A matter of life and death : substance-caused and substance-related fatalities in Ibiza in 2015

This is the pre-peer reviewed version of the following article: Rita Santacroce, et al, 'A matter of life and death: substance-caused and substance-related fatalities in Ibiza in 2015', Human Psychopharmacology: Clinical & Experimental, Vol. 32 (3), e2592, May 2017, which has been published in final form at DOI: 10.1002/hup.2592. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. The Accepted Manuscript is under embargo. Embargo end date: 18 May 2018.Objectives and methods: In the framework of the EU-funded project “EU-Madness,” we collected and analysed all the reports of fatalities directly or indirectly related to substances of abuse registered in Ibiza from January to September 2015, in order to analyse the characteristics of the sample, the identified substances, and the nature of deaths associated with their consumption. Results: A significant increase of substance-caused deaths with respect to the previous 4 years has been highlighted. Most of the subjects were young males, more than half were not Spanish. Males prevailed also amongst the victims of traffic accidents and suicides. The most commonly involved substances included MDMA, alcohol, cocaine, THC, opiates and prescription drugs. Conclusions: Although the use of NPS is rapidly increasing in Europe, according to the results from our sample, alcohol and well-known stimulants (MDMA and cocaine) are still the substances of abuse mainly involved in the cases of substance-caused and substance-related fatalities. The significant increase of fatalities in Ibiza in the last 5 years is an issue that must be taken into account and should be better investigated, as other theories besides NPS-increased diffusion should be proposed, and therefore, targeted prevention strategies should be designed.Peer reviewe

An agent-based modeling approach for real-world economic systems: Example and calibration with a Social Accounting Matrix of Spain

The global economy is one of today's major challenges, with increasing relevance in recent decades. A frequent observation by policy makers is the lack of tools that help at least to understand, if not predict, economic crises. Currently, macroeconomic modeling is dominated by Dynamic Stochastic General Equilibrium (DSGE) models. The limitations of DSGE in coping with the complexity of today's global economy are often recognized and are the subject of intense research to find possible solutions. As an alternative or complement to DSGE, the last two decades have seen the rise of agent-based models (ABM). An attractive feature of ABM is that it can model very complex systems because it is a bottom-up approach that can describe the specific behavior of heterogeneous agents. The main obstacle, however, is the large number of parameters that need to be known or calibrated. To enable the use of ABM with data from the real-world economy, this paper describes an agent-based macroeconomic modeling approach that can read a Social Accounting Matrix (SAM) and deploy from scratch an economic system (labor, activity sectors operating as firms, a central bank, the government, external sectors...) whose structure and activity produce a SAM with values very close to those of the actual SAM snapshot. This approach paves the way for unleashing the expected high performance of ABM models to deal with the complexities of current global macroeconomics, including other layers of interest like ecology, epidemiology, or social networks among others

Kinetic Monte Carlo simulation for semiconductor processing: A review

The Kinetic Monte Carlo (KMC) algorithm is a particularly apt technique to simulate the complex processing of semiconductor devices. In this review, some of the main processes used for semiconductor industries to manufacture transistor from semiconductor materials, namely implantation, annealing and epitaxial growth are reviewed. The evolution of defects created during such processing for the particular, and well known case, of silicon, is commented. Kinetic Monte Carlo modeling is introduced and contrasted briefly with a continuum approach. Particular models of different phenomena, using both object and lattice KMC, are shown: point defect migration, cluster formation, dopant activation and deactivation, damage accumulation, amorphization, recrystallization, solid phase and selective epitaxial regrowth, etc. In this work we describe the models, its implementation into KMC, and we show several comparisons with significant experimental data validating the KMC approach and showing its capabilities. How extra capabilities can be included by extending the models to current problems in the semiconductor industry is also commented, in particular the use of SiGe alloys and the introduction of stress dependencies

Comprehensive modeling of ion-implant amorphization in silicon

10.1016/j.mseb.2005.08.026Materials Science and Engineering B: Solid-State Materials for Advanced Technology124-125SUPPL.383-385MSBT

Rational selection of co-catalysts for the deaminative hydrogenation of amides

The catalytic hydrogenation of amides is an atom economical method to synthesize amines. Previously, it was serendipitously discovered that the combination of a secondary amide co-catalyst with (iPrPNP)Fe(H)(CO) (iPrPNP = N[CH2CH2(PiPr2)]2−), results in a highly active base metal system for deaminative amide hydrogenation. Here, we use DFT to develop an improved co-catalyst for amide hydrogenation. Initially, we computationally evaluated the ability of a series of co-catalysts to accelerate the turnover-limiting proton transfer during C–N bond cleavage and poison the (iPrPNP)Fe(H)(CO) catalyst through a side reaction. TBD (triazabicyclodecene) was identified as the leading co-catalyst. It was experimentally confirmed that when TBD is combined with (iPrPNP)Fe(H)(CO) a remarkably active system for amide hydrogenation is generated. TBD also enhances the activity of other catalysts for amide hydrogenation and our results provide guidelines for the rational design of future co-catalysts

Ion-implant simulations: The effect of defect spatial correlation on damage accumulation

10.1016/j.mseb.2005.08.100Materials Science and Engineering B: Solid-State Materials for Advanced Technology124-125SUPPL.386-388MSBT

Bimodal distribution of damage morphology generated by ion implantation

10.1016/j.mseb.2005.08.099Materials Science and Engineering B: Solid-State Materials for Advanced Technology124-125SUPPL.389-391MSBT

Modeling and simulation of the influence of SOI structure on damage evolution and ultra-shallow junction formed by Ge preamorphization implants and solid phase epitaxial regrowth

Materials Research Society Symposium Proceedings91299-104MRSP

Modeling and simulation of the influence of SOI structure on damage evolution and ultra-shallow junction formed by Ge preamorphization implants and solid phase epitaxial regrowth

Preamorphization implant (PAT) prior to dopant implantation, followed by solid phase epitaxial regrowth (SPER) is of great interest due to its ability to form highly-activated ultrashallow junctions. Coupled with growing interest in the use of silicon-on-insulator (SOI) wafers, modeling and simulating the influence of SOI structure on damage evolution and ultra-shallow junction formation is required. In this work, we use a kinetic Monte Carlo (kMC) simulator to model the different mechanisms involved in the process of ultra-shallow junction formation, including amorphization, recrystallization, defect interaction and evolution, as well as dopant-defect interaction in both bulk silicon and SOI. Simulation results of dopant concentration profiles and dopant activation are in good agreement with experimental data and can provide important insight for optimizing the process in bulk silicon and SOI. © 2006 Materials Research Society