Search Results (18,509)

Background: Fetal alcohol spectrum disorders (FASDs) are lifelong neurodevelopmental conditions resulting from prenatal alcohol exposure. Despite high FASD rates in high-risk adoptive populations, little is known about the experiences of adoptive parents in Israel, where underdiagnosis and limited professional awareness persist. Drawing on ecological and family systems theory, this study explored the daily challenges, coping strategies, and service-related barriers encountered by adoptive parents raising children with FASD. Methods: Using a qualitative, exploratory design, semi-structured interviews were conducted with 12 adoptive parents of children aged 9–18 years with a parent-reported clinical diagnosis of FASD made by a healthcare professional. The interviews focused on child-related difficulties, coping mechanisms, diagnostic experiences, and interactions with health, education, and welfare systems. The data were analysed using reflexive thematic analysis. Results: Five themes were identified: (a) child-related neurodevelopmental and behavioral challenges, (b) emotional and practical caregiving burden, (c) systemic barriers, including limited professional knowledge and fragmented diagnostic pathways, (d) coping strategies and family strengths, and (e) unmet needs and recommendations. Parents described fluctuating child functioning, chronic caregiving demands, and significant gaps in diagnostic and educational support. Conclusions: Adoptive parents raising children with FASD face substantial daily challenges compounded by systemic limitations. This study’s findings underscore the need for improved professional training, clearer diagnostic pathways, FASD-informed educational practices, and coordinated multidisciplinary services. These results provide direction for policy and service development to improve support for families affected by FASD. Full article

The mathematical characterization of non-stationary signals remains a significant challenge, particularly when impulsive components are obscured by high-dimensional noise and structural coupling. This paper proposes an application-driven mathematical methodology for a learnable discrete wavelet transform (LDWT) that combines classical multi-resolution analysis with task-optimized data-driven adaptivity. Rather than introducing entirely new foundational theory, our approach strategically relaxes constraints from orthogonal wavelet theory within the non-perfect reconstruction filter bank framework, enabling controlled spectral decomposition optimized for supervised fault diagnosis. We introduce a specialized regularization term based on the half-band property to ensure spectral complementarity and minimize cross-band correlation, while a Jacobian-based stabilization approach is formulated to ensure the convergence of filter coefficients during optimization. The proposed algorithmic architecture, LDBRFnet, features a dual-branch encoder system designed to capture the mathematical synergy between sub-band-level global statistics and time-domain local morphology. This dual-view representation effectively mitigates feature leakage and overconfidence in classification. Theoretical analysis and numerical experiments demonstrate that the learned filters satisfy the frequency-shift property and maintain robust spectral partitioning even under low signal-to-noise ratios. Validation on complex vibration datasets confirms that the framework achieves superior diagnostic accuracy (over 95.5%) and computational efficiency, reducing model parameters by 96.7% compared to state-of-the-art baselines. This work provides a generalizable mathematical approach for adaptive signal decomposition and robust pattern recognition in interdisciplinary applications. Full article

This study examines the bending of cross-ply laminated composite nanoplates coupled to a piezoelectric fiber-reinforced composite layer via the nonlocal strain gradient theory. The aim is to accurately capture size-dependent impacts and electromechanical interaction in nanoscale composite structures. The mechanical response is modeled utilizing a refined four-variable shear deformation theory, with the governing equilibrium equations developed using the virtual work assumption. The nanoplate is examined under simply supported boundary conditions exposed to both mechanical loading and applied electric voltage. A detailed parametric investigation is done to assess the contribution of non-local and strain gradient factors, imposed voltage, and geometric ratios on the bending behavior. The results show that the nonlocal parameter generates a softening result, increasing deflection, whereas the strain gradient parameter raises stiffness and minimizes deformation. Moreover, the applied voltage successfully controls the bending response by electromechanical actuation, underlining the potential of PFRC-integrated nanoplates in smart nanoscale systems. Full article

