Aims and scope

Journal of Engineering is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in several areas of engineering. The subject areas covered by the journal are:

    Chemical Engineering
    Civil Engineering
    Computer Engineering
    Electrical Engineering
    Industrial Engineering
    Mechanical Engineering

Special Issue on AI and Machine Learning in Renewable Energy Systems
Submission Date: 2026-01-16

Description

The global transition to renewable energy systems is critical for mitigating climate change, ensuring energy security, and fostering sustainable development. However, the complexity and variability inherent in renewable energy sources such as solar, wind, and hydro pose significant challenges to their efficient integration and operation within existing energy infrastructures. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated transformative potential in addressing these challenges, enabling smarter, more adaptive, and highly efficient renewable energy systems.

This Special Issue provides a comprehensive platform to explore the intersection of AI, ML, and renewable energy systems. By leveraging AI-driven innovations, researchers and practitioners can enhance forecasting accuracy, optimize energy production and storage, improve grid stability, and design intelligent control mechanisms. Furthermore, AI techniques have proven instrumental in fostering resilience, reducing costs, and promoting the scalability of renewable energy technologies.

The significance of this Special Issue lies in its focus on fostering interdisciplinary research that bridges AI technologies and energy sciences, driving innovation across diverse domains such as predictive analytics, autonomous systems, and decision-making algorithms. By assembling contributions from leading experts, this issue highlights innovative advancements, practical applications, and future research directions that collectively support the global agenda for a sustainable energy future. This Special Issue welcomes original research, case studies, and review articles that address critical challenges and opportunities, providing valuable insights for academia, industry, and policymakers.

Potential topics include but are not limited to the following:

    Forecasting and predictive analytics
    Optimization of energy production and storage
    Smart Grids and energy management systems
    Autonomous and intelligent control systems
    AI for decentralized energy systems
    Energy efficiency and system optimization
    AI in waste management and biomass energy
    Resilience and fault detection in renewable systems
    Sustainability and policy support
    Emerging AI techniques for renewable energy
    Challenges of AI integration in energy
    Addressing barriers to AI adoption

Special Issue on AI-Driven Modeling and Prediction Across Infrastructure Lifecycle Stages: From Design Optimization to Decommissioning Forecasts in Civil Engineering
Submission Date: 2026-02-27

Description

The civil engineering sector is facing unprecedented demands for robust, sustainable, and economically viable infrastructure solutions. As assets mature, financial constraints tighten and environmental challenges escalate, there is an increasing need for predictive models that enhance decision-making at every stage of the infrastructure lifecycle, from initial design to eventual decommissioning. Recent advances in artificial intelligence (AI) have revealed new opportunities for modelling and forecasting the performance, maintenance requirements, and financial implications of civil structures over time. This special issue aims to investigate the transformative potential of AI to tackle these urgent challenges. While this special issue primarily focuses on civil engineering applications, contributions exploring AI-driven insights from related engineering disciplines are welcome. AI-driven models are revolutionizing civil engineering by enabling professionals to analyze extensive datasets, uncover patterns, and derive insights that were previously unattainable through conventional methods. These models enhance design processes, optimize materials and structural parameters, and predict maintenance needs, thereby reducing downtime and costs. AI technologies are becoming indispensable for asset management, cost forecasting, and lifespan prediction, fostering more resilient and resource-efficient infrastructure systems. The following sections delve into the specific contributions of AI in these areas.

• Design Optimization: AI algorithms significantly enhance the design phase by optimizing structural parameters and predicting potential failures, leading to more efficient and reliable designs (Akhmedov et al., 2024).
• Machine learning methods are applied to improve workflows and design processes, ensuring that structures are both cost-effective and sustainable (Kamolov, 2024).
• Predictive Maintenance: AI-driven predictive maintenance models utilize sensor data and advanced analytics to anticipate equipment failures, allowing for proactive interventions that extend the lifespan of assets (Akhmedov et al., 2024). These models are integrated into various industries, reducing costs and improving quality by enabling continuous learning and automation (Zsombok & Zsombok, 2023).
• Asset Management: AI technologies in asset management provide comprehensive insights into asset portfolios, facilitating strategic decision-making and proactive maintenance protocols (Лю, 2024).
• Real-world case studies demonstrate the transformative potential of AI in revolutionizing conventional asset management methodologies, enhancing organizational performance (Лю, 2024).
• Structural Health Monitoring: AI plays a crucial role in structural health monitoring by automating tasks and predicting outcomes, which are essential for maintaining the integrity and safety of civil structures (Vinayak, 2024).

