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
Coastal Microplastic Pollution
Coastal microplastic pollution refers to the contamination of shorelines, beaches, intertidal sediments, and nearshore waters by synthetic plastic particles smaller than five millimeters in diameter, including fragments, fibers, granules, and films derived from the breakdown of larger plastic debris or from manufactured microbeads [1]. These particles accumulate on beaches through wave action, tidal deposition, and nearshore currents, where they become embedded in surface and subsurface beach sands and strandlines, and they act both as a temporary sink for debris and as a source for subsequent reworking and transport [2]. Because microplastics are present in a range of polymers including polyethylene, polypropylene, polystyrene, and polyester, and because they sorb hydrophobic organic contaminants and heavy metals from seawater, they exhibit a heterogeneous spatial distribution controlled by sediment grain size, hydrodynamic energy, and proximity to terrestrial and marine sources [3]. The particles are ingested by intertidal and coastal organisms and can be transferred through coastal food webs.
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  • 24 Sep 2026
Topic Review
Ground Source Heat Pump
A ground source heat pump is a heating and cooling system that transfers heat between a building and the shallow ground through a closed loop of circulating heat-exchange fluid buried in vertical boreholes or horizontal trenches [1]. The ground acts as a thermal reservoir whose temperature remains nearly constant throughout the year at depths of several meters, so that heat is extracted from the ground in winter and rejected into the ground in summer by means of a vapor-compression refrigeration cycle operating in reverse [2]. The ground heat exchanger consists of high-density polyethylene pipes embedded in the borehole and grouted with thermally conductive material, and the required borehole length is determined by the building's annual heating and cooling loads, the ground thermal conductivity, and the undisturbed ground temperature measured through an in-situ thermal response test. The heat pump unit raises the temperature of the extracted low-grade heat to a usable level for space heating and domestic hot water, and reverses its cycle to provide space cooling.
  • 8
  • 24 Sep 2026
Topic Review
Unsaturated Soil Mechanics
Unsaturated soil mechanics is the branch of geotechnical engineering concerned with the stress-strain, strength, volume-change, and flow behavior of soils in which the void space contains both water and air, so that the pore-fluid pressure is negative relative to atmospheric pressure [1]. The state of such soils is characterized by matric suction, defined as the difference between pore-air pressure and pore-water pressure, and by the soil-water characteristic curve that relates suction to volumetric water content [2]. Because effective stress in unsaturated soils depends on two independent stress variables—the net normal stress and the matric suction—the constitutive relations for shear strength, volume change, and hydraulic conductivity are expressed as functions of both variables rather than the single effective-stress variable used in saturated soil mechanics [1]. The framework also governs the one-dimensional consolidation process, in which water drains from the soil skeleton while suction gradients evolve, and it underpins the analysis of slopes, earthen covers, and shallow foundations located above the water table [3].
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  • 24 Sep 2026
Topic Review
Superconducting Magnet
A superconducting magnet is an electromagnet constructed from coils of superconducting wire that operate at temperatures below their critical temperature, at which the electrical resistance of the conductor vanishes and the material expels internal magnetic flux through the Meissner effect [1]. Because the zero-resistance state permits large persistent currents to circulate in a closed loop without Joule heating, such magnets can sustain intense, highly stable magnetic fields with negligible steady-state power consumption compared with conventional resistive electromagnets [2]. The winding must be maintained at cryogenic temperature, typically by liquid helium cooling for low-temperature superconductors such as niobium-titanium, and the design must account for the critical surface that bounds the combinations of current density, magnetic field, and temperature within which superconductivity is preserved [3]. The complete magnet system includes a superconducting winding, a cryostat, a power supply for energizing the coil, and protection circuitry that rapidly discharges stored energy if a localized transition to the normal state, or quench, occurs.
  • 14
  • 28 Sep 2026
Topic Review
Distributed Storage System
A distributed storage system is a software architecture that aggregates storage resources across multiple independent networked nodes to present a single logical storage abstraction to clients. Rather than relying on a single central storage device, the system partitions data into fixed-size blocks or chunks and places redundant replicas of each block across distinct physical machines, thereby achieving fault tolerance and horizontal scalability [1]. The system typically separates metadata management from data storage: one or more metadata servers maintain the namespace, directory hierarchy, and block-to-node mapping, while the data nodes store the actual block replicas and service client read and write traffic directly. To satisfy always-on availability requirements, such systems employ weakly consistent replication protocols, consistent hashing for decentralized data placement, and application-assisted conflict resolution under network partitions and node failures [2]. The storage interface may expose a file-system namespace, a key-value object store, or a block device, and the system dynamically rebalances data when nodes are added or removed.
  • 8
  • 28 Sep 2026
Topic Review
Precise Point Positioning
Precise point positioning is a global navigation satellite system positioning technique that determines the absolute coordinates of a single receiver using precise satellite orbit and clock products, rather than differencing measurements between a rover receiver and a nearby base station. The method processes pseudorange and carrier-phase observations from a single receiver with externally provided precise satellite ephemerides and satellite clock corrections, eliminating the need for simultaneous reference-station data and enabling standalone decimeter-to-centimeter level positioning [1]. The model corrects for or estimates a comprehensive set of error sources: satellite antenna phase center offsets, tropospheric delay, ionospheric delay either through dual-frequency ionosphere-free combination or by estimation, carrier-phase integer ambiguity resolution through uncalibrated phase delays, and receiver clock offset estimated as a free parameter. The solution typically converges over 20 to 30 minutes to centimeter-level accuracy when using undifferenced carrier-phase data, and rapid-convergence techniques accelerate this by estimating wide-lane and narrow-lane ambiguities separately or by applying ambiguity resolution processing strategies. The method is distinct from differential GNSS in that it provides absolute rather than relative positioning and does not require a local reference station, though it depends on high-rate precise satellite clock products [2]. The mathematical formulation uses the ionosphere-free combination of dual-frequency carrier phases and pseudoranges, and the position, receiver clock, and zenith tropospheric delay are estimated as unknown parameters in a least-squares or Kalman filter framework [3].
