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
Global Navigation Satellite Systems
Global Navigation Satellite Systems (GNSS) is the generic term for the satellite-based radio positioning, navigation, and timing systems that provide autonomous geospatial positioning and time references on a global or regional basis [1]. A GNSS constellation comprises a space segment of satellites in precisely modeled orbits that continuously transmit ranging signals carrying navigation data formatted according to standardized interface specifications [2], a control segment of ground stations that tracks the satellites, uploads ephemerides and clock corrections, and maintains the constellation, and a user segment of receivers that measure pseudorange and carrier-phase observables to derive position, velocity, and time [1]. Position is determined by trilateration: a receiver measures the travel time of signals from at least four satellites, converting geometric range into three-dimensional coordinates and a common clock offset through solution of the ranging equations [1]. GNSS includes fully global systems such as the United States GPS, Russian GLONASS, European Galileo, and Chinese BeiDou, as well as regional augmentation systems, and is distinguished from terrestrial navigation aids by its space-borne transmitters and global coverage [3].
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  • 16 Sep 2026
Topic Review
Extended Kalman Filter
The extended Kalman filter (EKF) is a recursive Bayesian state-estimation algorithm that extends the linear Kalman filter to systems governed by nonlinear state-transition or measurement functions [1]. Like the linear Kalman filter, the EKF operates in two alternating steps: a time update that propagates the state estimate and its associated error covariance forward through the nonlinear process model, and a measurement update that corrects the prediction using an available observation [2]. The nonlinear functions are linearized about the current state estimate by a first-order Taylor expansion, with the Jacobian matrices of the process and observation functions evaluated at the current estimate replacing the constant state-transition and observation matrices of the linear filter [1]. The EKF assumes additive Gaussian process and measurement noise and Gaussian initial uncertainty, and it propagates only the first two moments—mean and covariance—of the state distribution [3]. Because the linearization is performed around the current estimate rather than a fixed trajectory, the EKF is a nonlinear filter in which the gain and covariance depend on the actual measurements received [2]. It is distinguished from the linear Kalman filter by its handling of nonlinearity, and from second-order or sigma-point filters by its first-order Taylor approximation [4].
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  • 16 Sep 2026
Topic Review
Signal to Noise Ratio
Signal-to-noise ratio (SNR) is a dimensionless measure that quantifies the ratio of the power of a desired signal to the power of background noise in a communication or measurement system. Expressed in decibels (dB), SNR is calculated as ten times the base-10 logarithm of the signal power divided by the noise power, or equivalently as twenty times the logarithm of the amplitude ratio when signal and noise are measured across the same impedance [1]. A positive SNR indicates that the signal power exceeds the noise power, while a negative SNR indicates that noise dominates [2]. SNR serves as a fundamental metric for assessing system fidelity, receiver sensitivity, and the reliability of data transmission across analog and digital communication channels [3]. In digital communications, SNR is directly related to bit error rate through modulation-specific relationships, where higher SNR values correspond to lower error probabilities [4]. The measure is applied across electrical engineering, acoustics, imaging, and spectroscopy, although the precise definition of signal and noise power may vary by domain [5].
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  • 16 Sep 2026
Topic Review
Energy Dispersive X-Ray Spectroscopy
Energy-dispersive X-ray spectroscopy (EDS, also EDX or EDAX) is an analytical microanalysis technique used for the elemental characterization of a sample. When a focused beam of high-energy electrons is directed at a specimen — typically within a scanning electron microscope (SEM) or electron probe microanalyzer (EPMA) — inner-shell ionization of the atoms in the interaction volume produces characteristic X-rays whose photon energies are element-specific [1]. An energy-dispersive spectrometer, usually a cooled silicon drift detector or Si(Li) detector, records the emitted X-rays by energy simultaneously, producing a spectrum in which peak energies identify the elements present and peak areas quantify their concentrations [2]. The technique is conventionally applied to elements heavier than sodium (Z ≥ 11), although modern detectors extend routine light-element detection to beryllium [3]. EDS is distinguished from wavelength-dispersive X-ray spectroscopy (WDS) by its parallel acquisition of the full X-ray energy spectrum, shorter acquisition time, and generally lower energy resolution; quantitative analysis requires correction for atomic number effects, absorption, and secondary fluorescence, as specified in ASTM E1508 [3].
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  • 17 Sep 2026
Topic Review
Internal Combustion Engine
An internal combustion engine (ICE) is a heat engine in which the oxidation of a fuel–air mixture occurs inside the engine itself, within one or more cylinders, and the resulting high-temperature, high-pressure gaseous products act directly on a piston, rotor, or nozzle to produce mechanical work [1]. Energy is released by rapid combustion—either spark-ignited burning of a premixed charge or compression ignition of injected fuel—that converts the chemical enthalpy of the fuel into cylinder pressure, which is then transformed through a reciprocating or rotating mechanism into shaft work [2]. Reciprocating ICEs are classified by thermodynamic cycle as four-stroke or two-stroke devices and by ignition mode as spark-ignition (Otto) or compression-ignition (Diesel) engines, with air-standard cycles serving as idealized reference models of the real process [3]. The engine converts the expansion of hot gas against a moving boundary, transferring work through a crankshaft or turbine, and exhausts the products of combustion to the environment [2]. It is distinguished from external combustion engines, in which combustion takes place outside the working cylinder, by the internal conversion of chemical fuel energy to mechanical work [1].
