ILIS--An integrated laboratory information system. I. Biochemistry and hematology.

1982 ◽  
Vol 28 (2) ◽  
pp. 271-276 ◽  
Author(s):  
S U Deshpande
Keyword(s):  
Medical Records ◽  
Data Entry ◽  
Large Data ◽  
Work Load ◽  
Processing Unit ◽  
Patient Identification ◽  
Central Processing ◽  
Cathode Ray Tubes ◽  
Clinical Laboratories ◽  
On Line

Abstract IBM System 34 (central processing unit, 128 kilobytes; fixed disks, 128.4 megabytes) with seven cathode-ray tubes has been used by our clinical laboratories for the last 30 months. All data-entry programs are in a conversational mode, for on-line corrections of possible errors in patient identification and results. Daily reports are removed from the medical records after temporary and permanent cumulative weekly reports are received, which keep a three-month track of the results. The main advantages of the system are: (a) the increasing laboratory work load can be handled with the same staff; (b) the volume of the medical record files on the patients is decreased; (c) an easily retrievable large data base of results is formed for research purposes; (d) faster billing; and (e) the computer system is run without engaging any additional staff.

Multicriteria decision making based optimum virtual machine selection technique for smart cloud environment

2021 ◽  
pp. 1-15
Author(s):  
Raman Singh ◽  
Maninder Singh ◽  
Sheetal Garg ◽  
Ivan Perl ◽  
Olga Kalyonova ◽  
...  
Keyword(s):  
Virtual Machines ◽  
Smart Cities ◽  
Multicriteria Decision Making ◽  
Work Load ◽  
Load Factor ◽  
Processing Unit ◽  
Central Processing ◽  
Data Centre ◽  
Smart Data ◽  
Iot Devices

In the popular field of cloud computing, millions of job requests arrive at the data centre for execution. The job of the data centre is to optimally allocate virtual machines (VMs) to these job requests in order to use resources efficiently. In the future smart cities, huge amount of job requests and data will be generated by the Internet of Things (IoT) devices which will influence the designing of optimum resource management of smart cloud environments. The present paper analyses the performance efficiency of the data centre with and without job request consolidation. First, the work load performance of the data centre was analysed without job request consolidation, exhibiting that the job requests to VM assignment was highly imbalanced, and only 5% of VMs were running with a load factor of more than 70%. Then, the technique for order of preference by similarity to ideal solution-based VM selection algorithm was applied, which was able to select the best VM using parameters such as the provisioned or available central processing unit capacity, provisioned or available memory capacity, and state of machine (running, hibernated, or available). The Bitbrains dataset consisting of 1750 VMs was used to analyse the performance of the proposed methodology. The analysis concluded that the proposed methodology was capable of serving all job requests using less than 24% VMs with improved load efficiency. The fewer number of VMs with an improved load factor guarantees energy saving and an increase in the overall running efficiency of the smart data centre environment.

Causes, consequences, detection, and prevention of identification errors in laboratory diagnostics

2009 ◽  
Vol 47 (2) ◽  
Author(s):  
Giuseppe Lippi ◽  
Norbert Blanckaert ◽  
Pierangelo Bonini ◽  
Sol Green ◽  
Steve Kitchen ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Data Entry ◽  
Low Frequency ◽  
Clinical Decision ◽  
Laboratory Diagnostics ◽  
Inappropriate Therapy ◽  
Sample Collection ◽  
Patient Identification ◽  
Positive Identification ◽  
Clinical Laboratories

AbstractLaboratory diagnostics, a pivotal part of clinical decision making, is no safer than other areas of healthcare, with most errors occurring in the manually intensive preanalytical process. Patient misidentification errors are potentially associated with the worst clinical outcome due to the potential for misdiagnosis and inappropriate therapy. While it is misleadingly assumed that identification errors occur at a low frequency in clinical laboratories, misidentification of general laboratory specimens is around 1% and can produce serious harm to patients, when not promptly detected. This article focuses on this challenging issue, providing an overview on the prevalence and leading causes of identification errors, analyzing the potential adverse consequences, and providing tentative guidelines for detection and prevention based on direct-positive identification, the use of information technology for data entry, automated systems for patient identification and specimen labeling, two or more identifiers during sample collection and delta check technology to identify significant variance of results from historical values. Once misidentification is detected, rejection and recollection is the most suitable approach to manage the specimen.Clin Chem Lab Med 2009;47:143–53.

