Current data on the heat capacity of bismuth were obtained in the 1980s using various methods and measuring equipment. Data published in various sources differ by values exceeding the permissible error of the utilized measurement instruments and techniques. Furthermore, no information is provided about input data acquisition methods and/or non-standardized statistical processing techniques used. Therefore, it is not possible to assess the reliability of the results of measuring bismuth heat capacity. Pure bismuth and its alloys are widely used in the industry and engineering. However, its unreliable heat capacity data create several problems, including the lack of reference values of specific heat capacity necessary for incoming material inspection in the electrical industry, overestimation of tolerances during design calculations, and restricted operating ranges for alloys. This paper presents the results of studying specific heat capacity of pure bismuth conducted between 2021 and 2025. The study evaluates the stability of reproducing specific heat capacity of bismuth during storage without special conditions, as well as the ability of using this material to verify temperature scales and thermal analysis tools. The obtained results will be useful for metrologists, users of complex thermal analysis instruments, materials scientists studying thermal properties of semimetals and layered structures, developers in the field of electrical and instrument engineering, as well as process engineers working in the chemical and pharmaceutical industries.
In the context of non-invasive voice source analysis, a key area of acoustic measurement research, this paper addresses the measurement of excitation signal parameters and waveforms for linear predictive vocoders. The study highlights the critical challenge of high computational complexity in existing analysis-by-synthesis algorithms. In order to overcome this challenge, a high-speed acoustic measurement method has been proposed based on the criterion of minimum mean-square linear prediction error. This criterion is shown to reflect the principle of minimizing the energy consumption of a speaker during speech production. An example of technical implementation of the method is presented alongside an evaluation of its computational complexity. It is shown that compared to a standard method of multi-pulse excitation of a linear predictive vocoder using two address codebooks (adaptive and stochastic), the implementation costs of the proposed method are reduced by dozens of times. To validate these findings, a full-scale experiment was conducted using proprietary software on a set of vowel phonemes from a control speaker. It is shown that optimizing the excitation signal waveform significantly reduces the mean-square linear prediction error. Our results can be useful when developing new and upgrading existing systems and technologies for speech coding and synthesis, mobile speech communication, and other digital speech signal processing applications with data compression based on the linear prediction model.
We analyze the state of the reference base aimed at reproducing the unit of torque force within the range of its high values. The analysis of available publications dealing with reference installations confirms the urgency of development and implementation of high-precision reference installations reproducing the unit of torque within the range greater than 20 kN ⋅ m. To guarantee high accuracy of reproducing the unit of torque, special design solutions are used for the installations in order to exclude the influence of parasitic components of the force vector and the radius vector of the arm on the measured (reproduced) value, i.e., the modulus of the torque vector. Note that these solutions are connected with the use of costly materials and impose high demands on the precision of manufacturing the required parts. To successfully solve the problems of creating reference installations capable of reproducing the unit of torque force for the torques higher than 20 kN ⋅ m under the conditions of restricted financial possibilities, we developed a generalized physical and mathematical model of the process of measuring torque forces based on the classical definition of torque as the vector product of the force vector by the radius vector of the arm. The developed generalized physical and mathematical model makes it possible to take into account the influence of the specific features of measuring installations and various methodological and instrumental factors related to the procedures of measuring force vectors and the radius vector of the arm, as well as the technical means used to reproduce and transfer the unit of torque force (according to the applied measurement methods), on the measurement data. A preliminary mathematical modeling of the process of reproducing the unit of torque force has been carried out and the influence of the measurement error of the arm radius vector and the accuracy of location of the axis of rotation on the uncertainty of measuring the torque force has been estimated. As a result, the applicability of the developed generalized physical and mathematical model of the process of measuring of torque forces aimed at estimating the uncertainty of measurements by the Monte Carlo method and specifying the requirements to the components of the designed installation is demonstrated. The accumulated results make it possible to create installations intended for reproducing the unit of torque force within the range of its large values with the help of numerical modeling.
Modern additive manufacturing technologies, specifically selective laser melting, provide a means to create products with complex geometries and target physical and mechanical properties. The quality of the final product is determined by the composition of the gas environment in the working chamber of a selective laser melting system. The most critical factor is the oxidation of molten metal, which results in shape defects and a mechanical deterioration of the finished product. Existing standard monitoring systems with remote sensors do not provide exhaustive information about the gas environment in the working chamber, for example, about local fluctuations in the gas environment composition directly in the melt bath and the formation of gaseous oxidants (nitrogen oxides NOx and water vapor). The authors proposed and experimentally validated a method for the comprehensive monitoring of a gas environment, which relies on detecting the total concentration of molecular oxygen and NOx, as well as of moisture indicators H2 and NH3. The architecture of an information and measurement system for the comprehensive monitoring of a gas environment was developed. The system can be integrated into existing industrial equipment, with no changes to its design. Conducted using an EOS M 280 (Electro Optical Systems, Germany) and Farsoon FS 121M (Farsoon, China) systems, the experimental studies confirmed the formation of NOx during melting and showed that the proposed method can provide more accurate information about the gas environment composition compared to the use of standard systems. The developed information and measurement system is designed for multi-level control of the gas environment composition through automated control of gas supply based on real-time multiparameter analysis. The system provides more complete information about the chemical composition of the environment in the working chamber of a selective laser melting system and enables an appropriate response to oxidation risks, minimizing defects and improving product quality.
The article addresses the issue of automating the interpretation of remote sensing data on natural features. The existing methods of structural analysis of spectral data are shown to rely on expert parameter evaluation of the used algorithms. In order to automate the interpretation of remote sensing data, a modified structural analysis method using the components of a correlation coefficient for a pair of spectral features was developed. Each component is defined by the product of its constituents in the form of normalized spectral features. According to the signs of the components comprising the correlation coefficient (positive, negative, and alternating), four classes are identified. The obtained data is used to establish a decision rule for assessing whether a test situation in the space of a spectral feature pair belongs to one of the identified classes. On the example of identifying forest areas damaged by the Siberian silk moth, the authors compared the results of applying the proposed method and conventional methods for the decomposition of remote sensing data, which use NDVI (normalized difference vegetation index) and GNDVI (green normalized difference vegetation index). In order to characterize the classes identified via the modified method and determine the threshold values of NDVI and GNDVI, kernel density estimates were used. The article presents a procedure for optimizing the kernel density estimate that relies on selecting blur coefficients in kernel functions based on the maximum likelihood function. The modified structural analysis method helps to circumvent the problem of determining threshold values for difference vegetation indices.
