Summary

Eng (ISSN 2673-4117) is an international, peer-reviewed open access journal which publishes original papers, critical reviews, rapid communications, technical notes, and discussions on all areas of engineering. Eng's aim is to encourage scientists to publish their experimental and theoretical research relating to engineering science and technology in as much detail as possible. There is no restriction on the maximum length of the papers. Launched in 2020, Eng was indexed in Scopus in 2023. The journal was subsequently included in the Emerging Sources Citation Index (ESCI, Web of Science) in March 2024 and Ei Compendex in April 2025. It holds a 2025 Impact Factor of 3.5 and is ranked JCR Q1 in the “ENGINEERING, MULTIDISCIPLINARY” category. Its 2025 CiteScore is 4.1, and it currently ranks Q2 in Scopus’ “Engineering (miscellaneous)” category.

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Topic Review
Fuzzy Decision-making
Fuzzy decision-making is a decision-analysis framework that extends classical decision theory to handle imprecise, ambiguous, or linguistically expressed information by representing preferences, criteria weights, and alternative evaluations as fuzzy sets rather than exact numerical values. The foundational premise is that human judgments, risk assessments, and preference statements are inherently vague and gradable, and representing them with precise numbers discards this uncertainty, so membership functions that express the degree to which an element belongs to a set provide a more faithful representation [1]. The process typically involves defining fuzzy numbers for criteria weights and alternative ratings, applying fuzzy aggregation operators such as weighted arithmetic averaging, ordered weighted averaging, or fuzzy TOPSIS to combine evaluations, and then defuzzifying the resulting fuzzy set into a crisp ranking score for alternatives. The framework accommodates multiple-criteria decision problems where decision criteria are conflicting, information is incomplete, and stakeholder preferences are expressed in natural language rather than cardinal utility scales [2]. The conceptual basis is the mathematical theory of fuzzy sets, which replaces binary membership truth values with continuous membership grades on the interval [0,1], and the Bellman-Zadeh decision model, which defines a fuzzy decision as the intersection of fuzzy goals and fuzzy constraints, both represented as membership functions over the decision space [3].
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  • 24 Sep 2026
Topic Review
Particle Swarm Optimization
Particle swarm optimization is a population-based stochastic optimization algorithm that searches for optimal solutions by simulating the social behavior of a population of candidate solutions, called particles, moving through a multidimensional search space. Each particle maintains a position vector representing a candidate solution, a velocity vector determining its direction and step size, and memory of the best fitness position it has personally encountered and the best position discovered by any particle in its social neighborhood [1]. At each iteration, particle velocities are updated through a combination of inertial momentum, cognitive attraction toward the particle's own historical best position, and social attraction toward the global or neighborhood best position, weighted by acceleration coefficients and random stochastic factors that introduce exploration diversity. The position is then updated by integrating the new velocity, and the process repeats until a convergence criterion is met. The algorithm does not require gradient information, making it applicable to non-differentiable, discontinuous, or noisy objective functions, and its convergence behavior is governed by the balance between exploration (driven by random factors and inertia weight) and exploitation (driven by cognitive and social attraction to known good regions) [2]. The theoretical foundation treats the swarm as a dynamical system whose stability conditions depend on the constriction factor or inertia weight, and the algorithm's convergence to a local optimum is guaranteed under specific parameter regimes [3].
  • 14
  • 24 Sep 2026
Topic Review
Multi-Agent Systems
Multi-agent systems are computational systems composed of multiple interacting intelligent agents—autonomous software entities that perceive their environment through sensors and act upon it through actuators—operating within a shared environment to collectively solve problems that are beyond the capacity of any single agent. Each agent is characterized by autonomy, social ability, reactivity to environmental change, and proactivity toward goals, and agents may be homogeneous or heterogeneous in their capabilities, knowledge, and objectives [1]. The system-level behavior emerges from local interactions governed by communication protocols, negotiation strategies, and coordination mechanisms, rather than from centralized control. Key structural dimensions include the degree of agent autonomy, the topology of the interaction network, the distribution of information, and the conflict or cooperation relationships among agent goals. Agents may cooperate to achieve joint objectives, compete through auction or bidding mechanisms, or negotiate through argumentation protocols, and system properties such as global coherence, stability, and scalability emerge from these local interaction rules [2]. The theoretical foundation distinguishes multi-agent systems from distributed systems in that agents are intentionally autonomous and goal-directed, rather than merely executing distributed functions under central orchestration, and from expert systems in that problem-solving is inherently social and interactive [3].
