Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis

Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing modalities from given ones. Existing (supervised learning) methods often require a large number of paired multi-modal data to train an effective synthesis model. However, it is often challenging to obtain sufficient paired data for supervised training. In reality, we often have a small number of paired data while a large number of unpaired data. To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis. Specifically, an Edge-preserving Masked AutoEncoder (Edge-MAE) is first pre-trained in a self-supervised manner to simultaneously perform 1) image imputation for randomly masked patches in each image and 2) whole edge map estimation, which effectively learns both contextual and structural information. Besides, a novel patch-wise loss is proposed to enhance the performance of Edge-MAE by treating different masked patches differently according to the difficulties of their respective imputations. Based on this proposed pre-training, in the subsequent fine-tuning stage, a Dual-scale Selective Fusion (DSF) module is designed (in our MT-Net) to synthesize missing-modality images by integrating multi-scale features extracted from the encoder of the pre-trained Edge-MAE. Further, this pre-trained encoder is also employed to extract high-level features from the synthesized image and corresponding ground-truth image, which are required to be similar (consistent) in the training. Experimental results show that our MT-Net achieves comparable performance to the competing methods even using $70\%$ of all available paired data. Our code will be publicly available at https://github.com/lyhkevin/MT-Net.Comment: 13 pages, 15 figure

Failure mode and effects analysis on the air system of an aero turbofan engine sing the Gaussian model and evidence theory

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine

Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations

It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102

The Development of Computer Forensic Legal System in China

The computer forensic discipline was established around 2000 in China, which was further developed along with Chinese judicial appraisal system in 2005. The new criminal and civil procedure laws of the People’s Republic of China was enacted on 1 Jan 2013. The new laws specified electronic data is legal evidence and has great impact on the current practice on handling electronic evidence. This paper introduces the electronic data and electronic evidence examination procedure in mainland China, the general concept of computer forensic legal system, the management of computer judicial experts, the management of computer judicial expertise institutions. Keywords: China legal system, computer forensic, judicial expert, judicial expertise institution

A versatile hybrid polyphenylsilane host for highly efficient solution-processed blue and deep blue electrophosphorescence

A universal hybrid polymeric host (PCzSiPh) for blue and deep blue phosphors has been designed and synthesized by incorporating electron-donating carbazole as pendants on a polytetraphenylsilane main chain. The polymer PCzSiPh (4) has a wide bandgap and high triplet energy (ET) because of the tetrahedral geometry of the silicon atom in the tetraphenylsilane backbone. The distinct physical properties of good solubility, combined with high thermal and morphological stability give amorphous and homogenous PCzSiPh films by solution processing. As a result, using PCzSiPh as host with the guest iridium complex TMP-FIrpic gives blue phosphorescent organic light-emitting diodes (PhOLEDs) with overall performance which far exceeds that of a control device with poly(vinylcarbazole) (PVK) host. Notably, FIrpic-based devices exhibit a maximum external quantum efficiency (EQE) of 14.3% (29.3 cd A−1, 10.4 lm W−1) which are comparable to state-of-the-art literature data using polymer hosts for a blue dopant emitter. Moreover, the versatility of PCzSiPh extends to deep blue PhOLEDs using FIr6 and FCNIrpic as dopants, with high efficiencies of 11.3 cd A−1 and 8.6 cd A−1, respectively

An in-situ method for assessing soil aggregate stability in burned landscapes

Due to soil repellency in burned areas, slope runoff and soil erodibility escalates following forest fires, increasing the vulnerability to post-fire debris flows. Soil aggregate stability is a critical determinant of soil infiltration capacity and erosion susceptibility. The prevalent method of assessing soil aggregate stability in burned areas, the counting the number of water drop impacts (CND) method, is time-intensive and impractical for in-situ measurements. In response, this study introduces a novel technique based on the shock and vibration damage (SVD) effect for evaluating soil aggregate stability in burned areas. Thirteen distinct soil aggregate types were meticulously prepared for indoor simulated fire testing, with due consideration to factors such as bulk weight, organic matter content, and water repellency, which influence stability of soil aggregates. Employing a custom-built test apparatus, the mass loss rate (MLR) of soil aggregates was determined through orthogonal experiments using the SVD method and compared against the standard CND technique's quantification of water droplet-induced aggregate destruction. The findings demonstrated that SVD method, employing Test Scheme 6 (testing 20 aggregates, 1-meter impact height, 40% water content, and five impacts), exhibits excellent agreement (Kendall coefficient = 0.797) and correlation (R2 = 0.634) with CND method outcomes. This testing scheme, characterized by rapid determination and effective discrimination, is identified as the optimal testing approach. The SVD testing apparatus is straightforward, portable, and easily disassembled, rendering it suitable for on-site use. It can be used to distinguish the stability level of soil aggregates swiftly and quantitatively under various fire intensities in burned areas in situ, which is an important guiding significance for the study of soil erosion, erosion control, and post-fire debris flow initiation mechanism in burned areas

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed