Search Results (198)

Landscapes in semiarid regions are highly sensitive to climate change and anthropogenic activities, and their evolution directly influences ecosystem services and regional ecological security. Although previous research has examined land use changes, systematic quantitative analyses of long-term evolutionary trends and driving mechanisms, particularly the comprehensive relationships between key hydrological elements and landscape pattern evolution in water-scarce, semiarid watersheds, remain limited. To address the research gap in long-term, multifactor, and hydro–landscape integrated analysis, China’s Tuwei River watershed was selected as the study area in this study, and methods such as landscape pattern indices and gray relational analysis were employed to quantitatively reveal the spatiotemporal evolution of watershed landscape fragmentation from 1980 to 2020 and identify its dominant driving forces. The results revealed that (1) over the 40-year period, the land use structure of the watershed underwent significant restructuring, with developed land expanding by 1282%, cropland and bare land areas decreasing by 14.2% and 32.01%, respectively, and grassland and forestland areas increasing by 24.5% and 14.9%, respectively; (2) land-scape fragmentation continued to intensify, with the landscape fragmentation composite index (FCI) increasing by 37.6%, patch density (PD) continuously increasing, edge density (ED) and landscape shape index (LSI) increasing significantly, and landscape connectivity weakening; (3) natural and socioeconomic factors jointly drove landscape evolution, with temperature and mean annual flow contributing the most among natural factors and the urbanization rate and secondary industry output value serving as the core drivers among socioeconomic factors; and (4) the trend of landscape fragmentation was synchronized with changes in annual rainfall and runoff and exhibited a significant negative correlation with the groundwater level. In summary, through long-term, multifactor comprehensive analysis, the evolution characteristics and driving mechanisms of landscape patterns in the Tuwei River watershed were systematically revealed in this study. These findings not only deepen the understanding of landscape fragmentation processes under the dual pressures of climate change and anthropogenic activities but also provide scientific evidence for the sustainable management of landscapes and associated ecosystems in semiarid watersheds. Full article

With the continuous advancement of shale gas field development, well productivity following initial hydraulic fracturing often declines due to mechanisms such as proppant embedment and fracture conductivity degradation. However, such wells may still retain significant development potential, making re-fracturing crucial for restoring production and highlighting the critical importance of accurate candidate selection for re-fracturing. To improve the precision of candidate well selection for re-fracturing in shale gas wells, this study focuses on a shale gas block in the Southern Chuan Basin. Through comparative analysis of existing selection methods, 14 key parameters were finalized. The threshold values for some of these key parameters were recalibrated based on the specific geological, engineering, and production characteristics of the target block in the Southern Chuan Basin. Furthermore, the AHP-GRA (Analytic Hierarchy Process-Gray Relational Analysis) weighting method was integrated to achieve a balance between empirical knowledge and quantitative objectivity. Ultimately, a more targeted, comprehensive, and combined subjective–objective methodology for selecting re-fracturing candidate wells was developed. A computational tool developed in Python 3.9 was utilized to evaluate 13 candidate wells in the block, successfully identifying three high-priority wells for re-fracturing implementation. The reliability of this selection result was validated by analyzing production data before and after re-fracturing, confirming that the production performance of the selected wells showed relatively significant improvement post re-fracturing, with a notable increase in recovery factor. This model provides critical decision-making support for the low-cost and large-scale development of shale gas. It holds significant theoretical and practical value for promoting the secondary development of mature shale gas wells and contributes positively to the efficient utilization of unconventional natural gas resources and energy security. Full article

