A Novel Multi-objective Optimisation Algorithm for Routability and Timing Driven Circuit Clustering on FPGAs

Circuit clustering algorithms fit synthesised circuits into FPGA configurable logic blocks (CLBs) efficiently. This fundamental process in FPGA CAD flow directly impacts both effort required and performance achievable in subsequent place-and-route processes. Circuit clustering is limited by hardware constraints of specific target architectures. Hence, better circuit clustering approaches are essential for improving device utilisation whilst at the same time optimising circuit performance parameters such as, e.g., power and delay. In this paper, we present a method based on multi-objective genetic algorithm (MOGA) to facilitate circuit clustering. We address a number of challenges including CLB input bandwidth constraints, improvement of CLB utilisation, minimisation of interconnects between CLBs. Our new approach has been validated using the "Golden 20" MCNC benchmark circuits that are regularly used in FPGA-related literature. The results show that the method proposed in this paper achieves improvements of up to 50% in clustering, routability and timing when compared to state-of-the-art approaches including VPack, T-VPack, RPack, DPack, HDPack, MOPack and iRAC. Key contribution of this work is a flexible EDA flow that can incorporate numerous objectives required to successfully tackle real-world circuit design on FPGA, providing device utilisation at increased design performance

Periodicities in the Daily Proton Fluxes from 2011 to 2019 Measured by the Alpha Magnetic Spectrometer on the International Space Station from 1 to 100 GV

We present the precision measurement of the daily proton fluxes in cosmic rays from May 20, 2011 to October 29, 2019 (a total of 2824 days or 114 Bartels rotations) in the rigidity interval from 1 to 100 GV based on 5.5×109 protons collected with the Alpha Magnetic Spectrometer aboard the International Space Station. The proton fluxes exhibit variations on multiple timescales. From 2014 to 2018, we observed recurrent flux variations with a period of 27 days. Shorter periods of 9 days and 13.5 days are observed in 2016. The strength of all three periodicities changes with time and rigidity. The rigidity dependence of the 27-day periodicity is different from the rigidity dependences of 9-day and 13.5-day periods. Unexpectedly, the strength of 9-day and 13.5-day periodicities increases with increasing rigidities up to ∼10 GV and ∼20 GV, respectively. Then the strength of the periodicities decreases with increasing rigidity up to 100 GV.</p

A Note Regarding the Condition Number: The Case of Spurious and Latent Multicollinearity

condition index, condition number, eigenvalues, multicollinearity (spurious, latent), singular value decomposition, total variation,

Impairing proliferation of glioblastoma multiforme with CD44+ selective conjugated polymer nanoparticles

Glioblastoma is one of the most aggressive types of cancer with success of therapy being hampered by the existence of treatment resistant populations of stem-like Tumour Initiating Cells (TICs) and poor blood–brain barrier drug penetration. Therapies capable of effectively targeting the TIC population are in high demand. Here, we synthesize spherical diketopyrrolopyrrole-based Conjugated Polymer Nanoparticles (CPNs) with an average diameter of 109 nm. CPNs were designed to include fluorescein-conjugated Hyaluronic Acid (HA), a ligand for the CD44 receptor present on one population of TICs. We demonstrate blood–brain barrier permeability of this system and concentration and cell cycle phase-dependent selective uptake of HA-CPNs in CD44 positive GBM-patient derived cultures. Interestingly, we found that uptake alone regulated the levels and signaling activity of the CD44 receptor, decreasing stemness, invasive properties and proliferation of the CD44-TIC populations in vitro and in a patient-derived xenograft zebrafish model. This work proposes a novel, CPN- based, and surface moiety-driven selective way of targeting of TIC populations in brain cancer

Nonlinear models to predict nitrogen mineralization in an Oxisol Modelos não lineares para predizer a mineralização de nitogênio num latossolo

This work was carried out to evaluate the statistical properties of eight nonlinear models used to predict nitrogen mineralization in soils of the Southern Minas Gerais State, Brazil. The parameter estimations for nonlinear models with and without structure of autoregressive errors was made by the least squares method. First, a structure of second order autoregressive errors, AR(2) was considered for all nonlinear models and then the significance of the autocorrelation parameters was verified. Among the models, the Juma presented an autocorrelation of second order, and the model of Broadbent presented one of first order. In summary, these models presented significant autocorrelation parameters. To estimate the parameters of nonlinear models, the SAS procedure MODEL was used (SAS). The comparison of the models was made by measuring the fitted parameters: adjusted R-square, mean square error and mean predicted error. The Juma model with AR(2) best fitted for nitrogen mineralization without liming, followed by Cabrera, Stanford & Smith without autoregressive errors, for both with and without soil acidity correction.<br>Este trabalho teve por objetivo avaliar o grau do ajuste de oito modelos não lineares apresentados na literatura, utilizados para descrever a mineralização do nitrogênio em latossolo do sul de Minas Gerais incubado durante 28 semanas. A estimação dos parâmetros para os modelos de regressão não linear sem e com estrutura de erros autorregressivos foi feita pelo método de mínimos quadrados. A princípio, considerou-se para todos os modelos não lineares uma estrutura de erros autorregressivos de segunda ordem, AR(2) e, em seguida, verificou-se a significância dos parâmetros de autocorrelação. Apenas o modelo de Juma apresentou autocorrelação de segunda ordem, e o modelo de Broadbent apresentou autocorrelação de primeira ordem, ou seja, apenas estes modelos apresentaram parâmetros de autocorrelação significativos. Para estimação dos parâmetros dos modelos não lineares, utilizou-se o procedimento MODEL (SAS®). A comparação dos modelos foi feito por meio de critérios da qualidade do ajuste (coeficiente de determinação ajustado, quadrado médio do resíduo e erro de predição médio). O modelo de melhor ajuste foi o de Juma com AR(2), para a mineralização de N sem calagem, seguido pelos modelos de Cabrera, Stanford & Smith sem estrutura de erros autorregressivos, tanto para os dados com, quanto para aqueles obtidos sem a correção da acidez do solo