Original Approach Research Articles

About Calculus Through the Transfer Matrix Method of a Beam with Intermediate Support with Applications in Dental Restorations

This work presents an original and very interesting approach to a calculus problem involving beams with intermediate supports through the transfer-matrix method, a very easy method to program to quickly obtain good results. To exemplify the applicability of this approach in dentistry, the calculus of a dental bridge on three poles is explored. Dental restorations are very important for improving a person’s general state of health as a result of improving mastication and esthetic appearance. The approach used in this study consists of presenting a theoretical study about an indeterminate beam with an intermediate support and then particularizing it for application in a dental restoration case, with a dental bridge on three poles and two missing teeth between the three poles. The bridge is assimilated to a simple static indeterminate beam. This paper is unique in that it involves the application of the transfer-matrix method for a case study in dental restoration. The assimilation of a dental bridge with a statically undetermined beam, resting on the extremities and on an intermediate support, is an original approach. The results obtained in the presented case study were validated by comparison with those obtained through the classical calculation of the Resistance of Materials, with Clapeyron’s equation of three moments. Due to the ease and elegance of solving various problems with the TMM, this approach will continue to be relevant to other original case studies with different modeling requirements, and these applications will be presented in future research.

Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

The increasing complexity of modern plant systems and the limitations of precise mathematical modeling have led to a shift towards data-driven control methods. These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. Nonetheless, control performance can be limited by the fixed probability coefficient used in the original SEDA to disrupt design parameters, especially when balancing exploration and exploitation phases. This study proposes an enhanced version of the SEDA: the modified SEDA (MSEDA), to address this issue. The MSEDA introduces a dynamic probability coefficient that decreases with each iteration. The adjustment improves the balance between exploration and exploitation phases, which enhances control accuracy. The MSEDA was used to tune the brain emotional learning-based intelligent controller (BELBIC) together with a proportional-integral-derivative (PID) controller. The result was the BELBIC-PID controller inspired by the limbic system of the human brain, which has high precision. The effectiveness of the proposed MSEDA-BELBIC-PID was validated using simulations on multi-input multi-output (MIMO) systems, with a focus on tracking performance and computational efficiency. The statistical analysis of 30 independent trials demonstrated that the proposed MSEDA-BELBIC-PID was significantly improved over the original SEDA-BELBIC-PID. Wilcoxon's rank test yielded a fitness function p-value < 0.05, which confirmed the robustness and effect of the proposed enhancement. The comparative results demonstrated that the MSEDA-BELBIC-PID consistently performed better than the original approach and had improved fitness function values, reduced total integral square error, and lower total integral square input. These findings underscored the MSEDA suitability as a data-driven tool for controller design parameter optimization. Furthermore, the low computational burden of MSEDA rendered it a strong alternative to heuristic multi-agent algorithms, which frequently encounter high computational costs with large controller design parameters.

A tri-level model for optimal management of active distribution networks enabling two-layer local markets

The deregulation of distribution systems enable local energy trading under the umbrella of local market strategies, which are launched and cleared by local entities like distribution system operators. The emergence of active players connected to distribution networks such as microgrids path the way to two-layer market structures, within which different local markets must coexist and coordinate. This new paradigm advocates for developing new management and market models suitable for multi-level local markets. This paper focuses on this issue. In particular, a new management model for active distribution networks enabling two-layer local markets is developed, which casts as an original tri-level optimization approach. This paper applies the developed methodology to the case in which different microgrids connected to a distribution market launch local markets for energy trading within the microgrid, but it could be applied to similar frameworks such as energy communities or virtual power plants. The new approach stablishes a game-oriented market coordination which preserves the hierarchical feature of the coordinated market strategy. A case study on a 33-bus radial distribution network serves to validate the model and illustrates how active players such as distributed generators and flexible demands perform under local market rules. A number of scenarios are studied under different distributed generation and microgrid penetration, showing that incrementing the number of both leads to improve the economy of the distribution system operator and microgrids. Finally, a sensitivity analysis regarding the renewable generation potential is studied.

