Homographic Research Articles

Optimized Fault Classification in Electric Vehicle Drive Motors Using Advanced Machine Learning and Data Transformation Techniques

The increasing use of electric vehicles has made fault diagnosis in electric drive motors, particularly in variable speed drives (VSDs) using three-phase induction motors, a critical area of research. This article presents a fault classification model based on machine learning (ML) algorithms to identify various faults under six operating conditions: normal operating mode (NOM), phase-to-phase fault (PTPF), phase-to-ground fault (PTGF), overloading fault (OLF), over-voltage fault (OVF), and under-voltage fault (UVF). A dataset simulating real-world operating conditions, consisting of 39,034 instances and nine key motor features, was analyzed. Comprehensive data preprocessing steps, including missing value removal, duplicate detection, and data transformation, were applied to enhance the dataset’s suitability for ML models. Yeo–Johnson and Hyperbolic Sine transformations were used to reduce skewness and improve the normality of the features. Multiple ML algorithms, including CatBoost, Random Forest (RF) Classifier, AdaBoost, and quadratic discriminant analysis (QDA), were trained and evaluated using Bayesian optimization with cross-validation. The CatBoost model achieved the best performance, with an accuracy of 94.1%, making it the most suitable model for fault classification in electric vehicle drive motors.

Quantum Interpretation for Measurements in Physics

The postulate in the Copenhagen interpretation is for quantum measures that in experiments from a set of operators only a subset of commuting operators can be measured simultaneously, the rest remains undetermined. As example the Stern-Gerlach measure shows for spin a 90 degree change and measures for instance spin up and down of the outcome particles only in the z-direction. The spacial xy-coordinates remain undetermined.Using octonion coordinates [1,2] for the quantum range this view can be applied to all octonion base GF triples which use the three noncommuting Pauli matrices. The coordinates are enumerated by indices 0,1,2,…,7. Entanglements of different GF are possible. Two examples are the spin-lepton cases 123 (for xyz) and 145 (1 for electrical EM charge, 4 for magnetism, 5 for leptonic mass) either in the gyromagnetic relation (EM) or the helicty (neutral leptons).A in the second case the Copenhagen interpretation is extended to a quantum interpretation for measurements of the GF. The systems and energies involved can be different. The neutrino N oscillations show that also the Heisenberg uncertainties HU play a role. Spin is aligned with the space coordinate 1 and the momentum p = mv on 6. The HU means that 1,6 cannot be measured sharp. Hence the leptonic kg measure 5 changes for p along the world line of N stochasticaly, observed as oscillation. The kg GF is 257 and has 6 possible values for leptons. The weights of the three GF coordinates are mostly positive real or complex numbers. There are seven octonion GF 123, 145, 167 (for electromagnetic interaction EMI), 246 (for heat, acoustics), 257, 347 (for rotational energy), 356 (for a nucleon inner dynamics). Beside these are for the strong interaction SI three more GF 126 as rgb-graviton , 345 for its dual Dg and 037 for a newly postulated color charge force cc.The new cc force has a different symmetry than the Pauli quaternions.The six complex cross ratios invariant under the Moebius transformations of the Riemannian sphere are discussed in relation to quantum measures.

A refined grey Verhulst model for accurate degradation prognostication of PEM fuel cells based on inverse hyperbolic sine function transformation

Accurate prognostication of degradation plays an essential role in effectively enhancing the operational lifespan of proton exchange membrane fuel cells (PEMFCs). This paper proposes a novel enhanced correctional grey Verhulst model (IHS-CRGVM-RE), designed to prognosticate the degradation process of PEMFCs using the voltage of PEMFCs stack as a health indicator. First, the inverse hyperbolic sine function transformation is employed to attain optimal smoothness in data treatment. Then, the background value within the grey Verhulst model framework is modified based on cellular automata with rectangle techniques. Finally, a residual correction mechanism is applied to delineate the influences of error outcomes concerning PEMFCs degradation. Rigorous validation is provided via a comprehensive analysis based on two distinct PEMFCs datasets. The results demonstrate that the proposed model outperforms other data-driven models in prognostication accuracy, highlighting its significant importance for prognosticating the lifespan of PEMFCs.

