Quantitative Assessment of Hemodynamics Associated With Embolization Degree in Brain Arteriovenous Malformations.

BACKGROUND AND OBJECTIVES: Grading systems, including the novel brain arteriovenous malformation endovascular grading scale (NBAVMES) and arteriovenous malformation embocure score (AVMES), predict embolization outcomes based on arteriovenous malformation (AVM) morphological features. The influence of hemodynamics on embolization outcomes remains unexplored. In this study, we investigated the relationship between hemodynamics and embolization outcomes. METHODS: We conducted a retrospective study of 99 consecutive patients who underwent transarterial embolization at our institution between 2012 and 2018. Hemodynamic features of AVMs were derived from pre-embolization digital subtraction angiography sequences using quantitative digital subtraction angiography. Multivariate logistic regression analysis was performed to determine the significant factors associated with embolization outcomes. RESULTS: Complete embolization (CE) was achieved in 17 (17.2%) patients, and near-complete embolization was achieved in 18 (18.2%) patients. A slower transnidal relative velocity (TRV, odds ratio [OR] = 0.71, P = .002) was significantly associated with CE. Moreover, higher stasis index of the drainage vein (OR = 16.53, P = .023), shorter transnidal time (OR = 0.15, P = .013), and slower TRV (OR = 0.9, P = .049) were significantly associated with complete or near-complete embolization (C/nCE). The area under the receiver operating characteristic curve for predicting CE was 0.87 for TRV, 0.72 for NBAVMES scores (ρ = 0.287, P = .004), and 0.76 for AVMES scores. The area under the receiver operating characteristic curve for predicting C/nCE was 0.77 for TRV, 0.61 for NBAVMES scores, and 0.75 for AVMES scores. Significant Spearman correlation was observed between TRV and NBAVMES scores and AVMES scores (ρ = 0.512, P < .001). CONCLUSION: Preoperative hemodynamic factors have the potential to predict the outcomes of AVM embolization. A higher stasis index of the drainage vein, slower TRV, and shorter transnidal time may indicate a moderate blood flow status or favorable AVM characteristics that can potentially facilitate embolization.

Identifying spatial agglomeration and driving forces of land use by green industries: a case study of Jiangsu Province, China.

In response to China's aims of becoming "carbon-neutral," the development of green industries such as renewable energy and recycling has flourished. Based on 2015 and 2019 data, this study uses spatial autocorrelation to analyze the evolution of land use by the green industries in Jiangsu Province. The Geodetector model was also applied to identify the driving factors underlying these spatial patterns. The spatial variability of green industrial land use in Jiangsu Province is significant, with the land-use area gradually decreasing from Southern to Northern Jiangsu. In terms of spatial-temporal changes, there is an increase in land use and a trend of expansion in the central and northern regions of Jiangsu. Land use by green industries in the province exhibits a more significant spatial clustering pattern but with a weakened clustering effect. The clustering types are mainly H-H and L-L, with the H-H type distributed mainly in the Su-Xi-Chang region and the L-L type distributed mainly in the Northern Jiangsu region. The levels of technology, economic development, industrialization, and industrial diversification are important individual driving factors, and the interaction among the different factors enhances their driving forces. This study suggests that spatial spillover effects should be focused to promote the coordinated development of regional energy-saving and environmental protection industries. At the same time, joint efforts should be made from the aspects of resources, government, economy, and related industries to promote the agglomeration of land for energy-saving and environmental protection industries.

Low-cost laser cutting fabricated all-metallic metamaterial near-field focusing lens.

In this work, a low-cost all-metal metamaterial near-field lens based on laser cutting technology is proposed. A novel spiral-slot structure is proposed to achieve miniaturized unit cells with an adjustable 360-degree phase shift at a length smaller than 0.2 times the operating wavelength. Since the unit is entirely constructed of stainless steel, it is resistant to high temperatures and high pressures compared to existing results. Moreover, a four-layer structure is used to increase the transmission coefficient. The final L-band near-field lens is constructed of 20 × 20 units. Simulation and measured results show that the half-power beamwidth of the focus is less than 211 mm from 1.52 GHz to 1.68 GHz at the focal spot observation plane of 500 mm from the lens. Since numerically controlled machine tools and three-dimensional printing are prohibitively expensive for machining large metal components, a low-cost all-metallic lens was manufactured using laser cutting technology. The measured results are in agreement with the simulation results.

A multi-criteria group decision-making framework for investment assessment of offshore floating wind-solar-aquaculture project under probabilistic linguistic environment.

