Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
104,213
result(s) for
"Zhao, Chen-chen"
Sort by:
واقع وآفاق التعاون الصيني العربي وتأسيس المنطقة الاقتصادية التجريبية الداخلية المفتوحة بنينغشيا
يتحدث الكتاب علي مبادرة \"الحزام والطريق\" التي أطلقها الرئيس الصيني الحالي شي جين بينغ إجراء مهما فيما يتعلق بالانفتاح الصيني على الخارج وتقع منطقة نينغشيا الذاتية الحكم ضمن المناطق الداخلية الصينية في موقع مهم على الجسر الذي يربط بين آسيا وأوروبا وتتمتع بموقع جغرافي متميز يربط بين الصين والشرق الأوسط وآسيا الوسطى وباعتبارها أكبر منطقة لتجمع شعب قومية الصينية المسلمة على مستوى الصين فهناك تقارب بين عادات وتقاليد قومية هوية وعادات وتقاليد المسلمين حول العالم وقد ساعد هذا التوافق في العقيدة الدينية على التواصل الشعبي المستمر وأن تلعب المنطقة دورا مهما في التعاون بين الصين والدول العربية والإسلامية وفي سبيل دفع تحويل ننيغشيا إلى قاعدة استراتيجية للمنطقة الاقتصادية التجريبية المفتوحة وانفتاح الصين غربا إلى جانب تحويلها إلى محطة استراتيجية على طول الحزام الاقتصادي لطريق الحرير وينطلق هذا الكتاب من تأسيس منصة استراتيجية تجمع بين الصين والدول العربية والإسلامية ويعتمد على التعاون الصيني العربي وتأسيس المنطقة الاقتصادية التجريبية الداخلية المفتوحة بنينغشيا كموضوع رئيسي لدراسة التعاون الصيني العربي في مختلف المجالات.
BOOK
Photothermal conversion triggered thermal asymmetric catalysis within metal nanoparticles loaded homochiral covalent organic framework
For seeking high enantiopurity, the previously reported thermal asymmetric catalysis is usually carried out at low temperature sometimes with limited yield, that is, the high enantiomeric excess (ee) usually at the cost of high yield. Thus, the achieving both high stereoselectivity and yield is an enormous challenge. We report herein two metal nanoparticle (M NP)-loaded and porphyrin-containing homochiral covalent organic framework (CCOF)-based composite catalysts, and their application in the thermally-driven asymmetric one-pot Henry and A
3
-coupling reactions. All the reactions are conducted at elevated temperatures with both excellent stereoselectivity and yield which resulted from the synergy of CCOF confinement effect and M NP catalytic activation. Notably, the needed thermal energy for the asymmetric reactions herein is derived from the photothermal conversion via porphyrin-based CCOF upon irradiation with visible light. Remarkably, the CCOF confinement effect can be effectively maintained up to 100 °C for the asymmetric one-pot Henry and A
3
-coupling reactions herein.
Achieving both high stereoselectivity and yield is a challenge for conventional asymmetric catalysis. Here, the authors report two metal nanoparticle-loaded and porphyrin-containing homochiral covalent organic framework-based composite catalysts that exhibit high stereoselectivity and yield in the thermally-driven asymmetric reactions.
Journal Article
Graph Adaptive Attention Network with Cross-Entropy
Non-Euclidean data, such as social networks and citation relationships between documents, have node and structural information. The Graph Convolutional Network (GCN) can automatically learn node features and association information between nodes. The core ideology of the Graph Convolutional Network is to aggregate node information by using edge information, thereby generating a new node feature. In updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node; the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the adaptive attention mechanism (AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution (MHGC). Finally, we adopt the cross-entropy (CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on the AAM, MHGC, and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). The experiments show that classification accuracy achieves outstanding performances on Cora, Citeseer, and Pubmed datasets.
