Search Results (14,863)

The study explores γ-valerolactone (GVL) pulps as a sustainable approach to producing microfibrillated (MFC) and nanofibrillated (NFC) cellulose from sugarcane bagasse, a widely available agro-industrial by-product. Pulp was obtained by acid-catalyzed organosolv delignification with a GVL–water system. MFC was generated through a simple disc refiner, while NFC was produced by TEMPO-mediated oxidation followed by mechanical treatment in a colloidal mill. NFC and MFC produced using the same methodology from a commercial sugarcane totally chlorine-free (TCF) soda–anthraquinone (soda–AQ) pulp served as a reference. Structural and physicochemical characterization involved optical transmittance, turbidity, conductimetry, X-ray diffraction, viscosity, FTIR, carboxyl content, cationic demand, degree of polymerization, and morphology by scanning electron microscopy (SEM). Results demonstrated that xylan and residual lignin contents influenced MFC formation, and the NFC showed properties comparable to those of the commercial pulp with fewer fibrillation passes. The study highlights GVL pulping as a greener, efficient alternative to conventional processes, opening new pathways for producing viscosity-controlled nanocellulose suspensions suitable for advanced applications. Full article

Power transformers are critical components of power grids, and their operational status characterization and fault diagnosis are crucial for power system reliability. Oil quality assessment is a crucial method for determining transformer status, and the detection of impurity particles in oil has historically been a key approach. However, recent field tests have revealed the presence of numerous impurity particles less than 5 μm in transformer oil. Current power standards do not address these micron-sized particles, and their sources and mechanisms of action are largely unresolved. Therefore, this paper designed a localized overheating experiment, incorporating microflow imaging technology, to investigate the generation patterns of impurity particles under localized overheating and their quantitative correlation with heat. Field oil samples were also collected and tested to further explore the potential application of these micron-sized particles in transformer overheating assessment. The research results show that insulating oil can decompose and produce impurity particles at temperatures as low as 140 °C. When the temperature is below 140 °C, the number of particles at different heat levels is not significantly different from that of the non-overheated oil sample. However, when the temperature exceeds 140 °C, the number of particles increases significantly with increasing heat. Among the generated particles, particles with a diameter of less than 5 μm account for over 50% of the total number, and their number increases significantly with increasing heat. Their morphology is characterized by a smooth, regular, and spherical shape. Field test results of overheated oil samples are consistent with laboratory tests. Micron-sized particles are highly sensitive to changes in overheating conditions and have the potential to be used as a new characteristic parameter of transformer overheating conditions. In summary, this paper reveals the formation mechanism of impurity particles in insulating oil under localized overheating conditions. It was found that insulating oil can also decompose and generate impurity particles at 140 °C, with the pyrolysis products mainly consisting of particles smaller than 5 μm in diameter, which are not currently considered a concern in existing standards. Further research indicates that these micron-sized particles exhibit high sensitivity to changes in overheating conditions, demonstrating potential application value as a novel characteristic parameter of transformer overheating. Full article

In hybrid active–passive magnetic shielding systems, active compensation coils are used to suppress residual magnetic fields inside the shield. However, due to the intrinsic hysteresis of high-permeability materials, the compensation fields inevitably magnetize the passive layers. This process introduces new and unpredictable remanent magnetization, paradoxically worsening the remanence stability during active compensation. This study systematically investigates and quantifies how the number of passive shielding layers affects remanence instability. A combined approach of theoretical analysis, finite-element simulations, and experimental validation is employed. The results reveal a key counter-intuitive finding: although adding more shielding layers enhances the static attenuation of external fields, it markedly amplifies the remanence instability induced by active compensation. Specifically, multi-layer shields exhibit larger remanence changes under identical compensation-field excitations. This finding reveals a previously overlooked performance trade-off and provides new design insights for ultra-high-precision shielding systems. These findings provide essential guidance for optimizing the design and operation of next-generation ultra-high-precision magnetic shielding devices and their applications in frontier areas such as fundamental physics and biomedicine. Full article

