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
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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.

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-04-07-2020-10860
Total View : 350

Title : Technologies Of Developing The Intangible Incentive Staff Program Of An Educational Organization

Abstract :

The aim of the study is to analyze the level of satisfaction of employees of educational organizations with the intangible incentive staff program, to study its significance for teachers in a pandemic, and to rank promising forms of intangible incentive. A key research method is a questionnaire survey of employees (N = 193). The questionnaire was posted on the online platform Google. The results of the study showed that only every fifth teacher is completely satisfied with the intangible incentive staff program. At the same time, the organizational loyalty of employees is at a fairly high level. The survey found that traditional forms of intangible incentives (honor roll, birthday greetings, challenge prize for the best structural unit) are losing their significance in modern socio-economic conditions. The high level of workload, ongoing organizational changes in educational organizations reduce for teachers the relevance of such measures of intangible incentives, such as inclusion in the administrative personnel reserve. Workplace stability is more significant for teachers than career prospects. The survey results showed that the top lines of the rating in assessing the intangible incentive staff program take such forms as increasing the term of the employment contract, participating in innovative projects, and becoming part of creative teams. The recognition of colleagues, joining small groups, creative teams is considered as one of the most effective measures of intangible incentives, increasing the level of competitiveness of a teacher in the labor market

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Journal ID : TRKU-03-07-2020-10859
Total View : 332

Title : Multistage Flocculation Technique of Gradual Velocity Gradient to Improve Turbidity Removal from Water

Abstract :

The removal of fine materials from surface water represents a challenge. This research tries to improve the efficiency of flocculation process of low turbidity water by dividing the process into stage of gradual descending velocity gradient. A three stages flocculator model of continuous flow was designed and constructed. Combinations of three levels of velocity gradient of 60, 45 and 30 sec-1 were applied in the experiments in descending order. Tigris river water was used as raw water with 8-12 NTU turbidity. Alum was used as a coagulant at the optimum dosage. Turbidity removal percent was considered as an indicator of flocculation efficiency. The results showed a significant increase in turbidity removal percent with the decrease of velocity gradient at stage III. On the other hand, velocity gradient at stage I shows a direct relationship with turbidity removal, while the relationship is not clear at stage II. The best turbidity removal percent of more than 80% was obtained by the combinations 60*30*30 and 45*45* 30 sec-1. A regression model shows that velocity gradient at stage III was the most contributor to turbidity removal variation. The research recommended descending gradual velocity gradient flocculation at two stages

Full article

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