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The purpose of this paper is to present a method to obtain the inertia parameter of a captured unknown space target.

An inertia parameter identification method is proposed in the post-capture scenario in this paper. This method is to resolve parameter identification with two steps: coarse estimation and precise estimation. In the coarse estimation step, all the robot arms are fixed and inertia tensor of the combined system is first calculated by the angular momentum conservation equation of the system. Then, inertia parameters of the unknown target are estimated using the least square method. Second, in the precise estimation step, the robot arms are controlled to move and then inertia parameters are once again estimated by optimization method. In the process of optimization, the coarse estimation results are used as an initial value.

Numerical simulation results prove that the method presented in this paper is effective for identifying the inertia parameter of a captured unknown target.

The presented method can also be applied to identify the inertia parameter of space robot.

In the classic momentum-based identification method, the linear momentum and angular momentum of system, both considered to be conserved, are used to identify the parameter of system. If the elliptical orbit in space is considered, the conservation of linear momentum is wrong. In this paper, an identification based on the conservation of angular momentum and dynamics is presented. Compared with the classic momentum-based method, this method can get a more accurate identification result.

The widespread acceptance of various social platforms has increased the number of users posting about various services based on their experiences about the services. Finding out the intended ratings of social media (SM) posts is important for both organizations and prospective users since these posts can help in capturing the user’s perspectives. However, unlike merchant websites, the SM posts related to the service-experience cannot be rated unless explicitly mentioned in the comments. Additionally, predicting ratings can also help to build a database using recent comments for testing recommender algorithms in various scenarios.

In this study, the authors have predicted the ratings of SM posts using linear (Naïve Bayes, max-entropy) and non-linear (k-nearest neighbor, k-NN) classifiers utilizing combinations of different features, sentiment scores and emotion scores.

Overall, the results of this study reveal that the non-linear classifier (k-NN classifier) performed better than the linear classifiers (Naïve Bayes, Max-entropy classifier). Results also show an improvement of performance where the classifier was combined with sentiment and emotion scores. Introduction of the feature “factors of importance” or “the latent factors” also show an improvement of the classifier performance.

This study provides a new avenue of predicting ratings of SM feeds by the use of machine learning algorithms along with a combination of different features like emotional aspects and latent factors.

The Gulf Cooperation Council (GCC) countries have consistently ranked high in per capita carbon emissions, not to mention the fact that a lifestyle with a high ecological footprint in a fragile ecosystem can affect the regional environment, prosperity and social stability. The adoption of carbon capture and storage (CCS) in the GCC countries has been consistently gaining attention, as it is widely seen as a suitable mitigation measure, particularly in a region where heavy industry and geological exploitation have led to wealth and prosperity. Additionally, making captured CO₂ available for enhanced oil recovery is expected to create significant economic value. However, the lack of a coordinated environmental regulation regime to cap future carbon emissions is posing significant risks for further CCS development. The paper aims to discuss these issues.

This paper reviews the state of play with regard to CCS in the GCC region and investigate the opportunities and challenges facing CCS development in the UAE by use of the interview technique.

This paper finds that the lack of CCS‐related regulations, absence of CCS policy at a national level and limited human capital resources are impeding the development of CCS in the UAE. Findings from this study can offer GCC policy‐makers relevant insights into how best to develop CCS projects for the GCC region.

This is an original research, that has not been conducted before. This is first of a kind assessment for the GCC region.

Invoices, bills of lading, purchase orders, and the tons of costly paper transactions written by businesses each year may become relics of the past as industries implement Electronic Data Interchange (EDI). EDI, forecast to grow 73% annually over the next five years, could erode the stacks of paper files and bring many companies into the twenty‐first century with instant, direct transfer of business documents. A direct result of this growth, according to a new 199‐page report by Frost & Sullivan on The Electronic Data Interchange Market in the US. (♯A 1911) is the development of document format standards that enable computer‐to‐computer transmission of business forms to multiple industries.

The purpose of this study is to explore the information needs and behaviors of Chinese first-time mothers from the perspective of information journey theory.

This study uses the popular coding language Python to write a program to extract 366 posts published by first-time mothers in the Babytree community of China's largest maternal and infant platform. Content analysis is used to analyze and encode the collected data.

This study summarizes the information needs of first-time mothers in China specifically, which includes six aspects of health, pregnancy care, postpartum parenting, socialization, self-actualization, and entertainment. Then, based on its hierarchical and stage characteristics, more flexible information needs matrix for Chinese first-time mothers has been developed to reflect their changing 15 types of information needs more fully. Finally, it constructs the information journey model of Chinese first-time mothers including five stages.

This is the first time that information journey theory has been applied to the research on information needs and behaviors of first-time mothers. The authors have also further expanded the information seeking and acquisition phases and found new gaps in the phases to further refine the information journey model. This study can help us better understand the online information needs and behaviors of Chinese first-time mothers and provide support for organizations and online health community managers to develop new information services or improve the quality of existing services.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0538.