The global transition to renewable energy sources is essential to carbon neutrality and ensuring energy security. First, the paper presents a comprehensive literature review of the main technological breakthroughs in bioenergy, hydro energy, solar energy, onshore and offshore wind energy, ocean energy, and geothermal energy, selecting the latest papers published. Next, key scientific challenges, environmental and economic constraints, and future research priorities for each of the six renewable energies were outlined. Then, to emphasize the important contribution of renewable energies to total energy production and the proportions of each type of renewable energy, the evolution of global electricity generation from all six renewable sources between 2000 and 2023 was analyzed. Thus, in 2023, the global electricity generation weight of each renewable energy in total renewable energy ranks hydro energy (47.83%) first, followed by onshore and offshore wind energy (25.8%), solar energy (18.19%), bioenergy (7.07%), geothermal energy (1.1%), and ocean energy (0.01%). After that, the bibliometric analysis, conducted between 1 January 2021 and 1 October 2025 on the Web of Science (WoS) database and using the PRISMA approach and VOSviewer version 1.6.20 software, enabled the identification of the most cited papers, publications and citation number by WoS categories, topics, correlation with Sustainable Development Goals, authors’ affiliation, publication title, and publisher. Furthermore, the paper presents a network visualization of the link between co-occurrences and all keywords, imposing minimum thresholds of 10, 20, and 30 occurrences per keyword, and computes the network density based on the number of edges and nodes. Finally, additional analysis included the most used keywords in different co-occurrences, a word cloud of occurrences by total link strength, regression of occurrences versus total link strength, and correlations between citations and documents and between citations and authors. Carbon neutrality and a resilient energy future can only be achieved by integrating renewable sources into hybrid systems and optimized smart grids. Each technological progress stage will bring new challenges that must be addressed cost-effectively. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) enables the large-scale collection of ecological data, yet translating this dynamic sensor data into engaging, personalized public experiences remains a significant challenge. Existing solutions fall short: static exhibitions lack adaptability, while general-purpose LLM agents struggle with real-time responsiveness and reliability. To address this, we introduce CurationAgent, a novel intelligent agent built upon the State-Gated Agent Architecture (SGAA). Its core innovation is an advanced hybrid curation pipeline that synergizes Retrieval-Augmented Generation (RAG) for broad semantic recall with an Intent-Driven Curation (IDC) Funnel for precise intent formalization and narrative synthesis. This hybrid model robustly translates user intent into a curated, multi-modal narrative. We validate this framework in a proof-of-concept virtual exhibition of the Lalu Wetland’s biodiversity. Our comprehensive evaluation demonstrates that CurationAgent is significantly more responsive (1512 ms vs. 4301 ms), reliable (95% vs. 57% task success), and precise (85.5% vs. 52.7% query precision) than standard agent architectures. Furthermore, a user study with 27 participants confirmed our system leads to measurably higher user engagement. This work contributes a robust and responsive agent architecture that validates a new paradigm for interactive systems, shifting from passive information retrieval to active, partnered experience curation. Full article

Rising residential land prices push up housing prices and worsen credit misallocation. These patterns emerge amid cyclical real estate fluctuations and heavy land-based public finance. Such pressures undermine macroeconomic stability and sustainable land-use. The land price circuit breaker is widely applied with a price cap and state dependence, yet its trigger mechanism and interaction with inflation targeting remain underexplored. This study addresses three core questions. First, how does the circuit breaker’s discrete trigger and rule-switching logic differ from traditional static price ceilings? Second, can the mechanism, via the collateral channel, restrain excessive land price hikes, improve credit allocation, and, thereby, stabilize land price dynamics and long-run macroeconomic performance? Third, how does the circuit breaker interact with inflation targeting, and through which endogenous channels does a strict target dampen housing prices and raise activation probability? This study develops a multi-sector DSGE model with an embedded land price circuit breaker. The price cap is modeled as an occasionally binding constraint. A dynamic price band and trigger indicator capture the policy’s switch between slack and binding states. The framework incorporates interactions among local governments, the central bank, developers, and households. It also links firms and the secondary housing market. Under different inflation-targeting rules, this study uses impulse responses, an event study, and welfare analysis to assess trigger conditions and macroeconomic effects. The findings are threefold. First, a strict inflation target increases the probability of a circuit breaker being triggered. It channels housing-demand shocks toward land prices and creates a “nominal anchor–relative price constraint” linkage. Second, once activated, the circuit breaker narrows the gap between land price and house-price growth. It weakens the procyclicality of collateral values. It also restrains credit expansion by impatient households. These effects redirect credit toward firms, improve corporate financing, reduce the decline in investment, and accelerate output recovery. Third, the circuit breaker limits new land supply and shifts demand toward the secondary housing market. This generates a supply-side effect that releases existing stock and stabilizes prices, thereby weakening the amplification mechanism of housing cycles. This study identifies the endogenous trigger logic and cross-market transmission of the land price circuit breaker under a strict inflation target. It shows that the mechanism is not merely a price-management tool in the land market but a systemic policy variable that links the real estate, finance, and fiscal sectors. By dampening real estate procyclicality, improving credit allocation, and stabilizing macroeconomic fluctuations, the mechanism offers new insights for sustainable land-use policy and macroeconomic stabilization. Full article

