Investigation Forecasting Methods of Noise Level From Rail Trams in Lviv City

The method of localization of noise level calculation from a rail vehicle in the city of Lviv is investigated and developed. Models of noise load measurement have been adapted, the measured values have been unified and our own solution has been created on the basis of the road surface, the speed of rail transport and the distance from the noise source. According to the research methods, namely: Schall 03 (from Germany), Nordic Train (Scandinavian countries) was carried out compared to the Bland—Altmann schedule, with which we can adapt the studied results. distance from the noise source. As a result, models for predicting noise load measurements were adapted. The results were carried out compared to the Bland-Altmann plot, which helped us with the comparison table. The purpose of the study is to analyze methods for noise level measurements and adapt them into our realities. Based on the known methods of measuring the noise load of railway vehicles, we can assume that there are no correct methods for the cities of Ukraine (especially Lviv).

Restructured single parabolic band model for quick analysis in thermoelectricity

The single parabolic band (SPB) model has been widely used to preliminarily elucidate inherent transport behaviors of thermoelectric (TE) materials, such as their band structure and electronic thermal conductivity, etc. However, in the SPB calculation, it is necessary to determine some intermediate variables, such as Fermi level or the complex Fermi-Dirac integrals. In this work, we establish a direct carrier-concentration-dependent restructured SPB model, which eliminates Fermi-Dirac integrals and Fermi level calculation and emerges stronger visibility and usability in experiments. We have verified the reliability of such restructured model with 490 groups of experimental data from state-of-the-art TE materials and the relative error is less than 2%. Moreover, carrier effective mass, intrinsic carrier mobility and optimal carrier concentration of these materials are systematically investigated. We believe that our work can provide more convenience and accuracy for thermoelectric data analysis as well as instructive understanding on future optimization design.

Identifikasi Kandungan Hidrokuinon pada Krim Pemutih yang Beredar di Pasar Tradisional Wilayah Kabupaten Banjarnegara

Cosmetics that are sold in the market are in great demand by many women. The type of cosmetics that is often used is a whitening cream. This cream can provide a whitening effect on facial skin so that it can increase self-confidence. One of the ingredients that have skin whitening properties and is often added to whitening creams is hydroquinone. This material has the effect of inhibiting the formation of melanin so that it can whiten the skin. The use of hydroquinone without medical supervision is not allowed because it can have negative effects such as allergies, skin redness, and a burning feeling. In this paper, we will report the identification results of the hydroquinone content in the whitening cream which is not a distribution license number from the BPOM (Indonesia Food and Drug Supervisory Agency) which is marketed in the Banjarnegara Region area. Samples were taken from the Banjarnegara area and the analysis was carried out at the Pharmacy Laboratory of the Universitas Muhammadiyah Yogyakarta. The analytical method used was the identification of the hydroquinone content carried out by the TLC-densitometry method. Qualitative analysis used TLC, where the stationary phase used was silica GF254 while the mobile phase used was toluene - glacial acetic acid at a ratio of 8: 2. Samples were dotted using a microsyringe of 25 µL after extraction. Furthermore, the spot identification was carried out under 254 nm UV light and then the Rf value was calculated. Quantitative analysis was used. It was carried out by densitometry by looking at the area produced by the spots on TLC. In this analysis, the level calculation was carried out by comparing the area of the hydroquinone sample with the area of the positive control hydroquinone multiplied by the known hydroquinone level in the positive control. The results of the qualitative analysis showed that of the 21 samples, there were 6 samples or 28.57% of the samples containing hydroquinone. Quantitative analysis shows the levels in the six samples containing hydroquinone, namely sample no.11: 7,12%, sample no.12: 3.69%, sample no.15: 0.06%, sample no.16: 11.18%, sample no.18: 4.67%, and sample no.19: 1.07%. Based on the results of the analysis, several preparations contain hydroquinone exceeding a safe level of 5%, so it is necessary to regularly check testing and supervision from the authorized institution so that the circulating whitening cream is safe for use by the public.

Risk Level Calculation for IoT System

Due to the complexity and heterogeneity of technology, devices, data and computation, Internet of Things (IoT) systems are vulnerable to the cyber-attacks. Many cyber security risk assessment frameworks dedicated to IoT systems are under study. This study introduces the unique risk ranking method by calculating risk impact and risk likelihood by quantifying them. This unique computational approach is applied in the context of medial domain to calculate risk ranking of two medical devices used in medical IoT-based system.

