects, limited flexibility in the settings/timings/nature of review appointments, PrEP-related stigma and emerging stigmas around not using PrEP. Facilitators included flexible appointment scheduling, reminders, and processes to follow up non-attenders. Examples of the 25 recommendations include: emphasising benefits of PrEP reviews and providing appointments flexibly within individualised PrEP care; using clinic systems to remind/recall PrEP users; supporting PrEP conversations among sexual partners; clear on-demand dosing guidance; encouraging good PrEP citizenship; detailed discussion on managing side-effects and care/coping planning activities. Conclusions PrEP adherence and retention in care is challenging, reducing the effectiveness of PrEP at individual and population levels. We identify and provide solutions to where and how collaborative interventions across public health, clinical, and community practice could address these challenges

Gibbsian Method for the Self-Optimization of Cellular Networks

In this work, we propose and analyze a class of distributed algorithms performing the joint optimization of radio resources in heterogeneous cellular networks made of a juxtaposition of macro and small cells. Within this context, it is essential to use algorithms able to simultaneously solve the problems of channel selection, user association and power control. In such networks, the unpredictability of the cell and user patterns also requires distributed optimization schemes. The proposed method is inspired from statistical physics and based on the Gibbs sampler. It does not require the concavity/convexity, monotonicity or duality properties common to classical optimization problems. Besides, it supports discrete optimization which is especially useful to practical systems. We show that it can be implemented in a fully distributed way and nevertheless achieves system-wide optimality. We use simulation to compare this solution to today's default operational methods in terms of both throughput and energy consumption. Finally, we address concrete issues for the implementation of this solution and analyze the overhead traffic required within the framework of 3GPP and femtocell standards.Comment: 25 pages, 9 figures, to appear in EURASIP Journal on Wireless Communications and Networking 201