Differences between Human and Mouse IgM Fc Receptor (FcµR)

Both non-immune “natural” and antigen-induced “immune” IgM are important for protection against pathogens and for regulation of immune responses to self-antigens. Since the bona fide IgM Fc receptor (FcµR) was identified in humans by a functional cloning strategy in 2009, the roles of FcµR in these IgM effector functions have begun to be explored. In this short essay, we describe the differences between human and mouse FcµRs in terms of their identification processes, cellular distributions and ligand binding activities with emphasis on our recent findings from the mutational analysis of human FcµR. We have identified at least three sites of human FcµR, i.e., Asn66 in the CDR2, Lys79 to Arg83 in the DE loop and Asn109 in the CDR3, responsible for its constitutive IgM-ligand binding. Results of computational structural modeling analysis are consistent with these mutational data and a model of the ligand binding, Ig-like domain of human FcµR is proposed. Serendipitously, substitution of Glu41 and Met42 in the CDR1 of human FcµR with mouse equivalents Gln and Leu, either single or more prominently in combination, enhances both the receptor expression and IgM binding. These findings would help in the future development of preventive and therapeutic interventions targeting FcµR.     

Cycle-accurate macro-models for RT-level power analysis

In this paper, we present a methodology and techniques for generating cycle-accurate macro-models for register transfer (RT)-level power analysis. The proposed macro-model predicts not only the cycle-by-cycle power consumption of a module, but also the moving average of power consumption and the power profile of the module over time. We propose an exact power function and approximation steps to generate our power macro-model. First-order temporal correlations and spatial correlations of up to order three are considered in order to improve the estimation accuracy. A variable reduction algorithm is designed to eliminate the “insignificant” variables using a statistical sensitivity test. Population stratification is employed to increase the model fidelity. Experimental results show our macro-models with 15 or fewer variables, exhibit <5% error for average power and <20% errors for cycle-by-cycle power estimation compared to circuit simulation results using Powermill     

Model-order reduction using variational balanced truncation with spectral shaping

This paper presents a spectrally weighted balanced truncation (SBT) technique for tightly coupled integrated circuit interconnects, when the interconnect circuit parameters change as a result of statistical variations in the manufacturing process. The salient features of this algorithm are the inclusion of the parameter variation in the RLCK interconnect, the guaranteed passivity of the reduced transfer function, and the availability of provable spectrally weighted error bounds for the reduced-order system. This paper shows that the variational balanced truncation technique produces reduced systems that accurately follow the time- and frequency-domain responses of the original system when variations in the circuit parameters are taken into consideration. Experimental results show that the new variational SBT attains, in average, 30% more accuracy than the variational Krylov-subspace-based model-order reduction techniques.     

Fast Interconnect and Gate Timing Analysis for Performance Optimization

Static timing analysis is a key step in the physical design optimization of VLSI designs. The lumped capacitance model for gate delay and the Elmore model for wire delay have been shown to be inadequate for wire-dominated designs. Using the effective capacitance model for the gate delay calculation and model-order reduction techniques for wire delay calculation is prohibitively expensive. In this paper, we present sufficiently accurate and highly efficient filtering algorithms for interconnect timing as well as gate timing analysis. The key idea is to partition the circuit into low and high complexity circuits, whereby low complexity circuits are handled with efficient algorithms such as total capacitance algorithm for gate delay and the Elmore metric for wire delay and high complexity circuits are handled with sign-off algorithms. Experimental results on microprocessor designs show accuracies that are quite comparable with sign-off delay calculators with more than of 65% reduction in the computation times     

Energy minimization using multiple supply voltages

We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both nonpipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level (selected from a fixed and known number of voltage levels) to each operation in a data flow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using three supply voltage levels on a number of standard benchmarks, an average energy saving of 40.19% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using a single supply voltage level     

Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models

Nowadays, the use of artificial intelligence is extended to various scientific and engineering fields including water management and planning. This study investigates the performance of dynamic artificial neural network (ANN) models in prediction of water inflow into the Sefidruod dam reservoir (Iran). For this purpose, first, the discharge time series of tributaries of the Sefidruod dam were analyzed for trends for a 47 year time period (1967 to 2014) using parametric regression and non-parametric Mann–Kendall tests considering independence, short-term, and long-term persistence assumptions. Also, the homogeneity of the data was investigated using three statistical tests including Cumulative Deviations, Worsley's Likelihood Ratio, and Bayesian inference. Then, the inflow discharges into the reservoir of Sefidruod dam from GhezelOzan and Shahroud tributaries were simulated using dynamic Nonlinear Auto-Regressive (NAR) and Nonlinear Auto-Regressive with exogenous input (NARX) models. Further, water inflow values of both rivers were predicted for the next 5 years in future using dynamic NAR and NARX models. Finally, the simulated results were tested for trends. Obtained results showed a significant decreasing trend in both rivers. Results also showed a continuous downward trend for the following 5-year period predicted by NAR and NARX models. In addition, it was found that the results obtained by the NARX model were less accurate compared to those by the NAR model.

     

New permeation models for nanocomposite polymeric membranes filled with nonporous particles

In this article, permeation models for nanocomposite polymeric membranes (NCPMs) filled with nonporous particles are discussed and two new models for prediction of effective permeability of NCPMs are proposed. To derive these models, the presence of interfacial layer at the surface of the nanofiller particles as well as the impact of two important phenomena namely creating void volumes and increasing free volume at the interface layer are taken into account. The capability of the models for prediction of reliable results is checked against available experimental data on permeability of NCPMs and is also compared with other presented models for such membranes. The new proposed models show profound superiority over the well known models such as “Bruggeman model in limit” which offers fairly good prediction for NCPMs.     

Improving the efficiency of Monte Carlo power estimation [VLSI]

In this paper, we propose two efficient statistical sampling techniques for estimating the total power consumption of large hierarchical circuits. We first show that, due to the characteristic of the sampling efficiency in Monte Carlo simulation, granularity of samples is an important issue in achieving high overall efficiency. The proposed techniques perform sampling both temporally (across different clock cycles) and spatially (across different modules) so that a smaller sample granularity can be achieved while maintaining the normality of samples. The first proposed technique, which is referred to as the module-based approach, samples each module independently when forming a power sample. The second technique, which is referred to as the cluster-based approach, lumps the modules of a hierarchical circuit into a number of clusters on which sampling is then performed. Both techniques adapt stratification to further improve the efficiency. Experimental results show that these techniques provide a reduction of 23× in simulation run time compared to existing Monte-Carlo simulation techniques