Temperature offers a simple yet powerful signal to program cellular behavior. Here, we engineered and characterized a set of temperature-dependent genetic circuits that integrate RNA thermometers with site-specific DNA recombinases to achieve precise, irreversible control of gene expression. Using the serine recombinase Bxb1 placed under the control of the Salmonella FourU RNA thermometer, we demonstrate how promoter strength critically defines thermal sensitivity: weak promoters’ activity clears ON/OFF transitions, while strong promoters lead to continuous, quasi-temperature-independent recombination. Furthermore, temperature pulse duration and growth phase of cell culture were found to modulate recombination efficiency, providing additional layers of control. We illustrate the potential of this framework through proof-of-concept applications, including (i) the generation of spatial expression patterns on 2D surfaces via localized heating, (ii) a paper-based device capable of recording temperature gradients as stable genetic outputs, and (iii) a temperature-triggered lysis system for controlled cellular release. Together, these results establish temperature-regulated recombinase circuits as versatile and robust tools for programmable, spatially resolved, and irreversible control of gene expression, paving the way for new applications in synthetic biology, biosensing, and bioproduction. Full article

To address the limitations of the traditional Coati Optimization Algorithm (COA), such as insufficient global exploration, poor population cooperation, and low convergence efficiency in global optimization and photovoltaic (PV) model parameter identification, this paper proposes a Multi-strategy Enhanced Coati Optimization Algorithm (MECOA). MECOA improves performance through three core strategies: (1) Elite-guided search, which replaces the single global best solution with an elite pool of three top individuals and incorporates the heavy-tailed property of Lévy flights to balance large-step exploration and small-step exploitation; (2) Horizontal crossover, which simulates biological gene recombination to promote information sharing among individuals and enhance cooperative search efficiency; and (3) Precise elimination, which discards 20% of low-fitness individuals in each generation and generates new individuals around the best solution to improve population quality. Experiments on the CEC2017 (30/50/100-dimensional) and CEC2022 (20-dimensional) benchmark suites demonstrate that MECOA achieves superior performance. On CEC2017, MECOA ranks first with an average rank of 1.87, 2.07, 1.83, outperforming the second-best LSHADE (2.03, 2.43 and 2.63) and the original COA (9.93, 9.93 and 9.96). On CEC2022, MECOA also maintains the leading position with an average rank of 1.58, far surpassing COA (8.92). Statistical analysis using the Wilcoxon rank-sum test (significance level 0.05) confirms the superiority of MECOA. Furthermore, MECOA is applied to parameter identification of single-diode (SDM) and double-diode (DDM) PV models. Experiments based on real measurement data show that the SDM model achieves an RMSE of 9.8610 × 10⁻⁴, which is only 1/20 of that of COA. For the DDM model, the fitted curves almost perfectly overlap with the experimental data, with a total integrated absolute error (IAE) of only 0.021555 A. These results fully validate the effectiveness and reliability of MECOA in solving complex engineering optimization problems, providing a robust and efficient solution for accurate modeling and optimization of PV systems. Full article

