Quantized passive filtering for switched delayed neural networks

The issue of quantized passive filtering for switched delayed neural networks with noise interference is studied in this paper. Both arbitrary and semi-Markov switching rules are taken into account. By choosing Lyapunov functionals and applying several inequality techniques, sufficient conditions are proposed to ensure the filter error system to be not only exponentially stable, but also exponentially passive from the noise interference to the output error. The gain matrix for the proposed quantized passive filter is able to be determined through the feasible solution of linear matrix inequalities, which are computationally tractable with the help of some popular convex optimization tools. Finally, two numerical examples are given to illustrate the usefulness of the quantized passive filter design methods

New tumor-targeted nanosized delivery carrier for oligonucleotides: characteristics in vitro and in vivo

Tianyang Zhou1,2, Xin Jia1, Huixiang Li3, Jin Wang3, Hongling Zhang1,2, Youmei A1,2, Zhenzhong Zhang1,21School of Pharmaceutical Sciences, 2Nanotechnology Research Center for Drugs, 3Department of Pathology, Medical School of Zhengzhou University, Zhengzhou, People’s Republic of ChinaBackground: The purpose of this study was to investigate the in vitro and in vivo characteristics of a new tumor-targeted nanosized delivery carrier for antisense oligonucleotide (ASON).Methods: Polyethylenimine (PEI) was used to condense ASON to form nanosized complexes (PEI/ASON), which were then modified using asparagine-glycine-arginine (NGR) peptide to obtain a tumor-targeted nanosized delivery carrier (NGR/PEI/ASON). The conditions required to form PEI/ASON were investigated.Results: A linear correlation between the natural logarithm of the N/P ratio (PEI to ASON) and the zeta potential of the PEI/ASON complexes was found, ranging from 1.5 to 5.0. The pH of the solution strongly influenced the zeta potential of the PEI/ASON complexes. PEI/ASON and NGR/PEI/ASON were stable in RPMI-1640 culture medium in the presence of Dextran 70. Incorporation of ASON into PEI/ASON and NGR/PEI/ASON complexes prevented degradation of ASON by DNase I.Conclusion: Both ASON/PEI and NGR/PEI/ASON complexes enhanced the uptake of ASON by EC9706 cells in vitro. In vivo, NGR/PEI/ASON complexes had the ability to target tumor tissues effectively.Keywords: nanosized delivery system, squamous cell carcinoma, antisense oligonucleotid

Research on the Interaction between Ageing People and Urban Open Space in Chinese Cities – A Case Study of Beijing, China

This research has aimed to produce a comprehensive and effective conceptual framework which can be applied to urban open space design in Chinese cities, in order to improve the well-being and quality of life of their elderly people. Open space around residential communities contributes to the well-being of elderly people. The benefits of urban open space have been evidenced by many studies, but few of these focus on their psychological benefits. Inadequate understanding of the ways in which they can generate psychological satisfaction can lead to difficulty in making effective improvements in design for the ageing population. This research has four objectives: 1. To develop the nature of physical, and psychological dimensions relevant to the relationship ageing people have with open space and bring this understanding into a conceptual framework. 2. To test the initial conceptual framework and to analyse the way it works in relation to people in Beijing. 3. To reflect on the use of the initial conceptual framework, and revise it in light of the academic literature. 4. To make open space design recommendations focused on the social and perceptual dimensions of open space to promote the quality of life and wellbeing of the elderly. The first objective has been achieved by producing the initial conceptual framework, addressing the ways in which people, place, behaviour, emotional bonding and wellbeing impact upon psychological issues. This, in turn, determined the approaches and indicators employed in the methods used in this research, for example the mixed-method combination of quantitative and qualitative approaches with a questionnaire, semi-structured interview, observation and mapping which were applied to the investigation of five neighbourhood parks in Beijing. The second objective was achieved in three steps. The first tested the associations in the initial conceptual framework to discover the internal structure and key features of their relationships by using descriptive statistics, Goodman and Kruskal’s gamma correlation test, and comparative analysis based on the data from questionnaires and some indicators from the semi-structured interviews. The second part produced a behaviour map of ageing people based on observation and mapping. The third part uncovered features of usage, desire and thought of the elderly people, based on both observation and semi- structured interviews. The third objective was achieved by integrating the findings on the perceptual, behavioural, social and physical dimensions which emerged from the study carried out in accord with the second objective. The gap between the current state of neighbourhood parks and the desires of the elderly people who use them was identified by comparing observations of their behaviour and their thoughts as revealed in their semi-structured interviews. The final objective was achieved by decisions based on the predictions of the final conceptual framework and the gap identified in third objective. This research has produced five main contributions to the field of urban neighbourhood park and urban open space design, addressing its social and perceptual dimensions including both theoretical development and practical application. The first illustrates the associations between physical, social, and perceptual dimensions of neighbourhood park and psychological, behavioural, personal dimensions of the elderly which can fill the gap existing within the literature review of people-place relationship in social and perceptual dimension. The second is that it has uncovered the differences in performance in psychological, social, perceptual and behavioural dimensions of ageing people, in terms of both gender and ageing steps, filling the gap in studies of older people using urban space. This can help designers to meet the various different desires of the target group. The third contribution is that it exposes the gap between the desires of elderly people and the current environment of the neighbourhood parks in a way which may help designers and planners to find a more effective direction for ageing friendly design. The fifth contribution is the proposal of a conceptual framework for understanding the qualities of attractiveness in neighbourhood parks and in the group activities offered to elderly people there, helping designers to engage with the elderly, so that they can use neighbourhood parks to improve their quality of life and well-being

