Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university. Asia Life Sciences

12 Aug 2020

Day

Hour

Min

Sec

31 Jul 2020

Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:

Electrical Engineering,
Telecommunication Engineering,
Electro-mechanical System Engineering,
Biological Biosystem Engineering,
Integrated Engineering,
Electronic Engineering,
Hardware-software co-design and interfacing,
Semiconductor chip,
Peripheral equipments,
Nanotechnology,
Advanced control theories and applications,
Machine design and optimization ,
Turbines micro-turbines,
FACTS devices ,
Insulation systems ,
Power quality ,
High voltage engineering,
Electrical actuators ,
Energy optimization ,
Electric drives ,
Electrical machines,
HVDC transmission,
Power electronics.

Software Engineering,
Data Security ,
Computer Vision ,
Image Processing,
Cryptography,
Computer Networking,
Database system and Management,
Data mining, Big Data, Robotics ,
Parallel and distributed processing ,
Artificial Intelligence ,
Natural language processing ,
Neural Networking, Distributed Systems ,
Fuzzy logic, Advance programming,
Machine learning,
Internet & the Web,
Information Technology ,
Computer architecture,
Virtual vision and virtual simulations,
Operating systems,
Cryptosystems and data compression,
Security and privacy,
Algorithms,
Sensors and ad-hoc networks,
Graph theory,
Pattern/image recognition,
Neural networks.

Chemical engineering fundamentals,
Physical, Theoretical and Computational Chemistry,
Chemical engineering educational challenges and development,
Chemical reaction engineering,
Chemical engineering equipment design and process design,
Thermodynamics,
Catalysis & reaction engineering,
Particulate systems,
Rheology,
Multifase flows,
Interfacial & colloidal phenomena,
Transport phenomena in porous/granular media,
Membranes and membrane science,
Crystallization, distillation, absorption and extraction,
Ionic liquids/electrolyte solutions.

Food science,
Food engineering,
Food microbiology,
Food packaging,
Food preservation,
Food technology,
Aseptic processing,
Food fortification,
Food rheology,
Dietary supplement,
Food safety,
Food chemistry.

Astrophysics,
Atomic and molecular physics,
Biophysics,
Chemical physics,
Civil engineering,
Cluster physics,
Computational physics,
Condensed matter,
Cosmology,
Device physics,
Fluid dynamics,
Geophysics,
High energy particle physics,
Laser,
Mechanical engineering,
Medical physics,
Nanotechnology,
Nonlinear science,
Nuclear physics,
Optics,
Photonics,
Plasma and fluid physics,
Quantum physics,
Robotics,
Soft matter and polymers.

Actuarial science,
Algebra,
Algebraic geometry,
Analysis and advanced calculus,
Approximation theory,
Boundry layer theory,
Calculus of variations,
Combinatorics,
Complex analysis,
Continuum mechanics,
Cryptography,
Demography,
Differential equations,
Differential geometry,
Dynamical systems,
Econometrics,
Fluid mechanics,
Functional analysis,
Game theory,
General topology,
Geometry,
Graph theory,
Group theory,
Industrial mathematics,
Information theory,
Integral transforms and integral equations,
Lie algebras,
Logic,
Magnetohydrodynamics,
Mathematical analysis.

Journal ID : TRKU-19-06-2020-10818

Total View : 356

Provision of appropriate learning strategies will be able to improve concepts understanding and applications of graphic design concepts. This study aimed to determine the effect of Collaborative e-learning support strategies for a classroom and self-regulated learning (SRL) outcomes and concepts application. Data analysis was performed with the MANOVA statistical test. The results of the study concluded: (1) There is a significant difference in the learning outcomes of concepts understanding between students who implemented the Collaborative e-learning (CeL) strategy and students who implemented the Collaborative Non e-learning (CNeL) strategy (p = 0.000). (2) There is a significant difference in the learning outcomes of the concepts application between students who apply CeL strategies and students who apply CNeL strategies (p = 0.000). (3) There is a significant difference in the learning outcomes of concepts understanding between students who have high and low SRL (p = 0.049). (4) There is a significant difference in the learning outcomes of concepts application between students who have high and low SRL (p = 0,000). (5) There is an interaction between the CeL strategy and the CNeL strategy with high and low SRL towards the concepts understanding learning outcomes (p = 0.002). (6) There is no interaction between the CeL strategy and the CNeL strategy with high and low SRL to the learning outcomes of the concepts application (p = 0.761). The use of CeL strategies can improve SRL of understanding graphic design concepts and application. In building the ability to understand and apply graphic design concepts in the Mathematics Education Study Program, the application of CeL strategies in this study is better than CNeL strategies in terms of improving the application ability of students' mathematics learning concepts

Journal ID : TRKU-19-06-2020-10817

Total View : 321

Response surface methodology was used to determine for optimum processing condition that yield maximum water loss and weight reduction and minimum solid gain during osmotic dehydration of radish in salt solution. The experiments were conducted according to Box-Behnken Design. The independent process variables for osmotic dehydration were processing time (30-120min), temperature (40-60°C) and salt concentration (6-10%w/w). The osmotic dehydration process was optimized for water loss, solid gain, and weight reduction. The optimum conditions were found to be; temperature = 44.575°C, immersion time =30min, salt concentration =6%. At this optimum point, water loss, solid gain and weight reduction were found to be (27.87g/100g initial sample), (1.05g/100g initial sample) and (26.83 g/100g initial sample), respectively