Search Results (7,015)

We introduce and study a class of multi-composite sigmoid neural network operators for Banach space-valued approximation. The proposed operators are generated by density-type kernels induced by finite compositions of seven standard sigmoid-type activation functions. The approximation is considered for continuous functions on compact intervals of the real line and on the whole real line, with values in an arbitrary Banach space $(X,\parallel ·\parallel )$. We prove quantitative pointwise and uniform convergence results by means of Jackson-type inequalities expressed through the first modulus of continuity. Higher-order and fractional approximation results are also obtained in terms of Banach space-valued derivatives and Caputo–Bochner fractional derivatives. The associated feed-forward neural network representation has one hidden layer and uses the multi-composite sigmoid function as its activation. Numerical experiments are presented to validate the theoretical estimates and to illustrate the approximation behavior of the proposed operators. In particular, we compare classical tanh-based operators, normalized self-composed activation operators, and heterogeneous multi-composite activation operators. The results show that self-composition and heterogeneous composition may improve the uniform approximation error for certain activation families and parameter choices, while also indicating that the observed improvement is activation-dependent and influenced by the composition order, kernel localization, and the regularity of the target function. Full article

The fractional Whitham–Broer–Kaup model governs nonlinear wave propagation in memory-dependent media, including porous structures, viscoelastic fluids, and irregular seabeds, yet the full dynamical spectrum from quasi-periodicity to deterministic chaos, the role of stochastic forcing, and reliable identification from noisy data remains insufficiently explored, particularly how the fractional order $\beta$ influences these regimes. This study addresses these gaps through a comprehensive, multi-method dynamical analysis of a representative nonlinear oscillator embodying key FWBK features. Three-dimensional attractor visualizations, return maps, and surrogate data tests demonstrate a transition from quasi-periodic toroidal attractors to fully developed chaos via torus breakdown, confirming that observed complexity originates from deterministic nonlinearity. Poincaré sections reveal multistability and KAM-type structures, where coexisting attractors depend on initial conditions, while increasing noise progressively disrupts coherent dynamics. The OGY control method effectively stabilizes unstable periodic orbits across chaotic regimes with minimal perturbation, and Lyapunov analysis indicates that stochastic forcing attenuates chaos while enhancing dissipation. The Fokker–Planck framework shows that noise reshapes probability landscapes, driving transitions from unimodal to bimodal distributions. Comparative analysis of SINDy, JMAP and VBA highlights trade-offs in interpretability, computational efficiency, and uncertainty quantification, while an integrated Bayesian–PCE–Sobol approach quantifies parametric uncertainty and reveals time-dependent sensitivity variations. Additionally, the overlapping of soliton solutions extracted via the enhanced modified Sardar sub-equation method reveals structural relationships among soliton families and their stability under interaction. Soliton branches that maintain high overlap under noise correspond to stable regimes, while those losing coherence indicate the onset of chaos. Furthermore, while the reduced dynamics in $\eta$-space are independent of $\beta$, the fractional order controls spatial compression and temporal scaling in physical coordinates, directly influencing observable wave localization. These results imply that fractional effects can modify chaos transitions, support controllability through OGY, and influence noise–instability interactions depending on $\beta$. This framework provides a robust, transferable methodology for analyzing and controlling nonlinear oscillatory systems under deterministic and stochastic conditions, with direct applications to FWBK-based models in coastal engineering, fiber optics, and quantum interference systems. Full article

Background: Adolescent tobacco, e-cigarette, and alcohol use are important public health concerns in China. However, the associations of specific types and cumulative exposure to childhood family violence with different substance use outcomes remain insufficiently understood. This study examined these associations among Chinese adolescents. Methods: We analyzed data from a cross-sectional school-based survey of 41,146 students aged 10–19 years conducted from October 2022 to March 2023 in a mountainous city in western Guangdong Province, China. Childhood family violence was assessed using the validated Chinese Family Violence Questionnaire and a cumulative exposure index. Descriptive analyses and logistic regression models were conducted, with adjustment for demographic and environmental factors. Results: The mean age of participants was 14.8 years, and 51.7% were female. Overall, 25.1% of adolescents reported at least one type of childhood family violence. Verbal insults (18.6%) and emotional neglect (16.3%) were the most frequently reported types and were consistently associated with tobacco, e-cigarette, and alcohol use (adjusted odds ratios [ORs] = 1.4–1.5, p < 0.001). A cumulative exposure pattern was also observed. Compared with adolescents reporting no childhood family violence, those exposed to three or more types had higher odds of tobacco use (OR = 3.81; 95% CI: 3.42–4.23), e-cigarette use (OR = 3.90; 95% CI: 3.39–4.48), and alcohol use (OR = 3.95; 95% CI: 3.59–4.35). Peer smoking and access to tobacco products were also significantly associated with substance use. Conclusions: Childhood family violence, particularly verbal insults and emotional neglect, was associated with adolescent tobacco, e-cigarette, and alcohol use. The findings highlight the importance of considering emotional maltreatment, cumulative adversity, peer influences, and access to tobacco products in future prevention research and practice. Full article

