Search Results (5,077)

In the context of green development, promoting the development of data elements is crucial for advancing the green and low-carbon transition and achieving China’s “dual-carbon” targets. This study quantitatively evaluates China’s data element policies to identify their strengths and weaknesses and to assess their alignment with green development objectives. In this study, we examine 15 representative data element policy texts, evaluating their quality by integrating the Latent Dirichlet Allocation (LDA) topic model with the PMC-Index model. The LDA analysis identifies five core themes within the policy texts: the data element industry, data resource management, data element trading systems, service platform construction, and e-governments. The evaluation results show an average PMC-Index score of 6.03 for the 15 policies, with 9 rated as “Good” and 6 as “Acceptable”. This indicates that while the overall design of the current policy system is acceptable, there remains substantial room for improvement. Based on the average scores for the primary indicators, the policies perform relatively poorly in terms of green development assessment, policy timeliness, policy nature, and policy guarantee. Drawing from these findings, we propose recommendations to enhance China’s data element policies, offering insights for policymakers. Full article

The structural analysis of shelterbelts forms the foundation of their planning and management, yet the scientific and effective quantification of shelterbelt structures requires further investigation. This study developed an innovative heterogeneous analytical framework, integrating three key methodologies: the LeWoS algorithm for wood–leaf separation, TreeQSM for structural reconstruction, and 3D alpha-shape spatial quantification, using terrestrial laser scanning (TLS) technology. This framework was applied to three typical farmland shelterbelts in the Ulan Buh Desert oasis, enabling the first precise quantitative characterization of structural components during the leaf-on stage. The results showed the following to be true: (1) The combined three-algorithm method achieved ≥90.774% relative accuracy in extracting structural parameters for all measured traits except leaf surface area. (2) Branch length, diameter, surface area, and volume decreased progressively from first- to fourth-order branches, while branch angles increased with ascending branch order. (3) The trunk, branch, and leaf components exhibited distinct vertical stratification. Trunk volume and surface area decreased linearly with height, while branch and leaf volumes and surface areas followed an inverted U-shaped distribution. (4) Horizontally, both surface area density (Scd) and volume density (Vcd) in each cube unit exhibited pronounced edge effects. Specifically, the Scd and Vcd were greatest between 0.33 and 0.60 times the shelterbelt’s height (H, i.e., mid-canopy). In contrast, the optical porosity (Op) was at a minimum of 0.43 H to 0.67 H, while the volumetric porosity (Vp) was at a minimum at 0.25 H to 0.50 H. (5) The proposed volumetric stratified porosity (Vsp) metric provides a scientific basis for regional farmland shelterbelt management strategies. This three-dimensional structural analytical framework enables precision silviculture, with particular relevance to strengthening ecological barrier efficacy in arid regions. Full article

Background/Objectives: Pelvic organ prolapse (POP) is a common condition that significantly impacts quality of life. Research has focused largely on older women, while experiences of younger women remain relatively underexplored despite challenges unique to this population. Informed by the biopsychosocial model of illness, this study aims to assess the symptom burden, treatment goals, and information needs of younger women complaining of prolapse by analyzing questionnaire responses from an existing electronic Personal Assessment Questionnaire—Pelvic Floor (ePAQ-PF) dataset. Methods: Mixed-methods content analysis was conducted using free-text data from an anonymized multi-site ePAQ-PF dataset of 5717 responses collected across eight UK NHS trusts (2018–2022). A quantitative, deductive approach was first used to identify younger women (≤50 years old) with self-reported prolapse. ePAQ-PF scores for younger women with prolapse were compared with those aged >50 years, using Mann–Whitney tests. Free-text response data were analyzed inductively to qualitatively explore younger women’s symptom burden, treatment goals, and information needs. Results: Of the 1473 women with prolapse identified, 399 were aged ≤50 years. ePAQ-PF scores of the younger cohort demonstrated significantly greater symptom severity and bother than those aged >50, particularly in bowel, prolapse, vaginal, body image, and sexual health domains (p < adjusted threshold). Qualitative analysis undertaken to understand women’s concerns and priorities produced five health-related themes (physical health; functionality; psychosocial and emotional wellbeing; reproductive and sexual health; and healthcare journeys) and a sixth intersecting theme representing information needs. Conclusions: The findings highlight the substantial symptom burden of younger women with prolapse, as well as treatment goals and information needs specific to this population. The development of age-specific resources is identified as a requirement to support this group. Full article

