Real Process Research Articles

Inverse Problems are Solvable on Real Number Signal Processing Hardware

Despite the success of Deep Learning (DL) serious reliability issues such as non-robustness persist. An interesting aspect is, whether these problems arise due to insufficient tools or fundamental limitations of DL. We study this question from the computability perspective by characterizing the limits the applied hardware imposes. For this, we focus on the class of inverse problems, which, in particular, encompasses any task to reconstruct data from measurements. On digital hardware, a conceptual barrier on the capabilities of DL for solving finite-dimensional inverse problems has in fact already been derived. This paper investigates the general computation framework of Blum-Shub-Smale (BSS) machines, describing the processing and storage of arbitrary real values. Although a corresponding real-world computing device does not exist, research and development towards real number computing hardware, usually referred to by “neuromorphic computing”, has increased in recent years. In this work, we show that the framework of BSS machines does enable the algorithmic solvability of finite dimensional inverse problems. Our results emphasize the influence of the considered computing model in questions of accuracy and reliability.

Remotely controlled reality in the “Robotics Fundamentals” course for students of the “Information Systems and Technologies” speciality

Abstract This paper presents the distance learning technology in the “Robotics Fundamentals” course for students of the “Information Systems and Technologies” speciality. The purpose of the educational process in this course is to form students’ understanding a robotics unit and its software as an information system. We need to stress the students’ attention on importance of feedback in such complex systems. The main idea of our approach is to show the students the real production process of robotics system creation as a sequence of steps from modelling and simulation in virtual environment to testing it the in the appropriate real environment. Choice of the software and hardware for supporting the distance learning process is discussed. We suggest some technical decisions according to the remote laboratory and students’ remote communications with the robotics system. Results of conducted surveys are analysed to evaluate the efficiency of study process in the course as well to distinguish tasks for future development od our learning technology.

Exploring engineering applications of two-sided partial destructive disassembly line balancing problems under electrical limiting and time-of-use pricing

With the rising demand for energy and resource shortages, disassembly as energy-intensive industry should be more attention to energy conservation. However, the non-disassemblability of components (CND) and fixed-constant disassembly costs make this process relatively expensive compared to the potential profit. This paper introduces a two-sided partial destructive disassembly model to solve problem of CND. To more accurately reflect real-world conditions, this study incorporates into the model, which was not considered in previous work. A mixed integer planning model is developed by exploring the impact of time-of-use tariffs on energy consumption and profit, and its correctness is verified in a small-scale case. Subsequently, we propose an improved water cycle algorithm (IWCA) to increase the model solving efficiency. Using non-destructive, destructive, and human-robot hybrid disassembly as benchmark cases, we compare 11 algorithms from existing research, and verify the superiority of IWCA in solving different cases. Finally, the proposed method is validated using automotive engine as an example. The results show that: (1) IWCA is superior to the four latest algorithms in solving the two-sided partially destructive disassembly line balancing problem under electrical limiting and time-of-use electricity pricing. (2) The two-sided partial destructive disassembly effectively addresses the CND issue, reducing the smoothing index by 10.24% and increasing disassembly profit by 3.19% compared to other algorithms. (3) The partial destructive disassembly is better suited for addressing issues in real production processes than non-destructive or complete destructive disassembly methods.

UTILIZING GAUSSIAN PROCESS REGRESSION FOR NONLINEAR MAGNETIC SEPARATION PROCESS IDENTIFICATION

This paper presents a novel approach utilizing Gaussian Process Regression (GPR) to identify dynamic models with nonlinear parameters in magnetic separation processes. It aims to address the complex and dynamic nature of these processes by employing advanced modeling methods. The effectiveness of GPR is demonstrated through its application to simulated signals representing real iron ore separation processes, highlighting its potential to enhance existing models and optimize processes. Conducted within the MATLAB, this research lays the groundwork for further advancement and practical implementation. The utilization of GPR in magnetic separation offers innovative modeling of nonlinear dynamic processes, promising improved efficiency and precision in industrial applications.

