Search Results (22,268)

This paper addresses active-mode detection for switched discrete-time Takagi–Sugeno systems from noisy input–output data under candidate-dependent input correction and uncertainty in data-driven observability subspaces. A lifted input–output formulation is developed in which each candidate mode is associated with a mode-dependent forced-response correction and a nominal observability subspace identified offline from representative data. Based on this construction, a practical residual criterion is introduced together with an ideal residual criterion defined by the exact residual projector. An online verifiable sufficient condition is then derived to guarantee consistency between the practical and ideal residual orderings, yielding a conservative but theorem-consistent certification mechanism. To quantify the effect of measurement uncertainty, a component-wise noise-to-signal ratio (NSR) analysis is established, leading to explicit conservative NSR bounds when signal-floor conditions are available offline. These results motivate an adaptive observation-window strategy driven by an explicit online NSR estimate. In addition, an uncertainty-corrected discernibility index based on principal angles between estimated observability subspaces is introduced to assess offline mode separability. Simulations on a switched T–S benchmark show high practical detection accuracy, sound but conservative certification, informative NSR bounds, and stable adaptive-window regulation, including under reviewer-motivated switching stress tests and baseline comparison experiments. Full article

In this study, we examined the psychometric properties (reliability, validity, and underlying factor structure) and measurement invariance of a researcher-developed instrument: Motivated Strategies for Science Learning (MSSL). Data were collected from 2765 fifth-grade students across [state withheld] school districts. Results from exploratory and confirmatory factor analyses suggested satisfactory psychometric properties of MSSL. We further identified strong evidence to support configural, metric, scalar, and strict invariance across school location groups (i.e., rural and non-rural), confirming that the instrument can accurately capture the construct of science learning motivation among fifth-grade students from both rural and non-rural Texas schools. The study provides the first step toward measuring Texas rural and non-rural school students’ motivation and strategies for science learning. Recommendations for future research are discussed. Full article

This study explores what motivates farmers in Fars Province, Iran, to consider adopting LCA practices, with a focus on behavioral, moral, and institutional influences. Data were collected from 386 farmers selected through stratified random sampling and analyzed using Structural Equation Modeling (SEM) in Smart Partial Least Squares (PLS) 3. The results confirmed that the measurement model was reliable and valid, and the structural model showed strong explanatory power, explaining 76% of the variance in adoption intention (R² = 0.766) and 64% in moral norms (R² = 0.642). Farmers’ intentions were significantly shaped by attitude (β = 0.210, p < 0.001), perceived behavioral control (β = 0.175, p < 0.001), moral norms (β = 0.307, p < 0.001), policy support (β = 0.202, p = 0.003), and perceived climate risk (β = 0.176, p < 0.001). In contrast, subjective norms and trust in institutions did not directly influence intention, although trust strongly strengthened moral norms (β = 0.387, p < 0.001). In general, the findings highlight that farmers’ decisions are shaped not only by practical and economic considerations but also by their sense of responsibility, confidence in their abilities, and perceptions of climate risk and institutional support. The study contributes to sustainability research by integrating moral and institutional perspectives into behavioral models and offers practical insights for policymakers to support the transition toward low-carbon, climate-resilient agriculture in Iran. Full article

Dopamine (DA) is essential for motor control, motivation, and cognition, and its dysregulation is associated with neurological and psychiatric disorders such as Parkinson’s disease, schizophrenia, and addiction. Accurate and selective DA quantification in complex biological matrices is important, but remains challenging because of coexisting interferents and the low physiological concentration of DA. Here, we report a disposable electrochemical DA sensor based on screen-printed carbon electrodes (SPCEs) modified with metal–organic framework-derived gold nanocomposites (MOFD-AuNCs). The optimal material, synthesized with a 60 min NaBH₄ reduction step (MOFD-AuNC-60), exhibited superior electron-transfer kinetics compared with materials prepared at other reduction times. A single coating of MOFD-AuNC-60 on SPCEs enabled DA oxidation at a low potential (~0.05 V) with high selectivity in the presence of ascorbic acid and uric acid. In undiluted porcine serum, the sensor exhibited a dynamic range of 2.5–500 nM with a calculated detection limit of 0.5 nM. In undiluted human serum, it exhibited a dynamic range of 5–100 nM with a calculated detection limit of 4.4 nM. The MOFD-AuNC-60/SPCEs further demonstrated excellent reproducibility (relative standard deviation, 3%) and stability (7.5% current loss over 7 days). These results demonstrate that the proposed sensor provides a disposable, robust, and reliable sensing platform for direct DA detection in undiluted serum, showing promise for practical applications. Full article

