Model Based Evaluation of the Differences between Full and Partial Flow Particulate Matter Sampling Systems

PM has been shown to be harmful to people, animals, and the environment. For this reason PM is a regulated emission. As federal regulation of particulate matter (PM) becomes tighter, the need to accurately measure it becomes paramount. As the limit decreases, it becomes more difficult to measure PM due to the inaccuracies in the measurement equipment, and the nature of the particles to be lost in the sampling system.;This study investigated the error propagation and particle loss in two common PM mass measurement systems, the full flow sampling system, also called the constant volume sampling (CVS), and the partial flow sampling (PFS) systems. Computer models were created to simulate the propagation of inaccuracies in the components to total error, and to simulate how many and why particles get lost in the systems. The data that the models used came from transient testing of a 2004 model year heavy-duty diesel engine from which both a CVS and PFS system were used to measure the emissions of PM. Particle spectrometer data were also collected and used in the particle loss model. The models produced batch error results and integrated mass loss results. The models also produced continuous error propagation and continuous particle loss to give more insight as to when in the transient test the largest errors and particle losses were occurring. Another useful result from the particle loss model was showing which types of losses affect which size of particle. Four types of particle loss were considered: diffusion, thermophoretic, isokinetic, and bend. Each of the loss types affected the particle distribution differently.;The CVS system had less error in its measurement, but more particles were lost therein. The PFS system had much more error than the CVS, but fewer particles were lost. The relative error in the CVS system was minimal at approximately 10%, whereas the PFS system had a relative error of approximately 36%. The losses in the CVS system lowered the mass result by approximately 11%, and the losses in the PFS system lowered the mass result by approximately 5%

Social Impact Bonds: Overview and Considerations

One of the hottest topics in human services is "pay-for-success" approaches to government contracting. In this era of tight budgets and increased skepticism about the effectiveness of government-funded programs, the idea that the government could pay only for proven results has a broad appeal. And those who have identified prevention-focused models that have the potential to improve long-term outcomes and save the government money are deeply frustrated that they have been unable to attract the funding needed to take these programs to scale. Some advocates for expanded prevention efforts are confident that these programs could thrive under pay for success and see such an approach as a way to break out of the harmful cycle where what limited funds are available must be used to provide services for those who are already in crisis, and there are rarely sufficient funds to pay for prevention

Does a 6-point scale approach to post-treatment 18F-FDG PET-CT allow to improve response assessment in head and neck squamous cell carcinoma? A multicenter study

Abstract Purpose Response assessment to definitive non-surgical treatment for head and neck squamous cell carcinoma (HNSCC) is centered on the role of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET-CT) 12 weeks after treatment. The 5-point Hopkins score is the only qualitative system available for standardized reporting, albeit limited by suboptimal positive predictive value (PPV). The aim of our study was to explore the feasibility and assess the diagnostic accuracy of an experimental 6-point scale ("Cuneo score"). Methods We performed a retrospective, multicenter study on HNSCC patients who received a curatively-intended, radiation-based treatment. A centralized, independent qualitative evaluation of post-treatment FDG-PET/CT scans was undertaken by 3 experienced nuclear medicine physicians who were blinded to patients' information, clinical data, and all other imaging examinations. Response to treatment was evaluated according to Hopkins, Cuneo, and Deauville criteria. The primary endpoint of the study was to evaluate the PPV of Cuneo score in assessing locoregional control (LRC). We also correlated semi-quantitative metabolic factors as included in PERCIST and EORTC criteria with disease outcome. Results Out of a total sample of 350 patients from 11 centers, 119 subjects (oropharynx, 57.1%; HPV negative, 73.1%) had baseline and post-treatment FDG-PET/CT scans fully compliant with EANM 1.0 guidelines and were therefore included in our analysis. At a median follow-up of 42 months (range 5-98), the median locoregional control was 35 months (95% CI, 32-43), with a 74.5% 3-year rate. Cuneo score had the highest diagnostic accuracy (76.5%), with a positive predictive value for primary tumor (Tref), nodal disease (Nref), and composite TNref of 42.9%, 100%, and 50%, respectively. A Cuneo score of 5-6 (indicative of residual disease) was associated with poor overall survival at multivariate analysis (HR 6.0; 95% CI, 1.88-19.18; p = 0.002). In addition, nodal progressive disease according to PERCIST criteria was associated with worse LRC (OR for LR failure, 5.65; 95% CI, 1.26-25.46; p = 0.024) and overall survival (OR for death, 4.81; 1.07-21.53; p = 0.04). Conclusions In the frame of a strictly blinded methodology for response assessment, the feasibility of Cuneo score was preliminarily validated. Prospective investigations are warranted to further evaluate its reproducibility and diagnostic accuracy