Этнополитика современной Кабардино-Балкарии: генезис и специфика

The subject of the research in this article is the historically formed integral “locus” of ethnopoli-tics – the Kabardino-Balkarian Republic. During the existence of traditional society, the ties be-tween distinct ethnosocial groupings and their social elites within the territory of historical Ka-bardino-Balkaria were not viewed as interethnic and had no national-political implications. Pre-requisites for the emergence of regional ethnopolitics developed during the 19th century, when social and intersocial relations in the Central Caucasus became the subject of regulation by the state. The formation of the Soviet autonomy of Kabardino-Balkaria already acted as a real ethno-political process, the subject of which was the coordination of national interests of the Kabardian and Balkar peoples. The illegal act of expulsion from their homeland brought a traumatic begin-ning to the ethnosocial experience of the Balkar people, which has not been eradicated to this day. Ethnopolitical problems came to the forefront of the social and political life of the multi-ethnic and “two-subject” republic in the conditions of the crisis transition from the Soviet to the modern state and public system in the 1990s and their modern specificity took shape: the interweaving of the processes of socio-political consolidation of the Kabardino-Balkarian Republic and the nation-al consolidation of its “titular” peoples as a deep source of reproduction of ethnopolitics in the social life of the region; an urgent need to confirm the integral national identity of each of the peoples of the republic in the absence of institutional registration of its “two-subject” nature; his-torical and political discourse as the main field of the “struggle for identity”; the visible asym-metry of the ethno-social structure of the republic and the search for opportunities, conditions and forms of overcoming it as the driving force of Kabardino-Balkarian ethnopolitics at each stage of its actualization.

ФИЛОЛОГ-СТУДЕНТТЕРДІҢ ӘДІСТЕМЕЛІК ҚҰЗЫРЕТТІЛІГІН ҚАЛЫПТАСТЫРУ ЖОЛДАРЫ

This article discusses the ways of forming the methodological competence of a future teacher of the Kazakh language and literature. Theoretical works are summarized and studies related to the disclosure of the essence of the concept of "methodological competence of a university student" are analyzed. It is known that methodological training is an important component in the content of teaching philology students. The importance of methodological training as an integral part of the student's professional competence in the formation of methodological competence is determined. Using the example of the research directions of scientists, it was suggested that various factors influence the definition of ways to form methodological competence. In addition, the criteria for the formation of such types of methods as modeling of pedagogical situations, analysis of pedagogical situations, design, game method, case method, problem seminar, problem lecture, used in the formation of methodological competence of students, are clarified and defined. It is known that the formation of methodological competence of students of philology is an important and multifaceted process. Here are some main ways to achieve this goal: providing fundamental knowledge in the field of theoretical education, linguistics, literary studies and pedagogy, deep learning in teaching methods and technologies, and the development of practical skills. Conducting micro-lessons, drawing up curricula, analysis, development of educational materials, pedagogical practice, practical training in schools and other educational institutions, participation in the real educational process help to effectively form the methodological competence of students of philology. It was found that the personalization of this process is also important, taking into account the individual characteristics of each student.

Review of approximation formulas for fractional derivatives of variable order

Fractional differential equations of variable order, depending on a time or space variable, are successfully used to study time and/or spatial dynamics. The purpose of this review is to consider the latest research and results related to the basic definitions and approximation formulas for fractional derivatives of variable order. The review begins with an examination of existing definitions based on various physical and practical knowledge. The following are formulas for discretizing fractional derivatives, since they play a key role in numerical modeling, providing an effective means of describing complex systems with long-term memory, heterogeneous parameters and non-local interactions. Their use not only improves the accuracy of models, but also facilitates the numerical solution of differential equations, which is essential for analyzing and predicting the behavior of real processes in various fields of science and engineering. This review is intended to help readers select appropriate definitions and discretization formulas to effectively solve specific physical and engineering problems.

