Techno-enviro-economic analysis of grid-connected solar powered floating PV water pumping system for farmland applications: A numerical design model.

To meet the required load of a farm in the rural area in Mafraq, Jordan, the complete floating photovoltaic (FPV) water pumping sizing, modelling, and optimization of an on-grid PV system with comprehensive capacity, energy output cost, and emission estimations are outlined in this work. The novelty of this study lies in its comprehensive approach that integrates technical, environmental, and economic factors into a unified framework for designing a PV water pumping system, particularly in scenarios where grid supply is feasible or economically viable. A proposal has been made to install PV panels over the water lake to improve the overall system efficiency and to give an aesthetic appearance. The proposed system is composed of a 165 kW PV array and three 55 kW inverters, which cost 54696.92 JD as the initial cost, CO2 emission reduction of more than 5000 tons and produce electricity at 0.028 JD/kWh. The results indicated that the FPV option demonstrates an about 5 % increase in efficiency compared to the other two scenarios. Also, the FPV option has higher costs due to a 25 % increase in system cost but results in lower CO2 emissions compared to the other two options. Top of Form As shown from the results, the two sizing methods for solar water pumping systems, the equations-based method, and the PVsyst simulation tool give the same results. By following this methodology, one can assess the load, size the system, simulate its operation, and analyse the expected performance. Furthermore, the findings of this study could be valuable in designing a grid-connected FPV water pumping system.

Optimizing in silico drug discovery: simulation of connected differential expression signatures and applications to benchmarking.

BACKGROUND: We present a novel simulation method for generating connected differential expression signatures. Traditional methods have struggled with the lack of reliable benchmarking data and biases in drug-disease pair labeling, limiting the rigorous benchmarking of connectivity-based approaches. OBJECTIVE: Our aim is to develop a simulation method based on a statistical framework that allows for adjustable levels of parametrization, especially the connectivity, to generate a pair of interconnected differential signatures. This could help to address the issue of benchmarking data availability for connectivity-based drug repurposing approaches. METHODS: We first detailed the simulation process and how it reflected real biological variability and the interconnectedness of gene expression signatures. Then, we generated several datasets to enable the evaluation of different existing algorithms that compare differential expression signatures, providing insights into their performance and limitations. RESULTS: Our findings demonstrate the ability of our simulation to produce realistic data, as evidenced by correlation analyses and the log2 fold-change distribution of deregulated genes. Benchmarking reveals that methods like extreme cosine similarity and Pearson correlation outperform others in identifying connected signatures. CONCLUSION: Overall, our method provides a reliable tool for simulating differential expression signatures. The data simulated by our tool encompass a wide spectrum of possibilities to challenge and evaluate existing methods to estimate connectivity scores. This may represent a critical gap in connectivity-based drug repurposing research because reliable benchmarking data are essential for assessing and advancing in the development of new algorithms. The simulation tool is available as a R package (General Public License (GPL) license) at https://github.com/cgonzalez-gomez/cosimu.

BiSim Tool: a binding simulation tool to aid and simplify ligand-binding assay design and development.

Ligand-binding assays (LBAs) rely on the reversible, noncovalent binding between the analyte of interest and the assay reagents, and understanding their dynamic equilibrium is key to building robust LBA methods. Although the dynamic interplay of free and bound fractions can be calculated using mathematical models, these are not routinely applied. This approach is costly in terms of both assay development time and reagents, and can result in an under-exploration of the possible parameter combinations. Therefore, we have created a user-friendly simulation tool to facilitate LBA development (the BiSim Tool). We describe the models driving the mathematical simulations and the main features of our software solution by means of case studies, illustrating the tool's value in drug development. To support drug development for all patients worldwide, the BiSim Tool is now available as an open-source code project and as a free web-based tool at https://proteinbindingsimulation.shinyapps.io/BiSim-ProteinBindingSimulation [1].

[Box: see text].

[The SCATTER project: computer-based simulation in the strategic transfer of intensive care patients]. / Das SCATTER-Projekt: Computerbasierte Simulation zur Unterstützung bei der strategischen Verlegung von Intensivpatienten.