The integration of AI in structural analysis and risk assessment helps in identifying vulnerabilities and optimizing maintenance strategies(Vinayak, 2024).

While AI offers numerous benefits in civil engineering, challenges such as data quality and model interpretability remain. Additionally, the need for interdisciplinary collaboration and continued research is emphasized to fully leverage AI's potential in addressing evolving challenges and opportunities in the field(Vinayak, 2024).

This special issue invites original research, reviews, and case studies that exhibit innovative AI applications in infrastructure lifecycle management. Topics include but are not limited to:

Potential topics include but are not limited to the following:

    • Asset Management and Maintenance Optimization: Predictive AI models that facilitate the monitoring and maintenance of infrastructure integrity
    • Cost Forecasting and Budgeting: AI-enhanced financial modeling to refine cost accuracy throughout infrastructure lifecycle stages
    • Lifespan Prediction of Civil Structures: Models and algorithms that anticipate deterioration and enable prompt interventions
    • Sustainable Decommissioning Strategies: AI tools that assist in end-of-life planning to maximize reuse and minimize environmental repercussions
    • Integrated Lifecycle Management: AI solutions that optimize data and processes across design, construction, maintenance, and decommissioning phases.

Special Issue on Emerging Artificial Intelligence-Machine Learning Techniques for Monitoring, Mitigation and Management of Environmental Pollution
Submission Date: 2026-03-05

Description

Environmental pollution is one of the most critical and complex challenges facing our world today, impacting ecosystems, human health, and the sustainability of urban infrastructure. With rapid urbanization, industrialization, and agricultural intensification, pollution in water, air, soil, and waste has reached unprecedented levels. Conventional methods of pollution monitoring and management are often reactive and labour-intensive, making it difficult to address the growing scale and complexity of pollution problems. In this context, artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools for transforming the way we assess, mitigate, and manage pollution, offering advanced computational approaches that enhance efficiency, precision, and adaptability.

The global rise in pollution has led to significant environmental and public health risks. Water pollution, for example, is a leading cause of ecosystem degradation and contamination of drinking water, affecting millions of people worldwide. Soil pollution poses serious threats to agricultural productivity, food security, and biodiversity, while air pollution is directly linked to respiratory and cardiovascular diseases, particularly in urban areas. Furthermore, ineffective solid waste management continues to strain urban infrastructure, with increasing volumes of waste contributing to environmental degradation. Traditional pollution management methods often fall short in addressing these challenges due to their reliance on static models, limited data integration, and lack of real-time monitoring capabilities. The need for more efficient, predictive, and adaptive systems is urgent, as current technologies are inadequate in keeping pace with the dynamic nature of pollution and its widespread impacts.

This Special Issue aims to bring together cutting-edge research and innovative AI-ML solutions for pollution monitoring, mitigation, and resource recovery, with a focus on urban and ecosystem systems. It provides a platform for exploring new computational methods for real-time pollution monitoring, predictive analytics, and adaptive management strategies. Contributions that demonstrate how AI and ML can be integrated into water, air, soil, and waste management systems are particularly encouraged, especially those that offer solutions for improving efficiency, sustainability, and resilience in the face of growing environmental challenges. By fostering interdisciplinary collaboration among researchers, engineers, and policymakers, this Special Issue aspires to promote the development of AI-ML-driven methodologies that support sustainable environmental practices and help create cleaner, healthier, and more resilient urban and ecological systems for future generations.

Potential topics include but are not limited to the following:

