Design Review 2008 Portable [g8ni 92]

Nakajima's design featured a mid-mounted wing of small area and high aspect ratio, a tricycle landing gear and a large single-fin rudder. Power came from four 2,000 hp Nakajima NK9K-L "Homare" 24 radial engines with Hitachi 92 turbosuperchargers driving four-bladed propellers. The engines were cooled by counter-rotating fans positioned just inside the engine cowlings.[1] Defensive armament included power-operated nose, dorsal, ventral and tail turrets along with two free-swiveling machine guns at the beam positions.[2]

Just before Japan's surrender in August 1945 consideration was also briefly given to producing an all-steel version of the aircraft, to be designated G8N3 Renzan-Kai Model 23, but the cessation of hostilities precluded any further development.[2]

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The Gini Index has been utilized in different applications in machine learning, for example, extortion location, credit scoring, and client division. For example, in extortion discovery, the Gini Index can be utilized to distinguish designs in exchange data and recognize bizarre way of behaving. In credit scoring, the Gini Index can be utilized to foresee the probability of default in view of variables like income, relationship of outstanding debt to take home pay, and record of loan repayment. In client division, the Gini Index can be utilized to bunch clients in view of their way of behaving and inclinations.

Next generation sequencing methods have provided a wealth of new possibilities for the characterization of tumors in individual patients and laid the basis for new treatment options. However, under certain circumstances, the nature of the obtained data poses new risks to data privacy [1, 2]. Patients that provide samples within translational research projects at the National Center for Tumor Diseases (NCT) Heidelberg therefore sign a specific informed consent accounting this risk. The consent specifically addresses the issue of data privacy, which would not be as critical, if the biological specimen were anonymized for sequencing and further analysis. However, translational research and personalized medicine ultimately require a two-way information highway from bench to bedside and vice versa [3, 4]. Conducting research with anonymized data excludes the possibility of retrospective linkage to clinically relevant information, which is a fundamental ambition in biomedical translational research [3]. Without the possibility to de-pseudonymize a patient, he or she would be deprived of a direct benefit from research results. This is an ethical challenge, as under certain circumstances, individual research results in genetic and genomic research might lead to new treatment possibilities and therefore should be offered to study participants in a clinically relevant timeframe [5]. In a scenario where clinical follow-up data is later added to a biological sample to study outcome effects, anonymization is not applicable either. Furthermore, personalized medicine has to be based on the careful analysis of multifaceted data. By their very design next-generation-sequencing technologies and other high-throughput methods imply the involvement of many different persons and even external organizations in the data collection and analysis process, with all the corresponding additional risks to patient privacy [2, 6]. To retain a semantic reference between patient and sample, while still complying with data privacy requirements, pseudonymization is the method of choice [7, 8]. What we need is a data protection and privacy concept with solid pseudonymization and a streamlined de-pseudonymization process to transfer the results back to the clinic [9].

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