Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.Comment: IEEE Communications Magazine, to appea

EPISTEMIC MOTIVATION AND KNOWLEDGE CONTRIBUTION BEHAVIORS IN WIKI TEAMS: A CROSS-LEVEL MODERATION MODEL

Prior research on how to facilitate individuals’ participation in wiki knowledge contribution generally pays little attention to the differentiation of knowledge contributions and the embeddedness of individual team members in team context. This paper examines how an individual’s epistemic motivation and team task reflexivity interact to jointly influence adding, deleting and revising behaviors in distinct ways. Empirical data of 166 university students in 51 teams support our hypotheses. Individuals’ adding, deleting and revising behaviors on wikis are influenced differently by the interactive effect of individual epistemic motivation and team task reflexivity. First, the positive relationship between epistemic motivation and adding behaviors is stronger when the team’s task reflexivity is high. Second, the epistemic motivation stimulates deleting behaviors only when team task reflexivity is high. Third, epistemic motivation is significantly associated with more revising behaviors no matter the level of task reflexivity is high or low

Leczenie oksytocyną zapobiega stłuszczeniu szpiku kostnego obserwowanemu u królików z cukrzycą wywołaną alloksanem — badanie przy użyciu protonowej spektroskopii rezonansu magnetycznego

Introduction: Oxytocin might be used therapeutically as an ally to rescue osteopathy resulting from diabetes. However, the in vivo effects of oxytocin on marrow adipogenesis in diabetes remain unknown. In this longitudinal study, we aimed to investigate the protective ef­fects of oxytocin on diabetes-induced marrow adiposity in rabbits using proton MR spectroscopy. Material and methods: Forty-five female New Zealand rabbits were randomly divided into controls, diabetes, and diabetes treated with oxytocin (ip, 0.78 mg/kg) for six months. Marrow fat fraction (FF) was determined by proton MR spectroscopy at baseline, and at three and six months. Bone mineral density was measured by dual-energy X-ray absorptiometry. Serum biomarkers, glycolipid metabolism, and histological analysis of marrow adipocytes were determined. Results: Oxytocin treatment had positive metabolic effects in diabetic rabbits, which was based on the changes in glucose metabolism, insulin sensitivity, and lipid profiles. The diabetic rabbits demonstrated dramatic marrow adiposity in a time-dependent manner; at three and six months the FF percentage changes from baseline were 10.1% and 25.8%, respectively (all P < 0.001). Moreover, oxytocin treatment significantly reversed FF values and quantitative parameters of marrow adipocyte in diabetic rabbits to levels of naive control rabbits. Oxytocin improved bone formation marker in diabetic rabbits compared to the saline group. Also, treatment of diabetic rabbits with oxytocin significantly mitigated bone deterioration when compared with the saline-treated diabetic group (all P < 0.05). Conclusions: Oxytocin appears to alleviate harmful effects of hyperglycaemia on marrow adiposity. Proton MR spectroscopy may be a valuable tool, providing complementary information on efficacy assessments.Wstęp: Oksytocyna może być stosowana terapeutycznie w osteopatii wynikającej z cukrzycy, jednakże jej wpływ in vivo na stłuszczenie szpiku kostnego w przebiegu cukrzycy pozostaje niezbadany. Niniejsze badanie przekrojowe ma na celu zbadać ochronne działanie oksy­tocyny na wywołane cukrzycą stłuszczenie szpiku kostnego u królików przy użyciu protonowej spektroskopii rezonansu magnetycznego. Materiał i metody: Czterdzieści pięć samic królików nowozelandzkich podzielono losowo na grupę kontrolną, grupę z cukrzycą oraz grupę z cukrzycą leczoną oksytocyną (0.78 mg/kg, i.p.) przez sześć miesięcy. Frakcja tłuszczu (ang. fat fraction; FF) szpiku kostnego została określona za pomocą protonowej spektroskopii rezonansu magnetycznego na początku badania oraz po trzech i sześciu miesiącach. Gęstość mineralną kości zmierzono za pomocą absorpcjometrii promieniowania rentgenowskiego o podwójnej energii. Określono również biomarkery surowicy krwi, metabolizm glikolipidów oraz sporządzono analizę histologiczną adipocytów szpiku kostnego. Wyniki: Leczenie oksytocyną przyniosło pozytywne efekty metaboliczne u królików z cukrzycą, co stwierdzono na podstawie zmian w me­tabolizmie glukozy, wrażliwości na insulinę oraz profili lipidowych. Zauważono drastyczny wzrost stłuszczenia szpiku kostnego u królików z cukrzycą w sposób zależny od czasu; po trzech i sześciu miesiącach, procentowe zmiany frakcji tłuszczu w stosunku do wartości wyjściowej wynosiły odpowiednio 10,1% i 25,8% (wszystkie P &lt; 0.001). Co więcej, leczenie oksytocyną znacząco odwracało wartości frakcji tłuszczu oraz ilościowe parametry adipocytów szpiku kostnego u królików z cukrzycą do poziomu królików z grupy kontrolnej. Oksytocyna poprawiała marker tworzenia kości u królików z cukrzycą w porównaniu do grupy, której podawano sól fizjologiczną. Ponadto, leczenie oksytocyną królików z cukrzycą znacząco łagodziło niszczenie kości w porównaniu do grupy z cukrzycą, której podawano sól fizjologiczną (wszystkie P &lt; 0.05). Wnioski: Oksytocyna wydaje się zmniejszać szkodliwy wpływ hiperglikemii na stłuszczenie szpiku kostnego. Protonowa spektroskopia rezonansu magnetycznego może być cennym narzędziem, dostarczającym uzupełniających informacji na temat oceny skuteczności leczenia

