Variable Scale Research Articles

Effective and adaptable: Four studies on the shortened attitude toward the color blue marker variable scale.

This research is an extension of the recent scale development efforts for the marker variable Attitude Toward the Color Blue (ATCB), which addresses the efficacy of multiple shorter permutations of the scale. The purpose of this study is to develop a shorter version of an ideal marker variable scale used to detect common method variance. Potential uses of the shorter version of ATCB include intensive longitudinal studies, implementation of experience sampling methodology, or any brief survey for which the original version might be cumbersome to implement repeatedly or appear very odd to the respondent when paired with only a few other substantive items. Study 1, uses all six-, five-, and four-item versions of ATCB in confirmatory factor analysis (CFA) marker technique tests on a bivariate relationship. Study 2 analyzes the best- and worst-performing versions of reduced lengths of the ATCB scale found in the first study on another bivariate relationship. Study 3 compares the original seven-item version, as well as randomly selected reduced length versions in a data set with 15 model relationships. Study 4 uses an experiment to determine the efficacy of providing respondents with one of three shorter ATCB scales in a model of three substantive variables. Our findings indicate that ATCB of different permutations and lengths can detect CMV successfully, and that researchers should choose the length of scale based on their survey length. We conclude that ATCB is adaptable for a variety of research situations, presenting it as a valuable tool for high-quality research.

Examining the relationship between health literacy and quality of life in patients with coronary heart disease: a quantitative study at a cardiology clinic

Coronary Heart Disease (CHD) is the leading cause of death worldwide and is rising, annually. Improved health outcomes require early treatment and self-monitoring, which need health literacy. This study examined how health literacy affects CHD patients’ Quality of Life (QoL) at the Hospital’s Cardiology Clinic. This quantitative, descriptive correlation study was cross-sectional. The sample was 164 respondents, sampled consecutively. Patients must be willing to participate, diagnosed with CHD, and attend cardiology outpatient follow-up appointments. Health literacy was measured with the HLS-EU-SQ10 and QoL with the Seattle Angina Questionnaire. This study used chi-square to analyze the relationship between two ordinal scale variables in a contingency table. The average age was 58.07 years, according to research. Of the respondents, 59 (36.0%) had graduated high school. Health literacy was assessed in three QoL domains: physical limitation (P=0.024), angina frequency (P=0.570), and QoL (P=0.338). Service agencies should be able to use pamphlets to improve health literacy and provide rehabilitation to acute coronary syndrome patients to improve QoL.

Tracking the Evolution of Snow Drought in the U.S. Pacific Northwest at Variable Scales

AbstractLong‐term and year‐to‐year changes in climate can cause anomalously warm and/or dry conditions, leading to periods with snowfall deficits known as snow droughts. How the occurrence of distinct types of snow drought has evolved at different spatial and temporal scales is not well understood. Here, we investigate how snow droughts and associated seasonal snow conditions vary subseasonally in the US Pacific Northwest. We specifically consider differences with spatial unit (point vs. river basin), data set and intra‐annual timing of snow drought. Data sets included in the research come from in situ observations, gridded model outputs and optical remote sensing. We found that overall, snow drought occurrences have increased 10%–15% over the last 30 years based on decadal counts, but the change was non‐uniform across snow drought types and winter periods. Our research showed a decrease in dry snow droughts but an increase in warm/dry snow droughts according to in situ observations and only a modest increase in the model record. We also found that dry snow droughts were more prevalent in the eastern portion of our study region while warm/dry snow droughts were generally more common in the western part of the study area. With optical remote sensing data, it was possible to identify warm/dry snow drought conditions in the mid to late winter (January–May), but not possible to distinguish other types of snow drought in the early winter. This research points to the importance of data set choice, spatial resolution and spatial/temporal aggregation when selecting parameters for snow drought research.

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Testing for similarity of multivariate mixed outcomes using generalized joint regression models with application to efficacy-toxicity responses.