Study of the Academic Expectations of Bachelor Students for the Lecture Course in Didactics

The academic expectations of bachelor students regarding the didactics lecture course are a factor in the formation of motivated and creative teachers. The thesis is supported that if there is a correspondence between positive academic expectations of the students and the real learning process at the university, the students are motivated and participate actively. In this study an author's questionnaire about the academic expectations of students in the didactics lecture course is used. The analysis of various tools for assessing students' academic expectations showed that questionnaires structured by thematic areas are most often used. In the present study, the questionnaire is structured in three modules, corresponding to the three components in the system of expectations – cognitive, affective and conative. The current research has a practical orientation - the author's questionnaire should serve as an analysis of the students' academic expectations in the context of improving the lecture course on didactics.

Modeling Hemodynamics in Three-Dimensional, Biomimetic, Branched, Microfluidic, Vascular Networks.

Neovascularization has been extensively studied because of its significant role in both physiological processes and diseases. The significance of vascular microfluidic platforms lies in its essential role in recreating an in vitro environment capable of supporting cellular and tissue systems through the process of neovascularization. Biomechanical properties in a tissue engineered system use fluid flow and transport properties to recapitulate physiological systems. This enables mimicry of organ systems which can further personalized and regenerative medicine. Thus, fluid hemodynamics can be used to study these flow patterns and create a system that mimics real physiological pathways and processes. The establishment of stable flow pathways encourages endothelial cells (ECs) ECs to undergo neovascularization. Specifically, the shear stress applied in capillary beds generates the increased proliferation and differentiation of ECs to build larger microcirculatory beds. Here, we describe a mathematical model that uses branching patterns and vessel morphology to predict hemodynamic parameters in capillary beds. A retinal capillary bed is used as one-use case of our model to show how the mathematical framework can be used to determine hemodynamic parameters for any microfluidic system. In doing so, this tool can be altered to be used to supplement emerging research areas in neovascularization.

Construction of the Invoicing Process through Process Mining and Business Intelligence in the Colombian Pharmaceutical Sector

The invoicing process is critical to the financial management of organizations. However, modeling this process presents challenges such as data updating, information availability, and aligning planned activities with the actual execution of the process. One difficulty is that designing the invoicing process requires extensive knowledge of the activities involved, and process representations based on organizational repositories are not necessarily aligned with the actual invoicing processes in the organization. Process Mining is complemented by the use of dashboards, which are inherent to business intelligence and allow for visual tracking of process behavior. This paper explores how the combination of process mining and business intelligence can enable a new level of process modeling that addresses specific issues in constructing processes that are aligned with real-world activities. To accomplish this, we first propose the Design Science Research (DSR) methodology, which outlines how a researcher or practitioner should approach the task of modeling a specific process using Process Mining augmented with dashboard resources. The research strategy was to identify the most appropriate methodology to construct the actual billing process, which led to the identification of the DSR methodology. This methodology, with its 12-step plan, allowed the construction of an artifact representing the actual invoicing process. Ultimately, the objective of constructing a real invoicing process in the Colombian pharmaceutical sector is achieved through the development of an artifact, complemented by business intelligence dashboards that ensure the alignment of the execution of activities within the process.

Development and verification of a multi-component model for steam methane reforming

RELEVANCE. Steam methane reforming is the dominant method of hydrogen production. Its significant share in global CO2 emissions highlights the importance of optimizing technological parameters to reduce environmental impact. The developed multi-component model of steam methane reforming in COMSOL Multiphysics is relevant not only due to its applicability for optimizing existing production facilities but also for its potential in developing new methods for utilizing associated petroleum gas. In the context of import substitution in the hydrogen energy sector, this model is also of interest, allowing for the calculation of technological parameters of industrial installations.THE PURPOSE. The aim of the work is to develop and verify a multi-component model of steam methane reforming.METHODS. The research methodology includes the use of experimental data from the literature and industrial indicators for integration into a multi-component model in COMSOL Multiphysics. This enables the modelling of complex chemical interactions under conditions characteristic of the industrial steam methane reforming process.RESULTS. The developed multi-component model allows calculating key parameters of the steam methane reforming process, including the concentration of components (methane, hydrogen, carbon monoxide, and carbon dioxide) and temperature along the reactor. The model successfully describes the chemical interactions between components and takes into account the influence of operating conditions, such as temperature, pressure, and steam/gas ratio, on process efficiency. The model verification was carried out by comparing the modelling results with experimental data and indicators of real industrial processes. Their correspondence confirms the high degree of reliability and suitability of the model for practical application in engineering calculations and optimization of steam methane reforming processes.CONCLUSION. The conclusions made based on the modelling can be used for further improvement of methane conversion technologies, contributing to their efficiency and environmental friendliness. There is also potential for using the model to calculate the stages of installations for the utilization of products from the processing of associated petroleum gas.

