Search Results (10,391)

Under alkaline conditions, most commonly used preservatives exhibit limited efficacy and fail to meet the preservation requirements of coated tofu. This study aims to investigate the effects of metal-phenolic networks (MPNs) on quality deterioration, protein oxidation, conformation, and gel microstructure of coated tofu during cold storage (4 °C and 10 °C). The results showed that, compared with the untreated control group, MPNs treatment effectively inhibited protein oxidation, alleviated quality deterioration, delayed the degradation of color and texture, and reduced protein degradation, as evidenced by soluble protein contents that were 63.55% (4 °C) and 66.65% (10 °C) lower than those of the control group after 20 days of storage. MPNs treatment also improved the orderliness and stability of the protein secondary structure. In addition, electrophoretic analysis showed that MPNs markedly retarded the decline in band optical density of the 11S protein A subunit by 96.19% and 97.28% at 4 °C and 10 °C, respectively, and suppressed the increase in the B subunit by 13.28% and 73.20%, respectively. Moreover, MPNs treatment helped maintain a more compact gel network. Based on physicochemical characterization and Pearson correlation analysis, the preservative effect of MPNs on coated tofu under alkaline conditions was elucidated, revealing the internal correlation between the inhibition of quality deterioration and the regulation of protein oxidation. Specifically, MPNs mitigate protein disulfide bond loss, increase the β-sheet content, preserve the natural protein conformation and the relative proportion of 11S subunits, stabilize the gel microstructure, and thereby achieve quality preservation. These findings provide theoretical support and strategic reference for the development of preservation technologies for alkaline high-protein gel foods. Full article

Artificial intelligence (AI) tools are increasingly integrated into mathematics education, yet most reviews emphasize achievement rather than how AI shapes mathematical thinking. This scoping review mapped literature published between 2020 and 2026 on AI-supported mathematics learning through three cognition frameworks: APOS (Action–Process–Object–Schema), Sfard’s process–object duality and reification, and Conceptual Image theory. Searches were conducted in Scopus, Web of Science, ERIC, PsycINFO, Education Source, and IEEE Xplore, followed by duplicate removal and Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR)-aligned screening. Twenty-one peer-reviewed studies met inclusion criteria (18 empirical studies plus three theoretically oriented studies). Evidence growth accelerated after 2022, with most studies situated in secondary and higher education. Large language models (LLMs) and Intelligent Tutoring Systems (ITS) were the most frequently investigated modalities. Across studies, AI commonly supported theoretically inferred action-level execution and procedural management (APOS) via adaptive feedback, hinting, and stepwise scaffolding, and it often broadened learners’ conceptual images through multiple representations and generated explanations. However, these interpretations were necessarily cautious, because very few studies directly operationalized theory-linked conceptual mechanisms such as process internalization, object encapsulation, reification, or alignment between conceptual images and formal definitions. In LLM-supported contexts, gains in explanation quality coexisted with risks of procedural outsourcing when students relied on generated solutions without prior reasoning. By contrast, ITS-based environments more often supported tightly structured procedural engagement, suggesting that different AI modalities afford different forms of cognitive support and risk. Overall, AI’s conceptual impact appears to depend less on tool availability and more on instructional orchestration (task design, prompting, and teacher mediation). The findings also suggest that sustainability-related dimensions—particularly learner agency, transparency of AI support, and equitable participation—are closely connected to whether AI use promotes durable conceptual learning rather than superficial performance gains. Future research should operationalize cognitive transitions, assess structural understanding, and report AI-use conditions transparently to support cumulative, theory-driven synthesis. Full article

Farmland management rights (FMR) mortgage lending has been advanced as a central instrument of rural credit reform in China, yet the program has consistently failed to sustain itself in the absence of direct government facilitation. Drawing on five national and provincial pilot counties in Henan Province, this study investigates the structural factors underlying this sustainability failure. We employ a sequential mixed-methods design: grounded theory analysis of in-depth interviews, policy documents, and media reports from five focal sites to inductively construct a constraint framework, followed by structural equation modeling (SEM) validation using 1055 survey responses. Our grounded theory analysis identifies three internal constraint categories—property rights insecurity, a thin secondary land market, and subject-level agricultural risk—and one external environmental constraint, which together produce a state of mutual non-recognition: neither financial institutions nor farming households regard FMR as legitimate collateral. Notably, the effect of collateral acceptance on farmer mortgage willingness is statistically insignificant, revealing that demand-side barriers are more deeply entrenched than supply-side institutional improvements alone can resolve. These findings challenge the premise that legal formalization of land rights is sufficient to generate market-driven credit activity, and call attention to the equally important role of institutional ecosystem development—encompassing land markets, appraisal capacity, supervisory infrastructure, and rural credit culture. The insights carry direct relevance for developing economies exploring land-backed agricultural credit as a rural finance strategy. Full article