Asymptotic behaviour of the confidence region in orbit determination for hyperbolic maps with a parameter

When dealing with an orbit determination problem, uncertainties naturally arise from intrinsic errors related to observation devices and approximation models. Following the Least Squares Method and applying approximation schemes such as the differential correction, uncertainties can be geometrically summarized in confidence regions and estimated by confidence ellipsoids. We investigate the asymptotic behaviour of the confidence ellipsoids while the number of observations and the time span over which they are performed simultaneously increase. Numerical evidences suggest that, in the chaotic scenario, the uncertainties decay at different rates whether the orbit determination is set up to recover the initial conditions alone or along with a dynamical or kinematical parameter, while in the regular case there is no distinction. We show how to improve some of the results in Marò and Bonanno (2021), providing conditions that imply a non-faster-than-polynomial rate of decay in the chaotic case with the parameter, in accordance with the numerical experiments. We also apply these findings to well known examples of chaotic maps, such as piecewise expanding maps of the unit interval or affine hyperbolic toral transformations. We also discuss the applicability to intermittent maps.

A Mobius transformation-based liftoff effect reduction method for crack classification and prediction in eddy current testing

In eddy current (EC) defect evaluation, accurate determination of the crack depth is crucial. The lift-off signal on the complex impedance plane is not necessarily horizontal and mostly arc-shaped, which makes it difficult to separate from crack signals, which can have various orientations depending on probe characteristics and frequencies. Phase rotation is normally used to align the lift-off signal to the real axis and defect signal is read from the imaginary axis. However, this approach proves challenging to separate lift-off and defect signals within the complex plane in many cases.To address this issue, this study proposes and implements a Mobius transformation that linearizes the arc-shaped lift-off signals on the complex plane, enabling more accurate extraction of crack signals unaffected by lift-off signal height. Furthermore, it is demonstrated through k-nearest neighbors (kNN) that the transformed crack signals can still be effectively quantified.

A multi-stakeholder engagement framework for material-building-city synergy through circular transformation

Scholars, industrial stakeholders, and governmental institutions are developing the circular economy paradigm. However, the emergence of multiple perspectives has challenged its implementation. As the industry that is the biggest contributor to the negative impacts on the environment, the construction industry stakeholders are paving the way for more sustainable as well as circular and regenerative construction by considering all actors in the system. Yet, the construction industry has a complex supply chain that requires clear strategies and stakeholder engagement across materials, buildings, and cities for efficient flows in the supply chain. Nonetheless, there is a need for improvement in the engagement of construction stakeholders for circular transformation. Therefore, this study aims to develop a multi-stakeholder engagement framework through circular transformation to guide the decision-makers for circular city governance. It has identified critical success factors by considering the construction stakeholders. The framework includes strategies at the micro (material), meso (building), and macro (city) scales to strengthen the material-building-city synergy. It's a significant step toward advancing circular city governance by bridging the gap between theoretical understanding and practical implementation and establishing a robust engagement for material-building-city synergy. The study employs a systematic literature review to extract strategies and natural language processing to analyze the strategies by topic modeling and defines critical success factors for multi-stakeholder engagement at multiscale. The outcome introduces the REVERT framework, bridging resource, envisagement, validation, entity, regulation, and technology, to facilitate a seamless transition by material-building-city synergy advancing circular city governance.

Automated long-term monitoring of stereotypical movement in polar bears under human care using machine learning

The welfare of animals under human care is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant pacing. Traditional behaviour monitoring methods, however, are labour-intensive and subject to observer bias. Our study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in managed populations, exemplified by a longitudinal study of two polar bears. We designed an animal tracking framework to localise and identify individual animals in the enclosure. After determining their position on the enclosure map via homographic transformation, we refined the resulting trajectories using a particle filter. Finally, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %. The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological settings. Ultimately, we analysed 607 days for the occurrence of SB, allowing us to discuss seasonal patterns of SB in both the male and female polar bear monitored. This work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated detection and monitoring of stereotypical behaviour, paving the way for its application across various settings and species that can be continuously monitored with cameras. We made the code publicly available at https://github.com/team-vera/stereotypy-detector.

Gravitational wavefunctions in JT supergravity

We determine explicit expressions for the continuous two-sided gravitational wavefunctions in supersymmetric versions of JT gravity, focusing mainly on N\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\mathcal{N} $$\\end{document} = 2 JT supergravity. Our approach is based on representation theory of the associated supergroup, for which we determine the relevant mixed parabolic matrix elements that implement asymptotic AdS boundary conditions at the quantum level. We match our expressions with those found by solving the energy-eigenvalue equation [1]. We discuss gravitational applications by computing several amplitudes of interest, and address how our framework can be generalized further.