  • 14
  • 24 Sep 2026
Topic Review
Brain-Computer Interface
A brain-computer interface is a communication and control system that translates neural activity recorded directly from the brain into control signals that operate external devices without requiring the involvement of peripheral nerves or muscles. The system acquires electrophysiological signals—most commonly scalp-recorded electroencephalography, but also invasive cortical recordings, intracortical microelectrode arrays, or magnetoencephalography—and processes them through signal amplification, artifact removal, feature extraction, and classification algorithms to identify the user's intended command [1]. The distinguishing characteristic is that the output command is derived purely from brain signals, bypassing the normal motor output pathway, and the user learns to modulate specific neural patterns—such as sensory motor rhythms, P300 event-related potentials, or slow cortical potentials—to select among discrete control options. The closed-loop interaction includes feedback to the user, typically visual, auditory, or tactile, that informs them of the decoded command, enabling operant conditioning to improve signal modulation over training sessions. Signal classification algorithms range from linear discriminant analysis and support vector machines to deep neural networks, and the performance metric is information transfer rate measured in bits per minute [2]. The conceptual basis is that intentional cognitive states produce distinguishable spatiotemporal patterns of brain activity that can be decoded in real time, and the interface establishes a direct pathway from intention to action independent of neuromuscular output [3].
  • 18
  • 28 Sep 2026
Topic Review
Sparse Signal Processing
Sparse signal processing is a framework for representing, acquiring, and reconstructing signals that have a sparse or compressible representation in some dictionary or transform basis, meaning that only a small fraction of the coefficients are significant while most are zero or near-zero. The central result of compressed sensing is that such signals can be reconstructed exactly from far fewer linear measurements than the Nyquist-Shannon sampling theorem requires, provided the measurement matrix satisfies the restricted isometry property and the signal is sufficiently sparse [1]. The reconstruction problem is formulated as an underdetermined linear system solved through L1-norm minimization, which promotes sparsity by penalizing the sum of absolute coefficients, or through greedy algorithms such as orthogonal matching pursuit that iteratively select the dictionary atoms most correlated with the residual. The framework applies to image processing, MRI, radar, and communication systems where the cost or bandwidth of acquiring full Nyquist-rate samples is prohibitive. The mathematical conditions for exact recovery are governed by incoherence between the measurement basis and the sparsity basis, and the number of measurements required scales logarithmically with signal dimension and linearly with sparsity level [2]. The theory unifies signal representation, sampling, and inverse problem solving under the principle that exploiting sparsity is equivalent to exploiting low-dimensional structure, and it provides deterministic recovery guarantees under random measurement ensembles [3].
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  • 24 Sep 2026
Topic Review
Traffic Flow Optimization
Traffic flow optimization is the mathematical and control-theoretic problem of determining traffic signal timings, ramp metering rates, lane assignments, and variable speed limits that maximize roadway throughput, minimize average travel time, and reduce congestion on a transportation network. The problem is formulated on the basis of macroscopic traffic flow theory, which models traffic as a compressible continuum governed by conservation of vehicles and a fundamental diagram relating flow, density, and speed [1]. The Lighthill-Whitham-Richards model describes the evolution of density and flow over space and time through a nonlinear partial differential equation, and the optimization objective is to find control inputs that minimize total network travel delay while respecting capacity constraints, queue storage limits, and signal-cycle constraints. At the microscopic level, car-following models represent individual vehicle dynamics through response to lead-vehicle speed and spacing, forming the basis for adaptive cruise control and intersection management algorithms. The optimization may be solved through dynamic programming, model predictive control, queue-theoretic approximations, or metaheuristic search, depending on the network scale and real-time computation requirements [2]. The conceptual framework treats the traffic network as a controlled distributed system whose state evolves through nonlinear flow dynamics and whose performance is measured by total delay, travel time reliability, and fuel consumption [3].
  • 12
  • 24 Sep 2026
Topic Review
CO2 Enhanced Oil Recovery
CO2 enhanced oil recovery is a tertiary petroleum recovery process in which carbon dioxide is injected into a depleted oil reservoir to mobilize residual oil that cannot be displaced by primary production or secondary waterflooding. Under sufficient reservoir pressure and temperature conditions, injected CO2 becomes miscible or near-miscible with the crude oil, reducing oil viscosity, swelling the oil phase, and lowering interfacial tension so that oil flows more readily toward production wells [1]. The process operates through mechanisms of multi-contact miscibility development, where CO2 transfers between gas and oil phases across a transition zone and progressively extracts lighter hydrocarbons from the oil, progressively altering the phase behavior until the gas and oil become single phase at the minimum miscibility pressure. Reservoir engineering design determines injection patterns, slug size, WAG (water-alternating-gas) ratios, and reservoir pressure to optimize sweep efficiency while controlling gravity override, viscous fingering, and CO2 breakthrough. The process simultaneously serves as a carbon storage mechanism because a portion of the injected CO2 remains sequestered in the reservoir after field operations cease, making it a bridge between production and geologic storage objectives [2]. The key technical parameters are minimum miscibility pressure, MMP, minimum miscibility enrichment, residual oil saturation after waterflooding, and the storage capacity determined by pore volume and displacement efficiency [3].
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  • 24 Sep 2026
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