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

Accurately determining the stage of crop development holds significant importance for field crop management. With the advancement of smart agriculture, an increasing number of Internet of Things (IoT) devices are being integrated into agricultural production, enabling more efficient acquisition of high-precision crop images. Currently, research on detecting crop growth stages based on IoT device images remains relatively scarce. Most existing studies rely on a single network model for detection, often encountering issues such as low accuracy and overfitting. Therefore, in this study, we collected maize images using IoT devices and constructed an integrated deep learning model by utilizing four convolutional neural networks (CNNs) to detect the growth period of maize in real time. Additionally, we implemented several improvements on these four CNNs and subsequently tested the performance of the ensemble model on the maize dataset. Regarding the ensemble strategy for the ensemble model, we proposed a dynamic weighted voting method, building upon the original voting approach, which can mitigate model training fluctuations and expedite model convergence. Ultimately, we manually simulated various lighting conditions to assess their impact on the ensemble model. Experimental results demonstrate that the ensemble deep model proposed in this paper represents a robust method for detecting maize growth stages, achieving an accuracy rate of 0.976 on the maize dataset, effectively facilitating high-precision detection of maize growth stages in complex backgrounds.

Trade and Economic Activity: Nonlinear Modeling and Forecasting

ABSTRACTMotivated by the increasing role of trade in global economic developments, this paper derives novel econometric methods to forecast global trade by exploiting the relationship between economic activity and trade itself. We empirically document that the relation between trade and economic activity changes along the business cycle—the stronger the cycle, the larger their elasticity. Consistently with theoretical predictions, such cyclicality depends on two key factors: (i) the high pro‐cyclicalilty of the demand for intensively traded items and (ii) the presence of low‐frequency (“trend”) components in trade and GDP series. We show that the latter is key to generate a cyclical income elasticity of trade and that a linear relationship holds once those components are filtered out. These empirical findings are exploited in two original empirical approaches to map GDP forecasts, for which rather accurate and timely projections are available, into world trade forecast. In an out‐of‐sample real‐time forecasting exercise, with both the proposed methods, we obtain predictions that are vividly more accurate than naive linear models and nearly halve the forecast error of the IMF‐WEO.

The Chiro-Phenomenology of Vilém Flusser: Exploring the Gestures of the Hand

ABSTRACT Vilém Flusser’s chiro-phenomenology fills an essential gap in scholarship by expanding and reinterpreting the ontological significance of gestures, focusing on the hand as a crucial site for understanding the embodied nature of human existence. This article explores Flusser’s unique contribution to the phenomenology of the hand, highlighting why his approach is necessary for a fuller understanding of gesture and embodiment. I begin by emphasizing the relevance of the hand in understanding gestures, arguing that phenomenology provides rich resources for this inquiry. Within the phenomenological tradition, two distinct perspectives emerge: Husserl and Merleau-Ponty, who concentrate on the perceptive and kinesthetic dimensions of the hand, and Heidegger and Levinas, who explore its ontological significance, viewing the hand as essential to human existence. I position Flusser within this ontological framework but show how he departs from his predecessors by developing an original manual approach that centers on the ‘gesture of making.’ By analyzing the eleven constitutive moments of this gesture, I demonstrate how Flusser’s chiro-phenomenology views gestures not only as expressive or communicative acts but as fundamental possibilities for human being-in-the-world. His work thus offers a crucial extension of phenomenology’s understanding of the body and action.

On the finite Radon transform for the computational homogenization of conductivity

AbstractThis paper investigates the computational homogenization of thermal conductivity problems using a finite Radon transform as proposed by Derraz and coworkers, implemented using the Fourier slice theorem to allow utilization of the fast Fourier transform‐based frameworks and methods. For the finite Radon transform both the original approach and the consistent approach proposed by Jabs and Schneider are used. The two discretizations are compared to the Moulinec–Suquet discretization using numerical examples. In doing so the convergence of the consistent approach is shown and the discussion of the original Radon approach is extended.

Siamese neural network improves the performance of a convolutional neural network in colloidal self-assembly state classification.

Identifying the state of the colloidal self-assembly process is critical to monitoring and controlling the system into desired configurations. Recent application of convolutional neural networks with unsupervised clustering has shown a comparable performance to conventional approaches, in representing and classifying the states of a simulated 2D colloidal batch assembly system. Despite the early success, capturing the subtle differences among similar configurations still presents a challenge. To address this issue, we leverage a Siamese neural network to improve the accuracy of the state classification. Results from a Brownian dynamics-simulated electric field-mediated colloidal self-assembly system and a magnetic field-mediated colloidal self-assembly system demonstrate significant improvement from the original convolutional neural network-based approach. We anticipate the proposed improvement to further pave the way for automated monitoring and control of colloidal self-assembly processes in real time and real space.

