Search Results (24)

In this work, we develop a disturbance suppression-oriented fuzzy sliding mode secured sampled-data controller for third-order parabolic partial differential equations that ought to cope with nonlinearities, hybrid cyber attacks, and modeled disturbances. This endeavor is mainly driven by formulating an observer model with a T–S fuzzy mode of execution that retrieves the latent state variables of the perceived system. Progressing onward, the disturbance observers are formulated to estimate the modeled disturbances emerging from the exogenous systems. In due course, the information received from the system and disturbance estimators, coupled with the sliding surface, is compiled to fabricate the developed controller. Furthermore, in the realm of security, hybrid cyber attacks are scrutinized through the use of stochastic variables that abide by the Bernoulli distributed white sequence, which combat their unpredictability. Proceeding further in this framework, a set of linear matrix inequality conditions is established that relies on the Lyapunov stability theory. Precisely, the refined looped Lyapunov–Krasovskii functional paradigm, which reflects in the sampling period that is intricately split into non-uniform intervals by leveraging a fractional-order parameter, is deployed. In line with this pursuit, a strictly $({\Phi }_1,{\Phi }_2,{\Phi }_3)-\varrho$ dissipative framework is crafted with the intent to curb norm-bounded disturbances. A simulation-backed numerical example is unveiled in the closing segment to underscore the potency and efficacy of the developed control design technique. Full article

Background: The shift toward precision orthodontics necessitates population-specific cephalometric databases. Reliance on Eurocentric norms for ethnically diverse populations—particularly underrepresented Middle Eastern groups—represents a significant evidence gap. This study establishes initial normative cephalometric data for Yemeni and Northern Turkish Cypriot (NTC) adults. Methods: This retrospective comparative study analyzed 400 lateral cephalograms from skeletal Class I adults (200 Yemeni and 200 NTC; age 18–40; gender-balanced). Twenty standardized parameters were assessed using VistaDent OC™ software (version 4.2.61, GAC Orthodontic Software solutions, Birmingham, AL, USA). Analyses included *t*-tests, MANOVA, effect size computations (Cohen’s *d*), and variance partitioning. The False Discovery Rate method controlled multiple comparisons. Results: Yemeni adults exhibited a more vertical facial growth pattern (indicated by a lower Jarabak ratio: 60.18 ± 4.50% vs. 65.79 ± 5.20%; *d* = 1.15) and pronounced soft-tissue convexity (N-A-Pog: 5.76 ± 1.20 mm vs. 3.82 ± 1.10 mm; *d* =1.69). NTC adults showed a mild skeletal Class II tendency (ANB: 4.51 ± 1.70° vs. 3.35 ± 1.50°; *d* = 0.72). Ethnicity accounted for 21.3% of craniofacial variance (partial η² = 0.213). Conclusions: This study provides foundational cephalometric reference data for two underrepresented populations. The significant morphological distinctions quantified here underscore the necessity of developing population-specific norms. These data should be considered as one component within comprehensive, individualized diagnostic frameworks in orthodontics, rather than standalone diagnostic criteria. Full article

