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Abstract In the realm of deep learning-based networks for dehazing using paired clean-hazy image datasets to address complex real-world haze scenarios in daytime environments and cross-dataset challenges remains a significant concern due to algorithmic inefficiencies and color distortion. To tackle these issues, we propose SwinTieredHazymers (STH), a dehazing network designed to adaptively discern pixel intensities in hazy images and compute haze residue for clarity restoration. Through a unique three-branch design, we hierarchically modulate haze residuals by leveraging the global features brought by Transformer and the local features brought by Convolutional Neural Network (CNN) which has led to the algorithm’s widespread applicability. Experimental results demonstrate that our approach surpasses advanced single-image dehazing methods in both quantitative metrics and visual fidelity for real-world hazy image dehazing, while also exhibiting strong performance in cross-dataset dehazing scenarios.

Modelling the movement uncertainty of crowdsourced human trajectories in complex urban areas is useful for various human mobility analytics and applications. However, the existing human movement uncertainty modelling approaches only consider the largest movement distance or speed, and fixed sampling and measurement errors, resulting in limited accuracy in uncertainty prediction. To fill this gap, this paper presents a Bi-directional Long Short-Term Memory (Bi-LSTM) assisted framework for modelling the uncertainty of crowdsourced human trajectories under complex urban environments. The proposed movement uncertainty modelling framework adaptively integrates the pedestrian motion detection characteristics, including the real-time gait-length and heading deviation features under detected step period. The characteristics are further combined with the Global Navigation Satellite System (GNSS) originated location, speed and virtual heading information and constructed as the input features for the uncertainty prediction model. Comparison with the existing uncertainty modelling methods is conducted using the real-world datasets, and the results demonstrate the presented Bi-LSTM assisted framework’s robust outperformance in achieving more adaptive and accurate movement uncertainty prediction, as measured by multiple metrics. This study provides an accurate and practical solution for modelling the movement uncertainty of human trajectories under complex urban areas, and can support reliable analytics for crowdsourced urban big data.

The increasing amount of geotagged social media data provides a possible resource for location prediction. However, existing location prediction methods rarely incorporate temporal changes in mobility patterns, which could lead to unreliable predictions. In particular, human mobility patterns have changed greatly in the COVID-19 era. We propose a novel model to predict individuals’ movements in dynamic regions of interest (ROIs), taking into account changes in activity areas and movement regularity. To address changes in the activity areas, we design a new updating strategy that can ensure the realistic extraction of an individual’s ROIs. Then, we develop an integration model for changes in the movement regularity based on two newly proposed prediction methods that consider both rapid and slow changes. The proposed integration model is evaluated based on five real-world social media datasets; three Weibo datasets related to COVID-19 collected in three Chinese cities, one Twitter dataset collected in New York and one dense GPS dataset. The results demonstrate that the proposed model can achieve better performances than state-of-the-art models, especially when mobility patterns change greatly. Combined with related pandemic data, this study will benefit pandemic prevention and control.

Abstract Diffusion‐like scattering along multiple directions based on coding metasurface (CM) is proposed for both mono‐static and bi‐static radar cross section (RCS) reduction in this work. Different from the previous CMs achieving diffusion‐like scattering only in one direction, the proposed CMs can realize diffusion‐like scattering along multiple directions via designing the phase spatial distributions of the consisted coding elements. The multiple diffusion‐like scattering CMs were designed by using the co‐polarization reflection resonators under circularly polarized wave incidence based on Pancharatnam–Berry (PB) phase. By elaborately arranging the phase spatial distribution of coding elements, two, three, and four beams of diffusion‐like scattering were realized. The bi‐static RCS reduction was improved with increasing number of the backward diffusion‐like scattering beams. The measured results agree well with the simulations, and both demonstrated the excellent performance on mono‐static and bi‐static RCS reduction. Our strategy can help realize stealth applications under bi‐static detections.

