Search Results (2,336)

The changes in adolescents’ visions for the future are important to adolescents’ developmental trajectories, motivation, and educational outcomes, yet understudied. This study examined the change in possible selves and its association with grit during school closure and life interruption during COVID-19. We conducted a school-based longitudinal survey among 1577 students (Mage = 13.05, SD = 0.86) from 12 secondary schools at the start and end of an academic year prior to and during the COVID-19 pandemic with a 9-month interval. Demographic, grit, socioeconomic status (SES), self-control, and possible selves were measured. Paired t-tests indicated a significant decrease in academic possible selves and strategies. Hierarchical regression analysis results show that participants with higher grit scores reported higher academic and life possible selves; in particular, the effect of grit–perseverance was stronger than grit–passion after controlling self-control. SES moderated the effect of grit–passion on academic possible selves. The current longitudinal study provides important implications for education and youth social work practice for young people growing up with the influence of the pandemic. Full article

This work presents and compares different methodologies for the joint optimisation of the fractional derivative order and the parameters of the right-hand-side neural network in Neural Fractional Differential Equation models. The proposed strategies aim to tackle the training difficulties typically encountered when learning the fractional order $\alpha$ together with the network weights. One approach is based on regulating the gradient magnitude of the loss function with respect to $\alpha$, encouraging more stable and effective updates. Another strategy introduces an online pre-training scheme, where the network parameters are initially optimised over progressively longer time intervals, while $\alpha$ is updated more conservatively using the full time trajectory. The study focuses only on a foundational setting with one-dimensional problems, and numerical experiments demonstrate that the proposed techniques improve both training stability and accuracy. Nonetheless, the issue of non-uniqueness in the optimal derivative order remains, particularly in less well-posed scenarios, suggesting the need for further research in data-driven modelling of fractional-order systems. Full article

The floral transition in Chinese cabbage (Brassica rapa ssp. pekinensis) is governed by a complex interplay of gene expression and hormonal regulation. Temporal transcriptome profiling was conducted across three developmental stages: pre-bolting (PBS), bolting (BS), and flowering stages (FS), to investigate the underlying molecular mechanisms. A total of 7092 differentially expressed genes (DEGs) were identified, exhibiting distinct expression trajectories during the transition. Moreover, functional enrichment analyses revealed strong associations with plant hormone signaling, MAPK pathways, and developmental regulation processes. Key flowering-related genes, such as BrFLM, BrAP2, BrFD, BrFT, and BrSOC1s displayed antagonistic expression patterns. Hormonal pathways involving auxin, ABA, ET, BR, GA, JA, CK, and SA showed stage-dependent modulation. Further, orphan genes (OGs), especially EARLY FLOWERING 1 (EF1), showed significant upregulation during the transition, which exhibited 1.84-fold and 1.93-fold increases at BS and FS compared to PBS, respectively (p < 0.05). Functional validation through EF1 overexpression (EF1OE) in Arabidopsis consistently promoted early flowering. The expression levels of AtFT and AtSOC1 were significantly upregulated in EF1OE lines compared to wild-type (WT) plants. The findings contribute to understanding the coordinated genetic and hormonal events driving floral development in Chinese cabbage, suggesting EF1 as a candidate for bolting resistance breeding. This work also expands the existing regulatory framework through the successful integration of OGs into the complex floral induction system of Brassica crops. Full article