School dropout is an issue that requires the attention of institutions. Related research indicates that both family (e.g., parental monitoring) and personal (e.g., academic motivation and school engagement) factors affect adolescents’ decision to quit school. As no studies have jointly examined these variables in Italian adolescents over time, this two-wave study aimed to investigate the role of parental monitoring in the relationships among academic motivation, school engagement, and dropout intention. This study enrolled 377 adolescents (boys = 178; M_age = 14.41, SD_age = 0.72) from two public upper secondary schools in Italy, and followed them over 6 months from November 2024 (T0) to May 2025 (T1). They completed a questionnaire at T0 and T1 comprising the following measures: parental monitoring, academic motivation, school engagement, and dropout intention. Structural equation modelling showed a good fit to the data, χ²₍₅₄₎ = 84.589, p = 0.005, RMSEA = 0.04 [0.02–0.05], CFI = 0.99, TLI = 0.98, SRMR = 0.02. At T0, parental monitoring was positively associated with academic motivation and school engagement and negatively associated with dropout intention. A positive reciprocal association was observed between academic motivation and school engagement at T0 and T1. Academic motivation and school engagement at T0 were negatively associated with dropout intention at T1. Parental monitoring at T0 had a significant indirect effect on dropout intention at T1 via academic motivation and school engagement at T0. These findings suggest that interventions targeting family and personal factors may reduce school dropout rates among adolescents. Full article

Background/Objectives: There is a limited body of research on evidence-based food literacy education for adolescents. The inquiry-based curriculum, Teens CAN: Comprehensive Food Literacy in Cooking, Agriculture, and Nutrition, was designed to improve food literacy among adolescents ages 14–18 years. This study aimed to assess the Teens CAN curriculum by examining changes in food literacy outcomes among high school–aged adolescents and explore the effectiveness of undergraduate facilitators in implementing the curriculum with fidelity. Methods: This quasi-experimental pilot study was conducted among high school students comprising intervention (n =14) and comparison groups (n = 16). All Teens CAN lessons were delivered by trained undergraduate facilitators, and lesson fidelity was measured by a trained observer. Baseline and follow-up survey measures assessed various components of food literacy, including adolescent nutrition knowledge, diet quality, and intrinsic motivation to prepare healthy food (cooking self-efficacy). Between-group differences were examined using t-tests, and ANCOVA regression models assessed associations between changes in baseline to follow-up nutrition knowledge, diet quality, and cooking self-efficacy, adjusting for baseline values. Results: The adolescents in the intervention group had a significant increase in nutrition knowledge scores compared to the comparison group (4.6 ± 2.3 vs. 1.1 ± 3.7, respectively; p = 0.01). High fidelity (≥80%) was achieved across lessons and lesson components. In ANCOVA regression analyses, participation in the intervention was positively associated with nutrition knowledge (β = 3.3, 95% CI [0.87–5.80]; p = 0.01), providing evidence for future investigation. Conclusions: The findings from this pilot study suggest that Teens CAN has the potential to positively influence food literacy and related behaviors among adolescents, therefore warranting further investigation in a larger population. Full article