IntroductionSoccer events require a lot of energy, resulting in significant carbon emissions. To achieve carbon neutrality, it is crucial to reduce the cost and energy consumption of soccer events. However, current methods for cost minimization often have high equipment requirements, time-consuming training, and many parameters, making them unsuitable for real-world industrial scenarios. To address this issue, we propose a lightweight CNN model based on transfer learning to study cost minimization strategies for soccer events in a carbon-neutral context.MethodsOur proposed lightweight CNN model uses a downsampling module based on the human brain for efficient information processing and a transfer learning-based module to speed up the training progress. We conducted experiments to evaluate the performance of our model and compared it with existing models in terms of the number of parameters and computation and recognition accuracy.ResultsThe experimental results show that our proposed network model has significant advantages over existing models in terms of the number of parameters and computation while achieving higher recognition accuracy than conventional models. Our model effectively predicts soccer event data and proposes more reasonable strategies to optimize event costs and accelerate the realization of carbon neutral goals.DiscussionOur proposed lightweight CNN model based on transfer learning is a promising method for studying cost minimization strategies for soccer events in a carbon-neutral context. The use of a downsampling module based on the human brain and a transfer learning-based module allows for more efficient information processing and faster training progress. The results of our experiments indicate that our model outperforms existing models and can effectively predict soccer event data and propose cost optimization strategies. Our model can contribute to the realization of carbon-neutral goals in the sports industry.

Abstract As COVID-19 spread around the world, epidemic prevention and control policies have been adopted by many countries. This process has prompted online social platforms to become important channels to enable people to socialize and exchange information. The massive use of social media data mining techniques, to analyze the development online of public opinion during the epidemic, is of great significance in relation to the management of public opinion. This paper presents a study that aims to analyze the developmental course of online public opinion in terms of fine-grained emotions presented during the COVID-19 epidemic in China. It is based on more than 45 million Weibo posts during the period from December 1, 2019 to April 30, 2020. A text emotion extraction method based on a dictionary of emotional ontology has been developed. The results show, for example, that a high emotional effect is observed during holidays, such as New Year. As revealed by Internet users, the outbreak of the COVID-19 epidemic and its rapid spread, over a comparatively short period of time, triggered a sharp rise in the emotion “fear”. This phenomenon was noted especially in Wuhan and the immediate surrounding areas. Over the initial 2 months, although this “fear” gradually declined, it remained significantly higher than the more common level of uncertainty that existed during the epidemic’s initial developmental era. Simultaneously, in the main city clusters, the response to the COVID-19 epidemic in central cities, was stronger than that in neighboring cities, in terms of the above emotion. The topics of Weibo posts, the corresponding emotions, and the analysis conclusions can provide auxiliary reference materials for the monitoring of network public opinion under similar major public events.

One of the major pathogenesis mechanisms of SARS-CoV-2 is its potent suppression of innate immunity, including blocking the production of type I interferons. However, it is unknown whether and how the virus interacts with different innate-like T cells, including NKT, MAIT and γδ T cells. Here we reported that upon SARS-CoV-2 infection, invariant NKT (iNKT) cells rapidly trafficked to infected lung tissues from the periphery. We discovered that the envelope (E) protein of SARS-CoV-2 efficiently down-regulated the cell surface expression of the antigen-presenting molecule, CD1d, to suppress the function of iNKT cells. E protein is a small membrane protein and a viroporin that plays important roles in virion packaging and envelopment during viral morphogenesis. We showed that the transmembrane domain of E protein was responsible for suppressing CD1d expression by specifically reducing the level of mature, post-ER forms of CD1d, suggesting that it suppressed the trafficking of CD1d proteins and led to their degradation. Point mutations demonstrated that the putative ion channel function was required for suppression of CD1d expression and inhibition of the ion channel function using small chemicals rescued the CD1d expression. Importantly, we discovered that among seven human coronaviruses, only E proteins from highly pathogenic coronaviruses including SARS-CoV-2, SARS-CoV and MERS suppressed CD1d expression, whereas the E proteins of human common cold coronaviruses, HCoV-OC43, HCoV-229E, HCoV-NL63 and HCoV-HKU1, did not. These results suggested that E protein-mediated evasion of NKT cell function was likely an important pathogenesis factor, enhancing the virulence of these highly pathogenic coronaviruses. Remarkably, activation of iNKT cells with their glycolipid ligands, both prophylactically and therapeutically, overcame the putative viral immune evasion, significantly mitigated viral pathogenesis and improved host survival in mice. Our results suggested a novel NKT cell-based anti-SARS-CoV-2 therapeutic approach.

