Search Results (1,930)

This study investigates the dynamics of optical solitons for the variable-coefficient coupled higher-order nonlinear Schrödinger equation (VCHNLSE) enriched with $\beta$-derivatives. By employing an extended direct algebraic method (EDAM), we successfully derive explicit soliton solutions that illustrate the intricate interplay between nonlinearities and variable coefficients. Our approach facilitates the transformation of the complex NLS into a more manageable form, allowing for the systematic exploration of diverse solitonic structures, including bright, dark, and singular solitons, as well as exponential, polynomial, hyperbolic, rational, and Jacobi elliptic solutions. This diverse family of solutions substantially expands beyond the limited soliton interactions studied in conventional approaches, demonstrating the superior capability of our method in unraveling new wave phenomena. Furthermore, we rigorously demonstrate the robustness of these soliton solutions against various perturbations through comprehensive stability analysis and numerical simulations under parameter variations. The practical significance of this work lies in its potential applications in advanced optical communication systems. The derived soliton solutions and the analysis of their dynamics provide crucial insights for designing robust signal carriers in nonlinear optical media. Specifically, the management of variable coefficients and fractional-order effects can be leveraged to model and engineer sophisticated dispersion-managed optical fibers, tunable photonic devices, and ultrafast laser systems, where controlling pulse propagation and stability is paramount. The presence of $\beta$-fractional derivatives introduces additional complexity to the wave propagation behaviors, leading to novel dynamics that we analyze through numerical simulations and graphical representations. The findings highlight the potential of the proposed methodology to uncover rich patterns in soliton dynamics, offering insights into their robustness and stability under varying conditions. This work not only contributes to the theoretical foundation of nonlinear optics but also provides a framework for practical applications in optical fiber communications and other fields involving nonlinear wave phenomena. Full article

Ergothioneine is a potent non-toxic and very stable antioxidant which is synthesized by fungi, algae, and bacteria but not animals or higher plants. Ergothioneine has been widely used in cosmetics; dietary supplements; and medicine to treat diabetes, cancer, as well as cardiovascular, neurodegenerative, and liver diseases. Ergothioneine presents two tautomeric forms: thione, the majoritarian and more stable form (ERGO), and thiol (ERGT). Ergothioneine cannot cross cell membranes, and human cells rely on a specific transporter, OCTN1, to transport ingested ERGO to different parts of the body. Ergothioneine is very hydrophilic, and it is supposed to act at the water level but not at the membrane one. In this work, I studied the interaction of ERGO and ERGT with a complex biomembrane using molecular dynamics (MD). MD suggests that ERGO, but not ERGT, inserts spontaneously into the membrane interphase and can move from the membrane interphase to the water phase and vice versa, and no oligomerization was observed. Furthermore, ERGO, when inserted in the membrane, does not alter the hydrocarbon chain order. Therefore, ERGO (the thione form of ergothioneine), but not ERGT (the thiol form), might act at both the water and membrane interphase levels. Full article

Rural resilience building has gained increasing scholarly attention, yet existing literature overlooks the temporal dynamics of resilience evolution and lacks an integrative framework to explain cross-level mechanisms. This paper uses a longitudinal case study to explore how rural resilience transitions from a low-equilibrium to a high-equilibrium state and how neo-endogenous practices emerge in a weak institutional context. The study reveals three key findings. First, the village’s resilience evolved through three phases—institutional intervention, community capital activation, and resilience self-reinforcement—driven by co-evolutionary interactions between an enabling government and the rural community. This process is marked by chain effects of multidimensional community capital (e.g., cultural capital enhancing social capital) and overflow effects from resilience amplification (e.g., multi-scalar network). Second, exogenous resources and endogenous community capital are critical in the neo-endogenous model, but their synergy relies on vertical institutional interventions that foster horizontal networks and enhance communities’ resource absorption capacity. Third, the government enables resilience building by creating a support ecosystem that transitions from institutionally bundled resources to a higher-order composite space, facilitated by urban–rural interactions and community restructuring. The study makes three theoretical contributions: (1) it proposes an analytical framework integrating an enabling government, community capital, and ecosystem upgrading, thus advancing beyond the current community capital-centric paradigm; (2) it introduces a three-phase process model that unpacks spatiotemporal interactions across urban-rural interfaces, multi-scalar networks, and state-community relations, addressing the limitations of static factor-based analyses; (3) it reconceptualizes the role of government as an “enabling government” that mediates local and extra-local resource interfaces, challenging the neo-endogenous theories’ neglect of institutional agency. These insights contribute to rural resilience scholarship through a complex adaptive systems lens and offer policy implications for synergistic urban-rural revitalization. Full article

