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Background: This study aims to evaluate the efficacy of an oxygen scavenging mask device in reducing local oxygen concentrations from nasal cannula ventilation compared to a standard open facial surgical field. Methods: This is a controlled experiment using a custom-fabricated silicone midfacial oxygen scavenging device, SimMan airway management trainer manikin (Laerdal Medical, Stavanger, Norway), handheld oxygen detector (Forensics Detectors, Los Angeles, United States) and oxygen from a Datex Ohmeda Aisys Carestation anesthesia unit (GE HealthCare, Chicago, United States). Oxygen concentrations were measured at 18 facial landmarks (Fig. 1) with nasal cannula flow of 2, 4, and 6 L/min of 100% FiO2 in both masked and unmasked conditions (Fig. 2). Results: The mean oxygen concentration in the facial surgical field was 20.95% with the scavenger mask and 24.8% without (P < 0.001; two-tailed paired t-test). The unmasked condition was associated with suprathreshold oxygen concentration levels at 13 of 18 facial landmarks (Table 1). The device significantly reduced local oxygen concentration at 16 of 18 facial landmarks (Table 1). The device provided safe oxygen concentration levels at all three flow rates, and measured oxygen concentrations directly correlated with oxygen flow rate in the unmasked condition (Table 2). Conclusions: An oxygen scavenger mask device reduced local oxygen concentrations from nasal cannula ventilation to below the 23% fire threshold in the entire facial surgical field external to the mask in these experiments. The device may reduce intraoperative fire risk in patients that require supplementary oxygen during surgery. [ABSTRACT FROM AUTHOR]

Blended learning (BL) is regarded as an effective strategy for combining traditional face-to-face classroom activities with various types of online learning tools (e.g., e-books). An effective feature of e-books is the ability to use digital notes. When e-books are used in BL, the strategic adoption of note-taking provides benefits that influence the learners' progress for self-regulated learning (SRL) and course achievements. However, learners tend to be unsure about how note-taking is performed using online learning materials and lack knowledge of effective strategies for SRL. Furthermore, few studies have investigated blended learners' sequential patterns of e-book note-taking for SRL. Thus, in this paper, an exploratory study was conducted in an undergraduate course that implemented the BL design. The learning task for the blended learners in the present study was to study the learning material using BookRoll, an e-book system, during in-class and out-of-class learning sessions. Lag sequential analysis of the e-book learning behavior data was conducted to identify the blended learners' sequential behaviors of e-book note-taking for the cognitive strategy use of SRL. Moreover, the difference between higher- and lower-achievement blended learners in terms of their sequential behaviors of e-book note-taking for SRL was revealed. This study can help educators provide evidence-based educational feedback to learners regarding the identified sequential patterns of e-book note-taking that can be applied as effective strategies for promoting the cognitive strategy use of SRL and improvement of course achievement in BL. [ABSTRACT FROM AUTHOR]

When people perform the same task repeatedly, their behavior becomes habitual, or inflexible to changes in the goals or structure of a task. Although habits have been hypothesized to be a key aspect of motor skill acquisition, there has been little empirical work investigating the relationship between skills and habits. To better understand this relationship, we examined whether and when people's behavior would become habitual as they learned a challenging new motor skill: maneuvering an on-screen cursor with a nonintuitive bimanual mapping from hand to cursor position. After participants practiced using this mapping for up to 10 days, we altered the mapping between the hands and the cursor to assess whether participants could flexibly adjust their behavior or would habitually persist in performing the task the way they had originally learned. We found that participants' behavior became habitual within 2 days of practice, at which point they were still relatively unskilled. Further practice led to improved skill but did not alter the strength of habitual behavior. These data demonstrate that motor skills become habitual after relatively little training but can nevertheless further improve with practice. We suggest that building habits early in learning may be a crucial step in acquiring new motor skills. [ABSTRACT FROM AUTHOR]

Although much research on motor learning has focused on how we adapt our movements to maintain performance in the face of imposed perturbations, in many cases, we must learn new skills from scratch or de novo. Compared with adaptation, relatively little is known about de novo learning. In part, this is because learning a new skill can involve many challenges, including learning to recognize new patterns of sensory input and generate new patterns of motor output. However, even with familiar sensory cues and well-practiced movements, the problem of quickly selecting the appropriate actions in response to the current state is challenging. Here, we devised a bimanual hand-to-cursor mapping that isolates this control problem. We find that participants initially struggled to control the cursor under this bimanual mapping, despite explicit knowledge of the mapping. Performance improved steadily over multiple days of practice, however. Participants exhibited no aftereffects when reverting to a veridical cursor, confirming that participants learned the new task de novo, rather than through adaptation. Corrective responses to midmovement perturbations of the target were initially weak, but with practice, participants gradually became able to respond rapidly and robustly to perturbations. After 4 days of practice, participants' behavior under the bimanual mapping almost matched performance using a veridically mapped cursor. However, there remained a small but persistent difference in performance level. Our findings illustrate the dynamics and limitations of learning a novel controller and introduce a promising paradigm for tractably investigating this aspect of motor skill learning. [ABSTRACT FROM AUTHOR]

• Association of ADHD and aggressive driving disappeared after accounting for covariates. • Drivers with ADHD were more likely to perform improper braking or stopping. • Depression was not significantly associated with any unsafe driving behavior. Introduction: Road injuries remain a persistent public health concern across the world. The task of driving is complicated by mental health conditions, which may affect drivers' executive functioning and cognitive resource allocation. This study examines whether attention-deficit/hyperactivity disorder (ADHD) and depression are associated with unsafe driving behaviors. Method: Generalized linear mixed models were employed to estimate the association of self-reported ADHD and depression with 18 unsafe driving behavior types found prior to at-fault crashes and near-crashes using a large-scale naturalistic driving dataset. Driver demographics, cognitive traits, environmental factors, and driver random effects were included to reduce confounding and biases. Results: Controlling for other covariates, people with self-reported ADHD were more likely to have performed improper braking or stopping (OR = 4.89, 95% CI 1.82–13.17) prior to an at-fault crash or near-crash, while those with self-reported depression did not have a significant association with any unsafe driving behavior. Conclusions: After accounting for demographic, cognitive, and environmental covariates, individuals with ADHD and depression were not prone to purposefully aggressive or reckless driving. Instead, drivers with self-reported ADHD may unintentionally execute unsafe driving behaviors in particular driving scenarios that require a high level of cognitive judgment. Practical Applications: These findings can inform the curriculum design of driver's education programs that help learners with mental health conditions gain practice in certain road scenarios, for example, more practice on preemptively reducing speed instead of making sudden brakes and smooth turning on curved roads for students with ADHD. Furthermore, specific advanced driver assistance systems may prove particularly helpful for drivers with ADHD, such as detection of leading objects and curve speed warning. [ABSTRACT FROM AUTHOR]

