Search Results (130)

This study investigated the effects of curdlan (CUR) with different helical conformations on the gelling behavior and mechanisms of heat-induced soy protein isolate (SPI) gels. The results demonstrated that CUR significantly improved the functional properties of SPI gels, including water-holding capacity (0.31–5.06% increase), gel strength (7.01–240.51% enhancement), textural properties, viscoelasticity, and thermal stability. The incorporation of CUR facilitated the unfolding and cross-linking of SPI molecules, leading to enhanced network formation. Notably, SPI composite gels containing CUR with an ordered triple-helix bundled structure exhibited superior gelling performance compared to other helical conformations, characterized by a more compact and uniform microstructure. This improvement was attributed to stronger hydrogen bonding interactions between the triple-helix CUR and SPI molecules. Furthermore, the entanglement of triple-helix CUR with SPI promoted the formation of a denser and more homogeneous interpenetrating polymer network. These findings indicate that triple-helix CUR is highly effective in optimizing the gelling characteristics of heat-induced SPI gels. This study provides new insights into the structure–function relationship of CUR in SPI-based gel systems, offering potential strategies for designing high-performance protein–polysaccharide composite gels. The findings establish a theoretical foundation for applications in the food industry. Full article

This article explores the transformative changes in Catholic liturgy during the twentieth century and their implications for the stability of religious meaning and cultural identity in the West. In critical dialogue with Chantal Delsol’s diagnosis of the decline of Christianitas, this study argues that the reform of ritual following the Second Vatican Council, rather than political entanglements, played a decisive role in weakening the public credibility of Catholic truth claims. Drawing on Roy A. Rappaport’s theory of ritual as a stabilizer of cultural meaning, the author analyzes how this postconciliar liturgical reform altered the semiotic structure of Catholic worship—shifting communication from indexical to symbolic forms and reorienting the liturgy from a vertical–concentric order to a more decentralized horizontal dynamic. The chosen method combines theoretical reflection with liturgical anthropology to assess how changes in the Roman Missal, ritual posture, and spatial arrangement disrupted the transmission of canonical messages. The conclusion suggests that this semiotic transformation undermined the liturgy’s capacity to ritually confirm the truths of faith, contributing to the broader civilizational disintegration observed by Delsol. Ultimately, this article contends that any future revitalization of Catholic culture will depend less on political influence and more on recovering the liturgy’s ritual capacity to sustain belief in transcendent truth. Full article

Hyperspectral imaging (HSI) plays a pivotal role in modern agriculture by capturing fine-grained spectral signatures that support crop classification, health assessment, and land-use monitoring. However, the transition from raw spectral data to reliable semantic understanding remains challenging—particularly under fragmented planting patterns, spectral ambiguity, and spatial heterogeneity. To address these limitations, we propose UniHSFormer-X, a unified transformer-based framework that reconstructs agricultural semantics through prototype-guided token routing and hierarchical context modeling. Unlike conventional models that treat spectral–spatial features uniformly, UniHSFormer-X dynamically modulates information flow based on class-aware affinities, enabling precise delineation of field boundaries and robust recognition of spectrally entangled crop types. Evaluated on three UAV-based benchmarks—WHU-Hi-LongKou, HanChuan, and HongHu—the model achieves up to 99.80% overall accuracy and 99.28% average accuracy, outperforming state-of-the-art CNN, ViT, and hybrid architectures across both structured and heterogeneous agricultural scenarios. Ablation studies further reveal the critical role of semantic routing and prototype projection in stabilizing model behavior, while parameter surface analysis demonstrates consistent generalization across diverse configurations. Beyond high performance, UniHSFormer-X offers a semantically interpretable architecture that adapts to the spatial logic and compositional nuance of agricultural imagery, representing a forward step toward robust and scalable crop classification. Full article

