Photoelectrochemical properties of sol–gel obtained titanium oxide

The photoelectrochemical properties of a sol–gel prepared titanium oxide coating applied onto a Ti substrate were investigated. The oxide coating was formed from an inorganic sol thermally treated in air at 350 °C. The coating consisted of agglomerates of narrow size distribution around 100 nm. The photoelectrochemical characteristics were evaluated by investigating the changes in the open circuit potential, current transients and impedance characteristics of a Ti/TiO2 electrode upon illumination by UV light in H2SO4 solution and in the oxidation of benzyl alcohol. The electrode was found to be active for photoelectrochemical reactions in the investigated solutions

Laryngeal Manifestation of Forestier's Disease

BACKGROUND: Forestier's disease is a rare disorder involving bony growths that can occur in various parts of the spinal column, mostly asymptomatic, but these osteophytes, very rarely have been associated with serious complications. AIM: We report a 69-year-old man who was admitted at foniatric departement for evaluation of presenting hoarseness, dysphagia and laborious breathing.CASE PRESENTATION: Noninvasive endolaryngeal imaging and radiological examination revealed distortion of left side of the larynx pushing to the right due to bony mass of the anterior part of cervical spine which was prominent at the left side. Тhе symptoms of the patient presented were caused by Forestier's disease as found by the imiging. CONCLUSIONS: In clinical practice it is advisable to take into consideration Forestier's disease as a possible cause of hoarseness and dysphagia in rare cases

Assessment of Autonomic Nervous System Dysfunction in the Early Phase of Infection With SARS-CoV-2 Virus

BackgroundWe are facing the outburst of coronavirus disease 2019 (COVID-19) defined as a serious, multisystem, disorder, including various neurological manifestations in its presentation. So far, autonomic dysfunction (AD) has not been reported in patients with COVID-19 infection.AimAssessment of AD in the early phase of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 virus).Patients and methodsWe analyzed 116 PCR positive COVID-19 patients. After the exclusion of 41 patients with associate diseases (CADG), partitioned to patients with diabetes mellitus, hypertension, and syncope, the remaining patients were included into a severe group (45 patients with confirmed interstitial pneumonia) and mild group (30 patients). Basic cardiovascular autonomic reflex tests (CART) were performed, followed by beat-to-beat heart rate variability (HRV) and systolic and diastolic blood pressure variability (BPV) analysis, along with baroreceptor sensitivity (BRS). Non-linear analysis of HRV was provided by Poincare Plot. Results were compared to 77 sex and age-matched controls.ResultsAD (sympathetic, parasympathetic, or both) in our study has been revealed in 51.5% of severe, 78.0% of mild COVID-19 patients, and the difference compared to healthy controls was significant (p = 0.018). Orthostatic hypotension has been established in 33.0% COVID-19 patients compared to 2.6% controls (p = 0.001). Most of the spectral parameters of HRV and BPV confirmed AD, most prominent in the severe COVID-19 group. BRS was significantly lower in all patients (severe, mild, CADG), indicating significant sudden cardiac death risk.ConclusionCardiovascular autonomic neuropathy should be taken into account in COVID-19 patients’ assessment. It can be an explanation for a variety of registered manifestations, enabling a comprehensive diagnostic approach and further treatment

Evolving trends in the management of acute appendicitis during COVID-19 waves. The ACIE appy II study

Background: In 2020, ACIE Appy study showed that COVID-19 pandemic heavily affected the management of patients with acute appendicitis (AA) worldwide, with an increased rate of non-operative management (NOM) strategies and a trend toward open surgery due to concern of virus transmission by laparoscopy and controversial recommendations on this issue. The aim of this study was to survey again the same group of surgeons to assess if any difference in management attitudes of AA had occurred in the later stages of the outbreak. Methods: From August 15 to September 30, 2021, an online questionnaire was sent to all 709 participants of the ACIE Appy study. The questionnaire included questions on personal protective equipment (PPE), local policies and screening for SARS-CoV-2 infection, NOM, surgical approach and disease presentations in 2021. The results were compared with the results from the previous study. Results: A total of 476 answers were collected (response rate 67.1%). Screening policies were significatively improved with most patients screened regardless of symptoms (89.5% vs. 37.4%) with PCR and antigenic test as the preferred test (74.1% vs. 26.3%). More patients tested positive before surgery and commercial systems were the preferred ones to filter smoke plumes during laparoscopy. Laparoscopic appendicectomy was the first option in the treatment of AA, with a declined use of NOM. Conclusion: Management of AA has improved in the last waves of pandemic. Increased evidence regarding SARS-COV-2 infection along with a timely healthcare systems response has been translated into tailored attitudes and a better care for patients with AA worldwide

Feasibility Study of the Arenal Volcano Wind Project

Empresa de Servicios Publicos de Heredia (ESPH) in Costa Rica wants to develop a wind farm to complement hydropower generation. We explored the feasibility of building a wind farm at a site in Guanacaste for ESPH by determining potential energy output, feasible turbine placement, construction feasibility, financial feasibility and the social and environmental impacts. We proposed a design with a twelve-megawatt wind farm with a payback period of five years as the most cost-effective and efficient

An 8x1 Wideband Antenna Phased Array

In this project, we are trying to detect the direction of arrival of incoming radiation in the far-field region. To achieve this, we implemented a very flexible and low cost wideband 8x1 phased array antenna receiver system that performs digital beamforming. We designed and built ten aperture coupled patch antennas, radio-frequency (RF) front ends and intermediate-frequency (IF) stage blocks for each channel. Finally, we stored the data in the first-in-first-out (FIFO) memory placed on a Vertex6 FPGA on which we synthesized a microblaze microcontroller that uses SPI to control programmable RF devices and transfers data to the computer for further processing. Super-resolution direction of arrival and model order detection algorithms were implemented to perform the digital beamforming

Hybrid artificial intelligence model for prediction of heating energy use

Currently, in the building sector there is an increase in energy use due to the increased demand for indoor thermal comfort. Proper energy planning based on a real measurement data is a necessity. In this study, we developed and evaluated hybrid artificial intelligence models for the prediction of the daily heating energy use. Building energy use is defined by significant number of influencing factors, while many of them are difficult to adequately quantify. For heating energy use modelling, the complex relationship between the input and output variables is hard to define. The main idea of this paper was to divide the heat demand prediction problem into the linear and the non-linear part (residuals) by using different statistical methods for the prediction. The expectations were that the joint hybrid model, could outperform the individual predictors. Multiple Linear Regression was selected for the linear modelling, while the non-linear part was predicted using Feedforward and Radial Basis neural networks. The hybrid model prediction consisted of the sum of the outputs of the linear and the non-linear model. The results showed that both hybrid models achieved better results than each of the individual Feedforward and Radial Basis neural networks and Multiple Linear Regression on the same dataset. It was shown that this hybrid approach improved the accuracy of artificial intelligence models