Background: Theta-band cordance is a quantitative EEG (qEEG) metric that integrates absolute and relative spectral power and correlates with regional cerebral perfusion. Although widely applied in psychiatric and neurophysiological research, its longitudinal trajectory in healthy adults remains largely unknown. This study aimed to characterize multi-year changes in theta cordance across cortical regions, determine which areas show stability versus decline, and evaluate whether individuals maintain a trait-like cordance profile over time. Methods: Nineteen cognitively healthy, medication-free adults underwent resting-state EEG recordings at two time points, separated by an average of 6.4 years (range: 1.9–14.8). Theta cordance (4–8 Hz) was computed at 19 scalp electrodes using the Leuchter algorithm and aggregated into eight lobar regions (left/right frontal, temporal, parietal, occipital). Paired-samples t-tests assessed longitudinal changes. Inter-regional Pearson correlations examined evolving connectivity patterns. Canonical correlation analysis (CCA), validated via LOOCV and bootstrap confidence intervals, evaluated multivariate stability between baseline and follow-up cordance profiles. Results: Theta cordance remained normally distributed at both time points. Significant longitudinal decreases emerged in the right temporal (t(18) = 5.34, p < 0.001, d = 1.23) and right frontal (t(18) = 2.65, p = 0.016, d = 0.61) regions, while other lobes showed no significant change. Midline Cz demonstrated a robust increase over time (p < 0.001). CCA revealed a strong cross-time association (Rc = 0.999, p = 0.029), indicating preservation of a stable, frontally anchored cordance profile despite regional right-hemisphere decline. Inter-regional correlation matrices showed both preserved posterior synchrony and emerging inverse anterior–posterior and cross-hemispheric relationships, suggesting age-related reorganization of cortical connectivity. Conclusions: Theta cordance exhibits a mixed pattern of trait-like stability and region-specific aging effects. A dominant, stable fronto-central profile persists across years, yet the right frontal and right temporal cortices show significant decline, consistent with lateralized vulnerability in normative aging. Evolving inter-regional correlation patterns further indicate network-level reorganization. Longitudinal cordance assessment may provide a noninvasive marker of functional brain aging and help differentiate normal aging trajectories from early pathological change. This longitudinal quantitative EEG (qEEG) study examined theta-band cordance dynamics across cortical regions in healthy adults over an average follow-up of 6.4 years (range: 1.9–14.8). Resting-state EEGs were recorded at two time points from 19 participants and analyzed using Leuchter’s cordance algorithm across 19 scalp electrodes. Regional cordance values were computed for frontal, temporal, parietal, and occipital lobes. Paired-samples t-tests revealed significant longitudinal decreases in theta cordance in the right frontal (p = 0.016, d = 0.61) and right temporal lobes (p < 0.001, d = 1.23), while other regions remained stable. Inter-regional Pearson correlations showed strong bilateral synchrony in posterior regions and emergent inverse anterior–posterior relationships over time. Canonical correlation analysis revealed a robust multivariate association (Rc = 0.999, p = 0.029) between baseline and follow-up patterns. Partial correlations (controlling for follow-up interval) identified region-specific trait stability, highest in left occipital and right frontal cortices. These findings suggest that theta cordance reflects both longitudinally stable neural traits and regionally specific aging effects in cortical physiology. Full article

This paper develops the linear-algebraic foundations of the Convex Least Squares Programming (CLSP) estimator and constructs its modular two-step convex optimization framework, capable of addressing ill-posed and underdetermined problems. After reformulating a problem in its canonical form, ${\mathbf{A}}^{\left(r\right)}{\mathbf{z}}^{\left(r\right)}=\mathbf{b}$, Step 1 yields an iterated (if $r>1$) minimum-norm least-squares estimate ${\widehat{\mathbf{z}}}^{\left(r\right)}={\left({\mathbf{A}}_{\mathbf{Z}}^{\left(r\right)}\right)}^{†}\mathbf{b}$ on a constrained subspace defined by a symmetric idempotent $\mathbf{Z}$ (reducing to the Moore–Penrose pseudoinverse when $\mathbf{Z}=\mathbf{I}$). The optional Step 2 corrects ${\widehat{\mathbf{z}}}^{\left(r\right)}$ by solving a convex program, which penalizes deviations using a Lasso/Ridge/Elastic net-inspired scheme parameterized by $\alpha \in [0,1]$ and yields ${\widehat{\mathbf{z}}}^{*}$. The second step guarantees a unique solution for $\alpha \in (0,1]$ and coincides with the Minimum-Norm BLUE (MNBLUE) when $\alpha =1$. This paper also proposes an analysis of numerical stability and CLSP-specific goodness-of-fit statistics, such as partial $R^2$, normalized RMSE (NRMSE), Monte Carlo t-tests for the mean of NRMSE, and condition-number-based confidence bands. The three special CLSP problem cases are then tested in a 50,000-iteration Monte Carlo experiment and on simulated numerical examples. The estimator has a wide range of applications, including interpolating input–output tables and structural matrices. Full article

Inspired by the work of Gregori et al. and guided by some open direction, we propose the concepts of Cauchy sequence and convergent sequence in a fuzzy partial metric space by the residuum operator associated to a continuous t-norm. Based on these notions, we introduce the concepts of two kinds of fuzzy $\eta$-contractive mappings in fuzzy partial metric spaces and present related fixed point theorems. Full article