Background: Disparities secondary to underinsurance present throughout the surgical care continuum. Community free clinics are uniquely capable to provide health care services to the medically underserved, but surgery often falls outside their scope of care. Methods: Retrospective chart review was conducted on consecutive community free clinic patients receiving free surgical services via referral to a partnering ambulatory surgery center between March 2016 and September 2021. Those with documented contact information were recruited 1-3 years post-procedure for long-term quality-of-life (LTQOL) outcomes assessment via modified Veterans RAND 12-item health survey. Results: Of 142 included patients, 95.7% identified as Hispanic/Latino and 75.6% were uninsured. Twelve patients had cancerous or precancerous lesions detected and/or removed through diagnostic or definitive procedures. 3.5% experienced postoperative complication including bacterial (n = 2) or fungal (n = 1) surgical site infection and wound dehiscence (n = 2). With a 48.9% response rate, no significant differences in sociodemographic or clinical characteristics were found between surveyed vs non-surveyed patients. Of surveyed patients, 59.7% and 52.2% reported pre-/post-operative improvement in physical health and emotional health, respectively. Discussion: Free diagnostic screening procedures provided timely diagnoses while free definitive surgeries safely and positively impacted long-term patient-reported physical health. Longitudinal, multidisciplinary follow-up and social support may be warranted to concurrently improve emotional and mental health in similarly underinsured populations. [ABSTRACT FROM AUTHOR]

Introduction: Studies thus far have focused on automobile accidents that involve driver distraction. However, it is hard to discern whether distraction played a role if fault designation is missing because an accident could be caused by an unexpected external event over which the driver has no control. This study seeks to determine the effect of distraction in driver-at-fault events. Method: Two generalized linear mixed models, one with at-fault safety critical events (SCE) and the other with all-cause SCEs as the outcomes, were developed to compare the odds associated with common distraction types using data from the SHRP2 naturalistic driving study. Results: Adjusting for environment and driver variation, 6 of 10 common distraction types significantly increased the risk of at-fault SCEs by 20-1330%. The three most hazardous sources of distraction were handling in-cabin objects (OR = 14.3), mobile device use (OR = 2.4), and external distraction (OR = 1.8). Mobile device use and external distraction were also among the most commonly occurring distraction types (10.1% and 11.0%, respectively). Conclusions: Focusing on at-fault events improves our understanding of the role of distraction in potentially avoidable automobile accidents. The in-cabin distraction that requires eye-hand coordination presents the most danger to drivers' ability in maintaining fault-free, safe driving. Practical Applications: The high risk of at-fault SCEs associated with in-cabin distraction should motivate the smart design of the interior and in-vehicle information system that requires less visual attention and manual effort. [ABSTRACT FROM AUTHOR]

How do people learn to perform tasks that require continuous adjustments of motor output, like riding a bicycle? People rely heavily on cognitive strategies when learning discrete movement tasks, but such time-consuming strategies are infeasible in continuous control tasks that demand rapid responses to ongoing sensory feedback. To understand how people can learn to perform such tasks without the benefit of cognitive strategies, we imposed a rotation/mirror reversal of visual feedback while participants performed a continuous tracking task. We analyzed behavior using a system identification approach, which revealed two qualitatively different components of learning: adaptation of a baseline controller and formation of a new, task-specific continuous controller. These components exhibited different signatures in the frequency domain and were differentially engaged under the rotation/mirror reversal. Our results demonstrate that people can rapidly build a new continuous controller de novo and can simultaneously deploy this process with adaptation of an existing controller. [ABSTRACT FROM AUTHOR]

Research has revealed the positive effects of flipped classroom approaches on students' learning engagement and performance compared with conventional lecture-based classrooms. However, because of a lack of out-of-class learning support, many students fail to comprehensively prepare the provided lecture materials before class. One promising solution to this problem is recommendation systems in the educational area, which have been instrumental in helping learners identify useful and relevant lecture materials that satisfy their learning needs. Thus, in this study, we propose a summarized lecture material recommendation system, which is integrated into an e-book reading system as an enhancement of the flipped classroom approach. This system helps students identify pages that contain essential knowledge that must be thoroughly studied before class. The proposed system was constructed on the basis of our previous work. In this study, a quasi-experiment was conducted in a graduate course that implemented the flipped classroom model: experimental group students learned with the proposed system, whereas the control group students had no access to the additional features. The findings of this study suggest that students who learn with the proposed recommendation system significantly outperform those who learn without the system in a flipped classroom in terms of their learning outcomes and engagement in preclass preparation. [ABSTRACT FROM AUTHOR]

This methodological-theoretical synergy provides an integrative framework of learning analytics through the development of the human-and-machine symbiotic reinforcement learning. The framework intends to address the challenges of the current learning analytics model, including a lack of internal validity, generalizability, immediacy, transferability, and interpretability for precision education. The proposed framework consists of a master component (the brain) and its four subsuming components: social networking, the smart classroom, the intelligent agent, and the dashboard. The brain component takes in and analyzes multimodal streams of student data from the other components with the model-based reinforcement learning, which forms policies of adequate actions that maximize the long-term rewards for both the human and machine in the seamless learning environment. An example case plan in advanced statistics was demonstrated to illustrate the course description, data collected in each component, and how the components meet different features of the smart learning environment to deliver precision education. An empirical demonstration was provided using some selected mulitmodal data to inform the effectiveness of the proposed framework. The human-and-machine symbiotic reinforcement learning has theoretical and practical implications for the next-generation learning analytics models and research. [ABSTRACT FROM AUTHOR]

Precision education is now recognized as a new challenge of applying artificial intelligence, machine learning, and learning analytics to improve both learning performance and teaching quality. To promote precision education, digital learning platforms have been widely used to collect educational records of students' behavior, performance, and other types of interaction. On the other hand, the increasing volume of students' learning behavioral data in virtual learning environments provides opportunities for mining data on these students' learning patterns. Accordingly, identifying students' online learning patterns on various digital learning platforms has drawn the interest of the learning analytics and educational data mining research communities. In this study, the authors applied data analytics methods to examine the learning patterns of students using an ebook system for one semester in an undergraduate course. The authors used a clustering approach to identify subgroups of students with different learning patterns. Several subgroups were identified, and the students' learning patterns in each subgroup were determined accordingly. In addition, the association between these students' learning patterns and their learning outcomes from the course was investigated. The findings of this study provide educators opportunities to predict students' learning outcomes by analyzing their online learning behaviors and providing timely intervention for improving their learning experience, which achieves one of the goals of learning analytics as part of precision education. [ABSTRACT FROM AUTHOR]

Drug repositioning has drawn significant attention for drug development in pharmaceutical research and industry, because of its advantages in cost and time compared with the de novo drug development. The availability of biomedical databases and online health-related information, as well as the high-performance computing, empowers the development of computational drug repositioning methods. In this work, we developed a systematic approach that identifies repositioning drugs based on heterogeneous network mining using both pharmaceutical databases (PharmGKB and SIDER) and online health community (MedHelp). By utilizing adverse drug reactions (ADRs) as the intermediate, we constructed a heterogeneous health network containing drugs, diseases, and ADRs, and developed path-based heterogeneous network mining approaches for drug repositioning. Additionally, we investigated on how the data sources affect the performance on drug repositioning. Experiment results showed that combining both PharmKGB and MedHelp identified 479 repositioning drugs, which are more than the repositioning drugs discovered by other alternatives. In addition, 31% of the 479 of the discovered repositioning drugs were supported by evidence from PubMed. [ABSTRACT FROM AUTHOR]

