content optimization
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2022 ◽  
Vol 12 ◽  
Author(s):  
Xueyun Zeng ◽  
Xuening Xu ◽  
Yenchun Jim Wu

Application of artificial intelligence is accelerating the digital transformation of enterprises, and digital content optimization is crucial to take the users' attention in social media usage. The purpose of this work is to demonstrate how social media content reaches and impresses more users. Using a sample of 345 articles released by Chinese small and medium-sized enterprises (SMEs) on their official WeChat accounts, we employ the self-determination theory to analyze the effects of content optimization strategies on social media visibility. It is found that articles with enterprise-related information optimized for content related to users' psychological needs (heart-based content optimization, mind-based content optimization, and knowledge-based content optimization) achieved higher visibility than that of sheer enterprise-related information, whereas the enterprise-related information embedded with material incentive (benefits-based content optimization) brings lower visibility. The results confirm the positive effect of psychological needs on the diffusion of enterprise-related information, and provide guidance for SMEs to apply artificial intelligence technology to social media practice.


2021 ◽  
Vol 514 ◽  
pp. 230563
Author(s):  
Emily Cossar ◽  
Alejandro O. Barnett ◽  
Frode Seland ◽  
Reza Safari ◽  
Gianluigi A. Botton ◽  
...  

2021 ◽  
Vol 496 ◽  
pp. 229855
Author(s):  
Samindi Madhubha Jayawickrama ◽  
Dan Wu ◽  
Rei Nakayama ◽  
Shota Ishikawa ◽  
Xuanchen Liu ◽  
...  

2021 ◽  
Vol 640 (2) ◽  
pp. 022074
Author(s):  
A B Turalieva ◽  
M K Sadygova ◽  
T V Kirillova ◽  
M V Belova ◽  
T I Anikienko ◽  
...  

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Hotman Manurung ◽  
Donald Siahaan ◽  
Benika Naibaho ◽  
Rosnawyta Simanjuntak ◽  
Tumiur Gultom

It is not known how to extract carotenoid- which has a high value -optimally from palm oil  mesocarp fiber (POMF). The research objective was to determine the optimization of oil extraction from POMF waste and to determine the optimization of carotenoid extraction from POMF oil. The research was carried out in 2 stages: The first stage was oil extraction from the POMF with the treatment factor ratio of hexane to the weight of the POMF and the extraction time. Stage 2 Optimization of carotenoid  extraction using the solvolitic method with treatment: Minor solvent types Methyl ester (Me) caprylate-caprate (C8 –C10) and Me laurate-myristate (C12-C14) and minor solvent concentrations of 0.1% and 0, 25%. Parameters analyzed were: oil content, Deterioration bleaching of index (DOBI), and carotenoid concentration. Optimization ratio between hexane and POMF weight is 1:40 (vol / g) with an oil content of 2.938%. Optimization of extraction time for 100 minutes with 4.104% oil content. Optimization of carotenoid extraction is by using minor solvent Me C8-C10 with a solvent amount of 0.1% which results in a carotenoid concentration of 302.442 ppm and DOBI of 5.74. The increase in caroten concentration resulted from saponification reached 114.2 times from the carotenoid concentration in POMF oil.


2020 ◽  
Vol 849 ◽  
pp. 156640 ◽  
Author(s):  
Long-Qing Zhao ◽  
Cheng Wang ◽  
Jun-Chen Chen ◽  
Hong Ning ◽  
Zhi-Zheng Yang ◽  
...  

2020 ◽  
Vol 50 (4) ◽  
pp. 225-238
Author(s):  
Eunhye Song ◽  
Peiling Wu-Smith ◽  
Barry L. Nelson

A vehicle content portfolio refers to a complete set of combinations of vehicle features offered while satisfying certain restrictions for the vehicle model. Vehicle Content Optimization (VCO) is a simulation-based decision support system at General Motors (GM) that helps to optimize a vehicle content portfolio to improve GM’s business performance and customers’ satisfaction. VCO has been applied to most major vehicle models at GM. VCO consists of several steps that demand intensive computing power, thus requiring trade-offs between the estimation error of the simulated performance measures and the computation time. Given VCO’s substantial influence on GM’s content decisions, questions were raised regarding the business risk caused by uncertainty in the simulation results. This paper shows how we successfully established an uncertainty quantification procedure for VCO that can be applied to any vehicle model at GM. With this capability, GM can not only quantify the overall uncertainty in its performance measure estimates but also identify the largest source of uncertainty and reduce it by allocating more targeted simulation effort. Moreover, we identified several opportunities to improve the efficiency of VCO by reducing its computational overhead, some of which were adopted in the development of the next generation of VCO.


2020 ◽  
Vol MA2020-01 (37) ◽  
pp. 1588-1588
Author(s):  
Elena A. Baranova ◽  
Emily Cossar ◽  
Alejandro Oyarce Barnett ◽  
Frode Seland

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