BACKGROUND: The need for a concept for the nationwide strategic transfer of critical care patients in Germany was highlighted during the COVID-19 (coronavirus disease 2019) pandemic. Despite the cloverleaf concept developed specifically for this purpose, the transfer of large numbers of critical care patients represents a major challenge. With the help of a computer simulation, the SCATTER research project uses a fictitious example to test, develop, and recommend transfer strategies. METHOD: The simulation was programmed after collecting procedural and structural data on critical care transports within Germany. The simulation allows altering various parameters and testing different transfer scenarios. In a fictitious scenario, nationwide transfers starting from Schleswig-Holstein were simulated and evaluated using predetermined criteria. RESULTS: In the case of ground-based transfers, it became apparent that, depending on the selected target region, not all patients could be transferred due to the limited range of ground-based vehicles. Although a higher number of patients can be transferred by air, this is associated with additional gurney changes and potential risk to the patient. A distance-dependent transport strategy led to the identical results as purely air-bound transport, since air-bound transport was always chosen due to the long distances. DISCUSSION: The simulation can be used to develop recommendations and to draw important conclusions from different transfer strategies.

A coupled experimental and statistical approach for an assessment of SARS-CoV-2 infection risk at indoor event locations.

Indoor event locations are particularly affected by the SARS-CoV-2 pandemic. At large venues, only incomplete risk assessments exist, whereby no suitable measures can be derived. In this study, a physical and data-driven statistical model for a comprehensive infection risk assessment has been developed. At venues displacement ventilation concepts are often implemented. Here simplified theoretical assumptions fail for the prediction of relevant airflows for airborne transmission processes. Thus, with locally resolving trace gas measurements infection risks are computed more detailed. Coupled with epidemiological data such as incidences, vaccination rates, test sensitivities, and audience characteristics such as masks and age distribution, predictions of new infections (mean), situational R-values (mean), and individual risks on- and off-seat can be achieved for the first time. Using the Stuttgart State Opera as an example, the functioning of the model and its plausibility are tested and a sensitivity analysis is performed with regard to masks and tests. Besides a reference scenario on 2022-11-29, a maximum safety scenario with an obligation of FFP2 masks and rapid antigen tests as well as a minimum safety scenario without masks and tests are investigated. For these scenarios the new infections (mean) are 10.6, 0.25 and 13.0, respectively. The situational R-values (mean) - number of new infections caused by a single infectious person in a certain situation - are 2.75, 0.32 and 3.39, respectively. Besides these results a clustered consideration divided by age, masks and whether infections occur on-seat or off-seat are presented. In conclusion this provides an instrument that can enable policymakers and operators to take appropriate measures to control pandemics despite ongoing mass gathering events.

PCE simulation toolkit: a platform for perceptual crossing experiment research.

The Perceptual Crossing Experiment (PCE) has been the object of study for over a decade, and aims at explaining how we perceive, interact with, and understand each other in real-time. In addition to human participant studies, a number of computational models have investigated how virtual agents can solve this task. However, the set of implementation choices that has been explored to date is rather limited, and the large number of variables that can be used make it very difficult to replicate the results. The main objective of this paper is to describe the PCE Simulation Toolkit we have developed and published as an open-source repository on GitHub. We hope that this effort will help make future PCE simulation results reproducible and advance research in the understanding of possible behaviors in this experimental paradigm. At the end of this paper, we present two case studies of evolved agents that demonstrate how parameter choices affect the simulations.

SPECTRa: An Online Tool for Simulating Prehospital Patient Care.

OBJECTIVES: To (1) develop a simulation software environment to conduct prehospital research during the COVID-19 pandemic on paramedics' teamwork and use of mobile computing devices, and (2) establish its feasibility for use as a research and training tool. BACKGROUND: Simulation-based research and training for prehospital environments has typically used live simulation, with highly realistic equipment and technology-enhanced manikins. However, such simulations are expensive, difficult to replicate, and require facilitators and participants to be at the same location. Although virtual simulation tools exist for prehospital care, it is unclear how best to use them for research and training. METHODS: We present SPECTRa-Simulated Prehospital Emergency Care for Team Research-an online simulated prehospital environment that lets participants care concurrently for single or multiple patients remotely. Patient scenarios are designed using Laerdal's SimDesigner. SPECTRa records data about scenario states and participants' virtual interaction with the simulated patients. SPECTRa's supporting environment records participants' verbal communication and their visual and physical interactions with their interface and devices using Zoom conferencing and audiovisual recording. We discuss a pilot research implementation to assess SPECTRa's feasibility. RESULTS: SPECTRa allows researchers to systematically test small-team interaction in single- or multipatient care scenarios and assess the impact of mobile devices on participants' assessment and care of patients. SPECTRa also supports pedagogical features that could allow prehospital educators to provide individual trainees or teams with online simulation training and evaluation. CONCLUSIONS: SPECTRa, an online tool for simulating prehospital patient care, shows potential for remote healthcare research and training.

