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Driving intelligence tests are critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life-like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude. Tests for autonomous vehicles are usually made in the naturalistic driving environment where safety-critical scenarios are rare. Feng et al. propose a testing approach combining naturalistic and adversarial environment which allows to accelerate testing process and detect dangerous driving events.

The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—presents significant challenges in ensuring the safety of autonomous vehicles using deep learning. Looking at it from distinct perspectives, we identify three potential approaches for addressing the issue. The curse of rarity—the rarity of safety-critical events in high-dimensional variable spaces—presents significant challenges in ensuring the safety of autonomous vehicles using deep learning. Looking at it from distinct perspectives, the authors identify three potential approaches for addressing the issue.

Several epidemiological studies have identified diabetes as a risk factor for colorectal cancer (CRC). The potential pathophysiological mechanisms of this association include hyperinsulinemia, insulin-like growth factor (IGF) axis, hyperglycemia, inflammation induced by adipose tissue dysfunction, gastrointestinal motility disorder, and impaired immunological surveillance. Several studies have shown that underlying diabetes adversely affects the prognosis of patients with CRC. This review explores the novel anticancer agents targeting IGF-1R and receptor for advanced glycation end products (RAGE), both of which play a vital role in diabetes-induced colorectal tumorigenesis. Inhibitors of IGF-1R and RAGE are expected to become promising therapeutic choices, particularly for CRC patients with diabetes. Furthermore, hypoglycemic therapy is associated with the incidence of CRC. Selection of appropriate hypoglycemic agents, which can reduce the risk of CRC in diabetic patients, is an unmet issue. Therefore, this review mainly summarizes the current studies concerning the connections among diabetes, hypoglycemic therapy, and CRC as well as provides a synthesis of the underlying pathophysiological mechanisms. Our synthesis provides a theoretical basis for rational use of hypoglycemic therapies and early diagnosis and treatment of diabetes-related CRC.

The assembly stage is a vital phase in the production process and currently, there are still many manual tasks in the assembly operation. One of the challenges of manual assembly is the issue of blind area assembly since the visual obstruction of the hands or a part can lead to more errors and lower assembly efficiency. In this study, we developed an AR-assisted assembly system that solves the occlusion problem. Assembly workers can use the system to achieve comprehensive and precise hand–eye coordination (HEC). Additionally, we designed and conducted a user evaluation experiment to measure the learnability, usability, and mental effort required for the system for other HEC modes. Results indicate that hand position is the first visual information that should be considered in blind areas. Besides, the Intact HEC mode can effectively reduce the difficulty of learning and mental burden in operation, while at the same time improving efficiency.

For simulation to be an effective tool for the development and testing of autonomous vehicles, the simulator must be able to produce realistic safety-critical scenarios with distribution-level accuracy. However, due to the high dimensionality of real-world driving environments and the rarity of long-tail safety-critical events, how to achieve statistical realism in simulation is a long-standing problem. In this paper, we develop NeuralNDE, a deep learning-based framework to learn multi-agent interaction behavior from vehicle trajectory data, and propose a conflict critic model and a safety mapping network to refine the generation process of safety-critical events, following real-world occurring frequencies and patterns. The results show that NeuralNDE can achieve both accurate safety-critical driving statistics (e.g., crash rate/type/severity and near-miss statistics, etc.) and normal driving statistics (e.g., vehicle speed/distance/yielding behavior distributions, etc.), as demonstrated in the simulation of urban driving environments. To the best of our knowledge, this is the first time that a simulation model can reproduce the real-world driving environment with statistical realism, particularly for safety-critical situations. Simulation of naturalistic driving environment for autonomous vehicle development is challenging due to its complexity and high dimensionality. The authors develop a deep learning-based framework to model driving behavior including safety-critical events for improved training of autonomous vehicles.

In December 2019, the outbreak of a new coronavirus-caused pneumonia (COVID-19) in Wuhan attracted close attention in China and the world. The Chinese government took strong national intervention measures on January 23 to control the spread of the epidemic. We are trying to show the impact of these controls on the spread of the epidemic. We proposed an SEIR(Susceptible-Exposed-Infectious-Removed) model to analyze the epidemic trend in Wuhan and use the AI model to analyze the epidemic trend in non-Wuhan areas. We found that if the closure was lifted, the outbreak in non-Wuhan areas of mainland China would double in size. Our SEIR and AI model was effective in predicting the COVID-19 epidemic peaks and sizes. The epidemic control measures taken by the Chinese government, especially the city closure measures, reduced the scale of the COVID-19 epidemic.

