To investigate the influence of water on the mechanical behavior of rock surrounding hard-rock tunnels, a series of uniaxial and true-triaxial compression tests were performed on red sandstone ...samples with two different water contents (natural water content (NWC) and saturated water content (SWC)). The samples taken were cubic samples containing a circular hole and cylindrical samples. During the true-triaxial tests, hole failure was monitored and recorded in real-time with in-house developed monitoring equipment. The effects of water on the stress, energy, and fracture characteristics of rock failure in hard-rock tunnel were determined. The results indicate that, after reaching the SWC state, the strength and elastic modulus of red sandstone were reduced, and the shear characteristics became more obvious. The failure mode of the NWC holes was primarily slab ejection, while the failure mode of the SWC holes was primarily slab flaking. Water changes the mesoscopic mechanism of spalling and exhibits a double effect on hard-rock tunnels. The mechanisms of water on rockburst prevention are to reduce residual elastic strain energy, avoid excessive concentration of strain energy, and increase rockburst resistance. The ratio of the far-field maximum principal stress to the uniaxial compressive strength can be used as an index to evaluate the stability of hard-rock tunnels. The results help to rethink the influence of water on underground hard rock engineering, such as the failure mechanism of surrounding rock and the analysis of tunnel (or caverns) stability in water-rich stratum, and the mechanism of water on the rockburst prevention.
•The effect of water on hard-rock tunnel was studied by true-triaxial test.•The mechanism of water on the rockburst prevention was revealed•Water changes the mesoscopic mechanism of rock spalling.•Water exhibits a double effect on hard-rock tunnel.
Objectives
To investigate the potential of dual-energy computed tomography (DECT) parameters in identifying metastatic cervical lymph nodes in oral squamous cell carcinoma (OSCC) patients and to ...explore the relationships between DECT and pathological features.
Methods
Clinical and DECT data were collected from patients who underwent radical resection of OSCC and cervical lymph node dissection between November 2019 and June 2021. Microvascular density was assessed using the Weidner counting method. The electron density (ED) and effective atomic number (
Z
eff
) in non - contrast phase and iodine concentration (IC), normalized IC, slope of the energy spectrum curve (
λ
HU
), and dual-energy index (DEI) in parenchymal phase were compared between metastatic and non - metastatic lymph nodes. Student’s
t
-test, Pearson’s rank correlation, and receiver operating characteristic curves were performed.
Results
The inclusion criteria were met in 399 lymph nodes from 103 patients. Metastatic nodes (
n
= 158) displayed significantly decreased ED, IC, normalized IC,
λ
HU
, and DEI values compared with non-metastatic nodes (
n
= 241) (all
p
< 0.01). Strong correlations were found between IC (
r
= 0.776), normalized IC (
r
= 0.779),
λ
HU
(
r
= 0.738), DEI (
r
= 0.734), and microvascular density. Area under the curve (AUC) for normalized IC performed the highest (0.875) in diagnosing metastatic nodes. When combined with the width of nodes, AUC increased to 0.918.
Conclusion
DECT parameters IC, normalized IC,
λ
HU
, and DEI reflect pathologic changes in lymph nodes to a certain extent, and aid for detection of metastatic cervical lymph nodes from OSCC.
Key Points
• Electron density, iodine concentration, normalized iodine concentration, λ
HU
, and dual-energy index values showed significant differences between metastatic and non-metastatic nodes.
• Strong correlations were found between iodine concentration, normalized iodine concentration, slope of the spectral Hounsfield unit curve, dual-energy index, and microvascular density.
• DECT qualitative parameters reflect the pathologic changes in lymph nodes to a certain extent, and aid for the detection of metastatic cervical lymph nodes from oral squamous cell carcinoma.