Accurate estimation of aboveground biomass (AGB) in tree–shrub communities is critical for quantifying forest ecosystem productivity and carbon sequestration potential. Although generalized allometric equations offer expediency in natural forest AGB estimation, their neglect of interspecific variability introduces methodological pitfalls. Precise AGB prediction necessitates resolving two biological constraints: phylogenetic conservation of allometric coefficients and ontogenetic regulation of scaling relationships. This study establishes an integrated framework combining the following: (1) phylogenetic signal detection (Blomberg’s K/Pagel’s λ) across 157 species’ allometric equations, revealing weak but significant evolutionary constraints (λ = 0.1249, p = 0.0027; K ≈ 0, p = 0.621); (2) hierarchical error decomposition of 9105 stems in a Mt. Wuyishan forest dynamics plot (15 species), identifying family-level error stratification (e.g., Theaceae vs. Myrtaceae, Δerror > 25%); (3) ontogenetic trajectory analysis of Castanopsis eyrei between Mt. Wuyishan and Mt. Huangshan, demonstrating significant biomass deviations in small trees (5–15 cm DBH, p < 0.05). Key findings resolve the following hypotheses: (1) absence of strong phylogenetic signals validates generalized models for phylogenetically diverse communities; (2) ontogenetic regulation dominates error magnitude, particularly in early developmental stages; (3) differential modeling is recommended: species-specific equations for pure forests/seedlings vs. generalized equations for mixed mature forests. This work establishes an error hierarchy: ontogeny > taxonomy > phylogeny, providing a mechanistic basis for optimizing forest carbon stock assessments. Full article

The analysis of anomalous diffusion characteristics within single-particle tracking data is a key problem in several applied-science domains, including biosignal processing, bioinformatics, and biotechnology. This task becomes particularly challenging in the presence of short trajectories, localization errors, and non-ergodicity, features that are common in real experimental data. To address these limitations, this work proposes an approach that improves the robustness and accuracy of estimating the anomalous diffusion exponent α, even for very short trajectories of up to 10 points. The approach includes an ensemble-based variance estimation of the exponent α, along with a bias correction based on time–ensemble averaged mean squared displacement, which reduces the systematic bias. These components integrate well into neural network architectures and are suitable for analyzing experimental trajectories in biotechnology and bioprocess engineering applications. Full article

Inclusion in play, physical education, outdoor life, organized sports, and other movement-based activities can promote resilience and support physical, emotional, and social well-being. These arenas are particularly important for reducing health disparities and preventing social marginalization across the lifespan. Yet, children and adolescents from vulnerable or disadvantaged backgrounds encounter persistent barriers to participation, rooted in broader inequalities related to their socioeconomic position, disability, gender, ethnicity, and access to supportive environments. This perspective outlines how inclusive movement contexts, when informed by developmental systems theory and resilience frameworks, can interrupt trajectories of marginalization and promote long-term equity in health, education, and work inclusion. We emphasize the need for interdisciplinary approaches, combining longitudinal and qualitative methods, to uncover how vulnerability and participation interact dynamically over time. By integrating insights from developmental science, education, public health, and spatial ecology, we identify strategic pathways for research and action. Addressing these challenges requires coordinated efforts across sectors and stakeholders to co-create inclusive, context-sensitive interventions. Full article

Mangroves form the dominant coastal plant community of low-energy tropical intertidal habitats and provide critical ecosystem services to humans and the environment. However, more frequent and increasingly powerful hurricanes and storm surges are creating additional pressure on the natural resilience of these threatened coastal ecosystems. Advances in remote sensing techniques and approaches are critical to providing robust quantitative monitoring of post-storm mangrove forest recovery to better prioritize the often-limited resources available for the restoration of these storm-damaged habitats. Here, we build on previously utilized spatial and temporal ranges of European Space Agency (ESA) Sentinel satellite imagery to monitor and map the recovery of the mangrove forests of the British Virgin Islands (BVI) since the occurrence of back-to-back category 5 hurricanes, Irma and Maria, on September 6 and 19 of 2017, respectively. Pre- to post-storm changes in coastal mangrove forest health were assessed annually using the normalized difference vegetation index (NDVI) and moisture stress index (MSI) from 2016 to 2023 using Google Earth Engine. Results reveal a steady trajectory towards forest health recovery on many of the Territory’s islands since the storms’ impacts in 2017. However, some mangrove patches are slower to recover, such as those on the islands of Virgin Gorda and Jost Van Dyke, and, in some cases, have shown a continued decline (e.g., Prickly Pear Island). Our work also uses a linear ANCOVA model to assess a variety of geospatial, environmental, and anthropogenic drivers for mangrove recovery as a function of NDVI pre-storm and post-storm conditions. The model suggests that roughly 58% of the variability in the 7-year difference (2016 to 2023) in NDVI may be related by a positive linear relationship with the variable of population within 0.5 km and a negative linear relationship with the variables of northwest aspect vs. southwest aspect, island size, temperature, and slope. Full article