  • 21
  • 28 Sep 2026
Topic Review
Underwater Vehicle Control
Underwater vehicle control is the discipline of designing guidance, navigation, and feedback-control systems that stabilize the motion and steer the trajectory of unmanned underwater vehicles, including remotely operated vehicles and autonomous underwater vehicles within a six-degree-of-freedom hydrodynamic environment. The control problem is defined by the nonlinear, coupled equations of rigid-body motion expressed in a body-fixed reference frame, with hydrodynamic forces from added mass, linear and quadratic damping, buoyancy, thrust from propellers or fins, and environmental disturbances from currents and waves [1]. Because underwater vehicles are typically underactuated—possessing fewer control inputs than degrees of freedom—the control architecture must handle nonholonomic constraints, coupling between surge and sway modes, and buoyancy-induced pitch and roll moments that depend on center-of-gravity relative to center-of-buoyancy separation. Controllers are synthesized using model-based methods such as sliding-mode control, feedback linearization, sliding mode observers for estimating unmeasured velocities, and Lyapunov-based stability proofs that account for parametric uncertainty in hydrodynamic coefficients [2]. The guidance layer generates desired trajectories using waypoint navigation, path-following algorithms that satisfy surge and heading references, and dynamic positioning strategies that hold position against current disturbance while maintaining depth and heading stability [3].
  • 16
  • 23 Sep 2026
Topic Review
Multimodal Action Recognition
Multimodal action recognition is the computational task of identifying and classifying human physical actions by jointly interpreting information streams derived from multiple sensory modalities, typically including RGB video frames, depth maps, skeletal joint coordinates, inertial measurement unit signals, audio, and text descriptions. The core technical premise is that no single sensory channel captures the full discriminative information of a human action, and fusing complementary modalities yields action representations that are more robust to viewpoint variation, occlusion, and illumination change than unimodal approaches [1]. The recognition pipeline consists of modality-specific feature extraction—convolutional neural networks for visual frames, temporal convolutional networks or recurrent models for skeleton sequences, and spectral or temporal encoders for inertial signals—followed by cross-modal fusion that aligns features temporally and semantically before a classifier infers the action label. The fusion architecture may be early-level, combining raw signals before feature extraction, or late-level, combining modality-specific predictions, with intermediate-level feature fusion representing the dominant paradigm in deep-learning-based systems . The conceptual foundation is that human actions are inherently spatiotemporal events characterized by joint motion patterns, body shape deformation, environmental context, and vocal or textual cues, and each modality captures a different projection of this underlying event, such that multimodal fusion approximates the full action manifold more completely than any single projection [2].
  • 9
  • 24 Sep 2026
Topic Review
Climate Disaster Resilience
Climate disaster resilience is the capacity of a system, community, or region exposed to climate-related hazards to resist, absorb, accommodate, adapt to, transform, and recover from the effects of a hazard in a timely and efficient manner, including through the preservation and restoration of its essential basic structures and functions. The concept integrates exposure, vulnerability, and adaptive capacity into a dynamic property that evolves as hazards intensify and social conditions change [1]. It is characterized by four functional properties: robustness of critical infrastructure to withstand physical impact, redundancy of alternative systems to absorb failure, rapid recovery speed to restore operations, and adaptive capacity to learn and transform after each event. Resilience is distinguished from vulnerability and from hazard mitigation: whereas vulnerability describes susceptibility to harm, resilience describes the system's intrinsic ability to maintain function under stress and reorganize after disruption. The operational measurement of resilience combines physical infrastructure fragility, social capital and equity, institutional governance capacity, and ecosystem services that buffer climate extremes, all assessed at geographic scales ranging from individual buildings to national policy frameworks [2]. The conceptual model treats resilience not as a static target but as a continuous adaptive process shaped by iterative exposure, response, and recovery cycles under changing climate forcing [3].
  • 10
  • 23 Sep 2026
Topic Review
Sustainable Construction Management
Sustainable construction management is the integrated planning, coordination, and control of construction project resources, materials, processes, and supply chains to achieve project objectives while reducing environmental impacts and addressing social and economic considerations throughout the life cycle of the built asset [1][2]. It builds on traditional construction project management, which primarily focuses on cost, time, and quality, by incorporating the sustainability triple bottom line of environmental, social, and economic outcomes into project decision-making [1][2]. These sustainability considerations may include embodied carbon emissions, resource depletion, construction waste, worker health and safety, social equity, and long-term economic viability. In practice, sustainable construction management can be implemented through sustainable procurement, such as selecting materials with recycled or renewable content, products with verified environmental performance, and suppliers that meet defined environmental and social criteria [1][3]. Waste-reduction measures include accurate material planning, prefabrication and modular construction, on-site separation of waste streams, reuse of materials, and recycling of construction and demolition waste [1][3]. Design and construction strategies that facilitate disassembly, adaptability, reuse, and recycling can further improve resource efficiency and reduce waste over the building life cycle [3]. Sustainability performance can be evaluated using measurable indicators such as embodied carbon emissions (kg CO₂e/m²), construction waste generation (kg/m²), the percentage of construction waste diverted from landfill, recycled or reused material content (%), energy and water consumption, and project cost and schedule performance [2][4]. Social performance may additionally be assessed through indicators related to occupational health and safety, labor conditions, and stakeholder engagement [2][4]. Life-cycle assessment and other quantified environmental assessment methods can be used to evaluate environmental performance across the life cycle of a building [5]. Integrating these indicators into project planning, procurement, construction, and post-construction evaluation provides a basis for monitoring sustainability performance while balancing environmental, social, economic, cost, time, and quality objectives [2][4].
