Analytical Modeling for Translating Statistical Changes to Circuit Variability by Ultra-Deep Submicron Digital Circuit Design

This paper presents a physics-based compact gate delay model that includes all short-channel phenomena prevalent at the ultra-deep submicron technology node of 32 nm. To simplify calculations, the proposed model is connected to a compact α-power law-based (Sakurai-Newton) model. The model has been tested on a wide range of supply voltages. The model accurately predicts nominal delays and the delays under process variations. It has been shown that at lower technology nodes, the delay is more sensitive to threshold voltage variations, specifically at the sub-threshold operating region as compared with effective channel length variations above the threshold region.     

Sequence-Dependent Nanofiber Structures of Phenylalanine and Isoleucine Tripeptides

The molecular design of short peptides to achieve a tailor-made functional architecture has attracted attention during the past decade but remains challenging as a result of insufficient understanding of the relationship between peptide sequence and assembled supramolecular structures. We report a hybrid-resolution model to computationally explore the sequence–structure relationship of self-assembly for tripeptides containing only phenylalanine and isoleucine. We found that all these tripeptides have a tendency to assemble into nanofibers composed of laterally associated filaments. Molecular arrangements within the assemblies are diverse and vary depending on the sequences. This structural diversity originates from (1) distinct conformations of peptide building blocks that lead to different surface geometries of the filaments and (2) unique sidechain arrangements at the filament interfaces for each sequence. Many conformations are available for tripeptides in solution, but only an extended β-strand and another resembling a right-handed turn are observed in assemblies. It was found that the sequence dependence of these conformations and the packing of resulting filaments are determined by multiple competing noncovalent forces, with hydrophobic interactions involving Phe being particularly important. The sequence pattern for each type of assembly conformation and packing has been identified. These results highlight the importance of the interplay between conformation, molecular packing, and sequences for determining detailed nanostructures of peptides and provide a detailed insight to support a more precise design of peptide-based nanomaterials.     

Reduction of temperature influence in stress measurement using surface SH‐wave acoustoelasticity

Stress measurements are required to prevent collapse accidents of structures. Surface SH‐wave acoustoelasticity can measure principal stress difference nondestructively and easily. Currently, surface SH‐wave acoustoelasticity can measure with high precision using a T‐type surface SH‐wave sensor in ideal environments such as in the laboratory. However, in actual environments, it cannot be measured with high precision due to change in the temperature of the specimen. In this paper, temperature dependence of surface SH‐wave acoustoelastic constants was verified to investigate the influence of specimen temperature on surface SH‐wave acoustoelasticity. In addition, the measuring system of surface SH‐wave acoustoelasticity using a cross T‐type surface SH‐wave sensor to reduce the influence of specimen temperature was developed.     

Alcoholism detection based on Hu moment invariants of event-related potentials

Diagnostic efforts to identify alcoholism are frequently subjective; however, recent technological advances have allowed the automated assessment of objective signals. To improve the performance of the automated classification of patients with alcohol dependence (AD) and controls, we analyzed features of event-related potentials (ERPs) in 72 subjects with AD and 42 controls while the subjects underwent three different visual oddball tasks. In addition to such commonly used morphological features (MFs) as time-domain amplitude and latency components as well as discrete wavelet transform coefficient entropies (WEs), we analyzed Hu moment invariants (HMIs) in this study. Smaller P3 and P2 amplitudes, longer latencies, lower WE values, and lower HMI values were observed for AD subjects relative to those observed for controls. We used a Naïve Bayes algorithm to compare classification accuracies among MFs, WEs, HMIs, combinations of MFs/WEs, and combinations of all three characteristics. We found that HMIs improved the classification accuracy by 17%. Our findings demonstrated that a combined feature analysis of ERPs obtained during a visual oddball task can be a useful and discriminative tool for diagnostic purposes in alcoholism and that HMIs show promise for improving the objective diagnosis of alcoholism and similar diseases.     

Peaks Over Thresholds Modeling With Multivariate Generalized Pareto Distributions

When assessing the impact of extreme events, it is often not just a single component, but the combined behavior of several components which is important. Statistical modeling using multivariate generalized Pareto (GP) distributions constitutes the multivariate analogue of univariate peaks over thresholds modeling, which is widely used in finance and engineering. We develop general methods for construction of multivariate GP distributions and use them to create a variety of new statistical models. A censored likelihood procedure is proposed to make inference on these models, together with a threshold selection procedure, goodness-of-fit diagnostics, and a computationally tractable strategy for model selection. The models are fitted to returns of stock prices of four UK-based banks and to rainfall data in the context of landslide risk estimation. Supplementary materials and codes are available online.     

Testing the oil price efficiency using various measures of long-range dependence

In this paper, we empirically examine the application of a host of techniques employed to measure price efficiency through long-range dependence using prices of monthly oil contracts. Using a series of methods, we analyse the volatility (daily absolute returns) of WTI and Brent oil prices for nine different contracts with maturity, ranging from 1 to 9 months, during the sample period of 1990-2017. We use bootstrapping to compute the confidence interval of the parameter of long-range dependence. Our results indicate that on an average, there is no long-range dependence in the volatility of oil price contracts at least at the 10-percent level of significance. Moreover, our results of rolling estimates suggest that the normality assumption does not affect the results considerably, and the results are robust to different rolling window sizes. While the results of the efficiency index suggest that the efficiency of oil returns vary with time, the futures contracts for Brent oil are found to be less efficient compared to WTI oil. The long-term futures contracts are more efficient than short-term contracts.     

