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As the core carrier of information storage, a semiconductor memory device is a basic product with a large volume that is widespread in the integrated circuit industry. With the rapid development of semiconductor manufacturing processes and materials, the internal structure of memory has gradually shifted from a 2D planar packaging structure to a 3D packaging structure to meet industry demands for high-frequency, high-speed, and large-capacity devices with low power consumption. However, advanced 3D packaging technology can pose some reliability risks, making devices prone to failure, especially when used in harsh environmental conditions, including temperature changes, high temperature and humidity levels, and mechanical stress. In this paper, the authors introduce the typical structure characteristics of 3D packaged memory; analyze the reasons for device failure caused by stress; summarize current research methods that utilize temperature, mechanical and hygrothermal theories, and failure models; and present future challenges and directions regarding the reliability research of 3D packaged memory.

In 3D packaging memory devices, solder joints are critical links between the chip and the printed circuit board (PCB). Under severe working conditions, cracks inevitably occur due to thermal shock. If cracks grow in the solder joint, the chip will be disconnected with the PCB, causing its function failure. In this paper, the reliability of solder joints under thermal shock are evaluated for 3D packaging memory devices by means of the SEM and finite element analysis. As microscopically studied by the SEM, it is found out that the main failure mechanism of solder joints in such test is the thermal fatigue failure of solder joints. Finite element analysis shows that cracks are caused by the accumulation of plastic work and creep strain. The initiation and growth of cracks are mainly influenced by the inelastic strain accumulation. The trends of cracks are influenced by the difference between the coefficient of thermal expansion (CTE) of epoxy resin and that of the chip.

With the development of science and technology, consumers’ requirements for various electronic devices present a trend of more diverse functions and thinner bodies. This makes integrated circuits mounted in electronic products and their packaging more vital to satisfying the above requirements. Ball grid array (BGA) packaging is widely used in the field of microelectronic manufacturing industries due to its multiple I/O volumes and excellent electric characteristics. However, due to environmental loads such as vibration and impact during its production and application, defects inevitably emerge in BGA solder joint defects, which will lead to the failure of electronic products. This article summarizes the state-of-the-art research on the factors, analysis methods, and models for the fatigue failure of BGA chips. After rigorous discussions concerning this research, some theoretical suggestions are provided for BGA packaging in reliability analysis and the establishment of evaluation standards.

In order to facilitate the entire board reflow process, the pin of a DIP(Dual In-line Package) component is bent into a gull-wing type pin. However, after a short period of service, the failure of the Dual Inline Package surface-mounted solder joints of this type of product has occurred. In order to analyze the causes of failures, the DIP surface-mount solder joints were synthesized by means of metallographic sectioning, SEM (scanning electron microscope) analysis and numerical simulation analysis.After metallographic sectioning and SEM&EDS analysis, it is found that due to the possible poor coplanarity of the bent DIP pin array, some of the pins are deficient in solder under the reflow soldering, resulting in a solder joint with the effective connection area too small, so that it is cracked by environmental vibration stress during the service process. The main objective of the work is to evaluate the long-term reliability of DIP surface mounted solder joints, this study mainly used reliability simulation techniques to perform random vibration simulations on module components, and conducted stress analysis on the vibration response of solder joints to verify the location of solder joint cracking under the vibration load. The vibration responses of bent gull-wing solder joints and normal plug-in type solder joints were compared and analyzed through simulation techniques. The vibration reliability of plug-in solder joints was found to be significantly higher than that of gull-wing solder joints after design changes. As a result, for long-term reliability considerations, this design change is not recommended.

Without any organic solvent or activator, the selective oxidation of cyclohexane with air to afford cyclohexanol and cyclohexanone was investigated over Co/Al2O3 and Co-M/Al2O3 (M = Cu, Zn, and Ni) catalysts prepared by sol-gel method. The activity of the catalysts decreases in the following order: Co-Ni/Al2O3 > Co/Al2O3 > Co-Zn/Al2O3 > Co-Cu/Al2O3. The optimum contents of cobalt and nickel are 4.0 wt% and 3.0 wt% in Co-Ni/Al2O3 catalyst, respectively. The best result was obtained over Co-Ni/Al2O3 catalyst with a conversion of 9.9% and a selectivity of 94.6% and the ratio of cyclohexanone to cyclohexanol of 2.8 when the reaction was carried out at 443 K for 120 min under 4.5 MPa, and catalytic activity did not change after five consecutive reactions.