A common problem in clinical trials is to test whether the effect of an explanatory variable on a response of interest is similar between two groups, for example, patient or treatment groups. In this regard, similarity is defined as equivalence up to a pre-specified threshold that denotes an acceptable deviation between the two groups. This issue is typically tackled by assessing if the explanatory variable's effect on the response is similar. This assessment is based on, for example, confidence intervals of differences or a suitable distance between two parametric regression models. Typically, these approaches build on the assumption of a univariate continuous or binary outcome variable. However, multivariate outcomes, especially beyond the case of bivariate binary responses, remain underexplored. This paper introduces an approach based on a generalized joint regression framework exploiting the Gaussian copula. Compared to existing methods, our approach accommodates various outcome variable scales, such as continuous, binary, categorical, and ordinal, including mixed outcomes in multi-dimensional spaces. We demonstrate the validity of this approach through a simulation study and an efficacy-toxicity case study, hence highlighting its practical relevance.

An exploration of blood-based biomarkers of negative symptoms of psychosis in men

Negative symptoms in the context of psychosis are still poorly understood and diagnosed, which impairs the treatment efficacy of current therapies and patient's integration in society. In this study, we aimed to test hypothesis-based and exploratory associations of negative symptom domains, as defined by the Brief Negative Symptom Scale (BNSS), with hormonal and hematological variables, and, complementarily, with standard psychological/cognitive and psychopathological measures. Fifty-one male patients diagnosed with a psychotic disorder underwent a structured interview and blood collection. Standard Spearmen bivariate correlations were used for data analysis. We obtained evidence of hypothesis-based associations between specific negative symptoms and oxytocin, thyroid stimulating hormone levels and neutrophil-to-lymphocyte ratio; as well as novel and hypothesis-free associations with erythrocyte and lymphocyte count, mean corpuscular volume and red cell distribution width. Complementarily, we also obtained some validation of previous associations of negative symptoms with illness resolution, cognitive symptom severity and social performance, and a novel association with anger contagion. We hope our results can generate new hypotheses in psychosis research. Our work suggests further avenues in research on erythrocytic, inflammatory, thyroid and oxytocin-related markers and abnormalities in psychosis, especially in regards to specific negative symptoms, towards more precise and comprehensive etiological, diagnostic and therapeutic models.

Seminal electromagnetic fields from preinflation

We investigate the geometric dynamics of the primordial electric and magnetic fields during the early stages of the universe by extending a recently introduced quantum algebra (Bellini et al., 2023). We work on an extended model of gravity that considers the boundary terms from the Einstein–Hilbert action as geometric quantum fluctuations of the spacetime. We propose that the extended Riemann manifold is generated by a new connection δΓˆαβμ. This connection contains geometric information about the fluctuations of gravitational and electromagnetic fields in the vacuum, which could have been crucial during the primordial stages of the universe’s evolution. We revisit a preinflationary cosmological model (Bellini, 2023) with a variable time scale and negative spatial curvature, such that the universe begins with a null initial background energy density. We observed the emergence of large scale magnetic fields starting from small values during the early phases of the universe’s evolution. Subsequently, these fields decrease to reach present day values on the order of δBˆ≃10−12G on cosmological scales (between 1024 and 1026 meters). This significant deviation from inflationary models eliminates the need to impose excessively large initial values on these fields.

An Optimization Method for Lightweight Rock Classification Models: Transferred Rich Fine-Grained Knowledge.

Rock image classification represents a challenging fine-grained image classification task characterized by subtle differences among closely related rock categories. Current contrastive learning methods prevalently utilized in fine-grained image classification restrict the model's capacity to discern critical features contrastively from image pairs, and are typically too large for deployment on mobile devices used for in situ rock identification. In this work, we introduce an innovative and compact model generation framework anchored by the design of a Feature Positioning Comparison Network (FPCN). The FPCN facilitates interaction between feature vectors from localized regions within image pairs, capturing both shared and distinctive features. Further, it accommodates the variable scales of objects depicted in images, which correspond to differing quantities of inherent object information, directing the network's attention to additional contextual details based on object size variability. Leveraging knowledge distillation, the architecture is streamlined, with a focus on nuanced information at activation boundaries to master the precise fine-grained decision boundaries, thereby enhancing the small model's accuracy. Empirical evidence demonstrates that our proposed method based on FPCN improves the classification accuracy mobile lightweight models by nearly 2% while maintaining the same time and space consumption.