High levels of low-density lipoprotein cholesterol (LDL-c) have been recognized as the main causal factor of atherosclerotic cardiovascular disease (ASCVD) and are influenced by both genetic and environmental factors. Among genetic determinants, Familial Hypercholesterolemia (FH) is the most common monogenic disorder, caused by rare high-impact variants in genes involved in LDL uptake. Other monogenic causes of hypercholesterolemia include sitosterolemia, cerebrotendinous xanthomatosis and lysosomal acid lipase deficiency (LALD). However, monogenic disorders only account for a small proportion of inherited hypercholesterolemia. In many individuals, increased LDL-c levels are caused by the contemporary presence of different single-nucleotide polymorphisms (SNPs) with a moderate/low impact. These SNPs could be summarized through polygenic risk scores (PRS) that attribute relative weight to each of these. Another genetic determinant of hypercholesterolemic phenotypes is high levels of lipoprotein(a)—Lp(a). Lp(a) is an LDL particle modified by the binding of apolipoprotein(a)—apo(a)—which represents an independent risk factor for ASCVD. Lp(a) levels are mainly genetically determined by variation in the number of kringle IV type 2 (K-IV₂) repeats, as well as by several SNPs, and remain stable throughout life. The aim of this narrative review is to report an updated overview of the genetic mechanisms underlying hypercholesterolemia, including monogenic disorders, PRS and Lp(a), focusing on their potential repercussion in clinical practice by the integration into cardiovascular risk stratification beyond traditional clinical assessment. This integration could lead to a more comprehensive and individualized approach to cardiovascular prevention, with emerging perspectives including the possible use of artificial intelligence (AI). Full article

Introduction: Compassion is defined as the emotional response that arises when an individual perceives another’s suffering and is motivated to alleviate it. Purpose: To explore levels of self-compassion among nurses working in pediatric hospitals and examine their associations with nurses’ characteristics. Materials and Methods: This cross-sectional study included a convenience sample of 208 nurses from a public pediatric hospital. Data were collected through interviews using the Neff Self-Compassion Scale (SCS) which includes the following subscales: Self-Kindness, Common Humanity, Mindfulness, Self-Judgment, Isolation, and Over-Identification. The Greek-validated version of the instrument was used with acceptable internal consistency in the present sample (Cronbach’s alpha = 0.849). Data analysis included descriptive statistics and inferential tests (non-parametric comparisons and multiple linear regression), with statistical significance defined as p < 0.05. Results: The mean total Self-Compassion score was 83.24 ± 12.6 (range: 26–130). Regarding family-related factors, total Self-Compassion (p = 0.029), Common Humanity (p = 0.033), and Over-Identification (p = 0.041) were associated with the number of children. In relation to age, Self-Kindness (p = 0.033), Isolation (p = 0.005), and Over-Identification (p = 0.005) showed significant associations. Professional factors were also relevant, as Isolation was associated with total years of nursing experience (p = 0.032) and choice of nursing as a profession (p = 0.004), while Over-Identification was associated with years of experience in pediatric settings (p = 0.004) and choice of nursing as a profession (p = 0.049). Additionally, marital status was associated with Over-Identification (p = 0.045). Conclusions: Demographic and professional characteristics appear to influence the expression of Self-compassion. Healthcare organizations should implement targeted training programs to individualize professional development. Future research should explore work-related and personal factors influencing self-compassion to improve care quality and outcomes. Full article

