Variable Scale Research Articles

Signal-to-Noise Ratio in Estimating and Testing the Mediation Effect: Structural Equation Modeling versus Path Analysis with Weighted Composites

Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models are determined by the subjectively assigned scales of the latent variables. Thus, conclusions in existing studies comparing structural equation modeling (SEM) and path analysis with weighted composites (PAWC) on the accuracy and precision of the estimates of the indirect effect in mediation analysis have little validity. Instead of comparing the size on estimates of the indirect effect between SEM and PAWC, this article compares parameter estimates by signal-to-noise ratio (SNR), which does not depend on the metrics of the latent variables once the anchors of the latent variables are determined. Results show that PAWC yields greater SNR than SEM in estimating and testing the indirect effect even when measurement errors exist. In particular, path analysis via factor scores almost always yields greater SNRs than SEM. Mediation analysis with equally weighted composites (EWCs) also more likely yields greater SNRs than SEM. Consequently, PAWC is statistically more efficient and more powerful than SEM in conducting mediation analysis in empirical research. The article also further studies conditions that cause SEM to have smaller SNRs, and results indicate that the advantage of PAWC becomes more obvious when there is a strong relationship between the predictor and the mediator, whereas the size of the prediction error in the mediator adversely affects the performance of the PAWC methodology. Results of a real-data example also support the conclusions.

The Role of CPV as a Mediation of Factors That Influence Repurchase Intention in Outdoor Products

Consumers have an interest in repurchasing a product due to several factors, namely brand image, quality, price and the balance between the benefits obtained and the sacrifices made (Customer Perceived Value). This research aims to test the influence of brand image, quality, and price on repurchase interest in Merapi Mountain outdoor products, and to test the influence of brand image, quality, and price on repurchase intention with customer perceived value (CPV) as an intervening variable for outdoor products Merapi Mountain. The variable measurement scale used is a Likert scale with a score rating (1-5) and a sample of 122. Data processing in this research uses smartPLS (Partial Least Square) software with outer model and inner model measurement models. The results of this research show that brand image and quality have a positive and significant effect on repurchase intention. Price has an insignificant positive effect on repurchase interest. Brand image, quality, and price have a positive influence on repurchase intention with CPV as an intervening variable.

Nonlinear mechanisms of CO2 emissions in growing and shrinking cities: An empirical study on integrated effects of aging and industrial structure in Japan

For the first time, this research aims to reveal how population age changes the nexus of industrial structure upgrading-carbon emissions under urban growth and shrinkage using a panel threshold model. Population aging, industrial structure upgrading, and CO2 emission mitigation represent three main challenges for urban development globally. However, only a few studies have integrated these challenges under the same framework, especially in the context of differentiated urban development patterns. Taking Japan as an example, urban development patterns are classified into four types using urban population and population density data from 2010 to 2020, including growth type, potential shrinking type, smart shrinking type, and continuous shrinking type. Each type's CO2 emission patterns regarding energy consumption are then examined. After controlling for several socioeconomic and urban land scale variables, population aging and industry structure were used as the threshold variable and the key explanatory variable in the panel threshold model, respectively. The results showed that growing cities have lower carbon emissions than any of the shrinking cities in terms of CO2 emissions per capita. A single-threshold effect of population aging was detected that changes the nexus of industrial structure upgrading-CO2 emissions in potentially shrinking cities and continuously shrinking cities, while no such nonlinear effect was detected in the growing group. The mitigating effect of industrial structure upgrading on CO2 emissions per capita increases as the aging level crosses 23.71% in potentially shrinking cities. In contrast, the promoting effect of industrial structure upgrading on CO2 emissions per capita weakens as the aging level crosses 20.27% in continuously shrinking cities. This study implies a nonlinear mechanism of CO2 emissions considering the integrated effects of aging and industrial structure, contributing to developing a rational and efficient emission reduction strategy considering differentiated urban development patterns.