    AI-ML models for real-time water quality monitoring and prediction of pollutant levels in urban water systems
    Machine learning-based optimization of wastewater treatment processes for enhanced resource recovery
    Machine learning for the optimization of industrial wastewater treatment using advanced remediation techniques
    AI-ML approaches for the detection, monitoring, and mitigation of emerging pollutants in water and wastewater systems
    AI-enhanced predictive modeling and management strategies for nutrient pollution in freshwater and coastal ecosystems
    Machine learning models for predicting soil contaminant distribution and remediation efficiency in brownfield sites
    Deep learning algorithms for detecting and classifying heavy metal pollution in agricultural lands
    Predictive modeling of air quality using AI-ML for urban pollution control and public health risk mitigation
    Integration of AI-ML and IoT sensor networks for real-time air pollution monitoring and forecasting
    Development of AI-based smart waste segregation systems using computer vision and deep learning for efficient recycling processes
    AI-powered predictive modeling of leachate generation and contaminant transport for enhanced landfill lifespan management
    Hybrid AI-ML approaches for assessing microplastic pollution in aquatic ecosystems
    Development of intelligent decision support systems for sustainable solid waste management in smart cities
    AI-ML-driven optimization of biomass valorization processes for sustainable biofuel production and value-added bioproducts
    Application of reinforcement learning for dynamic environmental remediation strategies
    Big data and AI-ML integration for multi-pollutant assessment and environmental risk mapping
    Intelligent systems for adaptive management of pollution mitigation in response to climate change impacts on urban infrastructure
    AI-enhanced solutions for mitigating saltwater intrusion and optimizing desalination to combat water scarcity
    AI-enhanced predictive models for groundwater contamination assessment and remediation strategies

Special Issue on Stability Analysis and Control Validation of x-by-wire Chassis Architecture for Autonomous Driving Vehicles
Submission Date: 2026-03-12

Description

The evolution of autonomous driving technology, alongside the ongoing enhancement of automation levels, has spurred a rising demand for actuators with higher response speed and control precision. This evolution has also given rise to numerous innovations in chassis design. The x-by-wire chassis, integrating wheels with advanced electric drive systems such as steer-by-wire systems, brake-by-wire systems, and active suspension systems, has been regarded as one of the most promising electric vehicle architectures by international automotive scholars. Active safety control aimed at enhancing autonomous driving performance is a hot topic for both academia and industry, with the stability region being an important criterion in the vehicle's active safety system. The novel x-by-wire chassis architecture, characterized by significant redundancy features, has also introduced new control strategies through the integration of different actuators, thereby reshaping the vehicle's stability region.

Current research on the active safety control strategies of x-by-wire chassis is still based on the traditional vehicle stability region, resulting in conservative controller designs. Furthermore, the implementation framework of autonomous driving primarily involves the planner generating a collision-free trajectory, which would consider the vehicles’ stability performance. Extensive literature has developed various stability regions for traditional centralized driving chassis and integrated them into the autonomous path-planning and trajectory-tracking process. However, model mismatches can undermine safety planning, rendering the final control trajectory no longer safe.

In this Special Issue, we encourage everyone to focus on developing new solution paradigms for stability analysis and control validation of x-by-wire chassis, while expanding these to achieve autonomous driving planning and control with strong robustness. This includes, but is not limited to, explicitly analyzing stability performance to achieve robust stability under model-based design, as well as developing robust and reliable planning methods. Our focus extends beyond mere theoretical advancement and novelty; instead, we strongly encourage researchers to diligently address the challenges in the realms of stability analysis, motion planning, and active safety control strategies of autonomous driving vehicles, all prompted by the introduction of novel x-by-wire chassis.

Potential topics include but are not limited to the following:

    Stability Analysis and Control Validation of x-by-wire Chassis
    Modeling, States Estimation and Control of x-by-wire Systems
    Coordinated Control among Different x-by-wire Systems
    Handling Stability Control of Autonomous Driving
    Fault-tolerant Control Technology of x-by-wire Systems
    Efficient Torque Vectoring Control Strategies
    Robust Path Planning Methods with Stability Guarantees
    Robust Trajectory Tracking Control Methods
    Risk Aware Planning in Autonomous Driving
    Human-machine Interactive Dynamics Modeling and Control

Special Issue on Innovations in Compressible Multiphase Flows: Modeling, Simulation, and Applications
Submission Date: 2026-03-24

Description

Compressible multiphase flows are of significant importance in various engineering and scientific fields, such as aerospace, energy, environmental science, and industrial processes. These complex flows involve interactions between different phases (e.g., gas-liquid, gas-solid) under compressible conditions, presenting unique challenges in modeling, simulation, and experimental investigation. The accurate prediction and understanding of compressible multiphase flows are crucial for optimizing performance, enhancing safety, and reducing environmental impacts in numerous applications, such as rocket propulsion, gas-liquid reactors, and atmospheric phenomena. This special issue aims to provide a comprehensive platform for researchers worldwide to present their latest innovations, advancements, and breakthroughs in the modeling, simulation, and applications of compressible multiphase flows. This special issue will not only highlight the state-of-the-art research but also identify emerging trends and future directions, making it a valuable resource for both academic and industrial communities

