Magnetic Angle Changer for Studies of Electronically Excited Long-Living Atomic States

A new geometry of a magnetic angle changer (MAC) device is proposed, which allows experiments to be run on electron impact excitation of long-lived states of target atoms. The details of the device’s design are presented and discussed together with a numerical analysis of its magnetic field

A New Approach to the Use of Energy from Renewable Sources in Low-Voltage Power Distribution Networks

Currently, in rural networks with a large amount of distributed generation, PV installations are often disconnected due to the excessively high voltage in the network, which often exceeds the limit value, in accordance with the PN-EN 50160 standard. Disconnecting such an installation extends the return on investment costs by preventing the generation of electricity for the owner’s needs and results in the consumption of this energy from the grid. In such a case, the recipient has to bear the costs related to the purchase of this energy. In order to solve the problem of excessively high voltage in a low-voltage distribution network with a large amount of distributed generation, the authors of this article proposed a new approach to the use of electricity from these sources. In order to present the benefits of the proposed solution, a computer simulation was used. In order to carry it out, a mathematical model of a low-voltage power grid with distributed generation was developed using the electric multipole method and Newton’s method, which is discussed in the paper. To determine the advantages of the proposed solution, nine variants of the operation of an exemplary low-voltage power grid over one day were analyzed. The main conclusion based on the analysis of the results is that the proposed approach improves the operation of the power system by maintaining the voltage values within the standard range for the entire tested part of the network. In addition, the proposed approach does not increase the power or electricity when generating electricity from a PV installation. The proposed solution can also serve as a very attractive stimulus for the creation of energy cooperatives

Magnetic Angle Changer for Studies of Electronically Excited Long-Living Atomic States

A new geometry of a magnetic angle changer (MAC) device is proposed, which allows experiments to be run on electron impact excitation of long-lived states of target atoms. The details of the device’s design are presented and discussed together with a numerical analysis of its magnetic field

Intellectual Capital: A New Predictive Indicator for Project Management Improvement

Effective project management has contributed to successful operations and process management. The goal of this article is to look at the link between a project’s success (PS) and the amount of intellectual capital (IC) an organization has. Instead of being reactive to measuring the cost, timeliness, and quality (customer requirements), a more predictive indicator of a project’s success is needed. Nearly 300 people who work in the field of digital (information and communication) technology took part in the survey research. The survey contains 88 questions. Several statistical techniques are utilized for the data analysis. Based on the comprehensive surveys, the findings show the strong possibility for IC to be adapted as a predictor of the success of investment projects, especially for digital upgrade and improvement. IC plays a key role in assuring the effective (and successful) project management. The study highlights the impacts of effective project management on industrial and organizational operations. This highlight is based on the attempt to determine whether IC contributes to a PS. In this study, in addition to the three traditional factors of cost, timeliness, and quality (or requirements), IC should be considered as a prediction for the project management’s success. The survey was addressed to selected companies from the ICT industry (IT projects). The sample selection is based on non-probability sampling. The author’s method of converting the respondents’ answers into binary form was adopted

Ocena korelacji oznaczeń stężenia kortyzolu we włosach w porównaniu z kortyzolem w surowicy, ślinie i moczu

