Search Results (14,707)

The rapid advancement of communication technologies exacerbates severe electromagnetic interference (EMI) pollution. Conventional flexible shielding materials rely heavily on non-degradable petroleum-based polymers, aggravating the electronic waste crisis. To address this dual challenge, sustainable biomass-derived TEMPO-oxidized cellulose nanofibrils (TOCNFs) emerge as ideal structural substrates. However, their intrinsic electrical insulation necessitates integrating conductive two-dimensional (2D) MXene, which suffers from severe self-restacking and brittleness. Herein, TOCNFs@MXene hybrid films are manufactured via vacuum filtration and hot-pressing densification. TOCNFs inhibit MXene self-restacking, constructing a highly ordered layered architecture via a dense hydrogen-bonded network. The optimized ultrathin film T5@M20 (~4.92 μm) exhibits an electrical conductivity of 1.09 × 10⁶ ± 5.06 × 10⁴ s m⁻¹ and an X-band shielding effectiveness (SE_Total) of 25.55 dB. Demonstrating an ultrahigh thickness-normalized specific shielding effectiveness (SSE/t) of 51,934.72 dB·cm²·g⁻¹, this sustainable architecture shows exceptional potential for next-generation flexible electronics. Full article

The rapid proliferation of unmanned aerial vehicles (UAVs) has amplified the need for robust and efficient object detection in diverse aerial environments. However, detecting small objects under complex conditions (e.g., low illumination, cluttered backgrounds, and thermal–visual discrepancies) remains challenging. While many existing detectors emphasize real-time inference, they often rely on weak or late fusion strategies, resulting in suboptimal utilization of complementary multi-modal cues. To address this limitation, we propose DGE-YOLO, an enhanced YOLO-based framework for effective infrared–visible (IR–RGB) multi-modal fusion in UAV object detection. DGE-YOLO adopts a dual-branch architecture for modality-specific feature extraction, preserving modality-aware representations before fusion. To strengthen cross-scale semantics, we introduce an Efficient Multi-scale Attention (EMA) module that improves feature discrimination across spatial resolutions. Furthermore, we replace the conventional neck with a Gather-and-Distribute module to reduce information loss during feature aggregation and improve multi-scale feature propagation. Extensive experiments on the DroneVehicle dataset demonstrate that DGE-YOLO consistently outperforms state-of-the-art baselines, confirming its effectiveness and practicality as an applied multi-modal detection solution for UAV scenarios. Full article

Permanent magnet synchronous motor (PMSM)-driven position servo systems in high-speed flight vehicles face severe challenges from extreme thermal environments, which induce significant parameter variations up to 25% (e.g., motor torque constant) and complex multi-scale disturbances. This paper proposes a novel adaptive robust control strategy integrating three key components: (1) an ultra-local model formulation motivated by physically consistent thermal effect analysis of electromagnetic, mechanical, and tribological parameters; (2) a dual-layer disturbance observer architecture comprising a third-order finite-time convergent extended state observer (FTCESO) for fast-varying disturbances and a σ-modification adaptive estimator for slow-varying thermal drifts; and (3) a global nonlinear integral terminal sliding mode controller with a cycloidal reaching law. Stability analysis based on homogeneous system theory and Lyapunov methods establishes practical finite-time convergence with explicit bounds. The experimental results on a TMS320F28335-based servo platform demonstrate that the proposed method reduces the maximum position deviation by 83–94% compared to PID, LADRC, and conventional SMC controllers under the tested disturbance conditions, achieving settling time reductions exceeding 90%. Under combined thermal drift and external loading, the proposed approach limits the maximum tracking error to below 0.45° while maintaining a steady-state error under 0.08°. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