In the process of determining whether a health technology should be covered by healthcare systems, patients and their representatives were initially excluded from both evaluations and decision-making. In Europe, direct dialogue between patient organisations and regulatory authorities—particularly in the pharmaceutical sector—began in the early 1990s. It was only decades later, as the high cost of medicines created new challenges, that authorities recognised the necessity of engaging with patients. Patients’ contributions to the assessment of a health technology begin with discussions about the need for the technology in question. Initially, these discussions involve the developer, and later—after research and development—regulators, HTA assessors, and payers. Given that multiple technologies may be under development, patients and their organisations often prioritise those that generate the most interest within the patient community. They can then share their perspectives with evaluators during the horizon-scanning phase. Another key contribution is the role patients play in guiding clinical research by participating in scientific advice. Finally, during the assessment and appraisal stages, various methods are used to gather their views. Full article

The blast-wave model with Boltzmann–Gibbs statistics is used to examine the transverse momentum spectra of ${K\ast }^0$ mesons generated at the Relativistic High-Energy Collider (RHIC) Beam Energies with mid-rapidity ($\left|y\right|<1$) in symmetric $Au-Au$ collisions. There is a clear correlation between the extracted kinetic freeze-out temperature ($T_0$) and transverse flow velocity (${\beta }_T$) in various collision centralities and center-of-mass energies ($\sqrt{s_{NN}}$). Since a larger initial energy density delays freeze-out and a shorter system lifetime limits cooling, $T_0$ is directly proportional to both $\sqrt{s_{NN}}$ and peripheral collisions. On the other hand, ${\beta }_T$ drops in peripheral symmetric collisions due to weaker collective expansion, while it rises with $\sqrt{s_{NN}}$ because of larger pressure gradients. The concurrence between the thermal and collective energy components in the expanding fireball is reflected in the obvious anti-correlation between $T_0$ and ${\beta }_T$. These findings support hydrodynamic predictions and offer important new information about QGP’s freeze-out behavior. Full article

Integrated hydro-wind-solar-storage (HWSS) bases are pivotal for advancing new power systems under the low carbon goals. However, the independent decision-making of diverse generation investors, coupled with limited transmission capacity, often leads to a dilemma in which individually rational decisions lead to collectively suboptimal outcomes, undermining overall benefits. To address this challenge, this study proposes a novel cooperative game-based method that seamlessly integrates grid congestion into capacity allocation and benefit distribution. First, a bi-level optimization model is developed, where a congestion penalty is explicitly embedded into the cooperative game’s characteristic function to quantify the maximum benefits under different coalition structures. Second, an improved Shapley value model is introduced, incorporating a comprehensive correction factor that synthesizes investment risk, congestion mitigation contribution, and capacity scale to overcome the fairness limitations of the classical method. Third, a case study of a high-renewable-energy base in Qinghai is conducted. The results demonstrate that the proposed cooperative model increases total system revenue by 20.1%, while dramatically reducing congestion costs and wind/solar curtailment rates by 86.2% and 79.3%, respectively. Furthermore, the improved Shapley value ensures a fairer distribution, appropriately increasing the profit shares for hydropower (from 28.5% to 32.1%) and energy storage, thereby enhancing coalition stability. This research provides a theoretical foundation and practical decision-making tool for the collaborative planning of HWSS bases with multiple investors. Full article

This work reports the electrochemical behavior of a nickel hydroxide electrode, electrodeposited in a deep eutectic solvent (DES), in alkaline solutions of varying composition, aiming to elucidate the influence of the cation (Na⁺ vs. K⁺), urea, and carbonate ions on the mechanism and kinetics of anodic processes. Cyclic voltammetry and electrochemical impedance spectroscopy were employed to analyze the electrochemical responses of electrode processes in alkaline water electrolysis systems. For the urea oxidation reaction (UOR), the frequency-dependent characteristics were thoroughly characterized, and the impedance response was simulated according to the Armstrong–Henderson equivalent circuit. It was found that the addition of urea significantly transforms the impedance structure, sharply reducing the polarization resistance and increasing the pseudo-capacitive component of the constant phase element at low frequencies, indicating activation of the slow steps of urea oxidation via a direct mechanism and the formation of an extended adsorptive surface. It was demonstrated that, unlike conventional alkaline electrolysis where KOH-based systems are generally more effective, urea-assisted systems exhibit superior performance in NaOH-based electrolytes, which provides more favorable kinetics for the electrocatalytic urea oxidation process. Furthermore, the accumulation of carbonate ions was shown to negatively affect UOR kinetics by increasing polarization resistance and partially blocking surface sites, highlighting the necessity of controlling electrolyte composition in practical systems. These findings open new opportunities for the rational design of efficient urea-assisted electrolyzers for green hydrogen generation. Full article