Finite-time H-infinity synchronization of semi-Markov jump Lur'e systems

This paper investigates the problem of finite-time H-infinity synchronization for semi-Markov jump Lur'e systems with time-varying delay and external disturbance. The purpose of this work is to design a mode-dependent state-feedback controller to ensure that the synchronization-error system achieves finite-time synchronization with a prescribed H-infinity performance index. A criterion for the finite-time synchronization is proposed by using appropriate Lyapunov functional and two recently developed inequalities. Then, a design method for the required state-feedback controller is presented with the application of several decoupling techniques. Finally, an example is provided to illustrate the applicability of the proposed control method

Multi-resolution attention convolutional neural network for crowd counting

Estimating crowd counts remains a challenging task due to the problems of scale variations, non-uniform distribution and complex backgrounds. In this paper, we propose a multi-resolution attention convolutional neural network (MRA-CNN) to address this challenging task. Except for the counting task, we exploit an additional density-level classification task during training and combine features learned for the two tasks, thus forming multi-scale, multi-contextual features to cope with the scale variation and non-uniform distribution. Besides, we utilize a multi-resolution attention (MRA) model to generate score maps, where head locations are with higher scores to guide the network to focus on head regions and suppress non-head regions regardless of the complex backgrounds. During the generation of score maps, atrous convolution layers are used to expand the receptive field with fewer parameters, thus getting higher-level features and providing the MRA model more comprehensive information. Experiments on ShanghaiTech, WorldExpo’10 and UCF datasets demonstrate the effectiveness of our method.Info-communications Media Development Authority (IMDA)Accepted versionThis work was supported in part by the National Natural Science Foundation of China under Grant 61673244, Grant 61273277 and Grant 61703240) and was carried out at the Rapid-Rich Object Search (ROSE) Lab at the Nanyang Technological University, Singapore. The ROSE Lab is supported by the Infocomm Media Development Authority, Singapore

Research on the Application of Artificial Intelligence in Public Health Management: Leveraging Artificial Intelligence to Improve COVID-19 CT Image Diagnosis

Since the start of 2020, the outbreak of the Coronavirus disease (COVID-19) has been a global public health emergency, and it has caused unprecedented economic and social disaster. In order to improve the diagnosis efficiency of COVID-19 patients, a number of researchers have conducted extensive studies on applying artificial intelligence techniques to the analysis of COVID-19-related medical images. The automatic segmentation of lesions from computed tomography (CT) images using deep learning provides an important basis for the quantification and diagnosis of COVID-19 cases. For a deep learning-based CT diagnostic method, a few of accurate pixel-level labels are essential for the training process of a model. However, the translucent ground-glass area of the lesion usually leads to mislabeling while performing the manual labeling operation, which weakens the accuracy of the model. In this work, we propose a method for correcting rough labels; that is, to hierarchize these rough labels into precise ones by performing an analysis on the pixel distribution of the infected and normal areas in the lung. The proposed method corrects the incorrectly labeled pixels and enables the deep learning model to learn the infected degree of each infected pixel, with which an aiding system (named DLShelper) for COVID-19 CT image diagnosis using the hierarchical labels is also proposed. The DLShelper targets lesion segmentation from CT images, as well as the severity grading. The DLShelper assists medical staff in efficient diagnosis by providing rich auxiliary diagnostic information (including the severity grade, the proportions of the lesion and the visualization of the lesion area). A comprehensive experiment based on a public COVID-19 CT image dataset is also conducted, and the experimental results show that the DLShelper significantly improves the accuracy of segmentation for the lesion areas and also achieves a promising accuracy for the severity grading task