Background/Objectives: Vaccine attitudes are an individual’s beliefs, feelings, and evaluations regarding vaccines. Limited research has examined how students in faith-based university settings organize these attitudes. This study looked at vaccination attitudes among students at a religious university where faith, science, family, and politics often influence how students think and make decisions. Methods: This study used Q-methodology to examine shared viewpoints about vaccination. A concourse of 240 statements was developed from published literature, public discourse, and student interviews, then reduced to a 37-statement-Q-set. Undergraduate students enrolled in an introductory nonmajors biology course completed digital Q-sorts. We analyzed the data using by-person factor analysis, along with principal components analysis and Varimax rotation. Follow-up interviews helped us interpret the factors. Results: Three viewpoints explained 59% of the study variance. The first viewpoint, Faith-Integrated Institutional Trust, showed strong trust in science, public health agencies, and religious leaders. People in this group saw vaccination as both a moral duty and a way to protect others. The second viewpoint, Skeptical Autonomy and Institutional Distrust, emphasized personal choice, family influence, and distrust of government and official vaccine information. The third viewpoint, Pragmatic Autonomy and Science Confidence, endorsed vaccines and scientific evidence while also prioritizing individual decision-making over mandates. Conclusions: Science alone does not explain vaccination attitudes among college students. Trust, identity, and personal autonomy also play an important role. Vaccine communication should therefore connect scientific evidence with students’ moral commitments, trusted relationships, and concerns about freedom, especially in settings where faith influences health decision-making. Full article

Anemia represents one of the most frequent systemic complications of inflammatory bowel disease (IBD), with a consistently higher prevalence reported in patients with Crohn’s disease (CD) compared with ulcerative colitis (UC). While chronic inflammation, impaired iron absorption, and intestinal blood loss are recognized contributors, microbiome-mediated mechanisms influencing host iron availability remain insufficiently explored. Emerging evidence indicates that CD-associated dysbiosis is characterized by an increased abundance of siderophore-producing bacteria, particularly members of the Enterobacteriaceae family. Because siderophores are high-affinity iron-chelating molecules capable of competing with host iron acquisition systems and partially escaping lipocalin-2-mediated sequestration, their expansion may contribute to reduced luminal iron bioavailability. In this systematic review, we analyzed comparative microbiome studies published between 2016 and 2026 that directly evaluated microbial differences between CD and UC. CD microbiota consistently demonstrated enrichment in siderophore-associated taxa relative to UC. Based on these findings, we propose that microbiome-driven iron competition may represent an additional mechanistic contributor to the increased prevalence and persistence of anemia observed in CD. Although direct in vivo quantification of siderophore activity in IBD remains limited, the convergence of ecological, functional, and strain-level microbiome evidence supports a biologically plausible interaction between microbial iron-scavenging strategies and host iron metabolism. Full article

Eating behavior and eating habits are shaped from the earliest stages of life through interactions among biological, familial, social, and environmental factors. The aim of this narrative review is to integrate evidence on the early-life determinants of eating behavior and their influence on dietary intake from infancy to adolescence. A narrative review was conducted with a structured search approach prioritized on longitudinal studies, intervention trials, and policy evaluations when available, and using cross-sectional evidence mainly to describe patterns and sociodemographic factors. Synthesizing the current evidence, our framework proposes that breastfeeding, responsive complementary feeding, and self-regulatory parenting are associated with higher responsiveness to internal hunger, satiety cues, and preference for nutrient-dense foods. Conversely, coercive practices, early exposure to highly palatable foods, and the influence of food marketing are linked to dominant hedonic responses and impulsive consumption patterns. Furthermore, family environments characterized by stress or food insecurity, together with high access to low-nutrient foods, may increase vulnerability to poor eating habits and emotional eating during adolescence. Overall, the evidence highlights the need for preventive interventions that integrate parenting support, school food education, digital marketing regulation policies, and the promotion of healthy food environments across multiple sectors. Understanding the biological, psychological, and social factors linking early determinants to dietary intake and eating behaviors across development is essential for promoting a balanced relationship with food and preventing chronic diseases from an early age. Full article

