Analysis of Cache Performance for Processing XML-Based Application Data on Multi-core Processors

2008 ◽  
Author(s):  
Rajdeep Bhowmik ◽  
Madhusudhan Govindaraju
Keyword(s):  
Cache Performance ◽  
Application Data

Sistem Pendukung Keputusan Untuk Penerimaan Calon Karyawan Outsourcing Dengan Metode Simple Additive Weighting (SAW)

2018 ◽  
Vol 4 (1) ◽  
pp. 29-39 ◽  
Author(s):  
Otto Fajarianto ◽  
Ardiyanto S. Widodo ◽  
Muhammad Iqbal Hanafri ◽  
Arianto ◽  
Arif Muchlisin Fauzi
Keyword(s):  
Decision Making ◽  
Supporting System ◽  
Performance Result ◽  
Decision Supporting ◽  
Decision Supporting System ◽  
Application Data ◽  
Simple Additive Weighting

Decision making on hiring outsourcing employment candidate which still manually has impact on stucking application data, obstacle on placement process and unreal in scoring criteria. In deciding decision needs specific criteria for scoring performance result. This criterion can be standardized in deciding advisable candidate. The purpose of this study is building a decision supporting system, help the company in giving score and choosing new employment. SAW method uses for calculating criteria value base on the highest degree and calculating is taken from normalisation process. The data will be inputted in decision supporting system, and then it will be calculated base on specific criteria. The score from each criterion will be calculated to get the highest score. The result will give recommendation for company in deciding advisable candidate.

scholarly journals Design and Application Data-Based Employee Eat Barcode Scanner

2021 ◽  
Vol 1764 (1) ◽  
pp. 012065
Author(s):  
Ninik Sri Lestari ◽  
Sukirno ◽  
Hetty Fadriani ◽  
Ahmad Sujana ◽  
Yudi Herdiana ◽  
...  
Keyword(s):  
Application Data ◽  
Design And Application

scholarly journals Reinforcement Learning to Send Reminders at Right Moments in Smartphone Exercise Application: A Feasibility Study

2021 ◽  
Vol 18 (11) ◽  
pp. 6059
Author(s):  
Shihan Wang ◽  
Karlijn Sporrel ◽  
Herke van Hoof ◽  
Monique Simons ◽  
Rémi D. D. de Boer ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Physical Activities ◽  
Lessons Learned ◽  
Just In Time ◽  
Healthcare Intervention ◽  
Adaptive Intervention ◽  
Mobile Healthcare ◽  
Crucial Component ◽  
Application Data ◽  
The Right

Just-in-time adaptive intervention (JITAI) has gained attention recently and previous studies have indicated that it is an effective strategy in the field of mobile healthcare intervention. Identifying the right moment for the intervention is a crucial component. In this paper the reinforcement learning (RL) technique has been used in a smartphone exercise application to promote physical activity. This RL model determines the ‘right’ time to deliver a restricted number of notifications adaptively, with respect to users’ temporary context information (i.e., time and calendar). A four-week trial study was conducted to examine the feasibility of our model with real target users. JITAI reminders were sent by the RL model in the fourth week of the intervention, while the participants could only access the app’s other functionalities during the first 3 weeks. Eleven target users registered for this study, and the data from 7 participants using the application for 4 weeks and receiving the intervening reminders were analyzed. Not only were the reaction behaviors of users after receiving the reminders analyzed from the application data, but the user experience with the reminders was also explored in a questionnaire and exit interviews. The results show that 83.3% reminders sent at adaptive moments were able to elicit user reaction within 50 min, and 66.7% of physical activities in the intervention week were performed within 5 h of the delivery of a reminder. Our findings indicated the usability of the RL model, while the timing of the moments to deliver reminders can be further improved based on lessons learned.

scholarly journals Alpha Amylase from Bacillus pacificus Associated with Brown Algae Turbinaria ornata: Cultural Conditions, Purification, and Biochemical Characterization

Processes ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 16
Author(s):  
Mona Alonazi ◽  
Aida Karray ◽  
Ahmed Yacine Badjah-Hadj-Ahmed ◽  
Abir Ben Bacha
Keyword(s):  
Biochemical Characterization ◽  
Alpha Amylase ◽  
Bacterial Symbionts ◽  
Terminal Sequence ◽  
Turbinaria Ornata ◽  
Substrate Analogs ◽  
Cultural Conditions ◽  
Initial Ph ◽  
Application Data ◽  
High Degree

We aimed in the current study, the identification of a marine bacterial amylase produced by Bacillus pacificus, which was associated with Turbinaria ornata. Cultural conditions were optimized for the highest amylase production on Tryptic soy broth media supplemented with starch 1% at initial pH 9, 55 °C for 24 h. The newly purified amylase was characterized for a possible biotechnological application. Data indicated that the obtained amylase with a molecular weight of 40 kD and the N-terminal sequence of the first 30 amino acids of amBp showed a high degree of homology with known alpha amylase, and was stable at 60 °C of pH 11. Among the tested substrate analogs, amBp was almost fully active on Alylose and Alylopectine (97%), but moderately hydrolyzed glycogen < sucrose < maltose < lactose. Therefore, the current amylase mainly generated maltohexaose from starch. Mg2+ and Zn2+ improved amylase activity up to 170%. While ethylenediamine tetraacetic acid (EDTA) similarly induced the greatest activity with purified amylase, PCMB had the least effect. Regarding all these characteristics, amylase from marine bacterial symbionts amBp has a new promising feature for probable therapeutic, industrial, and nutritional applications.

Design of an Intelligent Data Cache with Replacement Policy

2019 ◽  
Vol 10 (2) ◽  
pp. 87-107
Author(s):  
B. Shameedha Begum ◽  
N. Ramasubramanian
Keyword(s):  
Real Time ◽  
Replacement Policy ◽  
Data Cache ◽  
Cache Performance ◽  
Performance Requirements ◽  
Novel Approach ◽  
Reconfigurable Caches ◽  
Real Time Applications ◽  
Hard Real Time ◽  
Replacement Algorithms

Embedded systems are designed for a variety of applications ranging from Hard Real Time applications to mobile computing, which demands various types of cache designs for better performance. Since real-time applications place stringent requirements on performance, the role of the cache subsystem assumes significance. Reconfigurable caches meet performance requirements under this context. Existing reconfigurable caches tend to use associativity and size for maximizing cache performance. This article proposes a novel approach of a reconfigurable and intelligent data cache (L1) based on replacement algorithms. An intelligent embedded data cache and a dynamic reconfigurable intelligent embedded data cache have been implemented using Verilog 2001 and tested for cache performance. Data collected by enabling the cache with two different replacement strategies have shown that the hit rate improves by 40% when compared to LRU and 21% when compared to MRU for sequential applications which will significantly improve performance of embedded real time application.

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