The scalable and sustainable production of graphene remains a significant challenge due to the high cost, complex processing, and environmental impact associated with fossil-derived graphite precursors. In this work, we report a biorenewable pathway for producing graphitic carbon from industrial hemp biomass, yielding a plant-derived material called CleanGraphene. This approach provides a renewable and potentially scalable alternative to petroleum- and coal-based graphene production while maintaining competitive structural and electrical performance. CleanGraphene samples are systematically characterized using X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA) to evaluate crystallographic order, layer stacking, defect density, surface chemistry, and thermal stability. The results show that optimized CleanGraphene materials consist of multilayer graphene-like platelets with compact interlayer spacing (d(002) ≈ 3.36–3.37 Å), extended crystallite coherence lengths (L_c up to ~75 nm), large in-plane sp² domains (L_a exceeding ~200 nm), and relatively low defect densities, indicating well-developed graphitic ordering. Electrical conductivity measurements using a binder-free pelletization method and four-point probe analysis demonstrate that the highest quality CleanGraphene samples achieve conductivities of (8.4–8.6) × 10⁴ S m⁻¹, surpassing leading commercial graphene benchmarks measured under identical conditions. Structure–property correlations confirm that electrical performance is governed primarily by crystallite coherence, defect density, and interlayer stacking order, while surface oxygen content plays a secondary role within an ordered graphitic framework. All CleanGraphene samples exhibit excellent thermal stability, retaining more than 95% mass up to ~800–900 °C under an inert atmosphere. Collectively, these findings establish quantitative quality benchmarks for hemp-derived graphene and demonstrate that biomass-based graphene can achieve electrical and thermal performance comparable to, and in some cases exceeding, conventional commercial products. This work highlights industrial hemp as a promising renewable precursor for the scalable production of high-performance graphitic nanomaterials for electrically and thermally conductive composite applications. Full article

With the rapid intensification of industrial and agricultural activities, water contamination by heavy metal ions has emerged as a critical global challenge, gravely imperiling ecosystem stability and public health. Among the various remediation technologies, adsorption has been widely adopted due to its high efficiency, low-cost water treatment, and simplicity of operation. However, conventional inorganic or synthetic adsorbents often exhibit poor degradability and pose a risk of secondary contamination, substantially limiting their sustainable application. Consequently, the development of environmentally benign and renewable adsorbent materials has become a central research focus in this field. Recently, cellulose-based composite hydrogels, derived from renewable resources and characterized by excellent eco-friendliness and highly tunable three-dimensional porous structures, have attracted considerable attention as promising green adsorption materials. These hydrogels demonstrate outstanding performance in the efficient sequestration of heavy metal contaminants from aqueous environments. This review systematically summarizes recent advances in cellulose-based composite hydrogels for heavy metal removal, to elucidate the structure–performance relationships linking material fabrication strategies, structural modulation, and adsorption efficiency. First, we outline the principal construction approaches, including physical crosslinking, chemical modification, and supramolecular self-assembly, and comprehensively analyze how different synthesis routes regulate pore architecture, mechanical properties, and the distribution of surface functional groups. Second, the underlying adsorption mechanisms, primarily coordination complexation, electrostatic interactions, and ion exchange, are discussed in detail. Finally, recent studies on the adsorption of cationic heavy metals (e.g., Pb(II), Cu(II), and Cd(II)) and anionic oxyanions (e.g., As(III) and Cr(VI)) are critically reviewed, with particular emphasis on the relationships between selective adsorption performance, material design principles, and specific recognition mechanisms. Overall, this review provides a theoretical foundation and practical guidance for the design and development of next-generation water treatment materials with high adsorption capacity, excellent selectivity, non-toxicity, and strong environmental compatibility, followed by future research recommendations. 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

