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Given the low carbon transformation of our energy systems, demand response has the potential to increase the adaptability of electricity demand to a volatile electricity supply. In this article, we investigate the demand response potential for the case where substituting technologies are available in an energy-intensive industrial production process. The available production technologies may not only differ in their technical characteristics, but also vary by the necessary input materials. We present a generic linear optimization model for such a production process and apply it to a real-world example in the paper industry. The results show that the question of which substituting technologies are used in an optimal production schedule to which degree, is highly influenced by the combination of current input parameters such as prices. In direct consequence, the corresponding demand response potential is not a fixed number. From an operational perspective, this input dependency implies that the price relation of raw input materials used in substituting technologies can be a crucial driving force for the ability and willingness of industrial enterprises to provide demand response. In addition, from a strategic perspective, long-run investments in demand response potentials may rely on expected price development of major input factors.

MiRNAs are discussed as diagnostic and therapeutic molecules. However, effective miRNA drug treatments with miRNAs are, so far, hampered by the complexity of the miRNA networks. To identify potential miRNA drugs in colorectal cancer, we profiled miRNA and mRNA expression in matching normal, tumor and metastasis tissues of eight patients by Illumina sequencing. We validated six miRNAs in a large tissue screen containing 16 additional tumor entities and identified miRNA-1, miRNA-129, miRNA-497 and miRNA-215 as constantly de-regulated within the majority of cancers. Of these, we investigated miRNA-1 as representative in a systems-biology simulation of cellular cancer models implemented in PyBioS and assessed the effects of depletion as well as overexpression in terms of miRNA-1 as a potential treatment option. In this system, miRNA-1 treatment reverted the disease phenotype with different effectiveness among the patients. Scoring the gene expression changes obtained through mRNA-Seq from the same patients we show that the combination of deep sequencing and systems biological modeling can help to identify patient-specific responses to miRNA treatments. We present this data as guideline for future pre-clinical assessments of new and personalized therapeutic options.

A method for modeling the effects of random process variation through measured transistor current is introduced. The methodology culminates by modeling random current variation at a given operating point above threshold as a zero-mean Additive White Gaussian Noise (AWGN) current source with a standard deviation dependent on nominal operating current and design variables, the transistor sizes, W and L, and the transistor operating points, [V.sub.gs] and [V.sub.ds.] The model has a simple posynomial form and is accurate compared to measured data within an RMS error of 5.4% for narrow-, wide-, short-, and long-channel transistors. The model's simplified form bridges the gap between existing statistical methods and circuit design. The efficacy of the model in the circuit design space will also be presented. In the analog domain, we will show that the contributions of this model can be used as a replacement for Monte Carlo methods for calculating circuit voltage and current variances. In the digital space, we will investigate the calculation of timing delay distributions. Results calculated via an alpha-power ([[alpha].sub.p]) law model for average current show that scaling the supply voltage by [gamma] results in an approximately 1/[[gamma].sup.[alpha]p+1.1] scaling in the path-delay standard deviation for a 65-nm process. Index Terms--Variability, mismatch, semiconductor device modeling, timing, scaling, variability-aware design.

Statistical circuit analysis and optimization are critical for robust nanoscale CMOS design. To accurately perform such analysis, primary process variation sources need to be identified and modeled for further circuit simulation. In this work, a rigorous method to extract process variations from in situ IV measurements is present. Transistor statistics are collected from a test chip fabricated in a 65-nm process. Gate length (L), threshold voltage ([V.sub.th]) and mobility ([mu]) are recognized as the leading variation sources, due to the tremendous process challenges in lithography, channel doping, and the stress engineering. To decompose these variations, three critical IV points from the cut-off and linear regions are identified. The extracted L, [V.sub.th] and [mu] variations are normally distributed, with negligible spatial correlation. By including extracted variations in the nominal model file, accurate prediction of the change of drive current in all operation regions and process corners is achieved. The new extraction method guarantees excellent model matching with hardware for further statistical circuit analysis. Index Terms--Compact modeling, process variation, spatial correlation, threshold voltage variation.