Purpose: User-generated content on social media sites, such as health-related online forums, offers researchers a tantalizing amount of information, but concerns regarding scientific application of such data remain. This paper compares and contrasts symptom cluster patterns derived from messages on a breast cancer forum with those from a symptom checklist completed by breast cancer survivors participating in a research study.Methods: Over 50,000 messages generated by 12,991 users of the breast cancer forum on MedHelp.org were transformed into a standard form and examined for the co-occurrence of 25 symptoms. The k-medoid clustering method was used to determine appropriate placement of symptoms within clusters. Findings were compared with a similar analysis of a symptom checklist administered to 653 breast cancer survivors participating in a research study.Results: The following clusters were identified using forum data: menopausal/psychological, pain/fatigue, gastrointestinal, and miscellaneous. Study data generated the clusters: menopausal, pain, fatigue/sleep/gastrointestinal, psychological, and increased weight/appetite. Although the clusters are somewhat different, many symptoms that clustered together in the social media analysis remained together in the analysis of the study participants. Density of connections between symptoms, as reflected by rates of co-occurrence and similarity, was higher in the study data.Conclusions: The copious amount of data generated by social media outlets can augment findings from traditional data sources. When different sources of information are combined, areas of overlap and discrepancy can be detected, perhaps giving researchers a more accurate picture of reality. However, data derived from social media must be used carefully and with understanding of its limitations. [ABSTRACT FROM AUTHOR]

Drug safety, also called pharmacovigilance, represents a serious health problem all over the world. Adverse drug reactions (ADRs) and drug-drug interactions (DDIs) are two important issues in pharmacovigilance, and how to detect drug safety signals has drawn many researchers' attention and efforts. Currently, methods proposed for ADR and DDI detection are mainly based on traditional data sources such as spontaneous reporting data, electronic health records, pharmaceutical databases, and biomedical literature. However, these data sources are either limited by under-reporting ratio, privacy issues, high cost, or long publication cycle. In this study, we propose a framework for drug safety signal detection by harnessing online health community data, a timely, informative, and publicly available data source. Concretely, we used MedHelp as the data source to collect patient-contributed content based on which a weighted heterogeneous network was constructed. We extracted topological features from the network, quantified them with different weighting methods, and used supervised learning method for both ADR and DDI signal detection. In addition, after identifying DDI signals, we proposed a new metric, named Interaction Ratio, to identify associated ADRs due to suspected interactions. The experiment results showed that our proposed techniques outperforms baseline methods. [ABSTRACT FROM AUTHOR]

Sepsis is one of the most challenging health conditions worldwide, with relatively high incidence and mortality rates. It is shown that preventing sepsis is the key to avoid potentially irreversible organ dysfunction. However, data-driven early identification of sepsis is challenging as sepsis shares signs and symptoms with other health conditions. This paper adopts a temporal pattern mining approach to identify frequent temporal and evolving patterns of physiological and biological biomarkers in sepsis patients. We show that using these frequent patterns as features for classifying sepsis and non-sepsis patients can improve the prediction accuracy and performance up to 7%. Most of the temporal modeling approaches adopted in the sepsis literature are based on deep learning methods. Although these approaches produce high accuracy, they generally have limited model explainability and interpretability. Using the adopted methods in this study, we could identify the most important features contributing to the patients' sepsis incidence, such as fluctuations in platelet, lactate, and creatinine, or evolution of patterns including renal and metabolic organ systems, and consequently, enhance the findings' clinical interpretability. • Using frequent temporal patterns can improve the performance of sepsis onset prediction. • The patients with more severe renal and metabolic organ systems' manifestations are more likely to be sepsis patients. • The identification of critical temporal patterns contributing to the prediction performance improves clinical interpretability. • The rich repository of frequent temporal and evolving patterns can inform personalized treatment and management of sepsis. [ABSTRACT FROM AUTHOR]

Objective: Pulsed ultrasound has been proposed as a tool to enhance ocular drug delivery, but its effects on drug potency are not well understood. Doxorubicin-HCl and cisplatin are two drugs commonly used to treat ocular melanoma. We report the effects of pulsed ultrasound on the cytotoxicity of doxorubicin-HCl and cisplatin in vitro. Methods: Cultured human retinal pigment epithelium cells (ARPE-19) were treated with doxorubicin-HCl or cisplatin in the presence or absence of ultrasound. MTT and Trypan blue assays were performed at 24 and 48 h post treatment to assess cell metabolism and death. Results: Cells treated with ultrasound plus doxorubicin-HCl demonstrated a significant decrease in metabolism compared with cells treated with doxorubicin-HCl alone. In contrast, cells treated with ultrasound plus cisplatin exhibited a significant increase in metabolism compared with cells treated with cisplatin alone for 48 h. Cells treated with cisplatin and pretreated with ultrasound (US–Cis) exhibited a significant decrease in metabolism. Cell death was similar in doxorubicin- and cisplatin-treated cells with and without ultrasound. Conclusion: Pulsed ultrasound enhances the cytotoxicity of doxorubicin-HCl at 24 and 48 h post treatment but abrogates cisplatin toxicity 48 h post treatment. This suggests ultrasound modulates cell–drug interactions in a drug-specific manner. These findings may influence the future development of ultrasound-assisted ocular drug delivery systems. [ABSTRACT FROM AUTHOR]

Objective: The rapid growth of online health social websites has captured a vast amount of healthcare information and made the information easy to access for health consumers. E-patients often use these social websites for informational and emotional support. However, health consumers could be easily overwhelmed by the overloaded information. Healthcare information searching can be very difficult for consumers, not to mention most of them are not skilled information searcher. In this work, we investigate the approaches for measuring user similarity in online health social websites. By recommending similar users to consumers, we can help them to seek informational and emotional support in a more efficient way.Methods: We propose to represent the healthcare social media data as a heterogeneous healthcare information network and introduce the local and global structural approaches for measuring user similarity in a heterogeneous network. We compare the proposed structural approaches with the content-based approach.Results: Experiments were conducted on a dataset collected from a popular online health social website, and the results showed that content-based approach performed better for inactive users, while structural approaches performed better for active users. Moreover, global structural approach outperformed local structural approach for all user groups. In addition, we conducted experiments on local and global structural approaches using different weight schemas for the edges in the network. Leverage performed the best for both local and global approaches. Finally, we integrated different approaches and demonstrated that hybrid method yielded better performance than the individual approach.Conclusion: The results indicate that content-based methods can effectively capture the similarity of inactive users who usually have focused interests, while structural methods can achieve better performance when rich structural information is available. Local structural approach only considers direct connections between nodes in the network, while global structural approach takes the indirect connections into account. Therefore, the global similarity approach can deal with sparse networks and capture the implicit similarity between two users. Different approaches may capture different aspects of the similarity relationship between two users. When we combine different methods together, we could achieve a better performance than using each individual method. [ABSTRACT FROM AUTHOR]

