Circuit convergence study using machine learning compact models

Machine learning (ML) compact device models (CM) have emerged as an alternative to physics-based CMs. ML CMs can find a mathematical model close to the device characteristics without much prior knowledge, which saves the time of model formation. Additionally, versatile capabilities such as process-awareness, model merging, and fitting new technologies, promote the usage of ML CMs. While ML CMs draw great attention in CAD, their convergence in SPICE has not been carefully studied. Here different activation functions are used to create ML CMs, and then the circuit convergence is tested. We found that inverse square root unit (ISRU) activation has the best convergence. Besides, gate-to-source and gate-to-drain capacitance is founded to benefit the convergence in transient analysis. The circuit convergence rate is 100% for ISRU, sigmoid, and tanh when the capacitor is present. On the other hand, ISRU significantly outperforms other activation functions in DC sweep, achieving 81% convergence. If quasi-static transient analysis is employed to replace DC sweep, 100% convergence is achieved by ISRU. Due to its superior convergence, ISRU is the most promising for future ML CMs in SPICE.

Merging the Social Influence Theory and the Goal-Framing Theory to Understand Consumers’ Green Purchasing Behavior: Does the Level of Sensitivity to Climate Change Really Matter?

This study explored the formation of consumers’ green purchasing behavior (GPB) and investigated the moderating effect of sensitivity to climate change (SCC) to address this current knowledge gap. An integrated model merging the Social Influence Theory and the Goal-framing Theory was developed with the Stimulus-Organism-Response (S-O-R) paradigm. An empirical study was conducted, surveying 583 respondents and analyzing the questionnaire results using structural equation modeling. The results show that media, family, and peer influence (PEI) can effectively activate the consumers’ goal frames. Hedonic and normative goals had significant positive influences on GPB, while gain goals had no significant effect. SCC was found to significantly moderate social influence on GPB through the consumers’ goal frames. This research provided strong empirical support on understanding the relationship between social influence and GPB through three goal frames. In addition, the potential differences of the GPB formation process in two subgroups (high SCC and low SCC) are also investigated. The results of this study can help green practitioners develop more effective marketing strategies and incentives targeted to consumers with varying levels of environmental consciousness or sensitivity.

COVID-19 Pandemic Development in Jordan—Short-Term and Long-Term Forecasting

In this study, we proposed three simple approaches to forecast COVID-19 reported cases in a Middle Eastern society (Jordan). The first approach was a short-term forecast (STF) model based on a linear forecast model using the previous days as a learning data-base for forecasting. The second approach was a long-term forecast (LTF) model based on a mathematical formula that best described the current pandemic situation in Jordan. Both approaches can be seen as complementary: the STF can cope with sudden daily changes in the pandemic whereas the LTF can be utilized to predict the upcoming waves’ occurrence and strength. As such, the third approach was a hybrid forecast (HF) model merging both the STF and the LTF models. The HF was shown to be an efficient forecast model with excellent accuracy. It is evident that the decision to enforce the curfew at an early stage followed by the planned lockdown has been effective in eliminating a serious wave in April 2020. Vaccination has been effective in combating COVID-19 by reducing infection rates. Based on the forecasting results, there is some possibility that Jordan may face a third wave of the pandemic during the Summer of 2021.

Using tracheid isometry to upscale water transport from pit to tree-rings

<p>Assessing the characteristic of water transport in wood is challenged by the complexity of its tissue composed by numerous different-sized and interconnected conduits. Current methods for measuring conductivity or flow resistance performed on a piece of wood usually have no direct link to the anatomy of the single conduits. Thus, despite the large application of these assessment for ecological studies, this integrated tissue-level approach hampers the possibility to extend the hydraulic assessment across time by using dated series of tree rings.</p><p>In this contribution we make use of tracheid versus pit isometry to propose a new hydraulic model merging existing morphological-based components of tracheid hydraulic to upscale water transport properties across time and environments. By using linear relations between tracheid dimeter and pit size as described in the literature, we applied our model to tracheids of increasing size to show that our assessments of the pit and tracheid resistances match with estimations performed in independent studies. We then apply the model to tracheid anatomical measurements from Larix sibirica tree-rings (from 1986 to 2015) formed under harsh conditions in southern Siberia to show the potential to reconstruct hydraulic properties across tree-rings and to quantify their intra- and inter-annual variability.</p><p>The proposed model (see 10.1093/jxb/eraa595 for more details on the performed study) not only provide means to derive realistic conduits hydraulic properties via accessible measures of cross-sectional tracheid size, but it also allows assessing how different-sized tracheid’s components contribute to the overall hydraulic properties. In particular, our up-scaled results from the study case with trees from Southern Siberia showed that the natural inter- and intra-ring anatomical variations had a substantial impact on the ring hydraulic properties and can consequently be applied to assess the impact of cell structural characteristics on the hydraulic functioning of trees.</p><p>We therefore conclude that this model, despite its early developmental stage, has the potential to provides a novel basis to investigate xylem structure-function relations across time and environmental conditions.</p>

