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Introduction: Internal fistula across the posterior wall of stomach and the transverse colon caused by foreign bodies in the alimentary tract presents an extremely rare medical entity. Presentation of case: We report an aschizophrenia female patient with onset of internal fistula across the posterior wall of stomach and the transverse colon triggered by swallowed magnetic metal beads. The patient was admitted to the emergency room of Northern Jiangsu People’s Hospital because of acute right lower abdominal pain. Emergency routine abdominal CT scan revealed acute appendicitis and a set of foreign body in digestive tract. Discussion: The foreign body in the stomach was removed by open surgery after tentative Endoscopic foreign body removal and laparoscopic appendectomy and exploration. In the process of exploring the gastric wall, it was found that one of magnet beads was embedded in the posterior wall of stomach and adhered to part of the transverse colon. After separation, it was found that an internal fistula was formed across the posterior wall of stomach and the transverse colon. As the patient ate only a small amount of food within 2 days, and the intestines were in good condition, we performed partial transverse colectomy, end-to-side anastomosis and gastric wall repair. Conclusion: This case shows that for long-term foreign bodies in the digestive tract, we should be beware of the onset of gastrointestinal perforation. Moreover, perforation caused by the force acting on a blunt foreign body often results in atypical imaging findings, and the diagnosis of perforation cannot be clearly determined by imaging findings such as the presence of free gas downstream of the diaphragm. This poses new challenges for clear diagnosis and treatment.

Electronic Government (e-Government) systems constantly provide greater services to people, businesses, organisations, and societies by offering more information, opportunities, and platforms with the support of advances in information and communications technologies. This usually results in increased system complexity and sensitivity, necessitating stricter security and privacy-protection measures. The majority of the existing e-Government systems are centralised, making them vulnerable to privacy and security threats, in addition to suffering from a single point of failure. This study proposes a decentralised e-Government framework with integrated threat detection features to address the aforementioned challenges. In particular, the privacy and security of the proposed e-Government system are realised by the encryption, validation, and immutable mechanisms provided by Blockchain. The insider and external threats associated with blockchain transactions are minimised by the employment of an artificial immune system, which effectively protects the integrity of the Blockchain. The proposed e-Government system was validated and evaluated by using the framework of Ethereum Visualisations of Interactive, Blockchain, Extended Simulations (i.e. eVIBES simulator) with two publicly available datasets. The experimental results show the efficacy of the proposed framework in that it can mitigate insider and external threats in e-Government systems whilst simultaneously preserving the privacy of information.

Background: There are few models to predict the survival of patients of different ethnicities initially diagnosed with metastatic gastric cancer (mGC). Therefore, the aim of this study was to construct a nomogram to predict the cancer-specific survival (CSS) of these patients. Methods: Data for 994 patients initially diagnosed with mGC between 2000 and 2013 were extracted from the Surveillance, Epidemiology, and End Results database. Patients were randomly classified into a training (n = 696) or internal validation (n = 298) cohort, and a cohort of 133 patients from Fudan cohort was used for external validation. A nomogram to predict the CSS of mGC patients was derived and validated using a concordance index (C-index), calibration curves, and decision-curve analysis (DCA). Results: Multivariate Cox regression indicated that five factors were independent predictors of CSS: differentiation grade, T stage, N stage, metastatic site at diagnosis, and with or without chemotherapy. Thus, these factors were integrated into the nomogram model. The C-index value of the nomogram model was 0.63 (95% CI: 0.60–0.65), and those of the internal and external validation cohorts were 0.60 (95%: CI 0.55–0.64) and 0.63 (95%: CI 0.57–0.69), respectively. The calibration curves showed good consistency between the actual and predicted survival rates in both the internal and external validation cohorts. The DCA also showed the clinical utility of the nomogram model. Conclusions: We established a practical nomogram to predict the CSS of patients initially diagnosed with mGC. The nomogram can be used for individualized prediction of survival and to guide clinicians in making treatment decisions.

