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Since researchers have utilized text signals to develop a mass of within-document visualization analysis tools for reading aid in a long document, there is an increasing need to study the relationship between readers’ behavior of using text signals for navigation and their reading performance in the tools. The purpose of this paper is to combine the text signals using behavior and reading performance in two kinds of analysis tools to verify their relationship and discover whether there is any efficient reading strategy when using text signals to navigate a long document.

The methodology is a case study. The authors reviewed related literature first. After explaining the design ideas, interface and functions of THC-DAT and BOOKMARK, which are two reading tools utilizing two main kinds of text signals, one utilizing topics and the other utilizing headings for reading aid, a case study was presented to collect click data on the text signals of participants and their reading effectiveness (score) and efficiency (time).

The results confirm that the text signals using behavior for navigation has a significant impact on reading efficiency and no impact on reading effectiveness in both BOOKMARK and THC-DAT. The discrete degree of clicks behavior on text signals has an impact on reading efficiency. The using behavior of different types of text signals has different impacts on reading efficiency.

Using text signals for navigation time evenly can help improve reading efficiency. And a basic strategy suggested to readers is focusing on reducing their time to find answers when using text signals for navigation in a long document. As to utilizing the two different kinds of text signals, readers can have different strategies. Accordingly, personalized recommendation based on interval of adjacent clicks will help to improve computer-aided reading tools.

This paper combines the text signals using behavior for navigation and reading performance in two kinds of visual analysis tools, studied the relationship between them and discovers some efficient reading strategies when using text signals for navigation to read a long document.

This paper aims to use nano-silver paste to design a new bonding method for super-large-area direct-bonded-aluminum (DBA) plates. It compared several frequently used bonding methods and proved the feasibility of an optimized low-pressure-assisted double-layer-printed silver sintering technology for large-area bonding to increase the thermal conductivity of power electronic modules with high junction temperature, higher power density and higher reliability.

The bonding profile was optimized by using transparent glasses as substrates. Thus, the bonding qualities could be directly characterized by optical observation. After sintering, the bonded DBA samples were characterized by nondestructive X-ray computed tomography system, scanning electron microscopy equipped with an energy dispersive spectrometer. Finally, bonding stress evolution was characterized by shear tests.

Low-pressure-assisted large-area double-layer-printed bonding process consisting of six-step was successfully developed to bond DBA substrates with the size of 50.8 × 25.4 mm. The thickness of the sintered-silver bond-line was between 33  and 74 µm with the average porosity of 12.5 per cent. The distribution of shear strength along the length of DBA/DBA bonded sample was from 9.7  to 18.8 MPa, with average shear strength of 15.5 MPa. The typical fracture primarily propagated in the sintered-silver layer and partially along the Ni layer.

The bonding stress needs to be further improved. Meanwhile, the thermal and electrical properties are encouraged to test further.

If nano-silver paste can be used as thermal interfacial material for super-large-area bonding, the thermal performance will be improved.

The paper accelerated the use of nano-silver paste for super-large-area DBA bonding.

The proposed bonding method greatly decreased the bonding pressure.

The purpose of this paper is to study the phenomenology of Al₂O₃-DBC substrate thermal-cracking under different high temperature cyclic loadings. The extremely low cycle fatigue (ELCF) life prediction model for ductile materials was used to describe the thermal fatigue life of Al₂O₃-DBC substrates.

Four groups of thermal cycling tests using Al₂O₃-DBC substrates with 0.65 mm thick copper were conducted using different peak temperatures. The failure samples were observed by optical microscope. The thermal plastic strain distribution in the Al₂O₃-DBC substrates was analyzed using a finite element method with the Chaboche model for describing plastic deformation of copper. The ELCF life prediction model was used to predict the life of Al₂O₃-DBC substrates under high temperature cyclic loadings.