Abstract Background Motivated by the need for precise epidemic control and epidemic-resilient urban design, this study aims to reveal the joint and interactive associations between urban socioeconomic, density, connectivity, and functionality characteristics and the COVID-19 spread within a high-density city. Many studies have been made on the associations between urban characteristics and the COVID-19 spread, but there is a scarcity of such studies in the intra-city scale and as regards complex joint and interactive associations by using advanced machine learning approaches. Methods Differential-evolution-based association rule mining was used to investigate the joint and interactive associations between the urban characteristics and the spatiotemporal distribution of COVID-19 confirmed cases, at the neighborhood scale in Hong Kong. The associations were comparatively studied for the distribution of the cases in four waves of COVID-19 transmission: before Jun 2020 (wave 1 and 2), Jul–Oct 2020 (wave 3), and Nov 2020–Feb 2021 (wave 4), and for local and imported confirmed cases. Results The first two waves of COVID-19 were found mainly characterized by higher-socioeconomic-status (SES) imported cases. The third-wave outbreak concentrated in densely populated and usually lower-SES neighborhoods, showing a high risk of within-neighborhood virus transmissions jointly contributed by high density and unfavorable SES. Starting with a super-spread which considerably involved high-SES population, the fourth-wave outbreak showed a stronger link to cross-neighborhood transmissions driven by urban functionality. Then the outbreak diffused to lower-SES neighborhoods and interactively aggravated the within-neighborhood pandemic transmissions. Association was also found between a higher SES and a slightly longer waiting period (i.e., the period from symptom onset to diagnosis of symptomatic cases), which further indicated the potential contribution of higher-SES population to the pandemic transmission. Conclusions The results of this study may provide references to developing precise anti-pandemic measures for specific neighborhoods and virus transmission routes. The study also highlights the essentiality of reliving co-locating overcrowdedness and unfavorable SES for developing epidemic-resilient compact cities, and the higher obligation of higher-SES population to conform anti-pandemic policies.

In this paper, a 5.8 GHz, high-gain flexible receiving antenna (FRA) is proposed, which can be used for wireless power transmission (WPT) systems. The FRA is designed by an iterative process and is etched on Kapton, with a coplanar waveguide (CPW) feed. In order to improve the gain of the FRA, an air-gap and a flexible reflector are loaded, and the gain is improved from 2.79 to 9.51 dBi at 5.8 GHz. To validate the stability of FRA, the fabricated prototype uses three configurations of 80, 70, and 50 mm. The impedance matching keeps well, and the gain of the FRA can sustain above 8.96 dBi. In addition, a miniaturized WPT system was built. The measured results show that the proposed FRA has a maximum harvesting efficiency (HE) of 71.3%, and it can maintain the maximum HE above 63% in bending conditions. The lightweight, high-gain, and high HE in various cases prove the huge potential of the FRA in WPT applications.

Invariant Natural Killer T (iNKT) cells are unconventional T cells that respond to glycolipid antigens found in microbes in a CD1d-dependent manner. iNKT cells exert innate-like functions and produce copious amounts of cytokines, chemokines and cytotoxic molecules within only minutes of activation. As such, iNKT cells can fuel or dampen inflammation in a context-dependent manner. In addition, iNKT cells provide potent immunity against bacteria, viruses, parasites and fungi. Although microbiota-iNKT cell interactions are not well-characterized, mounting evidence suggests that microbiota colonization early in life impacts iNKT cell homeostasis and functions in disease. In this study, we showed that CD1d−/− and Vα14 Tg mice, which lack and have increased numbers of iNKT cells, respectively, had no significant alterations in gut microbiota composition compared to their littermate controls. Furthermore, specific iNKT cell activation by glycolipid antigens only resulted in a transient and minimal shift in microbiota composition when compared to the natural drift found in our colony. Our findings demonstrate that iNKT cells have little to no influence in regulating commensal bacteria at steady state.Abbreviations: iNKT: invariant Natural Killer T cell; αGC: α-galactosylceramide

Modelling pedestrian movement uncertainty in complex urban environments is regarded as a meaningful and challenging task regarding the promotion of geospatial data mining and analysis. However, the traditional uncertainty prediction model only takes the movement distance or speed into consideration and is not able to adapt well to time-varying measurement errors. In this paper, a deep-learning framework is proposed for modelling pedestrian movement uncertainty in large-scale indoor areas, in which a hybrid deep-learning model combines a one-dimensional Convolutional Neural Network (1D-CNN) with a long short-term memory (LSTM) network is proposed for enhancing feature extraction performance and reducing time correlation errors. The proposed framework takes human motion related measurement features into consideration, in which the moving step-length and heading information during a time period are also reconstructed and modelled as the input to the deep-learning model. Compared with state-of-art algorithms applied to different real-world trajectory datasets, the proposed deep-learning approach demonstrates much better performance of uncertainty region prediction, including the different indexes (Euclidean error distance, completeness and density) This study has leaded to the provision of an effective and practical framework for modelling trajectory uncertainty of the pedestrian in challenging urban environments, and which is expected to benefit smart city and spatial perception related applications.