Techno-economic process modelling and Monte Carlo simulation data of uncertainty quantification in field-grown plant-based manufacturing.

This data article is related to the research article, "M.J. McNulty, K. Kelada, D. Paul, S. Nandi, and K.A. McDonald, Introducing uncertainty quantification to techno-economic models of manufacturing field-grown plant-made products, Food Bioprod. Process. 128 (2021) 153-165." The raw and analyzed data presented are related to generation, analysis, and optimization of ultra-large-scale field-grown plant-based manufacturing of high-value recombinant protein under uncertainty. The data have been acquired using deterministic techno-economic process model simulation in SuperPro Designer integrated with stochastic Monte Carlo-based simulation in Microsoft Excel using the Crystal Ball plug-in. The purpose of the article is to make techno-economic and associated uncertainty data available to be leveraged and adapted for other research purposes.

A Comprehensive Open-Source Simulation Framework for LiFi Communication.

Light Fidelity (LiFi) is a new candidate for wireless networking that utilizes the visible light spectrum and exploits the existing lighting infrastructure in the form of light-emitting diodes (LEDs). It provides point-to-point and point-to-multipoint communication on a bidirectional channel at very high data rates. However, the LiFi has small coverage, and its optical gain is closely related to the receiver's directionality vis-à-vis the transmitter, therefore it can experience frequent service outages. To provide reliable coverage, the LiFi is integrated with other networking technologies such as wireless fidelity (WiFi) thus forming a hybrid system. The hybrid LiFi/WiFi system faces many challenges including but not limited to seamless integration with the WiFi, support for mobility, handover management, resource sharing, and load balancing. The existing literature has addressed one or the other aspect of the issues facing LiFi systems. There are limited free source tools available to holistically address these challenges in a scalable manner. To this end, we have developed an open-source simulation framework based on the network simulator 3 (ns-3), which realizes critical aspects of the LiFi wireless network. Our developed ns-3 LiFi framework provides a fully functional AP equipped with the physical layer and medium access control (MAC), a mobility model for the user device, and integration between LiFi and WiFi with a handover facility. Simulation results are produced to demonstrate the mobility and handover capabilities, and the performance gains from the LiFi-WiFi hybrid system in terms of packet delay, throughput, packet drop ratio (PDR), and fairness between users. The source code of the framework is made available for the use of the research community.

A flexible ChIP-sequencing simulation toolkit.

BACKGROUND: A major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq. RESULTS: We present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips . CONCLUSIONS: ChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

Application of an economic calculator to determine the cost of porcine reproductive and respiratory syndrome at farm-level in 21 pig herds in Germany.

BACKGROUND: Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) continues to be a major economic issue for the swine industry worldwide, not only due to acute outbreaks but also endemic infections. PRRS disease severity and consequently financial losses can vary greatly between endemically infected farms and estimation of damage is challenging. This study aimed to assess the economic effect of PRRS in a systematic way at individual farm-level for endemically infected herds, using a PRRS cost simulation tool. In total 21 German sow herds with endemic PRRSV infection were investigated. Data on health and production performance, farm management and environment to be fed into the calculator was collected on each farm, and blood samples taken to confirm the PRRSV status. RESULTS: All study farms experienced a significant loss attributable to PRRS. The median farm budget across all farms was - 31 € per sow and year, compared to a median simulated farm budget of 248 € if these farms had been PRRSV negative. The median total loss attributable to PRRS was 74,181 € per farm per year, corresponding to a median total loss per sow and year of 255 €. The impact of PRRS on farm profits was - 19.1% on average and - 41% in the worst case. CONCLUSIONS: The calculated losses give a good hint of the economic damage due to PRRS for the pig industry. Even in endemically infected farms, farmers face a non-negligible damage and profit from a concerted PRRS control. The calculator has proven itself in the field to render a valid estimation of losses due to PRRS in endemically infected farms.

