Search Results (63)

Background/Objective: The Trail Making Test (TMT) is a widely used neuropsychological tool to assess processing speed (Part A) and executive function (Part B). However, the neuroanatomical substrates underlying its Black & White variant (TMT-B&W) and the influence of demographic factors remain poorly understood. This study aimed to identify gray matter (GM) correlates of TMT-B&W performance across unadjusted and covariate-adjusted models in cognitively healthy adults. Methods: In this cross-sectional study, 87 participants (40–80 years) underwent structural magnetic resonance imaging (MRI) and completed TMT-B&W. Whole-brain voxel-based morphometry (VBM) was conducted using FreeSurfer for preprocessing and Computational Anatomy Toolbox (CAT12)/Statistical Parametric Mapping (SPM12) for analysis. Two voxel-wise regression models (unadjusted and adjusted for age, education, gender, and total intracranial volume (TICV)) assessed GM associations with TMT-B&W-A-B performance. Statistical thresholds were voxel-level p < 0.001 (uncorrected) and cluster-level Family-Wise Error (FWE) correction (p < 0.001). Results: In unadjusted models, TMT-B&W-A performance correlated with GM reductions in the right orbitofrontal cortex (T = 42.64, equivk = 515.60, representing peak voxel level T-statistic and cluster size in voxels), while TMT-B&W-B linked to the right insular cortex (T = 50.65, equivk = 515.50). After adjustment, both tasks converged on the left thalamus (TMT-A: T = 8.05, equivk = 594; TMT-B: T = 8.11, equivk = 621), with TMT-B&W-B showing a denser thalamic cluster. Demographic covariates attenuated cortical associations, revealing thalamic integration as a shared mechanism. Conclusions: The thalamus emerges as a critical hub for TMT-B&W performance when accounting for demographic variation, while distinct cortical regions mediate task-specific demands in unadjusted models. These findings support the TMT-B&W as a practical, low-cost neurobehavioral marker of brain integrity in older populations. Full article

Background: Around 1 in 59 individuals is diagnosed with Autism Spectrum Disorder (ASD), according to CDS statistics. Conventionally, ASD has been diagnosed using functional brain regions, regions of interest, or multi-tissue-based training in artificial intelligence models. The objective of the exhibit study is to develop an efficient deep learning network for identifying ASD using structural magnetic resonance imaging (MRI)-based brain scans. Methods: In this work, we developed a VGG-based deep learning network capable of diagnosing autism using whole brain gray matter (GM) tissues. We trained our deep network with 132 MRI T1 images from normal controls and 140 MRI T1 images from ASD patients sourced from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Results: The number of participants in both ASD and normal control (CN) subject groups was not statistically different (p = 0.23). The mean age of the CN subject group was 14.62 years (standard deviation: 4.34), and the ASD group had mean age of 14.89 years (standard deviation: 4.29). Our deep learning model accomplished a training accuracy of 97% and a validation accuracy of 96% over 50 epochs without overfitting. Conclusions: To the best of our knowledge, this is the first study to use GM tissue alone for diagnosing ASD using VGG-Net. Full article

This longitudinal study applies decade-spanning socioeconomic indicators (2013–2022) from the Beijing–Tianjin–Hebei urban agglomeration. An integrated analytical framework was developed, merging the super-efficiency slack-based measurement (SBM) methodology with entropic weighting techniques to quantify tourism efficiency and economic development. Subsequent phases employed a multi-method analytical cascade: coupling coordination assessment modeling for system interaction analysis, standard deviation ellipses for spatial dispersion characterization, and Markovian transition matrices for temporal pattern identification. The investigation concludes with evolutionary trajectory projections using gray system forecasting GM(1,1) modeling. The analytical findings reveal the following patterns: (1) Within the Beijing–Tianjin–Hebei metropolitan cluster, tourism efficiency demonstrates a consistent upward trajectory, manifesting spatial differentiation characteristics characterized by a dual-core structure centered on Tianjin and Baoding, with higher values observed in northwestern areas compared to southeastern regions. Concurrently, regional disparities exhibit progressive convergence over temporal progression. (2) The level of economic development in the Beijing–Tianjin–Hebei city cluster has been rising steadily, demonstrating a geospatial distribution of ‘central concentration with peripheral attenuation, with the north-east being better than the southwest’, and the gap between the regional differences has become broader over time. (3) The coupling between tourism efficiency and the level of economic development in the Beijing–Tianjin–Hebei city cluster has generally improved, with Beijing and Tianjin predominantly in a coordinated regime, and some cities in Hebei Province about to shift from dysfunctional to coordinated, and, spatially, the coupling and coordination in northern sectors demonstrate superior performance compared to southern counterparts nationally. (4) The coupling coordination degree of the Beijing–Tianjin–Hebei city cluster in the next eight years is predicted by the gray GM(1,1) prediction model and the overall continuation of the growth trend of the Beijing–Tianjin–Hebei city cluster over the past ten years, thus verifying the importance of the regional integrated policy frameworks in the system integration of the Beijing–Tianjin–Hebei metropolitan system. Full article

This study investigated the enhancement of corn stover (CS) anaerobic digestion (AD) performance through low-temperature hydrothermal modification (HM) with Fe/C catalysts and developed two predictive models for biomethane yield (BY). CS was modified with Fe/C at 50 °C and then anaerobically digested. The results indicated that Fe/C significantly improved CS hydrolysis efficiency, indicated by increasing concentrations of glucose, mannose, xylose, and volatile fatty acids (VFAs), which were 1.9, 1.7, 3.0, and 1.8 times higher than those of HM alone, respectively. The enhanced hydrolysis of CS effectively improved AD performance, leading to a BY increase of 25.5% as compared to the control group. The time to reach 90% of the maximum BY (T90) was also reduced by 7 days. Furthermore, the developed GM(1,N) gray system model effectively simulated multi-parameter coupling effects in AD processes under small-sample conditions (n < 20), demonstrating high accuracy (average percentage deviation [APD] = 4.50%) and enabling correlation analysis between modification parameters and BY. The ANN-GA model exhibited superior accuracy in BY prediction. This study demonstrated the effectiveness of low-temperature HM-Fe/C in enhancing BY and the accuracy of two models in predicting BY. Full article

Background: Infant brain tissue segmentation from MRI data is a critical task in medical imaging, particularly challenging due to the evolving nature of tissue contrasts in the early months of life. The difficulty increases as gray matter (GM) and white matter (WM) intensities converge, making accurate segmentation challenging. This study aims to develop an improved U-net-based model to enhance the precision of automatic segmentation of cerebro-spinal fluid (CSF), GM, and WM in 10 infant brain MRIs using the iSeg-2017 dataset. Methods: The proposed method utilizes a U-net architecture with (2+1)Dconvolutional layers and skip connections. Preprocessing includes intensity normalization using histogram alignment to standardize MRI data across different records. The model was trained on the iSeg-2017 dataset, which comprises T1-weighted and T2-weighted MRI data from ten infant subjects. Cross-validation was performed to evaluate the model’s segmentation performance. Results: The model achieved an average accuracy of 92.2%, improving on previous methods by 0.7%. Sensitivity, precision, and Dice similarity scores were used to evaluate the performance, showing high levels of accuracy across different tissue types. The model demonstrated a slight bias toward misclassifying GM and WM, indicating areas for potential improvement. Conclusions: The results suggest that the U-net architecture is highly effective in segmenting infant brain tissues from MRI data. Future work will explore enhancements such as attention mechanisms and dual-network processing for further improving segmentation accuracy. Full article