Purpose: This study provides a critical examination of the literature on applying artificial intelligence (AI) and Extended Reality (XR) in restaurant settings and related foodservice operations. It focuses on how AI and XE influence consumer nutrition awareness and decision-making about food choices, and their implications for customer satisfaction, loyalty, and service delivery in foodservice environments. Design/methodology/approach: The study adopts a systematic literature review (SLR) approach following the PRISMA method. An initial search identified over 3900 academic papers published between 2016 and 2025. Studies were selected on the basis of predetermined inclusion and exclusion criteria, and 26 peer-reviewed articles were analyzed. The review provides a conceptual synthesis and develops propositions for practical applications and future research directions. Findings: The review reveals a shift from static systems that rely on optimization, toward adaptive and user-centered solutions that are behavior-oriented. AI applications predominate in the case of calorie tracking, personalized recommendations, and menu planning. Though deployment of XR technologies (e.g., AR and VR) is less prevalent, they offer potential for immersive, and real-time interventions. A key distinction emerges between studies demonstrating empirical effectiveness (e.g., improved understanding and healthier choices) and those focused on technical and/or conceptual developments. To date, there has been limited validation of behavioral impacts in foodservice settings. Originality: This study offers a theory-informed conceptualization of AI and XR applications in restaurant and foodservice contexts by integrating three perspectives: hospitality (menus and dining experience), nutrition (dietary awareness and healthier choices), and human–technology interaction (technology acceptance and user engagement). The study reconceptualizes AI- and XR-enabled systems as behavioral intervention tools and outlines a focused research agenda for advancing nutritional communication in foodservice environments. Full article

Drought in Angola is a recurrent and cyclical natural phenomenon that poses significant environmental, economic, and social challenges, affecting water resources, agriculture, ecosystems, livestock, and vulnerable communities. This study investigates the temporal evolution and spatial behavior of drought in the provinces of Cunene, Huila, and Namibe over the period 1980–2024. Drought conditions were assessed using the Standardized Precipitation Index (SPI) and the Standard Precipitation–Evapotranspiration Index (SPEI) at multiple time scales. Trends were evaluated using the Modified Mann–Kendall test and Sen’s slope estimator, while drought intensity was analyzed using run theory. The results reveal a clear intensification of drought conditions in the last decade, characterized by an increase in frequency and intensity, particularly after 2010. Extreme drought events were identified in the early 1980s, the mid-1990s, and more recently in 2019 and 2021. Despite some regional variability, the three provinces exhibit consistent temporal patterns, with drought events generally occurring simultaneously over the study period. These findings highlight the increasing pressure on water and environmental systems and underscore the need for improved drought monitoring and forecasting approaches to support more effective adaptation and decision-making. Full article

In this study, we evaluated various density functional theory (DFT) methods to obtain thermodynamic parameters, such as enthalpy and Gibbs free energy, and compared them with experimental values obtained from conductometric analysis. As a model system, we chose the heptakis(2,6-di-O-methyl)-β-cyclodextrin (DIMEB) complex with the recently synthesized fluorinated compound, butane-1,4-diyl bis(2,2,2-trifluoroethane-1-sulfonate) (BFS). The analysis was carried out in the temperature range of 293.15–313.15 K. A conformational search was performed to identify the most stable complexes. The final stage of optimization was conducted at the ωB97X-D4/6-31G(d,p) level of theory in the presence of water, modeled using the conductor-like polarizable continuum model (CPCM). The thermodynamic analysis indicates that almost all theoretical methods overestimate the enthalpy and Gibbs free energy. This also applies to Minnesota functionals, which are commonly recommended for thermochemistry studies. The best agreement with experimental results was obtained for the composite methods r²SCAN-3c and PBEh-3c, with the coefficient of determination (R² = 0.9972) indicating excellent correlation between r²SCAN-3c and experiment. Full article