Multi-feature parallel topology optimization of fiber-reinforced coated structures based on a dual variable scale filtering method

Due to their outstanding mechanical properties, fiber-reinforced polymer composite (FRPC) structures have become a prominent research topic. Yet, to surpass existing performance limits, their design potential should be expanded beyond conventional empirical methods. Moreover, because of the high manufacturing costs, the protection of FRPC structures should be considered in the design phase to extend their service life. In this paper, we introduce an innovative configuration wherein metal coatings are strategically applied to the surfaces of FRPC structures. This technique aims to protect the internal matrix and fiber materials, and to inhibit the interlaminar delamination of composite laminates. Then, a corresponding full-scale topology optimization framework is built based on a density-based method. The coating, matrix, and fiber materials are considered collectively, avoiding issues such as excessive computation, non-convex optimization, and fiber discontinuity. Furthermore, a dual variable scale filtering (DVSF) method and its simplified scheme are presented to optimize coating thickness based on the physical information obtained from finite element analysis. Meanwhile, the co-evolution of solid topology and fiber morphology, driven by algorithms, facilitates the parallel optimization of multiple geometric features. Several typical examples, including wing ribs, are provided to illustrate the effectiveness and superiority of our approach, highlighting its engineering relevance.

Clinical Course, Antifungal Susceptibility, and Genomic Sequencing of Trichophyton indotineae

Trichophyton indotineae is an emerging dermatophyte causing outbreaks of extensive tinea infections often unresponsive to terbinafine. This species has been detected worldwide and in multiple US states, yet detailed US data on infections with T indotineae are sparse and could improve treatment practices and medical understanding of transmission. To correlate clinical features of T indotineae infections with in vitro antifungal susceptibility testing results, squalene epoxidase gene sequence variations, and isolate relatedness using whole-genome sequencing. This retrospective cohort study of patients with T indotineae infections in New York City spanned May 2022 to May 2023. Patients with confirmed T indotineae infections were recruited from 6 New York City medical centers. Improvement or resolution at the last follow-up assessment. Among 11 patients with T indotineae (6 male and 5 female patients; median [range] age, 39 [10-65] years), 2 were pregnant; 1 had lymphoma; and the remainder were immunocompetent. Nine patients reported previous travel to Bangladesh. All had widespread lesions with variable scale and inflammation, topical antifungal monotherapy failure, and diagnostic delays (range, 3-42 months). Terbinafine treatment failed in 7 patients at standard doses (250 mg daily) for prolonged duration; these patients also had isolates with amino acid substitutions at positions 393 (L393S) or 397 (F397L) in squalene epoxidase that correlated with elevated terbinafine minimum inhibitory concentrations of 0.5 μg/mL or higher. Patients who were treated with fluconazole and griseofulvin improved in 2 of 4 and 2 of 5 instances, respectively, without correlation between outcomes and antifungal minimum inhibitory concentrations. Furthermore, 5 of 7 patients treated with itraconazole cleared or had improvement at the last follow-up, and 2 of 7 were lost to follow-up or stopped treatment. Based on whole-genome sequencing analysis, US isolates formed a cluster distinct from Indian isolates. The results of this case series suggest that disease severity, diagnostic delays, and lack of response to typically used doses and durations of antifungals for tinea were common in this primarily immunocompetent patient cohort with T indotineae, consistent with published data. Itraconazole was generally effective, and the acquisition of infection was likely in Bangladesh.

Exploring the geometry of the bifurcation sets in parameter space

By studying a nonlinear model by inspecting a p-dimensional parameter space through (p-1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(p-1)$$\\end{document}-dimensional cuts, one can detect changes that are only determined by the geometry of the manifolds that make up the bifurcation set. We refer to these changes as geometric bifurcations. They can be understood within the framework of the theory of singularities for differentiable mappings and, in particular, of the Morse Theory. Working with a three-dimensional parameter space, geometric bifurcations are illustrated in two models of neuron activity: the Hindmarsh–Rose and the FitzHugh–Nagumo systems. Both are fast-slow systems with a small parameter that controls the time scale of a slow variable. Geometric bifurcations are observed on slices corresponding to fixed values of this distinguished small parameter, but they should be of interest to anyone studying bifurcation diagrams in the context of nonlinear phenomena.