The offshore floating wind-solar-aquaculture (WSA) system with its advantages such as strong seakeeping ability, considerable power generation, and full utilization of ocean space and water resources will have a bright prospect in the future. In order to accelerate the sustainable development of the energy industry, it is very important to build a reasonable investment decision-making framework. Therefore, this paper aims to build a multi-criteria group decision-making (MCGDM) framework for investment decision-making of this project. Firstly, a comprehensive criteria system has been established. Secondly, probabilistic language term sets (PLTSs) are introduced to describe the uncertainty and fuzziness of decision information. Thirdly, the expert weight determination model is established based on the correlation measure and correlation coefficient of PLTSs, and the PL-fuzzy decision-making trial and evaluation laboratory (DEMATEL) method and the information entropy method are introduced to determine the subjective and objective weights of the criteria. In addition, considering the decision maker's psychological behavior, we choose probabilistic language the interactive and multiple attribute decision-making (TODIM) method to determine the optimal investment alternative. Finally, we apply the proposed framework to a case study. The results illustrate that the alternative A3 possesses the optimal comprehensive performance with the overall value is 1. Then, we conduct sensitivity analysis and comparative analysis to verify its robustness and feasibility. Scenario analysis in TODIM method showed that it is reasonable to express decision preference by setting different recession coefficients in the actual decision-making environment. This study can provide some reference for decision-makers, and also extend the method of decision-making field.

A Decision Tree Model for Analysis and Judgment of Lower Limb Movement Comfort Level.

To address the problem of ambiguity and one-sidedness in the evaluation of comprehensive comfort perceptions during lower limb exercise, this paper deconstructs the comfort perception into two dimensions: psychological comfort and physiological comfort. Firstly, we designed a fixed-length weightless lower limb squat exercise test to collect original psychological comfort data and physiological comfort data. The principal component analysis and physiological comfort index algorithm were used to extract the comfort index from the original data. Secondly, comfort degrees for each sample were obtained by performing K-means++ to cluster normalized comfort index. Finally, we established a decision tree model for lower limb comfort level analysis and determination. The results showed that the classification accuracy of the model reached 95.8%, among which the classification accuracy of the four comfort levels reached 95.2%, 97.3%, 92.9%, and 97.8%, respectively. In order to verify the advantages of this paper, the classification results of this paper were compared with the classification results of four supervised classification algorithms: Gaussian Parsimonious Bayes, linear SVM, cosine KNN and traditional CLS decision tree.

A deep transfer learning framework for the automated assessment of corneal inflammation on in vivo confocal microscopy images.

PURPOSE: Infiltration of activated dendritic cells and inflammatory cells in cornea represents an important marker for defining corneal inflammation. Deep transfer learning has presented a promising potential and is gaining more importance in computer assisted diagnosis. This study aimed to develop deep transfer learning models for automatic detection of activated dendritic cells and inflammatory cells using in vivo confocal microscopy images. METHODS: A total of 3453 images was used to train the models. External validation was performed on an independent test set of 558 images. A ground-truth label was assigned to each image by a panel of cornea specialists. We constructed a deep transfer learning network that consisted of a pre-trained network and an adaptation layer. In this work, five pre-trained networks were considered, namely VGG-16, ResNet-101, Inception V3, Xception, and Inception-ResNet V2. The performance of each transfer network was evaluated by calculating the area under the curve (AUC) of receiver operating characteristic, accuracy, sensitivity, specificity, and G mean. RESULTS: The best performance was achieved by Inception-ResNet V2 transfer model. In the validation set, the best transfer system achieved an AUC of 0.9646 (P<0.001) in identifying activated dendritic cells (accuracy, 0.9319; sensitivity, 0.8171; specificity, 0.9517; and G mean, 0.8872), and 0.9901 (P<0.001) in identifying inflammatory cells (accuracy, 0.9767; sensitivity, 0.9174; specificity, 0.9931; and G mean, 0.9545). CONCLUSIONS: The deep transfer learning models provide a completely automated analysis of corneal inflammatory cellular components with high accuracy. The implementation of such models would greatly benefit the management of corneal diseases and reduce workloads for ophthalmologists.

Worm-type Monte Carlo simulation of the Ashkin-Teller model on the triangular lattice.

We investigate the symmetric Ashkin-Teller (AT) model on the triangular lattice in the antiferromagnetic two-spin coupling region (J<0). In the J→-∞ limit, we map the AT model onto a fully packed loop-dimer model on the honeycomb lattice. On the basis of this exact transformation and the low-temperature expansion, we formulate a variant of worm-type algorithms for the AT model, which significantly suppress the critical slowing down. We analyze the Monte Carlo data by finite-size scaling, and locate a line of critical points of the Ising universality class in the region J<0 and K>0, with K the four-spin interaction. Further, we find that, in the J→-∞ limit, the critical line terminates at the decoupled point K=0. From the numerical results and the exact mapping, we conjecture that this "tricritical" point (J→-∞,K=0) is Berezinsky-Kosterlitz-Thouless-like and the logarithmic correction is absent. The dynamic critical exponent of the worm algorithm is estimated as z=0.28(1) near (J→-∞,K=0).