Rising production costs have increased interest in lower-cost, non-inversion crop establishment systems in Ireland, yet uptake remains relatively limited. Growers’ perceptions of relative performance of innovations compared to current practice are key determinants of adoption. We surveyed 154 Irish arable growers (77 plough-based, 59 min-till, 18 direct drill) to assess perceived performance of min-till and direct drill across multiple parameters relative to ploughing to identify potential barriers to adoption. Respondents rated impacts on Likert scales; analyses summarized response distributions and between-system differences. For example: >30% of min-till growers believed min-till winter cereal yields exceed ploughing, compared with 0% of plough and <10% of direct drill growers. Growers generally favoured their own establishment system, consistent with adoption theory. Potential barriers to non-inversion adoption included perceived lower establishment reliability, crop performance concerns (especially spring crops), and anticipated increases in weed pressure, herbicide reliance, and herbicide resistance development risk. Several perceptions diverged from the Ireland-relevant literature, revealing both knowledge gaps (notably establishment stability and winter/spring crop performance of establishment systems) and misperceptions (including establishment system on soil structure). Targeted research to address knowledge gaps, combined with focused, grower-centred knowledge exchange, is required to support evidence-based evaluation and sustainable adoption of establishment systems in Ireland. Full article

Traditional analyses of transport phenomena rely on prescribed geometric boundaries, yet natural flow systems dynamically evolve their architecture to maximize access to currents. To address this disparity, we propose a field-theoretic framework for the constructal law that treats physical geometry as a dynamic state variable, represented by a time-dependent conductivity tensor. Using a variational approach grounded in non-equilibrium thermodynamics, we derive a general tensor evolution equation. Within this framework, macroscopic flow architecture emerges deterministically from the continuous competition between non-linear flux-induced accretion, linear entropic relaxation, and spatial smoothing. Scaling analysis reduces this dynamic to a tri-parameter dimensionless phase space: a morphogenic number driving structural growth, a structural diffusion number governing spatial coherence, and a stochastic intensity number providing the microscopic seeds for symmetry breaking. Our principal result is the analytical prediction of a critical bifurcation. When the local morphogenic number strictly exceeds unity, the system escapes its stable, isotropic configuration and branches into highly conductive, anisotropic architectures. We demonstrate the predictive validity and trans-scalar applicability of this continuum theory by mapping it to highly diverse phase transitions, successfully capturing phenomena ranging from microscopic aerosol agglomeration and microbial resistance, to macroscopic coral plasticity and crystal growth instabilities, and finally to the astrophysical launching of relativistic jets from black holes. Full article

To address the deteriorated control performance of permanent magnet synchronous motor (PMSM) drive systems for new energy vehicles (NEVs) under complex conditions caused by multi-source disturbances (internal parameter perturbations and external load mutations), this paper proposes an improved terminal integral sliding mode control (ITISMC-ADERL) strategy integrating a piecewise adaptive terminal integral sliding mode surface and an ADERL. The proposed sliding mode surface adopts interval-adaptive switching between high- and low-order power terms, completely eliminating singularity and integral saturation defects of traditional terminal sliding mode control while ensuring fast convergence, and achieving an optimal structural balance between convergence speed and chattering suppression. The state-dependent ADERL leverages the synergy of error-sliding variable coupled dynamic gain adjustment and variable exponential power compensation, realizing dual-mode adaptive switching of “strong driving for fast approaching far from the sliding surface, weak gain for smooth regulation near the sliding surface”, which significantly improves control accuracy and anti-disturbance robustness. The finite-time convergence of the closed-loop system is rigorously proved via Lyapunov stability theory. Full-operating-condition comparative tests on a TMS320F28379D DSP platform show that the proposed strategy outperforms SMC-ERL, ISMC-ERL and ITISMC-ERL in all test scenarios (no-load startup, acceleration/deceleration, sudden load changes, flux linkage perturbation), meeting the requirements of high-performance NEV drive systems and possessing important engineering application potential. Full article