Restricted Boltzmann machines (RBMs) have demonstrated considerable success as variational quantum states; however, their representational power remains incompletely understood. In this work, we present an analytical proof that RBMs can exactly and efficiently represent stabilizer code states—a class of highly entangled quantum states that are central to quantum error correction. Given a set of stabilizer generators, we develop an efficient algorithm to determine both the RBM architecture and the exact values of its parameters. Our findings provide new insights into the expressive power of RBMs, highlighting their capability to encode highly entangled states, and may serve as a useful tool for the classical simulation of quantum error-correcting codes. Full article

Natural restoration of vegetation and plantation are effective land use measures to promote soil organic carbon (SOC) sequestration. How soil physicochemical properties, microorganisms, Glomalin-related soil proteins (GRSPs), and aggregates interact to regulate SOC accumulation and sequestration remains unclear. This study examined five land uses in the karst region of Southwest China: corn field (CF), corn intercropped with cabbage fields (CICF), orchard (OR), plantation (PL), and natural restoration of vegetation (NRV). The results revealed that SOC, total nitrogen (TN), total phosphorus (TP), total GRSP (T-GRSP), and easily extractable GRSP (EE-GRSP) contents were significantly higher under NRV and PL than in the CF, CICF, and OR, with increases ranging from 10.69% to 266.72%. Land use significantly influenced bacterial α-diversity, though fungal α-diversity remained unaffected. The stability of soil aggregates among the five land uses followed the order: PL > NRV > CF > OR > CICF. Partial least-squares path modeling (PLS-PM) identified land use as the most critical factor influencing SOC. SOC accumulation and stability were enhanced through improved soil properties, increased microbial diversity, and greater community abundance, promoting GRSP secretion and strengthening soil aggregate stability. In particular, soil microorganisms adhere to the aggregates of soil particles through the entanglement of fine roots and microbial hyphae and their secretions (GRSPs, etc.) to maintain the stability of the aggregates, thus protecting SOC from decomposition. Natural restoration of vegetation and plantation proved more effective for soil carbon sequestration in the karst region of Southwest China compared to sloping cropland and orchards. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

Accurate car-sharing demand prediction is a key factor in enhancing the operational efficiency of shared mobility systems. However, mobility data often exhibit temporal, spatial, and spatio-temporal interdependencies that pose significant challenges for conventional models. These models typically struggle to capture nonlinear and high-dimensional patterns. Existing methods struggle to model entangled relationships across these modalities and lack scalability in dynamic urban environments. This paper presents the Quantum-Inspired Spatio-Temporal Inference Network (QSTIN), an enhanced approach that builds upon our previously proposed Explainable Spatio-Temporal Inference Network (eX-STIN). QSTIN integrates a Quantum-Inspired Neural Network (QINN) into the fusion module, generating complex-valued feature representations. This enables the model to capture intricate, nonlinear dependencies across heterogeneous mobility features. Additionally, Quantum Particle Swarm Optimization (QPSO) is applied at the final prediction stage to optimize output parameters and improve convergence stability. Experimental results indicate that QSTIN consistently outperforms both conventional baseline models and the earlier eX-STIN in predictive accuracy. By enhancing demand prediction, QSTIN supports efficient vehicle allocation and planning, reducing energy use and emissions and promoting sustainable urban mobility from both environmental and economic perspectives. Full article

To address the technical challenge of high polymer gel viscosity reducers losing viscosity at elevated temperatures and difficulty in controlling fluid loss, a polymer-based nano calcium carbonate composite high-temperature tackifier named GW-VIS was prepared using acrylamide (AM), 2-acrylamido-2-methylpropanesulfonic acid (AMPS), N-vinylpyrrolidone (NVP), and nano calcium carbonate as raw materials through water suspension polymerization. This polymer gel can absorb water well at room temperature and has a small solubility. After a long period of high-temperature treatment, most of it can dissolve in water, increasing the viscosity of the suspension. The structure of the samples was characterized by infrared spectroscopy, thermogravimetric analysis, and scanning electron microscopy, and their performance was evaluated. Rheological tests indicated that the 0.5% water suspension had a consistency coefficient (k = 761) significantly higher than the requirement for clay-free drilling fluids (k > 200). In thermal resistance experiments, the material maintained stable viscosity at 180 °C (reduction rate of 0%), and only decreased by 14.8% at 200 °C. Salt tolerance tests found that the viscosity reduction after hot rolling at 200 °C was only 17.31% when the NaCl concentration reached saturation. Field trials in three wells in the Liaohe oilfield verified that the clay-free drilling fluid supported formation operations successfully. The study shows that the polymer gel has the potential to maintain rheological stability at high temperatures by forming a network structure through polymer chain adsorption and entanglement, with a maximum temperature resistance of up to 200 °C, providing an efficient drilling fluid for deep oil and gas well development. It is feasible to select nano calcium carbonate to participate in the research of high-temperature resistant polymer materials. Meanwhile, the combined effect of monomers with large steric hindrance and inorganic materials can enhance the product’s temperature resistance and resistance to NaCl pollution. Full article

