Mobile Edge Assisted Live Streaming System for Omnidirectional Video

As a popular form of virtual reality (VR) media, omnidirectional video (OV) has been continuously developed in recent years. OV contains the view of the scene in every direction, which will ask for around 120 Mbps with 8k resolution and 25 fps (frames per second). Although there has been a lot of work to optimize the transmission for on-demand of OV, the research on the live streaming of OV is still very lacking. Another big challenge for the OV live streaming system is the huge demand for computing resources. The existing terminal devices are difficult to completely carry tasks such as stitching, encoding, and rendering. This paper proposes a mobile edge assisted live streaming system for omnidirectional video (MELiveOV); the MELiveOV can intelligently offload the processing tasks to the edge computing enabled 5G base stations. The MELiveOV consists of an omnidirectional video generation module, a streaming module, and a viewpoint prediction module. A prototype system of MELiveOV is implemented to prove its complete end-to-end OV live streaming service. Evaluation result demonstrates that compared with the traditional solution, MELiveOV can reduce the network bandwidth requirement by about 50% and the transmission delay of more than 70% while ensuring the quality of the user’s experience

Evaluating the ecological health of aquatic habitats in a megacity through a multimetric index model based on macroinvertebrates

Globally, urban water bodies suffer from a variety of ecological pressures which profoundly change freshwater ecosystem services and pose a great threat to aquatic biodiversity. To identify the effects of these pressures on aquatic communities, we systematically investigated the macroinvertebrates in different water types (e.g., mountain rivers, plain rivers, lakes, and reservoirs) in all water systems in Beijing, a megacity in China, to reveal the key environmental factors and response mechanisms affecting the spatial distribution of macroinvertebrate communities. A total of 188 macroinvertebrate taxa were identified at 61 survey sections, and environmental factors such as flow velocity, water depth, water temperature, and total nitrogen content were found to substantially affect the structure and spatial distribution of the macroinvertebrate communities. A multimetric index (MMI) model based on macroinvertebrates was developed to assess the ecological quality of each water type, and the developed MMI was demonstrated to be widely applicable. In the MMI, each community metric was weighted based on the goodness of fit for each biological metric and environmental metric to obtain the observed MMI values of the measured sample sites, and further model training and prediction was performed based on all sample site data. The MMI results revealed that the overall ecological quality of mountain rivers with less anthropogenic interference was relatively good (MMI = 0.62 ± 0.28), the overall ecological quality of lakes experiencing ecological disturbance and undergoing ecological restoration practices was moderate (MMI = 0.43 ± 0.09), and the overall ecological quality of plain rivers and reservoirs with strong anthropogenic interference was relatively poor (MMI = 0.24 ± 0.12 and 0.32 ± 0.12, respectively). Specific recommendations for ecological protection of different water types were formulated, providing a scientific basis and decision-making support for urban ecological planning and sustainable development

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients

Abstract Background A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce the use of social and medical resources. Artificial intelligence (AI) technology is increasingly being used in medical decision-making systems to obtain optimal dosing combinations and improve the survival rate of sepsis patients. To meet the real-world requirements of medical applications and make the training model more robust, we replaced the core algorithm applied in an AI-based medical decision support system developed by research teams at the Massachusetts Institute of Technology (MIT) and IMPERIAL College London (ICL) with the deep deterministic policy gradient (DDPG) algorithm. The main objective of this study was to develop an AI-based medical decision-making system that makes decisions closer to those of professional human clinicians and effectively reduces the mortality rate of sepsis patients. Methods We used the same public intensive care unit (ICU) dataset applied by the research teams at MIT and ICL, i.e., the Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) dataset, which contains information on the hospitalizations of 38,600 adult sepsis patients over the age of 15. We applied the DDPG algorithm as a strategy-based reinforcement learning approach to construct an AI-based medical decision-making system and analyzed the model results within a two-dimensional space to obtain the optimal dosing combination decision for sepsis patients. Results The results show that when the clinician administered the exact same dose as that recommended by the AI model, the mortality of the patients reached the lowest rate at 11.59%. At the same time, according to the database, the baseline mortality rate of the patients was calculated as 15.7%. This indicates that the patient mortality rate when difference between the doses administered by clinicians and those determined by the AI model was zero was approximately 4.2% lower than the baseline patient mortality rate found in the dataset. The results also illustrate that when a clinician administered a different dose than that recommended by the AI model, the patient mortality rate increased, and the greater the difference in dose, the higher the patient mortality rate. Furthermore, compared with the medical decision-making system based on the Deep-Q Learning Network (DQN) algorithm developed by the research teams at MIT and ICL, the optimal dosing combination recommended by our model is closer to that given by professional clinicians. Specifically, the number of patient samples administered by clinicians with the exact same dose recommended by our AI model increased by 142.3% compared with the model based on the DQN algorithm, with a reduction in the patient mortality rate of 2.58%. Conclusions The treatment plan generated by our medical decision-making system based on the DDPG algorithm is closer to that of a professional human clinician with a lower mortality rate in hospitalized sepsis patients, which can better help human clinicians deal with complex conditional changes in sepsis patients in an ICU. Our proposed AI-based medical decision-making system has the potential to provide the best reference dosing combinations for additional drugs