Surface flaw inspection is of great importance for quality control in the field of manufacture. In this paper, a novel surface flaw inspection algorithm is proposed based on adaptive multiscale image ...collection (AMIC) using convolutional neural networks. First, the inspection networks are pretrained with ImageNet data set. Second, the AMIC is established, which consists of adaptive multiscale image extraction and with-contour local extraction from training images. Through the AMIC, the training data set is greatly augmented, and labels of images can be accomplished automatically without artificial consumption. Then, transfer learning is performed with the AMIC established from training data set. Finally, an automatic surface flaw inspection instrument for large-volume metal components embedded with the proposed inspection algorithm is designed. Experiments with small metal components are performed to analyze the influence of parameters, and comparative experiments are carried out. The inspecting precisions for indentation, scratch, and pitted surface of the proposed method are 97.3%, 99.5%, and 100%, respectively. The experimental results demonstrate the effectiveness of the proposed method in the detection of various surface flaws.
A synergistic catalytic method combining photoredox catalysis, hydrogen‐atom transfer, and proton‐reduction catalysis for the dehydrogenative silylation of alkenes was developed. With this approach, ...a highly concise route to substituted allylsilanes has been achieved under very mild reaction conditions without using oxidants. This transformation features good to excellent yields, operational simplicity, and high atom economy. Based on control experiments, a possible reaction mechanism is proposed.
A synergistic catalytic method of combining photoredox catalysis, hydrogen‐atom transfer, and proton‐reduction catalysis for the dehydrogenative silylation of alkenes was developed. The reaction features high regioselectivity, excellent tolerance of functional groups, wide substrate scope, and mild reaction conditions. Moreover, this oxidant‐free system offers a cleaner and more efficient method beyond traditional catalysis, which requires either stoichiometric or excess amounts of oxidants.
There is growing interest in multilabel image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix ...completion (MC) has recently been introduced as a method for transductive (semisupervised) multilabel classification, and has several distinct advantages, including robustness to missing data and background noise in both feature and label space. However, it is limited by only considering data represented by a single-view feature, which cannot precisely characterize images containing several semantic concepts. To utilize multiple features taken from different views, we have to concatenate the different features as a long vector. However, this concatenation is prone to over-fitting and often leads to very high time complexity in MC-based image classification. Therefore, we propose to weightedly combine the MC outputs of different views, and present the multiview MC (MVMC) framework for transductive multilabel image classification. To learn the view combination weights effectively, we apply a cross-validation strategy on the labeled set. In particular, MVMC splits the labeled set into two parts, and predicts the labels of one part using the known labels of the other part. The predicted labels are then used to learn the view combination coefficients. In the learning process, we adopt the average precision (AP) loss, which is particular suitable for multilabel image classification, since the ranking-based criteria are critical for evaluating a multilabel classification system. A least squares loss formulation is also presented for the sake of efficiency, and the robustness of the algorithm based on the AP loss compared with the other losses is investigated. Experimental evaluation on two real-world data sets (PASCAL VOC' 07 and MIR Flickr) demonstrate the effectiveness of MVMC for transductive (semisupervised) multilabel image classification, and show that MVMC can exploit complementary properties of different features and output-consistent labels for improved multilabel image classification.
A high-isolation eight-antenna multi-input multi-output (MIMO) array operating in the 3.5 GHz band (3.4-3.6 GHz) for future smartphones is proposed. Here, a novel balanced open-slot antenna is ...designed as an array antenna element, in which this antenna design can yield a balanced slot mode (with reduced ground effects) that can enhance the isolation between two adjacent input ports. Furthermore, by meticulously arranging the positions of the eight antenna elements, desirable polarization diversity can also be successfully achieved, which further mitigates the coupling between antenna elements. A prototype was manufactured to validate the simulation. A good impedance matching (return loss > 10 dB), high isolation (>17.5 dB), high total efficiency (>62%), and low envelope correlation coefficient (ECC, <0.05) were measured across the desired operation bandwidth. To verify the MIMO performance, ergodic channel capacity using the Kronecker channel model was calculated. The effects of hand phantom were also studied.
Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of ...multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the main problem of multi-view canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained. In addition, a non-linear extension of TCCA is presented. Experiments on various challenge tasks, including large scale biometric structure prediction, internet advertisement classification, and web image annotation, demonstrate the effectiveness of the proposed method.
The 2015 Lancet Commission on Health and Climate Change has been formed to map out the impacts of climate change, and the necessary policy responses, in order to ensure the highest attainable ...standards of health for populations worldwide. This Commission is multidisciplinary and international in nature, with strong collaboration between academic centres in Europe and China.
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