The shift toward remote and automated admission processes in higher education introduces new challenges, including evaluator subjectivity and risks of applicant fraud. The FAIR-VID project addresses these issues by developing an artificial intelligence system that integrates multimodal data fusion with semi-supervised deep learning to assess applicant video interviews, submitted documents, and form data. This paper presents the project’s data preprocessing pipeline, designed to fuse heterogeneous modalities and to support seamless interaction between AI agents and human decision-makers throughout the admission workflow. The proposed process is intentionally general, making it applicable not only to international university admissions but also to broader human resource management and hiring contexts. Emphasis is placed on the need for robust and transparent AI adoption in admission and recruitment, supported by open-source modules and models at every stage of interaction between applicants and institutions. As a proof of concept, we provide open-source solutions for the analysis of video interviews, images, and documents enriched with semantic descriptions generated by large multimodal and complementary AI models. The paper details the multi-phase implementation of this pipeline to create structured, semantically rich datasets suitable for training advanced deep learning systems for comprehensive applicant assessment and fraud detection. Full article

In the context of new-type urbanization and high-quality development, this study aims to construct a multi-objective synergistic land-use mechanism to tackle the “growth-equity-ecology” trilemma in the Chengdu–Chongqing Urban Agglomeration (CCUA). By building an economic–social–ecological benefit evaluation index system and applying TOPSIS with entropy weighting, the coupling coordination degree (CCD) model, and the spatial Durbin model (SDM), we systematically explore the spatiotemporal patterns of land-use benefit synergies and their driving mechanisms. The results reveal the following: (1) From 2015 to 2023, CCUA’s land-use CCD generally improved but showed marked core–periphery polarization. Chongqing’s economic agglomeration worsened regional gaps, while Sichuan’s intra-regional policies boosted internal balance; cross-jurisdictional collaboration eased border disparities but failed to stop overall polarization. (2) Spatial clustering identified hotspots in Chongqing’s main urban and suburban areas and cold spots in eastern Sichuan, reflecting the coexistence of factor agglomeration and cross-border policy synergy. (3) Road network expansion directly hindered CCD, and neighboring ecological protection triggered resource-competition spillovers, emphasizing the key role of cross-regional governance in balancing the “ecology-development” trade-off. This study puts forward spatially differentiated strategies and cross-jurisdictional coordination mechanisms to optimize land-use structures and advance sustainable development in urban agglomerations. Full article

Physical AI enables reliable and timely operations of autonomous systems such as robots and smart manufacturing equipment under diverse and dynamic execution environments. In these environments, computing resources are often limited, shared among tasks, and fluctuate over time. This makes it difficult to guarantee that tasks meet timing constraints. As a result, resource-aware execution time prediction becomes essential for efficient resource management in physical AI systems. However, existing methods typically assume specific environments or static resource usage and often fail to generalize to new environments. In this paper, we propose CARE-D (Calibration-Assisted Resource-aware Execution time prediction), which trains a deep neural network to model the nonlinear relationships among hardware characteristics, resource levels, and task features across environments. The model predicts the execution time of tasks under diverse hardware and dynamically allocated computing resources, using a few execution records from new environments. CARE-D applies few-history-based calibration using only 1 to k execution records from target environments to adjust predictions without retraining the model. Experiments show that CARE-D improves prediction accuracy by about 7.3% over zero-history predictors within a 10% relative error and outperforms regression and deep learning baselines, using only one to five records per target environment. Full article