Artificial underground compressed air energy storage (CAES) caverns have the advantages of large capacity and flexible location. However, the location selection of CAES in conditions of hard shallowly buried rock requires comprehensive consideration of multi-field coupling effects and engineering constraints, and the decision-making process involves multiple criteria and strong uncertainty. Aimed at addressing the problems of the evaluation index system not being detailed enough and the weight determination being biased to a single subjective or objective method in the existing research, this paper constructs a multi-criteria site selection evaluation method for an artificial underground CAES chamber in hard shallowly buried rock. Firstly, starting from the four criteria layers of ground environment, construction convenience, regional geological characteristics, and basic geological characteristics, combined with literature research and expert investigation, an evaluation index system containing 13 indicators was established. Secondly, the analytic hierarchy process (AHP) and entropy weight method (EWM) were introduced, the combination of subjective weight and objective weight realized through game theory, and the comprehensive weight of each index obtained. Then, the VIKOR method was used to rank the four candidate sites—A, B, C, and D—and the results were compared with those of the weighted TOPSIS method and the weighted gray relational analysis method. The engineering example shows that site B has advantages in group utility value, individual regret value, and compromise index. It is judged the optimal scheme by the three methods, and the ranking is stable under different decision-making mechanism coefficients, which verifies the robustness and applicability of the AHP–EWM–VIKOR model. The results show that the proposed method can distinguish different site selection schemes more clearly, effectively and comprehensively reflect suitability under complex geological and engineering conditions, and provide quantitative decision support for engineering site selection of artificial underground CAES caverns. Full article

Background/Objectives: Major Depressive Disorder (MDD) is a severe psychiatric disorder, and effective, efficient automated diagnostic approaches are urgently needed. Traditional methods for assessing MDD face three key challenges: reliance on predefined features, inadequate handling of multi-site data heterogeneity, and suboptimal feature fusion. To address these issues, this study proposes the Multimodal Multitask Dynamic Disentanglement (MMDD) Framework. Methods: The MMDD Framework has three core innovations. First, it adopts a dual-pathway feature extraction architecture combining a 3D ResNet for modeling gray matter volume (GMV) data and an LSTM–Transformer for processing time series data. Second, it includes a Bidirectional Cross-Attention Fusion (BCAF) mechanism for dynamic feature alignment and complementary integration. Third, it uses a Gradient Reversal Layer-based Multitask Learning (GRL-MTL) strategy for enhancing the model’s domain generalization capability. Results: MMDD achieved 77.76% classification accuracy on the REST-meta-MDD dataset. Ablation studies confirmed that both the BCAF mechanism and GRL-MTL strategy played critical roles: the former optimized multimodal fusion, while the latter effectively mitigated site-related heterogeneity. Through interpretability analysis, we identified distinct neurobiological patterns: time series were primarily localized to subcortical hubs and the cerebellum, whereas GMV mainly involved higher-order cognitive and emotion-regulation cortices. Notably, the middle cingulate gyrus showed consistent abnormalities across both imaging modalities. Conclusions: This study makes two major contributions. First, we develop a robust and generalizable computational framework for objective MDD diagnosis by effectively leveraging multimodal data. Second, we provide data-driven insights into MDD’s distinct neuropathological processes, thereby advancing our understanding of the disorder. Full article

The riveting process is conventionally performed at a constant feed rate, overlooking the distinct deformation mechanisms inherent in its successive stages. This study introduces a novel compound feed rate approach to enhance the riveting quality of 2A10 aluminum alloy countersunk head rivets. A three-dimensional finite element model, validated experimentally, was developed to simulate the riveting process, segmented into three stages: free upsetting, hole wall interference, and driven head formation. An orthogonal experimental design was employed to investigate the effects of varying feed rates (1, 5, 10 mm/s) within these stages on key quality metrics: interference distribution, uniformity, and driven head geometry. Results demonstrate that increasing the feed rate reduces average interference but increases the driven head diameter, revealing a stage-dependent influence. A multi-objective optimization framework, integrating gray relational analysis with the entropy weighting method, was applied to balance these competing objectives. The optimal compound feed rate scheme of 10-1-10 mm/s (for the three stages, respectively) was identified. This optimized scheme improved interference uniformity by 1%, increased the critical shank-end interference (Point H) by 10.9%, and enhanced driven head dimensions compared to conventional constant-rate riveting. Full article

Perforation completion is one of the primary methods for putting oil and gas wells into production, and the influencing factors and prediction of perforation skin factors have long been key research topics in the petroleum industry. This study systematically investigates the effects of multiple factors (including perforation depth, phase angle, shot density, hole diameter, damage zone depth, and damage severity) on the perforation skin factor in both low-permeability and medium-to-high-permeability reservoirs. The research first established a coupled flow model for conventional perforation completion using ANSYS Fluent 2024R1 numerical simulation software, which integrates perforation geometric parameters and formation damage-related parameters; then, conducting single-factor sensitivity analysis to obtain qualitative relationships between individual perforation parameters and well skin factor; it subsequently uses orthogonal experimental design to explore multi-factor combined effects and applied gray correlation theory to calculate factor-skin factor correlation coefficients; finally, it adopts the least squares method for linear and nonlinear fitting based on orthogonal experimental data (with linear fitting average error of 45.3% and nonlinear fitting error of 7.4%, thus selecting the nonlinear formula as the prediction model). The findings provide valuable insights for optimizing perforation parameters and predicting well skin factors under different reservoir conditions. Full article

Background: This case report aimed to quantify dental, alveolar, and skeletal changes, periodontal health, and sleep quality after treatment with a tooth-borne rapid palatal expander (RPE) in a young adult with bilateral posterior crossbite due to transverse maxillary deficiency. Tooth-borne RPE is typically indicated during the prepubertal or pubertal growth phases; however, some post-pubertal or young adult patients may still present with incomplete maturation of the midpalatal suture—the so-called “gray zone.” In clinical practice, treatment decisions should ideally consider multiple skeletal resistance areas (the zygomaticomaxillary buttress, the pterygomaxillary junction, the nasal aperture pillars), although midpalatal suture assessment often remains central to case selection. Methods: A 19-year-old male patient presented with a skeletal Class III tendency, dental crowding, and anterior and bilateral posterior crossbites, accompanied by snoring and breathing difficulties. The patient declined surgical- and miniscrew-assisted RPE. Cone-beam computed tomography (CBCT) scan revealed incomplete midpalatal suture maturation. Based on periodontal evaluation, a conventional tooth-borne RPE was chosen. Pre- and post-expansion CBCT scans were used to evaluate dental, skeletal, and periodontal outcomes. Results: After one year of treatment, bilateral posterior crossbite was successfully corrected. Buccal bone thickness showed a slight reduction only on the upper left first molar (from 1.2 mm to 0.9 mm), without evidence of dehiscence or fenestration. A 2° increase in the dental tipping angle (DTA) was observed on both molars, and the palatal alveolar angle (PAA) increased by 3°. Sutural separation expanded from 0.32 mm to 7.82 mm. The Midpalatal Opening Related to Expander Opening (MORE) factor was 0.54, indicating a predominantly skeletal response. Periodontal health remained stable, and CBCT analysis confirmed increases in intermolar width (from 36.08 mm to 50.02 mm) and palatal maxillary width (from 28.04 mm to 34.5 mm). A reduction in the Pittsburgh Sleep Quality Index (PSQI) from 7 to 3 was observed, though this finding should be interpreted cautiously due to its subjective nature and the absence of objective airway measurements. Conclusions: The present case report suggests that tooth-borne RPE may represent a viable and minimally invasive option for correcting posterior crossbite in carefully selected young adults with incomplete midpalatal suture maturation. However, the findings are limited to a single case with short follow-up and should be regarded as hypothesis-generating rather than conclusive. Full article

This paper proposes a multi-objective optimization method that integrates deep neural networks (DNN) with gray relational analysis (GRA) to optimize lay-up configurations for carbon fiber-reinforced plastic (CFRP) automotive components. Specifically, a lab-scale CFRP B-pillar structure was investigated to simultaneously maximize structural strength and failure safety. A DNN surrogate model was trained using finite element simulations of 2000 random stacking sequences to achieve high predictive accuracy. The trained model was then used to evaluate all possible lay-up combinations to derive Pareto optimal solutions. Gray relational analysis was subsequently employed to select the final optimal configurations based on designer preferences. The selected lay-up designs demonstrated improvements in both strength and failure safety. To validate the proposed framework, laboratory-scale CFRP B-pillar was fabricated using a prepreg compression molding process and subjected to bending tests. The experimental results confirmed an error below 5% and failure trends consistent with the simulation results, thereby validating the reliability of the proposed method. The proposed DNN-GRA approach enables efficient multi-objective optimization with reduced computational effort and flexibility in reflecting different engineering priorities. Full article