Mycosporine-like amino acids (MAAs), a class of secondary metabolites characterized by a cyclohexenone or cyclohexenimine ring structure bound to amino acid residues, are widely distributed in algae. These compounds exhibit strong ultraviolet-absorbing and antioxidant activities, making them attractive candidates for natural sunscreen formulations. However, the low productivity of MAAs in microalgae severely hampers commercial viability. Asterarcys sp., a fast-growing, heat- and light-tolerant microalga, has recently been demonstrated to produce high levels of MAAs under UV irradiation. In this study, phosphorus limitation was found to stimulate rapid MAAs accumulation in Asterarcys sp. SCSIO-46548. After eight days of cultivation, microalgal cells grown in phosphorus-free medium (0 mg L⁻¹) showed a sixfold higher MAAs content (1.08% DW) compared to the group supplied with 5.60 mg L⁻¹ phosphorus (0.18% DW). However, the accumulation of MAAs began to plateau under phosphorus deprivation. Based on integrated homology alignment with cyanobacteria and functional domain validation, a putative biosynthetic pathway for mycosporine-serine in Asterarcys sp. SCSIO-46548 was proposed. Importantly, the gene expression of desmethyl-4-deoxygadusol synthase (DDGS) exhibited a 2.75-fold upregulation under phosphorus limitation. Complementary bioinformatic analyses further characterized the subcellular localization and major physicochemical properties of the candidate enzymes involved. In conclusion, phosphorus limitation is an effective strategy to enhance MAAs production in Asterarcys sp. SCSIO-46548 by upregulating the expression of key biosynthetic genes, such as DDGS. This finding provides an effective solution to the low MAAs productivity in microalgae cultivation. Full article

Background/Objectives: Investigating the modified derivatives of known cell-penetrating peptides can highlight the important residues in the peptide sequence and help understand the cellular uptake mechanism better. Moreover, comparing peptides with different fluorescent-dye positions can highlight the importance of the conjugation site. Earlier, it was demonstrated that the fluorescence quencher 4-((4-(dimethylamino)phenyl)azo)benzoyl (Dabcyl) group can enhance the internalization efficiency of highly cationic oligoarginine peptides. However, its effect in the case of arginine-rich penetratin, a secondary amphipathic cationic CPP, remains undiscovered. Methods: Here, several penetratin derivatives were studied in which the aromatic residues were substituted and the effect of Dabcyl modification was also studied on the cellular uptake of peptides by flow cytometry. Results: The triple Nal-substituted penetratin and dodeca-penetratin with N-terminally positioned carboxyfluoresein (Cf) dye demonstrated remarkable internalization efficiency compared to penetratin. Moreover, almost all the Dabcyl-modified peptides were superior to penetratin except two peptides with C-terminal Cf-labelling. This result highlights the importance of the structure of the conjugate. The position of the cargo molecule may have a high impact on internalization ability. The relatively low cellular uptake of the Trp48 residue-substituted Dabcyl-Pen12 points to the importance of this residue in the cellular uptake of dodeca-penetratin. The confocal microscopic studies revealed that, besides the greater penetration efficiency of Dabcyl penetratin derivatives, these peptides enter the cytoplasm of cells in an increased manner. Conclusions: We identified several intriguing derivatives and expanded the applicability of Dabcyl, while also highlighting its limitations. Additionally, the critical role of Trp48 in the penetratin sequence was reaffirmed, along with the importance of the fluorescent molecule’s position. Full article

Polysaccharide-based hydrogels for gastric retention face the inherent challenge of achieving effective retention through swelling while avoiding mechanical failure. Here, we introduce a strategy by incorporating curdlan into chitosan/sodium polyacrylate interpenetrating networks to reinforce the hydrogel and regulate swelling-induced transport behavior. Curdlan-reinforced chitosan/polyacrylate (CS/CUR/PAAS) hydrogels with varying curdlan content (0–4 wt.%) were synthesized and characterized. Optimal reinforcement was achieved with 2 wt.% curdlan, yielding an indentation hardness of ~80 kPa and an elastic modulus of ~63 kPa without compromising swelling capacity. Under acidic conditions (pH 1.2), the hydrogel swelled rapidly (~50-fold at 3 h; ~140-fold at 8 h) while maintaining structural integrity. Using a dynamic in vitro human stomach simulator (DHSI-IV), the optimized hydrogel demonstrated gastric retention for up to 5 h, with ~60% of the initial mass retained at 6 h. Metformin hydrochloride release followed diffusion-controlled kinetics (~69% over 8 h), governed primarily by pH with secondary shear modulation. Microstructural and rheological analyses revealed that acidic conditions regulated network expansion, viscoelastic relaxation, and pore formation, which in turn controlled transport pathways and drug release. The findings highlight that curdlan reinforcement stabilizes swelling behavior under acidic conditions, offering a robust and pH-responsive strategy for designing mechanically stable, gastric-retentive hydrogels. Full article