Quantifying the impact of vegetation-based metrics on species persistence when choosing offsets for habitat destruction.

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area × habitat suitability; and condition × habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss.

Cuantificación del Impacto de las Medidas Basadas en la Vegetación sobre la Persistencia de las Especies cuando se Eligen las Compensaciones por la Destrucción del Hábitat Resumen Con frecuencia se requiere por ley que los desarrolladores compensen los impactos ambientales por medio de acciones de conservación. La mayoría de las políticas de compensación especifican medidas para calcular los requerimientos de cada compensación, generalmente mediante la evaluación de las condiciones de la vegetación. A pesar del uso extenso de estas medidas basadas en la vegetación, existe muy poca evidencia que respalde su efectividad para asegurar la persistencia de la biodiversidad. Comparamos los impactos a largo plazo de las compensaciones de biodiversidad basadas solamente en el área; solamente en la condición de la vegetación; la idoneidad del área x hábitat; y la idoneidad condición x hábitat en las simulaciones de desarrollo y restauración para la Región Hunter de Nueva Gales del Sur, Australia. Simulamos el desarrollo y las compensaciones subsecuentes mediante la restauración dentro de un paisaje virtual, conectando las simulaciones con los modelos de viabilidad poblacional para tres especies. Las ganancias del hábitat no aseguraron la persistencia de las especies. No hubo pérdida neta cuando el desempeño de las compensaciones se evaluó en relación con la persistencia. El mantenimiento de la persistencia ocurrió más seguido cuando se evitaron los impactos, lo que proporciona un mayor respaldo para mejorar la aplicación de la fase de prevención de la jerarquía de mitigación. Cuando el desarrollo afectó a las áreas con una alta calidad de hábitat para las especies, no se pudo garantizar la persistencia. Por lo tanto, las especies deben considerarse más explícitamente en las compensaciones, en lugar de sólo considerar a la vegetación o al hábitat. Las declinaciones causadas por la falta de consideración directa de las dinámicas poblacionales de las especies y de la conectividad opacaron los beneficios producidos por las grandes áreas de hábitat de alta calidad. Nuestro marco de trabajo para el modelado demostró que los beneficios producidos por las compensaciones son específicos para cada especie y que las medidas simples basadas en la vegetación pueden brindar impresiones mal informadas sobre qué tanto influyen las compensaciones de biodiversidad en la no pérdida neta.

Three days of training with a low-fidelity arthroscopy triangulation simulator box improves task performance in a virtual reality high-fidelity virtual knee arthroscopy simulator.

PURPOSE: The aim of this work is to evaluate the effectiveness of training with the low-fidelity ArthroBox® regarding performance of different basic arthroscopy tasks using a validated high-fidelity virtual reality simulator of the knee. METHODS: Nineteen volunteers (14 females and 5 males) without any previous experience in arthroscopy were randomly assigned either to the ArthroBox® training group (n =10) or the non-training group (n =9). The training group underwent a supervised ArthroBox ® training consisting of a daily 60-min session for three consecutive days. Both groups completed the basic and the final assessment using a validated virtual reality-based passive haptic knee arthroscopy simulator (ArthroS, VirtaMed™). The following three factors were measured in different exercises (explained in "Materials and methods"): amount of time to finish the task, length of camera and scope path within the joint. Furthermore, the volunteers' demographics (age, sex, dexterity, video game experience, sport activities and profession) was assessed but showed no differences between the groups. RESULTS: There were no significant differences between the training and non-training group regarding the above-mentioned demographic factors. However, the training group showed significant improvement from baseline to follow-up in most activities (e.g. task performance time in seconds, intra-articular camera and grasp distance in centimetres; see Table 1) in comparison to the non-training group. CONCLUSIONS: The results from this study demonstrate that training for three consecutive days using a portable and versatile low-fidelity simulator significantly improves arthroscopy performance when using a validated high-fidelity virtual knee simulator. Arthroscopic triangulation training outside the operating theatre with a portable, low-cost simulator has proven to be a valuable educational tool to improve the arthroscopic skills of trainee surgeons. LEVEL OF EVIDENCE: Diagnostic study, Level II.