Supply chain transformative capabilities and their microfoundations for circular economy transition: A qualitative study in Made in Italy sectors

AbstractIn today's rapidly evolving landscape, supply chains must develop specific dynamic capabilities for initiating and successfully governing their transformations toward new configurations to withstand pressures arising from competitions and global changes. Differently from dynamic capabilities that enable adaptation, supply chain transformative capabilities include four core dimensions: triggering, envisioning, navigating, and stabilizing capabilities. By using a multiple‐case study approach involving six Italian companies related to fashion and furniture sectors, we investigate these concepts and identify the microfoundations of supply chain transformative capabilities for circularity. They are classified with reference to individuals, processes, and structures. Our findings illustrate how supply chains can facilitate and effectively govern their circular transformation by developing the transformative capabilities and their microfoundations. This paper provides valuable theoretical insights and managerial perspectives, advancing the concept of transformative supply chain management.

Mapping Service-Based Retailing to Improve Sustainability Practices in the Fashion Industry

The fashion industry is recognized among the most impactful industries in relation to environmental and social damage due to unsustainable models. While countless research studies focus on the supply chain and product innovation, the presented inquiry studies the fashion retail sector with the goal of systematizing existing sustainable practices based on service offerings. We present the results of an extensive case-study analysis conducted on 370 initiatives of different natures gathered among international companies and local actors in the European context that offer services oriented toward sustainability. As a result, we propose an ontology that encompasses three areas (services for environmental sustainability, culture of sustainability, and implementation of social sustainability) and comprehensively describes existing sustainability strategies for helping retailers preserve the environment, while promoting socially inclusive practices and disseminating the culture of sustainability. The innovation of retailing from a similar perspective aims at rendering businesses more resilient to survive market and social transformations, especially in consideration of the boost to sustainable and circular transformation advocated by recent European legislation.

Confession in Fiction: Boundaries and Scope of the Concept

In the following article, difficulties connected with the diversity of scientific approaches to the study of confession in different discourses (religious, legal, psychoanalytical, etc.) are discussed. The article deals with the difficulties associated with the diversity of scientific approaches to the study of confession in different discourses (religious, legal, psychoanalytic, etc.). It is stated that a theoretical understanding of literary (fictional) confession is conducted regarding already existing ideas about its Christian origins and circular transformations in European culture (from Augustine to Rousseau). The analysis of the structural and content features of literary confession with the support of M. M. Bakhtin’s aesthetics of verbal creation allows us to draw a line between the literary and the live word, including pseudo-confessional texts of the 19th and 21st centuries. It is confirmed that the concrete forms of embodiment of artistic confession (considering their diffuseness, generic specificity, and dependence on the author's individuality) resist typological study and require priority comprehension from the point of view of historical poetics. In conclusion, the groundlessness of the literary identification of confession with confessionalism is proved.

A note on the dislocation hyperbolic transformation function

In work of Roman A. Polyak [R.A. Polyak, Math. Program. 92 (2002) 197–235.], the Modified Chen-Harker-Kanzow-Smale (CHKS) function was studied to relate a multiplier method and an Interior Prox method with the second order distance function. Independently, the dislocated hyperbolic penalty function (DHPF) was proposed by A.E. Xavier (1992). DHPF was rewritten and studied in [L. Mallma Ramirez, N. Maculan, A.E. Xavier and V.L. Xavier, RAIRO:RO 57 (2023) 2941–2950.] and [L. Mallma Ramirez, N. Maculan, A.E. Xavier and V.L. Xavier, J. Convex Anal. To appear (2024).]. Thus, this function was called the dislocated hyperbolic function (DHF). In this work, we note that DHF is a particular case of CHKS function. Then we will call the DHF function as the dislocation hyperbolic transformation function.

CIRCULAR BUSINESS MODELS IN AGRICULTURE: ELEMENTS, CREATION DRIVERS AND APPLICATION PERSPECTIVES

The article reveals the organizational and economic principles regarding the elements and drivers of creating circular business models in the agri-food sector of Ukraine. It substantiates the perspective business models of the circular economy for the agri-food sector of Ukraine. It is substantiated that circular business models are new ways of organizing economic ties within the agri-food system, which are tolerant towards the environment and natural resources and form prerequisites for increasing social well-being. The factors that become drivers for the creation of circular business models for the agri-food sector are determined; it is proved that the key to them is the consumer behavior of buyers and the level of social responsibility since consumer behavior and business interests must achieve a balance under the conditions that the result of the system will satisfy all interested parties. The elements of the circular business model of the agri-food sector are defined and focus on specific tools related to the R principles of creating, offering, and realizing value. Prospective agro-circular economy business models for the agri-food sector of Ukraine have been identified, namely: organic farming model, natural farming model, agroforestry improvement, biomass use, innovations in food packaging, eco-design, integrated system of organic farming, integration of value chains within agriculture and the agro-food sector, models of ecological bioprocessing of waste and resources. Modern problems of implementing circular models in the practice of business entities of the agro-food sector are outlined. Their solution is substantiated within the framework of a complex systemic approach to the management of circular transformations, which should be implemented at all levels of economic management and contribute to the achievement of a single strategic goal - increasing the level of sustainability of the domestic agro-food sector and the national economy as a whole.