Aviation is widely recognised to have global-scale climate impacts through the formation of ozone (O₃) in the upper troposphere and lower stratosphere (UTLS), driven by emissions of nitrogen oxides (NO_X). Ozone is known to be one of the most potent greenhouse gases formed from the interaction of aircraft emission plumes with atmospheric species. This paper follows up on previous research, where a Photochemical Trajectory Model was shown to be a robust measure of ozone formation along flight trajectories post-flight. We use a combination of a global Lagrangian chemistry-transport model and a box model to quantify the impacts of aircraft NO_X on UTLS ozone over a five-day timescale. This work expands on the spatial and temporal range, as well as the chemical accuracy reported previously, with a greater range of NO_X chemistry relevant chemical species. Based on these models, route optimisation has been investigated, through the use of network theory and algorithms. This is to show the potential inclusion of an understanding of climate-sensitive regions of the atmosphere on route planning can have on aviation’s impact on Earth’s Thermal Radiation balance with existing resources and technology. Optimised flight trajectories indicated reductions in O₃ formation per unit NO_X are in the range 1–40% depending on the spatial aspect of the flight. Temporally, local winter times and equatorial regions are generally found to have the most significant O₃ formation per unit NO_X; moreover, hotspots were found over the Pacific and Indian Ocean. Full article

Background/Objectives: Children with above-average cognitive functioning often present complex developmental profiles, combining high cognitive potential with heterogeneous socio-emotional and learning trajectories. Although the cognitive and behavioral characteristics of giftedness have been widely studied in Anglophone countries, evidence remains limited in Southern Europe. This study aimed to investigate the cognitive, academic, and emotional–behavioral profiles of Italian children and adolescents with above-average cognitive functioning, using an inclusive, dimensional approach (IQ > 114). Methods: We analyzed a cross-sectional sample of 331 children and adolescents (ages 2.11–16.5 years), referred for clinical cognitive or behavioral evaluations. Participants were assessed using the WPPSI-III or WISC-IV for cognitive functioning, the MT battery for academic achievement, and the Child Behavior Checklist (CBCL) for emotional and behavioral symptoms. Comparative and correlational analyses were performed across age, gender, and functional domains. A correction for multiple testing was applied using the Benjamini–Hochberg procedure. Results: Gifted participants showed strong verbal comprehension (mean VCI: preschoolers = 118; school-aged = 121) and relative weaknesses in working memory (WM = 106) and processing speed (PS = 109). Males outperformed females in perceptual reasoning (PR = 121 vs. 118; p = 0.032), while females scored higher in processing speed (112 vs. 106; p = 0.021). Difficulties in writing and arithmetic were observed in 47.3% and 41.8% of school-aged participants, respectively. Subclinical internalizing problems were common in preschool and school-aged groups (mean CBCL T = 56.2–56.7). Working memory negatively correlated with total behavioral problems (r = −0.13, p = 0.046). Conclusions: These findings confirm the heterogeneity of gifted profiles and underscore the need for personalized educational and psychological interventions to support both strengths and vulnerabilities in gifted children. Caution is warranted when interpreting these associations, given their modest effect sizes and the exploratory nature of the study. Full article