Hospital layouts play a critical role in supporting efficient care processes, which are continually evolving. While care processes adapt over time, the spatial needs of care professionals are expected to remain relatively stable. This study proposes an evaluation framework combining cognitive mapping and space syntax analysis (SSA) to assess how hospital layouts align with these spatial needs. The framework is applied to a real-world emergency department (ED) with two distinct layout configurations. Cognitive mapping captures spatial needs from the perspective of care professionals, while SSA evaluates how the layout supports or constrains these needs. Drawing on the open building approach, we interpret layout adaptability through a layered system of primary (rigid), secondary (adaptable), and tertiary (care process) levels. Our results show that the choices in primary and secondary system designs can limit the functionality of the tertiary system. This approach supports informed decision-making by addressing multiple spatial needs simultaneously, offering insights into the coherence between spatial configuration and care delivery, and enabling quantitative comparison across different layout designs. Full article

Mesoscale coastal eddies are key components of ocean circulation, mediating the transport of heat, nutrients, and marine debris. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution sea surface height data, offering a novel opportunity to improve the observation and characterization of these features, especially in coastal regions where conventional altimetry is limited. In this study, we investigate a mesoscale anticyclonic coastal eddy observed southwest of Mallorca Island, in the Balearic Sea, to assess the impact of SWOT-enhanced altimetry in resolving its structure and dynamics. Initial eddy identification is performed using satellite ocean color imagery, followed by a qualitative and quantitative comparison of multiple altimetric datasets, ranging from conventional nadir altimetry to wide-swath products derived from SWOT. We analyze multiple altimetric variables—Sea Level Anomaly, Absolute Dynamic Topography, Velocity Magnitude, Eddy Kinetic Energy, and Relative Vorticity—highlighting substantial differences in spatial detail and intensity. Our results show that SWOT-enhanced observations significantly improve the spatial characterization and dynamical depiction of the eddy. Furthermore, Lagrangian transport simulations reveal how altimetric resolution influences modeled transport pathways and retention patterns. These findings underline the critical role of SWOT in advancing the monitoring of coastal mesoscale processes and improving our ability to model oceanic transport mechanisms. Full article

Water quality should be tested to ensure it is acceptable for the healthy growth of plants and animals, and water hardness is one of the important testing indexes. Herein, a novel approach was proposed to achieve high accuracy and rapid quantitative analyses of water hardness by combining one-point calibration laser-induced breakdown spectroscopy (OPC–LIBS) and aerosolization. First, the water samples are aerosolized via the aerosol generation device and the LIBS spectra of aerosols are obtained. Then, a modified OPC–LIBS model is used to determine the elemental contents of the aerosols via LIBS spectra, in which the plasma temperature is calculated using the Multi-Element Saha–Boltzmann (ME–SB) plot. One suitable standard liquid sample (the concentrations of Ca, Mg, and Sr were 50 mg/L, 50 mg/L, and 500 mg/L, respectively) was selected to evaluate the quantitative performance of the modified OPC–LIBS. Then, the Ca and Mg concentrations in the three real water samples (from the Yangtze River, reservoir, and underground) were detected and quantified by the proposed method, and the quantitative results of three LIBS calibration methods were compared with that of inductively coupled plasma optical emission spectroscopy (ICP–OES). The average relative error of Ca and Mg found in the OPC–LIBS results was lower by 22.23% than the internal standard method and 14.50% lower than the external standard method. The method combining modified OPC–LIBS and aerosolization can achieve high-precision rapid quantification of water hardness detection, which provides a new path for rapid detection of water hardness and is expected to make online detection a reality in the water quality testing field. Full article