This study examines the interaction between cognitive demands and generative artificial intelligence (GenAI) technologies in shaping the quality and influence of academic research. While GenAI tools such as ChatGPT and Elicit are increasingly adopted to ease information processing and automate repetitive tasks, their broader impact on researchers’ cognitive performance remains underexplored. Using data from 998 researchers and applying structural equation modeling (SEM-PLS), we examined the effects of cognitive load, task fatigue, and resilience on research outcomes, with GenAI immersion as a higher-order moderator. Results reveal that both cognitive load and fatigue negatively affect research quality, while engagement and resilience offer partial protection. Unexpectedly, high immersion in GenAI intensified the negative impact of cognitive strain, suggesting that over-reliance on AI can amplify mental burden rather than reduce it. These results enhance the design and responsible integration of AI technologies in academic environments by demonstrating that sustainable adoption necessitates a balance between efficiency and human creativity and resilience. The study provides evidence-based insights for researchers, institutions, and policymakers seeking to optimize AI-supported workflows without compromising research integrity or well-being. Full article

Basolite^® F300 and synthetic nano-{Fe-BTC} MOFs, two iron-trimesate MOFs, have been investigated, demonstrating broad pH range adsorption for monomethylarsenate (MMA), cacodylic acid (DMAA), 4-aminophenylarsonate (ASA), and arsenate, while arsenite adsorption was notable at pH > 9.5. A similar uptake trend was found for both MOFs, with Basolite^® F300 being the more effective given its higher porosity and greater surface area. Pseudo-second-order kinetic models were followed by MMA, DMAA, ASA, and As(V), suggesting a chemisorption mechanism with arsenic species diffusion into MOF pores as the controlling step. Equilibrium data for DMAA and ASA fit the Langmuir model whereas MMA adsorption fits the Redlich–Peterson model. The uptake of MMA, DMAA, and ASA by both Fe-MOFs is mainly attributed to their coordination with Fe(III). Aromatic units in ASA enhance adsorption through П-П stacking interactions. The competition between all arsenic species for the sorption sites of the Fe-MOFs led to an uptake decrease of 10% for MMA and ASA and higher than 30% for DMAA and As(V) with respect to the individual uptakes. The Fe-MOFs can be reused for four cycles by washing with acidic methanol. Basolite^® F300 and synthetic nano-{Fe-BTC} effectively removed organic and inorganic arsenic species, exhibiting rapid adsorption, selective uptake, stability, and easy regeneration. Full article

This article proposes a multi-agent path planning and decision-making solution for high-tempo field robotic operations, such as search-and-rescue, in large-scale unstructured environments. As a representative example, the subterranean environments can span many kilometers and are loaded with challenges such as limited to no communication, hazardous terrain, blocked passages due to collapses, and vertical structures. The time-sensitive nature of these operations inherently requires solutions that are reliably deployable in practice. Moreover, a human-supervised multi-robot team is required to ensure that mobility and cognitive capabilities of various agents are leveraged for efficiency of the mission. Therefore, this article attempts to propose a solution that is suited for both air and ground vehicles and is adapted well for information sharing between different agents. This article first details a sampling-based autonomous exploration solution that brings significant improvements with respect to the current state of the art. These improvements include relying on an occupancy grid-based sample-and-project solution to terrain assessment and formulating the solution-search problem as a constraint-satisfaction problem to further enhance the computational efficiency of the planner. In addition, the demonstration of the exploration planner by team MARBLE at the DARPA Subterranean Challenge finals is presented. The inevitable interaction of heterogeneous autonomous robots with human operators demands the use of common semantics for reasoning across the robot and human teams making use of different geometric map capabilities suited for their mobility and computational resources. To this end, the path planner is further extended to include semantic mapping and decision-making into the framework. Firstly, the proposed solution generates a semantic map of the exploration environment by labeling position history of a robot in the form of probability distributions of observations. The semantic reasoning solution uses higher-level cues from a semantic map in order to bias exploration behaviors toward a semantic of interest. This objective is achieved by using a particle filter to localize a robot on a given semantic map followed by a Partially Observable Markov Decision Process (POMDP)-based controller to guide the exploration direction of the sampling-based exploration planner. Hence, this article aims to bridge an understanding gap between human and a heterogeneous robotic team not just through a common-sense semantic map transfer among the agents but by also enabling a robot to make use of such information to guide its lower-level reasoning in case such abstract information is transferred to it. Full article

