On the blockage problem and the non-analyticity of the current for the parallel TASEP on a ring

The Totally Asymmetric Simple Exclusion Process (TASEP) is an important example of a particle system driven by an irreversible Markov chain. In this paper we give a simple yet rigorous derivation of the chain stationary measure in the case of parallel updating rule. In this parallel framework we then consider the blockage problem (aka slow bond problem). We find the exact expression of the current for an arbitrary blockage intensity $\varepsilon$ in the case of the so-called rule-184 cellular automaton, i.e. a parallel TASEP where at each step all particles free-to-move are actually moved. Finally, we investigate through numerical experiments the conjecture that for parallel updates other than rule-184 the current may be non-analytic in the blockage intensity around the value $\varepsilon = 0$

On-Line Instruction-checking in Pipelined Microprocessors

Microprocessors performances have increased by more than five orders of magnitude in the last three decades. As technology scales down, these components become inherently unreliable posing major design and test challenges. This paper proposes an instruction-checking architecture to detect erroneous instruction executions caused by both permanent and transient errors in the internal logic of a microprocessor. Monitoring the correct activation sequence of a set of predefined microprocessor control/status signals allow distinguishing between correctly and not correctly executed instruction

Fatty acid methyl and ethyl esters as well as wax esters for evaluating the quality of olive oils

Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)A promising correlation between chemical analysis and sensorial evaluation was confirmed: extra virgin olive oils with low contents of methyl and ethyl esters of fatty acids as well as straight chain wax esters were sensorially evaluated as being of high quality, whereas some with high contents were even devaluated as not being of extra virgin quality. Methanol and ethanol formed during fermentation in degrading olives are esterified, largely by transesterification with fatty acids from the triglycerides, and in this way transferred into the pressed oil. The presence of high contents of methyl and ethyl esters in degrading olives was confirmed. Wax esters from the skin of the olives are extracted at low yields, whereby the yield increases when the olives are soft and possibly degrading. High wax ester contents may, therefore, stand for mild oils, but also for deficient oils

The decline in muscle strength and muscle quality in relation to metabolic derangements in adult women with obesity

Background & aims: The metabolic and functional characteristics related to sarcopenic obesity have not been thoroughly explored in the earlier stages of the aging process. The aim of the present study was to examine the phenotype of sarcopenic obesity, in terms of lean body mass, muscle strength and quality, in adult women with and without the Metabolic Syndrome (MetS), and its relationship with the features of myosteatosis. Methods: Study participants were enrolled at the Sapienza University, Rome, Italy. Body composition was assessed by DXA. The Handgrip strength test (HGST) was performed. HGST was normalized to arm lean mass to indicate muscle quality; intermuscular adipose tissue (IMAT) and intramyocellular lipid content (IMCL) were measured by magnetic resonance imaging and spectroscopy, as indicators of myosteatosis. Different indices of sarcopenia were calculated, based on appendicular lean mass (ALM, kg) divided by height squared, or weight. The NCEP-ATPIII criteria were used to diagnose the MetS. HOMA-IR was calculated. The physical activity level (PAL) was assessed through the IPAQ questionnaire. Results: 54 women (age: 48 ± 14 years, BMI: 37.9 ± 5.4 kg/m 2 ) were included. 54% had the MetS (metabolically unhealthy, MUO). HGST/arm lean mass was lower in MUO women than women without the MetS (6.3 ± 1.8 vs. 7.8 ± 1.6, p = 0.03). No differences emerged in terms of absolute ALM (kg) or other indices of sarcopenia (ALM/h 2 or ALM/weight) between metabolically healthy (MHO) vs. MUO women (p > 0.05). Muscle quality was negatively associated with HOMA-IR (p = 0.02), after adjustment for age, body fat, hs-CRP levels, and PAL. IMAT, but not IMCL, was significantly higher in obese women with the MetS compared to women without the MetS (p > 0.05). No association emerged between HGST/arm lean mass and IMAT or IMCL when HOMA-IR was included in the models. Conclusion: Insulin resistance, and not sarcopenia or myosteatosis per se, was associated with muscle weakness, resulting in the phenotype of “dynapenic obesity” in middle-aged women with the metabolic syndrome

Application of regression and remote sensing technology for determining sufficiency of contact-based sensors in long-term SHM of civil structures

For structural health monitoring (SHM) of civil structures, one needs to install sufficient sensors for measuring structural responses and influential environmental/operational (E/O) factors. Due to various reasons such as total budgets, weather conditions, structure locations, and monitoring target and duration, it may not be feasible to install all potential sensors. In order to devise and implement an affordable SHM program on large-scale civil structures, this paper proposes a new methodology for verifying the sufficiency of contact-based E/O sensors installed in long-span bridges by benefiting machine learning and spaceborne remote sensing. The main premise of the proposed methodology lies in the fact that structural responses obtained from some products of remote sensing allow civil engineers to investigate the sufficiency of contact sensors and also analyze the impacts of measured and unmeasured E/O factors. Using structural displacement responses obtained from remote sensing and limited measured E/O data from contact-based sensors, a regression model developed from a supervised artificial neural network is designed to evaluate the sufficiency of contact E/O sensors using the R-squared metric under three scenarios. Real-world long-span bridges are considered to testify the proposed methodology using displacement responses and air temperature data. Results demonstrate that the methodology presents an effective and practical strategy for affordable SHM programs