Unlike the hot-melting processing of thermoplastic plastics, the processing of starch-based material relies on the addition of solvents, resulting in their low productivity, hindering large-scale industrialized production. A strategy to realize the high production efficiency of starch-based material, an environmentally friendly modification process without waste liquid generation, was designed to prepare a hot-melting starch (HMS) that can be repeatedly hot melted. Ball milling, enzymatic digestion, and deep eutectic solvent (DES) plasticization modification were combined to prepare the HMS. Ball milling destroyed the starch’s particles and the crystallinity, exposing the hydroxyl group, which allowed amylase to achieve enzymatic hydrolysis more easily. After enzymatic hydrolysis, the molecular chains of modified starch were shortened and the entanglement of molecular chains was reduced, which promoted the slip of molecular chains. The plasticization of DES, which promoted by the broken starch particles and the destroyed crystal structure, formed stronger hydrogen bonds and facilitated hot melting. Furthermore, due to the excellent hot-melting properties, HMS can be combined with sisal fiber and polycaprolactone (PCL) under solvent-free conditions. The tensile strength of HMS/sisal fiber/PCL was increased by 109%; meanwhile, the water contact angle was stabilized at 104°, when the blending ratio of hot-melting starch was 67.5% compared with HMS. Full article

The properties of compatibilized blends of polyethylene (PE) and polypropylene (PP), having reduced macromolecular entanglements, were studied. The density of PP macromolecular entanglements was controlled by prior disentangling in solution. The polymer ratio in the blend was 4:1 or 1:4. An ethylene–octene copolymer was used as a compatibilizer. The melt blending process resulted in good dispersion of the minority component, with slightly larger inclusions when more disentangled PP was used. Rheological studies confirmed the achievement of different entanglement densities of PP macromolecules in the blends. The partial disentanglement did not affect the thermal stability of the blends. During the isothermal crystallization studies, faster growth of PP spherulites was observed in the blend with reduced entanglements, which also influenced the entire crystallization process. The recovery time of equilibrium entanglement was investigated and it turned out to be 45 min if the blend was annealed at 190 °C, which was shorter than in the analogous homopolymer. Studies of tensile properties showed that in blends with a majority share of polyethylene, the elongation at break increased with the disentanglement of the minority component, due to better bonding of the blend components and thus the reduction in microcavitation. Full article

In this paper, we analyze the distribution of bi-partite correlations in pure symmetric N-qubit states during local deterministic measurements, which ensure the same value of the reduced purities in the outcome states. It is analytically shown that all reduced purities grow in the process of deterministic measurements. This allows us to characterize the stability of bi-partite entanglement during the optimal correlation transfer under single-qubit measurements in the asymptotic limit $N\gg 1$. Full article

The study of system evolution in generalized amplitude damping is of great significance in quantum information science and quantum computing. As an important quantum noise channel, the generalized amplitude damping channel can describe the general phenomenon of the energy dissipation effect in quantum systems at finite temperature. In this paper, we study the use of weak measurement and reversal to protect the entanglement of X-type systems in generalized amplitude damping channels, and give an experimental scheme. The results show that the closer to zero the temperature environment, the better the protection effect of weak measurement and reversal, but the lower the success rate. Therefore, when choosing an experimental environment, it is important to consider not only the temperature factor but also the probability of success. Because all quantum systems work at finite temperatures, it is hoped that the study of generalized amplitude damping channels can help design more robust quantum algorithms and protocols to improve the efficiency and stability of quantum information processing. Full article