Background: Periodontal dysbiosis contributes to liver injury through systemic inflammation, oral–gut microbial translocation, and endotoxemia. Lipopolysaccharides (LPSs) and virulence factors derived from periodontal pathogens, particularly Porphyromonas gingivalis (P. gingivalis) activate Toll-like receptor (TLR) signaling, trigger NF-κB-mediated cytokine release (e.g., TNF-α, IL-1β, IL-6), and promote oxidative stress and Kupffer cell activation within the liver. The present systematic review summarized clinical evidence supporting these mechanistic links between periodontal pathogens and hepatic outcomes, highlighting the role of microbial crosstalk in liver pathophysiology. Methods: A PRISMA-compliant systematic review was conducted by searching PubMed, Scopus, and the Cochrane library, as well as gray literature. Eligible study designs were observational studies and trials evaluating P. gingivalis and other periodontal pathogens (Aggregatibacter actinomycetemcomitans, Prevotella intermedia, and Tannerella forsythia) for liver phenotypes (Non-Alcoholic Fatty Liver Disease [NAFLD]/Metabolic Dysfunction-Associated Steatotic Liver Disease [MASLD], fibrosis/cirrhosis, acute alcoholic hepatitis [AAH], and Hepatocellular carcinoma [HCC]). Risk of bias was assessed using the Newcastle–Ottawa Scale adapted for cross-sectional studies (NOS-CS) for observational designs and the RoB 2 scale for single randomized controlled trials (RCTs). Due to the heterogeneity of exposures/outcomes, results were summarized narratively. Results: In total, twenty studies (2012–2025; ~34,000 participants) met the inclusion criteria. Population-level evidence was conflicting (no clear association between anti-P. gingivalis serology and NAFLD), while clinical cohorts more frequently linked periodontal exposure, particularly to P. gingivalis, to more advanced liver phenotypes, including fibrosis. Microbiome studies suggested stage-related changes in oral communities rather than the effect of a single pathogen, and direct translocation into ascitic fluid was not observed in decompensated cirrhosis. Signals from interventional and behavioral research (periodontal therapy; toothbrushing frequency) indicate a potential modifiability of liver indices. The overall methodological quality was moderate with substantial heterogeneity, precluding meta-analysis. Conclusions: Current evidence supports a biologically plausible oral–liver axis in which periodontal inflammation, often involving P. gingivalis, is associated with liver damage. Causality has not yet been proven; however, periodontal evaluation and treatment may represent a low-risk option in periodontitis-associated NAFLD. Well-designed, multicenter prospective studies and randomized trials with standardized periodontal and liver measurements are needed. Full article

Variability in reclaimed asphalt pavement (RAP) properties, such as aggregate gradation, asphalt content, and moisture content, poses a significant challenge to producing consistent and reliable recycled asphalt mixtures. This study systematically evaluated processing techniques for mitigating variability through a comparative analysis of four fractionation strategies, i.e., unfractionated, two-fraction, four-fraction, and six-fraction processing. Corresponding to the four approaches, four distinct reference RAP mixtures were fabricated by proportionally recombining the obtained RAP fractions towards a target gradation. The gray relational analysis (GRA) was employed to quantify geometric similarity between the gradation curve of reclaimed aggregates from each fraction and the target gradation curve, thereby facilitating efficient determination of blending proportions without resorting to complex optimization algorithms. Statistical variability indicators, including range, standard deviation, and coefficient of variation (COV), were used to assess the effectiveness of each fractionation and recombining method. The results demonstrated that refined fractionation processing significantly reduced variability, particularly in gradation properties. Compared with the COV values from the commonly used two-fraction processing, those from the refined four-fraction and six-fraction processing methods decreased by up to 51.5% and 73.5%, respectively. While increasing the number of fractions generally enhanced homogeneity, the four-fraction approach emerged as the most technically reliable and economically viable strategy, achieving a desirable balance between processing effort and variability control. Furthermore, the GRA proved to be a practical and efficient tool for blend proportioning, reducing reliance on complex numerical methods. These findings reveal the importance of refined fractionated RAP processing in enabling the production of high-RAP recycled mixtures with improved uniformity and performance. Full article