Social media platforms allow users worldwide to create and share information, forging vast sensing networks that allow information on certain topics to be collected, stored, mined, and analyzed in a rapid manner. During the COVID-19 pandemic, extensive social media mining efforts have been undertaken to tackle COVID-19 challenges from various perspectives. This review summarizes the progress of social media data mining studies in the COVID-19 contexts and categorizes them into six major domains, including early warning and detection, human mobility monitoring, communication and information conveying, public attitudes and emotions, infodemic and misinformation, and hatred and violence. We further document essential features of publicly available COVID-19 related social media data archives that will benefit research communities in conducting replicable and reproducible studies. In addition, we discuss seven challenges in social media analytics associated with their potential impacts on derived COVID-19 findings, followed by our visions for the possible paths forward in regard to social media-based COVID-19 investigations. This review serves as a valuable reference that recaps social media mining efforts in COVID-19 related studies and provides future directions along which the information harnessed from social media can be used to address public health emergencies.

Abstract Background The urban built environment (BE) has been globally acknowledged as one of the main factors that affects the spread of infectious disease. However, the effect of the street network on coronavirus disease 2019 (COVID-19) incidence has been insufficiently studied. Severe acute respiratory syndrome coronavirus 2, which causes COVID-19, is far more transmissible than previous respiratory viruses, such as severe acute respiratory syndrome coronavirus, which highlights the role of the spatial configuration of street network in COVID-19 spread, as it is where humans have contact with each other, especially in high-density areas. To fill this research gap, this study utilized space syntax theory and investigated the effect of the urban BE on the spatial diffusion of COVID-19 cases in Hong Kong. Method This study collected a comprehensive dataset including a total of 3815 confirmed cases and corresponding locations from January 18 to October 5, 2020. Based on the space syntax theory, six space syntax measures were selected as quantitative indicators for the urban BE. A linear regression model and Geographically Weighted Regression model were then applied to explore the underlying relationships between COVID-19 cases and the urban BE. In addition, we have further improved the performance of GWR model considering the spatial heterogeneity and scale effects by adopting an adaptive bandwidth. Result Our results indicated a strong correlation between the geographical distribution of COVID-19 cases and the urban BE. Areas with higher integration (a measure of the cognitive complexity required for a pedestrians to reach a street) and betweenness centrality values (a measure of spatial network accessibility) tend to have more confirmed cases. Further, the Geographically Weighted Regression model with adaptive bandwidth achieved the best performance in predicting the spread of COVID-19 cases. Conclusion In this study, we revealed a strong positive relationship between the spatial configuration of street network and the spread of COVID-19 cases. The topology, network accessibility, and centrality of an urban area were proven to be effective for use in predicting the spread of COVID-19. The findings of this study also shed light on the underlying mechanism of the spread of COVID-19, which shows significant spatial variation and scale effects. This study contributed to current literature investigating the spread of COVID-19 cases in a local scale from the space syntax perspective, which may be beneficial for epidemic and pandemic prevention.

In this paper, a 5.8 GHz π-stub decoupling network is proposed to improve the performance of a receiving antenna array (RAA) in microwave wireless power transmission (MWPT) systems. A set of general design formulas was derived for determining the electric parameters of the required π-stubs. To validate the new technique, a π-stub decoupling network was combined with RAAs. The simulated and measured results show that the performance of the RAA is greatly improved by loading the π-stub decoupling network. In addition, a miniaturized MWPT system was built. System-level measurements indicate that the novel decoupling network enhances the receiving power of the RAA by up to 36.4%. An extended application also shows the scalability and effectiveness of the network, implying its huge potential in large-scale receiving arrays.

