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Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide- area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, without remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future.

Data caches of various forms have been widely deployed in the context of commercial and research and education networks, but their common positioning at the Edge limits their utility from a network operator perspective. When deployed outside the network core, providers lack visibility to make decisions or apply traffic engineering based on data access patterns and caching node location. As an alternative, in-network caching provides a different type of content delivery network for scientific data infrastructure, supporting on-demand temporary caching service. We will describe the status of in-network caching nodes deployed within ESnet in support of the US CMS data federation. We will describe the container and networking architecture used to deploy data caches within ESnet, and update on the evolving tooling around service management lifecycle. An analysis of cache usage will also be provided along with an outlook for expanding the in-network cache footprint.

The storage management system dCache acts as a disk cache for high-energy physics (HEP) data from the US ATLAS community. Since its disk capacity is considerably smaller than the total volume of ATLAS data, a heuristic is needed to determine what data should be kept on disks. An effective heuristic would be to keep the data files that are expected to be heavily accessed in the near future. Through a careful study of access statistics, we find a few most popular datasets are accessed much more frequently than others, even though these popular datasets change over time. If we could predict the near-term popularity of datasets, we could pin the most popular ones in the disk cache to prevent their accidental removal and guarantee their availability. To predict a dataset popularity, we present several methods for forecasting the number of times a dataset will be accessed in the next day. Test results show that these methods could predict the next-day access counts of popular datasets reliably. This observation is confirmed with dCache logs from two separate time ranges.

Massive Open Online Courses (MOOC) are the latest online teaching and learning medium through website and are seen as a key factor in future lesson delivery. One well-established and popular MOOC is Coursera which is provided by Stanford University. Online participation and interactions are complex phenomena and the answers to the questions in regard to Courserians' participation gave considerable insights into knowledge construction in discussion forums. The successful construction of knowledge requires active participation. This article is aimed to observe a Coursera community in its natural setting and to investigate the participants' experiences in Coursera discussion forum. The data was derived from over 60 online interview forums with Coursera learners. Field note data was extracted from 160 days of interaction with the participants. Analysis of the qualitative data revealed some insights about learners' feelings, challenges, ideas and recommendations.

Massive Open Online Courses (MOOC) are becoming increasingly difficult to ignore in online education. Recent developments in MOOC have heightened the need for understanding the MOOC. The issue of an in-depth understanding of the MOOC setting has been a key subject within the field of qualitative research. However, far too little attention has been paid to the MOOC setting. This paper seeks to put the Netnography methodology into context by presenting a description of the Coursera community to better understand this community. It contributes by providing details about Coursera community through our engagement in this community.

Recently researchers have shown an increased interest in cloud computing technology. It is becoming increasingly difficult to ignore cloud computing technology in education context. However rapid changes in information technology are having a serious effect on teaching framework designs. So far, however, there has been little discussion about cloud computing benefits in domains of teaching frameworks which propels us to study and redesign teaching frameworks considering cloud computing. The purpose of this paper is to review recent research into cloud computing and features which can be improved with this new technology. This paper studied several researches through literature to determine the main impact of cloud computing on "planning and preparation" and "instruction" domains as two main domains of teaching framework. Light will be then shed on the impact and potential benefits of cloud computing on teaching framework. The paper closes by proposing to design an evaluation table based on cloud computing artifacts to enhance teaching practice and highlights its offerings for educational institutions.

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926. [ABSTRACT FROM AUTHOR]

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as "Have-it-alls", who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth ("nesting period"), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as "Couples", who start careers and partnerships early but delay parenthood, and "Singles", who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention. [ABSTRACT FROM AUTHOR]

Obsessive‐compulsive disorder (OCD) commonly affects males and can manifest with a range of urological symptoms directly linked to the content of the OCD. In Part 1 of this article, the authors discussed the recognition and diagnosis of OCD. In Part 2 here, they consider its treatment and related sexual dysfunction. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs) have increasingly become an important element for individuals' learning and development. However, MOOCs mainly concentrate on duplicating knowledge instead of constructing it. This research aims to explore the structure of the MOOCs for fostering the knowledge construction in which educational professional build, develop, share one another' learning and reflections. This research focused on Coursera, a particular MOOC community, by drawing on the concepts of community of practice (CoP) as a theoretical lens. Three types of data were collected. The archival data consisted of the top and selected posts from online discussion forums, and the elicited data which was derived from over 60 interviews with Coursera learners. Meanwhile, field note data was extracted from 160 days of interaction with the participants. A qualitative research method using a netnographic methodology was employed. The findings contribute to the body of knowledge construction and online communities by providing an understanding of the domain, community and practice elements. The study on other elements such as the reinforcement of identity, formation of warrants and identification of mechanisms for legitimate peripheral participation can help to interpret the constitution of CoPs in MOOC. This research developed a Coursera community framework that generally makes a MOOC community more energetic to construct knowledge. [ABSTRACT FROM AUTHOR]