Association of size‐fractionated indoor particulate matter and black carbon with heart rate variability in healthy elderly women in Beijing

Associations between size‐fractionated indoor particulate matter (PM) and black carbon (BC) and heart rate variability (HRV) and heart rate (HR) in elderly women remain unclear. Twenty‐nine healthy elderly women were measured for 24‐hour HRV/HR indices. Real‐time size‐fractionated indoor PM and BC were monitored on the same day and on the preceding day. Mixed‐effects models were applied to investigate the associations between pollutants and HRV/HR indices. Increases in size‐fractionated indoor PM were significantly associated with declines in power in the high‐frequency band (HF), power in the low‐frequency band (LF), and standard deviation of all NN intervals (SDNN). The largest decline in HF was 19% at 5‐minute moving average for an interquartile range (IQR) increase (24 μg/m³) in PM_0.5. The results showed that smaller particles could lead to greater reductions in HRV indices. The reported associations were modified by body mass index (BMI): Declines in HF at 5‐minute average for an IQR increase in PM_0.5 were 34.5% and 1.0% for overweight (BMI ≥25 kg/m²) and normal‐weight (BMI <25 kg/m²) participants, respectively. Moreover, negative associations between BC and HRV indices were found to be significant in overweight participants. Increases in size‐fractionated indoor PM and BC were associated with compromised cardiac autonomic function in healthy elderly women, especially overweight ones.     

Assessment of chemical and microbiological parameters of indoor swimming pool atmosphere using multiple comparisons

The aim of this study was to evaluate the air quality of an indoor swimming pool, analyzing diurnal and seasonal variations in microbiological counts and chemical parameters. The results indicated that yeast and bacteria counts, as well as carbon dioxide (CO₂), nitrogen oxides (NO_x) and O₃ concentrations, showed significant diurnal difference. On the other hand, temperature, relative humidity (R.H.), yeast counts and concentrations of CO₂, particles, O₃, toluene, and benzene showed seasonal differences. In addition, the relationship between indoor and outdoor air and the degree of correlation between the different parameters have been calculated, suggesting that CO₂, fine particles and NO_x would have indoor origin due to the human activity and secondary reactions favored by the chemical and environmental conditions of the swimming pool; while O₃, benzene and toluene, would come from outside, mainly. The overall results indicated that indoor air quality (IAQ) in the swimming pool building was deficient by the high levels of CO₂ and microorganisms, low temperatures, and high R.H., because frequently the limits established by the legislation were exceeded. This fact could be due to the poor ventilation and the inadequate operation of heating, ventilation, and air‐conditioning systems.     

Assessing the cyclic-variability of spark-ignition engine running on methane-hydrogen blends with high hydrogen contents of up to 50%

The cyclic variability in a spark-ignition (SI) engine is examined fueled with methane/hydrogen blends with the use of an in-house computational fluid dynamics (CFD) code. A recent methodology is followed, which has been developed with the main aim at providing accurate predictions of the coefficient of variation (COV) of the indicated mean effective pressure (IMEP) in a fraction of time. Instead of simulating several tens of engine cycles, the methodology is based on the numerical results obtained from just 5 cycles, which are then processed for developing suitable fitted correlations of the main parameters as a function of a normalized distance. The latter expresses the distance of the spheres of the initial flame within the computational cell at the spark-plug region with the local turbulent eddy, and provides a smooth transition from the laminar burning regime to the fully turbulent one. This sub-model is included in the ignition numerical approach and is applied here in a SI engine with 3 different hydrogen contents, 10%, 30% and 50%, and three equivalence ratios, 1, 0.8 and 0.7, showing that the COV of IMEP is well predicted compared to the available measured data. Other parameters of engine cycle variations are also examined, such as the distribution of the IMEP. The variability of NO (nitric oxide) emissions is also examined, showing that for the stoichiometric cases it follows a distribution similar to a normal (Gaussian) one, while for lower ratios it is positively skewed. Overall, the methodology seems to provide reliable results for the whole range of the operating conditions examined, while the next steps of this activity will focus on similar cases for engine with variable speed and load, with the final goal to include additional mechanisms that contribute to the engine cycle variations.     

Process-induced variability modeling of subthreshold leakage power considering device stacking

The dominance of leakage currents in circuit design has been impelled by steady downscaling of MOSFET into nanometer regime, and has become a significant component of total IC power dissipation. The issue is further aggravated with the inability to gauge the tolerance of process parameters around their nominal value. Consequently, the drive to improve the static power prediction has enticed accurate and reliable modeling of leakage current, specifically for ultralow power applications. In contrast to gate- and band-to-band-tunneling leakages, subthreshold leakage exhibits high susceptibility to process variations and hence has been considered for variability modeling. Fluctuations in the device electrical and geometry parameters result in a wider distribution of subthreshold leakage current. Hence, taking into account stacking effect, an analytical variability model to estimate subthreshold leakage power in subthreshold circuits, in the presence of threshold voltage variations is proposed. Further, the impact of threshold voltage variability on subthreshold leakage power is modeled in conjunction with simultaneous variations in gate length and width. The leakage power variability is characterized by model-generated distributions obtained using Monte Carlo analysis and validated against SPICE simulations. The proposed model is about 700 computationally faster than SPICE simulations with mean error being less than 0.19%.