Application of an ensemble earthquake rate model in Italy, considering seismic catalogs and fault moment release

We develop an ensemble earthquake rate model that provides spatially variable time-independent (Poisson) long-term annual occurrence rates of seismic events throughout Italy, for magnitude bin of 0.1 units from Mw ≥ 4.5 in spatial cells of 0.1° × 0.1°. We weighed seismic activity rates of smoothed seismicity and fault-based inputs to build our earthquake rupture forecast model, merging it into a single ensemble model. Both inputs adopt a tapered Gutenberg-Richter relation with a single b-value and a single corner magnitude estimated by earthquakes catalog. The spatial smoothed seismicity was obtained using the classical kernel smoothing method with the inclusion of magnitude dependent completeness periods applied to the Historical (CPTI15) and Instrumental seismic catalogs. For each seismogenic source provided by the Database of the Individual Seismogenic Sources (DISS), we computed the annual rate of the events above Mw 4.5, assuming that the seismic moments of the earthquakes generated by each fault are distributed according to the tapered Gutenberg-Richter relation with the same parameters of the smoothed seismicity models. Comparing seismic annual rates of the catalogs with those of the seismogenic sources, we realized that there is a good agreement between these rates in Central Apennines zones, whereas the seismogenic rates are higher than those of the catalogs in the north east and south of Italy. We also tested our model against the strong Italian earthquakes (Mw 5.5+), in order to check if the total number (N-test) and the spatial distribution (S-test) of these events was compatible with our model, obtaining good results, i.e. high p-values in the test. The final model will be a branch of the new Italian seismic hazard map.

A Feature-Based Evaluation of Model Merge Methods for e-Health Solutions

Model-driven engineering (MDE) paradigm considers models as central artifacts for software development lifecycle during which models evolve. Developing an e-health solution using MDE poses challenges of model version control, model differencing and model merging, which requires appropriate software configuration management (SCM). In this paper we focus on model-driven merging, which refers to combining two or more versions of a model into a single consolidated version. SCM for model-driven merging leverages evolution of valid configurations, which is a highly desired behavior. Our investigation is based on the features that are required for model-driven SCM realization. Initially, we identify these features using which the existing model-driven merging techniques are evaluated. It is observed that though various proposals are made by academia and research community, a standard model-driven SCM solution that can cater to the needs of industry is still absent. This is in contrary to the situation of traditional SCM systems where standard solutions exist. We also present the usefulness of each technique along with the tradeoffs involved. Finally, guidelines are provided to select techniques appropriate for given circumstances.

A Feature-Based Evaluation of Model Merge Methods for e-Health Solutions

Model-driven engineering (MDE) paradigm considers models as central artifacts for software development lifecycle during which models evolve. Developing an e-health solution using MDE poses challenges of model version control, model differencing and model merging, which requires appropriate software configuration management (SCM). In this paper we focus on model-driven merging, which refers to combining two or more versions of a model into a single consolidated version. SCM for model-driven merging leverages evolution of valid configurations, which is a highly desired behavior. Our investigation is based on the features that are required for model-driven SCM realization. Initially, we identify these features using which the existing model-driven merging techniques are evaluated. It is observed that though various proposals are made by academia and research community, a standard model-driven SCM solution that can cater to the needs of industry is still absent. This is in contrary to the situation of traditional SCM systems where standard solutions exist. We also present the usefulness of each technique along with the tradeoffs involved. Finally, guidelines are provided to select techniques appropriate for given circumstances.