Cooperator driven oscillation in a time-delayed feedback-evolving game

Considering feedback of collective actions of cooperation on common resources has vital importance to reach sustainability. But such efforts may have not immediate consequence on the state of environment and it is unclear how they influence the strategic and environmental dynamics with feedbacks. To address this issue, we construct a feedback-evolving game model in which we consider the growth capacity of resources and the punishment efficiency on defectors who do not provide returns to the environment. Importantly, we further assume a delay in adopting the contribution of cooperative individuals to environmental change in our model. We find that when this contribution amount from cooperators' endowment is fixed, the time delay has no particular consequence on the coevolutionary dynamics. However, when the return is proportional to their endowment, then the time delay can induce periodic oscillatory dynamics of cooperation level and environment. Our work reveals the potential effects of time delay of cooperative actions on the coevolutionary dynamics in strategic interactions with environmental feedback

Loop closure detection of visual SLAM based on variational autoencoder

Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper, a loop closure detection method based on a variational autoencoder (VAE) is proposed. It is intended to be used as a feature extractor to extract image features through neural networks to replace the handicraft features used in traditional methods. This method extracts a low-dimensional vector as the representation of the image. At the same time, the attention mechanism is added to the network and constraints are added to improve the loss function for better image representation. In the back-end feature matching process, geometric checking is used to filter out the wrong matching for the false positive problem. Finally, through numerical experiments, the proposed method is demonstrated to have a better precision-recall curve than the traditional method of the bag-of-words model and other deep learning methods and is highly robust to environmental changes. In addition, experiments on datasets from three different scenarios also demonstrate that the method can be applied in real-world scenarios and that it has a good performance

Industrial steam consumption analysis and prediction based on multi-source sensing data for sustainable energy development

Centralized heating is an energy-saving and environmentally friendly way that is strongly promoted by the state. It can improve energy utilization and reduce carbon emissions. However, Centralized heating depends on accurate heat demand forecasting. On the one hand, it is impossible to save energy if over producing, while on the other hand, it is impossible to meet the heat demand of enterprises if there is not enough capacity. Therefore, it is necessary to forecast the future trend of heat consumption, so as to provide a reliable basis for enterprises to reasonably deploy fuel stocks and boiler power. At the same time, it is also necessary to analyze and monitor the steam consumption of enterprises for abnormalities in order to monitor pipeline leakage and enterprise gas theft. Due to the nonlinear characteristics of heat load, it is difficult for traditional forecasting methods to capture data trend. Therefore, it is necessary to study the characteristics of heat loads and explore suitable heat load prediction models. In this paper, industrial steam consumption of a paper manufacturer is used as an example, and steam consumption data are periodically analyzed to study its time series characteristics; then steam consumption prediction models are established based on ARIMA model and LSTM neural network, respectively. The prediction work was carried out in minutes and hours, respectively. The experimental results show that the LSTM neural network has greater advantages in this steam consumption load prediction and can meet the needs of heat load prediction