Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

Mental impact of Covid-19 among Spanish healthcare workers. A large longitudinal survey.

Aims Longitudinal data on the mental health impact of the coronavirus disease 2019 (Covid-19) pandemic in healthcare workers is limited. We estimated prevalence, incidence and persistence of probable mental disorders in a cohort of Spanish healthcare workers (Covid-19 waves 1 and 2) -and identified associated risk factors. Methods 8996 healthcare workers evaluated on 5 May–7 September 2020 (baseline) were invited to a second web-based survey (October–December 2020). Major depressive disorder (PHQ-8 ≥ 10), generalised anxiety disorder (GAD-7 ≥ 10), panic attacks, post-traumatic stress disorder (PCL-5 ≥ 7), and alcohol use disorder (CAGE-AID ≥ 2) were assessed. Distal (pre-pandemic) and proximal (pandemic) risk factors were included. We estimated the incidence of probable mental disorders (among those without disorders at baseline) and persistence (among those with disorders at baseline). Logistic regression of individual-level [odds ratios (OR)] and population-level (population attributable risk proportions) associations were estimated, adjusting by all distal risk factors, health care centre and time of baseline interview. Results 4809 healthcare workers participated at four months follow-up (cooperation rate = 65.7%; mean = 120 days S.D. = 22 days from baseline assessment). Follow-up prevalence of any disorder was 41.5%, (v. 45.4% at baseline, p < 0.001); incidence, 19.7% (S.E. = 1.6) and persistence, 67.7% (S.E. = 2.3). Proximal factors showing significant bivariate-adjusted associations with incidence included: work-related factors [prioritising Covid-19 patients (OR = 1.62)], stress factors [personal health-related stress (OR = 1.61)], interpersonal stress (OR = 1.53) and financial factors [significant income loss (OR = 1.37)]. Risk factors associated with persistence were largely similar. Conclusions Our study indicates that the prevalence of probable mental disorders among Spanish healthcare workers during the second wave of the Covid-19 pandemic was similarly high to that after the first wave. This was in good part due to the persistence of mental disorders detected at the baseline, but with a relevant incidence of about 1 in 5 of HCWs without mental disorders during the first wave of the Covid-19 pandemic. Health-related factors, work-related factors and interpersonal stress are important risks of persistence of mental disorders and of incidence of mental disorders. Adequately addressing these factors might have prevented a considerable amount of mental health impact of the pandemic among this vulnerable population. Addressing health-related stress, work-related factors and interpersonal stress might reduce the prevalence of these disorders substantially. Study registration number: NCT04556565post-print338 K

Four-month incidence of suicidal thoughts and behaviors among healthcare workers after the first wave of the Spain COVID-19 pandemic.

post-print499 K

Progress on Modular Unmanned Aircraft Technology

Modular unmanned aerial systems (UAS) are a new development in UAS architecture that holds promise for reusable, reconfigurable hosts for science and autonomy payloads. Modularity of airframe subcomponents lowers costs, facilitates rapid field repair, permits holistic optimization, and enables mass-customization of bespoke platforms customizing the aircraft around a given payload or payloads. Without modular UAS, sensors and instruments often must be designed to fit in a non-modifiable airframe. This talk will present how the nexus of modularity, rapid prototyping and design reuse opens up new tradeoffs, and discuss the envisioned benefits, price paid, and enhanced missions made possible by this new approach to aircraft development

Effects of Frequency Dependence of the External Quantum Efficiency of Perovskite Solar Cells

Perovskite solar cells are known to show very long response time scales, on the order of milliseconds to seconds. This generates considerable doubt over the validity of the measured external quantum efficiency (EQE) and consequently the estimation of the short-circuit current density. We observe a variation as high as 10% in the values of the EQE of perovskite solar cells for different optical chopper frequencies between 10 and 500 Hz, indicating a need to establish well-defined protocols of EQE measurement. We also corroborate these values and obtain new insights regarding the working mechanisms of perovskite solar cells from intensity-modulated photocurrent spectroscopy measurements, identifying the evolution of the EQE over a range of frequencies, displaying a singular reduction at very low frequencies. This reduction in EQE is ascribed to additional resistive contributions hindering charge extraction in the perovskite solar cell at short-circuit conditions, which are delayed because of the concomitant large low-frequency capacitance

Self-Tuning PID controller for autonomous car tracking in urban traffic

In this paper an on line self-tuned PID controller is proposed for the control of a car whose goal is to follow another one, at distances and speeds typical in urban traffic. The bestknown tuning mechanism is perhaps the MIT rule, due to its ease of implementation. However, as it is well known, this method does not guarantee the stability of the system, providing good results only for constant or slowly varying reference signals and in the absence of noise, which are unrealistic conditions. When the reference input varies with an appreciable rate or in presence of noise, eventually it could result in system instability. In this paper an alternative method is proposed that significantly improves the robustness of the system for varying inputs or in the presence of noise, as demonstrated by simulation