Since adverse drug reactions (ADRs) represent a significant health problem all over the world, ADR detection has become an important research topic in drug safety surveillance. As many potential ADRs cannot be detected though premarketing review, drug safety currently depends heavily on postmarketing surveillance. Particularly, current postmarketing surveillance in the United States primarily relies on the FDA Adverse Event Reporting System (FAERS). However, the effectiveness of such spontaneous reporting systems for ADR detection is not as good as expected because of the extremely high underreporting ratio of ADRs. Moreover, it often takes the FDA years to complete the whole process of collecting reports, investigating cases, and releasing alerts. Given the prosperity of social media, many online health communities are publicly available for health consumers to share and discuss any healthcare experience such as ADRs they are suffering. Such health-consumer-contributed content is timely and informative, but this data source still remains untapped for postmarketing drug safety surveillance. In this study, we propose to use (1) association mining to identify the relations between a drug and an ADR and (2) temporal analysis to detect drug safety signals at the early stage. We collect data from MedHelp and use the FDA's alerts and information of drug labeling revision as the gold standard to evaluate the effectiveness of our approach. The experiment results show that health-related social media is a promising source for ADR detection, and our proposed techniques are effective to identify early ADR signals. [ABSTRACT FROM AUTHOR]

PURPOSE: Machine learning (ML) algorithms are used for predictive modeling in medicine, but studies often do not evaluate or report on the potential biases of the models. Our purpose was to develop clinical prediction models for readmission after surgery in patients with colorectal cancer (CRC) and to examine their potential for racial bias. METHODS: We used the 2012-2020 American College of Surgeons' National Surgical Quality Improvement Program Participant Use File and Targeted Colectomy File. Patients were categorized into four race groups—White, Black or African American, other, and unknown/not reported. Potential predictive features were identified from studies of risk factors of 30-day readmission in patients with CRC. We compared four ML-based methods—logistic regression, multilayer perceptron, random forest, and XGBoost (XGB). Model bias was assessed using false-negative rate (FNR) difference, false-positive rate (FPR) difference, and disparate impact (DI). RESULTS: In all, 112,077 patients were included, 67.2% of whom were White, 9.2% Black, 5.6% other race, and 18% with race not recorded. There were significant differences in the area under the receiver operating characteristics curve, FPR, and FNR between race groups across all models. Notably, patients in the other race category had higher FNR compared with Black patients in all but the XGB model, while Black patients had higher FPR than White patients in some models. Patients in the other category consistently had the lowest FPR. Applying the 80% rule for DI, the models consistently met the threshold for unfairness for the other race category. CONCLUSION: Predictive models for 30-day readmission after colorectal surgery may perform unequally for different race groups, potentially propagating to inequalities in delivery of care and patient outcomes if the predictions from these models are used to direct care. Predictive models for CRC readmission may show bias, potentially risking unequal care and outcomes. [ABSTRACT FROM AUTHOR]

Detecting evolving Hidden communities witHin dynamic social networks Has attracted significant attention recently due to its broad applications in e-commerce, online social media, security intelligence, public HealtH, and otHer areas. Many community network detection tecHniques employ a two-stage approacH to identify and detect evolutionary relationsHips between communities of two adjacent time epocHs. THese tecHniques often identify communities witH HigH temporal variation, since tHe two-stage approacH detects communities of eacH epocH independently witHout considering tHe continuity of communities across two time epocHs. OtHer tecHniques require identification of a predefined number of Hidden communities wHicH is not realistic in many applications. To overcome tHese limitations, we propose tHe Dynamic StocHastic Blockmodel witH Temporal DiricHlet Process, wHicH enables tHe detection of Hidden communities and tracks tHeir evolution simultaneously from a network stream. THe number of Hidden communities is automatically determined by a temporal DiricHlet process witHout Human intervention. We tested our proposed tecHnique on tHree different testbeds witH results identifying a HigH performance level wHen compared to tHe baseline algoritHm. [ABSTRACT FROM AUTHOR]

E-patients seeking information online often seek specific advice related to coping with their health condition(s) among social networking sites. They may be looking for social connectivity with compassionate strangers who may have experienced similar situations to share opinions and experiences rather than for authoritative medical information. Previous studies document distinct technological features and different levels of social support interaction patterns. It is expected that the design of the social media functions will have an impact on the user behavior of social support exchange. In this part of a multipart study, we investigate the social support types, in particular information support types, across multiple computer-mediated communication formats (forum, journal, and notes) within an alcoholism community using descriptive content analysis on 3 months of data from a MedHelp online peer support community. We present the results of identified informational support types including advice, referral, fact, personal experiences, and opinions, either offered or requested. Fact type was exchanged most often among the messages; however, there were some different patterns between notes and journal posts. Notes were used for maintaining relationships rather than as a main source for seeking information. Notes were similar to comments made to journal posts, which may indicate the friendship between journal readers and the author. These findings suggest that users may have initially joined the MedHelp Alcoholism Community for information-seeking purposes but continue participation even after they have completed with information gathering because of the relationships they formed with community members through social media features. [ABSTRACT FROM AUTHOR]

Interdisciplinary research has been attracting more attention in recent decades. In this article, we compare the similarity between scientific research domains and quantifying the temporal similarities of domains. We narrowed our study to three research domains: information retrieval ( IR), database ( DB), and World Wide Web ( W3), because the rapid development of the W3 domain substantially attracted research efforts from both IR and DB domains and introduced new research questions to these two areas. Most existing approaches either employed a content-based technique or a cocitation or coauthorship network-based technique to study the development trend of a research area. In this work, we proposed an effective way to quantify the similarities among different research domains by incorporating content similarity and coauthorship network similarity. Experimental results on DBLP (DataBase systems and Logic Programming) data related to IR, DB, and W3 domains showed that the W3 domain was getting closer to both IR and DB whereas the distance between IR and DB remained relatively constant. In addition, comparing to IR and W3 with the DB domain, the DB domain was more conservative and evolved relatively slower. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes the development and use of a hydrogen infrastructure optimization model using the TIMES modeling framework, H2TIMES, to analyze hydrogen development in California to 2050. H2TIMES is a quasi-spatial model that develops the infrastructure to supply hydrogen fuel in order to meet demand in eight separate California regions in a least cost manner subject to various resource, technology and policy constraints. A Base case, with a suite of hydrogen policies now in effect or proposed in California (renewable hydrogen mandate, fuel carbon intensity constraint and prohibition on using coal without carbon capture and sequestration) leads to hydrogen fuel with significant reductions in carbon intensity (85% below gasoline on an efficiency-adjusted basis, 75% below on a raw energy basis) and competitive hydrogen costs (∼$4.00/kg in 2025–2050). A number of sensitivity scenarios investigate the cost and emissions implications of altering policy constraints, technology and resource availability, and modeling decisions. The availability of biomass for hydrogen production and carbon capture and sequestration are two critical factors for achieving low-cost and low-emission hydrogen. [Copyright &y& Elsevier]