scholarly journals B3: Fuzzy-Based Data Center Load Optimization in Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
M. Jaiganesh ◽  
A. Vincent Antony Kumar
Keyword(s):  
Cloud Computing ◽  
Data Center ◽  
Large Data ◽  
Service Level ◽  
Processing Unit ◽  
High Definition ◽  
Load Optimization ◽  
Central Processing ◽  
Order Of Magnitude ◽  
Internet Connections

Cloud computing started a new era in getting variety of information puddles through various internet connections by any connective devices. It provides pay and use method for grasping the services by the clients. Data center is a sophisticated high definition server, which runs applications virtually in cloud computing. It moves the application, services, and data to a large data center. Data center provides more service level, which covers maximum of users. In order to find the overall load efficiency, the utilization service in data center is a definite task. Hence, we propose a novel method to find the efficiency of the data center in cloud computing. The goal is to optimize date center utilization in terms of three big factors—Bandwidth, Memory, and Central Processing Unit (CPU) cycle. We constructed a fuzzy expert system model to obtain maximum Data Center Load Efficiency (DCLE) in cloud computing environments. The advantage of the proposed system lies in DCLE computing. While computing, it allows regular evaluation of services to any number of clients. This approach indicates that the current cloud needs an order of magnitude in data center management to be used in next generation computing.

Design of Gas On-Line Monitoring System Based on STM32 & Linux

2013 ◽  
Vol 722 ◽  
pp. 379-383
Author(s):  
Shun Jing Guo ◽  
Jing Li
Keyword(s):  
Monitoring System ◽  
Contact Surface ◽  
Hardware Design ◽  
Processing Unit ◽  
Single Chip ◽  
Central Processing ◽  
On Line ◽  
Software And Hardware ◽  
Machine Interaction ◽  
General Module

A new portable gas detect & alarm device which is controlled by Single Chip Micyoco is proposed in this paper, which means the hardware and drivers designation of system uses STM32F103 as its central processing unit and also expounds the detail of software and hardware design. The system communicates with metrical instrument by the RS485 BUS. It can monitor the mine gas in network. It has good man-machine interaction contact surface and can be inserted into intelligence instrument as a general module.

A Retrieval System for a Library of Pathology Reports, Slides and Kodachromes

1972 ◽  
Vol 11 (03) ◽  
pp. 152-162 ◽  
Author(s):  
P. GAYNON ◽  
R. L. WONG
Keyword(s):  
Medical Records ◽  
Operative Procedure ◽  
Clinical Findings ◽  
Free Text ◽  
X Ray ◽  
Inverted File ◽  
Natural Language Parsing ◽  
On Line ◽  
Pathology Reports ◽  
Frequency Counts

With the objective of providing easier access to pathology specimens, slides and kodachromes with linkage to x-ray and the remainder of the patient’s medical records, an automated natural language parsing routine, based on dictionary look-up, was written for Surgical Pathology document-pairs, each consisting of a Request for Examination (authored by clinicians) and its corresponding report (authored by pathologists). These documents were input to the system in free-text English without manual editing or coding.Two types of indices were prepared. The first was an »inverted« file, available for on-line retrieval, for display of the content of the document-pairs, frequency counts of cases or listing of cases in table format. Retrievable items are patient’s and specimen’s identification data, date of operation, name of clinician and pathologist, etc. The English content of the operative procedure, clinical findings and pathologic diagnoses can be retrieved through logical combination of key words. The second type of index was a catalog. Three catalog files — »operation«, »clinical«, and »pathology« — were prepared by alphabetization of lines formed by the rotation of phrases, headed by keywords. These keywords were automatically selected and standardized by the parsing routine and the phrases were extracted from each sentence of each input document. Over 2,500 document-pairs have been entered and are currently being utilized for purpose of medical education.