Mind mapping has always played a role in graphic design as a logical analysis and integration of ideas. By using the method of mind mapping it is possible to develop thinking and reasoning from a wider range or different perspectives, thus reorganising relevant factors to break through the original deconstruction to achieve innovative research in graphic design. At the same time, the structural order and visualisation of the mind map is very conducive to logical reasoning and building systems for graphic design. Therefore, in this paper, the application of the mind mapping method in graphic design is investigated with the example of the traditional Chinese local opera, the Qin Qiang. Using the logical tool of mind mapping, a set of graphic design works on the theme of Qin Qiang was completed by clarifying the theme, divergent thinking and determining the direction and method of research to integrate and unify the various forms of Qin Qiang. This paper uses mind mapping to provide a wealth of information and inspirational derivation for the graphic design about the Qin Qiang, allowing the design to visualise effective information in a more comprehensive and concrete way. At the same time, this allows the central idea of the Qin Qiang to be communicated more effectively and accurately in the graphic design than in previous graphic design works.

In recent years, increasing numbers of people have been choosing herbal medicines or products to improve their health conditions, either alone or in combination with others. Herbs are staging a comeback and herbal “renaissance” occurs all over the world. According to the World Health Organization, 75% of the world’s populations are using herbs for basic healthcare needs. Since the dawn of mankind, in fact, the use of herbs/plants has offered an effective medicine for the treatment of illnesses. Moreover, many conventional/pharmaceutical drugs are derived directly from both nature and traditional remedies distributed around the world. Up to now, the practice of herbal medicine entails the use of more than 53,000 species, and a number of these are facing the threat of extinction due to overexploitation. This paper aims to provide a review of the history and status quo of Chinese, Indian, and Arabic herbal medicines in terms of their significant contribution to the health promotion in present-day over-populated and aging societies. Attention will be focused on the depletion of plant resources on earth in meeting the increasing demand for herbs.

In this paper, hybrid models of Kalman filter and neural network for state estimation are reviewed of their corresponding academic achievements, the creation of which is a noteworthy development in state estimation. This paper aims to provide a summary of research progress on such hybrid models and emphasize their functions and advantages. First of all, the concept and feature are paid attention to about Kalman filter, and its transmutative modes are taken into consideration. Then several popular neural network algorithms are introduced in brief. Subsequently, research results on hybrid models are analysed and discussed comprehensively. Not only Kalman filter and neural network can be adopted in succession, but also can be mixed in structure. The mixed models can also be divided into two types, the equations or parameters of state-space model are trained by neural network for Kalman filter and the parameters of neural network are updated by Kalman filter. It is proved that the hybrid models outperform than single model of Kalman filter or neural network in accuracy and generalization. Last but not least, the effectiveness of state-space equations of Kalman filter can be established by neural network in nonlinear systems is verified.

APACHE IVa provides typically useful and accurate predictions on in-hospital mortality and length of stay for patients in critical care. However, there are factors which may preclude APACHE IVa from reaching its ceiling of predictive accuracy. Our primary aim was to determine which variables available within the first 24 h of a patient’s ICU stay may be indicative of the APACHE IVa scoring system making occasional but potentially illuminating errors in predicting in-hospital mortality. We utilized the publicly available multi-institutional ICU database, eICU, available since 2018, to identify a large observational cohort for our investigation. APACHE IVa scores are provided by eICU for each patient’s ICU stay. We used Lasso logistic regression in an aim to build parsimonious final models, using cross-validation to select the penalization parameter, separately for each of our two responses, i.e., errors, of interest, which are APACHE falsely predicting in-hospital death (Type I error), and APACHE falsely predicting in-hospital survival (Type II error). We then assessed the performance of the models with a random holdout validation sample. While the extremeness of the APACHE prediction led to dependable predictions for preventing either type of error, distinct variables were identified as being strongly associated with the two different types of errors occurring. These included a primary set of predictors consisting of mean SpO2 and worst lactate for predicting Type I errors, and worst albumin and mean heart rate for Type II. In addition, a secondary set of predictors including changes recorded in care limitations for the patient’s treatment plan, worst pH, whether cardiac arrest occurred at admission, and whether vasopressor was provided for predicting Type I error; age, whether the patient was ventilated in day 1, mean respiratory rate, worst lactate, worst blood urea nitrogen test, and mean aperiodic vitals for Type II. The two models also differed in their performance metrics in their holdout validation samples, in large part due to the lower prevalence of Type II errors compared to Type I. The eICU database was a good resource for evaluating our objective, and important recommendations are provided, particularly identifying key variables that could lead to APACHE prediction errors when APACHE scores are sufficiently low to predict in-hospital survival.