Introduction: Human immunodeficiency virus (HIV)-associated neurocognitive impairment (NCI) remains a concern despite combination antiretroviral therapy (cART), with cognitive problems often persisting even after viral suppression. The mechanisms underlying neurocognitive deterioration in people living with HIV (PLWH) and the role of plasma biomarkers remain unclear. This study aims to evaluate neurocognitive trajectories and biomarker changes in a real-world cohort of newly diagnosed PLWH initiating cART in Greece. Methods: This prospective, single-center study assessed neuropsychological performance and plasma biomarkers in treatment-naïve PLWH at baseline and 18 months after cART initiation. HIV-associated neurocognitive disorder (HAND) was classified using the Frascati criteria, and plasma biomarkers of inflammation and monocyte activation were measured. Correlations between biomarkers and cognitive performance were analyzed. Results: A total of 39 treatment-naïve PLWH were enrolled in this study. At baseline, 45.7% of participants met criteria for HAND, predominantly, asymptomatic neurocognitive impairment (ANI). Over 18 months, neurocognitive function improved, particularly in speed of information processing, executive function, and visuospatial ability, while verbal fluency, fine motor dexterity, and attention/working memory remained unchanged. Biomarkers of inflammation and monocyte activation decreased following cART, except for neopterin, which increased (10.6 vs. 13 ng/mL, p = 0.002), and plasma NFL (7.5 vs. 7.2 pg/mL, p = 0.54), which remained stable. A negative correlation between monocyte activation markers and cognitive performance was observed only at follow-up, suggesting that systemic inflammation may mask these associations in untreated PLWH. Conclusions: Early cART initiation supports neurocognitive recovery and reduces immune activation in PLWH. The observed correlation between cognitive performance and monocyte activation markers after viral suppression highlights the potential utility of plasma biomarkers in predicting cognitive impairment. Full article

Digital transformation (DT) has become a critical focus in both academia and industry. However, its rapid evolution complicates our understanding of its core concepts and developmental patterns. Understanding the development path of DT is crucial for both scholars and practitioners because it provides a structured view of how the field has progressed over time. This study employs main path analysis (MPA), a citation-based scientometric method, to systematically review and trace the intellectual trajectory of DT research over the past 30 years. Drawing on 1790 academic articles from the Web of Science database, the study identifies key influential works and maps the primary citation paths that shape the field. The analysis reveals three major developmental phases of DT research—engagement, enablement, and enhancement—each characterized by distinct thematic and conceptual shifts. Furthermore, five emerging research trends are uncovered: reinventing digital innovation affordance, value-creation paths of DT, synergistic DT with business and management practices, disciplinary boundaries of DT, and digital leadership. Understanding the intellectual trajectory and emerging trends of DT helps practitioners anticipate technological shifts and align transformation efforts, guiding decision-makers in effectively managing their DT processes. Also, these findings provide a structured framework for understanding the evolution of DT and offer valuable directions for future research in information systems and digital innovation. Full article

This study investigates the relationship between ESG performance and economic growth in BRICS nations from 2000 to 2020, aiming to understand how ESG practices influence development trajectories. By integrating economic theories with relevant conceptual frameworks, this study provides a comprehensive analysis of ESG dynamics in emerging economies. The purpose of this study is to determine how the economic growth of the BRICS countries between 2000 and 2020 was impacted by ESG performance at the national level. This work contributes to the body of knowledge by offering a fresh macroeconomic examination of the connection between economic growth and ESG performance in the BRICS nations, a topic that is still relatively unexplored in comparison to firm-level research. A significant knowledge gap on how developing economies strike a balance between rapid economic expansion and environmental and social sustainability is filled by the research’s use of a thorough national-level ESG framework. The study employed a dynamic panel auto regressive distributed lag (ARDL) model, utilising a dynamic pooled mean group (PMG) ARDL econometric technique for both short- and long-term estimates. The findings reveal a short-term negative relationship between ESG performance and economic growth in the BRICS countries, which implies that there are high transitional effects involved in sustainable growth solutions. It also highlights the structural and developmental heterogeneity among BRICS countries. Moreover, the study highlights that carbon emissions positively influence short-term economic growth, underscoring the challenge of balancing sustainability with the continued reliance on fossil fuels in these economies. However, the long-term results show that strong ESG practices ultimately positively affect economic growth, reinforcing the importance of investing in sustainable development for achieving high-quality, long-term prosperity. This conclusion emphasises that, while short-term trade-offs may exist, robust ESG frameworks are crucial for fostering enduring economic and environmental well-being. Full article