Sterowanie kobotem za pomocą wirtualnej rzeczywistości dla potrzeb Przemysłu 4.0

Industry 4.0 is undergoing continuous evolution. Management and monitoring systems implemented in the industrial infrastructure are able to collect and process more and more data, then, based on dedicated algorithms, make decisions, and consequently send specific control commands to devices and technological processes. At the same time, there is growing interest in combining devices with other seemingly disparate technologies. An example of creating such coexistence is the integration of sensory solutions with virtual reality. Such activities contribute to the creation of digital twins of devices and processes, which in turn enables remote and in many cases automatic control of physical objects and the course of real processes. In the course of this research, such an approach has been used to develop a solution that allows a robot to be controlled from anywhere and at any time. This work presents a prototype of a solution providing remote control of a cobot arm robot.

Multiphase time-varying batch process monitoring based on stage moving model migration

To improve product qualities and obtain maximum comprehensive economic benefits from batch process production, it is of great practical significance to optimize the operation of batch production processes. Considering the multiphase and slow time-varying characteristics, a multiphase time-varying batch process monitoring method is proposed based on stage moving model migration technology. Firstly, the sliding windows are constructed with moving phases, where the moving of different phases is decided by the influence of the process variables to the final product. And time slices are the basic units for building the regression models. Secondly, to analyze and capture the relationship between the process variables of both the old mode and new mode and the final quality variable, the JY-PLS algorithm is utilized on each time slice within sliding windows for quality prediction. Then, monitoring indexes and corresponding control limits are calculated and used for process monitoring. The application to a real multiphase time-varying batch process, the start-up process of injection molding process, proved the effectiveness of the proposed method.

Solar-activated tin oxide photocatalysis for efficient naphthenic acids removal and toxicity reduction in oil sands process water

This research studied, for the first time, the effect of activating tin oxide (SnO2) under simulated solar light for treating real oil sands process water (OSPW). The solar/SnO2 system effectively eliminated fluorophore organic contaminants, classical naphthenic acids (O2-NAs), and oxidized NAs (Oxy-NAs) from OSPW. The best experimental conditions to remove over 90 % of O2-NAs were found to be 0.5 g/L SnO2 under 8 h of irradiation. HO• and O2•– species identified by electron paramagnetic resonance (EPR) analysis played an important role in the degradation of NAs and other contaminants in real OSPW. The initial toxic effects of untreated OSPW were noticeably reduced after treatment, with a reduction of approximately 50 % in acute toxicity using Microtox® bioassay and over 80 % in the level of bioavailable hydrocarbons. In addition, the process also demonstrated a significant reduction in immunotoxicity as measured using an immune cell bioassay and reduced the toxic effects on Staphylococcus warneri using an adapted bacterial minimal inhibitory concentration (MIC) viability assay. These results suggest that treated OSPW by SnO2 under solar light has high environmental compatibility, indicating it is safe for reuse in further applications.

Using integrated transcriptomics and metabolomics to explore the effects of infant formula on the growth and development of small intestinal organoids.