The synergistic relation among land resources, water resources, and food production plays a crucial role in sustainable agricultural development. This research constructs a coupling coordination assessment system of the land–water–food (LWF) nexus from 2005 to 2020 for 31 provinces (municipal cities, autonomous regions) in China, and explores the current development status of land, water, and food systems at multiple scales as well as the coupling coordination characteristics of the LWF nexus. The exploring spatial data analysis and spatial Tobit model are used to explain the spatial correlations and influencing factors of coupling coordination development on the LWF nexus. On that basis, the gray GM (1,1) model is used to forecast the future development of the LWF nexus in China. The results show that the comprehensive development indexes of the land system, water system, food system, and LWF nexus are on the rise, but the land system lags behind the water system and food system. The coupling coordination degree of the LWF nexus in different regions ranges from 0.538 to 0.754, and the coupling coordination development of the LWF nexus in China has reached the preliminary coupled coordination type, with an evolutionary process similar to that of its comprehensive development level. Further empirical research shows that there is a significant positive spatial correlation between coupling coordination development levels for the LWF nexus in China. The level of urbanization and agricultural industry agglomeration have negative effects, while economic development, ecological environment, and scientific and technological progress have positive effects. The prediction results indicate that the coupling coordination degree of the LWF nexus in China will show a stable upward trend from 2024 to 2025, and most provinces will reach the intermediate coupled coordination type in 2025. This study can inform decision-making for policy-makers and practitioners and enrich the knowledge hierarchy of the LWF nexus’ sustainable development on the national and regional scales. Full article

The marine aquaculture industry holds a significant position in the development of Weihai City’s fishing industry, with its ecological efficiency having a direct impact on the sustainable progress of the regional fishing sector. Utilizing a three-stage DEA model as an unexpected output model, this study systematically assessed the ecological efficiency of Weihai City’s marine aquaculture industry. By employing kernel density estimation, we analyzed the temporal dynamic evolution of ecological efficiency within the marine aquaculture industry. The results indicate that the overall ecological efficiency of marine aquaculture in Weihai City has improved to some extent, influenced by environmental factors such as government support, urbanization level, and regional economic development level. After removing environmental and random factors, it was found that the overall ecological efficiency of the marine aquaculture industry in Weihai City shows a more stable upward trend. Furthermore, using a gray dynamic model, GM (1, 1), we predicted the trend of ecological efficiency in the marine aquaculture industry. The findings indicate that, with the progressive adoption of advanced aquaculture technologies, the ecological efficiency of Weihai City’s marine aquaculture is anticipated to continue growing in the future. However, the pace of growth has decelerated. To maximize ecological efficiency, it is imperative to optimize resource allocation, foster technological innovation, and elevate awareness regarding ecological and environmental preservation. By assessing the ecological efficiency of Weihai City’s marine aquaculture industry, this article aims to shed light on the industry’s progress, thereby promoting its high-quality and sustainable development. Full article

Formulating a scientifically sound and efficient approach to allocating carbon quota aligned with the carbon peaking goal is a fundamental theoretical and practical challenge within the context of climate-oriented trading in the power sector. Given the highly irrational allocation of carbon allowances in China’s power sector, as well as the expanding role of renewable energy, it is essential to rationalize the use of green energy in the development of carbon reduction in the power sector. This study addresses the risk of “carbon transfer” within the power industry and develops a predictive model for CO₂ emission based on multiple influential factors, thereby proposing a carbon quota distribution scheme adapted to green energy growth. The proposed model employs a hybrid of the gray forecasting model-particle swarm optimization-enhanced back-propagation neural network (GM-PSO-BPNN) for forecasting and allocating the total carbon quota. Assuming consistent total volume control through 2030, carbon quota is distributed to regional power grids in proportion to actual production allocation. Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. With the total national carbon emissions remaining unchanged in 2030, in the end, the regional grids received the following quota values: 873.29 million tons in North China, 522.69 million tons in Northwest China, 194.15 million tons in Northeast China, 1283.16 million tons in East China, 1556.40 million tons in Central China, and 1085.37 million tons in the Southern Power Grid. The power sector can refer to this carbon allowance allocation standard to control carbon emissions in order to meet the industry’s emission reduction standards. Full article

(1) Background: In the current competitive market environment, accurately forecasting user needs is crucial for business success. By analyzing user-generated content (UGC) on social network platforms, enterprises can mine potential user needs and discern shifts in these needs, thereby enabling more efficient and precise product design that aligns with user needs. For newly launched products with a limited presence in the market, the scarcity of UGC poses a challenge to businesses seeking to predict user needs from small datasets. (2) Methods: To address this challenge, this paper proposes a model using correlation analysis (CA) and linear regression (LR) combined with multidimensional gray prediction (a CA-LR-GM (1, N) model) to help enterprises use small sample data to predict user needs. Using the UGC of the Xiaomi SU7 as a case study, this paper demonstrates the prediction of user needs for the vehicle and refines the prediction outcomes through an optimization design informed by the principle of optimal key feature distribution. (3) Results: The findings validate the feasibility of the proposed theoretical framework, offering a technical solution for the identification and prediction of user need trends. (4) Conclusions: This research puts forward strategic recommendations for enterprises regarding the optimization of their products. Full article