Interface cracking was observed to initiate at the short edge of the bonded copper and deviated into the ceramic layer when the crack grew beyond the critical length of 0.1-0.8 mm. The interface crack deviated into the ceramic layer at different thickness and grew parallel to the interface layer between the ceramic layer and copper layer. The crack propagation stopped after certain cycles. The copper layer with 10-20 μm thick alumina inside was not split away totally from the ceramic layer. The ELCF life prediction model could predict the life of Al₂O₃-DBC substrates well under high temperature cyclic loading. The material constants in the extremely low fatigue life prediction model were obtained using thermal fatigue tests results.

The influence of copper layer thickness and ceramic layer thickness on thermal cracking characteristics of DBC substrate should be studied in the future. Failure models should also be further investigated.

The failure model of Al₂O₃-DBC substrates under high temperature cyclic loading was studied. A method for predicting the life of the substrate samples under high temperature cyclic loading was proposed.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

The purpose of this paper is to examine the difference between experts and novices when reading with navigational table of contents (N-TOC). Experts refer to readers with high level of domain knowledge; novices refer to readers with low level of domain knowledge.

The authors performed a controlled experiment of three reading tasks (including fact finding task, partial understanding task, and full-text understanding task) on an N-TOC system for 35 post-graduates of Wuhan University who have rich experience in reading with N-TOC. Participants’ domain knowledge was measured by pre-experiment questionnaires; reading performance data including score, time, navigation use, and evaluation of N-TOC were collected.

The results showed that there was significant difference in neither navigation use nor participants’ evaluation, but domain experts performed significantly better in both score and time of all tasks than domain novices, which revealed an “illusion of control” phenomenon that rich experience in reading with N-TOC enabled domain novices to achieve the same performance as domain experts. In addition, this research found that N-TOC was not suitable for domain novices to solve full-text understanding task because of “cognitive overload” phenomenon.

This study makes a good contribution to the literature on the effect of domain knowledge on reading performance during N-TOC reading and how to provide better digital reading service in the field of library science and information science.

With a mass of electronic multi-topic documents available, there is an increasing need for evaluating emerging analysis tools to help users and digital libraries analyze these documents better. The purpose of this paper is to evaluate the effectiveness, efficiency and user satisfaction of THC-DAT, a within-document analysis tool, in reading a multi-topic document.

The authors reviewed related literature first, then performed a user-centered, comparative evaluation of two within-document analysis tools, THC-DAT and BOOKMARK. THC-DAT extracts a topic hierarchy tree using hierarchical latent Dirichlet allocation (hLDA) method and takes the context information into account. BOOKMARK provides similar functionality to the Table of Contents bookmarks in Adobe Reader. Three novel kinds of tasks were devised for participants to finish on two tools, with objective results to assess reading effectiveness and efficiency. And post-system questionnaires were employed to obtain participants’ subjective judgments about the tools.

The results confirm that THC-DAT is significantly more effective than BOOKMARK, while not inferior in efficiency. There is some evidence that suggests THC-DAT can slow down the process of approaching cognitive overload and improve users’ willingness to undertake difficult task. Based on qualitative data from questionnaires, the results indicate that users were more satisfied when using THC-DAT than BOOKMARK.

Adopting THC-DAT in digital libraries or electrical document reading systems contributes to promoting users’ reading performance, willingness to undertake difficult task and general satisfaction. Moreover, THC-DAT is of great value to addressing cognitive overload problem in the information retrieval field.

This paper evaluates a novel within-document analysis tool in analyzing a multi-topic document, and proved that this tool is superior to the benchmark in effectiveness and user satisfaction, and not inferior in efficiency.

This paper aims to examine the effect of domain knowledge on eye-tracking measures and predict readers’ domain knowledge from these measures in a navigational table of contents (N-TOC) system.

A controlled experiment of three reading tasks was conducted in an N-TOC system for 24 postgraduates of Wuhan University. Data including fixation duration, fixation count and inter-scanning transitions were collected and calculated. Participants’ domain knowledge was measured by pre-experiment questionnaires. Logistic regression analysis was leveraged to build the prediction model and the model’s performance was evaluated based on baseline model.