Interdisciplinary Aspects of Industry Innovations Use in Study Process Management at University

The paper provides an analysis and exposes the need for more intensive use of ICT in combination with modern sources of renewable energy for efficient implementation of modern control and management technologies. Some interdisciplinary aspects of renewable energy and IC technologies use at universities are analyzed. The objective of this paper is to outline the original approach to the connection of the study process at university in the field of applied knowledge and skills in scope of energy informatics with use of novel principles of energy use for infrastructure support and development. The use of innovation in energy and computer technologies for university infrastructure may guarantee the construction of very effective base for providing of practical training and R&amp;D activities for students. Justification of the role of interdisciplinarity in form of energo informatics as provider of industry innovations use in study process at university is one of the tasks on the path to objective. Critical analysis of a concept, using the literature as data and hybrid discriptive method were implemented for assigned tasks realization. Concept of interdisciplinary approach at university in scope of innovative essence of energo informatics in theorethical and practical spheres will open not only new horizonts in educational process, but also provide a new technological solutions implementation in university study environment. The activities described in this article should be realized in the design and development of a complex, reliable, robust, and sustainable system for the interaction between different disciplines in the scientific field, in the field of education management, and also in the field of application of modern energy and IC technologies to ensure the sustainabiliy of the university infrastructure.

The simple view of teaching: authorised pedagogies, curriculum and the neoliberal learner in preservice teacher education

ABSTRACT In the context of a global teacher recruitment crisis, the English department for Education has responded by implementing a new, highly prescriptive curriculum for initial teacher education called the Initial Teacher Training Core Content Framework. Using a combination of content analysis and an original approach entitled “doppelganger as method,” we examine where a doubling has occurred, asking how this doubling functions. Our analysis of the Core Content Framework focuses on the construction of both learners and learning, teachers and teaching. We find that the complex, relationship-based, messy act of educating pre-service teachers is reduced to a simple view of teaching in which fidelity to the authorised curriculum content and pedagogy define quality. This doubling of teacher education into its doppelganger, teacher training is essentially dehumanising as it denies the personhood of the people involved and the complex, relational aspect of the process.

Complementing the method of areas of Gaussian characteristic figures for the rapid diagnosis of the technical condition of the low-pressure gas-phase injectors

The Fit for 55 package has significantly reduced CO2 emissions from combustion propulsion sources. Therefore, it is considered important to maintain the efficiency of drive units at the required level. One of the main causes of changes in this area is the failure of the power system. This study focuses on the diagnosis of the gas injector, where the Gauss characteristic figure area method was used. The choice of the method was due to its analytical approach, which is different from the commonly used ones, and its easy interpretation of the results. However, the Gauss method was found to lack sensitivity in certain areas. Hence the proposal to supplement it with a trend line, which is a novelty of the study. The trend line, and in fact its indicators, allowed for improved inference relative to the original Gauss approach. This eliminated the fact of equivalent fields of figures that are rotated or distorted. The trend line indicators accurately identified the position of the characteristic figure and its shape. To confirm the expediency of using the proposed approach, tests were carried out for injectors: brand-new, in-operation, end-of-life and remanufactured, as well as those with artificially simulated deficiencies. The test results and analysis presented in the paper can be useful for further work, but also as a guideline for supplementing existing test stands with the described diagnostic functions. The proposed innovative approach was used for the case of gas injectors, but it is possible to apply it to testing injectors of other fuels.

Unsupervised Coherent Noise Removal From Seismological Distributed Acoustic Sensing Data

AbstractRecent advances in sensing technologies, particularly Distributed Acoustic Sensing (DAS), have significantly improved the collection and analysis of seismological data. DAS is a powerful method for detecting vibrations from various sources, including earthquakes. However, the vast amount of data produced by DAS requires sophisticated analytical methods to differentiate between signals of interest and noise, such as traffic signals. We introduce an innovative approach by extending the Noise2Self framework to effectively remove unwanted, structured coherent noise from DAS data. By creating masks based on the characteristics of traffic signals, we isolate and preserve earthquake signals while maintaining the denoising performance of the original Noise2Self approach, which reduces noise without requiring clean reference data. To evaluate our method, we used synthetic data generated from seismic recordings of closely spaced seismometers and then applied our approach to data from a DAS array crossing the Alpine Fault near Haast, New Zealand. Our results demonstrate that our model successfully removes traffic noise and other non‐coherent noise while preserving seismic signals, leading to improvements in both Signal‐to‐Noise Ratio (SNR) and waveform coherence. Evaluations on real‐world DAS data further confirm the robustness of our method, positioning it as a valuable tool for analyzing large‐scale DAS data sets in various geoscientific contexts. This approach, which we refer to as “CoherentNoise2Self” to emphasize the extension of Noise2Self to coherent noise, advances the capabilities for near real‐time monitoring and analysis of seismic DAS data.

