1,267 research outputs found

    Molecular Modeling Studies of Hemoglobin Crosslinking Reactions

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    Multihop Rendezvous Algorithm for Frequency Hopping Cognitive Radio Networks

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    Cognitive radios allow the possibility of increasing utilization of the wireless spectrum, but because of their dynamic access nature require new techniques for establishing and joining networks, these are known as rendezvous. Existing rendezvous algorithms assume that rendezvous can be completed in a single round or hop of time. However, cognitive radio networks utilizing frequency hopping that is too fast for synchronization packets to be exchanged in a single hop require a rendezvous algorithm that supports multiple hop rendezvous. We propose the Multiple Hop (MH) rendezvous algorithm based on a pre-shared sequence of random numbers, bounded timing differences, and similar channel lists to successfully match a percentage of hops. It is tested in simulation against other well known rendezvous algorithms and implemented in GNU Radio for the HackRF One. We found from the results of our simulation testing that at 100 hops per second the MH algorithm is faster than other tested algorithms at 50 or more channels with timing ±50 milliseconds, at 250 or more channels with timing ±500 milliseconds, and at 2000 channels with timing ±5000 milliseconds. In an asymmetric environment with 100 hops per second, a 500 millisecond timing difference, and 1000 channels the MH algorithm was faster than other tested algorithms as long as the channel overlap was 35% or higher for a 50% required packet success to complete rendezvous. We recommend the Multihop algorithm for use cases with a fast frequency hop rate and a slow data transmission rate requiring multiple hops to rendezvous or use cases where the channel count equals or exceeds 250 channels, as long as timing data is available and all of the radios to be connected to the network can be pre-loaded with a shared seed

    Effective Recruitment Strategies to Attract Orthopaedic Surgeons to Rural Nebraska

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    Rural communities have difficulty establishing and maintaining a quality healthcare workforce. Twenty percent of Americans compose the rural populations in this country, but only nine percent of physicians practice in these rural areas. From 2000 to 2020, the demand for orthopaedic physicians is expected to increase by 23%, mainly due to the aging population of the United States. Although the demand for orthopaedic surgeons to these underserved, aging rural populations is increasing, there is a shortage which continues to grow in rural areas. One possible driving force of this shortage could be the recruitment strategies exercised by the healthcare managers in these rural communities. Orthopaedic surgeons may avoid work in a rural area due to its remoteness, lack of new technology, and few opportunities for family members. The objective of this study was to identify factors that could enhance recruitment of orthopaedic surgeons to health care facilities in rural Nebraska using Lexington Regional Health Center (LRHC), one of Nebraska’s critical access hospitals, as a case study. Strategies such as loan repayment plans, competitive pay, “humanizing” the work environment, and motivational analysis show promising solutions to recruitment strategies in rural healthcare

    Expanding Haptic Workspace for Coupled-Object Manipulation

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    Haptic force-feedback offers a valuable cue in exploration and manipulation of virtual environments. However, grounding of many commercial kinesthetic haptic devices limits the workspace accessible using a purely position-control scheme. The bubble technique has been recently presented as a method for expanding the user’s haptic workspace. The bubble technique is a hybrid position-rate control system in which a volume, or “bubble,” is defined entirely within the physical workspace of the haptic device. When the device’s end effector is within this bubble, interaction is through position control. When exiting this volume, an elastic restoring force is rendered, and a rate is applied that moves the virtual accessible workspace. Existing work on the bubble technique focuses on point-based touching tasks. When the bubble technique is applied to simulations where the user is grasping virtual objects with part-part collision detection, unforeseen interaction problems surface. This paper discusses three details of the user experience of coupled-object manipulation with the bubble technique. A few preliminary methods of addressing these interaction challenges are introduced

    Response to an Abnormal Ovarian Cancer-Screening Test Result: Test of the Social Cognitive Processing and Cognitive Social Health Information Processing Models

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    All cancer screening tests produce a proportion of abnormal results requiring follow up. Consequently, the cancer-screening setting is a natural laboratory for examining psychological and behavioural response to a threatening health-related event. This study tested hypotheses derived from the social cognitive processing and cognitive-social health information processing models in trying to understand response to an abnormal ovarian cancer (OC) screening test result. Women (n = 278) receiving an abnormal screening test result a mean of 7 weeks earlier were assessed prior to a repeat screening test intended to clarify their previous abnormal result. Measures of disposition (optimism, informational coping style), social environment (social support and constraint), emotional processing, distress, and benefit finding were obtained. Regression analyses indicated greater distress was associated with greater social constraint and emotional processing and a monitoring coping style in women with a family history of OC. Distress was unrelated to social support. Greater benefit finding was associated with both greater social constraint and support and greater distress. The primacy of social constraint in accounting for both benefit finding and distress was noteworthy and warrants further research on the role of social constraint in adaptation to stressful events

    Eliminating helper phage from phage display

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    Phage display technology involves the display of proteins or peptides, as coat protein fusions, on the surface of a phage or phagemid particles. Using standard technology, helper phage are essential for the replication and assembly of phagemid particles, during library production and biopanning. We have eliminated the need to add helper phage by using 'bacterial packaging cell lines' that provide the same functions. These cell lines contain M13-based helper plasmids that express phage packaging proteins which assemble phagemid particles as efficiently as helper phage, but without helper phage contamination. This results in genetically pure phagemid particle preparations. Furthermore, by using constructs differing in the form of gene 3 that they contain, we have shown that the display, from a single library, can be modulated between monovalent (phagemid-like) and multivalent display (phage-like) without any further engineering. These packaging cells eliminate the use of helper phage from phagemid-based selection protocols; reducing the amount of technical preparation, facilitating automation, optimizing selections by matching display levels to diversity, and effectively using the packaged phagemid particles as means to transfer genetic information at an efficiency approaching 100%

    Payload-Byte: A Tool for Extracting and Labeling Packet Capture Files of Modern Network Intrusion Detection Datasets

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    Adapting modern approaches for network intrusion detection is becoming critical, given the rapid technological advancement and adversarial attack rates. Therefore, packet-based methods utilizing payload data are gaining much popularity due to their effectiveness in detecting certain attacks. However, packet-based approaches suffer from a lack of standardization, resulting in incomparability and reproducibility issues. Unlike flow-based datasets, no standard labeled dataset exists, forcing researchers to follow bespoke labeling pipelines for individual approaches. Without a standardized baseline, proposed approaches cannot be compared and evaluated with each other. One cannot gauge whether the proposed approach is a methodological advancement or is just being benefited from the proprietary interpretation of the dataset. Addressing comparability and reproducibility issues, we introduce Payload-Byte, an open-source tool for extracting and labeling network packets in this work. Payload-Byte utilizes metadata information and labels raw traffic captures of modern intrusion detection datasets in a generalized manner. Moreover, we transformed the labeled data into a byte-wise feature vector that can be utilized for training machine learning models. The whole cycle of processing and labeling is explicitly stated in this work. Furthermore, source code and processed data are made publicly available so that it may act as a standardized baseline for future research work. Lastly, we present a brief comparative analysis of machine learning models trained on packet-based and flow-based data
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