A novel unconstrained methodology for economic analysis of the level of repair with a case study of a multi-indenture and multi-echelon repair network

The multi-indenture and multi-echelon economic analysis of repair levels is commonly modelled as a constrained 0–1 programming problem. However, with the increase in the scale of the decision variables, the proportion of feasible solutions in the solution space sharply decreases, posing a challenge for traditional solution methods. In this paper, the transformation of the initial problem into an unconstrained integer programming problem is demonstrated. Specifically, the total number of feasible decisions can be solved analytically through a decision flow method, and a bijective relationship between decision sequence numbers and feasible decisions is established using a mixed-radix system, converting the optimization variables to feasible decision sequence numbers; in this way, the initial problem becomes unconstrained. The advantages of this approach include the following: (1) All the solutions in the transformed solution space are feasible, which significantly improves the efficiency of the solution and reduces the dimensionality of the decision variables; and (2) the proposed model is highly flexible in terms of considering additional variables, such as decision time, transportation modes, and repair locations. Furthermore, the sequence numbers of each feasible solution are transformed into binary sequences, and the binary particle swarm optimizer (BPSO) algorithm can be employed to solve the model. The proposed methodology is applied to the case of a three-indenture and three-echelon repair network considering multiple fault modes, and the results are compared with those of previous studies. The results validate the rationality and substantial advantages of the proposed methodology for economic analysis in terms of computational speed and convergence performance.

Let's talk about sex! Why should healthcare professionals address sexual distress in breast cancer patients and survivors?

PurposeSexual distress impacts the quality of life (QoL) of breast cancer patients but is often overlooked in standard care pathways. This study evaluated the prevalence and factors of sexual distress among Dutch breast cancer patients, compared them to the general population, and explored how sexual distress is discussed in clinical settings from the perspectives of patients and healthcare professionals (HCPs). MethodsQuestionnaires containing the Female Sexual Distress Scale (FSDS) and demographic variables were distributed to women with breast cancer. The effect of breast cancer on sexual distress was assessed with a Mann-Whitney U test. Multivariable linear regression was used to analyze variables associated with FSDS. The Sexuality Attitudes and Beliefs Survey (SABS) was sent to HCPs. ResultsBreast cancer patients reported significantly higher sexual distress compared to a Dutch non-breast cancer cohort, respectively 16.38 (SD 11.81) and 23.35 (SD 11.39). Factors associated with higher sexual distress were psychological comorbidities, the body image scale, and being diagnosed >10 years ago. Sexual distress was not discussed as often as patients needed. Barriers to addressing sexual distress were time constraints, HCPs’ confidence in their ability to address sexual distress, and uncertainty about who is responsible for initiation. ConclusionsBreast cancer patients showed significantly higher sexual distress compared to the Dutch population. However, it was not frequently addressed in the consultation room. While some barriers have been identified, this study highlights the importance of further exploring obstacles to integrating discussions about sexual distress into routine care to improve QoL of breast cancer patients.

Cerebral organoids display dynamic clonal growth and tunable tissue replenishment.

During brain development, neural progenitors expand through symmetric divisions before giving rise to differentiating cell types via asymmetric divisions. Transition between those modes varies among individual neural stem cells, resulting in clones of different sizes. Imaging-based lineage tracing allows for lineage analysis at high cellular resolution but systematic approaches to analyse clonal behaviour of entire tissues are currently lacking. Here we implement whole-tissue lineage tracing by genomic DNA barcoding in 3D human cerebral organoids, to show that individual stem cell clones produce progeny on a vastly variable scale. By using stochastic modelling we find that variable lineage sizes arise because a subpopulation of lineages retains symmetrically dividing cells. We show that lineage sizes can adjust to tissue demands after growth perturbation via chemical ablation or genetic restriction of a subset of cells in chimeric organoids. Our data suggest that adaptive plasticity of stem cell populations ensures robustness of development in human brain organoids.