First-person subjective vs. Third-person objective; the universe is a collection of self-driven mathematical entities; the first-person subjective consciousness is the use of mathematical models by a Turing machine

Physical interactions among any number of elementary particles (EPs) are governed by physical laws (e.g., the Schrodinger equation). In the reality, the predetermined world lines of all EPs form a predetermined state machine. What a Turing machine perceives/predicts, is not the reality itself (but a mathematical model (MM) of the reality), but it is incorrectly treated by this Turing machine as the reality, when this Turing machine deals with everyday challenges. The subjective experience is actually the use of a MM by a Turing machine within its low-level calculation. For example, when a Turing machine uses its geometric model of the reality (GMR), it feels like the subjective experience of being immersed within a geometric structure. The GMR, which is a component of the mind, is a real-time representation of all the EPs within the reality; the GMR only includes the physical objects perceived in the mind. A naïve cognitive researcher might incorrectly treat her GMR as the real world. A Turing machine can use its GMR. Using the semantics of human language, the use of GMR is described as subjectively experiencing the GMR. The subjective experience shouldn’t be able to impact the predetermined world line of any EP within this world.

.The predetermined nature, and its mathematical models within the Turing machine

Physical interactions among any number of elementary particles (EPs) are governed by physical laws (e.g., the Schrodinger equation). Let’s call the predetermined state machine which is formed by the predetermined world lines of all EPs the destiny. To a human neural network, the reality is a snapshot of the destiny. What a neural network perceives/predicts, is not the destiny itself (but a mathematical model (MM) of the destiny), but it is incorrectly treated by this neural network as the destiny, when this neural network deals with everyday challenges. The subjective experience is actually the use of a MM by a neural network within its low-level calculation. For example, when a neural network uses its geometric model of the destiny (GMD), it feels like the subjective experience of being immersed within a geometric structure. The GMD, which is a component of the mind, is a real-time representation of all the EPs within the universe; the GMD only includes the physical objects perceived in the mind. A naïve cognitive researcher might incorrectly treat her GMD as the real world. A neural network can use its GMD. Using the semantics of human language, the use of GMD is described as subjectively experiencing the GMD. It’s possible that a neural network can’t subjectively experience its GMD. Otherwise, its subjective experience shouldn’t be able to impact the predetermined world line of any EP within this universe.

.The predetermined destiny, and its mathematical models within the Turing machine

Physical interactions among any number of elementary particles (EPs) are governed by physical laws (e.g., the Schrodinger equation). Let’s call the predetermined state machine which is formed by the predetermined world lines of all EPs the destiny. To a human neural network, the reality is a snapshot of the destiny. What a neural network perceives/predicts, is not the destiny itself (but a mathematical model (MM) of the destiny), but it is incorrectly treated by this neural network as the destiny, when this neural network deals with everyday challenges. The subjective experience is actually the use of a MM by a neural network within its low-level calculation. For example, when a neural network uses its geometric model of the destiny (GMD), it feels like the subjective experience of being immersed within a topological structure. The GMD, which is a component of the mind, is a real-time representation of all the EPs within the universe; the GMD only includes the physical objects perceived in the mind. A naïve cognitive researcher might incorrectly treat her GMD as the real world. A neural network can use its GMD. Using the semantics of human language, the use of GMD is described as subjectively experiencing the GMD. It’s possible that a neural network can’t subjectively experience its GMD. Otherwise, its subjective experience shouldn’t be able to impact the predetermined world line of any EP within this universe.

.The predetermined destiny, and its mathematical models within the neural network

Physical interactions among any number of elementary particles (EPs) are governed by physical laws (e.g., the Schrodinger equation). Let’s call the superdeterministic state machine which is formed by the world lines of all EPs the destiny. To a human neural network, the reality is a snapshot of the destiny. What a neural network perceives/predicts, is not the destiny itself (but a mathematical model (MM) of the destiny), but it is incorrectly treated by this neural network as the destiny, when this neural network deals with everyday challenges. The subjective experience is actually the use of a MM by a neural network within its low-level calculation. For example, when a neural network uses its geometric model of the destiny (GMD), it feels like the subjective experience of being immersed within a topological structure. The GMD, which is a component of the mind, is a real-time representation of all the EPs within the universe; the GMD only includes the physical objects perceived in the mind. A naïve cognitive researcher might incorrectly treat her GMD as the real world. A neural network can use its GMD. Using the semantics of human language, the use of GMD is described as subjectively experiencing the GMD. It’s possible that a neural network can’t subjectively experience its GMD. Otherwise, its subjective experience shouldn’t be able to impact the actual world line of any EP within this universe.