A recently proposed tensor–scalar extension of gravity coupled to extended Aharonov–Bohm electrodynamics admits one-variable traveling reductions in which a longitudinal electromagnetic scalar mode $S={\partial }_{\mu }{A}^{\mu }$ couples nonlinearly to gravitational scalars. In the weak-field regime outside sources, a one-dimensional traveling ansatz depending on $\xi =x-vt$ reduces the field equations to a coupled autonomous ODE system. The mathematical core of the reduction is a singular Newton-type equation whose classical mechanics counterpart is known; the novelty here lies in its derivation from the scalar–tensor/Aharonov–Bohm field system, in the physically motivated normalization of the traveling-wave families, and in the resulting phase–space interpretation for source-generated pulse selection. We provide a systematic classification of all admissible initial data and of the corresponding maximal solutions, distinguishing repulsive/attractive regimes and subcritical/critical/supercritical behaviors through a normalized parameter map. In particular, attractive branches may reach the singularity in finite time with a universal collision exponent $2/3$, while escaping branches exhibit asymptotically uniform motion with a computable logarithmic correction. Finally, we construct a numerical atlas of representative trajectories and validate the computations by cross-checking direct time integration against numerical inversion of the implicit quadrature, together with energy-defect diagnostics. Full article

Growing mobility demand and declining vehicle utilization motivate dual-use vehicles that can alternately transport passengers and freight. This work presents an early-stage utility value analysis to select a baseline concept and integrates it into model-based systems-engineering architecture development of an autonomous dual-use shuttle. Existing dual-use-capable shuttle concepts were screened and comparatively assessed using a utility value analysis with exclusion criteria and weighted evaluation criteria, including operational versatility, module exchange flexibility, infrastructure effort, battery positioning, and technology readiness. Criterion weights were derived by pairwise preference analysis, emphasizing the versatility of use scenarios. The highest-ranking concept, 101 Modular Mobility, was selected as the reference architecture. Subsequently, a SysML system model was developed in a MagicGrid-structured model-based systems-engineering (MBSE) process, covering stakeholder needs, key use cases such as transport service usage, module exchange, and automated charging, and the resulting system context and interfaces. The system model is augmented by a tailored Grey Box structural viewpoint within the MagicGrid workflow to make module boundaries and inter-module interfaces explicit for the modular dual-use shuttle architecture. The resulting model provides a traceable early architectural baseline for further refinement and subsequent verification activities. Full article

Neuropsychiatric disorders are characterized by complex etiologies, widespread involvement of brain regions, and pronounced clinical heterogeneity, with core pathological mechanisms closely associated with abnormal activity in deep brain structures and their functional networks. Although current pharmacological therapies and conventional neuromodulation techniques have shown therapeutic benefits in certain conditions, they are generally limited by insufficient stimulation depth or the risks associated with invasive procedures. Temporal interference (TI) electrical stimulation has recently emerged as a non-invasive deep neuromodulation technique that generates low-frequency difference-envelope fields through high-frequency carrier signals, thereby enabling relatively precise modulation of deep brain regions while maintaining favorable safety and tolerability. This technique provides a novel technical pathway for precision intervention in neuropsychiatric disorders. In this review, we summarize the principles and technical characteristics of TI stimulation and highlight its recent applications in mood and stress-related disorders, cognitive impairment and neurodegenerative diseases, movement disorders, addiction, and disorders associated with dysregulated neural excitability. We integrate its potential mechanisms across multiple levels, including neural oscillations, deep–cortical network synchronization, reward and motivational circuits, synaptic plasticity and structural remodeling, excitatory-inhibitory balance, and gene and epigenetic regulation. Current evidence suggests that TI stimulation can modulate electrophysiological activity and may engage molecular and network-level processes relevant to functional improvement, although durable clinical benefits remain to be established. Although clinical translation remains challenged by parameter optimization, interindividual variability, and long-term safety evaluation, advances in computational modeling, multimodal neuroimaging, and closed-loop stimulation strategies are expected to facilitate its development. Overall, TI stimulation represents a promising non-invasive deep neuromodulation approach for mechanistic investigation and precision treatment of neuropsychiatric disorders. Full article