Morphometric analysis of the Late CretaceousPlacenticerasof Alabama, USA: sexual dimorphism, allometry, and implications for taxonomy

AbstractA traditional typological approach to taxonomy often does not adequately account for intraspecific variation and can result in taxonomic oversplitting. For many groups, including ammonoids of thePlacenticerasgenus, intraspecific variation documented in recent studies (e.g., ontogenetic changes, sexual dimorphism, polymorphism) challenges the historic proliferation of species names. Here, we used a population approach to taxonomy and quantitatively evaluated morphometric variation in a sample of Late Cretaceous (Santonian–Campanian)Placenticerasfrom Alabama and adjacent counties.We used linear mixed models (LMMs) to characterize how morphological variables scale with conch size across the sample, exploiting mixed longitudinal data to evaluate individual variation in growth and inform interpretations of multivariate analyses. Extended LMMs incorporating geological formation evaluated morphological changes through time. Principal component analysis and clustering analysis were then used to evaluate the number of distinct clusters that emerged in multivariate morphospace independent of previous taxon name assignments.Discontinuous scaling relationships and distinct clusters in multivariate space suggest sexual dimorphism characterized by differences in adult size and, secondarily, shape. PreviousStantonocerasandPlacenticerasassignments broadly overlap in our morphospace, failing to justify this historic distinction (as sexual dimorphs or as genera or subgenera).Placenticerasconch morphology and ornament placement changed through time, suggesting a potential utility for coarse (stage-level) biostratigraphy. However, temporal changes were not associated with distinct clusters in morphospace, and our data fail to support the plethora of reported species names. As few as one or two (successive) species may be present in our sample (representing 130 years of collection effort). In addition to highlighting the need for a significant taxonomic revision of thePlacenticerasgenus, this study demonstrates the utility of LMMs for distinguishing between different sources of morphological variation, improving interpretations of morphospace under a population approach to taxonomy, and maximizing the amount of ontogenetic information that can be obtained nondestructively.

Social Media Data Mining and Mental Health Status Assessment Based on Sentiment Analysis

The usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.

EVALUATING THE EFFICIENCY OF COUNTRIES' DIGITALIZATION IN TERMS OF FINANCIAL INCLUSION BY USING DEA

The aim of the study is to evaluate the efficiency of countries in digitalization in terms of financial inclusion, in other words, their success in digitalization. Within the scope of the study, 117 countries were grouped as high, upper-middle, lower middle and low income and subjected to Data Envelopment Analysis. In the study conducted for the year 2021, four input variables related to the utilization and access of digital financial services were identified, along with ten corresponding output variables. Efficient countries were determined in the study based on the constant and variable return scale model for output. Digital efficiency has been identified in financial inclusion was detected in 25 countries based on variable returns and 18 countries based on constant returns in high-income countries. In upper-middle-income countries, digital efficiency in financial inclusion was identified in 12 countries based on variable returns and 9 countries based on constant returns. 16 countries in the lower middle and low income categories demonstrated digital efficiency in financial inclusion using variable returns, while 9 countries detected digital efficiency using constant returns. In the study, the comprehensive analysis of the success of digitalization in financial inclusion at the level of countries divided into income groups by using data envelopment analysis reveals the originality of the study.

Algorithm for Coal Mine Rock Foreign Object Detection Based on Enhanced YOLOv8

In addressing the challenge of suboptimal detection precision in coal mine rock foreign object detection due to complex environments and variable object scales, this paper proposes a coal mine rock foreign object detection algorithm based on YOLOv8. Initially, the incorporation of the BiFormer attention mechanism is advocated to refine the backbone network, augmenting the model's attention towards pivotal information regions, consequently enhancing localization and feature extraction capabilities. Secondly, a lightweight Content-Aware Recurrent Affine Feature Extraction (CARAFE) operator is utilized within the neck architecture to effectively capture and preserve intricate features at lower hierarchical levels. Finally, Wise-IoU v3 is adopted as the bounding box regression loss for the proposed algorithm, coupled with a prudent gradient allocation approach, thereby enhancing the model's localization capabilities. Empirical findings illustrate that compared to baseline algorithms, the proposed algorithm has fewer parameters, with an average mAP improvement of 2.5%, and a detection speed increase of 2fps/s.

