Technology Reports of Kansai University

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.

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Submission Deadline

Volume - 66 , Issue 01
20 Jan 2024

Upcoming Publication

Volume - 66 , Issue 01
31 Jan 2024

Aim and Scope

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 and Telecommunication Section:

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.

Computer Science Section :

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. Lizi Jiaohuan Yu Xifu/Ion Exchange and Adsorption Fa yi xue za zhi Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology Research Journal of Chemistry and Environment

Civil and architectural engineering :

Architectural Drawing, Architectural Style, Architectural Theory, Biomechanics, Building Materials, Coastal Engineering, Construction Engineering, Control Engineering, Earthquake Engineering, Environmental Engineering, Geotechnical Engineering, Materials Engineering, Municipal Or Urban Engineering, Organic Architecture, Sociology of Architecture, Structural Engineering, Surveying, Transportation Engineering.

Mechanical and Materials Engineering :

kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.

Chemical Engineering :

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 Engineering :

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

Physics Section:

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.

Mathematics Section:

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.

Latest Articles of

Technology Reports of Kansai University

Journal ID : TRKU-08-08-2021-11472
Total View : 657

Title : Online Learning: Enhancing Communicative-Based Japanese Competence Through Zoom and Aizuchi Skill-Based Multimedia

Abstract :

Communicative-based Japanese learning has been applied since the outbreak of the COVID-19 pandemic through Zoom and Aizuchi skill-based multimedia. This study aims to investigate the learners’ initial attitude of learning communicative-based Japanese using Zoom and multimedia-based methods, the impact of online learning using Zoom and multimedia to learn Japanese, the participants’ attitude towards multimedia that has characteristics, autonomous material and media effectiveness, as well as the advantages and disadvantages of learning communicative-based Japanese using Zoom and Aizuchi skill-based multimedia. The descriptive analysis is used as the method in this study. The data collected were processed using the scale of Likert and Guttman. This research was conducted for 6 months in the Japanese Literature Study Program of the University of Hasanuddin Makassar. The research subjects were 30 middle students who have been learning Japanese for more than 2 years. The results of the study proved that communicative-based Japanese learning using Zoom and multimedia for students is very positive ever since it began. The use of Zoom and multimedia provides motivation, comforts, and learners’ creativity, interaction with lecturers and colleagues and good access to learning despite the COVID-19 pandemic. Nonetheless, the amount of cost required concerning large internet quota needed particularly during bad weather is considered the biggest obstacle for learners

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Journal ID : TRKU-08-08-2021-11471
Total View : 577

Title : State of the Art of Deep Learning Method to Predict the Compressive Strength of Concrete

Abstract :

In recent years research in the field of Artificial Intelligence (AI) has shown a fairly rapid and extensive development. The application of AI in the form of artificial neural networks in the field of Civil Engineering has also shown significant developments. In line with scientific advances in the field of numerical computing, artificial intelligence, also known as AI, has gained popularity in recent years (AI). One of the branches of AI is Machine Learning (ML). Machine Learning is a smart system that can learn and predict output based on learning done on the system. One form of ML is an artificial neural network (artificial neural network, ANN). ANN is a numerical computation model created by mimicking the workings of the human brain. ANN then developed extensively, with a greater number of neurons or by increasing the number of hidden layers. This development leads to a learning model known as Deep Learning (DL). This paper has an objective to describes how deep learning method, one of AI branch, can be used to predict the compressive strength of concrete, especially geopolymer concrete

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