Building a model for evaluating the efficiency of circular transformations in the context of technology transfer

The object of this study is the process of circular transformations in the context of technology transfer. It has been proven that circular transformations in the context of technology transfer are a new challenge in the global directions of harmonization of economic processes. The relevance of the study is determined by the fact that the development of technologies, in particular "green" technologies under modern conditions has become a catalyst for changes in production, consumption, and management of resources, which contributes to the achievement of the goals of sustainable development of enterprises. This involves focusing on the formation and support of social integration of the production sector and society, reducing emissions into the environment, and ensuring the balanced development of business entities at different levels of management. The characteristics of the level of circular transformations in the countries of the world were studied. The relationship between technology transfer and circular transformations was established through indicators of the degree of greening of the economy. A SWOT analysis of circular transformations was conducted to identify key opportunities and threats that would affect the business environment. A model for evaluating the effectiveness of circular transformations has been built. Based on the proposed model, the GDP change forecast for the EU countries for 2023–2027 was calculated. Carbon dioxide emission trends were predicted under optimistic and pessimistic scenarios, which made it possible to justify the need to transition to a carbon-neutral model of economic development and the feasibility of implementing "green" technologies. The transition to a new model of economic development will allow for optimal management of depleted resources, wider use of resource-saving technologies and will reduce pressure on the environment

Полихудожественное взаимодействие обучающихся во время построения лингвокультурных сценариев как средство обогащения тематического словарного запаса студентов – будущих переводчиков (на примере английского языка)

The purpose of the study is to determine the influence of poly-artistic methodological support for the study of linguistic and cultural scenarios on the formation of lexical and grammatical foreign language skills among students at the Faculty of Translation. The initial understanding of the cognitive approach to teaching foreign languages in a narrow sense (only as a system-theoretical support for processes) led to its underestimation and oblivion. The article examines the current trends towards borrowing individual components of the cognitive approach for the development of a communicative method of learning foreign languages. The scientific novelty of the research consists in the introduction of a poly-artistic approach in the training of students – future translators through the adaptation of professional highly specialized exercises for the training of future actors, directors and theater critics for their subsequent integration into the process of studying English-language frame models and scenarios, which had not previously become the object of research. The relevance of the study lies in the fact that, despite many years of experience using a communicative approach in teaching foreign languages, many students still face difficulties activating passive thematic vocabulary and grammatical rules and, as a result, a language barrier. As a result of the study, it was found that in conditions of meaningful operation of language and speech skills, students present the educational process not as a set of rules or a set of information, but as a toolkit for independent creative language activity. Using the Kolmogorov-Smirnov λ-criterion and the Fisher φ*-criterion (circular transformation), the beneficial effect of artistic and methodological support for the construction of linguistic and cultural scenarios on the expansion of the thematic vocabulary of students within the framework of the theme “At the Restaurant” has been experimentally confirmed.

Improving the Efficacy of Continuous Monitoring of Patients with Parkinson's Disease Through the Use of an Intelligent Computing Architecture Within the Medical Internet of Things

Telemonitoring has grown in popularity these days, particularly to assist patients with serious illnesses such as Parkinson's disease (PD). This study relies on the Daphnet dataset, which was trained and utilized to track five patients' whereabouts for an extensive dataset. Fuzzy logic was used in conjunction with a linear and Mobius map as part of the fog computing freezing of gait (FoG) detection system to provide a multi-level output (MLFM-map) that takes advantage of various spatial resolutions in motion data processing. Two improved Salp Swarm Algorithms with Fuzzy Logic (Fuzzy-ISSA) and transient search optimization algorithm (TSO) have been employed for the methodical training of this tool at the level of fog computing. When compared to other machine learning techniques, this one trains the FOG detection system quite quickly. In order to maximize performance while lowering computational complexity and testing time, the model architecture and parameters have been carefully considered. On average, the suggested method detected over 90% of FoG occurrences with extremely low latency in the original (test) dataset, demonstrating good to exceptional classification performance. In addition, the algorithm demonstrated a specificity of over 90% when tested on the test set. Various algorithms are used to train this design.