Due to the advancement of Web 2.0 technologies, a large volume of Web opinions is available on social media sites such as Web forums and Weblogs. These technologies provide a platform for Internet users around the world to communicate with each other and express their opinions. Analysis of developing Web opinions is potentially valuable for discovering ongoing topics of interests of the public like terrorist and crime detection, understanding how topics evolve together with the underlying social interaction between participants, and identifying important participants who have great influence in various topics of discussions. Nonetheless, the work of analyzing and clustering Web opinions is extremely challenging. Unlike regular documents, Web opinions are short and sparse text messages with noisy content. Typical document clustering techniques with the goal of clustering all documents applied to Web opinions produce unsatisfactory performance. In this paper, we investigated the density-based clustering algorithm and proposed the scalable distance-based clustering technique for Web opinion clustering. We conducted experiments and benchmarked with the density-based algorithm to show that the new algorithm obtains higher microaccuracy and macroaccuracy. This Web opinion clustering technique enables the identification of themes within discussions in Web social networks and their development, as well as the interactions of active participants. We also developed interactive visualization tools, which make use of the identified topic clusters to display social network development, the network topology similarity between topics, and the similarity values between participants. [ABSTRACT FROM PUBLISHER]

Robotic-assisted ureteral reimplantations were performed on 3 patients at a single institution, 2 with Boari flap and psoas hitch and 1 with psoas hitch alone. These were for urothelial carcinoma of the distal ureter, ureteral obstruction caused by distal ureteral endometriosis, and ureteral transaction during gynecologic surgery. We used intraoperative ureteroscopy to confirm tumor margins as well as a simple technique for retrograde placement of transvesicle wire prior to ureteral anastomosis. Surgery and recovery were uneventful. This illustrates that robotic-assisted ureteral reimplantation with Boari flap and psoas hitch is a safe and viable approach for ureterovesicle reconstruction. [ABSTRACT FROM AUTHOR]

Abstract: California has taken steps to reduce greenhouse gas emissions from the transportation sector. One example is the recent adoption of the Low Carbon Fuel Standard, which aims to reduce the carbon intensity of transportation fuels. To effectively implement this and similar policies, it is necessary to understand well-to-wheels emissions associated with distinct vehicle and fuel platforms, including those using electricity. This analysis uses an hourly electricity dispatch model to simulate and investigate operation of the current California grid and its response to added vehicle and fuel-related electricity demands in the near term. The model identifies the “marginal electricity mix” – the mix of power plants that is used to supply the incremental electricity demand from vehicles and fuels – and calculates greenhouse gas emissions from those plants. It also quantifies the contribution from electricity to well-to-wheels greenhouse gas emissions from battery-electric, plug-in hybrid, and fuel cell vehicles and explores sensitivities of electricity supply and emissions to hydro-power availability, timing of electricity demand (including vehicle recharging), and demand location within the state. The results suggest that the near-term marginal electricity mix for vehicles and fuels in California will come from natural gas-fired power plants, including a significant fraction (likely as much as 40%) from relatively inefficient steam- and combustion-turbine plants. The marginal electricity emissions rate will be higher than the average rate from all generation – likely to exceed 600gCO2 equiv.kWh−1 during most hours of the day and months of the year – and will likely be more than 60% higher than the value estimated in the Low Carbon Fuel Standard. But despite the relatively high fuel carbon intensity of marginal electricity in California, alternative vehicle and fuel platforms still reduce emissions compared to conventional gasoline vehicles and hybrids, through improved vehicle efficiency. [Copyright &y& Elsevier]

Given the advance of Internet technologies, we can now easily extract hundreds or thousands of news stories of any ongoing incidents from newswires such as CNN.com, but the volume of information is too large for us to capture the blueprint. Information retrieval techniques such as Topic Detection and Tracking are able to organize news stories as events, in a flat hierarchical structure, within a topic. However, they are incapable of presenting the complex evolution relationships between the events. We are interested to learn not only what the major events are but also how they develop within the topic. It is beneficial to identify the seminal events, the intermediary and ending events, and the evolution of these events. In this paper, we propose to utilize the event timestamp, event content similarity, temporal proximity, and document distributional proximity to model the event evolution relationships between events in an incident. An event evolution graph is constructed to present the underlying structure of events for efficient browsing and extracting of information. Case study and experiments are presented to illustrate and show the performance of our proposed technique. It is found that our proposed technique outperforms the baseline technique and other comparable techniques in previous work. [ABSTRACT FROM AUTHOR]

Social network visualization has drawn significant attention over recent years. It creates images of social networks that provide investigators with new insights about network structures and helps them to communicate those insights to others. Visualization facilitates the social network analysis. It supports the investigators to discover patterns of interactions among the social actors including detecting subgroups, identifying central actors and their roles, and discovering patterns of interactions among social actors. However, visualizing a large heterogeneous social network has several challenges. The large size of networks, complex relations among social actors and limited number of available pixels on a screen make it difficult to present important information clearly to investigators and hence reduce the capability of investigators to explore the networks. In this work, we propose the fractal views to construct a visual abstraction of a large and complex social network with users selected social actors as focuses. The fractal views are focus and context visualization techniques using an information reduction approach. It controls the amount of information displayed by focusing on the syntactic structure of information. It is useful in discovering knowledge from terrorist social networks for combating the war on terrorism. Such application has formed an important research topic, known as intelligence and security informatics, in recent years due to the terrorist attacks of September 11 2001 (9/11) and several other terror attacks that have occurred within the last decade. We present several case studies to demonstrate the capability of the proposed technique on analyzing the Global Salafi Jihad terrorist social network. It extracts the hidden relationships among terrorists through user interactions. In addition, we have conducted a user evaluation to assess the efficiency and effectiveness of fractal views. It shows that fractal views outperform fisheye views and zoom-in windows to support users in visualizing and analyzing terrorist social networks. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates how California may reduce transportation greenhouse gas emissions 80% below 1990 levels by 2050 (i.e., 80in50). A Kaya framework that decomposes greenhouse gas emissions into the product of population, transport intensity, energy intensity, and carbon intensity is used to analyze emissions and mitigation options. Each transportation subsector, including light-duty, heavy-duty, aviation, rail, marine, agriculture, and off-road vehicles, is analyzed to identify specific mitigation options and understand its potential for reducing greenhouse gas emissions. Scenario analysis shows that, while California’s 2050 target is ambitious, it can be achieved in transport if a concerted effort is made to change travel behavior and the vehicles and fuels that provide mobility. While no individual ‘‘Silver Bullet” strategy exists that can achieve the goals, a portfolio approach that combines strategies could yield success. The 80in50 scenarios show the impacts of advanced vehicle and fuels technologies as well as the role of travel demand reduction, which can significantly reduce energy and resource requirements and the level of technology development needed to meet the target. [Copyright &y& Elsevier]