Remote sensing semantic segmentation is fundamental to applications such as land-cover mapping, urban analysis, and environmental monitoring. However, remote sensing scenes often exhibit pronounced scale variation, fragmented regions, dense small objects, and complex boundary transitions, making fine-grained prediction particularly challenging. Transformer-based architectures such as SegFormer have demonstrated a strong capability in modeling long-range context through hierarchical encoding, yet their lightweight decoders mainly rely on linear projection and feature fusion, providing limited capacity for local refinement after multi-scale aggregation. This limitation may reduce spatial precision in boundary-sensitive and small-object-rich regions. To address this issue, we propose the Post-fusion Enhanced Block (PFEB), a lightweight decoder-side refinement module inserted after multi-scale feature fusion and before pixel-wise classification. PFEB combines channel expansion, depthwise and pointwise convolutions, efficient channel attention (ECA), and residual learning to enhance local semantic refinement while largely preserving computational efficiency. Built upon SegFormer, the proposed method was evaluated on two widely used remote sensing benchmarks, i.e., LoveDA and ISPRS Vaihingen, under both Mix Transformer-B0 (MiT-B0) and Mix Transformer-B2 (MiT-B2) backbones. Experimental results show that PFEB consistently improves the SegFormer baseline across datasets and model scales. Under MiT-B2 backbone, our method achieves 53.82 ± 0.31 mean intersection over union (mIoU) on LoveDA and 74.84 ± 0.41 mIoU on ISPRS Vaihingen. Boundary- and size-aware evaluations further indicate that the gains are mainly reflected in improved semantic correctness near boundaries and in the recoverability of small objects. With only modest additional cost (approximately +0.53 M parameters and +8.7 G floating point operations (FLOPs)), PFEB provides a favorable accuracy–efficiency trade-off. These results suggest that PFEB is an effective and lightweight post-fusion refinement module for improving fine-grained remote sensing semantic segmentation. Full article

The conservation of tuff- and coral rock-built architectural heritage structures (AHS) is challenging because access to original tuff and coral rock has become difficult and severely limited due to urbanization, land reclamation, the depletion of stone quarries, anti-mining and anti-quarrying legislation. An emerging approach to address this issue is to create compatible “replacement” rocks via geopolymerization, a process that is more sustainable and greener than the use of conventional cement and concrete. To explore the potential of geopolymers for AHS conservation strategies, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were implemented; 103 eligible articles were identified and classified into geopolymers for AHS (34 articles), tuff-built AHS (60 articles), and coral rock-built AHS (9 articles). Tuff substrates in AHSs appear in a variety of colors (yellowish-brown, grayish-cream, reddish-brown, pale greenish-gray and pink hues), densities (1.0–2.5 g/m³), and compressive strengths (3–100 MPa). Meanwhile, coral rock substrates in AHSs appear in whitish-cream color and are coarse-pored (1–5 MPa), fine-grained (8–15 MPa), and calcarenite (50–60 MPa). In terms of geopolymer formulation, metakaolin was reported as the most popular main precursor or admixture, while NaOH and Na₂SiO₃ were used simultaneously as alkaline activators. Aggregates used in geopolymer formulations depended on local availability, including quartz sand, river sand, crushed stones, carbonate stones, volcanic rock, volcanic sand, tuff, brick, ceramic tiles, and waste materials. Aesthetics, chemical composition, physical attributes, and mechanical properties have been identified as key criteria to ensure geopolymer compatibility for AHS conservation application. To date, geopolymers have been applied for AHS conservation as repair mortars, consolidants (i.e., grout and adhesives), and masonry strengthening (i.e., fiber-reinforced mortar). Finally, geopolymers formulated for AHS conservation have similar durability as the original substrate based on accelerated aging tests (i.e., salt mist, wet-dry, and freeze–thaw) and long-term outdoor exposure experiments. Full article

Methane (CH₄) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH₄ emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH₄ emissions at the facility-level. Near-field quantification of methane (CH₄) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH₄ emissions to controlled single-point emissions between 0.4 and 5.2 kg CH₄ h⁻¹. Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain. Full article

Background: COVID-19 remains a substantial public health challenge in the Netherlands. Next-generation COVID-19 vaccine, mRNA-1283, is approved in the European Union, with potential for higher relative vaccine efficacy compared with originally licensed COVID-19 vaccines. Methods: The potential public health and economic impact of mRNA-1283 in adults ≥ 60 years and high-risk adults aged 18–59 years was modeled versus no vaccination and originally licensed mRNA-1273 and BNT162b2, adapting a published static Markov model with a 1-year time horizon. COVID-19 burden reflected two full post-pandemic seasons. Vaccine efficacy versus mRNA-1273 was based on pivotal phase 3 NextCOVE trial data; efficacy versus BNT162b2 was derived from an indirect treatment comparison. The economically justifiable price (EJP) of mRNA-1283 versus no vaccination and price premiums over existing vaccines were determined at a willingness-to-pay threshold of €50,000/quality-adjusted life-year (QALY) gained. Results: Without COVID-19 vaccination, an estimated 460,000 infections, 23,800 hospitalizations, and 5300 deaths would occur. With current coverage, mRNA-1283 was estimated to prevent 68,000 infections, 5400 hospitalizations, and 1200 deaths, saving 9667 QALYs and over €66.5 million in treatment costs. The EJP was €238 versus no vaccination. Compared with mRNA-1273 and BNT162b2, mRNA-1283 was estimated to prevent additional burden (e.g., 1309 and 1679 hospitalizations, respectively) and was cost-effective at an incremental EJP of €62 versus mRNA-1273 and €80 versus BNT162b2. Conclusions: The results support continued COVID-19 vaccination to mitigate the ongoing health and societal burden of SARS-CoV-2 in the Netherlands. The comparative analyses indicate that mRNA-1283 may be associated with substantial health benefits over originally licensed mRNA vaccines; consequently, its use may further improve health outcomes and economic efficiency within COVID-19 vaccination programs. Full article

Photocatalytic reduction of carbon dioxide (CO₂) into high-value multicarbon products, such as ethylene (C₂H₄), remains a significant challenge due to the difficult C-C coupling process. Potassium poly(heptazine imide) (K-PHI) is a promising photocatalyst, yet efficiently exchanging its interlayer cations to tune catalytic selectivity without causing structural degradation is difficult. Herein, an efficient and green supercritical CO₂ (SC CO₂) assisted ion-exchange strategy was developed to successfully prepare a series of mono-/di-/trivalent cation-doped M-PHI photocatalysts (M = H⁺, Na⁺, Sr⁺, Ca²⁺, Co²⁺, Fe³⁺). Systematic characterizations confirmed that the SC-CO₂ treatment successfully achieved in-depth cation substitution without destroying the intrinsic heptazine framework, effectively regulating the interlayer structure and significantly optimizing the photoelectrochemical charge separation. Among the prepared samples, H-PHI exhibited the optimal photocatalytic CO₂ reduction performance with an outstanding selectivity toward C₂H₄ generation. Under simulated sunlight irradiation for 3 h, the yields of CO, CH₄, and C₂H₄ C₂H₄ C₂H₄ reached 3564.87, 807.32, and 40.00 μmol·g⁻¹, respectively, significantly outperforming pristine K-PHI and other metal-doped samples. Crucially, isotope-tracing experiments utilizing a SC CO₂-DCl treatment detected deuterated CH₄ and C₂H₄ products, providing direct evidence that the hydrogen in the carbon products originates from the introduced protons, thereby elucidating the precise reaction pathway for C-C coupling. This study provides a green and efficient supercritical CO₂ ion exchange strategy for the cation engineering of crystalline carbon nitride, and also offers new ideas and methods for designing high-activity photocatalysts for photocatalytic CO₂ reduction. Full article

Turbid Class II inland waters such as Taihu Lake exhibit a “spectral uplift” effect driven by suspended particulate matter (SPM) scattering and colored dissolved organic matter (CDOM) absorption, which can obscure chlorophyll-a (Chl-a) signals in the visible–red-edge region and challenge retrieval under small-sample, collinear feature settings. Using multispectral observations from the BC-1A satellite (carrying the Lightweight Hyperspectral Remote Sensing Imager, LHRSI) and synchronous satellite–ground in situ measurements acquired over Taihu Lake in late autumn, this study proposes Chl-a-oriented PCA–RF (COP-RF), a leakage-safe inversion framework integrating correlation screening, principal component analysis (PCA), and random forest (RF) regression. Candidate band-combination features are generated, and PCA is applied for orthogonal compression to mitigate collinearity before RF learning. A stratified five-fold cross-validation based on Chl-a quantile bins is adopted, with screening, standardization, and PCA fitted only on training folds. COP-RF achieves stable performance under the current dataset ($R^2=0.671$, RMSE $=1.80\mathsf{\mu }$g/L, MAE $=1.25\mathsf{\mu }$g/L). Spatial inversion shows higher Chl-a near shores and bays and lower values in the lake center, consistent with Sentinel-2 hotspot ranks. Full article

Intramuscular fat (IMF) marbling is a key determinant of beef quality, yet predicting how breed-specific gene expression translates into tissue-scale fat patterning remains a major challenge. Using a small public transcriptomic dataset (n = 3 per breed), this study presents a proof-of-concept omics-to-tissue modeling framework that converts RNA-seq data into biophysically interpretable parameters governing intramuscular adipogenesis. Using transcriptomic profiles from GSE161967 (Japanese Black Wagyu versus Chinese Red Steppes), we derived composite indices capturing the adipogenic commitment (φ) and lipid droplet capacity (ψ) from curated gene modules. These indices were mapped via calibrated linear functions to a Cellular Potts Model (CPM), parameterizing the fibro-adipogenic progenitor (FAP) differentiation probability, lipogenesis rate, adipocyte cohesion, and progenitor abundance. The gene-derived parameters placed Wagyu in a high-adipogenic regime ($p_{F\to A}^{\mathrm{base}}$ = 0.65; $k_{lipogenesis}$ = 0.12), while Chinese Red Steppes resided in a low-adipogenic regime (0.25; 0.04). The CPM simulations revealed a sharp, predictive threshold at $p_{F\to A}^{\mathrm{base}}$ ≈ 0.55, below which IMF remained negligible and above which stable adipocyte clusters and 8–9% IMF emerged. Without post hoc tuning, the gene-derived parameters correctly predicted robust marbling in Wagyu and a lean phenotype in Chinese Red Steppes. A sensitivity analysis identified the adipogenic commitment as the primary control parameter, with lipogenesis acting as an amplifier. Together, these results demonstrate that transcriptomic measurements can quantitatively predict emergent marbling phenotypes through a small set of interpretable biophysical parameters, establishing a generalizable framework for forecasting complex tissue traits from omics data. Full article

Phyllotreta striolata is a global pest of cruciferous vegetables, and controlling its soil-dwelling larvae is challenging. The lack of standardized larval bioassay methods hinders the screening of effective biocontrol agents. In this study, we established a stable and standardized laboratory-efficacy trial system for P. striolata larvae. Indoor rearing techniques were optimized for Brassica juncea var. foliosa and Brassica juncea var. megarrhiza were identified as the optimal host plants, with ideal oviposition conditions at 26–28 °C using black flannel substrate, and soil-cultured Brassica rapa var. pekinensis as the host plant. Based on these findings, a larval bioactivity assay was established using B. juncea var. megarrhiza slices on water-agar. This system maintained a natural larval mortality rate below 5% within 48 h, meeting the bioassay requirements. The reliability of the system was validated by evaluating the activity of the engineered Bacillus thuringiensis (Bt) strain G033A against larvae, where the LC₅₀ value decreased from 23.013 mg/mL to 7.295 mg/mL with an extended treatment time (12–48 h). Using this standardized method, novel Cry proteins with high activity against P. striolata larvae were screened. Cry8Ca and Cry8Ga proteins exhibited LC₅₀ values of 2.243 mg/mL and 1.649 mg/mL, respectively. Full article