Quantum entanglement plays a fundamental role in quantum mechanics, with applications in quantum computing. This study introduces a new approach that integrates quantum simulations, noise analysis, and fuzzy clustering to classify and evaluate the stability of quantum entangled states under noisy conditions. The Fuzzy C-Means clustering model (FCM) is applied to identify different categories of quantum states based on fidelity and entropy trends, allowing for a structured assessment of the impact of noise. The presented methodology follows five key phases: a simulation of the Bell state, the introduction of the noise channel (depolarization and phase damping), noise suppression using corrective operators, clustering-based state classification, and interpretability analysis using Explainable Artificial Intelligence (XAI) techniques. The results indicate that while moderate noise levels allow for partial state recovery, strong decoherence, particularly under depolarization, remains a major challenge. Rather than relying solely on noise suppression, a classification-based strategy is proposed to identify states that retain computational feasibility despite the effects of noise. This hybrid approach combining quantum-state classification with AI-based interpretability offers a new framework for assessing the resilience of quantum systems. The results have practical implications in quantum error correction, quantum cryptography, and the optimization of quantum technologies under realistic conditions. Full article

As a renewable and degradable biomass material, cellulose diacetate (CDA) has significant development potential and has gained widespread interest from researchers. However, its poor thermal stability at high temperatures limits its practical use in the extrusion process and restricts its applications in other fields, such as high-heat airflow filters, battery separators and special textile materials. To enhance the thermal stability of CDA, three heat-resistance additives, i.e., polyphenylene sulfide (PPS), polycarbonate (PC) and polyimide (PI), were introduced to synthesize PPS/CDA, PC/CDA and PI/CDA composite materials through melt extrusion. The incorporation of three heat-resistant additives increased the glass transition temperature (T_g), initial melting temperature (T_mⁱ) and final melting temperature (T_m^f) of the composites, and it reduced the heat loss at 195 °C. After conducting the isothermal thermogravimetry test for 3 h at 215 °C in an air atmosphere, the weight loss of PPS/CDA, PC/CDA and PI/CDA composites was 4.6%, 4.1% and 3.7%, respectively, which was 5.1% lower than that of pure CDA. Morphology characterization tests using a 3D digital microscope and a field emission scanning electron microscope (FESEM) revealed the compatibility order with CDA as the following: PC > PPS > PI. Additionally, Fourier transform infrared spectroscopy (FT–IR) disclosed that hydrogen bonds were formed between heat-resistant additives and CDA molecules, and the carbonyl groups in CDA showed conjugation and hyperconjugation effects with the benzene rings in the additives. Therefore, the enhanced thermal stability of CDA composites can be attributed to the molecular entanglement and crosslinking between additives and CDA molecules. Full article

Hydrogels are promising materials for biomedical applications due to their tunable properties. Despite significant research on optimizing the mechanical and rheological properties of chitosan hydrogels, a comprehensive analysis incorporating pH and molarity of the neutralizing solution is still lacking. This study addresses this gap by evaluating how these factors influence the rheological characteristics of chitosan hydrogels. The hydrogels were prepared using an acidic blend and were neutralized with sodium hydroxide solutions. Rheological characterization demonstrated that all samples exhibited pseudoplastic behavior, with viscosity decreasing under shear stress. Hydrogels with higher pH values exhibited lower viscosity, which is attributed to the reduced protonation and weaker electrostatic repulsion between chitosan chains. In contrast, more acidic conditions resulted in increased viscosity and greater chain entanglements. NaOH concentration impacted gel stability; lower concentrations resulted in more stable gels, whereas higher concentrations increased crosslinking but compromised integrity at elevated pH. These findings provide essential insights for optimizing chitosan hydrogels with customized properties, making them highly suitable for specific biomedical applications, such as advanced 3D-printed wound dressings. Full article