SOFTWARE DESIGN PATTERN FOR EQUIPMENT OF AUTOMATIC MASK INSPECTION SYSTEMS IN MICROELECTRONICS

The paper investigates a relevant applied problem associated with software development for building equipment of automatic mask inspection systems and quality control topological structures in the microelectronics industry. This problem is one of key issues of design flow for equipment of automatic mask inspection systems and automatic defects detection. An original approach is proposed for selecting the sharpness function. Experiments have been conducted that confirm the effectiveness of original approach for obtaining high-quality initial images in equipment of automatic mask inspection systems using a technical vision system and, as a result, increases the percentage of yield of suitable products in microelectronics.

Laboratory and synchrotron x‐ray micro–computed tomography to shed light on degradation features of corroded Roman glass

AbstractThis study explores the three‐dimensional structure of Roman glass through the combined use of laboratory and phase‐contrast synchrotron x‐ray micro–computed tomography. This original approach validates a noninvasive analytical procedure designed to enhance the understanding of glass degradation mechanisms, crucial for the preservation of historical artefacts. The high‐resolution images obtained reveal the intricate internal structure and morphology of various forms of glass deterioration, including cracking, pitting, and the formation of multilayered iridescent patinas. The research provides compelling evidence of corrosion processes and interaction mechanisms with burial soil, shedding light on the chemical and physical interactions that occur over centuries. By characterizing and examining the long‐term alteration of ancient glass, this work contributes to the field of archaeometry, offering insights into how different types of glass withstand the test of time in diverse environmental conditions. The findings have important implications for conservation strategies, enabling better preservation techniques for glass artefacts.

Forecasting the quality of fluid seals and reservoirs for the Upper Jurassic deposits in the southeastern part of the West Siberian oil and gas province

Background. Upper Jurassic natural reservoirs are the main deposits containing 65–75% of recoverable hydrocarbon reserves in the Tomsk Region. They are overlapped by the fluid seals of the Bazhenov–Georgievsk complex, the quality of which deteriorates in the easterly direction. Therefore, to predict new promising areas, it is necessary to determine the quality and thickness of the Upper Jurassic seals and natural reservoirs. Objective. The need to forecast the quality of the Upper Jurassic fluid seals and reservoirs of the “Yu group” in the southeastern part of the West Siberian oil and gas province (Tomsk Region). Materials and methods. The paper uses regional and local studies of different years, materials and methods for interpreting geophysical well studies and seismic data from 2D reflection and core studies, databases of geological and geophysical information, methods of mathematical modeling of sedimentary basins and an original methodological approach proposed by the authors. Results. The time of the beginning of the lithification of the Bazhenov–Georgievsk rocks of the Upper Jurassic–Early Cretaceous (Aptian–Albian) was determined, and a regional model of the Georgievsk horizon distribution was constructed. The porosity of the Upper Jurassic natural reservoirs and the thickness of the Bazhenov fluid seal at the selected fields were calculated. Conclusions. The assessment of the quality of fluid seals and natural reservoirs made it possible to determine more promising areas for geological exploration using the oil and gas exploration criterion determined on the basis of the analysis of the thickness of the Georgievsk horizon (Kontorovich criterion). The efficiency of the proposed method is described using the example of the Upper Jurassic oil and gas fields in the Tomsk Region (Western Siberia).

Imaginative storytelling – novel immersive production practices and processes for mobile cinematic, interactive 360-degree and real-time VR

ABSTRACT This article adopts creative practice research to explore novel Virtual Reality (VR) production practices and processes for mobile cinematic VR (MCVR), parallel interactive narratives in VR (PIN VR) and real-time VR (RT VR). As a result of Creative Practice research, Imaginative Storytelling analyses and describes these original production approaches and processes for immersive storytelling and illustrates emerging aesthetic formations, which shape new imaginative experiences in VR. These diverse representations of VR are examined through three case studies, A New Dawn [A New Dawn. 2021. Dir. Schleser (VR). Australia. Swinburne University of Technology.], Table for Two [Table for Two. 2022. Dir. Remedios (VR). India. Indian Institute of Technology Hyderabad].) and Phases of Motion [Phases of Motion. 2021/2023. Dir. Berrett (VR). Australia. Swinburne University of Technology]. Each experience focuses on exploring innovative production processes for moile cinematic, interactive 360-degree and real-time VR. By means of outlining three distinctive approaches to VR production, contemporary developments and new directions in immersive storytelling are demonstrated. This research contributes to the expansion of storytelling and storyliving approaches in which navigators become co-creators of VR experiences.