Arbitration policies for on-demand user-level I/O forwarding on HPC platforms

I/O forwarding is a well-established and widely-adopted technique in HPC to reduce contention in the access to storage servers and transparently improve I/O performance. Rather than having applications directly accessing the shared parallel file system, the forwarding technique defines a set of I/O nodes responsible for receiving application requests and forwarding them to the file system, thus reshaping the flow of requests. The typical approach is to statically assign I/O nodes to applications depending on the number of compute nodes they use, which is not always necessarily related to their I/O requirements. Thus, this approach leads to inefficient usage of these resources. This paper investigates arbitration policies based on the applications I/O demands, represented by their access patterns. We propose a policy based on the Multiple-Choice Knapsack problem that seeks to maximize global bandwidth by giving more I/O nodes to applications that will benefit the most. Furthermore, we propose a user-level I/O forwarding solution as an on-demand service capable of applying different allocation policies at runtime for machines where this layer is not present. We demonstrate our approach's applicability through extensive experimentation and show it can transparently improve global I/O bandwidth by up to 85% in a live setup compared to the default static policy.This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Supenor - Brasil (CAPES) - Finance Code 001. It has also received support from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil. It is also partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grants PID2019-107255GB; and the Generalitat de Catalunya under contract 2014-SGR-1051. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Barcelona Supercomputing Center. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).Peer ReviewedPostprint (author's final draft

Resource Sharing for Multi-Tenant Nosql Data Store in Cloud

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud providers because it enables resource sharing at low operating cost. Multi-tenancy takes several forms depending on whether the back-end file system is a local file system (LFS) or a parallel file system (PFS), and on whether tenants are independent or share data across tenants In this thesis I focus on and propose solutions to two cases: independent data-local file system, and shared data-parallel file system. In the independent data-local file system case, resource contention occurs under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL stores, causing performance degradation for one tenant by another. We investigate the interference and propose two approaches. The first provides a scheduling scheme that can approximate resource consumption, adapt to workload dynamics and work in a distributed fashion. The second introduces a workload-aware resource reservation approach to prevent interference. The approach relies on a performance model obtained offline and plans the reservation according to different workload resource demands. Results show the approaches together can prevent interference and adapt to dynamic workloads under multi-tenancy. In the shared data-parallel file system case, it has been shown that running a distributed NoSQL store over PFS for shared data across tenants is not cost effective. Overheads are introduced due to the unawareness of the NoSQL store of PFS. This dissertation targets the key-value store (KVS), a specific form of NoSQL stores, and proposes a lightweight KVS over a parallel file system to improve efficiency. The solution is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not designed for. Results show the proposed system outperforms Cassandra and Voldemort in several different workloads

Towards zero-waste recovery and zero-overhead checkpointing in ensemble data assimilation

Ensemble data assimilation is a powerful tool for increasing the accuracy of climatological states. It is based on combining observations with the results from numerical model simulations. The method comprises two steps, (1) the propagation, where the ensemble states are advanced by the numerical model and (2) the analysis, where the model states are corrected with observations. One bottleneck in ensemble data assimilation is circulating the ensemble states between the two steps. Often, the states are circulated using files. This article presents an extended implementation of Melissa-DA, an in-memory ensemble data assimilation framework, allowing zero-overhead checkpointing and recovery with few or zero recomputation. We hide the checkpoint creation using dedicated threads and MPI processes. We benchmark our implementation with up to 512 members simulating the Lorenz96 model using 10 9 gridpoints. We utilize up to 8 K processes and 8 TB of checkpoint data per cycle and reach a peak performance of 52 teraFLOPS.Part of the research presented here has received funding from the Horizon 2020 (H2020) funding framework under grant/award number: 824158; Energy oriented Centre of Excellence II (EoCoE-II).Peer ReviewedPostprint (author's final draft