Introduction: Cortisol concentration is measured in blood, urine, and saliva samples. It has been recently proven that cortisol could also be detected in hair samples. Cortisol measurements in different samples have their own individual characteristics and clinical utility. We aimed to investigate the correlation between hair cortisol concentration and standard cortisol measurements used in clinical practice. Material and methods: Fifty adult volunteers with a negative history of endocrine disorders were enrolled in the study. Morning serum cortisol (MSC), evening serum cortisol (ESC), evening free salivary cortisol (EFSC), urine free cortisol (UFC), and hair cortisol concentration (HCC) were analysed in all participants. Eventually, 41 volunteers were included into the study, whose cortisol concentration in the 1 mg overnight dexamethasone suppression test (1 mg ONDST) were &lt; 50 nmol/L, and cortisol levels in serum, saliva, and urine were within reference ranges. Hair cortisol concentration test was performed for 20 mg of hair strands of the proximal 1 cm hair segments. Results: Hair cortisol concentration ranged from 0.3036 to 2.65 nmol/mg, and the average value was 0.8125 ± 0.4834 nmol/mg. No significant correlations were found between HCC and MSC (rho = 0.04419, p = 0.7838), HCC and ESC (rho = –0.2071, p = 0.1938), HCC and EFSC (rho = 0.1005, p = 0.532), or HCC and UFC (rho = 0.1793, p = 0.262).Conclusions: This work is another step in the discussion on the application of HCC determinations in clinical practice. Our results have showed no correlations between HCC and single point cortisol assessment in blood, saliva, and urine in patients with reference cortisol levels.Wstęp: Oznaczenia kortyzolu wykonywane różnymi metodami tradycyjnie podlegają próbki krwi, moczu i śliny. Dodatkowo, najczęściej w ramach badań naukowych, sugeruje się możliwość oznaczeń stężenia kortyzolu we włosach. Każde z tych badań ma swoją indywidualną charakterystykę oraz przydatność kliniczną. Celem pracy jest zbadanie możliwej korelacji pomiędzy stężeniem kortyzolu we włosach, a stężeniem kortyzolu w rutynowo wykonywanych oznaczeniach. Materiał i metody: Do badania włączono 50 pełnoletnich ochotników z negatywnym wywiadem w kierunku endokrynopatii skutkującej zaburzeniami osi podwzgórzowo- przysadkowo- nadnerczowej. Wszyscy ochotnicy mieli wykonane oznaczenie porannego kortyzolu w surowicy (MSC), wieczornego kortyzolu w surowicy (ESC), wieczornego wolnego kortyzolu w ślinie (EFSC), wolnego kortyzolu w moczu (UFC) i stężenia kortyzolu we włosach (HCC). Ostatecznie do badania włączono 41 ochotników, u których stężenie kortyzolu w teście nocnego hamowania z 1mg deksametazonu (1mgONDST) określono na < 50 nmol/l, a pozostałe wyniki oznaczeń laboratoryjnych mieściły się w granicach normy. Oznaczenie HCC zostało wykonane dla przy-cebulkowej 1 cm próbki włosów pobranej w ilości 20 mg. Wyniki: Stężenia kortyzolu we włosach mieściły się w przedziale od 0,3036 do 2,65 nmol/l/mg, a średnia wartość została określona na 0,8125 ± 0,4834 nmol/l/mg. Nie stwierdzono istotnych zależności pomiędzy ocenianymi parametrami, a uzyskane wyniki wynoszą odpowiednio: HCC a MSC (rho=0.04419, p=0.7838), HCC a ESC (rho=-0.2071, p=0.1938), HCC a EFSC (rho=0.1005, p=0.532) oraz HCC a UFC (rho = 0.1793, p = 0.262). Wnioski: Przedstawiona praca jest kolejnym krokiem w dyskusji nad zastosowaniem oznaczeń HCC w praktyce klinicznej. Przedstawione wyniki nie wykazały korelacji między HCC, a oceną kortyzolu we krwi, ślinie i moczu u pacjentów z normokortyzolemią

Using an LSTM network to monitor industrial reactors using electrical capacitance and impedance tomography - a hybrid approach

The article presents a new concept for monitoring industrial tank reactors. The presented concept allows for faster and more reliable monitoring of industrial processes, which increases their reliability and reduces operating costs. The innovative method is based on electrical tomography. At the same time, it is non-invasive and enables the imaging of phase changes inside tanks filled with liquid. In particular, the hybrid tomograph can detect gas bubbles and crystals formed during industrial processes. The main novelty of the described solution is the simultaneous use of two types of electrical tomography: impedance and capacitance. Another novelty is the use of the LSTM network to solve the tomographic inverse problem. It was made possible by taking the measurement vector as a data sequence. Research has shown that the proposed hybrid solution and the LSTM algorithm work better than separate systems based on impedance or capacitance tomography

Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms

The article deals with the problem of detecting moisture in the walls of historical buildings. As part of the presented research, the following four methods based on mathematical modeling and machine learning were compared: total variation, least-angle regression, elastic net, and artificial neural networks. Based on the simulation data, the systems for the reconstruction of “pixel by pixel” tomographic images were trained. In order to test the reconstructive algorithms obtained during the research, images were generated based on real measurements and simulation cases. The method comparison was performed on the basis of three indicators: mean square error, relative image error, and image correlation coefficient. The above indicators were applied to four selected variants that corresponded to various parts of the walls. The variants differed in the dimensions of the tested wall sections, the number of electrodes used, and the resolution of the 3D image meshes. In all analyzed variants, the best results were obtained using the elastic net algorithm. In addition, all machine learning methods generated better tomographic reconstructions than the classic Total Variation method

Historical Buildings Dampness Analysis Using Electrical Tomography and Machine Learning Algorithms

The article deals with the problem of detecting moisture in the walls of historical buildings. As part of the presented research, the following four methods based on mathematical modeling and machine learning were compared: total variation, least-angle regression, elastic net, and artificial neural networks. Based on the simulation data, the systems for the reconstruction of “pixel by pixel” tomographic images were trained. In order to test the reconstructive algorithms obtained during the research, images were generated based on real measurements and simulation cases. The method comparison was performed on the basis of three indicators: mean square error, relative image error, and image correlation coefficient. The above indicators were applied to four selected variants that corresponded to various parts of the walls. The variants differed in the dimensions of the tested wall sections, the number of electrodes used, and the resolution of the 3D image meshes. In all analyzed variants, the best results were obtained using the elastic net algorithm. In addition, all machine learning methods generated better tomographic reconstructions than the classic Total Variation method