Multispectral LiDAR can simultaneously obtain distance and spectral information and shows great potential for underwater detection. However, absorption and scattering caused by suspended particles in water lead to energy attenuation and multiple scattering, which affect echo intensity and ranging accuracy, while the propagation characteristics under multi-wavelength conditions remain insufficiently studied. In this study, a simplified underwater propagation simulation model for multispectral LiDAR is established based on the equivalent spherical-particle assumption, combining Mie scattering theory with a semi-analytical Monte Carlo method. The effects of particle size on echo intensity and ranging error are analyzed under fixed concentration conditions. Based on this model, a detection-threshold-constrained optimal wavelength selection criterion is formulated. Multi-distance analysis (3, 5, 8, and 15 m) confirms that the preferred wavelength is primarily governed by particle size and remains stable across depths. The results show that the optimal detection wavelength shifts with particle size, being about 560 nm for fine particles and gradually moving toward the 400–480 nm blue–green band for larger particles. Experimental validation shows that the simulation-based ranging correction reduces RMSE by 9.4–25.9% (average 18.1%) and MAE by 11.8–29.7% (average 22.0%) across five experimental distances. The results provide a preliminary reference for wavelength selection in multispectral LiDAR systems under simplified conditions. Full article

In the aerospace industry, thin-web gears are preferred for achieving high power-density transmission. However, thin-webbed structures always lead to out-of-plane resonance during the transmission process, which commonly happens in helical gears, manifesting as severe vibration at a specific rotational speed. To address this, a shaft–web–ring dynamic model is proposed. The shaft, gear web, and gear ring are modelled based on the Timoshenko straight beam, Mindlin plate, and Timoshenko bent beam theory. Simultaneously, the potential energy caused by the time-varying meshing stiffness is coupled to the gear ring. The kinetic and potential energies of each discretized finite element of the components are derived based on elastic deformation theory, and the governing equations of each element are obtained using Hamilton’s principle. The model is verified through a modal experiment. The comparison with traditional rotor-gear models has demonstrated the significance of gear body flexibility in helical gears with thin webs. The effects of the web thickness and helix angle on dynamic response are studied, revealing that gear web elasticity and an appropriately high helix angle can effectively reduce vibrations at the support bearing, prevent excessive vibrations, and contribute to vibration and noise reduction in the transmission system. Full article

Contemporary enterprise IT operations are largely implemented on Shannon–Turing computing models in which programs execute read–compute–write cycles over data structures, while governance—fault handling, configuration control, auditability, continuity, and accounting—is applied externally through infrastructure platforms, observability stacks, and human operational processes. This separation scales analytical throughput but accumulates what we term coherence debt: locally expedient operational commitments whose provenance and revisability degrade over time until exposed by failures, security incidents, regulatory demands, or architectural transitions. This paper examines the evolution of operational computing models that integrate com-pupation with regulation at two distinct levels. First, Distributed Intelligent Managed Elements (DIME) extend the classical Turing cycle toward a supervised execution loop—read–check-with-oracle–compute–write—by incorporating signaling overlays and FCAPS (Fault, Configuration, Accounting, Performance, and Security) supervision into computation in progress. Second, the Autopoietic Management and Orchestration System (AMOS), grounded in the General Theory of Information, the Burgin–Mikkilineni Thesis, and Deutsch’s epistemic framework, fully decouples process executors from governance by treating any Turing-equivalent engine as a replaceable execution substrate while elevating knowledge structures—encoded as local and global Digital Genomes—to first-class operational state within a governed knowledge network. Using a distributed microservice transaction testbed, we demonstrate how this approach operationalizes topology-as-data, a capability-oriented control plane, decoupled application-layer FCAPS independent of infrastructure management, and policy-selectable consistency/availability semantics. Our results show that the principal benefit of AMOS is not circumventing theoretical constraints such as the Consistency, Availability, and Partition tolerance (CAP) theorem, but governing their trade-offs as explicit, auditable commitments with defined convergence pathways and controlled return to a coherent system state, thereby reducing coherence debt and improving operational reliability in distributed AI-enabled enterprise systems. Full article