Resilience for large ensemble computations

With the increasing power of supercomputers, ever more detailed models of physical systems can be simulated, and ever larger problem sizes can be considered for any kind of numerical system. During the last twenty years the performance of the fastest clusters went from the teraFLOPS domain (ASCI RED: 2.3 teraFLOPS) to the pre-exaFLOPS domain (Fugaku: 442 petaFLOPS), and we will soon have the first supercomputer with a peak performance cracking the exaFLOPS (El Capitan: 1.5 exaFLOPS). Ensemble techniques experience a renaissance with the availability of those extreme scales. Especially recent techniques, such as particle filters, will benefit from it. Current ensemble methods in climate science, such as ensemble Kalman filters, exhibit a linear dependency between the problem size and the ensemble size, while particle filters show an exponential dependency. Nevertheless, with the prospect of massive computing power come challenges such as power consumption and fault-tolerance. The mean-time-between-failures shrinks with the number of components in the system, and it is expected to have failures every few hours at exascale. In this thesis, we explore and develop techniques to protect large ensemble computations from failures. We present novel approaches in differential checkpointing, elastic recovery, fully asynchronous checkpointing, and checkpoint compression. Furthermore, we design and implement a fault-tolerant particle filter with pre-emptive particle prefetching and caching. And finally, we design and implement a framework for the automatic validation and application of lossy compression in ensemble data assimilation. Altogether, we present five contributions in this thesis, where the first two improve state-of-the-art checkpointing techniques, and the last three address the resilience of ensemble computations. The contributions represent stand-alone fault-tolerance techniques, however, they can also be used to improve the properties of each other. For instance, we utilize elastic recovery (2nd contribution) for mitigating resiliency in an online ensemble data assimilation framework (3rd contribution), and we built our validation framework (5th contribution) on top of our particle filter implementation (4th contribution). We further demonstrate that our contributions improve resilience and performance with experiments on various architectures such as Intel, IBM, and ARM processors.Amb l’increment de les capacitats de còmput dels supercomputadors, es poden simular models de sistemes físics encara més detallats, i es poden resoldre problemes de més grandària en qualsevol tipus de sistema numèric. Durant els últims vint anys, el rendiment dels clústers més ràpids ha passat del domini dels teraFLOPS (ASCI RED: 2.3 teraFLOPS) al domini dels pre-exaFLOPS (Fugaku: 442 petaFLOPS), i aviat tindrem el primer supercomputador amb un rendiment màxim que sobrepassa els exaFLOPS (El Capitan: 1.5 exaFLOPS). Les tècniques d’ensemble experimenten un renaixement amb la disponibilitat d’aquestes escales tan extremes. Especialment les tècniques més noves, com els filtres de partícules, se¿n beneficiaran. Els mètodes d’ensemble actuals en climatologia, com els filtres d’ensemble de Kalman, exhibeixen una dependència lineal entre la mida del problema i la mida de l’ensemble, mentre que els filtres de partícules mostren una dependència exponencial. No obstant, juntament amb les oportunitats de poder computar massivament, apareixen desafiaments com l’alt consum energètic i la necessitat de tolerància a errors. El temps de mitjana entre errors es redueix amb el nombre de components del sistema, i s’espera que els errors s’esdevinguin cada poques hores a exaescala. En aquesta tesis, explorem i desenvolupem tècniques per protegir grans càlculs d’ensemble d’errors. Presentem noves tècniques en punts de control diferencials, recuperació elàstica, punts de control totalment asincrònics i compressió de punts de control. A més, dissenyem i implementem un filtre de partícules tolerant a errors amb captació i emmagatzematge en caché de partícules de manera preventiva. I finalment, dissenyem i implementem un marc per la validació automàtica i l’aplicació de compressió amb pèrdua en l’assimilació de dades d’ensemble. En total, en aquesta tesis presentem cinc contribucions, les dues primeres de les quals milloren les tècniques de punts de control més avançades, mentre que les tres restants aborden la resiliència dels càlculs d’ensemble. Les contribucions representen tècniques independents de tolerància a errors; no obstant, també es poden utilitzar per a millorar les propietats de cadascuna. Per exemple, utilitzem la recuperació elàstica (segona contribució) per a mitigar la resiliència en un marc d’assimilació de dades d’ensemble en línia (tercera contribució), i construïm el nostre marc de validació (cinquena contribució) sobre la nostra implementació del filtre de partícules (quarta contribució). A més, demostrem que les nostres contribucions milloren la resiliència i el rendiment amb experiments en diverses arquitectures, com processadors Intel, IBM i ARM.Postprint (published version