Potential topics include but are not limited to the following:

    Advanced numerical modeling techniques for compressible multiphase flows
    • Experimental studies and validation of compressible multiphase flow
    • Applications of compressible multiphase flows in aerospace engineering
    • Shock wave interactions with multiphase media
    • Droplet and bubble dynamics in compressible environments
    • Computational fluid dynamics (CFD) simulations of compressible multiphase flows
    • Innovative experimental methods for studying compressible multiphase flows
    • Industrial applications of compressible multiphase flow technologies
    • Environmental impacts and mitigation strategies for compressible multiphase flows
    • Data-driven approaches for analyzing compressible multiphase flow phenomena

Special Issue on AI Innovations in Engineering and Smart Manufacturing
Submission Date: 2026-07-30

Description

Artificial intelligence (AI) is reshaping engineering and advanced manufacturing, powering intelligent, interconnected production systems that define Industry 4.0. From machine learning enhancing design processes to cyber-physical systems enabling real-time adaptability, AI addresses critical challenges such as efficiency, safety, and sustainability with remarkable precision. This special issue of the Journal of Engineering invites submissions to investigate how AI is revolutionizing engineering applications and smart manufacturing, driving innovation across diverse industrial sectors.

This special issue aims to gather cutting-edge research and comprehensive reviews on the integration of AI in engineering and advanced manufacturing. By fostering interdisciplinary collaboration among engineers, AI specialists, and manufacturing experts, it seeks to illustrate how AI improves system design, optimizes production processes, and tackles industrial challenges like scalability and environmental impact, advancing the development of smart manufacturing technologies.

Significance of the Collection-

This special issue will serve as an essential resource for engineers, researchers, and industry leaders by presenting high-quality research on AI’s transformative role in engineering and manufacturing. It will enhance understanding of sustainable and innovative engineering practices, supporting initiatives like India’s Atmanirbhar Bharat campaign, and contribute to global competitiveness in smart manufacturing technologies.

Potential topics include but are not limited to the following:

    AI for Engineering Process Optimization: Machine learning and deep learning for real-time control, parameter optimization, and quality assurance in engineering processes (e.g., machining, assembly).
    Predictive Maintenance in Engineering Systems: AI-driven analytics for monitoring equipment health, detecting faults, and estimating the lifespan of industrial machinery.
    AI in Manufacturing Design: Applications of AI in optimizing product designs, defect prevention, and process simulation for advanced manufacturing techniques.
    Human-Machine Integration: AI-enabled systems for safe and efficient collaboration between humans and robots in engineering and manufacturing environments.
    Sustainable Engineering Solutions: AI strategies for reducing resource consumption, optimizing energy efficiency, and implementing digital twins for real-time system analysis.

Special Issue on Micro/nanofluidics for biomedical applications
Submission Date: 2026-12-01

Description

Micro/nanofluidics, the science of manipulating fluids and bioparticles within micro/nanoscale confinements, has ushered in a paradigm shift in biomedical research and clinical diagnostics. This technology presents compelling advantages over conventional methods, including minimal sample consumption, high operational efficiency, compact device architecture, integrated functionality, and exceptional resolution in manipulation. Its application spectrum spans efficient sample preparation, single-cell analysis, high-throughput cytometry, organ-on-a-chip systems, and advanced biosensing. These advancements have profoundly enhanced modern biomedical practices. A notable example is the isolation and detection of rare circulating tumor cells (CTCs) and circulating tumor DNA from blood, which provides a non-invasive "liquid biopsy" for early cancer detection, personalized therapy, and treatment monitoring. Consequently, numerous point-of-care testing (POCT) devices have been developed, with several achieving successful commercialization.

Journal of Nanotechnology is calling for submissions of original studies that describe cutting-edge technical innovations and developments in the field of micro/nanofluidics, with a dedicated focus on its biomedical applications. Reviews which are well summarized and of far-sighted prospects are also encouraged.

Potential topics include but are not limited to the following:

    Fundamentals of micro/nanofluidics;
    Emerging technologies for micro/nanofluidics
    Fabrication methods for micro/nanofluidics
    Micro/nanofluidics-based point-of-care testing (POCT) devices
    Application of micro/nanofluidics in biomedical applications