Mitigating future climate change and managing future air quality are inter-related fields that have the potential to benefit from coordinated strategies that leverage the efforts in one area to achieve positive outcomes in the other area. California plans to reduce greenhouse gas (GHG) emission by 80% (relative to year 1990) by the year 2050. The changes required to meet this target also have the potential to improve air quality. Previous work developed an energy-economic model CA-TIMES and an emission inventory model CA-REMARQUE to study the possible pathways of meeting the GHG mitigation target and the air pollutant emissions associated with those pathways. Here we update the CA-TIMES and CA-REMARQUE model framework and analyze six different scenarios: (i) BAU - a business-as-usual future reference scenario, (ii) CAP30 - a loose GHG reduction scenario that meets current policy references but only achieves a 40% GHG reduction (relative to 1990 levels) by the year 2030, (iii) GHGAi - a climate-friendly 80% GHG reduction scenario featuring broad adoption of advanced technologies and renewable energies, (iv) CCS - a scenario that achieves 80% net GHG reductions but allows for more fossil energy combustion by focusing on adoption of carbon capture and sequestration technology, (v) NGB– a variation on the GHGAi scenario that allows for more natural gas combustion for residential and commercial buildings, and (vi) NGT – a variation of the GHGAi scenario that allows for more natural gas combustion for electricity generation. Results show that the GHGAi deep GHG mitigation scenario significantly reduces emissions (−41% PM 0.1 , −8% PM 2.5 , and −26% NO X) and improves air quality (−1 μg m−3 PM 2.5) yielding public health benefits (+USD 20B yr−1) relative to the BAU scenario. The CCS scenario achieves the same GHG reductions but increases emissions in some areas (+2.5% PM 2.5) resulting in only one third of the public health benefits compared to the GHGAi scenario. The NGB and NGT scenarios show that an 18% increase in natural gas utilization in buildings or a 15% increase in natural gas power generation offsets 32% and 46% of the ultrafine particle emission reduction achieved in the GHGAi scenario but has little impact on PM 2.5 concentrations, producing approximately 90% of the public health benefits of the GHGAi scenario. These public health benefits should be considered when making decisions about future GHG mitigation strategies. • Six future energy scenarios were translated into detailed emissions inventories for air pollution in California. • Deep GHG mitigation scenarios significantly reduce emissions of air pollution precursors, yielding significant reductions in predicted ground-level PM 2.5 concentrations. • Carbon capture and sequestration strategies yielded only one third of the public health benefits compared to the deep GHG reduction approaches. • Deep GHG mitigation scenarios that used additional natural gas experienced higher concentrations of ultrafine particles. [ABSTRACT FROM AUTHOR]

Navigating through hyperlinks within a Web site to look for information from one of its Web pages without the support of a site map can be inefficient and ineffective. Although the content of a Web site is usually organized with an inherent structure like a topic hierarchy, which is a directed tree rooted at a Web site's homepage whose vertices and edges correspond to Web pages and hyperlinks, such a topic hierarchy is not always available to the user. In this work, we studied the problem of automatic generation of Web sites' topic hierarchies. We modeled a Web site's link structure as a weighted directed graph and proposed methods for estimating edge weights based on eight types of features and three learning algorithms, namely decision trees, naïve Bayes classifiers, and logistic regression. Three graph algorithms, namely breadth-first search, shortest-path search, and directed minimum-spanning tree, were adapted to generate the topic hierarchy based on the graph model. We have tested the model and algorithms on real Web sites. It is found that the directed minimum-spanning tree algorithm with the decision tree as the weight learning algorithm achieves the highest performance with an average accuracy of 91.9%. [ABSTRACT FROM AUTHOR]

• In-Cabin Objects and In Vehicle Technology presented high at-fault crash risks. • Teens and adults were more distraction-prone than seniors. • Seniors were more severely affected by certain distractions. • Distraction impacted females and males similarly. • Effects of distractions on at-fault crashes were greater than on all-cause crashes. Distracted driving has been well researched, however the comparison between different age-gender groups on the impact of distracted driving has not been explored. Most crash analysis research does not distinguish driver responsibility, so the role that distractions has in at-fault crashes is unknown. Without distinguishing at-fault crashes from all-cause crashes, distracted driving's detrimental effects could be underestimated. This study aims to systematically assess the risk of at-fault crashes associated with different sources of distraction among six groups by driver age (Teens 16–19, Adults 20–64, Seniors 65+) and gender. Crashes where a study participant was deemed at fault were identified using human expert annotated variables from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study dataset. Generalized linear mixed models were performed to assess the adjusted odds ratios of 10 distraction types associated with the at-fault crashes while controlling for environmental factors. The main findings are (1) The highest contributing distraction types in at-fault crashes were In-Cabin Objects, Mobile Device, External Scenes, and In-Vehicle Information Systems (IVIS) as indicated by their influence on multiple age-gender groups and the magnitude of odds ratios; (2) Teens and adults were more distraction-prone than seniors, although seniors had the greatest at-fault crash risks associated with In-Cabin Objects, Mobile Device, and IVIS; (3) Distractions impacted females and males similarly; (4) At-fault crashes were more likely to have the significant distraction types present than all-cause crashes. This study adds to the limited literature on at-fault crashes particularly as it explores the role of driver demographics and distracted driving. Analyzing the risks of distracted driving by age-gender group shows that specific activities can be riskier for a certain population. The effects of distractions may be overlooked without fault determination. Distractions by external scenes, in-vehicle technologies, and in-cabin objects should not be overlooked, in addition to mobile device use. [ABSTRACT FROM AUTHOR]

This article analyzes the collaboration trends, authorship and keywords of all research articles published in the Journal of American Society for Information Science and Technology (JASIST). Comparing the articles between two 10-year periods, namely, 1988–1997 and 1998–2007, the three-fold objectives are to analyze the shifts in (a) authors' collaboration trends (b) top authors, their affiliations as well as the pattern of coauthorship among them, and (c) top keywords and the subdisciplines from which they emerge. The findings reveal a distinct tendency towards collaboration among authors, with external collaborations becoming more prevalent. Top authors have grown in diversity from those being affiliated predominantly with library/information-related departments to include those from information systems management, information technology, businesss, and the humanities. Amid heterogeneous clusters of collaboration among top authors, strongly connected cross-disciplinary coauthor pairs have become more prevalent. Correspondingly, the distribution of top keywords' occurrences that leans heavily on core information science has shifted towards other subdisciplines such as information technology and sociobehavioral science. [ABSTRACT FROM AUTHOR]