Taking a metro over-track TOD(Transit-Oriented Development) project in Chongqing as the engineering background, this study adopts a combined research approach integrating field measurements and numerical simulation. A coupled finite element model of the train–track–tunnel–soil–building system and a regional acoustic model are established to systematically reveal the vibration response characteristics of building clusters above the depot induced by metro operation, the propagation mechanism of structure-borne secondary noise, and the distribution patterns of the regional acoustic environment, while identifying the areas where vibration and noise exceed the prescribed limits as well as the key influencing factors. On this basis, following a hierarchical mitigation strategy consisting of source control, path interruption, and receiver protection, an integrated control scheme is proposed through the coordinated application of track vibration reduction, building vibration isolation, acoustic environment optimization, and building sound insulation. The engineering applicability and control effectiveness of the proposed scheme are further verified by numerical simulation. The findings of this study can provide theoretical support and technical reference for the refined design and integrated prevention and control of vibration and acoustic environments in similar metro over-track development projects. Full article

The continued evolution of SARS-CoV-2 has enabled an escape from most monoclonal antibodies, yet a subset of broadly neutralizing antibodies targeting three newly identified super-conserved RBD epitopes—SCORE-A, SCORE-B, and SCORE-C—retains remarkable activity against even the most recent JN.1-derived sublineages. Here, we employed an integrated computational framework combining conformational dynamics, mutational scanning, MM-GBSA binding energetics, and frustration profiling to dissect the molecular mechanisms by which XGI antibodies achieve broad neutralization and resistance to immune escape. Structural analysis revealed that all three SCORE epitopes share a common architecture: a highly conserved, minimally frustrated core that provides stable anchoring, flanked by peripheral regions that accommodate antibody-specific variations. Conformational dynamics showed that SCORE-A antibodies (XGI-183) rigidify the lateral epitope while leaving the RBM partially mobile; SCORE-B antibodies (XGI-198, XGI-203) clamp the RBM apex, directly blocking ACE2; and SCORE-C antibodies (XGI-171) allosterically loosen the RBM loop, impairing receptor engagement indirectly. Mutational scanning identified a hierarchical hotspot organization where primary hotspots (e.g., K356, T500, Y380, T385) are evolutionarily constrained and minimally frustrated, while secondary hotspots (e.g., V503, Y508, S383) are neutrally frustrated and represent the principal sites of immune-driven mutations. MM-GBSA decomposition revealed that van der Waals-driven hydrophobic packing dominates binding, with electrostatic interactions providing auxiliary stabilization. Critically, frustration analysis demonstrated that immune escape hotspots reside precisely in zones of neutral frustration—”energetic playgrounds” that permit mutational exploration without destabilizing the RBD—while minimally frustrated cores are evolutionarily locked. The comparative analysis of conformational versus mutational frustration distributions revealed a unifying principle: aligned neutral frustration yields permissive, escape-prone interfaces; decoupling enables the targeting of constrained cores; and the convergence of minimal frustration in both distributions creates invulnerable interfaces. These findings establish that broad neutralization arises not from ultra-high-affinity anchors but from strategic energy distribution across rigid, evolutionarily informed interfaces, providing a roadmap for designing next-generation therapeutics that target the invulnerable cores of viral surface proteins. Full article

The study is devoted to determining the degree of influence of the first and second stages of AISI 321 steel surface treatment with a femtosecond laser, with unchanged laser parameter characteristics, on the blackening parameters and parameters characterizing the distribution of surface heights according to the ISO 25178 standard. Surface blackening is important for production automation for better visibility of markers by optical sensors. The assessment is carried out from the point of view of changing the degree of blackening of the studied surface. During the experiment, it was found that secondary surface treatment without changing the processing mode leads to an insignificant, up to 5%, increase in the degree of surface blackening. Secondary laser processing revealed a diminishing returns effect, where doubling the energy input in perpendicular scanning resulted in only a marginal (~5%) gain in blackening. This phenomenon stems from surface morphological saturation, as the primary roughness parameters Sq and Sdq attain their plateau values, without contributing to the further formation of hierarchical light-trapping structures. It was also found that during the blackening process, such parameters as the maximum peak height Sp, the ten-point surface height S10z, and the asymmetry Ssk increased more than others. After repeated treatment, the values of the parameters maximum valley depth Sv and the root mean square slope Sdq increased the most. At the same time, the nature of the normal Gaussian surface height distribution was preserved. As the Sdq value increases, the number of randomly located reflecting surfaces increases, and this leads to better scattering of the directed beam. On the other hand, the absence of random fragments on the vertices of the periodic surface structure, which corresponds to a high Sku index, allows such a surface to scatter light more effectively. Full article