Background and Objectives: Postoperative pancreatic fistula and post-hepatectomy liver failure remain significant complications after HPB surgery; however, superficial surgical site infection (SSI) is the most frequent wound-related complication. Closed-incision negative-pressure wound therapy (ciNPWT) has been proposed to reduce superficial contamination, yet no liver-focused quantitative synthesis exists. We aimed to evaluate the effectiveness and safety of prophylactic ciNPWT after hepatopancreatobiliary (HPB) surgery. Methods: MEDLINE, Embase, and PubMed were searched from inception to 30 April 2025. Randomized and comparative observational studies that compared ciNPWT with conventional dressings after elective liver transplantation, hepatectomy, pancreatoduodenectomy, and liver resections were eligible. Two reviewers independently screened, extracted data, and assessed risk of bias (RoB-2/ROBINS-I). A random-effects Mantel–Haenszel model generated pooled risk ratios (RRs) for superficial SSI; secondary outcomes were reported descriptively. Results: Twelve studies (seven RCTs, five cohorts) encompassing 15,212 patients (3561 ciNPWT; 11,651 control) met the inclusion criteria. Device application lasted three to seven days in all trials. The pooled analysis demonstrated a 29% relative reduction in superficial SSI with ciNPWT (RR 0.71, 95% CI 0.63–0.79; p < 0.001) with negligible heterogeneity (I² 0%). Absolute risk reduction ranged from 0% to 13%, correlating positively with the baseline control-group SSI rate. Deep/organ-space SSI (RR 0.93, 95% CI 0.79–1.09) and 90-day mortality (RR 0.94, 95% CI 0.69–1.28) were unaffected. Seven studies documented a 1- to 3-day shorter median length of stay; only two reached statistical significance. Device-related adverse events were rare (one seroma, no skin necrosis). Conclusions: Prophylactic ciNPWT safely reduces superficial SSI after high-risk HPB surgery, with the greatest absolute benefit when baseline SSI risk exceeds ≈10%. Its influence on deep infection and mortality is negligible. Full article

This review investigates recent developments in cardiac mechano-electrical-fluid interaction (MEFI) modeling, with a focus on multiphysics simulation platforms and digital twin frameworks developed between 2015 and 2025. The purpose of the study is to assess how computational modeling methods—particularly finite element and immersed boundary techniques, monolithic and partitioned coupling schemes, and artificial intelligence (AI)-enhanced surrogate modeling—capture the integrated dynamics of cardiac electrophysiology, tissue mechanics, and hemodynamics. The goal is to evaluate the translational potential of MEFI models in clinical applications such as cardiac resynchronization therapy (CRT), arrhythmia classification, atrial fibrillation ablation, and surgical planning. Quantitative results from the literature demonstrate <5% error in pressure–volume loop predictions, >0.90 F1 scores in machine-learning-based arrhythmia detection, and <10% deviation in myocardial strain relative to MRI-based ground truth. These findings highlight both the promise and limitations of current MEFI approaches. While recent advances improve physiological fidelity and predictive accuracy, key challenges remain in achieving multiscale integration, model validation across diverse populations, and real-time clinical applicability. The review concludes by identifying future milestones for clinical translation, including regulatory model certification, standardization of validation protocols, and integration of patient-specific digital twins into electronic health record (EHR) systems. Full article

A salient feature of modern material surfaces used in cutting-edge technologies is their structural and spatial complexity, which endows them with novel properties and multifunctionality. The quantitative characterization of material complexity is a challenge that must be addressed to optimize their production and performance. While numerous metrics exist to quantify the complexity of spatial structures in various scientific domains, methods specifically tailored for characterizing the spatial complexity of material surface morphologies at the micro- and nanoscale are relatively scarce. In this paper, we utilize the concept of multiscale entropy to quantify the complexity of surface morphologies of rough surfaces across different scales and investigate the effects of amplitude fluctuations (i.e., surface height distribution) in both stepwise and smooth self-affine rough surfaces. The crucial role of the binning scheme in regulating amplitude effects on entropy and complexity measurements is highlighted and explained. Furthermore, by selecting an appropriate binning strategy, we analyze the impact of 2D imaging on the complexity of a rough surface and demonstrate that imaging can artificially introduce peaks in the relationship between complexity and surface amplitude. The results demonstrate that entropy-based spatial complexity effectively captures the scale-dependent heterogeneity of stepwise rough surfaces, providing valuable insights into their structural properties. Full article

As a plant-specific peroxidase family, class III peroxidase (PRX) plays an important role in plant growth, development, and stress response. In this study, a preliminary functional analysis of CqPRX9L1 was conducted. Bioinformatics analysis revealed that CqPRX9L1 encodes a 349-amino acid protein belonging to the plant-peroxidase-like superfamily, featuring a transmembrane domain and cytoplasmic localization. The promoter region of CqPRX9L1 harbors various cis-acting elements associated with stress responses, hormone signaling, light regulation, and meristem-specific expression. The tissue-specific expression pattern of the CqPRX9L1 gene and its characteristics in response to different stresses were explored using subcellular localization, quantitative real-time PCR (qRT-PCR), and heterologous transformation into Arabidopsis thaliana. The results showed that CqPRX9L1, with a transmembrane structure, was localized in the cytoplasm, which encodes 349 amino acids and belongs to the plant-peroxisome-like superfamily. The promoter region contains stress-response elements, hormone-response elements, light-response elements, and meristem expression-related elements. The expression of CqPRX9L1 was relatively higher in ears and roots at the panicle stage than in stems and leaves. CqPRX9L1 showed a dynamic expression pattern of first decreasing and then increasing under abiotic stresses such as 15% PEG 6000, low temperature, and salt damage, with differences in response time and degree. CqPRX9L1 plays an important role in response to abiotic stress by affecting the activity of antioxidant enzymes such as superoxide dismutase (SOD) and peroxidase (POD), as well as the synthesis and decomposition of proline (Pro). CqPRX9L1 also affects plant bolting and flowering by regulating key flowering genes (such as FT and AP1) and gibberellin (GA)-related pathways. The results establish a foundation for revealing the functions and molecular mechanisms of the CqPRX9L1 gene. Full article