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors. [ABSTRACT FROM AUTHOR]

Obsessive‐compulsive disorder (OCD) commonly affects males and can manifest with a range of urological symptoms directly linked to the content of the OCD, including urinary urgency, urinary frequency, urinary retention and sexual dysfunction. Accurate recognition of OCD in such cases is fundamental to avoid misdiagnosis, risks to patients associated with untreated OCD (including suicidal behaviour), unnecessary physical health investigations and delayed treatment. In Part 1 here, the authors discuss the recognition and diagnosis of OCD. Part 2 will consider its treatment. [ABSTRACT FROM AUTHOR]

Association rule mining (ARM) is used for discovering frequent itemsets for interesting relationships of associative and correlative behaviors within the data. This gives new insights of great value, both commercial and academic. The traditional ARM techniques discover interesting association rules based on a predefined minimum support threshold. However, there is no known standard of an exact definition of minimum support and providing an inappropriate minimum support value may result in missing important rules. In addition, most of the rules discovered by these traditional ARM techniques refer to already known knowledge. To address these limitations of the minimum support threshold in ARM techniques, this study proposes an algorithm to mine interesting association rules without minimum support using predicate logic and a property of a proposed interestingness measure (g measure). The algorithm scans the database and uses g measure's property to search for interesting combinations. The selected combinations are mapped to pseudo-implications and inference rules of logic are used on the pseudo-implications to produce and validate the predicate rules. Experimental results of the proposed technique show better performance against state-of-the-art classification techniques, and reliable predicate rules are discovered based on the reliability differences of the presence and absence of the rule's consequence. [ABSTRACT FROM AUTHOR]

Ocular manifestations, comorbidities, and detection of severe acute respiratory syndrome-coronavirus 2 from conjunctiva in coronavirus disease 2019: a systematic review and meta-analysis. Coronavirus disease 2019 (COVID-19) is a novel worldwide pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this patient, bilateral intermediate uveitis following MIS-C secondary to COVID-19 infection was diagnosed based on the assumption that his symptoms occurred following a severe multisystem inflammation after a positive COVID-19 PCR test. [Extracted from the article]

Byline: Alex Tze Hiang Sim, Maria Indrawan, Bala Srinivasan Association Rule Mining (ARM) technique is one of the popular data mining techniques used to discover knowledge from a database. In this paper, we highlight the subsequent drawbacks to association rule mining without considering the absence of items. We propose a novel approach to mine both positive and negative association rules as rule pairs. Our approach ensures that impacts of negative associations are considered so that the drawbacks identified can be avoided. Our initial experiment results show that mining pairs of association rules invoking negative associations are small in number and easy to be appreciated for its implications for decision-making.

Traffic flow prediction is a key component of an intelligent transportation system. Accurate traffic flow prediction provides a foundation for other tasks, such as signal coordination and travel time forecasting. There are many known methods in literature for the short-term traffic flow prediction problem, but their efficacy depends heavily on the traffic characteristics. It is difficult, if not impossible, to pick a single method that works well over time. In this paper, we present an automated framework to address this practical issue. Instead of selecting a single method, we combine predictions from multiple methods to generate a consensus traffic flow prediction. We propose an ensemble learning model that exploits the temporal characteristics of the data, and balances the accuracy of individual models and their mutual dependence through a covariance-regularizer. We additionally use a pruning scheme to remove anomalous individual predictions. We apply our proposed model to multi-step-ahead arterial roadway flow prediction. In tests, our method consistently outperforms recently published ensemble prediction methods based on ridge regression and lasso. Our method also produces steady results even when the standalone models and other ensemble methods make wildly exaggerated predictions. [ABSTRACT FROM AUTHOR]