The synthesis of nitrogen-containing heterocycles remains a subject of significant interest due to their applications in medicinal chemistry and materials science. This paper describes the preparation of imidazo[1,2-a]pyridine using a catalytic system consisting of the deep eutectic solvent (DES) choline chloride (ChCl)–tartaric acid (1:2) and I₂ by reaction between 2-aminopyridines and chalcones (1,3-diphenylprop-2-en-1-ones). The proposed mechanism suggests the activation of the chalcone carbonyl by the DES, enhancing the polarization of the conjugated system which suffers electrophilic addition by I₂ to the C=C bond. The resulting intermediate undergoes a nucleophilic attack by 2-aminopyridine followed by cyclization and iodine-promoted oxidation and aromatization to yield the corresponding imidazo[1,2-a]pyridine products. The role of the DES is crucial, as it facilitates carbonyl activation through hydrogen bond interactions, stabilizes reactive intermediates, and promotes protonation–deprotonation steps, thereby eliminating the need for metal catalysts or toxic organic solvents. Theoretical calculations at the PM6 level of theory suggest that the DES acts as a catalyst in this reaction, due to the nature of its components enabling the development of more sustainable synthetic strategies. Full article

Deep neural networks (DNNs) are traditionally analyzed as black-box function approximators, yet their internal structure exhibits phase transitions characteristic of complex physical systems. In this study, we investigate topological–functional decoupling—the phenomenon whereby a network retains full graph connectivity while losing computational function—in trained neural networks through the lens of percolation theory. By subjecting three distinct architectures (Shallow, Deep, and Wide MLPs) to a unified edge-pruning analysis on Fashion-MNIST, we uncover a fundamental divergence between structural integrity and computational capacity in this experimental setting. We report three key phenomena observed in these experiments: (1) the zombie network state under stochastic pruning, where the system retains global connectivity ($P_{\infty }\approx 1.0$) yet suffers a catastrophic functional collapse (accuracy falls below 50% of baseline at prunning ratio $p_f\approx 0.35$–$0.68$ depending on depth), proves that graph reachability does not imply computational capability; (2) depth fragility, where increased network depth triggers multiplicative signal decay (the avalanche effect), rendering deep architectures exponentially more vulnerable to random edge removal than shallow ones ($p_f^{\mathrm{deep}}\approx 0.35$ vs. $p_f^{\mathrm{shallow}}\approx 0.68$); and (3) scale-free universality, observed under magnitude-based pruning, where a robust functional skeleton maintains accuracy near the baseline (∼89%) up to extreme sparsity ($p_f\approx 0.85$–$0.95$) across all three architectures. Robustness stems not from holographic redundancy in the overall connection count but from the emergent heavy-tailed rich-club organization of weight magnitudes—a sparse set of high-magnitude synapses that form the functional backbone of the network, decoupled from the redundant topological mass. These findings offer new physical constraints for the design of resilient neuromorphic hardware. Full article

The deployment of grid-forming microgrids has attracted growing attention as a pathway toward improving energy system resilience and supporting low-carbon transitions in decentralized power systems. However, the relative influence of distinct stakeholder groups on microgrid development performance remains inadequately understood in the extant literature. Grounded in stakeholder theory and informed by behavioral economics, this study develops and empirically tests a stakeholder-based framework that examines the effects of government support, investor participation, user acceptance, and utility participation on microgrid development performance. Survey data were collected from 200 stakeholders engaged in microgrid-related activities and analyzed using consistent Partial Least Squares Structural Equation Modeling (PLS-SEM). The structural model accounts for a substantial proportion of the variance in microgrid development performance (R² = 0.647). The quantitative results indicate that all four stakeholder constructs exert statistically significant positive effects on microgrid development performance. Investor participation emerges as the strongest driver (β = 0.399, p < 0.001), followed by user acceptance (β = 0.190, p < 0.001), government support (β = 0.175, p = 0.015), and utility participation (β = 0.170, p = 0.003). Interpreted through a behavioral economics lens, these findings demonstrate that development performance is governed primarily by behavioral and perceptual factors, namely capital confidence, risk tolerance, and demand-side acceptance, rather than by technical preparedness alone. Conventional assumptions of linear adoption driven by technical superiority are therefore insufficient to account for observed development outcomes in complex, decentralized energy systems. This study advances a stakeholder-centered and behaviorally grounded understanding of grid-forming microgrid development and offers empirical guidance for designing governance frameworks that align regulatory structures with market and user behavioral dynamics. Full article