DecTree: a physics-based geochemical surrogate for surface complexation of uranium on clay

Abstract. Geochemistry is usually the computational bottleneck in coupled reactive transport simulations, which hampers the complexity of the systems and of the processes they can investigate. In recent years, promising speedups have been obtained by substituting the numerical solution of geochemical models with approximated surrogates borrowed from artificial intelligence and machine learning (AI/ML). In the framework of the DONUT/EURAD project a set of benchmarks were defined to assess the performance and the accuracy of different surrogate approaches in settings relevant to the safety assessment of nuclear waste repositories, such as the surface complexation and exchange of U(VI) on clay. In this context, this work introduces am original surrogate modelling approach based on recursive partitioning of parameter space, which exploits prior domain knowledge for the training. The surrogate, which can be represented as a decision tree, hence the DecTree name, performs dimensionality reduction by identifying functional relationships between outputs and input variables using a straightforward non-monotonic extension of the Spearman's rank correlation coefficient. New predictions are then interpolated from the partitioned training data. Applied to a low-dimensional geochemical model, DecTree shows virtually no training time and excellent accuracy, ensuring a throughput of around 500 000 predictions per second on a single CPU core.

Nonlinear Dynamics of an Electromagnetically Actuated Cantilever Beam Under Harmonic External Excitation

The present work is devoted to the study of nonlinear vibrations of an electromagnetically actuated cantilever beam subject to harmonic external excitation. The soft actuator that controls the vibratory motion of such components of a robotic structure led to a strongly nonlinear governing differential equation, which was solved in this work by using a highly accurate technique, namely the Optimal Auxiliary Functions Method. Comparisons between the results obtained using our original approach with those of numerical integration show the efficiency and reliability of our procedure, which can be applied to give an explicit analytical approximate solution in two cases: the nonresonant case and the nearly primary resonance. Our technique is effective, simple, easy to use, and very accurate by means of only the first iteration. On the other hand, we present an analysis of the local stability of the model using Routh–Hurwitz criteria and the eigenvalues of the Jacobian matrix. Global stability is analyzed by means of Lyapunov’s direct method and LaSalle’s invariance principle. For the first time, the Lyapunov function depends on the approximate solution obtained using OAFM. Also, Pontryagin’s principle with respect to the control variable is applied in the construction of the Lyapunov function.

DEVELOPMENT OF HIGHER MEDICAL EDUCATION ABROAD IN THE STUDIES OF UKRAINIAN COMPARATIVISTS

The article presents a synthesized historiographical analysis of Ukrainian pedagogical comparativistsʼ works on the development of higher medical education in foreign countries, identifying trends, achievements, bottlenecks, and prospects for studying this problem. The presented sample of dissertations, monographs, and textbooks demonstrated their narrow country-specific vector and similarity in formulating the subject of research. Historiographical and comparative analysis revealed similar approaches of scientists to determining trends in the development and functioning of national systems of professional training for doctors. These concern the preservation and expansion of decentralization in higher medical education management; strengthening the interaction of its various links; shifting emphasis in determining the prerequisites for the development of national higher medical education systems from “traditional” factors (political, socio-economic, cultural situation of the country, etc.) to clarifying the influences of international medical organizations and documents in the field of healthcare that substantiate general strategies for training doctors in higher medical education institutions, etc. Scientists also propose original approaches to studying the functioning of national systems of higher medical education. These are manifested in the development of authors periodizations and classifications of the specified process; thorough characteristics of university environments for training future doctors in different countries; comprehensive understanding of the determinants of their formation and development, which, in addition to social, economic, and cultural factors of the country's development, relate to the level of education of the population, its multiculturalism, the specifics of educational systems, etc. In these perspectives, the analyzed studies mark two main country-specific vectors of research in higher medical education: “overseas” (USA and Canada) and “European” (mainly Great Britain, Poland, and German-speaking countries).

Reduced Scaling Correlated Natural Transition Orbitals for Multilevel Coupled Cluster Calculations.

Multilevel coupled cluster theory offers reduced scaling computation of intensive properties in systems that are too large for standard coupled cluster calculations. A significant benefit of the multilevel coupled cluster framework is the possibility of calculating intensive properties that are not tightly localized if an appropriate set of active orbitals is used. Correlated natural transition orbitals (CNTOs) are tailored to describe excitation processes. For multilevel coupled cluster singles and doubles (MLCCSD) and singles and perturbative doubles (MLCC2) calculations, the construction of CNTOs generally becomes the computational bottleneck. Here, we demonstrate how CNTOs can be obtained with operations, eliminating the -scaling steps involved in the original approach. This reduction in scaling moves the bottleneck of MLCC2 and MLCCSD calculations from the active orbital space preparation to the MLCC2 and MLCCSD equations with -scaling.