Reducing the stress of mothers in the postpartum period: psychological inflexibility or mother-infant bonding

ABSTRACT Background Parental stress is a critical problem because it affects both the mental health of the mother and children’s development. In addition to many factors related to birth and marriage, mother – infant bonding and psychological inflexibility are essential factors that can affect stress. In this study, we examined the effects of the psychological processes of mothers and factors related to pregnancy, mother, environment on parental stress, and their relationships. Methods A sociodemographic variables scale, Acceptance and Action Questionnaire-II, Postpartum Bonding Questionnaire, and Parental Stress Scale were completed by 115 mothers in their first postpartum year. The model created with the correlation and regression results was subjected to path analysis. Results Breastfeeding, psychological inflexibility, and mother – infant bonding are related to parental stress. The bonding problem is the mediator of the relationship between psychological inflexibility and parental stress. The entire effect of sleep quality on parental stress occurred through psychological inflexibility. Conclusion Efforts should be directed towards improving the bond between the mother and infant and enhancing the mother’s psychological flexibility to lessen the negative impacts of stress. Breastfeeding should not be treated categorically, and its potential adverse effects should not be ignored.

Evaluating the time to detect biological effects of ocean acidification and warming: an example using simulations of purple sea urchin settlement

Ocean acidification (OA) and ocean warming driven by climate change are important stressors for marine species and systems, but documenting and detecting their long-term impacts on biological responses outside of laboratory settings are challenging due to natural variability caused by complex processes and interactions. We used settlement of purple sea urchins Strongylocentrotus purpuratus in the Southern California Bight (USA) over 6 yr as an example data set to parameterize a simulation model for exploring the time needed to detect environmental effects on a biological response. A generalized linear model was used to describe an index of urchin settlement as functions of pH, sea surface temperature (SST), sea surface salinity (SSS), and spatio-temporal factors, demonstrating that settlement was negatively associated with pH (i.e. lower settlement at higher pH) and positively associated with SST and SSS. Monte Carlo simulations were developed from this base model under a variety of alternative scenarios to estimate the time to detect: (1) annual trends in pH and SST time series, (2) pH and SST effects on urchin settlement, and (3) annual trends in urchin settlement. Time to detect pH and SST effects was predominantly influenced by the underlying strength of the relationships and the model uncertainty. Time to detect annual trends in settlement was more sensitive to the severity of long-term OA and warming trends, which had cumulative (at times opposing) effects. This study highlights the variable time scales (2-60+ yr) that may be necessary to detect biological responses to OA and ocean warming and the sensitivity to different assumptions of the study system.

Social Work students’ attitudes toward religious diversity and perceptions of the relationship between religion and Social Work

ABSTRACT In a world characterized by its cultural diversity, religion influences the beliefs, interpretations, actions and behaviors of many people. This study analyzes attitudes toward religious diversity among students of the Degree in Social Work at the University of Alicante (Spain), as well as their perceptions of the relationship between religion and Social Work. The study also analyzes associations between these students’ attitudes and perceptions and their biographical and contextual variables. Thus, the study’s design was of a descriptive and explanatory quantitative nature, and univariate and bivariate. The data collection instrument was a questionnaire comprised of nominal, ordinal and scale variables. 270 students participated, with an average age of 21.2 (SD = 4.57; range: 17–56), and most were women (86%). The results show that, in general, students harbor favorable attitudes toward religious diversity, especially the right to religious freedom, as well as toward public guarantees of religious freedom and equal treatment of people of all faiths. Regarding the relationship between religion and Social Work, students’ perceptions vary depending on their own characteristics. These results are relevant in the context of a discipline that, from a theoretical and professional point of view, has responsibilities in terms of cultural diversity management.