This study aims to investigate the coupling and harmonization between land ecological security (LES) and high-quality agricultural development (HAD) in the Han River Basin (HRB), China, with the objective of promoting harmonious coexistence between agriculture and ecosystems. Using 17 cities in the HRB as the research objects, an evaluation index system of two systems, LES and HAD, was constructed, analyzed, and evaluated via projective tracer modeling for multiple intelligent genetic algorithms (MIGA-PTM). The degree of coupling coordination (DCC) was used to quantitatively evaluate the coupling coordination development status of the two systems, the obstacle model (OM) was used to identify the main influencing factors, and the gray predictive model first-order univariate model (GM (1, 1)) was used to predict the DCC of the LES and HAD from 2025 to 2040. The results show the following: (1) the LES and HAD levels of the 17 cities in the HRB tended to increase during the study period, and there was a large gap between cities; (2) the spatial distributions of the DCCs of the LES and HAD in the HRB were uneven, with high values in the southern and low values in the central and northern parts, and the overall degree of coupling tended to fluctuate. The overall DCC showed a fluctuating upward trend; (3) the degree of obstacles, per capita water resources, greening coverage, and rate of return on financial expenditure are the main influencing factors; and (4) the prediction results of GM (1, 1) indicate that the LES and HAD of the HRB will be close to reaching the intermediate stage of coupling in 2035. This research offers critical insights into sustainable development practices that facilitate the alignment of agricultural growth with ecological preservation. Full article

When using seawater and sea sand as mixes, the mechanical properties and durability of concrete are adversely affected because the raw materials themselves contain harmful ions. Fly ash is the tailings formed in the process of industrial production, the use of which does not require the burning of clinker, reducing CO₂ emissions. Moreover, it belongs to a new type of cementitious materials with low emissions and high environmental protection. Fly ash enhances the properties of concrete and reduces the effect of harmful ions on concrete. Based on the above considerations, the corresponding specimens were prepared and subjected to cubic compressive strength, flexural strength, and seawater freezing and thawing resistance tests by using fly ash admixture as the main variable. A combination of macro-analysis and micro-analysis was used to investigate the effect of fly ash on the performance of seawater sea sand concrete. The results showed that fly ash significantly enhanced the mechanical properties and resistance to seawater freezing and thawing of seawater sea sand concrete. The best improvement in compressive strength and resistance to seawater freezing and thawing was achieved at a substitution rate of 20%. The maximum increase in compressive strength was 13.22%. The maximum reduction in mass loss rate was 57.26% and the strength loss rate was 43.14% after the specimens were subjected to seawater freezing and thawing 75 times. The maximum enhancement in flexural strength was 17.06% for a substitution rate of 10%. Through microanalysis, it can be seen that the incorporation of coal ash can enhance the compactness of concrete through the microaggregate effect as well as the volcanic ash reaction to promote the secondary hydration reaction, so as to strengthen the seawater freeze–thaw resistance of seawater sea sand concrete. Finally, the damage prediction model established using the mean GM (1, 1) model of gray system theory meets the requirements of the first level of prediction accuracy and can accurately predict the damage of seawater sea sand concrete under seawater freezing and thawing. Full article