Infant formulas are designed to provide sufficient energy and the necessary nutrients to support the growth and development of newborns. Currently, research on the functions of formula milk powder focuses on clinical research and cell experiments, and there were many cell experiments that investigated the effect of infant formulas on cellular growth. However, most of the cells used are tumor cell lines, which are unable to simulate the real digestion process of an infant. In this study, we innovatively proposed a method that integrates human small intestinal organoids (SIOs) with transcriptomics and metabolomics analysis. We induced directed differentiation of human embryonic stem cells into SIOs and simulated the intestinal environment of newborns with them. Then, three kinds of 1-stage infant formulas from the same brand were introduced to simulate the digestion, absorption, and metabolism of the infant intestine. The nutritional value of each formula milk powder was examined by multi-omics sequencing methods, including transcriptomics and metabolomics analysis. Results showed that there were significant alterations in gene expression and metabolites in the three groups of SIOs after absorbing different infant formulas. By analyzing transcriptome and metabolome data, combined with GO, KEGG, and GSEA analysis, we demonstrated the ability of SIOs to model the different aspects of the developing process of the intestine and discovered the correlation between formula components and their effects, including Lactobacillus lactis and lactoferrin. The study reveals the effect and mechanisms of formula milk powder on the growth and development of infant intestines and the formation of immune function. Furthermore, our method can help to construct a multi-level assessment model, detect the effects of nutrients, and evaluate the interactions between nutrients, which is helpful for future research and development of infant powders.

Large-scale data-driven uniformity analysis and sensory prediction of commercial banana ripening process

Artificial intelligence (AI) and machine learning (ML) have found prominent yet mostly academic applications in the food supply chain specifically to preserve and optimize the quality of fresh produce and achieve uniformity across the various stages of the cold chain. Nevertheless, the practical use of AI/ML for predictive analytics within real and large-scale commercial food processes, such as banana ripening, is sparse. This study proposes a novel data-driven approach tested and validated on two new large-scale datasets to automate and optimize the banana ripening process in refrigerated marine containers by successfully employing ML in uniformity analysis of the peel color and the pulp temperature of bananas based on atmospheric conditions. The results demonstrate high correlations between the gas concentrations and the uniformity of the process, suggesting that the uniformity of the peel color and the pulp temperature of fruit can be achieved by controlling the concentrations of the CO2 and O2 gas levels. Furthermore, this study, for the first time, achieves accurate algorithmic predictions of oxygen levels from other atmospheric variables to provide an alternative approach for continuous, improved and more cost-effective monitoring of the atmospheric conditions during ripening. A wide-range of predictive models are tested and validated where the Long Short Term Memory regression provides the lowest root-mean-square-errors (0.033 and 0.202) with robust R-squared values (0.999 and 0.959) for two datasets.

Enhancing Manufacturing Processing Stability and Efficiency with Linear-Regression Analysis: Modeling on a Flow-Drill Screw (FDS) Joining Process

The instability (in processing time) in the flow-drill screwing process is undesired but inescapable due to variations in material property, gauge, and process parameters. A substantial number of materials and lab labor need to be used to test and control the variability of the real manufacturing joining process. To enhance the stability and efficiency of the screwing process, this study seeks multi-disciplinary collaboration by applying linear-regression modeling. Six hundred and forty-eight data points were collected and split into an 80% training set for model building and a 20% test set for model validation. A multiple linear-regression model was built. The results indicated that, compared to variable base level (6000 rpm rotational speed and 1100 N downforce), higher rotational speed (8000 rpm, 7000 rpm), greater downforce (1200 N, 1300 N), and their interaction were significantly associated with passage (processing) time, while the switch point did not significantly affect passage time. The interaction plot and effect size were adopted to provide measurements of the effect magnitude on processing time. The coefficient of determination indicated that 86% of the variability in the passage time can be explained by this model. Statistical analysis, such as data visualization, statistical modeling, and other data-driven analysis methods, can be used to detect underlying relationships between variables, investigate variations, and make predictions in the manufacturing process. The outcomes from the data-driven analysis can benefit from improving the economical manufacturing system, refining the processing setting, and reducing test material costs, labor, and lead time.

In situ Synchrotron X-ray Metrology Boosted by Automated Data Analysis for Real-time Monitoring of Cathode Calcination.