Abstract: Hydrogen infrastructure costs will vary by region as geographic characteristics and feedstocks differ. This paper proposes a method for optimizing regional hydrogen infrastructure deployment by combining detailed spatial data in a geographic information system (GIS) with a technoeconomic model of hydrogen infrastructure components. The method is applied to a case study in Ohio in which coal-based hydrogen infrastructure with carbon capture and storage (CCS) is modeled for two distribution modes at several steady-state hydrogen vehicle market penetration levels. The paper identifies the optimal infrastructure design at each market penetration as well as the costs, CO2 emissions, and energy use associated with each infrastructure pathway. The results indicate that aggregating infrastructure at the regional-scale yields lower levelized costs of hydrogen than at the city-level at a given market penetration level, and centralized production with pipeline distribution is the favored pathway even at low market penetration. Based upon the hydrogen infrastructure designs evaluated in this paper, coal-based hydrogen production with CCS can significantly reduce transportation-related CO2 emissions at a relatively low infrastructure cost and levelized fuel cost. [Copyright &y& Elsevier]

Abstract: A future hydrogen economy would interact with and influence the electricity grid in numerous ways. This paper presents several concepts for understanding a hydrogen economy in the context of the co-evolution with the electricity sector and lays out some of the opportunities and challenges. and electricity are complementary energy carriers that have distinct characteristics, which lead to more or less utility in different applications. Despite their differences, it is possible to understand a future hydrogen economy using some of the same techniques as electricity system analysis. Hydrogen pathways will lead to additional electric demands that will influence the structure, operation and emissions in the electric sector. Examples of convergence between these sectors include a number of options for and electricity co-production and interconversion. [Copyright &y& Elsevier]

Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractals—mathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents. [ABSTRACT FROM AUTHOR]

Modern information retrieval systems use keywords within documents as indexing terms for search of relevant documents. As Chinese is an ideographic character-based language, the words in the texts are not delimited by white spaces. Indexing of Chinese documents is impossible without a proper segmentation algorithm. Many Chinese segmentation algorithms have been proposed in the past. Traditional segmentation algorithms cannot operate without a large dictionary or a large corpus of training data. Nowadays, the Web has become the largest corpus that is ideal for Chinese segmentation. Although most search engines have problems in segmenting texts into proper words, they maintain huge databases of documents and frequencies of character sequences in the documents. Their databases are important potential resources for segmentation. In this paper, we propose a segmentation algorithm by mining Web data with the help of search engines. On the other hand, the Romanized pinyin of Chinese language indicates boundaries of words in the text. Our algorithm is the first to utilize the Romanized pinyin to segmentation. It is the first unified segmentation algorithm for the Chinese language from different geographical areas, and it is also domain independent because of the nature of the Web. Experiments have been conducted on the datasets of a recent Chinese segmentation competition. The results show that our algorithm outperforms the traditional algorithms in terms of precision and recall. Moreover, our algorithm can effectively deal with the problems of segmentation ambiguity, new word (unknown word) detection, and stop words. [ABSTRACT FROM AUTHOR]

With the overwhelming volume of information, the task of finding relevant information on a given topic on the Web is becoming increasingly difficult. Web search engines hence become one of the most popular solutions available on the Web. However, it has never been easy for novice users to organize and represent their information needs using simple queries. Users have to keep modifying their input queries until they get expected results. Therefore, it is often desirable for search engines to give suggestions on related queries to users. Besides, by identifying those related queries, search engines can potentially perform optimizations on their systems, such as query expansion and file indexing. In this work we propose a method that suggests a list of related queries given an initial input query. The related queries are based in the query log of previously submitted queries by human users, which can be identified using an enhanced model of association rules. Users can utilize the suggested related queries to tune or redirect the search process. Our method not only discovers the related queries, but also ranks them according to the degree of their relatedness. Unlike many other rival techniques, it also performs reasonably well on less frequent input queries. [ABSTRACT FROM AUTHOR]

Hydrogen delivery is a critical contributor to the cost, energy use and emissions associated with hydrogen pathways involving central plant production. The choice of the lowest-cost delivery mode (compressed gas trucks, cryogenic liquid trucks or gas pipelines) will depend upon specific geographic and market characteristics (e. G. City population and radius, population density, size and number of refueling stations and market penetration of fuel cell vehicles). We developed models to characterize delivery distances and to estimate costs, emissions and energy use from various parts of the delivery chain (e. G. Compression or liquefaction, delivery and refueling stations). Results show that compressed gas truck delivery is ideal for small stations and very low demand, liquid delivery is ideal for long distance delivery and moderate demand and pipeline delivery is ideal for dense areas with large hydrogen demand. [Copyright &y& Elsevier]

Collecting dense range measurements in uncontrolled environments is a challenging problem, as the quality of the measurements is highly dependent on the lighting conditions and the texture of the target surfaces. This dependence affects the registration and data-fusion processes and, consequently, degrades the accuracy of the surface or occupancy models that are computed from the range measurements. Typical approaches to address this issue have concentrated on improving a specific type of range sensor. On the other hand, the overall quality of the sensing can also be enhanced through the development of a mechanism that combines the various range-sensing technologies to form a multimodal range sensor. The resulting problem of the merging datasets can then be solved in two ways: system calibration of the multimodal sensor or data fitting of all the datasets into a single model, of which the latter is more widely implemented. The lack of multimodal-system calibration approaches is due to their complicated and lengthy nature, where individual calibration procedures must be applied to each subsystem and then applied between the subsystems of the multimodal range sensor. This paper proposes a technique to alleviate the problems encountered in a multimodal-system calibration. Straightforward and generic guidelines for the calibration are defined and applied to an in-house integrated multimodal system built from a laser-range-finder system, two structured-lighting systems, and a stereovision system. The system's intracalibration and intercalibration processes are detailed. Reconstructed renderings of the datasets collected with the calibrated multimodal range sensor, without the use of data fitting, are also presented. From these results, the potential benefits of multimodal calibration over the computationally intensive data-fitting methods and the advantages of merging the subsystem's strengths to complement other subsystem's weaknesses are put in evidence. [ABSTRACT FROM AUTHOR]

Based on the salient features of the documents, automatic text summarization systems extract the key sentences from source documents. This process supports the users in evaluating the relevance of the extracted documents returned by information retrieval systems. Because of this tool, efficient filtering can be achieved. Indirectly, these systems help to resolve the problem of information overloading. Many automatic text summarization systems have been implemented for use with different languages. It has been established that the grammatical and lexical differences between languages have a significant effect on text processing. However, the impact of the language differences on the automatic text summarization systems has not yet been investigated. The authors provide an impact analysis of language difference on automatic text summarization. It includes the effect on the extraction processes, the scoring mechanisms, the performance, and the matching of the extracted sentences, using the parallel corpus in English and Chinese as the tested object. The analysis results provide a greater understanding of language differences and promote the future development of more advanced text summarization techniques. [ABSTRACT FROM AUTHOR]