Background: Despite being considered a country with a larger health workforce in Africa, the South African health workforce continues to experience shortages and a maldistribution of health workers across regions and sectors. Current projections suggest that the workforce is expected to decline further, especially among doctors, nurses and midwives, in large part, due to attrition—which could compromise the delivery of primary health and maternity services. These health workforce shortages and uneven distribution threaten the sustainability and effectiveness of health services in South Africa and drives the need to investigate the factors that may be influencing career choice and change decisions among health professionals in South Africa. Methods: A qualitative exploratory study, making use of purposive sampling and semi-structured interviews, was conducted to investigate the factors influencing career choice and change decisions among health professionals in South Africa. The participants were qualified health professionals in the fields of medicine, nutrition, pharmacy, nursing, and psychology working in the private, public, and academic sectors. Data was collected until saturation was achieved and then thematically analyzed using MAXQDA 24. Results: A total of 10 participants made up of three males and seven females were interviewed. These participants worked in different employment sectors with some having dual roles in private practice, public sector, and academia. The analysis revealed three major themes that capture the nature of and factors influencing career choice and career changes occurring in South Africa. The first theme related to factors influencing career choice (including altruism, family influence, personal experiences, financial/job security, academic achievement, career guidance, and opportunity for change). The second theme focused on career change dynamics (nature of career changes and career transitions occurring in the form of specialization, switching health professions, exiting health professions, adding non-health interests, and shifting focus areas). The third theme revealed factors influencing career change. These were categorized into personal and individual factors, workplace or job-specific factors, and administrative factors. This study has contributed to understanding the career choices and career changes taking place within the health professions in South Africa. It has also revealed a need for reforms in policy and practice for the current health professionals who have no intention of changing their careers while highlighting implications for future training of health professionals. Also, addressing the challenges of poor working conditions, lack of support, unemployment and placement delays, and other administrative barriers will help mitigate some of the issues leading to health workforce shortages and inequities in the South African context. Conclusions: The strongest motivator for choosing a career in health professions is the desire to care for others, while retention of the health workforce is challenged by personal, workplace, and administrative factors. Enhancing workplace conditions and support systems, implementing policy reforms, and minimizing administrative barriers is essential for achieving universal health coverage and sustaining a resilient health workforce in South Africa. Full article

Language embodies traditions, values, and collective identity, bridging gaps between generations and geographies. Maintaining consistent language policies at home and in communities remains challenging, with research showing that only a few families have explicit rules about language use and few enforce them regularly. The study explores strategies for heritage language maintenance (HLM) to mitigate language attrition among Anaañ bilinguals residing in the Akpabuyo and Calabar South Local Government Areas of Cross River State, Nigeria. The study draws on social identity theory, which links language use to identity, motivation, and group affiliation, thereby influencing language maintenance. Using a qualitative approach, data were collected through semi-structured interviews with 40 participants, selected purposively from Anaañ speakers in the study areas. Thematic analysis is employed to identify patterns and themes, revealing approaches for heritage language maintenance to curb language attrition. Findings reveal that despite some Anaañ speakers’ negative attitude towards their HL, others value it, keep it alive, and are ready to pass it to the future generations. This will preserve cultural identity and foster a sense of pride, belonging and shared values among Anaañ people, especially those residing in Southern Cross River State. Full article

Precision agriculture increasingly relies on machine learning applied to high-resolution data acquired by unmanned aerial vehicles (UAVs) to support crop monitoring, stress detection, and yield forecasting. This survey presents a structured review of machine learning methods for UAV-enabled precision agriculture and organizes over 100 peer-reviewed studies within a unified four-dimensional taxonomy defined by sensing modality, data type, model family, and analytical task. The taxonomy enables systematic comparison across RGB, multispectral, hyperspectral, LiDAR, and IoT data sources and across classical machine learning, deep learning, hybrid sequential models, and emerging transformer-based architectures. We analyze how modeling choices interact with data characteristics to influence robustness, cross-environment generalization, computational efficiency, and deployment feasibility on UAV and edge platforms. Recurring challenges include limited labeled data, domain shift across seasons and fields, multimodal heterogeneity, occlusion, and real-time processing constraints. We identify emerging research directions, including data-efficient learning, representation-level multimodal fusion, domain adaptation, lightweight architectures for embedded deployment, and uncertainty aware decision support. By formalizing the landscape through a unified taxonomy, this survey provides a foundation for designing scalable, robust, and deployable machine learning systems for next-generation precision agriculture. Full article

Accurate airfoil reconstruction is crucial for aerodynamic analysis, geometric modeling, and computational design applications. This study proposes an optimized M-Hermite interpolation framework for high-accuracy airfoil reconstruction and geometric preservation. Unlike classical Hermite interpolation, the proposed framework integrates a truncated M-derivative formulation through M-inspired parameter-dependent scaling into the interpolation structure, enabling adaptive local geometric control via fractional parameters $\alpha$ and $\beta$. Additionally, a tangential scaling coefficient is incorporated to improve curvature adaptation and reconstruction stability in critical geometric regions. The proposed framework is evaluated using 11 reference airfoil geometries and compared with widely used interpolation methods, including Cubic Spline, B-Spline, Bézier, Catmull-Rom, Classical Hermite, and unoptimized M-Hermite interpolation. Reconstruction performance was assessed using both global and local geometric validation metrics, including RMSE, MAE, maximum error, Hausdorff distance, leading-edge RMSE, trailing-edge RMSE, thickness retention error, and curvature retention error. Experimental results demonstrated that the optimized M-Hermite framework achieved the best overall reconstruction performance and geometric consistency across the evaluated airfoil families. The proposed framework improved reconstruction accuracy, particularly in high-curvature leading-edge regions, while preserving geometrically relevant shape descriptors known to influence aerodynamic behavior, including thickness distribution, camber-line consistency, and curvature structure. Optimization analyses further revealed that reconstruction performance is strongly dependent on geometry-adaptive parameter configurations, particularly the $\beta$ parameter, which governs local geometric behavior. These findings demonstrate that the proposed optimized M-Hermite framework provides an adaptive and computationally efficient interpolation strategy for accurate airfoil reconstruction and geometric shape preservation applications. Full article