Since China is the largest developing country in the world, its urban reconstruction has significance for countries undergoing rapid urbanization. The recent development of its central cities has gradually changed from large-scale incremental expansion to stock optimization. District boundary reorganization has become a new trend in reforming China’s administrative division of central cities. From the perspective of scalar reorganization and regional reconstruction, the adjustment of urban administrative divisions can be regarded as a regional construction strategy initiated by city governments to reshape the spatial structure, improve the governance relationship, and enhance the cities’ competitiveness. This study takes Hangzhou, an important central city in eastern China, as a case study to illustrate two ways in which a central city can optimize its urban spaces through district boundary recombination: scale recombination and regional recombination. The findings demonstrate two advantages of district boundary reorganization for China’s central cities: it eases the integration of new city districts and urban areas and promotes balanced development within the city. In the future, more Chinese cities will likely choose to reorganize their district boundaries.

Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user's aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fréchet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user.

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals.

Egocentric action recognition has been intensively studied in the fields of computer vision and clinical science with applications in pervasive health-care. The majority of the existing egocentric action recognition techniques utilize the features extracted from either the entire contents or the regions of interest (ROI) in video frames as the inputs of action classifiers. The former might suffer from moving backgrounds or irrelevant foregrounds usually associated with egocentric action videos, while the latter may be impaired by the mismatch between the calculated and the ground truth ROI. This paper proposes a new gaze-informed feature extraction approach, by which the features are extracted from the regions around the gaze points and thus representing the genuine ROI from a first person of view. The activity of daily life can then be classified based only on the identified regions using the extracted gaze-informed features. The proposed approach has been further applied to a memory support system for people with poor memory, such as those with amnesia or dementia, and their carers. The experimental results demonstrate the efficacy of the proposed approach in egocentric action recognition, and thus the potential of the memory support tool in health care.

The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel, and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are online tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms that the proposed network is capable of producing better control performances with high computational efficiency.

The brain emotional learning (BEL) system was inspired by the biological amygdala-orbitofrontal model to mimic the high speed of the emotional learning mechanism in the mammalian brain, which has been successfully applied in many real-world applications. Despite of its success, such system often suffers from slow convergence for online humanoid robotic control. This paper presents an improved fuzzy BEL model (iFBEL) neural network by integrating a fuzzy neural network (FNN) to a conventional BEL, in an effort to better support humanoid robots. In particular, the system inputs are passed into a sensory and emotional channels that jointly produce the final outputs of the network. The non-linear approximation ability of the iFBEL is achieved by taking the BEL network as the emotional channel. The proposed iFBEL works with a robust controller in generating the hand and gait motion of a humanoid robot. The updating rules of the iFBEL-based controller are composed of two parts, including a sensory channel followed by the updating rules of the conventional BEL model, and the updating rules of the FNN and the robust controller which are derived from the “Lyapunov” function. The experiments on a three-joint robot manipulator and a six-joint biped robot demonstrated the superiority of the proposed system in reference to a conventional proportional-integral-derivative controller and a fuzzy cerebellar model articulation controller, based on the more accurate and faster control performance of the proposed iFBEL.

This paper aims to present a novel efficient scheme in order to more effectively control the multiple input and multiple output (MIMO) uncertain nonlinear systems. A wavelet fuzzy brain emotional learning controller (WFBELC) model is proposed, which is comprises the benefit of wavelet function, fuzzy theory and brain emotional neural network. When it is used as the main tracking controller for a MIMO uncertain nonlinear systems, the performances of the system, such as the approximation ability, the learning performance and the convergence rate, will be effectively improved. Meanwhile, the gradient descent method is used to adjust the parameters online of WFBELC and the Lyapunov function is employed to guarantee the rapid convergence of the control systems. For the sake of the further illustrating the superiority of this model, two examples of uncertain nonlinear systems, a Duffing-Holmes chaotic system and a Chua's chaotic circuit, are studied. After compared with other models, the test results show that the proposed model can be applied to obtain more satisfactory control performance and be more suitable to deal with the influence of the uncertainty of the MIMO nonlinear systems.