To achieve a scientific and efficient emergency response, a dynamic route-planning strategy for emergency vehicles based on government–enterprise collaboration was studied. Firstly, a hybrid evaluation approach was developed, integrating the Analytic Hierarchy Process, Entropy Weight Method, and Gray Relation Analysis-TOPSIS to quantitatively assess the urgency of demands at disaster sites. Secondly, a government–enterprise coordinated route-planning strategy was designed, leveraging the government’s strong mobilizing capabilities and enterprises’ flexible operational mechanisms. Thirdly, to optimize scheduling efficiency, a dynamic route-planning model was proposed, incorporating multiple distribution conditions to minimize scheduling time, delay penalties, and unmet demand rates. A two-stage cellular genetic algorithm was employed to address realistic constraints such as demand splitting, soft time windows, open scheduling, and differentiated services. Numerical simulations of potential flooding in Hunan Province revealed that the collaborative strategy significantly improved performance: the demand satisfaction rate rose from 70.1% (government-led) to 92.3%, while the material transportation time per unit decreased by 23.6% (from 1.61 to 1.23 min/unit). Vehicle path characteristics varied under different operational behaviors, aligning with theoretical expectations. Even under sudden road disruptions, the model maintained a 98% demand satisfaction rate with only a negligible 0.076% increase in system loss. This research fills the gaps in previous studies by comprehensively addressing multiple factors in emergency vehicle route planning, offering a practical and efficient solution for post-disaster emergency response. Full article

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Accurate determination of epidermal growth factor receptor (EGFR) mutation status is essential for selecting patients eligible for tyrosine kinase inhibitors (TKIs). However, invasive genotyping is often limited by tissue accessibility and sample quality. This study presents a non-invasive machine learning model combining clinical data, CT morphological features, and radiomic descriptors to predict EGFR mutation status. A retrospective cohort of 138 patients with confirmed EGFR status and pre-treatment CT scans was analyzed. Radiomic features were extracted with PyRadiomics, and feature selection applied mutual information, Spearman correlation, and wrapper-based methods. Five Random Forest models were trained with different feature sets. The best-performing model, based on 11 selected variables, achieved an AUC of 0.91 (95% CI: 0.81–1.00) under stratified five-fold cross-validation, with an accuracy of 0.88 ± 0.03. Subgroup analysis showed that EGFR-WT had a performance of precision 0.93 ± 0.04, recall 0.92 ± 0.03, F1-score 0.91 ± 0.02, and EGFR-Mutant had a performance of precision 0.76 ± 0.05, recall 0.71 ± 0.05, F1-score 0.68 ± 0.04. SHapley Additive exPlanations (SHAP) analysis identified tobacco use, enhancement pattern, and gray-level-zone entropy as key predictors. Decision curve analysis confirmed clinical utility, supporting its role as a non-invasive tool for EGFR-screening. Full article

