A microscopy technique based on bio-impedance sensors

It is proposed a microscopy for cell culture applications based on impedance sensors. The imagined signals are measured with the Electrical Cell-Substrate Spectroscopy (ECIS) technique, by identifying the cell area. The proposed microscopy allows real-time monitoring inside the incubator, reducing the contamination risk by human manipulation. It requires specific circuits for impedance measurements, a two-dimensional sensor array (pixels), and employing electrical models to decode efficiently the measured signals. Analogue Hardware Description Language (AHDL) circuits for cell-microelectrode enables the use of geometrical and technological data into the system design flow. A study case with 8x8 sensor array is reported, illustrating the evolution and power of the proposed image acquisition.Junta de Andalucía P0-TIC-538

Plan de adecuación del alumbrado público del centro histórico de la ciudad de Ibiza

Este proyecto consiste en la elaboración de un plan director para el alumbrado exterior del casco histórico de la ciudad de Ibiza. En él, se parte de una situación inicial obsoleta de las instalaciones actuales, y se marcan las directrices a seguir para que el rendimiento de las mismas sea óptimo, generando la mínima cantidad de residuos y contaminación al medio. Una vez estudiada la situación actual, fijamos un resultado con unos valores deseados, y con estos, diseñamos la instalación para que estos se cumplan. También se describe las actuaciones futuras para impedir que esta instalación vuelva a quedar obsoleta en poco tiempo, estudiando su vida útil y mantenimiento. Con una mejor iluminación se consigue remarcar la importancia de esta zona, nombrada patrimonio de la humanidad por la UNESCO, obteniendo así el resultado exigido por el ayuntamiento de la ciudad

Data-Analytics Modeling of Electrical Impedance Measurements for Cell Culture Monitoring

High-throughput data analysis challenges in laboratory automation and lab-on-a-chip devices’ applications are continuously increasing. In cell culture monitoring, specifically, the electrical cell-substrate impedance sensing technique (ECIS), has been extensively used for a wide variety of applications. One of the main drawbacks of ECIS is the need for implementing complex electrical models to decode the electrical performance of the full system composed by the electrodes, medium, and cells. In this work we present a new approach for the analysis of data and the prediction of a specific biological parameter, the fill-factor of a cell culture, based on a polynomial regression, data-analytic model. The method was successfully applied to a specific ECIS circuit and two different cell cultures, N2A (a mouse neuroblastoma cell line) and myoblasts. The data-analytic modeling approach can be used in the decoding of electrical impedance measurements of different cell lines, provided a representative volume of data from the cell culture growth is available, sorting out the difficulties traditionally found in the implementation of electrical models. This can be of particular importance for the design of control algorithms for cell cultures in tissue engineering protocols, and labs-on-a-chip and wearable devices applicationsEspaña, Ministerio de Ciencia e Innovación y Universidades project RTI2018-093512-B-C2

Cell Biometrics Based on Bio-Impedance Measurements

Open Access.This work is in part supported by the funded Project: Auto-calibración y auto-test en circuitos analógicos, mixtos y de radio frecuencia: Andalusian Government project P0-TIC- 5386, co-financed with the FEDER program.Peer Reviewe

Bioimpedance real-time charazterization of neointimal tissue inside stents

It is hereby presented a new approach to monitor restenosis in arteries fitted with a stent during an angioplasty. The growth of neointimal tissue is followed up by measuring its bioimpedance with Electrical Impedance Spectroscopy (EIS). Besides, a mathematical model is derived to analytically describe the neointima’s histological composition from its bioimpedance. The model is validated by finite-element analysis (FEA) with COMSOL Multiphysics®. Satisfactory correlation between the analytical model and the FEA simulation is achieved for most of the characterization range, detecting some deviations introduced by the thin "double layer" that separates the neointima and the blood. It is shown how to apply conformal transformations to obtain bioimpedance models for stack-layered tissues over coplanar electrodes. Particularly, this is applied to characterize the neointima in real-time. This technique is either suitable as a main mechanism of restenosis follow-up or it can be combined with proposed blood-pressure-measuring intelligent stents to auto-calibrate the sensibility loss caused by the adherence of the tissue on the micro-electro-mechanical sensors (MEMS).Ministerio de Economía, Industria y Competitividad (Spain): projects TEC2013-46242-C3-1-PMinisterio de Economía, Industria y Competitividad (Spain): projects TEC2013-46242-C3-2-

Use of CALPUFF to predict airborne Mn levels at schools in an urban area impacted by a nearby manganese alloy plant

Children are susceptible to the health effects derived from elevated manganese (Mn) environmental exposure; residents living in urban areas where ferromanganese alloy plants are located are usually exposed to high Mn levels. In this work, a dispersion model developed by the USEPA, CALPUFF, has been used to estimate the airborne Mn levels near educational centers located in Santander bay, Northern Spain, an urban area where high Mn levels have been measured in the last decade. The CALPUFF model was validated in a previous work from a multi-site one-year observation dataset. Air manganese levels in 96 primary, secondary and high schools located in Santander bay were estimated using the CALPUFF model for two months corresponding to warm and cold periods using real meteorological data and Mn emission rates corresponding to different emission scenarios. Results show that when the emission scenario that best represented the observations dataset is used, the air Mn levels exceed the WHO guideline (i.e. 150?ng?Mn/m3) in 24% and 11% of the studied schools in the cold and warm periods respectively. These exceedances depend on the distance from the FeMn alloy plant and the direction of the prevailing winds. Additional emission scenarios based on the implementation of preventive and corrective measures are simulated and analysed in terms of the number of exceedances of the WHO guideline. The age range of children has been also considered in the analysis.This work was supported by the Spanish Ministry of Economy and Competitiveness (MINECO) through the CTM2013-43904R Project. This funding source was not involved in the study design; data collection, analysis, or interpretation; the writing of the article; or the decision to submit for publication

Large Language Models Still Can't Plan (A Benchmark for LLMs on Planning and Reasoning about Change)

Recent advances in large language models (LLMs) have transformed the field of natural language processing (NLP). From GPT-3 to PaLM, the state-of-the-art performance on natural language tasks is being pushed forward with every new large language model. Along with natural language abilities, there has been a significant interest in understanding whether such models exhibit reasoning capabilities with the use of reasoning benchmarks. However, even though results are seemingly positive, these benchmarks prove to be simplistic in nature and the performance of LLMs on these benchmarks cannot be used as evidence to support, many a times outlandish, claims being made about LLMs' reasoning capabilities. Further, these only represent a very limited set of simple reasoning tasks and we need to look at more sophisticated reasoning problems if we are to measure the true limits of such LLM-based systems. Motivated by this, we propose an extensible assessment framework to test the capabilities of LLMs on reasoning about actions and change, a central aspect of human intelligence. We provide multiple test cases that are more involved than any of the previously established benchmarks and each test case evaluates a different aspect of reasoning about actions and change. Results on GPT-3 (davinci), Instruct-GPT3 (text-davinci-002) and BLOOM (176B), showcase subpar performance on such reasoning tasks.Comment: An updated version of this work is here: arXiv:2302.06706 Accepted at Foundation Models for Decision Making Workshop at Neural Information Processing Systems, 202