A Comparative Study on Structural Displacement Prediction by Kernelized Regressors under Limited Training Data

An accurate prediction of the structural response in the presence of limited training data still represents a big challenge if machine learning-based approaches are adopted. This paper investigates and compares two state-of-the-art kernelized supervised regressors to predict the structural response of a long-span bridge retrieved from spaceborne remote sensing technology. The kernelized super- vised procedure is either based on a support vector regression or on a Gaussian process regression. A small set of displacement time histories and corresponding air temperature data are fed into the regressors to predict the actual structural response. Results demonstrate that the proposed regression techniques are reliable, even when only 30% of the training data are used at the learning stage

An innovative predictive method based on supervised teacher-student learning for forecasting limited structural responses of long-span bridges from satellite images

Forecasting the responses of large-scale civil structures offers an alternative to field measurement. Recently, spaceborne remote sensing technology has been increasingly adopted to monitor complicated and large structures. This approach involves extracting structural displacements from synthetic aperture radar images. To overcome some important restrictions associated with these images, the best solution is to exploit machine learning-aided prediction of displacement responses. For this purpose, it is necessary to measure key external factors, particularly environmental and operational conditions. In some cases, installing sensors for these factors may not be tractable, in which case some unmeasured and unknown conditions, which can affect structural responses, are not incorporated into the prediction process. To avoid poor performances and inaccurate forecasting outputs, this paper proposes a predictive solution using the idea of supervised teacher-student learning. This method consists of two parts of an elaborate regression model via a long-short-term-memory neural network acting as a teacher and a simple model through a single-hidden-layer feedforward neural network behaving as a student. The effectiveness and success of the proposed method are benchmarked by limited information of a long-span bridge. Results show that this method can adequately forecast limited bridge responses in the presence of the impacts of unmeasured predictors

A systematic correlation analysis for regression model selection: Application to bridge response prediction using contact and remote sensor systems

Correlation analysis is a crucial step before undertaking any regression modeling for data prediction because it helps reveal the relationships between predictors and responses, especially in terms of linearity and nonlinearity. This analysis is often essential for selecting the most appropriate regression model. A major challenge is that linear correlation measures are suitable only for linear relationships, and there are limited measures for assessing nonlinearity. Moreover, a significant issue arises from the influence of unknown predictor data, which can lead to unrealistic and inaccurate outputs from both linear and nonlinear correlation measures. To address these challenges, this paper proposes a systematic correlation analysis that first assesses the impact of unknown predictors and then selects the most suitable regressor for modeling and forecasting. The proposed method utilizes a linear measure known as canonical correlation analysis and a nonlinear measure called maximal information criterion. Based on the correlation values obtained from these measures, one can suggest low, moderate, and high correlation levels. The effectiveness of the proposed method is demonstrated using measured data related to long-span bridge structures. This data includes temperature records, serving as a single predictor, and bridge displacement responses obtained from synthetic aperture radar images as products of remote sensing technology. Results confirm that the proposed method is highly effective and applicable for selecting the best regression model for prediction

A Parsimonious Yet Robust Regression Model for Predicting Limited Structural Responses of Remote Sensing

Small data analytics, at the opposite extreme of big data analytics, represent a critical limitation in structural health monitoring based on spaceborne remote sensing technology. Besides the engineering challenge, small data is typically a demanding issue in machine learning applications related to the prediction of system evolutions. To address this challenge, this article proposes a parsimonious yet robust predictive model obtained as a combination of a regression artificial neural network and of a Bayesian hyperparameter optimization. The final aim of the offered strategy consists of the prediction of structural responses extracted from synthetic aperture radar images in remote sensing. Results regarding a long-span steel arch bridge confirm that, although simple, the proposed method can effectively predict the structural response in terms of displacement data with a noteworthy overall performance

Tuning the magnetic coupling of a molecular spin interface via electron doping

Mastering the magnetic response of molecular spin interfaces by tuning the occupancy of the molecular orbitals, which carry the spin magnetic moment, can be accomplished by electron doping. We propose a viable route to control the magnetization direction and magnitude of a molecular spin network, in a graphene-mediated architecture, achieved via alkali doping of manganese phthalocyanine (MnPc) molecules assembled on cobalt intercalated under a graphene membrane. The antiparallel magnetic alignment of the MnPc molecules with the underlying Co layer can be switched to a ferromagnetic state by electron doping. Multiplet calculations unveil an enhanced magnetic state of the Mn centers with a 3/2 to 5/2 spin transition induced by alkali doping, as confirmed by the steepening of the hysteresis loops, with higher saturation magnetization values. This new molecular spin configuration can be aligned by an external field, almost independently from the hard-magnet substrate effectively behaving as a free magnetic layer