124 research outputs found

    How Reliable are Compositions of Series and Parallel Networks Compared with Hammocks?

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    A classical problem in computer/network reliability is that of identifying simple, regular and repetitive building blocks (motifs) which yield reliability enhancements at the system-level. Over time, this apparently simple problem has been addressed by various increasingly complex methods. The earliest and simplest solutions are series and parallel structures. These were followed by majority voting and related schemes. For the most recent solutions, which are also the most involved (e.g., those based on Harary and circulant graphs), optimal reliability has been proven under particular conditions. Here, we propose an alternate approach for designing reliable systems as repetitive compositions of the simplest possible structures. More precisely, our two motifs (basic building blocks) are: two devices in series, and two devices in parallel. Therefore, for a given number of devices (which is a power of two) we build all the possible compositions of series and parallel networks of two devices. For all of the resulting twoterminal networks, we compute exactly the reliability polynomials, and then compare them with those of size-equivalent hammock networks. The results show that compositions of the two simplest motifs are not able to surpass size-equivalent hammock networks in terms of reliability. Still, the algorithm for computing the reliability polynomials of such compositions is linear (extremely effcient), as opposed to the one for the size-equivalent hammock networks, which is exponential. Interestingly, a few of the compositions come extremely close to size-equivalent hammock networks with respect to reliability, while having fewer wires.

    Modeling Climate Change Impacts On Coastal Resources With Enhanced Simulation Model Mantra

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    Accelerated sea level rise (SLR) and precipitation change in response to climate change is well underway, the impacts of which call for appropriate climate action SDG 13. The associated increase in surface seawater inundation and subsurface saltwater intrusion will reduce the availability of fresh groundwater due to permanent salinization of groundwater. Further, increased levels of soil salinity and decreased freshwater inputs may alter coastal ecosystems by facilitating the establishment of plants with higher salinity and flooding tolerance. This thesis focuses on the modelling and analysis of climate change impacts on the availability and quality of coastal groundwater as well as on the potential changes in coastal vegetation. For this purpose, the simulation model MANTRA is enhanced and used in this thesis. The hydrology-salinity-vegetation model MANTRA was developed by coupling the vegetation competition model MANHAM and groundwater flow and solute transport model SUTRA. SUTRA is first verified against standard density-dependent flow benchmarks for the purpose of ensuring correct understanding and implementation of SUTRA. Further simulation and analysis are then performed to provide insights on the response of an atoll island’s fresh groundwater lens to SLR and changes in precipitation. The potential of harvesting rainwater to mitigate the impact of SLR on coastal aquifer is also explored

    Real-Time Scheduling for Time-Sensitive Networking: A Systematic Review and Experimental Study

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    Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many time- and mission-critical industrial applications, e.g., automotive and aerospace systems. Given the stringent real-time communication requirements raised by these applications, the Time-Aware Shaper (TAS) draws special attention among the many traffic shapers developed for TSN, due to its ability to achieve deterministic latency guarantees. Extensive efforts on the designs of scheduling methods for TAS shapers have been reported in recent years to improve the system schedulability, each with their own distinct focuses and concerns. However, these scheduling methods have yet to be thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding on their performance using different evaluation metrics in various application scenarios. In this paper, we fill this gap by presenting a comprehensive experimental study on the existing TAS-based scheduling methods for TSN. We first categorize the system models employed in these work along with their formulated problems, and outline the fundamental considerations in the designs of TAS-based scheduling methods. We then perform extensive evaluation on 16 representative solutions and compare their performance under both synthetic scenarios and real-life industrial use cases. Through these experimental studies, we identify the limitations of individual scheduling methods and highlight several important findings. This work will provide foundational knowledge for the future studies on TSN real-time scheduling problems, and serve as the performance benchmarking for scheduling method development in TSN.Comment: 22 pages, ac

    Dynamic planning and control for large-scale infrastructure projects : route 3N as a case study

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (leaves 131-137).by Margaret Fulenwider.S.M

    A Machine Learning Recommender Model for Ride Sharing Based on Rider Characteristics and User Threshold Time