Solid sampling determination of ZnO nanoparticles in eyeshadows by graphite furnace atomic absorption spectrometry

The application of nanoparticles (NPs) in science and technology is a fast growing field. Therefore, reliable and straightforward analytical methods are required for their fast determination in different types of samples. In this work, a method that enables the determination of the average size of ZnO NPs, besides their concentration, discriminating them from ionic zinc, has been optimized. The method is based on solid sampling high-resolution continuum source electrothermal atomic absorption spectrometry (SS-HR-CS-GFAAS), and has been applied to determination and characterization of ZnO NPs in cosmetic samples. Recently, graphite furnace atomic absorption spectrometry has been introduced as a new tool to determine the size of nanoparticles by evaluation of the following parameters: atomization delay (tad) and atomization rate (kat). In this work both parameters (besides peak area) have been obtained from absorbance signals for a line of Zn with low sensitivity. Two multiple response surface designs have been used in order to optimize the adequate furnace program to achieve our aims. All the optimization experiments were performed using baby´s skin irritation protective cream. The optimized furnace program is shown in Table 1. Table 1. Optimized furnace program The size calibrations were performed against solid (powered) ZnO standards, from 50-nm to 500-nm sized nanoparticles. The correlation coefficients (R value) of the linear calibration were not worse than 0.9982. The optimized method was tested in other types of cosmetic samples such as eyeshadow samples with good results. The determination of the MNPs’ size was validated by transmission electron microscopy (TEM) and the Zn concentration in the solid samples was validated by atomic fluorescence spectroscopy (AFS). Acknowledgements (optional) [Garamond font, 10 points] The authors would like to thank Plan propio “Proyecto Puente” de la Universidad de Málaga for financial support of this work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Tuning de una Red de Clientes Ultralivianos para ambientes de desarrollo JAVA

Este artículo presenta el tuning de una red de clientes ultralivianos utilizados para ambientes de desarrollo JAVA, con tecnología SUN2, en el marco del proyecto COE3.\nSe investigó la performance en dicho entorno, analizando el backend de los clientes ultralivianos (SunRays), de las aplicaciones, la red, así como los parámetros que afectan su rendimiento. Se estudiaron alternativas para mejorar la performance en general y se realizaron ajustes en consecuencia que implicaron cambios en la topología de la red, en los servicios y en su configuración.\nNuestra investigación muestra como es posible lograr un desempeño aceptable a partir de este tipo de tecnología, partiendo de un uso convencional de la misma y luego extendiéndolo de manera tal de permitir el desarrollo de software por parte de los alumnos de la Facultad de Informática de la Universidad Nacional de La Plata, cumpliendo con los requerimientos que ello impone.Eje: Arquitectura, Redes y Sistemas Operativo

Rapid magnetic microextraction to preconcentrate and determinate cadmium from environmental samples

A novel and greener ultrasonically assisted/modified cloud point extraction procedure for the preconcentration and extraction of trace cadmium from environmental samples was developed. The sorbent material was fabricated by scattering of magnetic iron nanoparticles (MNPs) over graphene oxide (GO) to obtain magnetic graphene oxide (MGO). The material was suspended in the ionic liquid 1-n-butyl-3-metilimidazolium tetrafluoroborate [BMIM][BF4]. The obtained stable colloidal suspension is named ferrofluid. GO presents excellent adsorbent properties for organic species due to the presence of the electronic π system. For this reason, the organic ligand [1,5-bis-(2-dipyridyl) methylene] thiocarbonohydrazide (DPTH) was used in order to form an organic complex of Cd. Once the DPTH ligand has been added to sample, the ferrofluid was injected and finely dispersed in the sample solution in order to extract the formed chelate as shown in Fig. 1. The complete adsorption of the quelate took place within few seconds, after that the solid was separated from the solution with the aid of a strong magnet. Cadmium ions were desorbed from the material with 1 mL of 5% acid nitric solution and quantified by electrothermal atomic absorption spectrometry (ETAAS). The main parameters affecting the extraction and the furnace program were optimized using one-at-time method. The analytical performance under optimum conditions are the following: LOD: 0.005 µg/L, LOQ: 0.017 µg/L. Linear calibration: 0-0.25 µg/L and EF: 200. The reliability of the developed procedure was tested by relative standard deviation (% RSD), which was found to be < 5% (1.3%). The performance of the proposed procedure was checked by applying to certified reference material and spiking standard in real samplesUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech