Search Results (453)

The high prevalence of cardiovascular diseases and the extensive application wearable electrocardiogram (ECG) devices for long-term monitoring have posed significant challenges for the transmission, storage, and real-time processing of massive amounts of ECG data. Consequently, efficient ECG compression and reconstruction have become a research priority in remote ECG monitoring. Traditional compressed sensing is complex and has high computational overhead, while single deep learning models cannot simultaneously extract local waveforms and model temporal dependencies. To address these shortcomings in the reconstruction process, this paper presents a CNN-LSTM-Attention hybrid model. This model utilizes a convolutional neural network (CNN) to capture local ECG waveform features, employs a long short-term memory (LSTM) network to learn long-term temporal dependencies, and introduces an attention mechanism to weight and fuse key diagnostic features, enabling accurate focus on key components including the QRS complex and ST segment. Experimental results on the MIT-BIH Arrhythmia dataset demonstrate that across the full compression range of 0.1–0.9, the proposed model achieves favorable comprehensive performance. Its PRD is stabilized at 10–12%, the SNR stays above 20 dB, and the RMSE is mostly lower than 0.25 mV. In terms of reconstruction accuracy and stability, our model outperforms the single CNN and CNN-LSTM models by a large margin. Full article

Abiotic stresses limit crop productivity by disrupting water relations, carbon assimilation, nutrient acquisition, membrane stability, and redox homeostasis. Partial root-zone irrigation (PRI), commonly implemented as partial root-zone drying (PRD), is often viewed as a deficit-irrigation strategy to improve water-use efficiency; however, this view underestimates the biological consequences of spatial root-zone heterogeneity. This review evaluates PRI as a spatially structured, priming-like framework for crop adaptation to abiotic stress. Available evidence indicates that localized drying and wet-side water uptake can coordinate root sensing, hydraulic–chemical signaling, abscisic acid delivery, hormone crosstalk, xylem-mediated regulation, and stomatal control. Beyond gas exchange, PRI is associated with photosynthetic maintenance, osmotic adjustment, antioxidant and redox regulation, root architectural plasticity, nutrient acquisition, and metabolic reprogramming. Evidence is strongest for drought, whereas responses to low temperature, salinity, heat-associated evaporative demand, and combined stresses remain more context-dependent. Emerging work also links PRI to rhizosphere restructuring and microbiome shifts, but the causal mechanisms and field reproducibility remain unresolved. We argue that future progress requires matched PRI–deficit-irrigation comparisons, standardized switching thresholds, shared physiological and molecular readouts across crops, high-resolution root biology, and commercially realistic field validation. This framing distinguishes conserved physiological outcomes from mechanisms that may differ among crops, genotypes, and irrigation designs. Full article

Nitrogen oxides (NO_x), comprising nitric oxide (NO) and nitrogen dioxide (NO₂), are key precursors of atmospheric nitrate, a major component of fine particulate matter (PM_2.5) that critically affects air quality, human health, and ecosystems. Emission inventories provide detailed spatial and temporal information on NO_x sources, while stable isotope techniques offer an additional constraint for source apportionment. Here, we incorporated stable nitrogen isotopes (¹⁴N, ¹⁵N) into the widely used Multi-resolution Emission Inventory for China (MEIC) over South China, with a focus on the Pearl River Delta (PRD) region, one of the most highly urbanized and industrialized regions in China, using an isotopic mass–balance model. The 2008 MEIC inventory indicated that NO_x emissions across South China were spatially heterogeneous, dominated by transportation sources, and concentrated mainly in the PRD and other urban clusters. We then compared the simulated isotopic composition of emitted NO_x with atmospheric measurements to assess the role of emission sources in controlling atmospheric nitrate (NO₃⁻). The simulated δ¹⁵N(NO_x) values were found to generally underestimate the observed δ¹⁵N(NO₃⁻) values. This discrepancy highlights the need for future ¹⁵N-enabled air quality modeling to better represent both source contributions and atmospheric processing, thereby improving source apportionment, emission inventory evaluation, and our understanding of reactive nitrogen cycling. Full article

Drought severely limits maize yields. Water–saving irrigation methods like partial root–zone drying (PRD) can improve water use efficiency (WUE) but often result in variable yield responses among genotypes. We hypothesized that differences in root hydrotropism might contribute to some of this variability. Seven maize varieties were evaluated for hydrotropic response in a controlled moisture–gradient assay and then grown for five weeks under fixed PRD versus full irrigation in a greenhouse. The different maize varieties exhibited distinct hydrotropic behaviors: roots of V6 and V7 bent toward water much faster and more strongly, while V2 responded slowly with minimal curvature. Under PRD, genotypes also differed in root distribution and shoot performance. However, hydrotropism alone did not guarantee good shoot maintenance. One strongly hydrotropic genotype (V7) still suffered a large biomass reduction under PRD. Overall, genotypes that maintained better shoot water status, along with larger stem diameter and higher shoot water content, achieved the highest WUE under PRD. These results indicate that root hydrotropism varies widely in maize varieties. This variation was associated with shoot traits and WUE under PRD, suggesting that the benefit of hydrotropism for drought adaptation may depend on complementary shoot characteristics. Breeding for drought–resilient maize may therefore require combining strong root hydrotropism with the ability to maintain shoot function under water deficit. Full article

Quantifying anthropogenic CO₂ increments is vital for assessing emission reductions. Using a seamless XCO₂ dataset over China reconstructed from OCO-2/3 satellite retrievals and machine learning, combined with EOF decomposition and LISA analysis, this study investigates XCO₂ anomalies and local anthropogenic increments (dXCO₂) at national and urban agglomeration scales. Nationally, XCO₂ anomalies exhibit a “southeast positive, northwest negative” spatial pattern aligning with human activities and a “winter high, summer low” seasonal cycle. EOF analysis reveals four dominant modes: anthropogenic–natural trade-offs, East Asian summer monsoon modulation, local emissions, and baseline context. At the regional scale, multi-year mean dXCO₂ (2015–2019) in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are 3.46 ± 0.45 ppm, 1.30 ± 0.36 ppm, and 0.08 ± 0.14 ppm, respectively, showing higher values in northern heavy industrial zones. During the 2020–2022 pandemic, dXCO₂ decreased in BTH (2.28 ± 0.73 ppm) and YRD (1.16 ± 0.43 ppm) but increased in PRD (0.28 ± 0.27 ppm). Compared to pre-pandemic levels, lockdowns saw dXCO₂ decrease slightly in YRD while increasing in BTH and PRD, reflecting differential responses of regional industrial structures. This study demonstrates the potential of seamless XCO₂ data for monitoring anthropogenic enhancement signals, and the proposed LISA-based method offers new support for regionally differentiated emission reduction assessments. Full article

To address the issue of motion artifact interference faced by wearable ECG monitoring devices in dynamic environments, this paper proposes an adaptive motion artifact removal framework based on improved Variational Mode Decomposition (VMD). By designing a parameter self-adjustment mechanism and a multi-feature fusion mode selection strategy, the algorithm’s adaptability to non-stationary ECG signals and noise separation accuracy are enhanced. Experiments on the MIT-BIH Arrhythmia Database demonstrate that the improved VMD algorithm outperforms traditional wavelet transform, Recursive Least Squares (RLS), and conventional VMD methods in multiple performance metrics. Specifically, the signal-to-noise ratio (SNR) is improved by 5.17 dB, the Percentage Root Mean Squared Difference (PRD) is reduced to 49.13%, the correlation coefficient is increased to 0.88, and high real-time processing capability (Real-Time Processing Ratio, RTR = 22.5) is maintained, meeting the low-latency requirements of wearable devices. Moreover, case studies on pathological recordings (e.g., Wolff–Parkinson–White syndrome and third-degree atrioventricular block) reveal that the improved VMD better preserves clinically significant features such as delta waves and dissociated P waves. Furthermore, a downstream arrhythmia classification task using a CWT-CNN classifier achieves 91.67% accuracy on denoised heartbeats, which is 2.67 percentage points higher than that on raw noisy signals (89.00%), confirming the practical benefit of the proposed preprocessing for AI-based diagnosis. This study provides an effective processing solution for improving the signal quality of wearable ECG monitoring. Full article

The flexible job shop scheduling problem (FJSP), which simultaneously involves machine assignment and operation sequencing under multiple constraints, is a typical NP-hard combinatorial optimization problem, and efficient scheduling is of great importance for improving production efficiency and manufacturing flexibility. To address this problem, the success-history beaver behavior optimizer (SHBBO) is introduced to solve FJSP with the objective of minimizing the makespan. First, considering the discrete characteristics of FJSP, an effective encoding and decoding scheme is designed to represent operation sequences and machine assignments. Then, the adaptive success-history mechanism of SHBBO is employed to dynamically adjust the search parameters during the optimization process, enabling a better balance between global exploration and local exploitation. Meanwhile, the behavioral update strategy of SHBBO is adapted to the scheduling environment so that candidate solutions can be effectively evolved in the discrete solution space. In addition, a population updating strategy and elite-guided search mechanism are incorporated to enhance solution quality and convergence performance. Finally, extensive experiments are conducted on benchmark FJSP instances to verify the effectiveness of the proposed method. Experimental results show that SHBBO achieves the best average results on 11 out of 12 CEC2022 benchmark functions, with particularly notable improvements over the original beaver behavior optimizer (BBO) on functions such as F6 (56.69%), F5 (12.20%), and F10 (9.18%). On the BRdata benchmark instances, SHBBO obtains the best or tied-best makespan on all 10 instances, with an average percentage relative deviation (PRD) of 0, and reduces the makespan by 7.69% on MK10 and 6.25% on MK06 compared with BBO. Full article

Urban agriculture plays a critical yet increasingly complex role in sustainable urban development, especially in high-density regions undergoing rapid transformation. Accurate mapping of its spatial distribution and functional composition remains a methodological challenge due to its fragmented landscape, small plot sizes, and multifunctional nature. This study addresses this gap by developing and applying a novel hierarchical classification framework that integrates agricultural land cover types with key socio-economic functions to map urban agriculture in the Pearl River Delta (PRD), China. This framework is structured around agricultural land categories (i.e., cropland, garden, forest, grass, and water body) and further delineated by two primary production functions, planting and breeding, with a third functional dimension, leisure activities, proposed as a conceptual extension for future research. Using unmanned aerial vehicle (UAV) imagery and high-resolution satellite data, we constructed a spatial sample database for urban agriculture. The random forest algorithm was applied to classify urban agriculture with Gaofen-2 imagery, generating detailed spatial distribution maps across the study area, with consistently reliable overall accuracy (79.07–81.82%), though this may be slightly optimistic due to potential spatial autocorrelation between training and testing samples. While the framework performed exceptionally well for spectrally and spatially distinct classes such as water bodies and perennial plantations, challenges remained in discriminating among annual field crops due to spectral similarity. These findings underscore the potential of integrating multi-temporal remote sensing data to capture phenological variations for improved classification. This study provides a replicable, functionally informed mapping approach that not only advances the methodological toolkit for urban agriculture characterization but also offers a valuable evidence base for land use planning, agricultural policy, and sustainable urban development in rapidly urbanizing regions. Full article

Power battery enterprises are a key link in the new energy vehicle (NEV) industry chain. However, studies analyzing the investment layout of power battery enterprises from a micro perspective are relatively scarce. This study takes Contemporary Amperex Technology Co. Limited (CATL) as a case and employs various spatial analysis methods and an optimal parameter-based geographical detector (OPGD) to analyze the spatiotemporal evolution and driving mechanisms of its investment layout from 2020 to 2024. The results indicate that CATL’s investment center has shifted from Jiangxi to Hubei, and the spatial expansion axis has changed from a northwest–southeast to a southwest–northeast direction. The investment layout has evolved from a “one core with two secondary cores” structure to a “provincial dual core, multi-core outside the province” structure and, ultimately, to a nationwide networked pattern. By 2024, CATL’s investment network covered the southeastern coast, the Yangtze River Delta (YRD), the Pearl River Delta (PRD), central China, and southwestern regions. County-level spatial autocorrelation analysis shows that the investment agglomeration effect has continuously strengthened (with the global Moran’s I increasing from 0.006 to 0.025). High–high agglomeration areas gradually expanded from the southeastern coast to Xiamen and several provinces in central and western China, while high–low agglomeration areas, as early signals of investment diffusion, initially expanded and then contracted. The driving mechanism analysis reveals that fiscal support (q = 0.668), industrial structure upgrading (q = 0.585), tax burden (q = 0.543), and economic development (q = 0.536) are the primary factors driving investment layout, with significant synergistic effects between these factors. The synergy between industrial structure upgrading and clean energy supply stands out as particularly prominent. These findings contribute to optimizing the spatial layout of the NEV industry and promoting regional economic development. Full article

Compound Drought and Heatwave (CDH) events increasingly threaten terrestrial carbon uptake, yet the spatiotemporal heterogeneity of Gross Primary Productivity (GPP) responses in urban agglomerations remains unclear. This study analyzed CDH impacts in China’s three major urban agglomerations, namely the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) regions, using ERA5 and satellite GPP data (GOSIF and FluxSat) for representative CDH years (2007 for BTH; 2022 for YRD and PRD). CDH conditions exhibited a coherent hot–dry coupling, with temperature anomalies of 0.46–1.26 K and soil moisture deficits of −0.042 to −0.169 m³ m⁻³, accompanied by enhanced atmospheric dryness. Pronounced spatial heterogeneity in GPP responses aligned with regional climatic regimes and ecosystem types. The water-limited BTH region exhibited significant GPP deficits, with anomalies of −1.13 Standard Deviations (STD) and −0.96 STD for GPP_FluxSat and GPP_GOSIF, respectively. Conversely, the energy-limited regions showed positive anomalies: the YRD recorded +0.32 and +1.79 STD, while the PRD reached +1.86 and +1.06 STD for GPP_FluxSat and GPP_GOSIF, respectively. Mechanistically, the north–south contrast suggests a transition from water-limited vulnerability to energy-limited resilience, with vegetation traits and management (e.g., potential irrigation buffering in croplands and deeper water access in forests) modulating sensitivity to atmospheric dryness. These findings provide quantitative benchmarks for improving regional carbon-cycle assessments and adaptation planning under increasing compound extremes. Full article

Aerosol optical depth (AOD) is a key indicator for quantifying aerosol radiative effects and evaluating air quality. However, atmospheric chemical transport models often exhibit systematic AOD biases, and model capability for column-integrated optical properties is not always consistent with that for near-surface particulate matter concentrations. Here, we evaluate AOD simulated by the Community Multiscale Air Quality (CMAQ) model over five major urban clusters in China, including the Beijing-Tianjin-Hebei (BTH) region, Fenwei Plain (FWP), Sichuan Basin (SCB), Yangtze River Delta (YRD), and Pearl River Delta (PRD), using satellite retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), ground-based retrievals from the Aerosol Robotic Network (AERONET), and vertical extinction profiles from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). CMAQ reproduces the major spatial patterns and exhibits relatively small biases in near-surface PM_2.5. However, it persistently underestimates AOD relative to MODIS, with the largest negative bias occurring in April (i.e., a typical spring month). This contrast indicates a pronounced inconsistency between column-integrated aerosol amount and surface mass density. Relative to AERONET, CMAQ shows a negative bias (NMB = −38%), whereas MODIS shows a positive bias (NMB = 56%), suggesting that both model and retrieval uncertainties contribute to the CMAQ–MODIS disagreements. CALIPSO-constrained vertical analysis further suggests that insufficient extinction above the planetary boundary layer (PBL) is an important contributor to the negative AOD bias, although the relative roles of boundary-layer and upper-layer contributions vary across regions, underscoring the importance of accurately representing aerosol vertical transport and optical processes. These results indicate that evaluations based solely on surface observations may fail to fully capture the overall structure of AOD errors, particularly given the clear differences between near-surface mass concentrations and column optical properties, which vary across regions. This also highlights the importance of improving the representation of aerosol vertical transport and optical processes in chemical transport models. Full article

Naphthalene and methylnaphthalene (Nap and MN) are the most abundant polycyclic aromatic hydrocarbons (PAHs) and are important precursors of secondary organic aerosol (SOA) in the atmosphere. 1.2-Phthalic acid (1,2-PhA) and 4-methylphthalic acid (4-MPhA) are usually treated as tracers of SOA from Nap and MN. However, the two tracers also have primary sources, and directly using the tracers to estimate SOA would lead to an overestimation. In this study, we conducted a one-year synchronous observation of the two-ring PAH SOA (SOA_2-rings) tracers at nine sites in the Pearl River Delta (PRD) region. We measured and filtered the suitable emission characteristics of SOA_2-rings tracers for biomass burning, coal combustion, industrial processes and vehicle exhaust sources. Then, we developed a method to distinguish 1,2-PhA and 4-MPhA from primary emissions and secondary formation. The average proportions of 1,2-PhA_pri and 4-MPhA_pri in 1,2-PhA and 4-MPhA were 26.7% and 29.2%, respectively. The direct application of measured 1,2-PhA for estimating SOA_2-rings would lead to an overestimation exceeding 30% in the PRD. Furthermore, we estimated SOA_2-rings using the separated 1,2-PhA_sec and 4-MPhA_sec by the tracer-based method. The average contribution of MN to SOA was around three times that of Nap. In addition, when combined with monocyclic aromatic SOA (SOA_1-ring) and biogenic SOA, the contributions of SOA_1-ring (21%) and SOA_2-rings (25%) to total SOA were comparable. SOA_2-rings was even the largest contributor to total SOA (~44%) in winter. This study revealed that whether to separate the SOA_2-rings tracers for primary emissions and secondary formation is essential in SOA estimation and highlighted that two-ring PAHs make a significant contribution to SOA in the PRD. Full article

Neurodegenerative diseases feature diverse pathological protein aggregates, including Lewy bodies in Alzheimer’s disease (AD) and skein-like filaments in amyotrophic lateral sclerosis (ALS). The physical mechanisms underlying this morphological diversity remain unclear. Here, we demonstrate that aggregation of the prion-like domain of hnRNPA1 (A1PrD), implicated in AD and ALS, is driven by solution composition and phase transition dynamics. Utilizing 3D timelapse and fluorescence lifetime imaging microscopy, we show that solution conditions modulate phase separation, gelation, and fibrillation, resulting in distinct structures such as fibril, gel, and starburst morphologies. Homotypic and heterotypic interactions between A1PrD and RNA were observed to shift the balance between pathological and physiological condensates. Importantly, amyloid-rich starbursts displayed prion-like infection capabilities toward amyloid-poor condensates. Our findings highlight how the interplay between solution composition and kinetic balances of liquid-liquid phase separation, gelation, and fibrillation shapes the diverse pathological aggregate morphologies characteristic of neurodegenerative diseases. Full article

The deep integration of green finance and the digital economy serves as a critical lever for achieving the “dual carbon” goals and the “Digital China” strategy. This study constructs a “Technology–Capital–Environment” (TCE) analytical framework and integrates a coupling coordination degree model with a dynamic Qualitative Comparative Analysis (QCA) approach. Based on panel data of the Pearl River Delta urban agglomeration from 2014 to 2023, we investigate the synergistic development level, multiple pathways, and dynamic evolution between the two systems. Key findings include: (1) The coupling coordination degree of the two systems has steadily increased, yet significant spatial heterogeneity persists. The average annual growth rate of potential catch-up cities (3.37%) surpasses that of core leading cities (1.77%). (2) Four equifinal driving pathways are identified, which can be summarized into three patterns: technology-dominated institutional synergy, human capital–policy dual-core guidance, and technology–infrastructure synergistic driven. (3) Dynamic analysis reveals that pathways embedded with digital human capital and new infrastructure exhibit stronger resilience to shocks, whereas pathways reliant on institutional synergy demonstrate higher vulnerability. (4) Guangzhou and Shenzhen have already exhibited “ecosystem-level” synergistic characteristics, rendering existing configurational models limited in explanatory power. This study provides a theoretical foundation for promoting regionally differentiated deep integration of green finance and the digital economy and for building a resilience-oriented synergistic development system. Full article

China’s stringent clean air policies have substantially reduced nitrogen oxides (NO_x) emissions, leading to a general decline in nitrogen dioxide (NO₂). However, surface ozone (O₃) pollution remains severe, creating a complex challenge due to the non-linear relationship between O₃ and its precursors. To disentangle the drivers behind these trends, this study quantifies the impacts of interannual variations in top-down constrained NO_x emissions on surface NO₂ and O₃ concentrations from 2014 to 2021 across mainland China and five national urban agglomerations. We employed the WRF-CMAQ model with a fixed-emission simulation approach, using an observationally optimized NO_x emission inventory derived from the assimilation of surface NO₂ measurements. Results reveal that NO₂ reductions were predominantly emission-driven (>80% post-2017), with declines most pronounced in winter. A strong linear consistency was found between interannual changes in top-down NO_x emissions and attributed NO₂ concentration variations, validating the methodology. In contrast, O₃ responses to NO_x reductions were spatially and seasonally heterogeneous, reflecting a non-linear photochemical regime. In major urban agglomerations (e.g., Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD)), NO_x reductions post-2018 showed limited effectiveness in mitigating summertime O₃ and even increased O₃ in spring and autumn, indicating a prevalent VOC-sensitive regime where NO_x reduction can disinhibit O₃ formation. Conversely, certain provinces (e.g., Anhui, Shanxi, Jilin) exhibited O₃ decreases, suggesting a NO_x-sensitive regime. The area benefiting from NO_x reductions expanded steadily in summer after 2017 but not in other seasons. This study confirms the efficacy of NO_x-focused policies for reducing primary NO₂ pollution but highlights that mitigating persistent O₃ requires a strategic shift to synergistic, region-specific control of volatile organic compounds alongside NO_x, informed by local chemical sensitivity. Full article