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The main purpose of this paper is to measure the status and quality of library and information science (LIS) open-access (OA) journals in the Social Science Citation Index (SSCI).

The study selected 86 source journals of LIS in the SSCI as a sample and measured their status of open access. Analytic hierarchy process (AHP) was used to analyze 36 OA journals of 86 source journals, especially their production capability, academic influence and network communication ability.

The results indicate that OA journals have become an increasingly important part of LIS journals. Production capability, academic influence and network communication ability are important factors affecting the quality of OA journals. These three evaluation indicators of LIS OA journals are high, but many still have room for improvement.

As the paper is limited by collecting data, the indicators of OA journals’ quality are not all-around. So, they cannot reflect the quality of LIS OA journals. In the selection of the evaluation method, the evaluation results are limited because only one AHP method is used.

The research on evaluation of OA journals can help library and scientific research personnel use OA journals effectively. Identifying key factors on evaluation can help researchers to construct OA journals better.

The research on OA journals’ quality can also promote the study on OA process in academic circles and promote the communication, development and utilization of academic information. Such research can also enrich the theory of OA, and provide some new perspectives for the study of journals’ evaluation.

This paper measures the quality of LIS OA journals by analyzing production capability, academic influence and network communication ability. Rather than the traditional research methods, the focus of this study is on the value of the Web as a source of impact indices. It contributes to the scholarly impact measurements of OA journals.

With the development of automation technology, the accuracy, bearing capacity and self-adaptation requirements of wheeled mobile robots are more and more demanding under various complex conditions, which will urge designers such shortcomings as the low accuracy, poor flexibility and weak obstacle crossing ability of traditional heavy haul vehicles and improve the wear resistance and bearing capacity of traditional omnidirectional wheels.

The optimal configuration for heavy payload transportation is obtained by building sliding friction consumption model of traditional wheels with different driving types based on Hertz tangential contact theory. The heavy payload omnidirectional wheel with a double-wheel steering and a coupled differential wheel driving is designed with the optimal configuration. The wheel consists of a differential gear train unit and a nonindependent suspension unit. Kinematics model of the wheel is established and relative parameters are optimized.

The prototype experiments show that the wheel has higher motion accuracy and environment adaptability. The results are consistent with the theoretical calculation, which show that the accuracy is more than 50% higher than that of differential prototype. The motion stability and the accuracy of the coupled differential omnidirectional wheel are better than those of the traditional omnidirectional wheels during the moving and obstacle crossing process under complex conditions, which verifies the correctness and advantages of the design.

Aiming at the specific application of heavy payload omnidirectional transportation, a new omnidirectional mobile mechanism with a two-wheel coupling drive structure and an adaptive mechanism is proposed. The simulation and experimental results show that it can realize the high-precision heavy-load omnidirectional movement, the effective contact with the ground and improve the adaptability to the rugged ground. It is flexible, simple and modular and can be widely applied to transportation, exploration, detection and other related industrial fields.

This paper aims to identify a variety of binary system solders by alloying, and relevantly derive multiple system Pb-free solders from the former, attempting to replace the high temperature Sn-Pb solder.

The basis of the paper is the synthesis of previous studies. In terms of some binary high temperature solder alloys, such as Au-20Sn, Bi-2.5Ag, Sn-5Sb, Au-12.5Ge, Zn-6Al and Zn-Sn, taking the alloy phase diagram as the starting point, the melting characteristics, microstructure, mechanical properties, wetting ability and reliability of solder joint are analysed and the prospect is consequently indicated.

Based on the analysis of the six groups of Pb-free solders, the present binary system solder alloys, from the perspective of melting properties, mechanical properties, soldering or reliability of solder joint, rarely meet the comprehensive requirements of replacing the high-temperature Sn-Pb solder. It is assumed to be a solution that multiple-system Pb-free solders derive from a variety of binary system solders by means of alloying. The future development of high temperature Pb-free solder may focus on some factors such as physical properties, mechanical properties, processing, reliability of solder joint, environmental performance and expense.

The paper concentrates on the issue of Pb-free solders at high temperature. From a specific perspective of binary system solders, the presently available Pb-free solders are suggested from the starting point of the alloy phase diagram and the prospect of alternatives of Sn-Pb solders at high temperature are indicated.

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

In this paper, a light-weight, low-power atmospheric multi-parameter sensor (AMPS), which could be mounted on small flying platforms such as a tethered balloon, a quad-rotor unmanned aerial vehicle (UAV), a UAV helicopter, etc., is implemented and integrated to sample vertical distribution of aerosols with integrated parameters of aerosol particle concentration, temperature, relative humidity and atmospheric pressure.

The AMPS integrates three kinds of probes in an embedded system. A synchronous method based on GPS is proposed to drive the laser aerosol particle sensor, the temperature and humidity probe and the pressure probe to sample four channels approximately simultaneously. Different kinds of housing are designed to accommodate various flying platforms, and the weight is controlled to adapt the payload of each platform.

A series of validation tests show that while the AMPS achieves high precision, its power consumption is less than 1.3 W, which is essential for light flying platforms. The AMPS was mounted on different flying platforms and the difference was evaluated. For three times every five days, vertical profiles of PM2.5 and PM10 concentrations were observed by the AMPS mounted on a quad-rotor UAV, which revealed the significant correlation between the aerosol particle concentration and atmospheric parameters.

A new light-weight and low-power AMPS for small flying platforms is designed and tested, which provides an effective way to explore the properties of aerosol vertical distribution, and to monitor pollutants flexibly.

The anomaly detection task for oil and gas pipelines based on acoustic signals faces issues such as background noise coverage, lack of effective features, and small sample sizes, resulting in low fault identification accuracy and slow efficiency. The purpose of this paper is to study an accurate and efficient method of pipeline anomaly detection.

First, to address the impact of background noise on the accuracy of anomaly signals, the adaptive multi-threshold center frequency variational mode decomposition method(AMTCF-VMD) method is used to eliminate strong noise in pipeline signals. Secondly, to address the strong data dependency and loss of local features in the Swin Transformer network, a Hybrid Pyramid ConvNet network with an Agent Attention mechanism is proposed. This compensates for the limitations of CNN’s receptive field and enhances the Swin Transformer’s global contextual feature representation capabilities. Thirdly, to address the sparsity and imbalance of anomaly samples, the SpecAugment and Scaper methods are integrated to enhance the model’s generalization ability.

In the pipeline anomaly audio and environmental datasets such as ESC-50, the AMTCF-VMD method shows more significant denoising effects compared to wavelet packet decomposition and EMD methods. Additionally, the model achieved 98.7% accuracy on the preprocessed anomaly audio dataset and 99.0% on the ESC-50 dataset.

This paper innovatively proposes and combines the AMTCF-VMD preprocessing method with the Agent-SwinPyramidNet model, addressing noise interference and low accuracy issues in pipeline anomaly detection, and providing strong support for oil and gas pipeline anomaly recognition tasks in high-noise environments.