Objective Online health communities (OHCs) have become a major source of support for people with health problems. This research tries to improve our understanding of social influence and to identify influential users in OHCs. The outcome can facilitate OHC management, improve community sustainability, and eventually benefit OHC users. Methods Through text mining and sentiment analysis of users' online interactions, the research revealed sentiment dynamics in threaded discussions. A novel metric—the number of influential responding replies—was proposed to directly measure a user's ability to affect the sentiment of others. Results Using the dataset from a popular OHC, the research demonstrated that the proposed metric is highly effective in identifying influential users. In addition, combining the metric with other traditional measures further improves the identification of influential users.

Social bookmarking sites typically visualize user-generated tags as tag clouds. While tag clouds effectively show the relative frequency and thus popularity of tags, they fail to convey two aspects to the users: (1) the similarity between tags, and (2) the abstractness of tags. We suggest an alternative to tag clouds known as tag hierarchies. Tag hierarchies are based on a minimum evolution-based greedy algorithm for tag hierarchy construction, which iteratively includes optimal tags into the tree that introduce minimum changes to the existing taxonomy. Our algorithm also uses a global tag ranking method to order tags according to their levels of abstractness as well as popularity such that more abstract tags will appear at higher levels in the taxonomy. Based on the tag hierarchy, we derive a new tag recommendation algorithm, which is a structure-based approach that does not require heavily trained models and thus is highly efficient. User studies and quantitative analysis suggest that (1) the tag hierarchy can potentially reduce the user's tagging time in comparison to tag clouds and other tag tree structures, and (2) the tag recommendation algorithm significantly outperforms existing content-based methods in quality.

Many users join online health communities (OHC) to obtain information and seek social support. Understanding the emotional impacts of participation on patients and their informal caregivers is important for OHC managers. Ethnographical observations, interviews, and questionnaires have reported benefits from online health communities, but these approaches are too costly to adopt for large-scale analyses of emotional impacts. A computational approach using machine learning and text mining techniques is demonstrated using data from the American Cancer Society Cancer Survivors Network (CSN), an online forum of nearly a half million posts. This approach automatically estimates the sentiment of forum posts, discovers sentiment change patterns in CSN members, and allows investigation of factors that affect the sentiment change. This first study of sentiment benefits and dynamics in a large-scale health-related electronic community finds that an estimated 75\%--85\% of CSN forum participants change their sentiment in a positive direction through online interactions with other community members. Two new features, \textit{Name} and \textit{Slang}, not previously used in sentiment analysis, facilitate identifying positive sentiment in posts. This work establishes foundational concepts for further studies of sentiment impact of OHC participation and provides insight useful for the design of new OHC's or enhancement of existing OHCs in providing better emotional support to their members.

In many social networks, the connections between actors are formed because they participate in the same event, such as a set of scholars coauthoring a paper and a person making phone calls or having teleconferences with his friends. Therefore, we propose an event-driven framework for creating network growth models. We also notice that in evolving networks, both the behavior of the whole network and the behavior of nodes evolve over time. For example, we observe in collaborative networks that the growth rates of the communities and the average number of coauthors in papers change as the network sizes increase over time, and researchers' interactions with local groups and remote groups also evolve over time with their experience (degree). These observations motivate us to propose a behavior evolution-aware event-driven locality and attachedness based model to capture the growth dynamics in social networks. Based on some informative metrics of network structure and properties, such as degree distribution, degree-dependent clustering coefficients, and degree-dependent average degree of neighbors, the experiments suggest that our model can better characterize the growing process and simulate important network structures observed in real networks than other non-event driven and non-behavior aware models.

Along with more and more use of high density plastic ball grid array (PBGA) in portable electronic products and military electronic equipments, the vibration fatigue reliability of solder joints for PBGA becomes a critical concern. In this paper, a quarter 3D symmetric model was developed by ANSYS software, then modal analysis of PCBA assembly and random vibration response analysis of mixed solder joints were implemented. The effect of fixation points on mode shape and the size parameters for solder joints on von mises stress were discussed. Finally, the influence of every step mode on the PCB bending deflection was analyzed. The results showed that the first bend mode was the main factor which influences the bend deformation of the PCB. Adopting more fixations can reduce the influence of the first mode. Solder joint with maximum von mises stress (critical solder joint) was occurred on the outmost corner of PBGA which is mounted at the position of PCB largest deformation, and the accurate critical interface was located at the soldering interface between PCB and solder ball. More deflection of PCB causes larger stress of solder joint. The further research found that the first four step natural frequencies are less than 2000Hz, and they are within the vibration frequency range of 5Hz to 2000Hz caused by the transport and application. So it is necessary to optimize the fixation design of PCB assembly. Moreover, within a certain range, the thicker of PCB vehicle can get smaller stress, while the greater height of solder ball would cause greater stress.