Long, Atomically Precise Donor–Acceptor Cove-Edge Nanoribbons as Electron Acceptors

This Communication describes a new molecular design for the efficient synthesis of donor–acceptor, cove-edge graphene nanoribbons and their properties in solar cells. These nanoribbons are long (∼5 nm), atomically precise, and soluble. The design is based on the fusion of electron deficient perylene diimide oligomers with an electron rich alkoxy pyrene subunit. This strategy of alternating electron rich and electron poor units facilitates a visible light fusion reaction in >95% yield, whereas the cove-edge nature of these nanoribbons results in a high degree of twisting along the long axis. The rigidity of the backbone yields a sharp longest wavelength absorption edge. These nanoribbons are exceptional electron acceptors, and organic photovoltaics fabricated with the ribbons show efficiencies of ∼8% without optimization

High-phase purity two-dimensional perovskites with 17.3% efficiency enabled by interface engineering of hole transport layer

International audienceState-of-the-art p-i-n-based 3D perovskite solar cells (PSCs) use nickel oxide (NiOX) as an efficient hole transport layer (HTL), achieving efficiencies >22%. However, translating this to phase-pure 2D perovskites has been unsuccessful. Here, we report 2D phase-pure Ruddlesden-Popper BA2MA3Pb4I13 perovskites with 17.3% efficiency enabled by doping the NiOX with Li. Our results show that progressively increasing the doping concentration transforms the photoresistor behavior to a typical diode curve, with an increase in the average efficiency from 2.53% to 16.03% with a high open-circuit voltage of 1.22 V. Analysis reveals that Li doping of NiOX significantly improves the morphology, crystallinity, and orientation of 2D perovskite films and also affords a superior band alignment, facilitating efficient charge extraction. Finally, we demonstrate that 2D PSCs with Li-doped NiOX exhibit excellent photostability, with T99 = 400 h at 1 sun and T90 of 100 h at 5 suns measured at relative humidity of 60% ± 5% without the need for external thermal management

High-phase purity two-dimensional perovskites with 17.3% efficiency enabled by interface engineering of hole transport layer

State-of-the-art p-i-n-based 3D perovskite solar cells (PSCs) use nickel oxide (NiOX) as an efficient hole transport layer (HTL), achieving efficiencies >22%. However, translating this to phase-pure 2D perovskites has been unsuccessful. Here, we report 2D phase-pure Ruddlesden-Popper BA2MA3Pb4I13 perovskites with 17.3% efficiency enabled by doping the NiOX with Li. Our results show that progressively increasing the doping concentration transforms the photoresistor behavior to a typical diode curve, with an increase in the average efficiency from 2.53% to 16.03% with a high open-circuit voltage of 1.22 V. Analysis reveals that Li doping of NiOX significantly improves the morphology, crystallinity, and orientation of 2D perovskite films and also affords a superior band alignment, facilitating efficient charge extraction. Finally, we demonstrate that 2D PSCs with Li-doped NiOX exhibit excellent photostability, with T99 = 400 h at 1 sun and T90 of 100 h at 5 suns measured at relative humidity of 60% ± 5% without the need for external thermal management