  • 18
  • 29 Sep 2026
Topic Review
Wave-Structure Interaction
Wave-structure interaction is the hydrodynamic phenomenon describing the mutual dynamic coupling between surface gravity waves and submerged or floating solid bodies, wherein the wave field exerts time-varying pressure and force on the structure while the structure's presence modifies the local wave field through reflection, diffraction, transmission, and radiation. The problem is formulated as a boundary-value problem in which the fluid domain satisfies Laplace's equation under the assumption of inviscid, irrotational flow, and the body surface imposes a kinematic boundary condition that matches fluid particle velocity to structural motion [1]. Linear potential theory decomposes the solution into incident waves that would exist without the structure, diffracted waves caused by the fixed body blocking the wave field, and radiated waves generated by the body's own oscillatory motion. The hydrodynamic force on the body is then expressed as the sum of Froude-Krylov pressure from undisturbed incident waves, diffraction forces from the scattering body, and radiation forces that include added-mass and wave-damping terms proportional to body acceleration and velocity [2]. The motion equations couple these hydrodynamic coefficients to structural mass, stiffness, and mooring restoring forces, yielding the frequency response of the structure to irregular sea states described by wave spectral density functions [3].
  • 8
  • 23 Sep 2026
Topic Review
Building Energy Efficiency
Building energy efficiency refers to the ability of a building and its energy-using systems to provide required services, such as thermal comfort, lighting, ventilation, and domestic hot water, with a reduced amount of energy input [1]. It should be distinguished from energy consumption, which refers to the actual amount of energy used by a building during a specified period. Energy efficiency therefore concerns the relationship between energy inputs and the services or performance delivered, whereas energy consumption describes the quantity of energy consumed [1][2]. Building energy efficiency is influenced by the thermal performance of the building envelope, the efficiency of heating, cooling, and ventilation systems, lighting and appliances, building operation, and occupant-related factors. Assessment may be based on calculated or measured energy performance and can consider delivered energy, primary energy, or other energy-related metrics [1][2]. ISO 52000-1 provides a framework for assessing the overall energy performance of new and existing buildings using measurement or calculation. In practice, building energy efficiency is evaluated using indicators such as energy use intensity (EUI), expressed as energy consumption per unit of floor area and time; primary energy use per unit of reference floor area; and energy performance ratings or benchmark scores [2][3]. EUI can be calculated from measured energy consumption and used to compare a building with similar buildings, historical performance, or a defined target. Building energy standards and performance requirements may specify minimum envelope insulation levels, minimum system efficiencies, maximum allowable energy use, or other performance targets [4]. Energy performance certificates and benchmarking schemes similarly provide standardized approaches for reporting or comparing building performance.
  • 11
  • 29 Sep 2026
Topic Review
Discrete Element Method
The discrete element method (DEM) is a numerical technique that models the mechanical behavior of discontinuous media by representing the material as an assembly of discrete, interacting bodies whose motion and contact forces are computed explicitly through time-stepping algorithms. Each element is treated as a rigid or deformable particle, block, or clump, and interactions between neighboring elements are resolved through contact detection, contact force laws, and integration of Newton's equations of motion [1]. The method is fundamentally distinguished from continuum finite-element methods in that it does not enforce a continuum assumption; instead, it captures fracture, fragmentation, mixing, segregation, and large deformation as emergent properties of particle-particle contacts. Contact forces are computed using constitutive laws that model normal and shear stiffness, friction, damping, and, in bonded-particle implementations, cementation or cohesive bonds between particles that break when stress thresholds are exceeded [2]. The governing computational cycle consists of detecting all contacts within a time step, applying contact force laws, solving the equations of motion for each body, and updating particle positions and orientations, with the time step constrained by the critical contact oscillation period to ensure numerical stability [3].
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  • 23 Sep 2026
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