The authors propose a heuristic method for Chinese automatic text segmentation based on a statistical approach. This method is developed based on statistical information about the association among adjacent characters in Chinese text. Mutual information of bi-grams and significant estimation of tri-grams are utilized. A heuristic method with six rules is then proposed to determine the segmentation points in a Chinese sentence. No dictionary is required in this method. Chinese text segmentation is important in Chinese text indexing and thus greatly affects the performance of Chinese information retrieval. Due to the lack of delimiters of words in Chinese text, Chinese text segmentation is more difficult than English text segmentation. Besides, segmentation ambiguities and occurrences of out-of-vocabulary words (i.e., unknown words) are the major challenges in Chinese segmentation. Many research studies dealing with the problem of word segmentation have focused on the resolution of segmentation ambiguities. The problem of unknown word identification has not drawn much attention. The experimental result shows that the proposed heuristic method is promising to segment the unknown words as well as the known words. The authors further investigated the distribution of the errors of commission and the errors of omission caused by the proposed heuristic method and benchmarked the proposed heuristic method with a previous proposed technique, boundary detection. It is found that the heuristic method outperformed the boundary detection method. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) networks have been drawing significant attention in the area of information management and sharing recently. Unlike the World Wide Web (WWW), every peer in a P2P network is both client and server. It hosts information for sharing and submits queries to search for information from all of the other peers at the same time. There are a large number of studies related to the information behavior of WWW search engines in the US and Europe. Several issues have been investigated in these studies including number of unique and repeat queries, length of queries, terms being used in queries, successive searching, multitasking searching, multiple searching sessions, changes of topics, multimedia searching, etc. The number of similar studies in P2P networks is significantly less. We have previously studied the information behavior of Gnutella P2P network based on data collected in 2002. In this paper, we present changes in Gnutella queries, based on the previously collected data and new data collected in 2003. Similar metrics to those used by Spink, Wolfram, Jansen, and Saracevic and in our previous study are used. We found that the number of non-English queries has increased. The number of repeat queries in P2P has decreased but is still more than that for WWW search engines. The length of queries has been increased by about 40%. The topics of interest have shifted from entertainment and sexuality to computers and entertainment. In general, the information behavior of P2P users has been changing as the P2P technology becomes more mature and the P2P users become more familiar with the technology. As the P2P technology begins to be adopted in e-Business, we foresee that there will be more changes in the future. [ABSTRACT FROM AUTHOR]

Efficient and effective retrieval techniques of images are desired because of the explosive growth of digital images. Content-based image retrieval is a promising approach because of its automatic indexing and retrieval based on their semantic features and visual appearance. The similarity of images depends on the feature representation and feature dissimilarity function. However, users have difficulties in representing their information needs in queries to content-based image, retrieval systems. In this paper, we investigate two approaches, query by example and image browsing map. Activities to support the information seeking behavior are analyzed. The performance of these approaches is measured by a user evaluation. It is found that the image browsing map provides more functionalities and capabilities to support the features of information seeking behavior and produces better performance in searching images. [ABSTRACT FROM AUTHOR]

Tolerance design plays a significant role in the relationship between the performance and the manufacture of a product. It is important to maintain valid tolerances when product design is constrained by the relationship between the dimensions of entities of a component and the functional requirement of the design. Increasing the tolerance may decrease the manufacturing cost, but will also worsen the performance. In this paper, an efficient algorithm is proposed for computerized optimal allocation of tolerance among the components of a complex assembly with a large number of constraints and entities. Basic concepts of hierarchical interval constraint networks have been used in combination with an iterative relative sensitivity analysis procedure for modeling and solving the tolerance allocation problem. The proposed algorithm can handle practically any number of constraints and entities with the required level of accuracy. It can also accept any type of cost-tolerance relationship for modeling. Examples have been discussed and the results oftolerance design obtained using the proposed method are presented. [ABSTRACT FROM AUTHOR]

As the demand for global information increases significantly, multilingual corpora has become a valuable linguistic resource for applications to cross-lingual information retrieval and natural language processing. In order to cross the boundaries that exist between different languages, dictionaries are the most typical tools. However, the general-purpose dictionary is less sensitive in both genre and domain. It is also impractical to manually construct tailored bilingual dictionaries or sophisticated multilingual thesauri for large applications. Corpusbased approaches, which do not have the limitation of dictionaries, provide a statistical translation model with which to cross the language boundary. There are many domain-specific parallel or comparable corpora that are employed in machine translation and cross-lingual information retrieval. Most of these are corpora between Indo-European languages, such as English/French and English/Spanish. The Asian/Indo-European corpus, especially English/Chinese corpus, is relatively sparse. The objective of the present research is to construct English/Chinese parallel corpus automatically from the World Wide Web. In this paper, an alignment method is presented which is based on dynamic programming to identify the one-to-one Chinese and English title pairs. The method includes alignment at title level, word level and character level. The longest common subsequence (LCS) is applied to find the most reliable Chinese translation of an English word. As one word for a language may translate into two or more words repetitively in another language, the edit operation, deletion, is used to resolve redundancy. A score function is then proposed to determine the optimal title pairs. Experiments have been conducted to investigate the performance of the proposed method using the daily press release articles by the Hong Kong SAR government as the test bed. The precision of the result is 0.998 while the recall is 0.806. The release... [ABSTRACT FROM AUTHOR]

The information available in languages other than English in the World Wide Web is increasing significantly. According to a report from Computer Economics in 1999, 54% of Intemet users are English speakers ("English Will Dominate Web for Only Three More Years," Computer Economics, July 9, 1999, http://www.computereconomics. com/new4/pr/pr990610.html). However, it is predicted that there will be only 60% increase in Internet users among English speakers verses a 150% growth among nonEnglish speakers for the next five years. By 2005, 57% of Internet users will be non-English speakers. A report by CNN.com in 2000 showed that the number of Internet users in China had been increased from 8.9 million to 16.9 million from January to June in 2000 ("Report: China Internet users double to 17 million," CNN. com, July, 2000, http://cnn.org/2000/TECH/computing/07/27/ china.internet.reut/index.html). According to Nielsen/ NetRatings, there was a dramatic leap from 22.5 millions to 56.6 millions Internet users from 2001 to 2002. China had become the second largest global at-home Internet population in 2002 (US's Internet population was 166 millions) (Robyn Greenspan, "China Pulls Ahead of Japan," Internet.com, April 22, 2002, http://cyberatlas.internet. com/big_picture/geographics/article/0,,5911_1013841,00. html). All of the evidences reveal the importance of crosslingual research to satisfy the needs in the near future. Digital library research has been focusing in structural and semantic interoperability in the past. Searching and retrieving objects across variations in protocols, formats and disciplines are widely explored (Schatz, B., & Chen, H. (1999). Digital libraries: technological advances and social impacts. IEEE Computer, Special Issue on Digital Libraries, February, 32(2), 45-50.; Chen, H., Yen, J., & Yang, C.C. (1999). International activities: development of Asian digital libraries. IEEE Computer, Special Issue on Digital Libraries, 32(2), 48-49... [ABSTRACT FROM AUTHOR]

Presents a Web personal agent that utilizes user's profiles and relevance feedback to search Chinese financial news articles. On-line database indexing and searching; Client-based searching agents; Components of the system architecture of the intelligent Chinese Web financial news retrieval system.