A Novel Multi-camera Fusion Approach at Plant Scale: From 2D to 3D

Non-invasive crop phenotyping is essential for crop modeling, which relies on image processing techniques. This research presents a plant-scale vision system that can acquire multispectral plant data in agricultural fields. This paper proposes a sensory fusion method that uses three cameras, Two multispectral and a RGB depth camera. The sensory fusion method applies pattern recognition and statistical optimization to produce a single multispectral 3D image that combines thermal and near-infrared (NIR) images from crops. A multi-camera sensory fusion method incorporates five multispectral bands: three from the visible range and two from the non-visible range, namely NIR and mid-infrared. The object recognition method examines about 7000 features in each image and runs only once during calibration. The outcome of the sensory fusion process is a homographic transformation model that integrates multispectral and RGB data into a coherent 3D representation. This approach can handle occlusions, allowing an accurate extraction of crop features. The result is a 3D point cloud that contains thermal and NIR multispectral data that were initially obtained separately in 2D.

Influence of building spatial patterns on wind environment and air pollution dispersion inside an industrial park based on CFD simulation.

The "spatial pattern-wind environment-air pollution" within building clusters is closely interconnected, where different spatial pattern parameters may have varying degrees of impact on the wind environment and pollutant dispersion. Due to the complex spatial structure within industrial parks, this complexity may lead to the accumulation and retention of air pollutants within the parks. Therefore, to alleviate the air pollution situation in industrial parks in China and achieve the circular transformation and construction of parks, this study takes Hefei Circular Economy Demonstration Park as the research object. The microscale Fluent model in computational fluid dynamics (CFD) is used to finely simulate the wind flow field and the diffusion process of pollutants within the park. The study analyzes the triad relationship and influence mechanism of "spatial pattern-wind environment-air pollution" within the park and studies the influence of different spatial pattern parameters on the migration and diffusion of pollutants. The results show a significant negative correlation between the content of pollutants and wind speed inside the industrial park. The better the wind conditions, the higher the air quality. The spatial morphology parameters of the building complex are the main influences on the condition of its internal wind environment. Building coverage ratio and degree of enclosure have a significant negative correlation with wind conditions. Maintaining them near 0.23 and 0.37, respectively, is favorable to the quality of the surrounding environment. Moreover, the average height of the building is positively correlated with the wind environment condition. The rate of transport and dissipation of pollutants gradually increases as the average building height reaches 16 m. Therefore, a reasonable building planning strategy and arrangement layout can effectively improve the wind environment condition inside the park, thus alleviating the pollutant retention situation. The obtained results serve as a theoretical foundation for optimizing morphological structure design within urban industrial parks.

Circular supply chain management: Antecedent effect of social capital and big data analysis capability and their impact on sustainable performance

AbstractThe circular economy era has begun, and circular supply chain management (CSCM) is widely acknowledged as a management paradigm that promotes firm sustainability. Through previous research, we have learned that the key challenge in the circular transformation of manufacturing firms is to encourage supply chain partners to collaborate and effectively participate in resource cycles. Drawing inspiration from social capital theory and resource‐based views, we explored how social capital effect sustainable performance through CSCM and the moderating effect of big data analytics capability (BDAC). We tested this by collecting data from 414 Chinese manufacturing firms. The results indicate that both internal and external social capital are positively correlated with CSCM. In addition, CSCM mediates the relationship between social capital and organizational environmental and social sustainability. The results also indicate that BDAC strengthen the impact of external social capital on CSCM, but the moderating effect on the relationship between internal social capital and CSCM is not significant. This essay advances the theory and practice of CSCM by offering fresh perspectives on how social capital influences sustainable performance of firms.

Parabolic Tamari Lattices in Linear Type B

We study parabolic aligned elements associated with the type-$B$ Coxeter group and the so-called linear Coxeter element. These elements were introduced algebraically in (Mühle and Williams, 2019) for parabolic quotients of finite Coxeter groups and were characterized by a certain forcing condition on inversions. We focus on the type-$B$ case and give a combinatorial model for these elements in terms of pattern avoidance. Moreover, we describe an equivalence relation on parabolic quotients of the type-$B$ Coxeter group whose equivalence classes are indexed by the aligned elements. We prove that this equivalence relation extends to a congruence relation for the weak order. The resulting quotient lattice is the type-$B$ analogue of the parabolic Tamari lattice introduced for type $A$ in (Mühle and Williams, 2019).