Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) was originally founded in 1959. The publisher of the journal is Wuhan University of Technology. JWUT first got the scopus license in the year 2001. The journal generally publishes all aspect of engineering sciences like: physics, chemistry, mathematics, and all sorts of general engineering.
Scopus Indexed(2026)
Submission Deadline
Volume
50
, Issue
03
02 Apr 2026
Day
Hour
Min
Sec
Publish On
Volume
49
, Issue
06
31 Jul 2025
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Aim and Scopes
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) (ISSN:2095-3844) 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:
Computer Science Section :
Civil and architectural 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 :
Food Engineering :
Physics Section:
Mathematics Section:
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Mathematical Modelling of Zika Virus Transmission with Parameter Estimation and Optimal Control
This paper discusses estimating parameters and optimal control for mathematical modelling of the Zika virus transmission. First, we consider an SEIR (susceptible-exposed-infected-recovered) model of the Zika virus dynamics, which consists of four ordinary differential equations. This SEIR model expresses the interaction among the four compartments. Assuming that the solution of the model is known and storing it for estimating the model parameters. To do so, a least squares optimization problem is introduced, and the Gauss-Newton recursion equation is derived to estimate the model parameters. In addition, an optimal control law is designed so that the model can reach the equilibrium early. The contributions of this paper are the use of the Gauss-Newton method for parameter estimation in a dynamical system and the linearized optimal control law for stabilizing the SEIR model. The simulation results showed the parameter estimates' accuracy and the controller's efficiency. Therefore, the study demonstrates the usefulness of parameter estimation and optimal control in the mathematical modelling of the Zika virus transmission, which will impact future studies on epidemic modelling.
A Novel Deep Learning Framework for Enhanced Facial Emotion Recognition
Facial expression is a crucial component of biometry; it has emerged as a vibrant and significant study domain in the past decade owing to its role in conveying individuals' emotional states. Although this analysis can be conducted using alternative features such as voice, body gestures, and social and contextual parameters, facial expression remains the most expressive medium for humans to convey emotions due to its high levels of directness, friendliness, convenience, and robustness. Artificial intelligence research uses deep learning techniques in human-computer interactions as an efficient system application process. An average person can exhibit or possess seven distinct facial expressions depending on the circumstance: surprise, disgust, anger, sadness, happiness, neutrality, and fear. These emotions are considered universal across humanity in most studies.The Automated FER System (AFERS or FERS) is a complex process that enables machines to autonomously identify emotions without the assistance of human beings. Researchers in this subject are striving to improve models and techniques, as well as extract various aspects, to allow for more accurate computer predictions of emotion. Deep learning architectures can manage vast volumes of data while producing superior results compared to typical emotional analysis methods. In the current study, three techniques are presented to enhance the accuracy of face expression categorization. The first suggested technique is based on the CycleGAN method, but the second proposed method uses a deep learning architecture after preprocessing the dataset. The experimental findings reveal that the test accuracy of the two suggested approaches on the FER2013 data set is 92% and 97%, respectively. The third proposed method indicates that the training accuracy and validation accuracy of this method on the Cohn-Kanade (CK+) data set have reached 99.7% and 99.5%, respectively.
A Novel Deep Learning Framework for Enhanced Facial Emotion Recognition
Facial expression is a crucial component of biometry; it has emerged as a vibrant and significant study domain in the past decade owing to its role in conveying individuals' emotional states. Although this analysis can be conducted using alternative features such as voice, body gestures, and social and contextual parameters, facial expression remains the most expressive medium for humans to convey emotions due to its high levels of directness, friendliness, convenience, and robustness. Artificial intelligence research uses deep learning techniques in human-computer interactions as an efficient system application process. An average person can exhibit or possess seven distinct facial expressions depending on the circumstance: surprise, disgust, anger, sadness, happiness, neutrality, and fear. These emotions are considered universal across humanity in most studies.The Automated FER System (AFERS or FERS) is a complex process that enables machines to autonomously identify emotions without the assistance of human beings. Researchers in this subject are striving to improve models and techniques, as well as extract various aspects, to allow for more accurate computer predictions of emotion. Deep learning architectures can manage vast volumes of data while producing superior results compared to typical emotional analysis methods. In the current study, three techniques are presented to enhance the accuracy of face expression categorization. The first suggested technique is based on the CycleGAN method, but the second proposed method uses a deep learning architecture after preprocessing the dataset. The experimental findings reveal that the test accuracy of the two suggested approaches on the FER2013 data set is 92% and 97%, respectively. The third proposed method indicates that the training accuracy and validation accuracy of this method on the Cohn-Kanade (CK+) data set have reached 99.7% and 99.5%, respectively.
Benchmarking Task Scheduling Algorithms for Independent Non-Preemptive Tasks
This study conducts a comprehensive evaluation of scheduling algorithms specifically designed for independent, non-preemptive tasks within multiprocessor systems that operate under soft real-time constraints. The research introduces the Optimized Performance-Based Genetic Algorithm (OPBGA) and compares its performance against that of the Earliest Deadline First (EDF), Least Laxity First (LLF), and the Evolutionary Fuzzy-Based Scheduling Algorithm (EFSBA). The evaluation encompasses three distinct processor configurations. Utilizing randomized task sets with varying load factors, the study simulates low, medium, and high demand scenarios, ensuring a thorough performance assessment. Key performance metrics, including Average Turnaround Time (ATAT), Average Response Time (ART), and Deadline Misses (DLM), illustrate the enhanced scalability and efficiency of OPBGA. In circumstances of elevated load, traditional algorithms such as EDF and LLF exhibit significant limitations, while EFSBA demonstrates only moderate adaptability. In contrast, OPBGA consistently achieves the lowest ATAT and ART with a record of zero DLM across all configurations. The findings position OPBGA as a robust and scalable solution for real-time scheduling in multiprocessor environments, optimizing task allocation and ensuring superior performance across diverse load conditions.
Dynamic Architecture Between Theory And Implementation
Recently, computer applications have made a great effect on the culture and science of architecture and have added new dimensions to design, Schools of architecture have evolved noticeably, Schools of the digital era have emerged, including dynamic architecture and movement in buildings and not relying solely on static in buildings. The research study is based on the study of each of the aspects of moving surfaces (formas - colors - size - texture - lighting), Actual movement patterns in architecture ( rigid elements - deformable elements - soft and flexible elements - elastic elements - pneumatic forms ), Movement trends in dynamic buildings (static or dynamic motion static and dynamic motion) And identify the movement of building parts (Movement of furniture - movement of openings- movement of ceilings - movement of floors - movement of some floors of the building - movement of the whole building), Also Identify the actual components of dynamic architecture, At the end of the research, the design criteria for dynamic architecture are reached.
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/Journal of Wuhan University of Technology (Transportation Science and Engineering)