Policy Feedback

This element explores early and more recent contributions of the policy feedback literature to clarify the meaning of this concept and its contribution to both political science and policy studies. This element also discusses the practical implications of policy feedback research through a discussion of its potential impact on policy design

Approche pour l'identification des causes de la mauvaise décantation des solides biologiques

Les procédés d'épuration biologique à culture libre (boues activées) comprennent habituellement un décanteur qui permet de concentrer les solides biologiques en vue de leur recirculation en tête du réacteur biologique. Lorsque ce décanteur fonctionne mal on observe une perte de solides biologiques (SB), ce qui se traduit par une augmentation de la concentration des matières en suspension (MES) dans l'effluent du décanteur secondaire et par une baisse des performances du procédé d'épuration. Lorsque la concentration de MES dans l'effluent du décanteur secondaire est trop élevée on mesure l'indice de volume des boues (IVB). Un IVB faible indique que les solides biologiques ont de bonnes caractéristiques de décantation de sorte que la cause de la mauvaise efficacité du décanteur est d'ordre physique et peut être identifiée facilement. Lorsque l'IVB est élevé, la mauvaise décantation est alors causée par un désordre de l'écosystème qui se traduit le plus souvent par une croissance excessive d'organismes filamenteux. Les causes et les solutions d'un tel problème sont alors difficiles à identifier. Pour ce travail, les auteurs ont réalisé une importante revue bibliographique dont les résultats sont présentés sous la forme d'un cheminement critique (fig. 1). Dans cette figure, les cases numérotées de 1 à 48 sont liées par des énoncés logiques. Ainsi, en répondant à des questions simples, il est possible de cheminer dans la figure 1 et d'identifier les causes les plus probables du déséquilibre microbiologique ainsi que les solutions qui ont déjà été apportées avec succès. De plus les auteurs ont associé à chaque case une fiche technique (portant le même numéro que la case) sur laquelle sont présentées des explications et la liste des références consultées.Activated sludge is a microbiological aerated sewage treatment process which includes a secondary clarifier to separate the treated effluent from the biological solids. Part of the concentrated solids is recirculated to maintain an adequate concentration of mixed liquor suspended solids (MLSS) In the aerated basin. When the secondary clarifier malfunctions, some biological solids are lost to the effluent : the process efficiency drops and the concentration of suspended solids (SS) increases. When the SS in the effluent is too high the sludge volume index (SVI) must be measured. A low SVI means that the biological solids have good sedimentation characteristics : the problem is thon physical in nature and is easily identified. When the SVI is high, the problem is due to a disturbance of the microbiological ecosystem, which is at the origin of excessive filamentous organism growth. The origins and solutions of such a problem are much harder to find. To this end the authors proceeded with an important review of the literature, the results of which are summarized through a critical path, in figure 1. Files from 1 to 48 are linked by logical statements in such a way that by answering simple questions, one can proceed through the files and identify the must probable cause of the biological disturbance as well as the solution which has already proven successful. Furthermore, the authors have linked each file to a technical file which bears the same number and on which an explanation and references are found.Before proceeding with figure 1 to identify a problem in real life, one must obtain information, resulting from an analysis and observations, with regard to plant effluent, primary clarifier effluent and activated sludge characteristics, including the MLSS concentration. One must also know the chemical oxygen demand (COD), the soluble and total biochemical oxygen demand (BOD5), as well as the nitrogen and phosphorus concentrations in the plant influent. Furthermore, one must also be told of the presence of toxic material or industrial wastes in the sewage and of the fraction of pollution load which is in the form of particulates. Whether sudden changes in the quality of the plant influent have occurred is worth knowing. The concentration of oxygen or hydrogen sulfide in the primary clarifier is also important. One must also gather data related to the activated sludge treatment itself : type of reactor (completely mixed or plug flow), mixed liquor volatile suspended solids (MLVSS) concentration, dissolved oxygen concentration, rate of oxygen uptake and pH. Finally, the results of a microbiological analysis of the sludge are very useful.To illustrate the use of figure 1, let us say that we have the following data :a) Many filamentous microorganisms are present in the MLSS, in particular Microthrix parvicella, type 0092, and Thiothrix sp;b) The rate of dissolved oxygen uptake is 12 mg O2/g of SS - h;c) The rate of COD removal is 0,48 Kg/Kg of SS -d;d) There are no toxic substances in the plant influent;e) There are no abrupt changes in plant influent quality;f) The pHs of the plant influent and of the MLSS are 7,0 and 6,8 respectively;g) The ammonia nitrogen concentration of the plant influent is 1,2 mg/L (N);h) The phosphorus concentration of the plant influent is 4,4 mg/L (P);i) The total and soluble BOD5 concentrations of the plant influent are 400 and 80 mg/L respectively.With this information, we are ready to proceed through figure 1. From file one, one goes to file 2, since the rate of oxygen uptake is sufficient. Otherwise, we would have proceeded to file 32. The reactor being completely mixed, the next step is file 3, where it is said that, because of the low soluble BOD5 concentration one must go to file 9, where we find a fast of filamentous microarganisms which may be responsible for the disturbance. Since two of these microorganisms are effectively present in the mixed liquor suspended solids (MLSS), Microthrox parvicella and type 0092, we are invited to go to file 35, where it is stated that someone has already solved a similar problem by creating a modified contact zone to increase the substrats (organic matter) concentration around the microbiological flocs. The third filamentous microorganism is not identified in file 9. As a second possibility one may assume, in file 2. That the mixing is not complete, which is often the case. With the help of information and results of analyses already available, we proceed, through file 4, 14, 15 and 16, to file 20 where Thiothrixsp is included in the microorganisms listed. File 20 is linked to file 41, where it is said that the controlled addition of nitrogen in the plant influent has already been used to solve this type of problem.The critical path presented in this article is the result of an elaborate study. It may be used as a tool to identify the causes of bad biological flocs sedimentation in the secondary clarifier and select solutions that have already been used successfully

