Study on Energy Consumption and Coverage of Hierarchical Cooperation of Small Cell Base Stations in Heterogeneous Networks

The demand for communication services in the era of intelligent terminals is unprecedented and huge. To meet such development, modern wireless communications must provide higher quality services with higher energy efficiency in terms of system capacity and quality of service (QoS), which could be achieved by the high-speed data rate, the wider coverage and the higher band utilization. In this paper, we propose a way to offload users from a macro base station(MBS) with a hierarchical distribution of small cell base stations(SBS). The connection probability is the key indicator of the implementation of the unload operation. Furthermore, we measure the service performance of the system by finding the conditional probability-coverage probability with the certain SNR threshold as the condition, that is, the probability of obtaining the minimum communication quality when the different base stations are connected to the user. Then, user-centered total energy consumption of the system is respectively obtained when the macro base station(MBS) and the small cell base stations(SBS) serve each of the users. The simulation results show that the hierarchical SBS cooperation in heterogeneous networks can provide a higher system total coverage probability for the system with a lower overall system energy consumption than MBS.Comment: 6 pages, 7 figures, accepted by ICACT201

End to End Performance Analysis of Relay Cooperative Communication Based on Parked Cars

Parking lots (PLs) are usually full with cars. If these cars are formed into a self-organizing vehicular network, they can be new kind of road side units (RSUs) in urban area to provide communication data forwarding between mobile terminals nearby and a base station. However cars in PLs can leave at any time, which is neglected in the existing studies. In this paper, we investigate relay cooperative communication based on parked cars in PLs. Taking the impact of the car's leaving behavior into consideration, we derive the expressions of outage probability in a two-hop cooperative communication and its link capacity. Finally, the numerical results show that the impact of a car's arriving time is greater than the impact of the duration the car has parked on outage probability.Comment: 7 pages, 7 figures, accepted by ICACT201

Mining Time-Changing Data Streams

Streaming data have gained considerable attention in database and data mining communities because of the emergence of a class of applications, such as financial marketing, sensor networks, internet IP monitoring, and telecommunications that produce these data. Data streams have some unique characteristics that are not exhibited by traditional data: unbounded, fast-arriving, and time-changing. Traditional data mining techniques that make multiple passes over data or that ignore distribution changes are not applicable to dynamic data streams. Mining data streams has been an active research area to address requirements of the streaming applications. This thesis focuses on developing techniques for distribution change detection and mining time-changing data streams. Two techniques are proposed that can detect distribution changes in generic data streams. One approach for tackling one of the most popular stream mining tasks, frequent itemsets mining, is also presented in this thesis. All the proposed techniques are implemented and empirically studied. Experimental results show that the proposed techniques can achieve promising performance for detecting changes and mining dynamic data streams

Multisource multimedia data understanding: special theme issue of Multimedia Tools and Applications, Vol. 78, No. 33221

Multimedia Tools and Applications gratefully acknowledges the editorial work of the scholarslisted below on the special issue entitled 'Multisource Multimedia Data Understanding'. Of 23 papers submitted to this issue, 11 were eventually accepted after a stringent peer-review process

Lonicera japonica polysaccharides attenuate ovalbumin-induced allergic rhinitis by regulation of Th17 cells in BALB/c mice

Lonicera japonica Thunb. has been widely used as food ingredients and healthy drinks in the Asian countries, which was reported to possess some good activities. However, it remains unknown in the immunomodulation of Lonicera japonica polysaccharides (LJP) on allergic rhinitis (AR). This study aimed to investigate the impact of LJP on ovalbumin-induced AR in BALB/c mice model. LJP significantly inhibited AR symptoms and eosinophil number in nasal mucosa. Besides, the increased serum levels of IgE, TNF-α, IL-1β and IL-17 were markedly decreased when AR mice were treated with LJP. The mRNA expression levels of IL-4, IL-5, IL-6, IL-17, IL-23, ROR-γt and STAT3 in OVA group were increased, and SOCS3 was reduced, while LJP inhibited the changes. The present study indicated that LJP suppressed the inflammatory response in AR sensitized by ovalbumin, showing that LJP has the potential to treat AR through the regulation of Th17 cells

Photo-Induced Depolymerisation: Recent Advances and Future Challenges

Facing the growing environmental issues provoked by the use of nondegradable polymers in many fields (for example, packing, building, and clothing), tremendous efforts have been made to explore photodegradable materials to alleviate the increase in plastic pollution. Photodegradable materials would exploit significant advantages presented by the use of light, such as abundance, safety and the ability to easily tune intensity and wavelength. In particular, photo-induced depolymerisation has received increasing attention, which could enable polymers to degrade to their original monomers or small molecules under certain photoirradiation conditions. Most importantly, the obtained molecules or monomers via photo-induced depolymerisation could be conveniently recycled or further transformed to other high-value-added products, which is of great benefit for environmental protection. This Review summarizes recent advances in the growing field of photo-induced depolymerisation and also considers future challenges that must be addressed. It aims to encourage new researchers to enter this flourishing area and presents a brief guide to the field

BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning

Epilepsy is one of the most serious neurological diseases, affecting 1-2% of the world's population. The diagnosis of epilepsy depends heavily on the recognition of epileptic waves, i.e., disordered electrical brainwave activity in the patient's brain. Existing works have begun to employ machine learning models to detect epileptic waves via cortical electroencephalogram (EEG). However, the recently developed stereoelectrocorticography (SEEG) method provides information in stereo that is more precise than conventional EEG, and has been broadly applied in clinical practice. Therefore, we propose the first data-driven study to detect epileptic waves in a real-world SEEG dataset. While offering new opportunities, SEEG also poses several challenges. In clinical practice, epileptic wave activities are considered to propagate between different regions in the brain. These propagation paths, also known as the epileptogenic network, are deemed to be a key factor in the context of epilepsy surgery. However, the question of how to extract an exact epileptogenic network for each patient remains an open problem in the field of neuroscience. To address these challenges, we propose a novel model (BrainNet) that jointly learns the dynamic diffusion graphs and models the brain wave diffusion patterns. In addition, our model effectively aids in resisting label imbalance and severe noise by employing several self-supervised learning tasks and a hierarchical framework. By experimenting with the extensive real SEEG dataset obtained from multiple patients, we find that BrainNet outperforms several latest state-of-the-art baselines derived from time-series analysis

Involvement of Endoplasmic Reticulum Stress in Myocardial Apoptosis of Streptozocin-Induced Diabetic Rats

Apoptosis plays critical role in diabetic cardiomyopathy and endoplasmic reticulum stress (ERS) is one of intrinsic apoptosis pathways. For previous studies have shown that endoplasmic reticulum become swell in diabetic myocardium and ERS was involved in diabetes mellitus and heart failure, this study aimed to demonstrate whether ERS was induced in myocardium of streptozocin (STZ)-induced diabetic rats. We established type 1 diabetic rat model with STZ intraperitoneal injection, used echocardiographic evaluation, hematoxylin-eosin staining and the terminal deoxynucleotidyl transferase-mediated DNA nick-end labeling staining to identify the existence of diabetic cardiomyopathy and enhanced apoptosis in the diabetic heart. We performed immunohistochemistry, Western blot and real time PCR to analysis two hallmarks of ERS, glucose regulated protein78 (Grp78) and Caspase12. We found both Grp78 and Caspase12 had enhanced expression in protein and mRNA levels in diabetic myocardium than normal rat’s, and Caspase12 was activated in diabetic heart. Those results suggested that ERS was induced in STZ-induced diabetic rats’ myocardium, and ERS-associated apoptosis took part in the pathophysiology of diabetic cardiomyopathy