2,226 research outputs found

    Improving Early Sepsis Recognition: Resocializing Intensive Care Unit Nurses in a Large Hospital on the Inpatient Sepsis Bundle Checklist

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    Problem: Sepsis is a life threatening disease that has caused over a million deaths annually in the nation. Early recognition of sepsis is highly crucial for health care professionals to know to prevent an increase of mortality and morbidity rates. This quality improvement project aimed to increase sepsis bundle checklist awareness to the staff and compliance in the Intensive Care Unit to improve the sepsis cases. Context: Clinical Nurse Leader students completed a microsystem assessment of the Intensive Care Unit at Hospital X in San Mateo County. This unit cares for patients with sepsis, septic shock, severe sepsis, organ failure, and stroke. Interventions: The implemented intervention of sepsis bundle resocialization was ineffective. Measures: After completing an assessment of the microsystem, the students collected data to evaluate if the nurses were aware of the sepsis bundle checklist. The post-survey was to measure if the intervention of resocializing the sepsis bundle checklist was effective. Results: Analysis from the initial survey showed that 42% nurses were aware of the sepsis bundle checklist. From the post survey it showed that 0% nurses were aware of the sepsis bundle checklist. Conclusions: In collaboration, with my CNL colleague student, sepsis champion, registered nurses, assistant nurse managers, and quality improvement consultant, a need for sepsis bundle adherence and awareness was identified at Hospital X in the ICU. This study has the potential to expand on the project of reducing sepsis cases of staffing being aware and adhering to the sepsis bundle checklist. Keywords: sepsis, sepsis bundle, sepsis ICU, sepsis bundle checklist, and sepsis awarenes

    Energy Prediction Based Intrusion Detection In Wireless Sensor Networks

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    A challenge in designing wireless sensor networks is to maximize the lifetime of the network with respect to limited resources and energy. These limitations make the network particularly vulnerable to attacks from adversaries. Denial of Service (DOS) is considered a severely damaging attack in monitoring applications when intruders attack the network and force it to lose its power and die early. There are intrusion detection approaches, but they require communications and calculations which waste the network’s limited resources. In this paper, we propose a new intrusion detection model that is suitable for defending against DOS attacks. We use the idea of energy prediction to anticipate the energy consumption of the network in order to detect intruders based on the each individual node’s excessive usage of power. Our approach does not require a lot of communications or calculations between the nodes and the cluster head. It is energy efficient and accurate in detecting intruders. Simulations show that our energy aware intrusion detection approach can effectively detect intruders based on energy consumption rate

    Immunoglobulin Polygeny: An Evolutionary Perspective

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    Energy Prediction Based Intrusion Detection In Wireless Sensor Networks

    Get PDF
    A challenge in designing wireless sensor networks is to maximize the lifetime of the network with respect to limited resources and energy. These limitations make the network particularly vulnerable to attacks from adversaries. Denial of Service (DOS) is considered a severely damaging attack in monitoring applications when intruders attack the network and force it to lose its power and die early. There are intrusion detection approaches, but they require communications and calculations which waste the network’s limited resources. In this paper, we propose a new intrusion detection model that is suitable for defending against DOS attacks. We use the idea of energy prediction to anticipate the energy consumption of the network in order to detect intruders based on the each individual node’s excessive usage of power. Our approach does not require a lot of communications or calculations between the nodes and the cluster head. It is energy efficient and accurate in detecting intruders. Simulations show that our energy aware intrusion detection approach can effectively detect intruders based on energy consumption rate
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