Salience Interest Option: Temporal abstraction with salience interest functions

Reinforcement Learning (RL) is a significant machine learning subfield that emphasizes learning actions based on environment to obtain optimal behavior policy. RL agents can make decisions at variable time scales in the form of temporal abstractions, also known as options. The issue of discovering options has seen a considerable research effort. Most notably, the Interest Option Critic (IOC) algorithm first extends the initial set to the interest function, providing a method for learning options specialized to certain state space regions. This approach offers a specific attention mechanism for action selection. Unfortunately, this method still suffers from the classic issues of poor data efficiency and lack of flexibility in RL when learning options end-to-end through backpropagation. This paper proposes a new approach called Salience Interest Option Critic (SIOC), which chooses subsets of existing initiation sets for RL. Specifically, these subsets are not learned by backpropagation, which is slow and tends to overfit, but through particle filters. This approach enables the rapid and flexible identification of critical subsets using only reward feedback. We conducted experiments in discrete and continuous domains, and our proposed method demonstrate higher efficiency and flexibility than other methods. The generated options are more valuable within a single task and exhibited greater interpretability and reusability in multi-task learning scenarios.

To behave or not (un)ethically? The meditative effect of mindfulness on statistics anxiety and academic dishonesty moderated by risk aversion

Despite the growing interest in mindfulness in higher education, the literature on its relation to decision-making under risk (i.e. academic misconduct) and statistics anxiety is scarce. The present research shall fill this gap. Based on the prospect theory, we assessed the mediating effect of mindfulness on the relationship between statistics anxiety and academic dishonesty moderated by risk aversion. Data were collected from 791 undergraduate students in six Israeli academic institutions studying for bachelor’s degrees in social sciences. Questionnaires included the following measures: risk behaviour according to the prospect theory framework, Mindful Attention Awareness Scale, Statistics Anxiety Rating Scale, Academic Misconduct Scale and sociodemographic variables. Correlations among these variables were explored. The data was analysed using Structural Equation Modelling (SEM). The results indicate that the variance in academic dishonesty is explained by students' statistics anxiety with a mediation of Mindfulness moderated by Risk Aversion. Mindfulness negatively affects Academic Dishonesty, while Risk Aversion has a significant positive effect on Mindfulness. Finally, among individuals with high statistics anxiety, Risk Averse individuals show significantly higher Mindfulness than Risk Seekers. We conclude that mindfulness-based interventions might be a constructive tool to reduce risk-taking and promote ethical decision-making among individuals who experience high levels of statistics anxiety. Furthermore, developing mindful skills may help individuals with higher anxiety levels neutralize these unwanted feelings and get along with their learning tasks. Hence, avoid academic unethical behaviours.

Application of facial expression recognition technology based on feature fusion in teaching

With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved

Computer Modeling Analysis of Electric Vehicle's Choice Behavior Considering Latent Variables

At present, there are few researches on the influence of psychological latent variables on the choice behavior of electric vehicles, and lack of consideration of individual heterogeneity. In this study, computer modeling was used to construct not only a traditional discrete choice model that takes into account individual heterogeneity, but also a hybrid choice model,which takes into account psychological latent variables are constructed to explore the mutual effects of observable personal attributes, vehicle attributes and difficult to directly observe psychological latent variables on consumers’ choice behavior of electric vehicles. The estimation results of hybrid choice model with psychological latent variables and the traditional discrete choice model without considering psychological latent variables are compared. The result show that perceived usefulness, perceived risk and purchasing attitude have a significant impact on consumers’ choice behavior of electric vehicles. Compared with traditional discrete choice model, the hybrid choice model, which takes into account psychological latent variables has significantly higher prediction accuracy and has better interpretation ability and fitting effect. The research results can provide theoretical support for further expanding the electric vehicle market and formulating relevant policieshe usage of the word Power quality in recent times acquired intensified interest due to the complex industrial processes. The usage of intelligent tools to improve power quality is increasing day by day, as assumption of present day power system as a linear model is unsatisfactory. This paper deals with analysis of Differential Evolution (DE), Hybrid Differential Evolution (HDE) and Variable Scaling Hybrid Differential Evolution for harmonic reduction in the source current with optimal tuning of PI controller gain values. Shunt Active power Filter is one of the better solution to suppress the source current harmonics which are induced into power system because of nonlinear loads. Current controller called HBCC is considered for gating operation of switches in Voltage Source Inverter. The Intelligent tuned PQ theory is used for reference current generation. The then obtained compensating currents are injected at point of common coupling for current disturbance mitigation. Simulations of MATLAB/SIMULINK environment of the present work shows the efficacy.