Human–computer interaction (HCI) plays a crucial role across various fields, with eye-tracking technology emerging as a key enabler for intuitive and dynamic control in assistive systems like Assistive Robotic Arms (ARAs). By precisely tracking eye movements, this technology allows for more natural user interaction. However, current systems primarily rely on the single gaze-dependent interaction method, which leads to the “Midas Touch” problem. This highlights the need for real-time eye movement classification in dynamic interactions to ensure accurate and efficient control. This paper proposes a novel Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) classification algorithm aimed at overcoming the limitations of traditional methods in dynamic human–robot interactions. By incorporating sum of squared error (SSE)-based feature extraction and hierarchical training, the proposed algorithm achieves a classification accuracy of 94.39%, significantly outperforming existing approaches. Furthermore, it is integrated with a robotic arm system, enabling gaze trajectory-based dynamic path planning, which reduces the average path planning time to 2.97 milliseconds. The experimental results demonstrate the effectiveness of this approach, offering an efficient and intuitive solution for human–robot interaction in dynamic environments. This work provides a robust framework for future assistive robotic systems, improving interaction intuitiveness and efficiency in complex real-world scenarios. Full article

Consciousness remains one of the most critical yet least understood functions of the brain, not only in humans but also in certain highly organized animal species. In this review, we propose treating consciousness as an emergent, goal-directed informational system organized by the subjective “self” as an active system-forming factor. We present an integrative theoretical–systems framework in which subjectivity functions as system-forming factor of consciousness (SFF) throughout biological evolution. Beginning with proto-conscious invertebrates, we trace progressive elaborations of working and long-term memory, the refinement of behavioral programs, and the emergence of an internal arbiter capable of resolving competing drives. In endothermic vertebrates, subjectivity acquires distinct functional features—sensory filtering, causal reasoning, and adaptive arbitration—underpinned by increasingly complex neural architectures. This evolutionary trajectory culminates in humans, where subjectivity attains its highest level of organization through culturally mediated networks. Although the framework does not assume any specific neural substrate, it provides a testable roadmap linking evolutionary biology, information theory, and quantitative modeling. By clarifying why consciousness arose and how subjectivity shapes complex networks, this perspective also lays the groundwork for exploring possible nonbiological extensions of subjectivity. Full article

Underwater exploration is vital for advancing scientific understanding of marine ecosystems, biodiversity, and oceanic processes. Autonomous underwater vehicles and sensor platforms play a crucial role in continuous monitoring, but their operational endurance is often limited by energy constraints. Various control strategies have been proposed to enhance energy efficiency, including robust and optimal controllers, energy-optimal model predictive control, and disturbance-aware strategies. Recent work introduced a variable structure depth controller for a sensor platform with a variable buoyancy module, resulting in a 22% reduction in energy consumption. This paper extends that work by providing a formal stability proof for the proposed switching controller, ensuring safe and reliable operation in dynamic underwater environments. In contrast to the conventional approach used in controller stability proofs for switched systems—which typically relies on the existence of multiple Lyapunov functions—the method developed in this paper adopts a different strategy. Specifically, the stability proof is based on a novel analysis of the system’s trajectory in the net buoyancy force-versus-depth error plane. The findings were applied to a depth-controlled sensor platform previously developed by the authors, using a well-established system model and considering physical constraints. Despite adopting a conservative approach, the results demonstrate that the control law can be implemented while ensuring formal system stability. Moreover, the study highlights how stability regions are affected by different controller parameter choices and mission requirements, namely, by determining how these aspects affect the bounds of the switching control action. The results provide valuable guidance for selecting the appropriate controller parameters for specific mission scenarios. Full article