Delivering reform in English healthcare: an ideational perspective

A variety of perspectives has been put forward to understand reform across healthcare systems. Recently, some have called for these perspectives to give greater recognition to the role of ideational processes. The purpose of this article is to present an ideational approach to understanding the delivery of healthcare reform. It draws on a case of English healthcare reform – the Next Stage Review led by Lord Darzi – to show how the delivery of its reform proposals was associated with four ideational frames. These frames built on the idea of “progress” in responding to existing problems; the idea of “prevailing policy” in forming part of a bricolage of ideas within institutional contexts; the idea of “prescription” as top-down structural change at odds with local contexts; and the idea of “professional disputes” in challenging the notion of clinical engagement across professional groups. The article discusses the implications of these ideas in furthering our understanding of policy change, conflict and continuity across healthcare settings

Shaping Policy Change in Population Health: Policy Entrepreneurs, Ideas, and Institutions

Political realities and institutional structures are often ignored when gathering evidence to influence population health policies. If these policies are to be successful, social science literature on policy change should be integrated into the population health approach. In this contribution, drawing on the work of John W. Kingdon and related scholarship, we set out to examine how key components of the policy change literature could contribute towards the effective development of population health policies. Shaping policy change would require a realignment of the existing school of thought, where the contribution of population health seems to end at knowledge translation. Through our critical analysis of selected literature, we extend recommendations to advance a burgeoning discussion in adopting new approaches to successfully implement evidence-informed population health policies

Policy Feedback and the Politics of the Affordable Care Act

There is a large body of literature devoted to how “policies create politics” and how feedback effects from existing policy legacies shape potential reforms in a particular area. Although much of this literature focuses on self‐reinforcing feedback effects that increase support for existing policies over time, Kent Weaver and his colleagues have recently drawn our attention to self‐undermining effects that can gradually weaken support for such policies. The following contribution explores both self‐reinforcing and self‐undermining policy feedback in relationship to the Affordable Care Act, the most important health‐care reform enacted in the United States since the mid‐1960s. More specifically, the paper draws on the concept of policy feedback to reflect on the political fate of the ACA since its adoption in 2010. We argue that, due in part to its sheer complexity and fragmentation, the ACA generates both self‐reinforcing and self‐undermining feedback effects that, depending of the aspect of the legislation at hand, can either facilitate or impede conservative retrenchment and restructuring. Simultaneously, through a discussion of partisan effects that shape Republican behavior in Congress, we acknowledge the limits of policy feedback in the explanation of policy stability and change

Accelerating Training of MLIPs Through Small-Cell Training

While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density functional theory (DFT) calculations that have the same cell size as the simulations that the potential is explicitly trained to model. Here, we demonstrate an easy-to-implement small-cell training protocol and use it to model the Zr-H system. This training leads to a potential that accurately predicts known stable Zr-H phases and reproduces the α-β pure zirconium phase transition in molecular dynamics simulations. Compared to traditional active learning, small-cell training decreased the training time of the α-β zirconium phase transition by approximately 20 times. The potential describes the phase transition with a degree of accuracy similar to that of the large-cell training method

Shaping Policy Change in Population Health: Policy Entrepreneurs, Ideas, and Institutions

Abstract Political realities and institutional structures are often ignored when gathering evidence to influence population health policies. If these policies are to be successful, social science literature on policy change should be integrated into the population health approach. In this contribution, drawing on the work of John W. Kingdon and related scholarship, we set out to examine how key components of the policy change literature could contribute towards the effective development of population health policies. Shaping policy change would require a realignment of the existing school of thought, where the contribution of population health seems to end at knowledge translation. Through our critical analysis of selected literature, we extend recommendations to advance a burgeoning discussion in adopting new approaches to successfully implement evidence-informed population health policies

Enhanced heterogeneously catalyzed Suzuki–Miyaura reaction over SiliaCat Pd(0)

The SiliaCat Pd(0) solid catalyst can be efficiently employed in the Suzuki–Miyaura cross-coupling of an ample variety of haloarenes, including economically viable chloroarenes. The catalyst can be extensively recycled without loss of activity and with low leaching of valued palladium, opening the route to widespread utilization of the method to afford high yields of biaryls devoid of contaminating by-products

A set of moment tensor potentials for zirconium with increasing complexity

Machine learning force fields (MLFFs) are an increasingly popular choice for atomistic simulations due to their high fidelity and improvable nature. Here, we propose a hybrid small-cell approach that combines attributes of both offline and active learning to systematically expand a quantum mechanical (QM) database while constructing MLFFs with increasing model complexity. Our MLFFs employ the moment tensor potential formalism. During this process, we quantitatively assessed structural properties, elastic properties, dimer potential energies, melting temperatures, phase stability, point defect formation energies, point defect migration energies, free surface energies, and generalized stacking fault (GSF) energies of Zr as predicted by our MLFFs. Unsurprisingly, model complexity has a positive correlation with prediction accuracy. We also find that the MLFFs wee able to predict the properties of out-of-sample configurations without directly including these specific configurations in the training dataset. Additionally, we generated 100 MLFFs of high complexity (1513 parameters each) that reached different local optima during training. Their predictions cluster around the benchmark DFT values, but subtle physical features such as the location of local minima on the GSFE surface are washed out by statistical noise