Discrete Sugeno integrals form a significant family of aggregation functions. Several variants of these integrals have been proposed, most of which replace the minimum operation with alternative operations, such as product, overlap functions, and t-norms. Notably, the associativity of t-norms in discrete Sugeno integrals has no significant consequences, leading us to relinquish this property and introduce the concept of partial t-norms. Although interval-valued versions of the discrete Sugeno integral or its variants have been limited, this paper presents a novel approach based on interval-valued partial t-norms, admissible orders, and interval-valued fuzzy measures. We provide rigorous proofs of the properties of this operator class. Full article

The adoption of electric motorcycles is critical for reducing transportation-related greenhouse gas emissions in Indonesia, which reached 674.54 million t of CO₂ in 2023. This study integrates the Theory of Planned Behavior with situational, contextual, and demographic factors to explore the determinants of electric motorcycle adoption intentions and actual usage. Data were collected from 1602 respondents across ten provinces with the highest motorcycle sales using purposive sampling and analyzed through Partial Least Squares—Structural Equation Modeling. Findings reveal that psychological factors—attitude, subjective norms, and perceived behavioral control—significantly influence purchase intentions, while personal moral norms do not. Situational factors such as technology and cost indirectly affect adoption intentions through attitude and perceived behavioral control. Contextual factors show mixed results; government policies effectively shape attitudes and perceived behavioral control, but infrastructure remains inadequate to influence attitudes directly. Demographic analysis highlights gender as a moderating factor, with men showing higher moral-driven adoption intentions. These results imply that the government and manufacturers need to develop the appropriate strategy to foster public interest in adopting electric motorcycles to increase the adoption rate of pro-environmental vehicles. Government policies such as purchase price subsidies, tax reductions, and charging rate discounts can motivate the intention to adopt electric motorcycles. In addition, manufacturers could improve technical performance and reduce the total cost of ownership, such as the purchase price and battery replacement costs. Full article

This paper extends the well-known transfinite interpolation formula, which was developed in the late 1960s by the applied mathematician William Gordon at the premises of General Motors as an extension of the pre-existing Coons interpolation formula. Here, a conjecture is formulated, which claims that the meaning of the involved blending functions can be enhanced, such that it includes any linear independent and complete set of functions, including piecewise-linear, trigonometric functions, Bernstein polynomials, B-splines, and NURBS, among others. In this sense, NURBS-based isogeometric analysis and aspects of T-splines may be considered as special cases. Applications are provided to illustrate the accuracy in the interpolation through the L₂ error norm of closed-formed functions prescribed at the nodal points of the transfinite patch, which represent the solution of partial differential equations under boundary conditions of the Dirichlet type. Full article

Background/Objectives: The primary aim of this study was to characterize athletes approaching an outpatient interdisciplinary and multidisciplinary consultation structure for athletes with a suspected relative energy deficiency in sport (REDs) cross-sectionally and longitudinally to prove treatment efficacy. Methods: Data of 58 athletes suspected of REDs were collected at the onset (t₀) and completion (t₁) of interdisciplinary and multidisciplinary REDs treatment (clinical practice) between January 2019 and December 2022. The data included extracted information from medical records, anthropometric characteristics, physical performance diagnostics, laboratory values, dietary records, and partially gynecological and psychosomatic diagnostics. Results: The sample primarily consisted of female athletes (97%) under 18 years of age (66%) who were underweight with a body mass index (BMI) < 18.5 kg/m² and BMI percentile below the 10th percentile for their age and gender-specific norms (59%), and experienced menstrual disorders (93%). The dietary behavior is characterized by plant-based and low-energy-dense foods. Eating disorders (anorexia nervosa and bulimia nervosa) were diagnosed in 40% of the athletes according to the International Statistical Classification of Diseases and Related Health Problems 11th revision criteria. During the program, 64% of the athletes exhibited a mean weight gain of 7 (±6) kg (p < 0.001), excluding those still undergoing treatment (36%). Conclusions: The proposed interdisciplinary and multidisciplinary treatment approach proved effective and holds promise for future evidence-based developments in REDs treatment. Full article