Clinical phenotypes of comorbidities in end-stage knee osteoarthritis: a cluster analysis

ObjectivesComorbidities, as components of these heterogeneous features, often coexist with knee osteoarthritis, and are particularly prevalent in end-stage knee osteoarthritis. Here, we attempted to identify the different clinical phenotypes of comorbidities in patients with end-stage knee osteoarthritis by cluster analysis.MethodsA total of 421 inpatients diagnosed with end-stage knee osteoarthritis who underwent inpatient surgery were included in this cross-sectional study. 23 demographic, comorbidity, inflammatory immune and evaluation scale variables were collected. Systematic clustering after factor analysis and separate two-step cluster analysis were performed for individual comorbidity variables and all variables, respectively, to objectively identify the different clinical phenotypes of the study patients.ResultsFour clusters were finally identified. Cluster 1 had the largest proportion of obese patients (93.8%) and hypertension was common (71.2%). Almost all patients in cluster 2 were depressed (95.8%) and anxiety disorders (94.7%). Cluster 3 combined patients with isolated end-stage knee osteoarthritis and a few comorbidities. Cluster 4 had the highest proportion of patients with rheumatoid arthritis (58.8%).ConclusionsPatients with end-stage knee osteoarthritis may be classified into four different clinical phenotypes: "isolated end-stage knee osteoarthritis"; "obesity + hypertension"; "depression + anxiety"; and "rheumatoid arthritis", which may help guide individualized patient care and treatment strategies.

Educational technology for patients with external fixator in the lower limbs

The objective of the work is to develop and validate an educational technology in the form of a booklet for guidance on the care of orthopedic patients using external fixators in the lower limbs. This is a study of methodological, bibliographical development, with a qualitative-quantitative approach. A questionnaire containing Likert scale variables was used as an instrument for evaluating educational technology, and the Content Validity Index for each validation stage was used for analysis, considering items equal to or greater than 0.80 to be valid. Validation was carried out by 18 expert judges, among them 8 (44.44%) were nurses, 3 (16.66%) doctors, 3 (16.66%) physiotherapists and 4 (22.22%) nursing technicians , while validation by the target audience occurred with 20 patients undergoing treatment with an external fixator in the lower limbs. It is considered validated by professionals with a global Content Validity Index of 1.0 and by patients, with a global Content Validity Index of 0.98. The educational material proved to be valid in terms of appearance and content, and suitable to contribute to health promotion and use in trauma-orthopedics care practice.

A novel fault diagnosis method for permanent magnet synchronous motor based on VS-Inception

The new energy vehicle industry has an increasing demand for safety, stability, and efficiency in the operation of essential equipment, particularly in the intelligent means of fault diagnosis of motor equipment, which is rising annually. For the past several years, there has been an increasing demand for accuracy and precision in troubleshooting fault diagnosis of permanent magnet synchronous motors. This paper presents a variable-scale Inception (VS-Inception) permanent magnet motor malfunction diagnostic method on the basis of R parameters. Firstly, the Time Series Generative Adversarial Network is utilized to expand the dataset samples of the collected vibration signals, resulting in many virtual signals with motor fault characteristics. Then, the variable parameter variable scale structure of the VS-Inception model is employed to carry out the fault diagnosis of permanent magnet synchronous motors on the expanded dataset. Finally, comparison experiments of VS-Inception with the existing state-of-the-art methods and with the original method are conducted to confirm the superiority even further and stability of the VS-Inception method in the field of permanent magnet synchronous motor malfunction diagnostic.