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Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

Abstract For Solid Oxide Fuel Cells (SOFCs) to become an economically attractive energy conversion technology, suitable materials and structures which enable operation at lower temperatures, while retaining high cell performance, must be developed. Recently, the perovskite-type La 0.6 Ca 0.4 Fe 0.8 Ni 0.2 O 3 oxide has shown potential as an intermediate temperature SOFC cathode. An equivalent circuit describing the cathode polarization resistances was constructed from analyzing impedance spectra recorded at different temperatures in oxygen. A competitive electrode polarization resistance is reported for this oxygen electrode using a Ce 0.8 Gd 0.2 O 1.9 electrolyte, determined by impedance spectroscopy studies of symmetrical cells sintered at 800 °C and 1000 °C. Scanning electron microscopy (SEM) studies of the symmetrical cells revealed the absence of any reaction layer between cathode and electrolyte, and a porous electrode microstructure even when sintered at a temperature of only 800 °C. The performance of this cathode shows favorable oxygen reduction reaction (ORR) properties potentially making it an excellent choice for IT-SOFC application.

Diesel engine exhausts from a common rail 3.0 L F1C diesel engine were analyzed at two different load conditions of the WLTC testing cycle downstream of both the diesel particulate filter (DPF) and selective catalytic reactor (SCR) to verify their effect on the characteristics of carbon particulate matter. An array of chemical, physical and spectroscopic techniques (gas chromatography coupled with mass spectrometry (GC-MS), mobility analyzer, UV-Visible absorption and fluorescence spectroscopy) was applied for characterizing polycyclic aromatic hydrocarbons (PAH), heavy aromatic compounds and soot, constituting the particulate matter (PM) sampled from the exhaust. The engine was operated in half load (HL) (188 Nm, representing the more common condition for engine in urban traffic) and full load (FL) (452 Nm, representing the best performance of the engine operation) conditions, at the same engine speed (2000 rpm). Soot formation was enhanced in HL condition, with respect to FL, but, just because of the much lower soot amount, the after-treatment systems in this last condition resulted to be less efficient in the soot abatement. Indeed, the abatement through DPF was about 40% lower in the FL condition with respect to HL condition, and any significant further concentration decrease was found after SCR, in both conditions. By contrast, PAH concentration after DPF abatement was found to be higher in the HL with respect to FL condition. A further PAH concentration decrease of about 30% was found after the SCR in the HL condition whereas in FL the reduction was only about 5-6%. Also the heavy aromatic compounds having molecular weight above the GC-MS detection limit (300 u), were mitigated by SCR. Therefore, SCR did not cause a further soot reduction, whereas it was effective in largely reducing PAH and heavy aromatics emissions, especially in the lower temperature condition featuring the half-load condition, when combustion efficiency is worse. Moreover, SCR system reduced the emission of small particles probably due to an enhanced agglomeration of particles, with beneficial effect on the harmfulness to human health.

Spanish "Ministerio de Educacion, Cultura y Deporte'' within the "FPU'' and the “Doctorados Industriales” programs [grants FPU15/04375, DI-17-09596]

Abstract Electrolytic pickling of hot-rolled 304 stainless steel in Na 2 SO 4 electrolyte was investigated with electrochemical, weight loss and SEM–EDX measurements. Pickling took place upon both active and transpassive polarisations. Mechanism and kinetics of pickling during active and transpassive polarisations were unravelled. Metallic phase in oxide layer was dissolved during active polarisation while the oxide layer was significantly undercut. Chromium oxide in the oxide layer was oxidised to soluble anions during transpassive polarisation, while iron oxide and metallic phase could either be remained or removed. Pickling due to undercutting with active polarisation was highly pronounced.

Arsenic contamination in drinking water resources is of major concern in the Ganga delta plains of West Bengal in India and Bangladesh. Here, several laboratory and field studies on arsenic removal from drinking water resources were conducted in the past and the application of strong-oxidant-induced co-precipitation of arsenic on iron hydroxides is still considered as the most promising mechanism. This paper suggests an autonomous, solar driven arsenic removal setting and presents the findings of a long term field test conducted in West Bengal. The system applies an inline-electrolytic cell for in situ chlorine production using the natural chloride content of the water and by that substituting the external dosing of strong oxidants. Co-precipitation of As(V) occurs on freshly formed iron hydroxide, which is removed by Manganese Greensand Plus® filtration. The test was conducted for ten months under changing source water conditions considering arsenic (187 ± 45 µg/L), iron (5.5 ± 0.8 mg/L), manganese (1.5 ± 0.4 mg/L), phosphate (2.4 ± 1.3 mg/L) and ammonium (1.4 ± 0.5 mg/L) concentrations. Depending on the system setting removal rates of 94% for arsenic (10 ± 4 µg/L), >99% for iron (0.03 ± 0.03 mg/L), 96% for manganese (0.06 ± 0.05 mg/L), 72% for phosphate (0.7 ± 0.3 mg/L) and 84% for ammonium (0.18 ± 0.12 mg/L) were achieved—without the addition of any chemicals/adsorbents. Loading densities of arsenic on iron hydroxides averaged to 31 µgAs/mgFe. As the test was performed under field conditions and the here proposed removal mechanisms work fully autonomously, it poses a technically feasible treatment alternative, especially for rural areas.

The Covid-19 crisis has had a major impact on electricity markets, affecting power plant input and output prices. In this paper Spanish electricity and natural gas prices and international carbon prices are used to calculate the variable margin of natural gas combined cycles (NGCC), i.e. the Clean Spark Spread (CSS). The stochastic behavior of the CSS is modeled using an Ornstein-Uhlenbeck (OU) process because of its properties. The expected first semester 2020 CSS results based on the fitted model with daily 2016-2019 data, taking the end of 2019 as a starting point, are compared with the actual figures for the same period. In the first half of 2020 electricity and natural gas prices are significantly lower than expected at the end of 2019, but carbon allowance prices have decreased less in percentage terms. The monthly CSS values in the first half of 2020 are significantly lower than expected for March-May 2020, with the April value being -€4.15/WWh lower figure than the €7.16/MWh expected. This work calculates distributions of daily and monthly CSS values. This research is supported by the Basque Government through the BERC 2018–2021 programme and by the Spanish Ministry of the Economy and Competitiveness (MINECO) through BC3 María de Maeztu excellence accreditation MDM-2017-0714 . Further support is provided by the project MINECO RTI 2018-093352-B-I00 .