In evolving social networks, nodes join, make connections, or leave over time. In this paper, we introduce node event sequences that record activities of every node over time. Node event sequences are suitable for microscopic analysis of node behaviors in social networks. As preliminary results of taking advantage of node event sequences (as well as snapshots of networks), we study the health of the community of Nanotechnology based on the analysis of Seniority of nodes, identify the intrinsic dynamics of formation of new edges and its relation to Seniority of nodes, and the changes in the node behavior according to the nodes' Seniority.

Understanding how topics in scientific literature evolve is an interesting and important problem. Previous work simply models each paper as a bag of words and also considers the impact of authors. However, the impact of one document on another as captured by citations, one important inherent element in scientific literature, has not been considered. In this paper, we address the problem of understanding topic evolution by leveraging citations, and develop citation-aware approaches. We propose an iterative topic evolution learning framework by adapting the Latent Dirichlet Allocation model to the citation network and develop a novel inheritance topic model. We evaluate the effectiveness and efficiency of our approaches and compare with the state of the art approaches on a large collection of more than 650,000 research papers in the last 16 years and the citation network enabled by CiteSeerX. The results clearly show that citations can help to understand topic evolution better.

Community discovery has drawn significant research interests among researchers from many disciplines for its increasing application in multiple, disparate areas, including computer science, biology, social science and so on. This paper describes an LDA(latent Dirichlet Allocation)-based hierarchical Bayesian algorithm, namely SSN-LDA (simple social network LDA). In SSN-LDA, communities are modeled as latent variables in the graphical model and defined as distributions over the social actor space. The advantage of SSN-LDA is that it only requires topological information as input. This model is evaluated on two research collaborative networkst: CtteSeer and NanoSCI. The experimental results demonstrate that this approach is promising for discovering community structures in large-scale networks.

The development of Web Information Systems(WIS) is a hot research topic both in industry and academia. This paper presents a platform for ontology driven WIS development: the Web Information System auto-construction Environment(WISE). The novel features of WISE are: 1) Employing WIS ontology to specify user requirements; 2) Providing graphical tools to support ontology construction; 3) Drawing user interfaces and generating source code automatically from WIS ontology.

The load shedding problem is vital to a Data Stream Management System (DSMS). This paper presents the design, implementation, and evaluation of a load shedding method under the guide of the feedback control theory, in order to solve practical problems in medical environment. Thus, the using of operator selectivity, which has been proven not stable enough, is avoided. This paper focuses on the restriction of memory resource, this prevents the overflow of both CUP and memory resource. Our method can well support ad-hoc queries, while it is not so in a DSMS using current load shedding method because of the instability of operator selectivity. Our method also ensures a higher query precision when the system is over loaded and is easy to be implemented. The analytical and experimental results show that our method can be applied to medical data stream systems efficiently.

This paper proposes a general consistency management methodology within ontology-driven WIS development. The contributions of this paper are follows: 1) introduces the concept structures for representing model ontologies, mappings ontology and their instances. These ontologies and their instances greatly enhance the representation and reasoning capabilities of mapping and also simplify the manipulation 2) Defines and implements a series of general operators to manipulate mapping and model ontologies. 3) Formalizes the consistency management problem and gives an algorithm basing on general operators. 4) Implements a prototype and integrates it with WISE.

Complex structure and varying requirements increase the difficulties in developing domain specific Web Information Systems. People appeal to a smart tool to customize Web Information Systems automatically. To achieve that goal, this article makes following contributions: (1) Proposes the concept of Web Information System ontology to express the integrated domain semantics and maintain their relationships as a hierarchal structure; (2) Implements the Personalized tool for Ontology Development in domain specific Web Information System(PODWIS) with algorithms for (a) leaning the user's behaviors while building the ontology, (b) mining the repeated contents and composing reusable components, (c) guiding developer's work efficiently with high quality; (3)Designs two different self-learning strategies: online learning and offline learning to meet different situations; (4) Gives a semi-automatic tool to generate the personalized information system(includes user interface, operations and database access) based on ontology; (5)Shows the feasibility of this technique in practical application.

In many social networks, the connections between actors are formed because they participate in the same event, such as a set of scholars co-authoring a paper or colleagues having a teleconference. Therefore, we propose an event-driven model to capture the growth dynamics of social networks through modelling of the social events. We also investigate the evolution of event formation and the joint effect of attachedness and locality on the selection of participants for events in real social networks. We incorporate the evolution of event formation and the joint effect of attachedness and locality into our model. The experimental results suggest that our approach can simulate important network structures, such as hierarchical communities and assortativity, and better characterise the growing process of real networks than non-event driven models.