Abstract Background The P38 mitogen-activated protein kinase (MAPK) pathway plays an essential role in CVB3-induced diseases. We previously demonstrated microRNA-21 has potential inhibitory effect on the MAP2K3 which locates upstream of P38 MAPK and was upregulated in mouse hearts upon CVB3 infection. However, the effect and underlying mechanism of miRNA-21 on CVB3 infection remain unclear. Methods We detected continuous changes of cellular miRNA-21 and P38 MAPK proteins expression profiling post CVB3 infection in vitro within 12 h. P38 MAPK signaling was inhibited by the specific inhibitor, small interfering RNA and miRNA-21 mimic in vitro, CVB3 replication, cell apoptosis rate and proliferation were detected. Viral load in the mice heart, cardiomyocyte apoptosis rate and histological of the heart were also detected in the mice model of viral myocarditis pretreated with miRNA-21-lentivirus. Results We observed significant upregulation of miRNA-21 expression followed by suppression of the MAP2K3/P38 MAPK signaling in CVB3-infected Hela cells. The inactivation of the MAP2K3/P38 MAPK signaling by P38 MAPK specific inhibitor, small interfering RNA against MAP2K3, or miRNA-21 overexpression significantly inhibited viral progeny release from CVB3-infected cells. Mechanistically, when compared with control miRNA, miRNA-21 showed no effect on capsid protein VP1 expression and viral load within host cells, while significantly reversing CVB3-induced caspase-3 activation and cell apoptosis rate, further promoting proliferation of infected cells, which indicates the inhibitory effect of miRNA-21 on CVB3 progeny release. In the in vivo study, when compared with control miRNA, miRNA-21 pretreatment remarkably inactivated the MAP2K3/P38 MAPK signaling in mice and protected them against CVB3 infection as evidenced by significantly alleviated cell apoptosis rate, reduced viral titers, necrosis in the heart as well as by remarkably prolonged survival time. Conclusions miRNA-21 were reverse correlated with P38 MAPK activation post CVB3 infection, miRNA-21 overexpression significantly inhibited viral progeny release and decreased myocytes apoptosis rate in vitro and in vivo, suggesting that miRNA-21 may serve as a potential therapeutic agent against CVB3 infection through targeting the MAP2K3/P38 MAPK signaling.

Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.

Change detection (CD) is one of the most important topics in remote sensing. In this paper, we propose a novel higher-order clique conditional random field model to unsupervised CD for remote sensing images (termed HOC2RF), by defining a higher-order clique potential. The clique potential, constructed based on a well-designed higher-order clique of image objects, takes the interaction between the neighboring objects in both feature and location spaces into account. HOC2RF consists of five principle steps: (1) Two difference images with complementary change information are produced by change vector analysis and using the spectral correlation mapper, which describe changes from the perspective of the vector magnitude and angle, respectively. (2) The fuzzy partition matrix of each difference image is calculated by fuzzy clustering, and the fused partition matrix is obtained by fusing the calculated partition matrices with evidence theory. (3) An object-level map is created by segmenting the difference images with an adaptive morphological reconstruction based watershed algorithm. (4) The energy function of the proposed HOC2RF, composed of unary, pairwise, and higher-order clique potentials, is computed based on the difference images, the fusion partition matrix, and the object-level map. (5) The energy function is minimized by the graph cut algorithm to achieve the binary CD map. The proposed HOC2RF CD approach combines the complementary change information extracted from the perspectives of vector magnitude and angle, and synthetically exploits the pixel-level and object-level spatial correlation of images. The main contributions of this article include: (1) proposing the idea of using the interaction between neighboring objects in both feature and location spaces to enhance the CD performance; and (2) presenting a method to construct a higher-order clique of objects, developing a higher-order clique potential function, and proposing a novel CD method HOC2RF. In the experiments on three real remote sensing images, the Kappa coefficient/overall accuracy values of the proposed HOC2RF are 0.9655/0.9967, 0.9518/0.9910, and 0.7845/0.9651, respectively, which are superior to some state-of-the-art CD methods. The experimental results confirm the effectiveness of the proposed method.

Landslide trails are important elements of landslide inventory maps, providing valuable information for landslide risk and hazard assessment. Compared with traditional manual mapping, skeletonization methods offer a more cost-efficient way to map landslide trails, by automatically generating centerlines from landslide polygons. However, a challenge to existing skeletonization methods is that expert knowledge and manual intervention are required to obtain a branchless skeleton, which limits the applicability of these methods. To address this problem, a new workflow for landslide trail extraction (LTE) is proposed in this study. To avoid generating redundant branches and to improve the degree of automation, two endpoints, i.e., the crown point and the toe point, of the trail were determined first, with reference to the digital elevation model. Thus, a fire extinguishing model (FEM) is proposed to generate skeletons without redundant branches. Finally, the effectiveness of the proposed method is verified, by extracting landslide trails from landslide polygons of various shapes and sizes, in two study areas. Experimental results show that, compared with the traditional grassfire model-based skeletonization method, the proposed FEM is capable of obtaining landslide trails without spurious branches. More importantly, compared with the baseline method in our previous work, the proposed LTE workflow can avoid problems including incompleteness, low centrality, and direction errors. This method requires no parameter tuning and yields excellent performance, and is thus highly valuable for practical landslide mapping.

