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Concerning the radiation effects on the three-dimensional (3D) packaging in space environment, this study aims to investigate the influence of the total dose effect on the transmission characteristics of high-frequency electrical signals using experimental and simulation methods.

This work carries out the irradiation test of the specimens and measures their S21 parameters before and after irradiation. A simulation model describing the total dose effect was built based on the experimental test results. And, the radiation hardening design is evaluated by the simulation method.

The experimental results demonstrate that the S21 curve of the interconnection decreases with the increase of the irradiation dose, indicating that the total dose effect leads to the decline of its signal transmission characteristics. According to the simulation results, decreasing the height of the through silicon via (TSV), increasing the radius of the TSV, reducing the length of Si and increasing the number of grounded through silicon via have positive effects on improving the radiation resistance of the interconnection structure.

This work investigates the effect of radiation on the transmission characteristics of interconnection structures for 3D packaging and proposes the hardening design methods. It is meaningful for improving the reliability of 3D packaging in space applications.

The purpose of this paper is to investigate the effects of Sn-Ag-x leveling layers on the mechanical properties of SnBi solder joints. Four Sn-Ag-x (Sn-3.0Ag-0.5Cu, Sn-0.3Ag-0.7Cu, Sn-0.3Ag-0.7Cu-0.5 Bi-0.05Ni and Sn-3.0Ag-3.0 Bi-3.0In) leveling layers were coated on Cu pads to prepare SnBi/Sn-Ag-x/Cu solder joints. The microstructure, hardness, shear strength and fracture morphology of solder joints before and after aging were studied.

The interfacial brittleness of the SnBi low-temperature solder joint is a key problem affecting its reliability. The purpose of this study is to improve the mechanical properties of the SnBi solder joint.

Owing to the addition of the leveling layers, the grain size of the ß-Sn phase in the SnBi/Sn-Ag-x/Cu solder joint is significantly larger than that in the SnBi/Cu eutectic solder joint. Meanwhile, the hardness of the solder bulk in the SnBi/Cu solder joint shows a decrease trend because of the addition of the leveling layers. The SnBi/Cu solder joint shows obvious strength drop and interfacial brittle fracture after aging. Through the addition of the Sn-Ag-x layers, the brittle failure caused by aging is effectively suppressed. In addition, the Sn-Ag-x leveling layers improve the shear strength of the SnBi/Cu solder joint after aging. Among them, the SnBi/SACBN/Cu solder joint shows the highest shear strength.

This work suppresses the interfacial brittleness of the SnBi/Cu solder joint after isothermal aging by adding Sn-Ag-x leveling layers on the Cu pads. It provides a way to improve the mechanical performances of the SnBi solder joint.

The purpose of this research study is to improve sentiment analysis (SA) at the aspect level, which is accomplished through two independent goals of aspect term and opinion extraction and subsequent sentiment classification.

The proposed architecture uses neighborhood and dependency tree-based relations for target opinion extraction, a domain–ontology-based knowledge management system for aspect term extraction, and deep learning techniques for classification.

The authors use different deep learning architectures to test the proposed approach of both review and aspect levels. It is reported that Vanilla recurrent neural network has an accuracy of 83.22%, long short-term memory (LSTM) is 89.87% accurate, Bi-LSTM is 91.57% accurate, gated recurrent unit is 65.57% accurate and convolutional neural network is 82.33% accurate. For the aspect level analysis, ρ_aspect comes out to be 0.712 and Δ²_aspect is 0.384, indicating a marked improvement over previously reported results.

This study suggests a novel method for aspect-based SA that makes use of deep learning and domain ontologies. The use of domain ontologies allows for enhanced aspect identification, and the use of deep learning algorithms enhances the accuracy of the SA task.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach.

An enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing.

The enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools.

The study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.