Despite the rapid progress in the capabilities of large language models, they still lack a reliable and efficient method of storing and retrieving new information conveyed over the course of their interaction with users upon deployment. In this paper, we use reinforcement learning methods to train a team of smaller language models, which we frame as options, on reward-respecting subtasks, to learn to use SQL commands to store and retrieve relevant information to and from an external SQL database. In particular, we train a storage language model on a subtask for distinguishing between user and assistant in the dialogue history, to learn to store any relevant facts that may be required to answer future user queries. We then train a retrieval language model on a subtask for querying a sufficient number of fields, to learn to retrieve information from the SQL database that could be useful in answering the current user query. We find that training our models on their respective subtasks results in much higher performance than training them to directly optimize the reward signal and that the resulting team of language models is able to achieve performance on memory tasks comparable to existing methods that rely on language models orders of magnitude larger in size. In particular, we were able to able to achieve a 36% gain in accuracy over a prompt engineering baseline and a 13% gain over a strong baseline that uses the much larger GPT-3.5 Turbo on the MSC-Self-Instruct dataset. Full article

This study is based on an experiment and a molecular dynamics simulation to investigate the distribution states and property variation laws of crude oil in nanopores, aiming to provide theoretical support for efficient unconventional oil and gas development. Focus is placed on the distribution mechanisms of multicomponent crude oil in oil-wet siltstone (SiO₂) and dolomitic rock (dolomite, CaMg₃(CO₃)₄) nanopores, with comprehensive consideration of key factors including pore size, rock type, and CO₂ flooding on crude oil distribution at 353 K and 40 MPa. It is revealed that aromatic hydrocarbons (toluene) in multicomponent crude oil are preferentially adsorbed on pore walls due to π-π interactions, while n-hexane diffuses toward the pore center driven by hydrophobic effects. Pore size significantly affects the distribution states of crude oil: ordered adsorption structures form for n-hexane in 2 nm pores, whereas distributions become dispersed in 9 nm pores, with adsorption energy changing as pore size increases. Dolomite exhibits a significantly higher adsorption energy than SiO₂ due to surface roughness and calcium–magnesium ion crystal fields. CO₂ weakens the interaction between crude oil and pore walls through competitive adsorption and reduces viscosity via dissolution, promoting crude oil mobility. Nuclear magnetic resonance (NMR) experiments further verified the effect of CO₂ on crude oil stripping in pores. This study not only clarifies the collaborative adsorption mechanisms and displacement regulation laws of multi-component crude oil in nanopores but also provides a solid theoretical basis for CO₂ injection strategies in unconventional reservoir development. Full article

Room-temperature self-healing polyurethanes (PUs) generally show limited mechanical properties. In order to improve the mechanical properties of PUs without sacrificing their self-healing ability, in this study, different amounts of halloysite clay filler were added. Thus, intrinsically self-healing PUs were synthesized using polycarbonate diol polyol, aliphatic diisocyanate, 1,4-butanediol, and different amounts (0.5–10 wt.%) of thermally treated halloysite. During synthesis, the halloysite clay was added to the polyol. The structural, thermal, viscoelastic, and mechanical properties of the resulting halloysite-filled PUs were evaluated. All halloysite-filled PUs retained their room-temperature self-healing capability while exhibiting improved mechanical strength. The PU with 0.5 wt.% halloysite (E0.5) showed the most balanced performance, with well-dispersed halloysite nanotubes intercalated within the soft segments, enhancing chain mobility and soft segment ordering. Higher halloysite loadings (1–3 wt.%) led to increased mechanical properties but also some round clay particle agglomeration and surface migration, leading to limited halloysite–polyurethane interactions. The addition of more than 3 wt.% halloysite did not result in further improvements in mechanical properties. The findings of this study provide new insight into the filler–polymer interaction mechanism and establish a foundation for the design of multifunctional PUs with both autonomous self-repair and enhanced mechanical performance. Full article