Using the Dual Concept of Evolutionary Game and Reinforcement Learning in Support of Decision-Making Process of Community Regeneration—Case Study in Shanghai

Under the digital revolution that spawned in recent years, AI support is raised in the context of urban design and governance as it aims to match the operation of the urban developing process. It offers more chances for ensuring equality in public participation and empowerment, with the possibility of projection and computation of integrated social, cultural, and physical spaces. Therefore, this research explored how scenario simulation of social attributes and social interaction dimensions can be incorporated into digital twin city research and development, which is seen as a problem to be addressed in the refinement and planning of future digital platforms and management in terms of decision-making. To achieve the research aim, this paper examined the evolution of social governance state and strain decision models, built a simulation method for the evolution of complex systems of social governance driven by the fusion of data and knowledge, and proposed a system response to residents’ ubiquitous perception and ubiquitous participation. The findings can help inspire the application of computational decision-making support in urban governance, and enhance the internal drive for comprehensive and sustainable urban regeneration. Moreover, they imply the role of the updated iterations of physical space and social interaction on social attributes

Gait Recognition Based on the Feature Extraction of Gabor Filter and Linear Discriminant Analysis and Improved Local Coupled Extreme Learning Machine

A gait energy image contains much gait information, which is one of the most effective means to recognize gait characteristics. The accuracy of gait recognition is greatly affected by covariates, such as the viewing angle, occlusion of clothing, and walking speed. Gait features differ somewhat by angles. Therefore, how to improve the recognition accuracy of a cross-view gait is a challenging task. This study proposes a new gait recognition algorithm structure. A Gabor filter is used to extract gait features from gait energy images, since it can extract features of different directions and scales. We use linear discriminant analysis (LDA) to tackle the problem that the feature dimension restricts the process. Finally, the improved local coupled extreme learning machine based on particle swarm optimization is used for the classification process of the extracted features of the gait. The proposed method and other current mainstream algorithms are compared in terms of the recognition accuracy based on the CASIA-A and CASIA-B datasets, and the simulation results show that the proposed algorithm has good performance and performs well at cross-view gait recognition

Diverse Banana Pseudostems and Rachis Are Distinctive for Edible Carbohydrates and Lignocellulose Saccharification towards High Bioethanol Production under Chemical and Liquid Hot Water Pretreatments

Banana is a major fruit crop throughout the world with abundant lignocellulose in the pseudostem and rachis residues for biofuel production. In this study, we collected a total of 11 pseudostems and rachis samples that were originally derived from different genetic types and ecological locations of banana crops and then examined largely varied edible carbohydrates (soluble sugars, starch) and lignocellulose compositions. By performing chemical (H2SO4, NaOH) and liquid hot water (LHW) pretreatments, we also found a remarkable variation in biomass enzymatic saccharification and bioethanol production among all banana samples examined. Consequently, this study identified a desirable banana (Refen1, subgroup Pisang Awak) crop containing large amounts of edible carbohydrates and completely digestible lignocellulose, which could be combined to achieve the highest bioethanol yields of 31–38% (% dry matter), compared with previously reported ones in other bioenergy crops. Chemical analysis further indicated that the cellulose CrI and lignin G-monomer should be two major recalcitrant factors affecting biomass enzymatic saccharification in banana pseudostems and rachis. Therefore, this study not only examined rich edible carbohydrates for food in the banana pseudostems but also detected digestible lignocellulose for bioethanol production in rachis tissue, providing a strategy applicable for genetic breeding and biomass processing in banana crops