On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures.

NarrowBand IoT (NB-IoT) is emerging as a promising communication technology offering a reliable wireless connection to a large number of devices employed in pervasive monitoring scenarios, such as Smart City, Precision Agriculture, and Industry 4.0. Since most of the NB-IoT transmissions occur in the uplink, the random access channel (that is the primary interface between devices and the base station) may usually become the main bottleneck of the entire system. For this reason, analytical models and simulation tools able to investigate its behavior in different scenarios are of the utmost importance for driving current and future research activities. Unfortunately, scientific literature partially addresses the current open issues by means of simplified and, in many cases, not standard-compliant approaches. To provide a significant step forward in this direction, the contribution of this paper is three-folded. First, it presents a flexible, open-source, and 3GPP-compliant implementation of the NB-IoT random access procedure. Second, it formulates an analytical model capturing both collision and success probabilities associated with the aforementioned procedure. Third, it presents the cross-validation of both the analytical model and the simulation tool, by taking into account reference applications scenarios of sensor networks enabling periodic reporting in monitoring infrastructures. Obtained results prove the remarkable accuracy, demonstrating a well-calibrated instrument, which will be also useful for future research activities.

A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification.

BACKGROUND: An integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, only a few simulation tools that simulate multiple types of omics data and model their relationships with disease status are available, and these tools have their limitations in simulating the multi-omics data. RESULTS: We developed the multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., single-nucleotide polymorphisms [SNPs] and copy number variations), epigenomics (i.e., bisulphite sequencing), transcriptomics (i.e., RNA sequencing), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as methylation quantitative trait loci (SNPs influencing methylation), expression quantitative trait loci (SNPs influencing gene expression), and expression quantitative trait methylations (methylations influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and runtime. We also used OmicsSIMLA to simulate a multi-omics dataset with a scale similar to an ovarian cancer multi-omics dataset. The neural network-based multi-omics analysis method ATHENA was applied to both the real and simulated data and the results were compared. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and ATHENA showed the highest prediction accuracy when the effects of multi-omics features (e.g., SNPs, copy number variations, and gene expression levels) on the disease were strong. Furthermore, similar results can be obtained from ATHENA when analyzing the simulated and real ovarian multi-omics data. CONCLUSIONS: OmicsSIMLA will be useful to evaluate the performace of different multi-omics analysis methods. Sample sizes and power can also be calculated by OmicsSIMLA when planning a new multi-omics disease study.

A discrete event simulation tool to support and predict hospital and clinic staffing.

We demonstrate how to develop a simulation tool to help healthcare managers and administrators predict and plan for staffing needs in a hospital neonatal intensive care unit using administrative data. We developed a discrete event simulation model of nursing staff needed in a neonatal intensive care unit and then validated the model against historical data. The process flow was translated into a discrete event simulation model. Results demonstrated that the model can be used to give a respectable estimate of annual admissions, transfers, and deaths based upon two different staffing levels. The discrete event simulation tool model can provide healthcare managers and administrators with (1) a valid method of modeling patient mix, patient acuity, staffing needs, and costs in the present state and (2) a forecast of how changes in a unit's staffing, referral patterns, or patient mix would affect a unit in a future state.

Economic impact of a modification of the treatment trajectories of patients with end-stage renal disease.