Background: Mitral valve replacement presents considerable challenges in the field of cardiothoracic surgery, particularly in patients aged 50 to 69, where the decision between bioprosthetic and mechanical valves is critical. Nevertheless, the optimal selection of prosthetic valves for candidates within this age-related gray zone remains inadequately defined, necessitating a thorough evaluation of long-term outcomes and associated risks. Objective: This study aims to assess mid-term outcomes of MIMVR in patients aged 50 to 69, comparing reoperation rates, prosthesis-related morbidity, and overall survival between bioprosthetic and mechanical valves. While many prior studies on valve choice in patients aged 50 to 69 years are derived from sternotomy cohorts, the novelty of our work lies in the exclusive focus on patients undergoing minimally invasive techniques. Methods: A retrospective analysis was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, including 172 patients aged 50–69 years who underwent minimally invasive mitral valve replacement via right minithoracotomy at a high-volume center in Germany between 2011 and 2023. Of the 172 patients, 95 underwent MIMVR using biological prostheses, while 77 received mechanical prostheses. Comprehensive data on demographics, surgical procedures, and postoperative complications, as well as long-term outcomes, were analyzed. Results: With a mean follow-up of 7.1 years, early outcomes revealed no significant differences in 30-day mortality (7.4% for bioprosthetic vs. 2.6% for mechanical; p = 0.06). There was no significant differences in all-cause mortality at 1 year (8.4% vs. 3.9%; p = 0.22), 3-year (9.5% vs. 7.8%; p = 0.69), and 5-year (13.7% vs. 10.4%; p = 0.19), or at the longest follow-up (13.7% vs. 10.4%; p = 0.51). Kaplan–Meier analysis showed no significant difference in long-term survival between the groups (p = 0.5427). Postoperative arrhythmia occurred significantly more frequently in the biologic group compared to the mechanical group (18.9% vs. 6.5%; p = 0.01). Conclusions: For patients aged 50–69 undergoing MIMVR using a bioprosthetic or mechanical valve, the mid-term survival and incidence of reoperation and re-hospitalization were comparable up to 7 years. This provides evidence supporting the safe application of the MICS approach with either valve type in this gray-zone age group. Full article

Purpose: Focal cortical dysplasia (FCD) is a common developmental malformation disease of the cerebral cortex. Although mounting evidence has suggested that FCD lesions have variable locations and topographies throughout the cortex, few studies have explored consistencies in structural connectivity among different lesion types. In this study, we analyzed microscopic structural changes via lesion analysis and explored structural changes in nonlesion regions across the brain. Methods: Diffusion tensor imaging (DTI) and magnetization transfer imaging were used to compare FCD lesions and contralateral normal appearing gray/white matter (cNAG/WM). Voxel-based morphometry was calculated for 28 children with FCD and 34 sex- and age-matched healthy participants. DTI indices of the FCD and healthy control groups were analyzed via the tract-based spatial statistic method to evaluate the microstructure abnormalities of WM fiber tracts in individuals with FCD. Results: In terms of FCD lesions, compared with those of the cNAG, the fractional anisotropy (FA) values were decreased, and the mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) values were increased; the magnetization transfer ratios were also decreased. In terms of whole-brain changes due to FCD, compared with the healthy control group, the FCD group showed a decrease in the volume of the right hippocampus and left anterior cingulate cortex. FCD patients had lower FA values, higher MD values, lower AD values, and mainly increased RD values in relation to WM microstructure. Conclusions: Microstructural abnormalities outside lesion regions may be related to injury to the epileptic network, and the identification of such abnormalities may complement diagnoses of FCD in pediatric patients. Full article

Design for remanufacturing (DfRem) is a green design mode that ensures good remanufacturability at the end-of-life (EOL) of the product. However, the diversity of service environments and operating modes makes it difficult to generate accurate DfRem solutions for the smooth implementation of remanufacturing. Moreover, the historical remanufacturing process contains a great deal of information conducive to DfRem. It will greatly enhance the efficiency and accuracy of remanufacturing design by feeding effective remanufacturing information back into the product design process. Unfortunately, there is a lack of direct correlation between them, which prevents remanufacturing information from effectively guiding DfRem. To improve the accuracy of DfRem solutions and the utilization rate of remanufacturing information, an intelligent design method for product remanufacturing based on remanufacturing information reuse is proposed. Firstly, rough set theory (RST) is used to identify key remanufacturability demand, and the quality function development (QFD) is used to establish a relationship between remanufacturability demand and engineering characteristics, which can accurately obtain the design objectives. Then, the correlation between remanufacturability demand, remanufacturing information, and DfRem parameters is analyzed, and the ontology technology is applied to construct the DfRem knowledge by ingratiating remanufacturing information. In addition, case-based reasoning (CBR) is applied to search for design cases from DfRem knowledge that best match the design objectives, and gray relational analysis (GRA) is used to calculate the similarity between design knowledge. Finally, the feasibility of the method is verified by taking an ordinary lathe as an example. This method has been implemented as a DfRem interface application using Visual Studio 2022 and Microsoft SQL Server 2022, and the research results indicate that this design method can accurately generate a reasonable DfRem scheme. Full article