In addition, online music paying users have increased to 42.7 million with a year-over-year increase of 50%. According to Tencent Music Entertainment, the revenue from online music subscriptions is increased by 70% in the first quarter of the year 2020. This pandemic resulted in an increasing number of users, who are engaged in live streaming through platforms like Instagram and YouTube. Additionally, low profit, expenditure on the licensing process, and high competition are some of the major factors that restrict the growth of the market during the forecast period.Īlmost every country has been affected by the spread of the COVID-19 virus and declared a lockdown in their country. Although, growing piracy in the world is acting as a restraint of the market.
Moreover, some technological development like growth in voice recognition technology to improve consumer experience is another reason behind the growing market of streaming music during the forecast period. Additionally, the rising digitization and customer preference to adopt advanced consumer electronic products are some of the major reasons that will fuel the market during the forecast period.įurthermore, the availability of unique and dormant music according to the user's time and mood is another reason to fuel the growth of the market during the forecast period. There is a rising graph of internet users and increasing usage of smartphones and laptops in the world that are contributing to the growth of the market during the forecast period. It also offers a person to access millions of songs as per their choice. The music streaming services do not occupy any space in the storage device. It can be recovered and played, according to the user's time and convenience without downloading it manually. The transmission of music in a compressed state by utilizing the internet is termed Music streaming. The Global Music Streaming Market size is expected to reach $60.5 billion by 2026, rising at a market growth of 16% CAGR during the forecast period. 11, 2021 (GLOBE NEWSWIRE) - The "Global Music Streaming Market By Content Type (Audio and Video), By Platform (Apps and Browsers), By Service Type (On-demand Streaming and Live Streaming), By End User (Individual and Commercial) By Region, Industry Analysis and Forecast, 2020 - 2026" report has been added to 's offering.
ĭetailed characterizations of materials the morphology and microstructure of GO digital photos of the ZHS and powder enhancement of LOI compared to PIZG0 of PP composites HRR, THR, TSP, MLR, RSR, O 2C, CO 2P, and COP curves of PP and its composites SEM images of fractured sections of ZHS/GO-0.Dublin, Jan.
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The Supporting Information is available free of charge at. The analysis principles and facile fabrication strategies proposed in this work could be important for developing highly flame retardant composites. The resultant composites with the substitution of intumescent flame retardant (IFR) by 1.0, 2.0, and 4.0 wt % conferred better flame retardancy compared with PP composite containing only IFR, reflected by the efficient increase of LOI value and V0 rating of UL-94 vertical tests. The selected samples were analyzed to confirm the predictive LOI model. The GO sheets were like “light yarns” wrapped onto the ZHS via hydrogen bonding in an ethanol solution. We proposed a structure of nano graphene oxide (GO) wrapped micro zinc hydroxystannate (ZHS) in a simple but effective way as a novel flame-retardant agent to enhance the flame retardancy and mechanical properties of polypropylene (PP) composites.
Meanwhile, the increasing structural design of flame retardants is crucial to flame-retardant polymer composites. The data analysis allows us for a better understanding of the flame-retardant mechanism and components, and the equation has good accuracy in guiding the design of composites with high flame-retardant performance. To explore the relationship between limit oxygen index (LOI) and components, we prepared 20 composites and then trained a simple equation for the LOI using the method sure independence screening and sparsifying operator (SISSO). Inspired by the effective new paradigm of data-driven material discovery, we used machine learning to analyze experimental data to accelerate the development of new flame-retardant polymers. Designing flame-retardant polymers with high performance is a long-standing challenge, partly because of the time-consuming traditional approaches based on experiential intuition and trial-and-error screenings.
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