BACKGROUND: This study assumed that some patients currently treated at hospital-based haemodialysis centres can be treated with another renal replacement therapy (RRT) modality without any increase in mortality risk and sought to evaluate the monthly cost impact of replacing hospital-based haemodialysis, for which fees are highest, by different proportions of other modalities. METHODS: We used a deterministic model tool to predict the outcomes and trajectories of hypothetical cohorts of incident adult end-stage renal disease (ESRD) patients for 15 years of RRT (10 different modalities). Our estimates were based on data from 67 258 patients in the REIN registry and 65 662 patients in the French national health insurance information system. Patients were categorized into six subcohorts, stratified for age and diabetes at ESRD onset, and analyses run for each subcohort. We simulated new strategies of care by changing any or all of the following: initial distributions in treatment modalities, transition rates and some costs. Strategies were classified according to their monthly per-patient cost compared to current practices (cost-minimization analysis). RESULTS: Simulations of the status quo for the next 15 years predicted a per-patient monthly cost of €2684 for a patient aged 18-45 years without diabetes and €7361 for one older than 70 years with diabetes. All of the strategies we analysed had monthly per-patient costs lower than the status quo, except for daily home HD. None impaired expected survival. Savings varied by strategy. CONCLUSIONS: Alternative strategies may well be less expensive than current practices. The decision to implement new strategies must nonetheless consider the number of patients concerned, feasibility of renal care reorganization, and investment costs. It must also take into account the role of patients' choice and the availability of professionals.

Simulation tool for assessing the release and environmental distribution of nanomaterials.

An integrated simulation tool was developed for assessing the potential release and environmental distribution of nanomaterials (RedNano) based on a life cycle assessment approach and multimedia compartmental modeling coupled with mechanistic intermedia transport processes. The RedNano simulation tool and its web-based software implementation enables rapid "what-if?" scenario analysis, in order to assess the response of an environmental system to various release scenarios of engineered nanomaterials (ENMs). It also allows for the investigation of the impact of geographical and meteorological parameters on ENM distribution in the environment, comparison of the impact of ENM production and potential releases on different regions, and estimation of source release rates based on monitored ENM concentrations. Moreover, the RedNano simulation tool is suitable for research, academic, and regulatory purposes. Specifically, it has been used in environmental multimedia impact assessment courses at both the undergraduate and graduate levels. The RedNano simulation tool can also serve as a decision support tool to rapidly and critically assess the potential environmental implications of ENMs and thus ensure that nanotechnology is developed in a productive and environmentally responsible manner.

Data Exporter: A complementary tool to export data simulation from neuron / Data Exporter: Una herramienta complementaria para exportar datos obtenidos de simulaciones con neuron

Objectives: To develop a computational tool for NEURON simulation environment, user friendly, to store the results generated during simulation and to make subsequent analysis with other tools such as Matlab and IgorPRo Materials and Methods: Data Exporter was implemented in the programming language hoc. This algorithm is divided in 13 sections or blocks. The first section is necessary to edit in a new simulation. These new configuration determine the geometry, biophysic properties the neurons to simulate and path to save data. Results: To check the efficiency of the algorithm, we simulated the propagation of action potential in TRN(thalamic reticular nucleus) neuron in different large of simulation. We determine that the time of simulation is linear respect to time of simulation. Conclusions: Data Exporter makes easier to start to neural simulation in NEURON reducing the steps of programming to geometry and biophysical properties of the neuron and to allow save data to next steps of analysis.

Objetivos: Desarrollar una herramienta computacional en ambiente lenguaje de programación hoc de NEURON y de fácil uso, que permita el rápido almacenamiento de los resultados obtenidos para su posterior análisis en otros software tales como Matlab or IgorPro. Materiales y métodos: Para el desarrollo de Data Exporter se escribió un algoritmo en lenguaje de programación hoc de NEURON. El algoritmo, escrito en un único archivo de texto, esta dividido en 13 bloques, de los cuales solo el primero debe ser modificado para adaptarlo a una geometría y biofísica neuronal particular y para determinar la ruta de almacenamiento de los datos. Resultados: Se desarrollo un software que simula la propagación de potenciales de acción a través de geometrías neuronales complejas. El uso de esta herramienta permite el almacenamiento de los resultados obtenidos, como potenciales y corrientes de membrana en diferentes puntos de toda la neurona, sin incremento significativo en el tiempo para el desarrollo de los procesos. Conclusiones: Data Exporter es un software que le da mayor flexibilidad a NEURON facilitando el acceso a nuevos neurocientíficos, los cuales pueden usarlo con solo conocer los códigos necesarios para el desarrollo de los archivos relacionados con las propiedades geométricas y biofísicas neuronales.