Perangkat keras komputer

2020 ◽  
Author(s):  
Roudati jannah
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras premprosesan (processing/central processing unit) – perangkat keras luaran (output/output device system) – perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (storage device system/external memory).

CENTRAL PROCESSING UNIT (CPU)

2020 ◽  
Author(s):  
Ika Milia wahyunu Siregar
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Processing Unit ◽  
Central Processing

Perkembangan IT di dunia sangat pesat, mulai dari perkembangan sofware hingga hardware. Teknologi sekarang telah mendominasi sebagian besar di permukaan bumi ini. Karena semakin cepatnya perkembangan Teknologi, kita sebagai pengguna bisa ketinggalan informasi mengenai teknologi baru apabila kita tidak up to date dalam pengetahuan teknologi ini. Hal itu dapat membuat kita mudah tergiur dan tertipu dengan berbagai iklan teknologi tanpa memikirkan sisi negatifnya. Sebagai pengguna dari komputer, kita sebaiknya tahu seputar mengenai komponen-komponen komputer. Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware komputer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Central Processing System/ Central Processing Unit (CPU) adalah salah satu jenis perangkat keras yang berfungsi sebagai tempat untuk pengolahan data atau juga dapat dikatakan sebagai otak dari segala aktivitas pengolahan seperti penghitungan, pengurutan, pencarian, penulisan, pembacaan dan sebagainya.

Perangkat lunak komputer

2020 ◽  
Author(s):  
Intan khadijah simatupang
Keyword(s):  
Data Storage ◽  
Central Processing Unit ◽  
External Memory ◽  
Storage Device ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device

Komputer adalah serangkaian mesin elektronik yang terdiri dari jutaan komponen yang dapat saling bekerja sama, serta membentuk sebuah sistem kerja yang rapi dan teliti. Sistem ini kemudian digunakan untuk dapat melaksanakan pekerjaan secara otomatis, berdasarkan instruksi (program) yang diberikan kepadanya. Istilah Hardware computer atau perangkat keras komputer, merupakan benda yang secara fisik dapat dipegang, dipindahkan dan dilihat. Software komputer atau perangkat lunak komputer merupakan kumpulan instruksi (program/prosedur) untuk dapat melaksanakan pekerjaan secara otomatis dengan cara mengolah atau memproses kumpulan instruksi (data) yang diberikan. Pada prinsipnya sistem komputer selalu memiliki perangkat keras masukan (input/input device system) – perangkat keras pemprosesan (processing/ central processing unit) – perangkat keras keluaran (output/output device system), perangkat tambahan yang sifatnya opsional (peripheral) dan tempat penyimpanan data (Storage device system/external memory).

PERANGKAT KERAS ( HARDWARE ) PADA KOMPUTER

2020 ◽  
Author(s):  
Siti Kumala Dewi
Keyword(s):  
Processing System ◽  
Central Processing Unit ◽  
Input Device ◽  
Processing Unit ◽  
Central Processing ◽  
Output Device ◽  
System P ◽  
System 2

Perangkat keras komputer adalah bagian dari sistem komputer sebagai perangkat yang dapat diraba, dilihat secara fisik, dan bertindak untuk menjalankan instruksi dari perangkat lunak (software). Perangkat keras komputer juga disebut dengan hardware. Hardware berperan secara menyeluruh terhadap kinerja suatu sistem komputer. Berdasarkan fungsinya, perangkat keras terbagi menjadi :1.Sistem Perangkat Keras Masukan (Input Device System )2.Sistem Pemrosesan ( Central Processing System/ Central Processing Unit(CPU)3.Sistem Perangkat Keras Keluaran ( Output Device System )4.Sistem Perangkat Keras Tambahan (Peripheral/Accessories Device System)

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