Testing and verification of neural-network-based safety-critical control software: A systematic literature review

Context: Neural Network (NN) algorithms have been successfully adopted in a number of Safety-Critical Cyber-Physical Systems (SCCPSs). Testing and Verification (T&V) of NN-based control software in safety-critical domains are gaining interest and attention from both software engineering and safety engineering researchers and practitioners. Objective: With the increase in studies on the T&V of NN-based control software in safety-critical domains, it is important to systematically review the state-of-the-art T&V methodologies, to classify approaches and tools that are invented, and to identify challenges and gaps for future studies. Method: We retrieved 950 papers on the T&V of NN-based Safety-Critical Control Software (SCCS). To reach our result, we filtered 83 primary papers published between 2001 and 2018, applied the thematic analysis approach for analyzing the data extracted from the selected papers, presented the classification of approaches, and identified challenges. Conclusion: The approaches were categorized into five high-order themes: assuring robustness of NNs, assuring safety properties of NN-based control software, improving the failure resilience of NNs, measuring and ensuring test completeness, and improving the interpretability of NNs. From the industry perspective, improving the interpretability of NNs is a crucial need in safety-critical applications. We also investigated nine safety integrity properties within four major safety lifecycle phases to investigate the achievement level of T&V goals in IEC 61508-3. Results show that correctness, completeness, freedom from intrinsic faults, and fault tolerance have drawn most attention from the research community. However, little effort has been invested in achieving repeatability; no reviewed study focused on precisely defined testing configuration or on defense against common cause failure.Comment: This paper had been submitted to Journal of Information and Software Technology on April 20, 2019,Revised 5 December 2019, Accepted 6 March 2020, Available online 7 March 202

Self adaptive global-local feature enhancement for radiology report generation

Automated radiology report generation aims at automatically generating a detailed description of medical images, which can greatly alleviate the workload of radiologists and provide better medical services to remote areas. Most existing works pay attention to the holistic impression of medical images, failing to utilize important anatomy information. However, in actual clinical practice, radiologists usually locate important anatomical structures, and then look for signs of abnormalities in certain structures and reason the underlying disease. In this paper, we propose a novel framework AGFNet to dynamically fuse the global and anatomy region feature to generate multi-grained radiology report. Firstly, we extract important anatomy region features and global features of input Chest X-ray (CXR). Then, with the region features and the global features as input, our proposed self-adaptive fusion gate module could dynamically fuse multi-granularity information. Finally, the captioning generator generates the radiology reports through multi-granularity features. Experiment results illustrate that our model achieved the state-of-the-art performance on two benchmark datasets including the IU X-Ray and MIMIC-CXR. Further analyses also prove that our model is able to leverage the multi-grained information from radiology images and texts so as to help generate more accurate reports

The role of TRIM family in metabolic associated fatty liver disease

Metabolic associated fatty liver disease (MAFLD) ranks among the most prevalent chronic liver conditions globally. At present, the mechanism of MAFLD has not been fully elucidated. Tripartite motif (TRIM) protein is a kind of protein with E3 ubiquitin ligase activity, which participates in highly diversified cell activities and processes. It not only plays an important role in innate immunity, but also participates in liver steatosis, insulin resistance and other processes. In this review, we focused on the role of TRIM family in metabolic associated fatty liver disease. We also introduced the structure and functions of TRIM proteins. We summarized the TRIM family’s regulation involved in the occurrence and development of metabolic associated fatty liver disease, as well as insulin resistance. We deeply discussed the potential of TRIM proteins as targets for the treatment of metabolic associated fatty liver disease

Research on neural network prediction method for upgrading scale of natural gas reserves

With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields

A case report and literature review: pheochromocytoma-mediated takotsubo cardiomyopathy, which is similar to acute myocardial infarction

A 52-year-old Chinese woman was admitted to a cardiac intensive care unit (CCU) due to nausea, vomiting, and dyspnea, which began a day before her hospitalization. Metoprolol succinate and conventional treatment for acute myocardial infarction (AMI) were initially administered to the patient based on electrocardiogram (ECG) findings and elevated cardiac troponin I (cTnI). However, the following day, she developed aggravated nausea, vomiting, fever, sweating, a flushed face, a rapid heart rate, and a significant rise in blood pressure. Furthermore, ultrasonic cardiography (UCG) displayed takotsubo-like changes; nevertheless, ECG indicated inconsistent cTnI peaks with extensive infarction. After coronary computed tomography angiography (CTA) ruled out (AMI), and in conjunction with the uncommon findings, we strongly suspected that the patient had a secondary condition of pheochromocytoma-induced takotsubo cardiomyopathy (Pheo-TCM). In the meanwhile, the use of metoprolol succinate was promptly discontinued. This hypothesis was further supported by the subsequent plasma elevation of multiple catecholamines and contrast-enhanced computed tomography (CECT). After one month of treatment with high-dose Phenoxybenzamine in combination with metoprolol succinate, the patient met the criteria for surgical excision and successfully underwent the procedure. This case report demonstrated that pheochromocytoma could induce TCM and emphasized the significance of distinguishing it from AMI (in the context of beta-blocker usage and anticoagulant management)