Intention To Buy Products with Environmentally Friendly Packaging in Jakarta, Indonesia

The objective of this study is to ascertain the elements influencing consumers' inclination to buy goods packaged in environmentally sustainable materials. The factors are consumers’ attitudes towards purchase behaviors, the influence of social factors, effectiveness perception, self-image considerations, community sense, quality of eco-friendly packaging, products with eco-friendly packaging pricing, and environmental knowledge. This research is conducted to complement the discoveries of the preceding research, with the distinction that this study is carried out in Indonesia. Primary data was obtained by conducting a direct survey with a sample size of 393 respondents using convenience sampling methods. This research is measured through scale variables, and is being analyzed with Cronbach Alpha, factor analysis, and other tools to identify the most influential factors to the least one which will be input into the regression model. The findings are that there are seven factors that impacting consumers' intention to purchase in Jakarta, which are the attitude of consumers toward purchasing behaviors, effectiveness perception, the pricing of products with eco-friendly packaging, community sense, self-image considerations, eco-friendly packaging quality, and environmental knowledge.

Physics-informed neural networks for fully non-linear free surface wave propagation

This study proposed fully nonlinear free surface physics-informed neural networks (FNFS-PINNs), an advancement within the framework of PINNs, to tackle wave propagation in fully nonlinear potential flows with the free surface. Utilizing the nonlinear fitting capabilities of neural networks, FNFS-PINNs offer an approach to addressing the complexities of modeling nonlinear free surface flows, broadening the scope for applying PINNs to various wave propagation scenarios. The improved quasi-σ coordinate transformation and dimensionless formulation of the basic equations are adopted to transform the time-dependent computational domain into the stationary one and align variable scale changes across different dimensions, respectively. These innovations, alongside a specialized network structure and a two-stage optimization process, enhance the mathematical formulation of nonlinear water waves and solvability of the model. FNFS-PINNs are evaluated through three scenarios: solitary wave propagation featuring nonlinearity, regular wave propagation under high dispersion, and an inverse problem of nonlinear free surface flow focusing on the back-calculation of an initial state from its later state. These tests demonstrate the capability of FNFS-PINNs to compute the propagation of solitary and regular waves in the vertical two-dimensional scenarios. While focusing on two-dimensional wave propagation, this study lays the groundwork for extending FNFS-PINNs to other free surface flows and highlights their potential in solving inverse problems.

Multigranulation variable-scale fuzzy neighborhood measures and corresponding Choquet-like integrals for feature selection

Choquet-like integrals, a nonlinear fuzzy aggregation function, are diffusely applied in several real problems. However, the corresponding fuzzy measures in them are provided by human intervention and not by data-driven methods. As an effective knowledge representation tool, rough set models in different approximation spaces are established for data processing. Particularly, the multigranulation fuzzy β-covering approximation space has been concerned with only a single scale β∈(0,1]. However, different granulation structures should have different scales. Therefore, establishing a multigranulation approximation space with a variable scale by inducing fuzzy measures through data-driven methods and using corresponding Choquet-like integrals in practical problems is a significant challenge. To address this challenge, the notion of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented here, as well as multigranulation variable-scale fuzzy neighborhood measures in it. Furthermore, Choquet-like integrals with the presented measures are constructed to solve the issue of feature selection (i.e., attribute reduction) under the new approximation spaces. Firstly, the concept of multigranulation variable-scale fuzzy Θ-covering group approximation space is presented, where different fuzzy β-coverings have different scales β∈Θ. Moreover, multigranulation variable-scale fuzzy neighborhood measures in it, as fuzzy measures, are presented. Subsequently, Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures are constructed. A novel attribute reduction method under Choquet-like integrals with multigranulation variable-scale fuzzy neighborhood measures is proposed for any decision information table. Finally, the presented method is selected for solving problems of classification and diagnosis of rolling bearing faults. Several public data sets are used to demonstrate the effectiveness and feasibility of the presented methods mentioned above.

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.