The amphibian microbiome plays a vital role in host health, yet the bacterial communities of caecilians (Order: Gymnophiona) remain largely uncharacterised. We investigated this by providing the first characterisation of the gut and skin microbiome of larval Ichthyophis bannanicus, a carnivorous caecilian, using 16S rRNA gene metabarcoding. Our analyses show distinct communities between the faecal samples and skin, with significant enrichment of Laribacter in faeces and Flavobacterium on skin. Despite significant variation in their community structures, the core genera Escherichia-Shigella were shared between both regions, suggesting similar microbial exchange in the aquatic environments. Skin bacterial diversity exhibited relatively higher richness, but lower evenness than that of faeces. Further, the skin bacterial community exhibited more complex interactions, suggesting stronger resilience to changes. The relationships and interactions of skin and faecal bacterial communities suggest their interactive effects on the host’s overall health. Compared with anuran tadpoles, the I. bannanicus larval microbiome showed taxonomic overlap, but possessed certain unique core bacteria. This work on an understudied amphibian lineage is foundational, highlighting how diet, phylogeny, and aquatic environment shape microbial communities and informing future research into amphibian health and disease. Full article

As AI systems become more integrated into society, the relationship between humans and AI is shifting from simple automation to co-creative collaboration. This evolution is particularly important in education, where human intuition and imagination can combine with AI’s computational power to enable innovative forms of learning and teaching. This study is grounded in the #ppAI6 model, a framework that describes six levels of creative engagement with AI in educational contexts, ranging from passive consumption to active, participatory co-creation of knowledge. The model highlights progression from initial interactions with AI tools to transformative educational experiences that involve deep collaboration between humans and AI. In this study, we explore how educators and learners can engage in deeper, more transformative interactions with AI technologies. The #ppAI6 model categorizes these levels of engagement as follows: level 1 involves passive consumption of AI-generated content, while level 6 represents expansive, participatory co-creation of knowledge. This model provides a lens through which we investigate how educational tools and practices can move beyond basic interactions to foster higher-order creativity. We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting the levels of creative engagement with AI tools in education. This review synthesizes existing literature on various levels of engagement, such as interactive consumption through Intelligent Tutoring Systems (ITS), and shifts focus to the exploration and design of higher-order forms of creative engagement. The findings highlight varied levels of engagement across both learners and educators. For learners, a total of four studies were found at level 2 (interactive consumption). Two studies were found that looked at level 3 (individual content creation). Four studies focused on collaborative content creation at level 4. No studies were observed at level 5, and only one study was found at level 6. These findings show a lack of development in AI tools for more creative involvement. For teachers, AI tools mainly support levels two and three, facilitating personalized content creation and performance analysis with limited examples of higher-level creative engagement and indicating areas for improvement in supportive collaborative teaching practices. The review found that two studies focused on level 2 (interactive consumption) for teachers. In addition, four studies were identified at level 3 (individual content creation). Only one study was found at level 5 (participatory co-creation), and no studies were found at level 6. In practical terms, the review suggests that educators need professional development focused on building AI literacy, enabling them to recognize and leverage the different levels of creative engagement that AI tools offer. Full article

With the expansion of industrial production capacity, a substantial volume of hazardous wastewater containing Pb (II) and Ni (II) requires treatment. Kaolin, a low-cost adsorbent with strong adsorption properties, was modified through thermal activation at 750 °C, 850 °C, and 950 °C to enhance its adsorption capacity. Following the optimization of pH, reaction time, temperature, heavy metal concentrations, and adsorbent amount, the 850-K was found to have the best removal efficiency, achieving removal rates > 90% for both PbCl₂ and NiCl₂, and the removal efficiency of PbCl₂ was higher compared to NiCl₂. The pseudo-second-order kinetics and Langmuir model could reasonably match the adsorption processes of PbCl₂/NiCl₂. The experimental findings were corroborated through simulations of adsorption distance, variations in bond length/bond angle, adsorption energy, frontier molecular orbital, charge density, and differential charge density. The differences in reactions between adsorbents and PbCl₂/NiCl₂ were primarily due to the electron transfer direction and bonding mechanisms. The O atoms were the main reactive atoms of the adsorbents, capable of forming covalent bonds with both PbCl₂ and NiCl₂, and the Cl atoms could form either ionic or covalent bonds with the adsorbent. Pb could form covalent bonds with the adsorbent, while Ni might be adsorbed through electrostatic interactions. Full article