The weak grid strength in regions with large-scale renewable energy integration has emerged as a universal challenge, limiting the further expansion of renewable energy development. Currently, the short-circuit ratio (SCR) is widely used to quantify the relative strength between AC systems and renewable energy. To address this issue, this study first analyzes and compares how different reactive power compensation methods enhance the SCR. It then proposes calculation frameworks for both the SCR and critical short-circuit ratio (CSCR) in renewable energy grid-connected systems integrated with reactive power compensation. Furthermore, based on these formulations, a quantitative evaluation methodology for voltage support strength is developed to systematically assess the improvement effects of various compensation approaches on grid strength. Finally, case studies verify that reactive power compensation provided by synchronous condensers effectively strengthens grid strength and facilitates the safe expansion of the renewable energy integration scale. Full article

Agrivoltaic (Agri-PV) systems face the critical challenge of balancing photovoltaic energy generation with crop productivity, yet systematic approaches to quantifying the trade-offs between these objectives remain scarce. In this study, we identify nine essential design indicators: panel tilt angle, elevation, photovoltaic coverage ratio, shading factor, land equivalent ratio, photosynthetically active radiation (PAR) utilization, crop yield stability index, water use efficiency, and return on investment. We introduce a novel dual matrix Analytic Hierarchy Process (AHP) to evaluate their relative significance. An international panel of eighteen Agri-PV experts, encompassing academia, industry, and policy, provided pairwise comparisons of these indicators under two objectives: maximizing annual energy yield and sustaining crop output. The high consistency observed in expert responses allowed for the derivation of normalized weight vectors, which form the basis of two Weighted Influence Matrices. Analysis of Total Weighted Influence scores from these matrices reveal distinct priority sets: panel tilt, coverage ratio, and elevation are most influential for energy optimization, while PAR utilization, yield stability, and elevation are prioritized for crop productivity. This methodology translates qualitative expert knowledge into quantitative, actionable guidance, clearly delineating both synergies, such as the mutual benefit of increased elevation for energy and crop outcomes, and trade-offs, exemplified by the negative impact of high photovoltaic coverage on crop yield despite gains in energy output. By offering a transparent, expert-driven decision-support tool, this framework enables practitioners to customize Agri-PV system configurations according to local climatic, agronomic, and economic contexts. Ultimately, this approach advances the optimization of the food energy nexus and supports integrated sustainability outcomes in Agri-PV deployment. Full article

Calorific value and moisture content are the key indices to evaluate Caragana pellet fuel’s quality and combustion characteristics. Calorific value is the key index to measure the energy released by energy plants during combustion, which determines energy utilization efficiency. But at present, the determination of solid fuel is still carried out in the laboratory by oxygen bomb calorimetry. This has seriously hindered the ability of large-scale, rapid detection of fuel particles in industrial production lines. In response to this technical challenge, this study proposes using hyperspectral imaging technology combined with various chemometric methods to establish quantitative models for determining moisture content and calorific value in Caragana korshinskii fuel. A hyperspectral imaging system was used to capture the spectral data in the 935–1720 nm range of 152 samples from multiple regions in Inner Mongolia Autonomous Region. For water content and calorific value, three quantitative detection models, partial least squares regression (PLSR), random forest regression (RFR), and extreme learning machine (ELM), respectively, were established, and Monte Carlo cross-validation (MCCV) was chosen to remove outliers from the raw spectral data to improve the model accuracy. Four preprocessing methods were used to preprocess the spectral data, with standard normal variate (SNV) preprocessing performing best on the quantitative moisture content detection model and Savitzky–Golay (SG) preprocessing performing best on the calorific value detection method. Meanwhile, to improve the prediction accuracy of the model to reduce the redundant wavelength data, we chose four feature extraction methods, competitive adaptive reweighted sampling (CARS), successive pojections algorithm (SPA), genetic algorithm (GA), iteratively retains informative variables (IRIV), and combined the three models to build a quantitative detection model for the characteristic wavelengths of moisture content and calorific value of Caragana korshinskii fuel. Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient $\left(R_P^2\right)$, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. At the same time, the moisture content distribution map of Caragana fuel particles is established by using this model. The SG-CARS-RFR modeling combination was the best for calorific value prediction, with its $R_P^2$, RMSEP, and RPD of 0.8037, 0.3219, and 2.2864, respectively. This study provides an innovative technical solution for Caragana fuel particles’ value and quality assessment. Full article

7,12-Dimethylbenzo[a]anthracene (DMBA-7,12), a highly toxic and environmentally persistent polycyclic aromatic hydrocarbon (PAH), poses significant threats to marine biodiversity and human health due to its bioaccumulation through the food chain. Conventional chromatographic methods, while achieving comparable detection limits, are hindered by the need for expensive instrumentation and prolonged analysis times, rendering them unsuitable for rapid on-site monitoring of DMBA-7,12 in marine environments. Therefore, the development of novel, efficient detection techniques is imperative. In this study, we have successfully developed an electrochemical immunosensor based on a polydopamine (PDA)–chitosan (CTs) composite interface to overcome existing technical limitations. PDA provides a robust scaffold for antibody immobilization due to its strong adhesive properties, while CTs enhances signal amplification and biocompatibility. The synergistic integration of these materials combines the high efficiency of electrochemical detection with the specificity of antigen–antibody recognition, enabling precise qualitative and quantitative analysis of the target analyte through monitoring changes in the electrochemical properties at the electrode surface. By systematically optimizing key experimental parameters, including buffer pH, probe concentration, and antibody loading, we have constructed the first electrochemical immunosensor for detecting DMBA-7,12 in seawater. The sensor achieved a detection limit as low as 0.42 ng/mL. In spiked seawater samples, the recovery rates ranged from 95.53% to 99.44%, with relative standard deviations (RSDs) ≤ 4.6%, demonstrating excellent accuracy and reliability. This innovative approach offers a cost-effective and efficient solution for the in situ rapid monitoring of trace carcinogens in marine environments, potentially advancing the field of marine pollutant detection technologies. Full article

Accurate and reliable detection of coal mine gases is the key to ensuring the safe service of coal mine production. Fourier Transform Infrared (FTIR) spectroscopy, due to its high sensitivity, non-destructive nature, and potential for online monitoring, has emerged as a key technique in gas detection. However, the complex underground environment often causes baseline drift in IR spectra. Furthermore, the variety of gas species and uneven distribution of concentrations make it difficult to achieve precise and reliable online analysis using existing quantitative methods. This paper aims to perform a quantitative analysis of coal mine gases by FTIR. It utilized the adaptive smoothness parameter penalized least squares method to correct the drifted spectra. Subsequently, based on the infrared spectral distribution characteristics of coal mine gases, they could be classified into gases with mutually distinct absorption peaks and gases with overlapping absorption peaks. For gases with distinct absorption peaks, three spectral lines, including the absorption peak and its adjacent troughs, were selected for quantitative analysis. Spline fitting, polynomial fitting, and other curve fitting methods are used to establish a functional relationship between characteristic parameters and gas concentration. For gases with overlapping absorption peaks, a wavelength selection method bassed on the impact values of variables and population analysis was applied to select variables from the spectral data. The selected variables were then used as input features for building a model with a backpropagation (BP) neural network. Finally, the proposed method was validated using standard gases. Experimental results show detection limits of 0.5 ppm for CH₄, 1 ppm for C₂H₆, 0.5 ppm for C₃H₈, 0.5 ppm for n-C₄H₁₀, 0.5 ppm for i-C₄H₁₀, 0.5 ppm for C₂H₄, 0.2 ppm for C₂H₂, 0.5 ppm for C₃H₆, 1 ppm for CO, 0.5 ppm for CO₂, and 0.1 ppm for SF₆, with quantification limits below 10 ppm for all gases. Experimental results show that the absolute error is less than 0.3% of the full scale (F.S.) and the relative error is within 10%. These results demonstrate that the proposed infrared spectral quantitative analysis method can effectively analyze mine gases and achieve good predictive performance. Full article