  • 17
  • 16 Sep 2026
Topic Review
Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is a noninvasive imaging modality that forms spatially resolved maps of the internal structure and chemical composition of an object from the nuclear magnetic resonance (NMR) signals of atomic nuclei, most commonly the protons of water and fat, within a strong static magnetic field [1]. In MRI, the object is placed in a homogeneous static field that establishes a net longitudinal magnetization, while radiofrequency pulses tip this magnetization into the transverse plane, where precessing spins emit an induced detectable signal [2]. Spatial localization is achieved by superimposing time-varying magnetic field gradients that encode spin precession frequency and phase according to position, so that the acquired raw signal in k-space can be reconstructed by Fourier transformation into a two- or three-dimensional image [2]. Image contrast arises from differences in proton density and in the longitudinal (T1) and transverse (T2) relaxation times of tissue, which reflect the local biochemical and physical environment [3]. MRI is distinguished from other tomographic techniques by its reliance on resonant interactions of spins with magnetic fields rather than on transmission of ionizing radiation, and by the dependence of contrast on tissue relaxation properties [1].
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  • 17 Sep 2026
Topic Review
Proton Exchange Membrane
A proton exchange membrane (PEM) is a semipermeable polymer electrolyte that facilitates the transport of protons while acting as an electronic insulator and gas separator in electrochemical devices [1]. Typically constructed from perfluorosulfonic acid polymers, the membrane contains sulfonic acid groups that enable proton conduction through water-mediated mechanisms, requiring controlled hydration to maintain ionic conductivity [2]. In fuel cell and electrolyzer assemblies, the PEM is positioned between the anode and cathode, allowing protons to migrate between the electrodes while preventing the direct mixing of fuel and oxidant or of product gases [3]. The membrane must simultaneously satisfy requirements for high proton conductivity, low electronic conductivity, chemical stability under oxidizing and reducing conditions, and mechanical integrity across hydration and temperature cycles [4]. PEMs are distinguished from other electrolyte types—such as alkaline membranes or solid oxides—by their polymer-based architecture and by the proton, rather than hydroxyl or oxide ion, as the charge carrier [5].
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  • 16 Sep 2026
Topic Review
Generative Adversarial Networks
A generative adversarial network (GAN) is a class of generative model in machine learning composed of two neural networks trained simultaneously within a minimax two-player game framework [1]. The two networks are a generator, which maps samples drawn from a latent prior distribution to synthetic data samples, and a discriminator, which distinguishes between real samples drawn from the training distribution and fake samples produced by the generator [1]. During training, the generator minimizes a value function by producing samples that the discriminator classifies as real, while the discriminator maximizes the same function by correctly classifying real and fake samples; the equilibrium occurs when the generator’s distribution matches the data distribution and the discriminator’s output is one-half everywhere [1]. Under an appropriate choice of divergence, the training objective corresponds to minimization of the Jensen–Shannon divergence between the empirical data distribution and the learned model distribution [2]. GANs differ from other generative models—such as variational autoencoders and autoregressive models—in that they do not explicitly model the data density, instead learning an implicit distribution through adversarial training, and they are characterized by the joint optimization of two parametric networks without an explicit likelihood [3].
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  • 16 Sep 2026
Topic Review
Monte Carlo Simulation
Monte Carlo simulations (or Monte Carlo methods) are a class of computational algorithms that rely on repeated random sampling to obtain numerical results; the underlying idea is to use randomness to solve problems that may be deterministic in principle [1]. A Monte Carlo simulation constructs a stochastic model of the system under study, generates samples from the relevant probability distributions, and aggregates the sample statistics to estimate quantities such as expected values, probabilities, or integrals [2]. The procedure typically proceeds by defining a domain of possible inputs, generating inputs randomly from a specified probability distribution over that domain, performing a deterministic computation on each sampled input, and aggregating the results [3]. As the sample size increases, the law of large numbers ensures that the estimate converges to the true value, with the statistical error decreasing proportionally to the inverse square root of the number of independent samples [4]. Monte Carlo simulations are distinguished from deterministic numerical methods by their stochastic nature and from analytic probabilistic techniques by their reliance on numerical computation rather than closed-form solution [2]. They are applicable to high-dimensional problems and to systems whose governing equations lack tractable analytic solutions [3].
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  • 16 Sep 2026
Topic Review
Greenhouse Gas Emissions
Greenhouse gas emissions refer to the release of radiatively‑active atmospheric gaseous constituents, including carbon dioxide, methane, nitrous oxide and fluorinated gases, from natural processes or anthropogenic activities into the Earth’s atmosphere over a defined time period [1][2]. These emissions comprise mass flows of gases capable of absorbing and re‑emitting infrared terrestrial radiation, thereby modifying the planetary radiative energy balance and driving the enhanced greenhouse effect [2][3]. Greenhouse gas emissions can be categorised by emission origin, scope, and gas species, and are commonly aggregated and quantified using carbon‑dioxide‑equivalent metrics to enable comparative assessment of their net radiative forcing impact [1][4]. The term encompasses both direct point‑source releases and diffuse non‑point‑source discharges generated across industrial, built‑environment, agricultural, energy‑generation, and waste‑management systems.
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  • 14 Sep 2026
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