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To create a low-cost ventilator that could be constructed with readily-available hospital equipment for use in emergency or low-resource settings. Main methods: The novel ventilator consists of an inspiratory limb composed of an elastic flow-inflating bag encased within a non-compliant outer sheath and an expiratory limb composed of a series of two, one-way bidirectional splitter valves derived from a self-inflating bag system. An Arduino Uno microcontroller controls a solenoid valve that can be programmed to open and close to produce a set respiratory rate and inspiratory time. Using an ASL 5000 Lung Simulator, we obtained flow, pressure, and volume waveforms at different lung compliances. Key findings: At a static lung compliance of 50 mL/cm H 2 O and an airway resistance of 6 cm H 2 O/L/s, ventilated at a PIP and PEEP of 16 and 5 cm H 2 O, respectively, tidal volumes of approximately 540 mL were achieved. At a static lung compliance of 20 mL/cm H 2 O and an airway resistance of 6 cm H 2 O/L/s, ventilated at a PIP and PEEP of 38 and 15 cm H 2 O, respectively, tidal volumes of approximately 495 mL were achieved. Significance: This novel ventilator is able to safely and reliably ventilate patients with a range of pulmonary disease in a simulated setting. Opportunities exist to utilize our ventilator in emergency situations and lowresource settings.

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In theconventional way of convert data into a singleton or merging has many drawbacks mainly computational complexity. In this context hierarchical clustering method for quantitative measures of similarity among objects that could keep not only the structure of categorical attributes but also relative distance of numeric values. For numeric data the number of clusters can be validated through integral data, the hierarchical and partitioning methods the relationships among categorical items. In This Paper we hereinvestigate linkage criterions in hierarchical clustering algorithm performance calculations using with Euclidian distance measure and some clustering techniques and their applications have been discussed. It also describes the necessities to be calculated for constructing a well-organized to handle the huge data sets. As the study initially investigates distinct issues for creating clusters with numeric attributes. The efficiency is obtained by clustering of datasets that comprises of numeric attributes related to distinct applications. Significant issues such merging object naturally uses Euclidean distance is resolved by using Agglomerative methods.

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