Despite significant advances in AI language models, Modern Standard Arabic (MSA) remains a linguistically complex domain in which apparent fluency often masks deeper grammatical instability. This study investigates recurrent grammatical error patterns in ChatGPT-generated Arabic texts, focusing on how these patterns reflect underlying morpho-syntactic challenges and the constraints of probabilistic language generation. Adopting a qualitative, pattern-oriented analytical framework, the study draws on online focus group discussions with secondary-level Arabic teachers, who served as expert linguistic evaluators. Participants collaboratively examined a set of AI-generated texts to identify and interpret systematic grammatical deviations across five key domains: agreement, inflection and case marking, sentence structure, prepositions and transitivity, and cross-linguistic influence. The findings indicate that grammatical errors in AI-generated Arabic are not random but occur as recurring, structured patterns, particularly in contexts involving long-distance dependencies and morphologically complex constructions. These patterns suggest a reliance on surface-level fluency at the expense of deeper grammatical coherence, reflecting limitations in maintaining consistent morpho-syntactic relationships. This study contributes by identifying and characterizing systematic grammatical patterns in AI-generated MSA as interpreted through expert linguistic judgment, offering a qualitative perspective that complements existing quantitative approaches and advances understanding of how large language models engage with morphologically rich languages. Full article

In deep-buried tunnels, the loads acting on supporting structures continuously increase due to the rheological behavior of surrounding rock, while the performance of the secondary lining gradually degrades under environmental effects. These delayed features have significant implications for tunnel safety but are rarely incorporated into the reliability evaluation of tunnels. In this study, the surrounding rock is modeled using the Burgers model, and an analytical solution is developed by incorporating the degradation and damage of the secondary lining. Parametric analysis is conducted to identify the key factors governing tunnel response. Subsequently, limit state functions are established, and a time-dependent system reliability analysis is performed. Results indicate that tunnel response and reliability are highly sensitive to rheological parameters. Among the rheological parameters, the elastic shear modulus of the Maxwell elements G_e has the most pronounced influence on deformation, whereas the elastic shear modulus of the Kelvin elements G_k governs the stress response of the secondary lining. The time-dependent failure probability increases rapidly in the early stage and gradually stabilizes thereafter. Insufficient initial support strength is identified as the dominant failure mode of system failure. Furthermore, G_e and G_k are the key parameters affecting tunnel reliability, and increasing G_k improves the reliability index by more than 1500%. Meanwhile, the variation in system reliability is mainly affected by the failure mode of insufficient initial support strength. These findings provide quantitative guidance for the design, construction, and long-term maintenance of deep-buried tunnels in rheological rock. Full article

Artificial Intelligence systems increasingly shape environmental decision making, infrastructure planning, and resource use across public and urban domains. However, prevailing AI trust and governance mechanisms, including labels, certifications, and assurance schemes, remain primarily focused on ethical and legal accountability, with limited operational attention to environmental sustainability. This paper reconceptualises AI trust mechanisms as socio-technical governance infrastructures that can support both ethical assurance and environmental accountability. Drawing on a comparative qualitative analysis of nine AI trust initiatives, the study develops a three-dimensional analytical framework embedding Environmental Performance Indicators across three governance dimensions: trust-building effectiveness, governance readiness, and sustainable adoption. Applying a systems governance lens, the framework examines how governance instruments structure information flows, institutional practices, and lifecycle feedback relevant to environmental performance. It is analytically illustrated through two urban mobility cases, Helsinki’s Whim application and Barcelona’s smart mobility system, to examine how governance conditions enable or constrain the integration of Environmental Performance Indicators in practice. Findings show that current trust mechanisms lack measurable and publicly visible environmental criteria, indicating a gap between AI assurance and environmental governance. The study contributes a systems-oriented framework for evaluating AI trust mechanisms as governance instruments capable of supporting environmental accountability. While exploratory and based on secondary data, the results indicate that future AI trust mechanisms must incorporate measurable sustainability indicators to support eco-efficient and accountable digital transformation. Full article