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    In the present age, human life is prospering incredibly due to the 4th Industrial Revolution or The Age of Digitization and Computing. The ubiquitous availability of the Internet and advanced computing systems have resulted in the rapid development of smart cities. From connected devices to live vehicle tracking, technology is taking the field of transportation to a new level. An essential part of the transportation domain in smart cities is Ride Sharing. It is an excellent solution to issues like pollution, traffic, and the rapid consumption of fuel. Even though Ride Sharing has several benefits, the current usage is significantly low due to limitations like social barriers and long rider waiting times. The thesis proposes a novel Ride Sharing model with two matching layers to eliminate most of the observed issues in the existing Ride Sharing applications like UberPool and LyftLine. The first matching layer matches riders based on specific human characteristics, and the second matching layer provides riders the option to restrict the waiting time by using personalized threshold time. At the end of trips, the system collects user feedback according to five characteristics. Then, at most, two main characteristics that are the most important to riders are determined based on the collected feedback. The registered characteristics and the two main determined characteristics are fed as the inputs to a Machine Learning classification module. For newly registering users, the module predicts the two main characteristics of riders, and that assists in matching with other riders having similar determined characteristics. The thesis includes subjecting the proposed model to an extensive simulation for measuring system efficiency. The model simulations have utilized the real-time New York City Cab traffic data with real-traffic conditions using Google Maps Application Programming Interface (API). Results indicate that the proposed Ride Sharing model is feasible, and efficient as the number of riders increases while maintaining the rider threshold time. The expected outcome of the thesis is to help service providers increase the usage of Ride Sharing, complete the pool for the maximum number of trips in minimal time and perform maximum rider matches based on similar characteristics, thus providing an energy-efficient and a social platform for Ride Sharing

    Indian River Lagoon surface water improvement and management (SWIM) plan, 2002 update.

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    In recognition of the need to place additional emphasis on the restoration, protection, and management of the surface water resources of the state, the Florida Legislature, through the Surface Water Improvement and Management (SWIM) Act of 1987, directed the state’s water management districts to “design and implement plans and programs for the improvement and management of surface water” (Section 373.451, Florida Statutes [FS]). The SWIM legislation requires the water management districts to protect the ecological, aesthetic, recreational, and economic value of the state’s surface water bodies, keeping in mind that water quality degradation is frequently caused by point and nonpoint source pollution and that degraded water quality can cause both direct and indirect losses of aquatic habitats. This 2002 update is the second update of the Indian River Lagoon SWIM Plan. This 2002 plan update includes a status report on the state of the Lagoon, a summary of progress on projects undertaken since the last update, and recommendations for future projects and other actions over the next 5 years. (262pp.

    Towards Scalable Personalization

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    The ever-growing amount of online information calls for Personalization. Among the various personalization systems, recommenders have become increasingly popular in recent years. Recommenders typically use collaborative filtering to suggest the most relevant items to their users. The most prominent challenges underlying personalization are: scalability, privacy, and heterogeneity. Scalability is challenging given the growing rate of the Internet and its dynamics, both in terms of churn (i.e., users might leave/join at any time) and changes of user interests over time. Privacy is also a major concern as users might be reluctant to expose their profiles to unknown parties (e.g., other curious users), unless they have an incentive to significantly improve their navigation experience and sufficient guarantees about their privacy. Heterogeneity poses a major technical difficulty because, to be really meaningful, the profiles of users should be extracted from a number of their navigation activities (heterogeneity of source domains) and represented in a form that is general enough to be leveraged in the context of other applications (heterogeneity of target domains). In this dissertation, we address the above-mentioned challenges. For scalability, we introduce democratization and incrementality. Our democratization approach focuses on iteratively offloading the computationally expensive tasks to the user devices (via browsers or applications). This approach achieves scalability by employing the devices of the users as additional resources and hence the throughput of the approach (i.e., number of updates per unit time) scales with the number of users. Our incrementality approach deals with incremental similarity metrics employing either explicit (e.g., ratings) or implicit (e.g., consumption sequences for users) feedback. This approach achieves scalability by reducing the time complexity of each update, and thereby enabling higher throughput. We tackle the privacy concerns from two perspectives, i.e., anonymity from either other curious users (user-level privacy) or the service provider (system-level privacy). We strengthen the notion of differential privacy in the context of recommenders by introducing distance-based differential privacy (D2P) which prevents curious users from even guessing any category (e.g., genre) in which a user might be interested in. We also briefly introduce a recommender (X-REC) which employs uniform user sampling technique to achieve user-level privacy and an efficient homomorphic encryption scheme (X-HE) to achieve system-level privacy. We also present a heterogeneous recommender (X-MAP) which employs a novel similarity metric (X-SIM) based on paths across heterogeneous items (i.e., items from different domains). To achieve a general form for any user profile, we generate her AlterEgo profile in a target domain by employing an item-to-item mapping from a source domain (e.g., movies) to a target domain (e.g., books). Moreover, X-MAP also enables differentially private AlterEgos. While X-MAP employs user-item interactions (e.g., ratings), we also explore the possibility of heterogeneous recommendation by using content-based features of users (e.g., demography, time-varying preferences) or items (e.g., popularity, price)
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