Using a molecular modeling approach for Tau-binding sites, we modified our previously reported imaging agent, [¹²⁵I]INFT, for the potential improvement of binding properties to Tau in an Alzheimer’s disease (AD) brain. Two new derivatives, namely [¹²⁵I]ISAS and [¹²⁵I]NIPZ, were designed, where binding energies at site 1 of Tau were −7.4 and −6.0 kcal/mole, respectively, compared to [¹²⁵I]INFT (−7.6 kcal/mole). The radiosynthesis of [¹²⁵I]ISAS and [¹²⁵I]NIPZ was carried out by using iodine-125 and purified chromatographically to achieve >90% purity. In vitro binding affinities (IC₅₀) for Tau were as follows: INFT = 7.3 × 10⁻⁸ M; ISAS = 4.7 × 10⁻⁸ M; NIPZ > 10⁻⁶ M. The binding of [¹²⁵I]ISAS to gray matter (GM) correlated with the presence of Tau in the AD brain, confirmed by anti-Tau immunohistochemistry. [¹²⁵I]NIPZ did not bind to Tau, with similar levels of binding observed in GM and white matter (WM). Four radiotracers were compared and the rank order of binding to Tau was found to be [¹²⁵I]IPPI > [¹²⁵I]INFT > [¹²⁵I]ISAS >>> [¹²⁵I]NIPZ with GM/WM ratios of [¹²⁵I]IPPI = 7.74 > [¹²⁵I]INFT = 4.86 > [¹²⁵I]ISAS = 3.62 >> [¹²⁵I]NIPZ = 1.24. The predictive value of Chimera–AutoDock for structurally related compounds binding to the Tau binding sites (measured as binding energy) was good. A binding energy of less than −7 kcal/mole is necessary and less than −8 kcal/mole will be more suitable for developing imaging agents. Full article

The influence of different recycled brick coarse aggregate (RBA) substitution rates on the mechanical performance and frost resistance of concrete was observed. The test findings revealed that RBA deteriorated the compressive and flexural properties in concrete and improved the tensile properties and plasticity in concrete to some extent. The frost resistance of concrete can be effectively improved by adding RBA. The influence degree of the RBA concrete frost resistance factor was quantified by gray entropy correlation theory, and the gray entropy correlations between freezing and thawing cycles, natural coarse aggregate substitution rate, recycled brick aggregate substitution rate, and freezing and thawing damage value (D_N) were 0.9979, 0.9914, and 0.9876, respectively. Moreover, the freezing and thawing damage model about GM(1, 1) theory was developed (R² > 0.87), which can better predict the freezing and thawing damage of RBA concrete. The damage mechanism of RBA concrete during freezing and thawing was revealed. Full article

The automatic delineation and segmentation of the brain tissues from Magnetic Resonance Images (MRIs) is a great challenge in the medical context. The difficulty of this task arises out of the similar visual appearance of neighboring brain structures in MR images. In this study, we present an automatic approach for robust and accurate brain tissue boundary outlining in MR images. This algorithm is proposed for the tissue classification of MR brain images into White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). The proposed segmentation process combines two algorithms, the Hidden Markov Random Field (HMRF) model and the Whale Optimization Algorithm (WOA), to enhance the treatment accuracy. In addition, we use the Whale Optimization Algorithm (WOA) to optimize the performance of the segmentation method. The experimental results from a dataset of brain MR images show the superiority of our proposed method, referred to HMRF-WOA, as compared to other reported approaches. The HMRF-WOA is evaluated on multiple MRI contrasts, including both simulated and real MR brain images. The well-known Dice coefficient (DC) and Jaccard coefficient (JC) were used as similarity metrics. The results show that, in many cases, our proposed method approaches the perfect segmentation with a Dice coefficient and Jaccard coefficient above 0.9. Full article

To investigate the effect of hybrid fibers on the compressive strength of aeolian sand concrete, compressive strength tests were conducted on aeolian sand concrete with single polypropylene fibers and aeolian sand concrete with mixed polypropylene fibers and calcium carbonate whisker, and their variation rules were studied. Using scanning electron microscopy and nuclear magnetic resonance, the microstructure and pore structure of specimens were analyzed, and a mathematical model of the relationship between compressive strength and pore structure was established with gray entropy analysis. The results show that the compressive strength of hybrid fiber aeolian sand concrete first increases and then decreases with an increase in whisker content. When the replacement rate of wind-accumulated sand is 80% and the fiber content is 0.1%, the optimal volume content of whisker is 0.4%, and the 28 d compressive strength of whisker is 24.8% higher than that of aeolian sand concrete. The average relative errors of compressive strength at 7 d and 28 d are 8.16% and 7.48%, respectively, using the GM (1,3) model. This study can provide effective theoretical support for the application of calcium carbonate whisker and polypropylene fibers in aeolian sand concrete. Full article