Journal Article
(HTBNet)Arbitrary Shape Scene Text Detection with Binarization of Hyperbolic Tangent and Cross-Entropy
The existing segmentation-based scene text detection methods mostly need complicated post-processing, and the post-processing operation is separated from the training process, which greatly reduces the detection performance. The previous method, DBNet, successfully simplified post-processing and integrated post-processing into a segmentation network. However, the training process of the model took a long time for 1200 epochs and the sensitivity to texts of various scales was lacking, leading to some text instances being missed. Considering the above two problems, we design the text detection Network with Binarization of Hyperbolic Tangent (HTBNet). First of all, we propose the Binarization of Hyperbolic Tangent (HTB), optimized along with which the segmentation network can expedite the initial convergent speed by reducing the number of epochs from 1200 to 600. Because features of different channels in the same scale feature map focus on the information of different regions in the image, to better represent the important features of all objects in the image, we devise the Multi-Scale Channel Attention (MSCA). Meanwhile, considering that multi-scale objects in the image cannot be simultaneously detected, we propose a novel module named Fused Module with Channel and Spatial (FMCS), which can fuse the multi-scale feature maps from channel and spatial dimensions. Finally, we adopt cross-entropy as the loss function, which measures the difference between predicted values and ground truths. The experimental results show that HTBNet, compared with lightweight models, has achieved competitive performance and speed on Total-Text (F-measure:86.0%, FPS:30) and MSRA-TD500 (F-measure:87.5%, FPS:30).
Journal Article
Physics-informed learning of governing equations from scarce data
Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel approach called physics-informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this discovery approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to approximate the solution of system variables, compute essential derivatives, as well as identify the key derivative terms and parameters that form the structure and explicit expression of the equations. The efficacy and robustness of this method are demonstrated, both numerically and experimentally, on discovering a variety of partial differential equation systems with different levels of data scarcity and noise accounting for different initial/boundary conditions. The resulting computational framework shows the potential for closed-form model discovery in practical applications where large and accurate datasets are intractable to capture.
Recovery of underlying governing laws or equations describing the evolution of complex systems from data can be challenging if dataset is damaged or incomplete. The authors propose a learning approach which allows to discover governing partial differential equations from scarce and noisy data.
Journal Article
Dynamic optimization of high-bandwidth multi-receiver signals for civil aircraft flight tests telemetry
Civil aircraft flight tests are distinguished by parallel multi-task operations, multi-sensor collaboration, and high-frequency data acquisition. The exponential growth in telemetry data generated in a single flight imposes stringent requirements on the high-bandwidth transmission capabilities of telemetry systems and the real-time optimization of multi-receiver signals. This study focuses on addressing the dynamic optimization of high-bandwidth multi-receiver signals during flight tests, aiming to enhance demodulation accuracy and ensure stable real-time transmission of large-scale telemetry data. A channel model for complex scenarios was constructed to analyze signal redundancy and dynamic switching in multi-receiver links, while an improved routing protocol integrated with multi-dimensional signal evaluation was investigated. A modular simulation model for the multi-receiver telemetry system was developed within a highly dynamic wireless ad hoc network environment. By enhancing the AODV and LEACH routing protocols, multi-path backup and energy consumption optimization were achieved. A multi-dimensional signal optimization algorithm was utilized to dynamically evaluate and fuse bit synchronization and frame synchronization via voting, enabling real-time selection of the optimal received signal in complex environments. The findings indicate that high-bandwidth multi-receiver signal optimization and enhanced routing strategies effectively alleviate challenges associated with multipath fading, channel obstruction, and high-speed mobility in flight test scenarios, thereby facilitating real-time channel monitoring and dynamic switching while improving data timeliness and accuracy. Simulation results demonstrate that under high-dynamic conditions, the proposed algorithm reduces the average BER to 3e -6 at 10 dB SNR, improves the frame synchronization success rate to 98.9%, and reduces invalid channel switching frequency by approximately 84% compared to traditional methods.
Journal Article
MOF derived core-shell CuO/C with temperature-controlled oxygen-vacancy for real time analysis of glucose
Introducing oxygen-vacancy into the surface of the non-enzymatic sensor is supposed to be an effective way to improve inherently low catalytic activity and specificity of non-enzymatic sensors. In this work, CuO/C was synthesized at different temperatures using metal-organic frameworks as sacrificial templates to receive additional content of oxygen-vacancy. The product with the highest oxygen vacancy was found at 400 °C (named CuO/C-400 °C), which increased catalytically active sites and enhanced the charge-transfer efficiency. The sensing performance was afterward explored by amperometry under an optimal applied potential at 0.5 V (vs. SCE), presenting a broad detection range from 5.0 µM to 25.325 mM (R
2
= 0.9998) with a sensitivity of 244.71 µA mM
− 1
cm
− 2
, and a detection limit of 1 µM. Furthermore, the reliability and selectivity of CuO/C-400 °C sensors were extensively explored in the presence of artificial serum/saliva samples with gradient glucose concentrations. The human blood samples were also detected with high recoveries compared with the clinical Hexokinase method. Hence, the prepared CuO/C-400 °C sensor with a broad detection range and high selectivity can be applied for the diabetes diagnosis ex vivo without further dilution for real-time analysis in practical applications.
Journal Article