Action observation (AO) generates event-related desynchronization (ERD) suppressions in the human brain by activating partial regions of the human mirror neuron system (hMNS). The activation of the hMNS response to AO remains controversial for several reasons. Therefore, this study investigated the activation of the hMNS response to a speed factor of AO by controlling the movement speed modes of a humanoid robot's arm movements. Since hMNS activation is reflected by ERD suppressions, electroencephalography (EEG) with BCI analysis methods for ERD suppressions were used as the recording and analysis modalities. Six healthy individuals were asked to participate in experiments comprising five different conditions. Four incremental-speed AO tasks and a motor imagery (MI) task involving imaging of the same movement were presented to the individuals. Occipital and sensorimotor regions were selected for BCI analyses. The experimental results showed that hMNS activation was higher in the occipital region but more robust in the sensorimotor region. Since the attended information impacts the activations of the hMNS during AO, the pattern of hMNS activations first rises and subsequently falls to a stable level during incremental-speed modes of AO. The discipline curves suggested that a moderate speed within a decent inter-stimulus interval (ISI) range produced the highest hMNS activations. Since a brain computer/machine interface (BCI) builds a path-way between human and computer/mahcine, the discipline curves will help to construct BCIs made by patterns of action observation (AO-BCI). Furthermore, a new method for constructing non-invasive brain machine brain interfaces (BMBIs) with moderate AO-BCI and motor imagery BCI (MI-BCI) was inspired by this paper.

Inspired by infant development theories, a robotic developmental model combined with game elements is proposed in this paper. This model does not require the definition of specific developmental goals for the robot, but the developmental goals are implied in the goals of a series of game tasks. The games are characterized into a sequence of game modes based on the complexity of the game tasks from simple to complex, and the task complexity is determined by the applications of developmental constraints. Given a current mode, the robot switches to play in a more complicated game mode when it cannot find any new salient stimuli in the current mode. By doing so, the robot gradually achieves it developmental goals by playing different modes of games. In the experiment, the game was instantiated into a mobile robot with the playing task of picking up toys, and the game is designed with a simple game mode and a complex game mode. A developmental algorithm, “Lift-Constraint, Act and Saturate,” is employed to drive the mobile robot move from the simple mode to the complex one. The experimental results show that the mobile manipulator is able to successfully learn the mobile grasping ability after playing simple and complex games, which is promising in developing robotic abilities to solve complex tasks using games.

As compared to the two-fluid single-pressure model, the two-fluid seven-equation two-pressure model has been proved to be unconditionally well-posed in all situations, thus existing with a wide range of industrial applications. The classical 1st-order upwind scheme is widely used in existing nuclear system analysis codes such as RELAP5, CATHARE, and TRACE. However, the 1st-order upwind scheme possesses issues of serious numerical diffusion and high truncation error, thus giving rise to the challenge of accurately modeling many nuclear thermal-hydraulics problems such as long term transients. In this paper, a semi-implicit algorithm based on the finite volume method with staggered grids is developed to solve such advanced well-posed two-pressure model. To overcome the challenge from 1st-order upwind scheme, eight high-resolution total variation diminishing (TVD) schemes are implemented in such algorithm to improve spatial accuracy. Then the semi-implicit algorithm with high-resolution TVD schemes is validated on the water faucet test. The numerical results show that the high-resolution semi-implicit algorithm is robust in solving the two-pressure two-fluid two-phase flow model; Superbee scheme and Koren scheme give two highest levels of accuracy while Minmod scheme is the worst one among the eight TVD schemes.

Egocentric video analysis is an important tool in healthcare that serves a variety of purposes, such as memory aid systems and physical rehabilitation, and feature extraction is an indispensable process for such analysis. Local feature descriptors have been widely applied due to their simple implementation and reasonable efficiency and performance in applications. This paper proposes an enhanced spatial and temporal local feature descriptor extraction method to boost the performance of action classification. The approach allows local feature descriptors to take advantage of saliency maps, which provide insights into visual attention. The effectiveness of the proposed method was validated and evaluated by a comparative study, whose results demonstrated an improved accuracy of around 2%.

This paper presents a novel developmental learning approach for hand-eye coordination in an autonomous robotic system. Robotic hand-eye coordination plays an important role in dealing with real-time environments. Under the approach, infant developmental patterns are introduced to build our robot's learning system. The method works by first constructing a brain-like computational structure to control the robot, and then by using infant behavioural patterns to build a hand-eye coordination learning algorithm. This work is supported by an experimental evaluation, which shows that the control system is implemented simply, and that the learning approach provides fast and incremental learning of behavioural competence.