Play is a universal activity. Yet there are cultural and linguistic differences in how families engage in adult–child play. In the present study, Thai–English bilingual mother–child dyads completed a toy play task in both languages. The results revealed cross-linguistic differences in bilingual mothers’ and children’s conversation styles. When speaking Thai, the nature of bilinguals’ dyadic play was more adult-centered, characterized by the use of directives by the mothers and use of repetitions by the children, which was congruent with parent–child interpersonal dynamics in high-power-distance Asian cultures. When speaking English, the play session was more child-centered, evidenced by children’s use of directives and encouragements, which was congruent with behavioral norms in low-power-distance Western cultures. Bilingual mothers and children exhibited positive associations in their narrative styles during both the Thai and English sessions. Additionally, the preliminary results provided evidence that cross-linguistic differences in mother–child speech patterns may be moderated by child gender. These findings suggest that the communicative and interactional patterns that bilingual caregivers modeled for bilingual children varied across languages and that preschoolers aligned their behaviors with those exemplified by their mothers. We conclude that bilingualism influences early social communication, with theoretical and applied implications for researchers, educators, and clinicians. Full article

Little is known about parents’ attitudes towards their children’s accent and the role they play in transmitting an accent (and attitudes about accents) to their children. Even less is known about how these perceptions and attitudes emerge and are transmitted in multilingual families. We draw on an online survey of parental language attitudes in Australia (n = 267), where 45% of respondents were born overseas, and 61% reported speaking a language other than English. Parents were asked whether they think their children speak with an Australian or another accent; whether they change their accent when they speak to their children, and whether their children’s accents change when they speak to their parents as compared to other people. A total of 14% of parents reported that one or more of their children had an accent that was not Australian, with about half of these children having reportedly hybrid or mixed accents, and the other half no accent at all. Over 11% of parents reported frequently or occasionally changing their accent with their children. Of those parents, several disclosed specific strategies in changing their accents around their children, such as making a conscious effort to sound ‘clear’ or deliberately exposing their children to different accents. A total of 15% of parents reported that their children’s accents frequently or occasionally change when speaking to them. These findings have implications for the complex influences on children’s language and dialect repertoires as they relate to language attitudes, language ecology, and linguistic identity. Full article

Background/Objectives: Recent advances in automated mitosis detection in H&E histopathology have expanded AI applications in digital pathology for tumor grading and proliferation assessment. However, reported performance remains difficult to interpret because it is strongly influenced by benchmark selection and heterogeneous evaluation regimes. This review examined how recent methodological advances, dataset context, and evaluation-regime stratification shape performance interpretation. Methods: We conducted a systematic review of peer-reviewed English-language studies published between January 2018 and December 2025. PubMed, Scopus, and IEEE Xplore were searched for mitosis detection, localization, or counting in H&E histopathology. After screening and full-text assessment, 66 studies met the inclusion criteria. We synthesized 60 method papers and considered 6 dataset/challenge descriptor papers separately. Extracted data included task formulation, datasets, evaluation regime, and outcomes. Results: The 60 method papers showed a methodological shift from patch/cell-level classification toward one-stage and two-stage detectors, dense segmentation/heatmap approaches, hybrid pipelines, and emerging robustness-oriented methods. F1 was reported in 59/60 studies, but evaluation practice was heterogeneous: custom hold-out testing predominated, whereas external validation and explicit domain-generalization protocols were uncommon. Evidence remained concentrated in legacy breast benchmarks, while MIDOG-family datasets anchored most robustness-oriented studies. Importantly, dataset names alone were insufficient to determine comparability; for example, “testing on ICPR2014” could refer to organizer-governed hidden-test scoring, post-challenge labels, or author-defined splits of public data. Conclusions: Automated mitosis detection research has diversified rapidly, but cross-study comparability remains limited by inconsistent evaluation and scarce cross-domain testing. Clearer reporting of dataset partitions, evaluation governance, and metrics, with more routine external or domain-held-out evaluation, would strengthen evidence for AI-driven digital pathology and precision oncology. Full article