Traditional lectures in veterinary anatomy often limit student engagement and higher-order thinking. The flipped classroom (FC) model shifts foundational content to independent study using interactive tools such as H5P^® and Wooclap^®, reserving classroom time for collaborative problem-solving. Objective: To evaluate the impact of the FC model on student engagement, preparation habits, and cognitive performance in veterinary anatomy, focusing on the respiratory and cardiovascular systems. Methodology: The intervention was implemented over two academic years (2023/24 and 2024/25) and included continuous assessment, cognitive-level evaluations based on Marzano’s taxonomy, platform analytics, and anonymous student surveys. Results: Platform data showed high engagement, with completion rates exceeding 90%. Students who prepared 2–3 days in advance performed better on application and integration tasks. Survey responses indicated a shift from passive video viewing to active learning strategies, such as structured note-taking and strategic time management. By 2024/25, 85% of students dedicated 30+ min to preparation, compared to 48% the previous year. Conclusion: The FC model fostered autonomy, spatial reasoning, and clinical contextualization. Aligned with constructivist principles, it supported Intended Learning Outcomes through adaptive scaffolding. Despite institutional challenges, the model proved scalable and pedagogically coherent, warranting further longitudinal research and broader curricular integration. Full article

Chañar brea gum exhibits properties that make it a promising material for paper adhesion. As the concentration of chañar brea gum (CBG) in solution increases, the following key changes are observed in its properties, which are relevant to its use as an adhesive. The surface tension (σ) decreases with increasing gum concentration. Viscosity (η) increases dramatically with increasing chañar brea gum concentration. While higher viscosity is often desirable for many adhesives, excessive viscosity, as may be observed at very high concentrations, can hamper application. However, adequate viscosity is crucial to ensure initial bond strength, as it allows for the formation of a uniform layer and prevents excessive penetration into porous substrates. A concentration of 10% wt. by weight offers this balance. Surface adsorption (Γ₂₍₁₎) increases linearly with gum concentration, indicating that higher interfacial adsorption is crucial for the formation of an effective adhesive layer. The contact angle (θ) increases slightly with concentration; although a lower contact angle typically indicates better wetting, the increase is marginal (from 88° ± 4 for water to 99° ± 4 for 30% wt.), so wetting is still acceptable. Chañar brea gum exhibits good surface adsorption capacity and reduced surface tension, which favors interaction with the paper surface. The adhesive strength of chañar brea gum (CBG) clearly depends on its concentration, increasing from 6.36 ± 0.22 MPa (at 5% wt.) to a significant maximum of 50.24 ± 1.19 MPa at a concentration of 10% wt. The decrease in adhesive strength at concentrations above 10% wt. by weight is an important aspect to consider in order to optimize its performance as a paper adhesive. Therefore, intermediate concentrations, such as 10% wt., offer the most favorable balance of properties for achieving good adhesive performance. Full article

The degradation of Brilliant Coomassie Blue G-250 (BCB) was investigated using the thermally activated persulfate (TAP) process in deionized water. A kinetic model incorporating both hydroxyl (^●OH) and sulfate (SO₄^●–) radicals was developed to predict pseudo-first-order rate constants (kₒ) for the interaction of BCB with these radicals. Experimental results demonstrated efficient BCB degradation under TAP treatment. A parametric study examining the effects of initial conditions such as solution pH, persulfate concentration, initial BCB concentration, and temperature revealed that higher persulfate dosages, lower BCB concentrations, and alkaline pH enhanced degradation performance. Complete removal of BCB was achieved within 20 min under optimal conditions ([BCB]₀ = 10 mg/L, [PS]₀ = 2 mg/L, neutral pH). The kinetic model showed strong agreement with experimental data across a broad range of pH and persulfate concentrations. The rate constants for BCB reactions with ^●OH and SO₄^●– were determined through simulation to be 4.731 × 10⁹ M⁻¹s⁻¹ and 1.07 × 10⁹ M⁻¹s⁻¹, respectively. The selectivity analysis results revealed that SO₄^●– radicals played a dominant role in the degradation process across the various initial persulfate concentration scenarios. The remaining degradation was attributed to the contribution of ^●OH radicals. These findings are linked to the higher reactivity of BCB with SO₄^●– compared to ^●OH. Overall, the results demonstrate that TAP process is an effective method for the removal of emerging contaminants such as BCB from water. Full article