The development of high-performance and sustainable carbon electrodes is increasingly important for next-generation supercapacitors, yet controlling heteroatom doping and hierarchical pore evolution in biomass-derived carbons remains a key challenge. Lignin, as an abundant aromatic biopolymer, offers a structurally rich platform for designing functional carbons, but its rigid cross-linked architecture limits precise pore regulation and efficient nitrogen incorporation. In this work, nitrogen-doped hierarchical porous carbons were engineered from enzymatically treated lignin through a synergistic urea-assisted nitrogen doping and KOH activation strategy. The urea–KOH co-activation drives the coordinated evolution of micropores and mesopores. This approach yields an optimized carbon material possessing a high BET surface area of 2569 m² g⁻¹, an interconnected micro–mesoporous architecture, and a favorable distribution of pyridinic, pyrrolic, and graphitic nitrogen species. The engineered pore hierarchy is correlated with enhanced ion transport kinetics, as evidenced by a high b value of 0.99 and a capacitive contribution of 98.5% at 100 mV s⁻¹; nitrogen functionalities introduce redox-active sites and improve interfacial wettability. As a result, the selected material delivers a high specific capacitance of 221 F g⁻¹ at 0.5 A g⁻¹, strong rate capability with 84.4% retention at 20 A g⁻¹, and excellent cycling durability with 90.7% capacitance retention after 50,000 cycles. This study demonstrates a potentially mechanistically informed, scalable pathway for coupling enzymatic structural regulation with chemical activation, offering a sustainable route for transforming lignin into high-value carbon electrodes suitable for advanced supercapacitor applications. Full article

The MADS-box family is a multifunctional family of transcription factors characterized by the presence of a unique MADS domain, which plays an important part in regulating essential biological processes, including metabolic synthesis and the stress response. In this review, we analyze the structural features and classification of MADS-box proteins, then summarize the functions of the MADS-box family in the stress response. The MADS-box family can directly regulate downstream functional genes by binding to the CArG-box in the promoters of target genes, thereby influencing growth, development, and stress responses. Also, MADS-box transcription factors can form protein complexes with both MADS-box proteins and other types of transcription factors and chromatin regulatory proteins to modulate the chromatin state or transcriptional activation. Furthermore, they can regulate plant physiological responses by facilitating the synthesis of essential signaling molecules, including hormones and non-coding RNA. Finally, we discuss the potential of the MADS-box family in crop molecular breeding, offering a novel approach for developing high-yield and stress-resistant cultivars for solving global food security and climate change challenges. Full article

Background: The reliable co-loading of paclitaxel (PTX) and indocyanine green (ICG) into a single lipid nanoparticle (LNP) enables synergistic antitumor delivery but remains challenging due to their distinct physicochemical properties. Methods: This study integrated COSMO-RS calculations, molecular dynamics simulations, and in vitro assays to systematically investigate the effects of lipid composition, drug modification, particle size, and solvent environment on dual-drug loading. Results: This work indicate that DMPS lipid membranes featuring highly polar headgroups and ordered bilayer structures stably bind both ICG and PTX, achieving drug-loading efficiencies (DLEs) of 7.2% and 5.6%, respectively. Carboxylation of PTX enhanced hydrogen bonding with DMPS, while alkyl chain modifications improved membrane insertion, though excessive chain length (e.g., C12) reduced stability due to increased flexibility. Increasing the LNP size from 50 nm to 250 nm raised the DLE of PTX from 4.7% to 8.1%, while sizes beyond 500 nm led to membrane destabilization. The use of 20 vol% ethanol increased total drug loading by 51% by disrupting the hydration shell of ICG and suppressing PTX aggregation; however, ethanol concentrations exceeding 40 vol% intensified drug–solvent competition and weakened membrane binding. Conclusions: This study provides a comprehensive elucidation of the multifactorial regulatory mechanisms underlying dual-drug loading in LNPs, offering a theoretical basis for the rational design of efficient co-delivery systems. Full article