The results showed that novices spent significantly more time in fixating on text area than experts, because of the difficulty of understanding the information of text area. Total fixation duration on text area (TFD_T) was a significantly negative predictor of domain knowledge. The prediction performance of logistic regression model using eye-tracking measures was better than baseline model, with the accuracy, precision and F(β = 1) scores to be 0.71, 0.86, 0.79.

Little research has been reported in literature on investigation of domain knowledge effect on eye-tracking measures during reading and prediction of domain knowledge based on eye-tracking measures. Most studies focus on multimedia learning. With respect to the prediction of domain knowledge, only some studies are found in the field of information search. This paper makes a good contribution to the literature on the effect of domain knowledge on eye-tracking measures during N-TOC reading and predicting domain knowledge.

This study aims to explore the gap between information needs and services for autism in China.

The gap is revealed by investigating the status quo of autism information needs and services. The authors extracted categories and subcategories of information needs through content analysis of academic documents and then supplemented the subcategories through text mining of an online forum. Meanwhile, categories and subcategories of information services were extracted through content analysis of autism websites. Finally, the authors matched the two to explore the gap and designed a quantitative index to measure it.

A total of eight and ten categories of information needs and services are extracted, respectively. In total, six categories of information needs can be partly matched, but nearly half of the subcategories failed. Huge gaps in economic support, sociality and policy mechanisms categories are observed through the quantitative index and medium gaps in social resource services and employment categories while almost no gaps in psychological/emotional counseling, rehabilitation skills training and professional knowledge/information categories.

This study takes a deep insight into the gap between autism information needs and services in China, providing evidences and suggestions for information providers to improve their services. Academic documents and online forum data are adopted to avoid the impact of stigmatization, which provides a multi-source data analysis approach for the information needs of special groups.

Significant differences exist in terms of use and demand of urban public space in different districts and communities. In this study, PSPL survey was made in about one year to investigate the type and occurring time of outdoor activities and the visitors' demand difference of Round-City-Park in Xi'an, China. The survey method for collecting data included spatial classification, photographic recording, field observation, questionnaires and interviews. Then a new method of Virtual Typical Day (VTD) was put forward to analyze usage pattern of public space in the daily life. According to our results, laying out more public spaces close to residential area can make a more vigorous city. And the results also reveal that there exist some problems of uneven usage periods in different spaces, various space requirement from different age groups, insufficient support of space and facilities and so on. Based on the survey, an optimizing strategy of adaptive design is proposed such as setting mobile “stage” and providing flexible “props” according to the changing demands. The proposed design approach can encourage people to participate in outdoor activities, improve usage frequency of public spaces, and stimulate vitality of the city. This may also apply to other Chinese cities.

Social media are central to the creation and maintenance of social relations, including romantic relations. While much of the scholarship has examined how social media play a role in the initiation and maintenance of romantic relations, little is known about their role in romantic dissolution. This chapter fills this gap by examining the kinds of strategies young adults employ to cope with the aftermath of a romantic breakup complicated by Facebook. Based on qualitative analysis of 10 interviews with young adults, the authors propose a typology of Facebook coping strategies for romantic breakups. The typology includes seven types of coping strategies with 12 subtopics and 25 specific actions linked to the subtopics. The authors find that those coping with a breakup engage in erasing and avoiding breakup reminders and digital traces, as they perceive them as hurtful. They regulate their use of Facebook and limit Facebook communication with the ex. An important coping strategy used was to express their emotions via Facebook content, often aimed at the ex-partner. They also use Facebook to seek support and new information about the ex, including about potential new romantic interests. Others preferred to avoid information about the ex and either deleted the ex and their friends or chose to unsubscribe to updates. Coping strategies included preventative measures such as changing passwords, blocking the ex, and adjusting privacy settings. We found our participants engaged in both goal-directed and emotionally based coping strategies. Yet, the findings suggest a tendency to rely more extensively on goal-directed coping strategies by actively addressing the breakup stressor. We also note that coping strategies specific to Facebook are more likely to be used than traditional offline forms of coping. This exploratory study serves as a starting point to better understand the role that Facebook plays in mediating online behaviors following a romantic dissolution, such that these strategies can be subsequently evaluated in terms of usefulness in clinical coping recommendations.