Abstract Background Interleukin (IL)-37 has emerged as a novel anti-inflammatory cytokine that play an immunosuppressive role in regulating inflammatory response. This study aimed to measure IL-37 levels in the plasma and peripheral blood mononuclear cells (PBMCs) of patients with systemic juvenile idiopathic arthritis (sJIA), and to establish the correlation between IL-37 levels and disease activity, laboratory parameters and inflammatory cytokines. Methods The mRNA levels of IL-37 in PBMCs and plasma IL-37 concentrations in 46 sJIA patients and 30 age- and sex-matched healthy controls were measured by real-time polymerase chain reaction (RT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively. The correlations between plasma IL-37 levels and disease activity, laboratory parameters and inflammatory cytokines in sJIA were analyzed by Spearman correlation test. PBMCs from the sJIA patients were stimulated with recombinant human IL-37 (rhIL-37) protein, expressions of IL-1β, IL-6, TNF-α and IL-17 were detected by RT-PCR and ELISA. Results Plasma levels of IL-37 and relative IL-37 mRNA expression were significantly elevated in sJIA patients, especially in active sJIA patients, when compared with the healthy controls (P

Remote sensing change detection (CD) plays an important role in Earth observation. In this paper, we propose a novel fusion approach for unsupervised CD of multispectral remote sensing images, by introducing majority voting (MV) into fuzzy topological space (FTMV). The proposed FTMV approach consists of three principal stages: (1) the CD results of different difference images produced by the fuzzy C-means algorithm are combined using a modified MV, and an initial fusion CD map is obtained; (2) by using fuzzy topology theory, the initial fusion CD map is automatically partitioned into two parts: a weakly conflicting part and strongly conflicting part; (3) the weakly conflicting pixels that possess little or no conflict are assigned to the current class, while the pixel patterns with strong conflicts often misclassified are relabeled using the supported connectivity of fuzzy topology. FTMV can integrate the merits of different CD results and largely solve the conflicting problem during fusion. Experimental results on three real remote sensing images confirm the effectiveness and efficiency of the proposed method.

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.

Along with the increase of big data and the advancement of technologies, comprehensive data-driven knowledge of urban systems is becoming more attainable, yet the connection between big-data research and its application e.g., in smart city development, is not clearly articulated. Focusing on Human Mobility, one of the most frequently investigated applications of big data analytics, a framework for linking international academic research and city-level management policy was established and applied to the case of Hong Kong. Literature regarding human mobility research using big data are reviewed. These studies contribute to (1) discovering the spatial-temporal phenomenon, (2) identifying the difference in human behaviour or spatial attributes, (3) explaining the dynamic of mobility, and (4) applying to city management. Then, the application of the research to smart city development are scrutinised based on email queries to various governmental departments in Hong Kong. The identified challenges include data isolation, data unavailability, gaming between costs and quality of data, limited knowledge derived from rich data, as well as estrangement between public and private sectors. With further improvement in the practical value of data analytics and the utilization of data sourced from multiple sectors, paths to achieve smarter cities from policymaking perspectives are highlighted.

Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts„ of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.

Adult central nervous system (CNS) neurons down-regulate growth programs after injury, leading to persistent regeneration failure. Coordinated lipids metabolism is required to synthesize membrane components during axon regeneration. However, lipids also function as cell signaling molecules. Whether lipid signaling contributes to axon regeneration remains unclear. In this study, we showed that lipin1 orchestrates mechanistic target of rapamycin (mTOR) and STAT3 signaling pathways to determine axon regeneration. We established an mTOR-lipin1-phosphatidic acid/lysophosphatidic acid-mTOR loop that acts as a positive feedback inhibitory signaling, contributing to the persistent suppression of CNS axon regeneration following injury. In addition, lipin1 knockdown (KD) enhances corticospinal tract (CST) sprouting after unilateral pyramidotomy and promotes CST regeneration following complete spinal cord injury (SCI). Furthermore, lipin1 KD enhances sensory axon regeneration after SCI. Overall, our research reveals that lipin1 functions as a central regulator to coordinate mTOR and STAT3 signaling pathways in the CNS neurons and highlights the potential of lipin1 as a promising therapeutic target for promoting the regeneration of motor and sensory axons after SCI. [ABSTRACT FROM AUTHOR]