Scenario generation for long term fuel prices

In this article we deal with the problem of scenario generation for fuel prices in the long term. The solution of many decision making problems in the energy sector such as the optimal mix of energy productions among different technologies, requires to model the dynamic of fuel prices and forecast their possible scenarios over time. We present two different approaches for scenario generation: a Vector autoregressive approach and a Monte Carlo approach; The first one is based on the estimate of a Vector Auto Regressive model i.e. a set of simultaneous equations. The second one is based of the assumption that the returns dynamics follow a generalised Weiner process. Using the two approaches we forecast prices’ scenarios

A procedure for the optimal management of medium-voltage AC networks with distributed generation and storage devices

A medium-voltage AC network with distributed generation and storage devices is considered for which set points are assigned in each time period of a given time horizon. A set point in a time period is defined by modules and phases of voltages in all nodes, active and reactive powers, on load tap changer and variable loads. When some parameters vary, in order to restore feasibility new set points need to be determined so as to minimize the variations with respect to the initial ones. This can be done by minimizing distributor’s redispatching costs, which are modeled by means of binary variables, while satisfying service security requirements and ensuring service quality, which are represented by nonlinear constraints, such as the nodal balance of active and reactive power and the current transits on lines and transformers for security. Storage devices are modeled by means of constraints that relate adjacent time periods. A two-step solution procedure is proposed, which is based on decoupling active and reactive variables: in the first step a MILP model determines the active power production and the use of storage devices that minimize redispatching costs over all time periods in the time horizon; in the second step, given the optimal active power production computed in the first step, reactive variables in each time period are computed by solving a nonlinear programming model

A risk averse stochastic optimization model for power generation capacity expansion

We present a mathematical model for maximizing the benefit of a price-taker power producer who has to decide the power generation capacity expansion planning in a long time horizon under uncertainty of the main parameters. These parameters are the variable production costs of the power plants already owned by the producer as well as of the candidate plants of the new technologies among which to choose; the market electricity price along the horizon, as well as the price of green certificates and CO2 emission permits; the potential market share that can be at hand for the power producer. These uncertainties are represented in a two stage scenario tree, so the model is a two stage stochastic integer optimization one, subject to technical constraints, market opportunities and budgetarial constraints, whose first stage variables represent the number of new power plants for each candidate technology to be added to the existing generation mix (whose construction has to start in) every year of the planning horizon. The second stage variables (i.e., scenario dependent) are the continuous operation variables of all power plants in the generation mix along the time horizon. We start presenting the maximization of the net present value of the expected profit over the scenarios along the time horizon (i.e., considering the so named risk neutral strategy). Alternatively, we consider different risk averse strategies (i.e., Conditional Value at Risk, Shortfall Probability, Expected Shortage and First- and Second-order Stochastic Dominance constraint integer-recourse strategies). By using a pilot case we report the main results of considering the six strategies under different hypotheses of the available budget, analysing the impact of each risk averse strategy on the expected profit. For that purpose we use a state-of-the-art MIP solver, concluding that 1. the technical advantage of replacing the risk neutral with the risk averse strategies needs a substantial increase in the computing requirements, but it strongly reduces the risk of non-wanted scenarios at a price of a relatively small reduction on the expected profit; 2. the risk averse strategies considered provide consistent solutions, since for all of them the optimal generation mix mainly consists of conventional thermal power plants, for low risk aversion, which are replaced by renewable energy sources plants, as risk aversion increases; 3. it is mandatory to replace the plain use of the solver with ad-hoc decomposition algorithms that have the additional feature of tackling cross-scenario constraints

In vivo imaging of extracellular matrix remodeling by tumor-associated fibroblasts.

