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.     

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%.     

Self-adaptation of mobile systems driven by the Common Variability Language

The execution context in which pervasive systems or mobile computing run changes continually. Hence, applications for these systems require support for self-adaptation to the continual context changes. Most of the approaches for self-adaptive systems implement a reconfiguration service that receives as input the list of all possible configurations and the plans to switch between them. In this paper we present an alternative approach for the automatic generation of application configurations and the reconfiguration plans at runtime. With our approach, the generated configurations are optimal as regards different criteria, such as functionality or resource consumption (e.g. battery or memory). This is achieved by: (1) modelling architectural variability at design-time using the Common Variability Language (CVL), and (2) using a genetic algorithm that finds nearly-optimal configurations at run-time using the information provided by the variability model. We also specify a case study and we use it to evaluate our approach, showing that it is efficient and suitable for devices with scarce resources.     

Genetic diversity by AFLP analysis within Jatropha curcas L. populations in the State of São Paulo,Brazil

The genetic diversity of 5 populations of Jatropha curcas L. (Euphorbiaceae), a species cultivated in tropical countries and used in biodiesel production, was analyzed by amplified fragment length polymorphism (AFLP). Plants from distinct populations found in the State of São Paulo, Brazil, had their genetic diversity characterized by using three primer combinations. The number of polymorphic bands obtained reached 184 and the base pair length of bands ranged from 75 to 350, with average PIC values of 0.418. Accessions from the Aguas de Santa Barbara population presented the highest percentage of polymorphic loci (89.76%), followed by the populations of Catanduva (84.24%), Jales (80.98%), Jurucê (78.80%) and Taquaritinga (70.65%). Plants collected from the populations of Taquaritinga and Jales presented the smallest and highest genetic diversities, respectively, measured by using both Nei's genetic variability index (h = 0.2242 and 0.2973) and Shannon's index (I = 0.3359 and 0.4319). The results obtained indicated that 73.1% of genetic variability corresponds to intrapopulational variation and 26.8% to variation among populations. The clustering dendrogram using Jacquard showed four clusters. Three clusters with low genetic diversity grouped most of individuals collected in distinct regions (63.3% JU, 47.0% JA and 82.5% TA) and the fourth with the higher genetic diversity was composed with basically individuals collected in CA and AS, but it also had samples collected in JU, JA and TA, where it is possible to select individuals to be included in breeding programs.     

Influence of physical and biological variability and solution methods in fruit and vegetable quality nondestructive inspection by using imaging and near-infrared spectroscopy techniques: A review

Over the past decades, imaging and spectroscopy techniques have been rapidly developing and widely applied in nondestructive fruit and vegetable quality assessment. The physical properties (including size, shape, color, position, and temperature) and biological properties (including cultivar, season, maturity level and geographical origin) of fruits and vegetables vary from one to another. A great variety of physical and biological properties of agricultural products influence the optical propagation properties and interaction behaviors with incident light, thus decreasing the quality inspection accuracy. Many attempts have been made in image correction and spectral compensation methods to improve the inspection accuracy. This paper gives a detailed summary about influence of physical and biological variability, as well as the correction and compensation methods for eliminating or reducing the effects in fruit and vegetable quality nondestructive inspection by using imaging and spectroscopy techniques. The advantages and disadvantages of the solution methods are discussed and summarized. Additionally, the future challenges and potential trends are also reported.     

Estimates of pollutant temporal and spatial variability in commercial buildings from the joint urban 2003 field experiments

In this paper, we report on the indoor concentrations from a suite of full-scale outdoor tracer-gas point releases conducted in the downtown area of Oklahoma City in 2003. A point release experiment consisted of releases of sulfur hexafluoride (SF₆) in multiple buildings and from different outdoor locations. From the measurements, we are able to estimate the concentration variations indoors for a building operating under “typical” operating conditions. The mean indoor spatial coefficients of variation are 30% to 45% from a daytime outdoor release are around 80% during an outdoor evening release. Having estimates of the spatial coefficient of variation provides stakeholders, including first responders, with the likely range of concentrations in the building when little is known about the building characteristics and operating behavior, such as developing urban-scale hazard and consequence analyses. We show differences in indoor measurements at different distances to the release points, floors of the building, and heating, ventilation, and air conditioning system (HVAC) operation. We also show estimates at different time resolutions. The statistics show that in the studied medium to large commercial buildings, spatial differences would result in peak indoor concentrations in certain parts of the buildings that may be substantially higher than the building average. To our knowledge, very few tracer gas measurements have been conducted in buildings of this scope, particularly with measurements on multiple floors and within a floor. The resulting estimates of spatial variability provide a unique opportunity for hazard assessment, and comparison to multi-zone models.     

An augmented subcutaneous type 1 diabetic patient modelling and design of adaptive glucose control

In the present work, an augmented subcutaneous (SC) model of type 1 diabetic patients (T1DP) is proposed first by estimating the model parameters with the aid of nonlinear least square method using the physiological data. Next, a nonlinear adaptive controller is proposed to tackle two important issues of intra-patient variability (IPV) and uncertain meal disturbance (MD). The proposed patient model agrees quite well with the responses of one of the most popular existing nonlinear model used in the research of artificial pancreas. Further, the developed adaptive control is shown to be capable of providing desired glycemic control without feed-forward action for meal compensation or safety algorithms to avoid hypoglycemia. Due to the simple structure and capability of handling intra-patient variability of the adaptive controller, it can find immediate applicability in the development of the in-silico artificial pancreas.