Optimizing Video Streaming in Dynamic Networks : An Intelligent Adaptive Bitrate Solution Considering Scene Intricacy and Data Budget

Adaptive Bitrate (ABR) algorithms have become increasingly important for delivering high-quality video content over fluctuating networks. Considering the complexity of video scenes, video chunks can be separated into two categories: those with intricate scenes and those with simple scenes. In practice, it has been observed that improving the quality of intricate chunks yields more substantial improvements in Quality of Experience (QoE) compared with focusing solely on simple chunks. However, the current ABR schemes either treat all chunks equally or rely on fixed linear-based reward functions, which limits their ability to meet real-world requirements. To tackle these limitations, this paper introduces a novel ABR approach called CAST (Complex-scene Aware bitrate algorithm via Self-play reinforcemenT learning), which considers the scene complexity and formulates the bitrate adaptation task as an explicit objective. Leveraging the power of parallel computing with multiple agents, CAST trains a neural network to achieve superior video playback quality for intricate scenes while minimizing playback freezing time. Moreover, we also introduce a new variant of our proposed approach called CAST-DU, to address the critical issue of efficiently managing users' limited cellular data budgets while ensuring a satisfactory viewing experience. Furthermore, we present CAST-Live, tailored for live streaming scenarios with constrained playback buffers and considerations for energy costs. Extensive trace-driven evaluations and subjective tests demonstrate that CAST, CAST-DU, and CAST-Live outperform existing off-the-shelf schemes, delivering a superior video streaming experience over fluctuating networks while efficiently utilizing data resources. Moreover, CAST-Live demonstrates effectiveness even under limited buffer size constraints while incurring minimal energy costs

Synthesis and Characterization of the Optical Properties of Pt-TiO 2

Composite Pt-doped TiO2 nanotubes (Pt-TNTs) were synthesized via alkaline fusion-hydrothermal method (AFHM) under ambient atmosphere pressure. Further systematic characterization of Pt-TNTs was performed by using XPS, surface photovoltage spectroscopy (SPS), electric field-induced surface photovoltage spectroscopy (FISPS), UV-Vis diffuse reflectance spectrophotometry (UV-Vis), TEM, and XRD. XPS spectrum showed double peaks which accounted for the presence of platinum dioxide and platinum oxide (PtO2 and PtO, PtOxδ+). Composition analysis showed that the particulate matters on surface of Pt-TNTs were composed of PtOxδ+ and TiO2. The results of SPS and FISPS demonstrated that the bound exciton showed sub-band gap transition characteristics with the asymmetric changes of photoelectric property corresponding to changes in polarity and strength of the external electric field. Furthermore, the influence of the changed microstructure morphology of Pt-doped TNTs on both the photovoltage spectroscopy and the lifetime of photogenerated carriers which occurred at the interfaces of Pt-TNTs was observed. Result of XRD indicated that a mixture of anatase and rutile phases prevailed in Pt-TNTs. Contact potential barriers consisting of PtOxδ+, anatase, rutile, and PtOxδ+ are presumed to form upon PtOxδ+ particle that deposited on the surface of Pt-TNTs

Specific fungi associated with response to capsulized fecal microbiota transplantation in patients with active ulcerative colitis

ObjectiveFecal microbiota transplantation (FMT) is a novel microbial treatment for patients with ulcerative colitis (UC). In this study, we performed a clinical trial of capsulized FMT in UC patients to determine the association between the gut fungal community and capsulized FMT outcomes.DesignThis study recruited patients with active UC (N = 22) and healthy individuals (donor, N = 9) according to the criteria. The patients received capsulized FMT three times a week. Patient stool samples were collected before (week 0) and after FMT follow-up visits at weeks 1, 4, and 12. Fungal communities were analysed using shotgun metagenomic sequencing.ResultsAccording to metagenomic analysis, fungal community evenness index was greater in samples collected from patients, and the overall fungal community was clustered among the samples collected from donors. The dominant fungi in fecal samples collected from donors and patients were Ascomycota and Basidiomycota. However, capsulized FMT ameliorated microbial fungal diversity and altered fungal composition, based on metagenomic analysis of fecal samples collected before and during follow-up visits after capsulized FMT. Fungal diversity decreased in samples collected from patients who achieved remission after capsulized FMT, similar to samples collected from donors. Patients achieving remission after capsulized FMT had specific enrichment of Kazachstania naganishii, Pyricularia grisea, Lachancea thermotolerans, and Schizosaccharomyces pombe compared with patients who did not achieve remission. In addition, the relative abundance of P. grisea was higher in remission fecal samples during the follow-up visit. Meanwhile, decreased levels of pathobionts, such as Candida and Debaryomyces hansenii, were associated with remission in patients receiving capsulized FMT.ConclusionIn the metagenomic analysis of fecal samples from donors and patients with UC receiving capsulized FMT, shifts in gut fungal diversity and composition were associated with capsulized FMT and validated in patients with active UC. We also identified the specific fungi associated with the induction of remission. ClinicalTrails.gov (NCT03426683)

Hybrid Algorithm of Fixed Point for Weak Relatively Nonexpansive Multivalued Mappings and Applications

The purpose of this paper is to present the notion of weak relatively nonexpansive multi-valued mapping and to prove the strong convergence theorems of fixed point for weak relatively nonexpansive multivalued mappings in Banach spaces. The weak relatively nonexpansive multivalued mappings are more generalized than relatively nonexpansive multivalued mappings. In this paper, an example will be given which is a weak relatively nonexpansive multivalued mapping but not a relatively nonexpansive multivalued mapping. In order to get the strong convergence theorems for weak relatively nonexpansive multivalued mappings, a new monotone hybrid iteration algorithm with generalized (metric) projection is presented and is used to approximate the fixed point of weak relatively nonexpansive multivalued mappings. In this paper, the notion of multivalued resolvent of maximal monotone operator has been also presented which is a weak relatively nonexpansive multivalued mapping and can be used to find the zero point of maximal monotone operator