This study investigates how Chinese learners of English express manners of motion, examining systematic features, cognitive motivations, and compensatory strategies. While Talmy’s motion events typology and Levin’s verb classification system provide important foundations, both have limitations in capturing the internal semantic granularity of manner verbs and the complexity of learner acquisition. To address this, we construct a 10-level verb typology establishing a “semantic granularity” continuum from concrete to abstract, physical to metaphorical, and lexical to grammatical. Using experimental data (N = 600) and corpus comparisons (COCA vs. learner corpus), we analyze Chinese learners’ manner expression patterns. Results reveal the following: (1) Chinese learners prioritize Path over Manner, overusing lower-level verbs (go, walk, run) while underusing higher-level and fine-grained manner verbs (stroll, scramble), which are preferred by native speakers. (2) Learners favor semi-tight or loose syntactic structures and show a preference for describing Manner precisely by adding other modifiers such as adverbials, prepositions, complements, or subordinate clauses. When it comes to precisely describing specific manners of motion, Chinese learners of English tend to use four strategies—analytic manner externalization, path salience, image-schematic transfer, and semantic simplification—whereas native English speakers typically rely on single verbs with high semantic density. These findings suggest learners’ expression of manner involves both L1 syntactic transfer and target language conceptual adaptation. The 10-level classification continuum advances the theoretical understanding of motion event lexicalization patterns, provides new perspectives for conceptual transfer research, and offer pedagogical implications for Chinese English learners’ accuracy of their expression of manner. Full article

Malnutrition—including undernutrition, micronutrient deficiencies and overweight—remains a major public health concern in sub-Saharan Africa, largely driven by food insecurity. Edible insects have been proposed as a sustainable, nutrient-dense dietary alternative with potential to improve food security and nutritional outcomes. This review analyses studies published until January 2024 in PubMed and Google Scholar assessing the prevalence, acceptability and nutritional impact of insect-based diets in sub-Saharan Africa. Thirteen original studies, predominantly qualitative, conducted in 8 of 47 countries in the region, met inclusion criteria. Two reviews provided additional evidence. Most studies focused on acceptability, which was strongly influenced by cultural and religious norms. Higher acceptance was observed among older individuals and those with lower educational attainment, while younger and more urbanized populations showed greater reluctance. Reported motivations for consumption included tradition, taste and perceived nutritional value. Some studies highlighted potential health risks related to food safety and the need for improved regulatory frameworks. The available nutritional analyses showed that edible insects are rich in protein and essential micronutrients, particularly iron and zinc, suggesting their potential to address common deficiencies. Although evidence on long-term nutritional impact remains limited, current findings support the feasibility and potential public health relevance of promoting insect-based diets in low-income settings. Full article

Background: Ultraviolet (UV) radiation is a major risk factor for skin cancer. Nevertheless, many people tan in the sun and in tanning beds. The aim of this study was to explore the reasons behind these behaviors and to investigate whether the reasons for tanning in the sun and in tanning beds differ. Methods: We used data from a nationwide survey study conducted in Germany, which included n = 4156 individuals aged 16 to 65 years. We assessed different reasons for outdoor and indoor tanning, as well as sociodemographic characteristics, skin type, and tanning behaviors. Results: While both outdoor and indoor tanners frequently reported relaxation, as well as feeling of light and warmth as reasons, outdoor tanners placed a greater emphasis on increasing vitamin D levels and health benefits. In contrast, indoor tanners were more focused on enhancing attractiveness and pre-tanning for holidays. Individuals who sought a tan more frequently—whether through indoor or outdoor tanning—were more likely to agree with the various reasons provided, compared to those who tanned less often. In addition, we found associations with sex, age, immigrant background, education, occupation, and skin type. Conclusions: There were differences as well as similarities in the reasons for indoor and outdoor tanning. This indicates that overarching prevention strategies could be effective. Additionally, targeted measures specifically for indoor and outdoor tanning could also be beneficial in raising awareness about the risks of UV radiation and, in the long term, reducing the incidence of skin cancer. Full article