Synchrotron X-ray-based in situ metrology is advantageous for monitoring the synthesis of battery materials, offering high throughput, high spatial and temporal resolution, and chemical sensitivity. However, the rapid generation of massive data poses a challenge to on-site, on-the-fly analysis needed for real-time process monitoring. Here, a weighted lagged cross-correlation (WLCC) similarity approach is presented for automated data analysis, which merges with in situ synchrotron X-ray diffraction metrology to monitor the calcination process of the archetypal nickel-based cathode, LiNiO2. The WLCC approach, incorporating variables that account for peak shifts and width changes associated with structural transformations, enables rapid extraction of phase progression within 10 seconds from tens of diffraction patterns. Details are captured, from initial precursors to intermediates and the final layered LiNiO2, providing information for agile on-site adjustments during experiments and complementing post hoc diffraction analysis by offering insights into early-stage phase nucleation and growth. Expanding this data-powered platform paves the way for real time calcination process monitoring and control, which is pivotal to quality control in battery cathode manufacturing.

Simulasi Metode Monte Carlo untuk Mengatur Sistem Antrian Truk

This research focuses on optimizing pozzolan truck queue management at PT. Danas Putra Mandiri through the application of the Monte Carlo method. The main objective of this research is to develop and implement a simulation application that is able to predict and calculate the average availability of trucks in a certain time unit, with the ultimate goal of increasing the company's operational efficiency. In industries that rely on the transportation of raw materials such as pozzolan, effective truck queue management is key to avoiding distribution delays and reducing operational costs. A queue is a service from one or more services that is caused by the need for services exceeding the capacity of the service or service facilities, so that customers who arrive cannot immediately receive service due to busyness in the service. The method used in this research is the Monte Carlo method. Monte Carlo is an experiment of various elements of probability using random samples. The Monte Carlo method is useful for solving quantitative problems with real or physical processing. This method has the ability to simulate and manage queues that occur in companies. PT Danas Putra Mandiri is one of the companies operating in the mining sector which supplies pozzolan to PT Semen Padang. The pozzolan delivery process uses trucks. The delivery process using trucks can affect the availability of the number of trucks in the company. The data used in this research is data from January 2023 - December 2023 with a total of 1619 data. Data taken through the admin of PT. DANAS PUTRA MANDIRI. Based on simulation predictions of queues on trucks, results were obtained with an average accuracy of 80.6%. The queuing simulation results show that the application of the Monte Carlo method can effectively reduce truck waiting times and increase the availability of trucks for rental, which ultimately contributes to increasing the company's operational efficiency.

Revealing Real Active Sites in Intricate Grain Boundaries Assemblies on Electroreduction of CO2 to C2+ Products

AbstractAlthough intricate structural assemblies contribute to enhancing the activity of electrocatalytic CO2 reduction (ECR) to C2+ products, blindly coupling multiple design strategies may not yield the expected results, and even inhibit the activity of intrinsic catalytic sites. Therefore, elucidating the promoting or inhibitory effects of each design strategy on the CO2‐to‐C2+ conversion to clarify the real active sites and dynamic oxidation processes is of paramount importance. Here, commonly used grain boundaries (GBs), oxidation states, and alloying strategies are focused on, constructing four different types of catalysts structures: original Cu GBs, oxygen‐enriched grain boundary oxidation (GBO), Ag‐enriched GBO, and Cu/Ag GBs. Multiple operando characterizations reveal that GBs and GBO strengthen the resistance of the oxidative Cu species to the electrochemical reduction. The in situ generated strongly oxidative hydroxyl radicals alter the local reaction environment on the catalyst surface, inducing and stabilizing oxidative Cuδ+ species. Catalytic activity comparisons indicate that the oxidation state of Cu plays a decisive role in the CO2‐to‐C2+ conversion, and the nanoalloy effect tends to favor the CH4 production in intricate GBs assemblies. Theoretical calculations suggest that weak CO adsorption on GBO structures facilitates hydrogenation, promoting C–C coupling toward C2+ products.