Here we integrated multiphoton laser scanning microscopy and the registration of second harmonic generation images of collagen fibers to overcome difficulties in tracking stromal cell-matrix interactions for several days in live mice. We show that the matrix-modifying hormone relaxin increased tumor-associated fibroblast (TAF) interaction with collagen fibers by stimulating beta1-integrin activity, which is necessary for fiber remodeling by matrix metalloproteinases

Membrane-type 1 matrix metalloproteinase-mediated progelatinase A activation in non-tumorigenic and tumorigenic humaneratinocytes

Elevated expression of type IV collagenases (MMP-2 and MMP-9) has been strongly correlated with tumour progression and metastasis in various tumours. Here, we analysed expression and activation of these MMPs in non-tumourigenic HaCaT cells and the malignant HaCaT variant II-4 rt. In monolayer cultures, both cell types secreted latent MMP-2 (proMMP-2) in comparable amounts, while MMP-9 production was clearly higher in II-4 rt cells. Upon contact with fibrillar collagen type I the malignant II-4 rt cells, but not the HaCaT cells, gained the capability to activate proMMP-2. This process is shown to be membrane-associated and mediated by MT1-MMP. Surprisingly, all membrane preparations from either HaCaT cells or II-4 rt cells grown as monolayers, as well as within collagen gels, contained considerable amounts of active MT1-MMP. However, within collagen gels HaCaT cells showed significantly higher TIMP-2 levels compared to II-4 rt cells. This indicates that TIMP-2 might play a central role for MT1-MMP-mediated gelatinolytic activity. Indeed, collagen type I-induced MT1-MMP-mediated proMMP-2 activation by II-4 rt membranes could be completely abolished by an excess of TIMP-2. In conclusion, our data suggest that MT1-MMP-mediated proMMP-2 activation might be associated with malignant progression of epidermal tumour cells. © 2000 Cancer Research Campaig

Fibroblast heterogeneity in the cancer wound

Fibroblasts regulate the structure and function of healthy tissues, participate transiently in tissue repair after acute inflammation, and assume an aberrant stimulatory role during chronic inflammatory states including cancer. Such cancer-associated fibroblasts (CAFs) modulate the tumor microenvironment and influence the behavior of neoplastic cells in either a tumor-promoting or tumor-inhibiting manner. These pleiotropic functions highlight the inherent plasticity of fibroblasts and may provide new avenues to understand and therapeutically intervene in malignancies. We discuss the emerging themes of CAF biology in the context of tumorigenesis and therapy

Persisting inhibition biases efficient rule inference under uncertainty

IntroductionTask set inhibition supports optimal switching among tasks by actively suppressing the interference from recently executed competing task sets. It is typically studied in cued task-switching paradigms where there is no uncertainty about the task set or rule to prepare for on each trial. While inhibition has been shown to influence the speed and the accuracy of task execution, affecting task set retrieval, preparation, or implementation in conditions of task set switching, it remains uninvestigated whether it also affects rule selection under uncertainty.MethodsWe implemented an ad-hoc four-rule card sorting task and categorized the rules selected by participants after a rule shift according to the recency of their last usage. We included a measure of working memory capacity (WMC) to control for its involvement in the rule selection process.ResultsParticipants exhibited a reduced preference for recently abandoned rules than less recently abandoned ones. Furthermore, we found that such a preference was not associated with WMC.DiscussionThe results suggest that decision-making processes underlying rule inference and selection may be influenced by task-set inhibition, configuring as a conflict adjustment mechanism to the sequential history of rules application

Plasmacytoid dendritic cell activation is dependent on coordinated expression of distinct amino acid transporters

Human plasmacytoid dendritic cells (pDCs) are interleukin-3 (IL-3)-dependent cells implicated in autoimmunity, but the role of IL-3 in pDC biology is poorly understood. We found that IL-3-induced Janus kinase 2-dependent expression of SLC7A5 and SLC3A2, which comprise the large neutral amino acid transporter, was required for mammalian target of rapamycin complex 1 (mTORC1) nutrient sensor activation in response to toll-like receptor agonists. mTORC1 facilitated increased anabolic activity resulting in type I interferon, tumor necrosis factor, and chemokine production and the expression of the cystine transporter SLC7A11. Loss of function of these amino acid transporters synergistically blocked cytokine production by pDCs. Comparison of in vitro-activated pDCs with those from lupus nephritis lesions identified not only SLC7A5, SLC3A2, and SLC7A11 but also ectonucleotide pyrophosphatase-phosphodiesterase 2 (ENPP2) as components of a shared transcriptional signature, and ENPP2 inhibition also blocked cytokine production. Our data identify additional therapeutic targets for autoimmune diseases in which pDCs are implicated