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction. Full article

In this paper, we present a more general approach based on a Picard integral scheme for nonlinear partial differential equations with a variable time-fractional derivative of order $\alpha (\mathbf{x},t)\in (1,2)$ and space-fractional order $s\in (0,1)$, where $v=u^{\prime }(t)$ is introduced as the new unknown function and u is recovered using the quadrature. In order to get rid of the constraints of traditional plans considering the half-time situation, integration by parts and the regularity process are introduced on the variable v. The convergence order can reach $O({\tau }^2+h^2)$, different from the common $L_{1,2-\alpha }$ schemes with convergence rate $O({\tau }^{2,3-\alpha (\mathbf{x},t)})$ under the infinite norm. In each integer time step, the stability, solvability and convergence of this scheme are proved. Several error results and convergence rates are calculated using numerical simulations to evidence the theoretical values of the proposed method. Full article

Although prior research has highlighted the importance of social media in promotion and communication, a comprehensive framework to clarify how to use social media as a value co-creation platform to promote a green lifestyle has yet to be developed. This research aims to create and test a conceptual model for using social media as a value co-creation platform to encourage and motivate people to adopt a sustainable green lifestyle, besides mapping the process of green lifestyle adoption from the actual social media user behaviors. Two hundred and eighty-nine (289) subjects participated in an online survey in the first half of 2022, and the data collected have been analyzed using regression. The three key findings: (1) social media contact is positively associated with a sustainable green lifestyle (β = 0.234, p < 0.001); (2) value co-creation partially mediates the relationship between social media contact and a sustainable green lifestyle (indirect effect = 0.113, with Sobel test’s t-value = 5.762); and (3) surprisingly, the moderating role of social media influencers and social norms in the social media contact–sustainable green lifestyle relationship is not supported. In addition, this research supplied some reasonable and practical implementations that can help green agents and policymakers promote green behaviors. Full article

We generalize the concept of a fuzzy metric by introducing its approximating counterpart in order to make it more appropriate for the study of some problems related to combinatorics on words. We establish close relations between fuzzy approximating metrics in the case of special t-norms and approximating parametrized metrics, discuss some relations between fuzzy approximating metrics and fuzzy partial metrics, as well as showing some possible applications of approximating parametrized metrics in the problems of combinatorics on words. Full article

This paper reveals some relations between fuzzy logic and quantum logic on partial residuated implications (PRIs) induced by partial t-norms as well as proposes partial residuated monoids (PRMs) and partial residuated lattices (PRLs) by defining partial adjoint pairs. First of all, we introduce the connection between lattice effect algebra and partial t-norms according to the concept of partial t-norms given by Borzooei, together with the proof that partial operation in any commutative quasiresiduated lattice is partial t-norm. Then, we offer the general form of PRI and the definition of partial fuzzy implication (PFI), give the condition that partial residuated implication is a fuzzy implication, and prove that each PRI is a PFI. Next, we propose PRLs, study their basic characteristics, discuss the correspondence between PRLs and lattice effect algebras (LEAs), and point out the relationship between LEAs and residuated partial algebras. In addition, like the definition of partial t-norms, we provide the notions of partial triangular conorms (partial t-conorms) and corresponding partial co-residuated lattices (PcRLs). Lastly, based on partial residuated lattices, we define well partial residuated lattices (wPRLs), study the filter of well partial residuated lattices, and then construct quotient structure of PRMs. Full article

In many domains of our daily life (e.g., agriculture, forestry, health, etc.), both laymen and experts need to classify entities into two binary classes (yes/no, good/bad, sufficient/insufficient, benign/malign, etc.). For many entities, this decision is difficult and we need another class called “maybe”, which contains a corresponding quantifiable tendency toward one of these two opposites. Human domain experts are often able to mark any entity, place it in a different class and adjust the position of the slope in the class. Moreover, they can often explain the classification space linguistically—depending on their individual domain experience and previous knowledge. We consider this human-in-the-loop extremely important and call our approach actionable explainable AI. Consequently, the parameters of the functions are adapted to these requirements and the solution is explained to the domain experts accordingly. Specifically, this paper contains three novelties going beyond the state-of-the-art: (1) A novel method for detecting the appropriate parameter range for the averaging function to treat the slope in the “maybe” class, along with a proposal for a better generalisation than the existing solution. (2) the insight that for a given problem, the family of t-norms and t-conorms covering the whole range of nilpotency is suitable because we need a clear “no” or “yes” not only for the borderline cases. Consequently, we adopted the Schweizer–Sklar family of t-norms or t-conorms in ordinal sums. (3) A new fuzzy quasi-dissimilarity function for classification into three classes: Main difference, irrelevant difference and partial difference. We conducted all of our experiments with real-world datasets. Full article