Self-healing materials (SHMs) are a class of bio-inspired materials capable of autonomously repairing damage, similar to how living organisms heal wounds. The core motivation behind SHMs is to extend the service life of components while enhancing safety and reducing maintenance or replacement needs. SHMs can be broadly categorized into intrinsic systems, which rely on reversible internal bonds (dynamic covalent or supramolecular interactions) to heal repeatedly, and extrinsic systems, which embed external healing agents (e.g., microcapsules or vascular networks) that are released upon damage to effect repairs. Researchers have demonstrated self-healing behavior in diverse material families, including polymers, metals, ceramics/cementitious materials, and protective coatings, thereby improving crack resistance, fatigue life, and reliability across aerospace, automotive, civil infrastructure, energy storage, and microelectronics applications. Advances in material design and additive manufacturing have started integrating SHMs into practical structures. However, challenges such as scaling up production, maintaining mechanical performance, and ensuring long-term durability remain. Reported healing efficiencies in self-healing materials typically range from ~50% to near-complete recovery (~100%), depending on material systems and testing conditions, highlighting key trade-offs between healing performance, mechanical integrity, and scalability. Overall, SHMs represent a promising strategy for creating safer and more sustainable engineering systems, with ongoing developments aimed at overcoming current limitations and expanding their capabilities. This review highlights key trade-offs between healing efficiency, mechanical performance, and scalability, providing insights into the design and application of next-generation self-healing materials. Full article

Surface protection and functional modification of aircraft-certified aluminum alloys are essential for corrosion resistance, durability, and long-term airworthiness. At the same time, increasingly restrictive environmental regulations motivate the development of alternatives to legacy wet-chemical surface treatments. This study presents an integrated assessment of ultrafast femtosecond laser surface texturing as a surface functionalization approach for Aluminum 6061 alloys within an aerospace manufacturing and sustainability context. Ultrashort-pulse laser processing enables controlled micro- and nano-scale surface topographical modification with limited thermal impact, allowing adjustment of wettability and surface functionality while preserving bulk material integrity. As a dry and contactless process, femtosecond laser treatment eliminates the use of hazardous chemicals, reduces consumable inputs, and generates minimal secondary waste. A streamlined cradle-to-gate life cycle assessment conducted in accordance with ISO 14040/14044 indicates a lower global-warming potential per functional unit compared with conventional surface treatments, including anodization, plasma-assisted coatings, and organic coating systems. Complementary qualitative analyses addressing environmental health and safety, supply-chain risk, and ESG alignment indicate potential advantages related to occupational safety, regulatory compliance, waste management, and end-of-life recyclability. The investigation is performed on planar Aluminum 6061 reference surfaces with a treated area of 25 mm², providing a controlled laboratory-scale basis for analyzing process behavior, functional surface modification, and associated environmental metrics. Within this defined scope, the results support further evaluation of femtosecond laser surface texturing as a surface engineering option for future aerospace manufacturing. Full article

For battery management systems, accurate remaining useful life (RUL) prediction is important, yet models trained offline may not remain well matched to individual cells during operation, because degradation trajectories differ across cells and evolve over aging stages. This study examines a lightweight online personalization strategy under a representative convolutional neural network–long short-term memory (CNN–LSTM) online-transfer setting while keeping the backbone architecture and fixed input length unchanged. The proposed method restricts online updates to a small adaptation path and adjusts the effective history span according to recent degradation behavior. Experiments on 22 test cells under unseen protocols show that the method improves average post-adaptation RUL performance relative to the representative baseline, reducing the root mean square error (RMSE) from 186.00 to 160.58. The number of trainable parameters involved in online updating is reduced from 74,880 to 2193, while the average update time per step decreases slightly from 2.54 s to 2.29 s. Cell-level analysis further shows that the benefit is not uniform across all cells, motivating more selective updating for safer deployment. Overall, the results indicate that lightweight online personalization can improve the accuracy–cost trade-off of deployment-oriented battery prognostics. Full article